CN116745858A

CN116745858A - Neuromelanin sensitive MRI and methods of use thereof

Info

Publication number: CN116745858A
Application number: CN202180077062.0A
Authority: CN
Inventors: S·克拉克; C·卡西迪; P·罗莎-内都; K·温格勒; G·霍尔加赫尔南德茨
Original assignee: Institute Of Mental Health University Of Ottawa; Mental Health Research Foundation Co ltd; Royal Academic Promotion Institution Mcgill University; Human Bioscience Co ltd; Columbia University in the City of New York
Current assignee: Institute Of Mental Health University Of Ottawa; Mental Health Research Foundation Co ltd; Royal Academic Promotion Institution Mcgill University; Human Bioscience Co ltd; Columbia University in the City of New York
Priority date: 2020-11-16
Filing date: 2021-11-16
Publication date: 2023-09-12

Abstract

A neuromelanin-sensitive magnetic resonance imaging ("MRI") technique, method, and computer-accessible medium for measuring the extent of one or more neurological disorders, providing a diagnosis of one or more neurological disorders, monitoring treatment of one or more neurological disorders, evaluating novel treatments for one or more neurological disorders, or determining prognosis associated with one or more neurological disorders.

Description

Neuromelanin sensitive MRI and methods of use thereof

Cross reference to related applications

The present application claims priority and benefit from U.S. provisional application Ser. No. 63/114,304, filed on even 16 months 11 and 2020, ser. No. 63/120,105, filed on even 1 month 12 and 2021, filed on even 9 months 11 and 2021, the contents of each of which are incorporated herein by reference in their entirety.

Technical Field

The present disclosure relates generally to magnetic resonance imaging ("MRI"), and more particularly to exemplary embodiments of systems, methods, and computer-accessible media for neuromelanin-sensitive MRI techniques as a non-invasive measure of neurological disorders.

Background

Alzheimer's Disease (AD) is a common form of neurodegenerative disease that leads to dementia, also known as Alzheimer's senile dementia and Alzheimer's primary dementia, alzheimer's Disease (AD). Dementia is a very interesting public health problem, with a new case diagnosed every 7 seconds worldwide. Alzheimer's disease was described by the German psychiatrist and neuropathologist Alzheimer's (Alois Alzheimer) in 1906 and named by his name. The disease does not heal, worsens as the condition progresses, and eventually dies within 7 years. Only less than three percent of people survive more than fourteen years after diagnosis. People diagnosed with AD are typically over 65 years old, diagnosed by standard language and visual memory testing, decision making, and problem solving tasks. In 2006, there were 2660 tens of thousands of patients worldwide, 500 tens of thousands of which are in the united states. By 2050, 1 out of 85 worldwide is expected to have alzheimer's disease. Early symptoms are often mistakenly considered 'age-related' problems, or pressure manifestations. When AD is suspected, diagnosis is usually confirmed by testing for evaluation of behavioral, memory, cognitive and mental capabilities, followed by brain scan studies.

Neurodegenerative diseases fall into two general categories of brain diseases. These diseases fall inaccurately into two groups: 1. disorders affecting memory, commonly associated with dementia, such as Alzheimer's disease; disorders that lead to motor problems, such as Parkinson's disease. The most widely known neurodegenerative diseases include Alzheimer's disease (or Alzheimer's disease), its precursor Mild Cognitive Impairment (MCI), parkinson's disease (including dementia with Parkinson's disease), and multiple sclerosis and many others. A comprehensive list including tens of less well known neurodegenerative disease names can be found on the website of the national nervous system disorders and stroke institute (National Institute of Neurological Disorders and Stroke, NINDS) of the national institutes of health (National Institutes of Health, NIH). It is understood that such diseases are often referred to by multiple names, and disease taxonomies may overly simplify the pathology of the combined occurrence or atypical or nonstandard pathology. Certain other conditions, such as post-operative cognitive dysfunction, have been described recently, and they may also involve anesthesia and post-operative neurodegenerative disorders. Other diseases such as epilepsy may not be neurodegenerative at all, but at a particular stage of the progression of the condition it may involve neurodegeneration.

Although the pathology of each of the aforementioned neurodegenerative diseases differs in at least some respects, the pathology and symptoms they share generally make it possible to treat them with similar therapeutic agents and methods. Thus, the methods described herein can be used with a selected variety of therapeutic agents described to treat most of these neurodegenerative diseases. Many publications describe common features of neurodegenerative diseases (Dale e.bredesen, rammohan v. Rao and Patrick mehlen.cell death in the nervous system. Nature 443 (2006): 796-802;Christian Haass.Initiation and propagation of neurodegeneration.Nature Medicine 16 (2010): 1201-1204;Michael T.Lin and m.flint bal. Mitochondral dysfunction and oxidative stress in neurodegenerative diseases. Nature 443 (2006) 787-795).

Disease symptoms of AD can include confusion, irritability, aggression, mood swings, language disorders, and long-term memory loss. Ill persons often leave the home and society. AD is an incurable degenerative disease that the patient needs to rely on the help and care of others. Caregivers are typically members of the family, spouse or close relatives, pose a great burden to them, and are one of the diseases that brings about great economic loss to society and families.

The etiology and progression of Alzheimer's disease is not yet clear. Studies have shown that this disease is associated with plaques and tangles in the brain. Current treatments can only help alleviate symptoms of the disease. No treatment is available to prevent or reverse the progression of the disease. By 2008, more than 500 clinical trials have been conducted to find a way to treat the disease, but it is not clear whether any of the tested treatments would be effective. Mental irritation, exercise, NSAID intake and balanced diet are considered as possible ways to delay symptoms in healthy elderly people. However, these have not proven to be effective treatments once symptoms appear.

The course of this disease is divided into four phases according to the progressive pattern of cognitive and functional impairment. 1. Pre-dementia; 2. mild early onset; 3. moderate progressive deterioration; 4. severe or advanced, i.e. the last stages where the individual is completely unable to live independently and is bedridden.

Alzheimer's disease is characterized by accumulation of neurofibrillary tangles (tau protein ) and neuritic plaques (amyloid beta) in the brain, particularly affecting degeneration of neurons in the olfactory bulb and its linked brain structures. These brain structures were the Entorhinal Cortex (EC), hippocampal structures, amygdala, michaelsholtzia basal nucleus (nucleus basalis of Meynert), blue spots and brainstem central nuclei, all of which were projected into the olfactory bulb (fig. 14). Degenerative changes lead to loss of memory and cognitive function. Severe loss of cortical and hippocampal cholinergic transferase activity occurs and degeneration of basal forebrain cholinergic neurons occurs. The loss of smell in alzheimer's disease is caused by necrosis and/or apoptosis of olfactory neurons, olfactory bulb, olfactory tract, anterior piriform cortex and inner olfactory cortex.

Etiology and neuropathophysiology: the etiology of most cases of Alzheimer's disease is unknown. The amyloid hypothesis assumes that amyloid β (aβ) deposits are the root cause of the disease. Furthermore, the major genetic risk factor APOE4 of AD also leads to excessive accumulation of amyloid in the brain before symptoms of AD appear. Thus, aβ deposition occurs prior to clinical AD. Interestingly, an experimental vaccine was found in early human trials to clear amyloid plaques, but it did not have any significant effect on dementia. Studies have shown that the culprit leading to the disease may be a close relative to the β -amyloid protein, not necessarily the β -amyloid itself. One study in 2004 found that amyloid plaque deposition was not associated with neuronal loss and memory loss. This observation confirms the τ hypothesis; i.e. theory and proposal that tau abnormality triggers a disease cascade. Eventually, tau forms neurofibrillary tangles in nerve cells, causing microtubules to disintegrate, disrupting the neuronal transport system, disrupting biochemical communication between neurons, and subsequently leading to cell death. Herpes simplex virus type 1 is thought to play a pathogenic role in people carrying a susceptible ApoE gene. Another hypothesis states that demyelination occurring in the elderly leads to interruption of axonal transport, leading to neuronal loss. Iron released during myelination and its vascular complex have also been hypothesized and considered a causative factor. I believe that the destruction of BV and the release of iron from the hemoglobin surrounding sphingomyelin and the nerve fiber network, causing an iron-catalyzed hydrogen peroxide reaction called Fenton's reaction, results in Reactive Oxygen Species (ROS) being generated during these demyelination events, which can adversely affect neurons, causing their apoptosis, and thus Alzheimer's disease.

Interestingly, AD individuals exhibited 70% loss of blue spot cells, while these cells provided norepinephrine. The blue spot cells are located in the bridge of the brain, projecting and innervating the spinal cord, brainstem, cerebellum, hypothalamus, thalamus relay nucleus, amygdala, basal end brain and cortex. Norepinephrine from LC has an excitatory effect on most parts of the brain, mediating wakefulness and exciting brain neurons activated by stimuli. Norepinephrine from this nucleus stimulates microglia to inhibit aβ -induced cytokine production and its phagocytosis of aβ, suggesting that blue-spot degeneration may be responsible for the increased deposition of aβ in AD brain initially. This nucleus in the brain bridge (part of the brain stem) is involved in physiological responses to stress and panic and is the main site for synthesis of norepinephrine (norepinephrine) by the brain in addition to the adrenal gland.

To date, there is no absolute diagnostic method for Alzheimer's disease, and there is a critical clinical need to develop a sensitive non-invasive diagnostic method. Diagnosis and monitoring of Alzheimer's disease patients is critical to assessing the severity of progression in response to proper preventative care. Timely intervention during the onset of Alzheimer's disease can save lives. Comprehensive imaging modalities for assessing alzheimer's disease remain a highly desirable clinical need.

To understand how such key neuromodulators contribute to cognitive, neurodevelopmental, aging and neuropsychiatric disorders in humans, in vivo measurement of dopamine activity has been used. In medicine, such measurements can yield objective biomarkers that will predict clinical outcome, including alzheimer's disease, ideally through the use of procedures that capture underlying pathophysiology while being readily available in the clinical setting. Blue print (LC) is the major part of norepinephrine neurons in the human brain, which start to degenerate in the early stages of Alzheimer's Disease (AD), and there is evidence that it is the first brain region that accumulates hyperphosphorylated tau in Braak phase 0. Despite extensive research into this structure in the context of AD, there are many unclear places concerning the time of LC changes and their correlation with characteristic aspects of AD pathophysiology and clinical features.

The central noradrenergic system plays a key role in the arousal and consolidation of emotional memory. Blue Spots (LCs), which are the main sites of noradrenergic neurons in the brain, have a projected topological pattern in which the tail region of the LC transmits the downstroke, thereby modulating autonomic signaling. Noradrenergic system disorders are related to theoretical explanations of PTSD, particularly with respect to the symptoms of excessive wakefulness and Major Depressive Disorder (MDD). Despite the strong theoretical basis, understanding of noradrenergic dysfunction in PTSDs and MDDs is incomplete, preventing the study of novel therapeutic approaches to target this system in PTSDs. Recent work has led to the use of a specialized neuro-imaging technique, neuromelanin-Sensitive MRI (NM-MRI), a non-invasive method of probing human noradrenergic system function in vivo by examining signal contrast in LCs. The NM-MRI signal here is positively correlated with emotional memory performance and autonomic functions (characterized by salivary alpha amylase or heart rate variability), but has not been studied in individuals with PTSD. On the other hand, low LC NM-MRI signals have been observed in major depressive states. We hypothesize that the overstrain symptoms of PTSD and MDD are positively correlated with the NM-MRI signal in tail LC.

Neuromelanin ("NM") is a dark pigment synthesized by iron-dependent oxidation of cytoplasmic dopamine and subsequently associated with proteins and lipids in midbrain dopamine neurons. NM pigments accumulate within specific autophagic organelles containing NM-iron complexes, lipids and various proteins. The organelles containing NM accumulate gradually over time in the dopamine neuronal cell bodies in the substantia nigra ("SN"), a core named for the high concentration of NM that gives it a black appearance, and are cleared from the tissue only after the cells die by microglial action. Since NM-iron complexes are paramagnetic, they can be imaged using MRI. The family of MRI sequences called NM-MRI captures a population of neurons with high NM content, such as in SN, that exhibit high density regions.

It would therefore be advantageous to provide a system, program, method and computer-accessible medium for neuromelanin-sensitive MRI that overcomes the above-described drawbacks. Different neurological and psychiatric disorders are associated with changes in neuromelanin in two major areas, the substantia nigra pars compacta (SNc) and the blue spot (LC). It is quite difficult to distinguish between different diseases with similar clinical manifestations based on the main symptoms alone, as these symptoms often overlap between related disorders.

There is no FDA approval for software as a medical device to measure NM in SN or LC.

An unmet medical need to be addressed herein is the ability to distinguish between related conditions such as parkinson's disease, multiple system atrophy and progressive supranuclear palsy, and different dementias such as, for example, alzheimer's disease and lewis body dementia. An increase in the ability to differentiate between related disorders will drive an improvement in patient outcome.

Disclosure of Invention

Provided herein, inter alia, are methods for determining the presence of alzheimer's disease in a subject and determining the change in nerve melanin concentration in the subject over time. The concentration of neuromelanin may vary due to the normal course of Alzheimer's disease or due to therapeutic intervention. In a first aspect, a method of determining whether a concentration of neuromelanin in a brain of a subject will change over time is provided. In a preferred embodiment, the subject is a patient suffering from Alzheimer's disease. The method includes obtaining a first neuro-melanin magnetic resonance image of the subject at a first time point. Subsequently, a second neuro-melanin magnetic resonance image is obtained at a second time point. The first magnetic resonance image is compared with the second magnetic resonance image, thereby determining whether a change in nerve melanin concentration occurs between the first time point and the second time point.

The present disclosure describes the combined use of two fully automated algorithms to measure the concentration and volume of Neuromelanin (NM) in two different brain regions (SNc and LC) to improve the ability to differentiate between related disorders. Voxel-based analysis algorithms (previously described in WO 2020/077098 and WO 2021/034770, the entire contents of each of which are incorporated herein by reference) are used to measure NM in SNc. However, since LC is much smaller and may not be well suited for voxel-based analysis on 3T MRI (the most clinically used scanner), the university of wortmania (University of Ottawa) invents a new algorithm to measure NM in LC. This LC algorithm is called a segmentation-based analysis algorithm. The present disclosure describes a combination of two algorithms in a software package that can be used to help diagnose and differentiate between neuropsychiatric disorders that are difficult to differentiate based on symptoms alone.

In this disclosure, software uses two algorithms. Voxel-based analysis algorithms are used to measure NM in SNc, while segmentation-based analysis algorithms are used to measure NM changes in LC. The software will report to the physician the NM levels and volumes in both brain regions. The combination of these two algorithms improves the ability to distinguish between related neurological conditions. Inclusion of these algorithms in fully automated software enables them to be widely used clinically.

In one embodiment, the present disclosure relates to a method of diagnosing alzheimer's disease in a subject, the method comprising:

(i) Performing a neuromelanin-magnetic resonance imaging (NM-MRI) scan, measuring neuromelanin levels,

(ii) The nerve melanin level is compared to previous scans and/or reference values,

and is also provided with

(iii) Diagnosis of Alzheimer's disease is provided.

In one embodiment, the present disclosure relates to a method of monitoring the progression of alzheimer's disease in a subject, the method comprising:

and is also provided with

(iii) The progression of Alzheimer's disease was determined.

In one embodiment, the present disclosure relates to a method of providing a prognosis of alzheimer's disease in a subject, the method comprising:

and is also provided with

(iii) Optionally providing a prognosis for Alzheimer's disease.

In one embodiment, the present disclosure relates to a method of monitoring treatment of alzheimer's disease in a subject, the method comprising:

and is also provided with

(iii) The effect of treatment of Alzheimer's disease was evaluated.

In one embodiment, the present disclosure relates to determining a first signal intensity from a first neuro-melanin magnetic resonance image and a second signal intensity from a second neuro-melanin magnetic resonance image, and comparing the first magnetic resonance image to the second magnetic resonance image comprises comparing the first signal intensity to the second signal intensity.

In one embodiment, the control is a level of neuromelanin present at about the same level in the population of subjects, or the standard control is an approximate average level of neuromelanin present in the population of subjects.

In one embodiment, a neuromelanin gradient phantom is used to measure the level, signal, and/or concentration of neuromelanin.

In one embodiment, the neuro-melanin phantom concentration gradient is scanned about once an hour, about once a day, about once a week, or about once a month, about once per patient.

In one embodiment, the neuromelanin phantom gradient is scanned daily.

In one embodiment, the neuromelanin phantom gradient of each patient is scanned.

In one embodiment, the present disclosure relates to a method of assessing neuromelanin in a subject, the method comprising:

performing a neuromelanin-magnetic resonance imaging (NM-MRI) scan on a subject;

acquiring a neuromelanin dataset from a NM-MRI scan;

optionally encrypting the neuromelanin dataset;

uploading the neuromelanin dataset to a remote server;

optionally decrypting the data set;

performing an analysis on the neuromelanin dataset, wherein the analysis includes one or more of:

(i) Comparing the neuro-melanin dataset with one or more neuro-melanin datasets previously obtained from the subject;

(ii) Comparing the neuromelanin dataset to a control dataset;

(iii) Comparing the neuro-melanin dataset with one or more neuro-melanin datasets previously acquired from different subjects;

generating a report comprising a neuromelanin analysis;

optionally encrypting the report;

uploading the report to a remote server;

The report is optionally decrypted.

In one embodiment, the present disclosure relates to an in vivo method of determining the progression of alzheimer's disease in a subject over time, the method comprising:

(i) Obtaining a first neuro-melanin magnetic resonance image at a first time point;

(ii) After step (i), comparing the first neuro-melanin magnetic resonance image to an age-matched control;

(iii) Determining the level, signal and/or concentration of neuromelanin present between the first time point and the second time point.

In one embodiment, the present disclosure relates to an in vivo method of diagnosing alzheimer's disease comprising:

(ii) Obtaining a second neuro-melanin magnetic resonance image at a second time point after step (i);

(iii) Comparing the first neuro-melanin magnetic resonance image with the second neuro-melanin magnetic resonance image, thereby determining whether a change in one or more of the level, signal, or concentration of neuro-melanin occurs between the first time point and the second time point.

In one embodiment, the present disclosure relates to a method of providing a treatment regimen to a patient, the method comprising performing a NM-MRI scan; acquiring NM signals in a region of interest from a NM-MRI scan; comparing NM signal data from the NM-MRI scan in the region of interest with an age-matched database number; if the NM signal is less than a predetermined value, a corresponding treatment regimen is administered.

In one embodiment, the subject exhibits symptoms of alzheimer's disease.

In one embodiment, the patient has a condition that is commonly misdiagnosed as alzheimer's disease.

In one embodiment, NM-MRI scanning and analysis will discriminate Alzheimer's disease from Parkinson's disease. In one embodiment, the NM-MRI scan and analysis identifies and may identify related disorders (e.g., lewy body dementia), respectively. In one embodiment, NM-MRI scans and analysis may monitor the progress of conditions associated with Alzheimer's disease, monitor the treatment of such conditions, and provide a prognosis for such conditions.

In one embodiment, the present disclosure relates to a method of determining whether a subject has or is at risk of developing alzheimer's disease, the method comprising analyzing one or more neuromelanin-magnetic resonance imaging (NM-MRI) scans of a region of interest of the brain of the subject, wherein the analyzing comprises:

receiving imaging information of a region of interest of the brain; and is also provided with

Determining NM concentration in the brain region of interest using segmentation analysis based on the imaging information;

wherein said determining whether the subject has or is at risk of developing Alzheimer's disease comprises:

(1) If the one or more NM-MRI scans have reduced NM signals compared to one or more control scans performed without the Alzheimer's disease condition, the subject has or is at risk of developing Alzheimer's disease; or alternatively

(2) If the one or more NM-MRI scans have NM signals comparable to those of one or more control scans performed without an Alzheimer's disease condition, the subject is not suffering from or at risk of developing Alzheimer's disease.

In one embodiment, the present disclosure relates to a method of treating a subject having alzheimer's disease, the method comprising analyzing a neuromelanin-magnetic resonance imaging (NM-MRI) scan of a region of interest of the brain of the subject, wherein the analyzing comprises:

receiving imaging information of a region of interest of the brain at a first point in time;

receiving imaging information of a region of interest of the brain at a second point in time;

determining, based on the imaging information, NM concentration in the brain region of interest at a first time point and a second time point using a segmentation analysis; and is also provided with

The NM concentrations at the first time point and the second time point are compared,

Wherein the method of treatment further comprises:

(1) Administering one or more of levodopa (levodopa) and carbidopa (carbidopa) if the NM-MRI scan at the second time point has a reduced NM signal compared to the NM signal at the first time point; or alternatively

(2) If the NM-MRI scan at the second time point has an increased NM signal compared to the NM signal at the first time point, one or more of levodopa and carbidopa are not administered.

In one embodiment, the subject exhibits one or more symptoms of alzheimer's disease.

In one embodiment, the method provides diagnosis of Alzheimer's disease prior to the occurrence of clinical symptoms.

In one embodiment, the NM-MRI method distinguishes Alzheimer's disease from Parkinson's disease.

In one embodiment, the NM-MRI method diagnoses the patient as suffering from Alzheimer's disease or not suffering from Alzheimer's disease; and indicates the diagnosis to the user via the user interface.

In one embodiment, the analysis is a segmentation analysis.

In one embodiment, the segmentation analysis includes determining at least one topological pattern within the brain region of interest.

In one embodiment, the method further comprises calculating using a value representing the volume of the neuromelanin voxel or segment.

In one embodiment, the segmented region of interest is a locus bluish.

In one embodiment, the present disclosure relates to a diagnostic system for providing diagnostic information for alzheimer's disease, the diagnostic system comprising:

an MRI system configured to generate and acquire a neuromelanin sensitive MRI scan and a series of neuromelanin data for voxels or segments located within a region of interest of a subject brain;

a signal processor configured to process a series of neuro-melanin data to produce a processed neuro-melanin MRI spectrum; and

a diagnostic processor configured to process the processed neuromelanin MRI spectrum to:

the measured values in the region of interest corresponding to the neuromelanin are extracted at a certain point in time,

comparing the measurement with one or more control measurements taken prior to the time point;

if the measurement is more than about 25% lower than the control measurement, a diagnosis of Alzheimer's disease is provided.

In one aspect, the present disclosure relates to a method for determining whether brain tissue of a subject contains abnormal levels of neuromelanin. The method includes detecting a level of neuromelanin in the tissue. The levels of neuromelanin were compared to a standard control. If a level of neuromelanin below the standard control is detected, this is indicative of Alzheimer's disease.

In one embodiment, a method for determining whether Alzheimer's disease therapy administered to a subject is effective is provided. The method comprises the step of detecting endogenous levels of neuromelanin in the tissue at a first time point. In a subsequent step, the therapy is administered to the subject. Then, the level of neuromelanin in the tissue at the second time point is determined. Thereafter, the levels of neuromelanin at the first time point are compared to the levels of neuromelanin at the second time point. A level of neuromelanin at the second time point that is greater than or equal to the level at the first time point indicates that the therapy is effective. Alternatively, a level of neuromelanin at the second time point that is lower than the level at the first time point indicates that the therapy administered to the subject is ineffective.

In one embodiment, a method for treating a patient suffering from Alzheimer's disease is provided. In one embodiment, the method comprises administering to the patient an initial amount of an Alzheimer's disease therapeutic agent. In one embodiment, the method includes monitoring the concentration of neuromelanin in a region of interest in the brain of the patient and assessing treatment-related adverse events during an initial treatment period. In one embodiment, if during initial treatment, the patient exhibits one or more of the following:

i) A decrease in the concentration of neuromelanin in a region of interest of the brain of the patient; and

ii) no treatment-related adverse or side effects;

the dosage of the therapeutic agent for Alzheimer's disease is increased in the subsequent treatment period;

wherein the treatment results in an improvement in the symptoms of Alzheimer's disease in the patient.

In one embodiment, the method of treatment comprises the steps of:

repeating steps a) -c) until the patient does not exhibit one or more of i) -ii) in step c).

In one aspect, the present disclosure relates to a method of diagnosing a neurological disorder in a subject, determining the progression of the neurological disorder over time, or providing a prognosis of the neurological disorder, the method comprising:

(i) Obtaining a first neuro-melanin magnetic resonance imaging (NM-MRI) scan at a first time point;

(ii) After step (i), obtaining a second NM-MRI scan at a second point in time;

(iii) Performing a segmentation-based algorithmic analysis to determine the level, concentration, and/or volume of Neuromelanin (NM) in the Locus Coeruleus (LC);

(iv) Performing voxel-based algorithmic analysis to determine the level, concentration, and/or volume of neuromelanin in the substantia nigra pars compacta (SNc);

(v) Comparing the first neuro-melanin magnetic resonance image with the second neuro-melanin magnetic resonance image, thereby determining whether a change in level, signal, and/or concentration of neuro-melanin occurs in the LC between the first time point and the second time point determined using a voxel-based algorithm and determined using a segmentation-based algorithm.

(vi) Diagnosis, progression over time, or prognosis of neurological disorders is provided based on differences in NM levels in SNc between the first and second scans, and differences in NM levels in LC between the first and second scans.

In one aspect, the present disclosure relates to an in vivo method of selecting a treatment regimen for preventing or treating a neurological disorder in a subject, the method comprising:

(ii) After step (i), obtaining a second NM-MRI scan at a second point in time;

(iii) Performing a segmentation-based algorithmic analysis and determining the level, concentration, and/or volume of Neuromelanin (NM) in the Locus Coeruleus (LC);

(iv) Performing voxel-based algorithmic analysis and determining the level, concentration, and/or volume of neuromelanin in the substantia nigra pars compacta (SNc);

(v) Comparing the first neuro-melanin magnetic resonance image with the second neuro-melanin magnetic resonance image, thereby determining whether a change in level, signal, and/or concentration of neuro-melanin occurs in the LC between the first time point and the second time point determined using a voxel-based algorithm and determined using a segmentation-based algorithm;

(vi) A treatment regimen corresponding to the determined neurological condition is administered.

In one aspect, the present disclosure relates to a method for distinguishing between motion disorders having similar cardinal symptoms, the method comprising:

(i) A check is performed to determine a unified parkinson's disease rating scale (Unified Parkinson's Disease Rating Scale) score;

(ii) Obtaining a first neuro-melanin magnetic resonance imaging (NM-MRI) scan at a first time point;

(iii) After step (i), obtaining a second NM-MRI scan at a second point in time;

(iv) Performing voxel-based analysis and determining concentration and/or volume of NM in SNc;

(v) Performing a segmentation-based analysis and determining the concentration and/or volume of NM in the LC;

(vi) Comparing the first neuro-melanin magnetic resonance image with the second neuro-melanin magnetic resonance image, thereby determining whether a change in the level, signal, and/or concentration of neuro-melanin occurs in the SNc and LC between the first time point and the second time point;

In one aspect, the present disclosure relates to a method of diagnosing a patient having a neurological disorder, the method comprising:

(i) Measuring the concentration and/or volume of neuromelanin in the SNc using a voxel-based analysis method and measuring the concentration and/or volume of neuromelanin in the LC using a segmentation-based analysis method;

(ii) Comparing the level of neuromelanin in SNc to a standard control level of neuromelanin in SNc, and comparing the level of neuromelanin in LC to a standard control level of neuromelanin in LC,

(iii) Diagnosis of neurological disorders is provided if the magnitude or ratio of SNc and LC neuromelanin in their respective corresponding regions is below or above a standard control.

(iii) Diagnosis of neurological disorders is provided if the magnitude or ratio of SNc and LC neuromelanin in their respective corresponding regions is lower or higher than a standard control according to a predetermined value.

In one embodiment, the methods described herein are used with a second imaging method, wherein the second imaging method is selected from the group consisting of: positron Emission Tomography (PET); tau-PET; structural MRI, including functional MRI (fMRI), blood Oxygen Level Dependent (BOLD) fMRI, iron sensitive MRI; quantitative susceptibility imaging (QSM); diffusion tensor imaging DTI; single Photon Emission Computed Tomography (SPECT), daTscan, and datquat.

In one embodiment, the methods described herein are used with a second imaging method, wherein the second imaging method is Positron Emission Tomography (PET).

In one embodiment, the methods described herein are used with a second imaging method, wherein the second imaging method is structural MRI.

In one embodiment, the methods described herein are used with a second imaging method, wherein the second imaging method is functional MRI (fMRI).

In one embodiment, the methods described herein are used with a second imaging method, wherein the second imaging method is Blood Oxygen Level Dependence (BOLD) fMRI.

In one embodiment, the methods described herein focus on the levels, concentrations, volumes, or patterns of neuromelanin within symptom-specific voxels and/or disease-specific voxels in SNc.

In one embodiment, the methods described herein focus on the levels, concentrations, volumes, or patterns of neuromelanin within the symptom-specific segment and/or the disease-specific segment in LC.

In one embodiment, the methods described herein focus on the levels, concentrations, volumes, or patterns of neuromelanin within symptom-specific voxels and/or disease-specific voxels in SNc and the levels, concentrations, volumes, or patterns of neuromelanin within symptom-specific segments and/or disease-specific segments in LC.

In one embodiment, the methods described herein focus on the levels, concentrations, volumes, or patterns of neuromelanin within SNc and within symptom-specific and/or disease-specific segments in LC.

In one embodiment, the methods described herein focus on the levels, concentrations, volumes, or patterns of neuromelanin within symptom-specific voxels and/or disease-specific voxels in SNc and the levels, concentrations, volumes, or patterns of neuromelanin within LC.

In one embodiment, the methods discussed herein are directed to one or more neurological disorders.

In one embodiment, the methods discussed herein are directed to one or more neurological disorders, wherein the neurological disorder is selected from the group consisting of schizophrenia, cocaine (cocaine) usage disorders, parkinson's disease, neuropsychiatric symptoms of alzheimer's disease, major depression, and/or post-traumatic stress disorder.

Drawings

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Fig. 1 shows neuroimaging measurements. Upper graph: use of radiotracers for tau load in representative Cognitive Normal (CN) and Alzheimer's Disease (AD) participants [ ¹⁸ F]MK6420 (left) and use against beta-amyloid ¹⁸ F]PET imaging measurements performed with AZD4694 (right). Base map: blue spot (LC) imaging. Lower left panel: NM-MRI images obtained from CN elderly in vivo. The following diagram: an enlarged view of the brain bridge from this participant and from a representative AD patient. This non-invasive procedure clearly delineates LC (yellow arrow) with high density voxels. In AD, LC degeneration begins at an early stage of disease, resulting in a significant decrease in LC NM-MRI signals. Lower right diagram: the 3D structure of the human LC revealed by computer reconstruction, showed a distribution of noradrenergic LC cells (orange) determined based on post-mortem cell count.

Fig. 2 shows the prediction of neuropsychiatric symptom severity for n=73 cognitively impaired elderly based on LC NM-MRI (left panel), τ burden (middle panel) or β -amyloid burden (right panel). NPS severity was adjusted based on covariate age, physiological gender (sex), dementia severity and PET measurements (LC profile) or LC signals (τ and amyloid profile). All measurements clearly predicted the MBI total score (Pearson r=0.37, 0.44, 0.40, respectively) and the MBI impulse runaway subfield score (not shown in the figure, r=0.35, 0.30, 0.29, respectively).

Fig. 3 shows NM-MRI images taken from a representative subject at 7 Tesla (Tesla) and 3 Tesla. Yellow arrows point to LC. The ultra-high field strength (7T) enables an increase in-plane resolution (0.7X0.7 mm at 3T versus 0.4X0.4 mm at 7T; axial view) and thinner layers (1.8 mm versus 1.0mm; coronal view) compared to 3T; therefore, at 7T, the voxel volume is 5.5 times smaller. The lower resolution may result in noise in the LC NM-MRI signal due to partial volume effects that exist in the case of single voxels combining LC and non-LC tissue. Therefore, the signal from this smaller structure is preferably measured with ultra-high field NM-MRI.

Fig. 4 shows the measurement of LC NM-MRI signals. A. NM-MRI visualization templates created by averaging many NM-MRI images in MNI space. B. And C, overlaying an over-inclusion mask (LC) of the manual tracking on the enlarged view of the template. This mask was divided into 4 extended beak-tail sections (color coding is found in B). D. NM-MRI images of representative subjects, showing the bridge in the original space (active space). The over inclusion LC mask is converted from the MNI space to the original space, thereby creating a search space (yellow) in which the LC is located. Lc (yellow) is identified on both sides with the brightest cluster formed by 4 adjacent voxels within the search space. F. The contrast to noise ratio (CNR) of all voxels is calculated relative to the signal in the reference region without NM (white circle). Averaging the CNR values from all LC voxels in the extended coracoid-caudal region yields LC NM-MRI signals. In AD, the intermediate beak extension (yellow) showed extremely pronounced degradation, and the signal here was used for all analyses.

FIG. 5 shows the relationship of LC NM-MRI signals to Braak stage and severity of dementia. Left diagram: schematic representation of LC in coronal view showing a zonal pattern of NM-MRI signal loss in tau-positive individuals. The LC is divided into 5 segments (each segment 3mm long) on the left and right sides. The tau status was divided into 3 levels (tau negative, braak zone 1 positive, braak zone 3 positive). Each segment of LC is color coded according to the relationship of NM-MRI signals in that segment to tau state (t statistics resulting from robust linear regression of control age and physiological gender). The strongest relationship occurs in the middle LC section (MNI space z coordinate = -22 to-25; the yellow LC section shown in fig. 1 is surrounded and matched with yellow). The LC NM signal from both sides of this segment is the NM-MRI index selected for all subsequent analyses. Middle diagram: a scatter plot of LC NM-MRI signals is shown in all study groups. The Braak 3 positive cases (dark red) showed reduced signals relative to the tau negative cases and the Braak 1 positive cases. Error bars represent standard error of the mean. Right figure: a scatter plot showing the correlation of LC NM-MRI signals with cognitive impairment (error on MMSE, top panel) and dementia stage (CDR score, bottom panel). L: left, R: right, CN-: normal tau-negative individuals are cognitively, ci+: cognition impaired Braak 1 positive individuals, ci++: cognitive impaired Braak 3 positive individuals, MMSE: simple mental state checklist, CDR: clinical dementia rating scale.

FIG. 6 shows the voxel-by-voxel correlation of LC NM-MRI signals with [18F ] MK-6240 uptake in the whole brain.

Fig. 7 shows the measurement of LC NM-MRI signals. Left diagram: a visualization template in MNI space created by averaging spatially normalized NM-MRI images from all participants. Middle diagram: an enlarged view of the template is visualized overlying the overly inclusive LC mask. This mask was manually tracked on a visualization template of the high density area around the LC and divided into 5 extended beak-tail sections (shown in different colours), each section spanning 3mm in the z-axis. Upper right diagram: untreated NM-MRI images showing the brain bridge of a representative individual; the central pontic reference area is surrounded by white. The contrast-to-noise ratio of all voxels is calculated with respect to the signal from this region. Lower right diagram: segmentation of LC in original space. The over-inclusion LC mask (yellow) is deformed from the MNI space to the original space to provide a search space, with the LC identified on the left and right with the brightest 4 neighboring voxels. To minimize partial volume effects, only the brightest 1 voxel out of 4 voxels is retained per side and slice, and the LC NM-MRI signal is calculated by averaging the CNR values of these voxels for each of the 5 segments.

FIG. 8 shows a positive correlation between tail LC NM-MRI signals and CAPS-5 excessive arousal symptom severity.

Fig. 9 shows a negative correlation between LC NM-MRI signals and BDI depression severity.

Fig. 10 shows LC signals for PTSD patients and healthy individuals. Tail LC signal increased significantly in PTSD group of control age (t34=2.08, p=0.046, kohn d value (cohen's d) =0.71; linear regression of control age).

FIG. 11 shows the relationship of clinical and physiological measurements of excessive arousal to LC NM-MRI signals. Left diagram: stronger LC NM-MRI signals in 24 canadian armed forces refund soldiers with a history of combat deployment were significantly correlated with more severe over-awakening symptoms (r=0.52, p=0.019). Right figure: preliminary data indicate that during fMRI fear conditioning procedures, LC NM-MRI signals from 7 young healthy individuals correlated positively with skin conductance response (phase max, conditioned stimulus-unconditioned stimulus) (age-related and diagnostic-related increases in NM signals may explain lower LC NM-MRI signal values than left panels).

FIG. 12 shows that LC localization by NM-MRI supports fMRI analysis. Due to the small size, it is not recommended to check LC activity using standard BOLD fMRI pre-treatment and analysis methods. Recent studies have shown an improved method of performing a first level fMRI analysis in raw space without smoothing and providing a personalized LC locator using NM-MRI signals [46,47]. We used this method in a subject with PTSD and examined the functional link of LC. At rest (top), we observed that the functional connectivity pattern is very similar to previous reports [46], centered on LC (white), and including brainstem and intra-cerebellar structures. After the same individual is exposed to personalized wound words (basal), the connection of LC increases, and many structures are known to project to or from LC, including hypothalamus, hippocampus, and cerebral cortex. In the present proposal, the fMRI paradigm consists of fear conditioning reflections rather than the wound evoked here shown; nevertheless, these results demonstrate the feasibility of methods for studying LC functional activity using BOLD fMRI.

Fig. 13 shows the SNc and LC mask. The software automatically applies a custom SN mask to the SNc to select regions for the voxel-based algorithm and applies a custom LC mask to the LC to select regions for the segmentation algorithm.

Fig. 14 shows the use of voxel-based and segmentation-based algorithms for measuring NM in Parkinson's disease patients. Voxel-based algorithms showed significant changes in SNc compared to healthy controls (left panel). Segmentation-based algorithms showed no significant change in LC compared to healthy controls (right panel).

Fig. 15 shows the use of voxel-based and segmentation-based algorithms for measuring NM in Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) patients. Voxel-based algorithms showed no significant change in SNc compared to healthy controls (left panel). Segmentation-based algorithms showed significant changes in LC compared to healthy controls (right panel).

Fig. 16 shows the use of voxel-based and segmentation-based algorithms for measuring neuropsychiatric symptoms NM in Alzheimer's Disease (AD). Segmentation-based algorithms showed a significant increase in NM in LC compared to healthy controls (left panel). Voxel-based algorithms showed a significant reduction in NM compared to NM in SNc of healthy controls (right panel).

Fig. 17 shows the use of voxel-based and segmentation-based algorithms for measuring NM in schizophrenic patients. Voxel-based algorithms showed significant changes in SNc compared to healthy controls (left panel). Segmentation-based algorithms showed no significant change in LC compared to healthy controls (right panel).

Fig. 18 shows the application of voxel-based and segmentation-based algorithms in post-traumatic stress disorder. Voxel-based algorithms showed that disease severity was not significantly correlated with NM levels in SNc compared to healthy controls (left panel). Segmentation-based algorithms showed significant changes in LC compared to healthy controls, and an increase in NM levels correlated significantly with disease severity (right panel).

Fig. 19 shows the application of voxel-based and segmentation-based algorithms in depressed patients. Voxel-based algorithms showed that disease severity was not significantly correlated with NM levels in SNc compared to healthy controls (left panel). Segmentation-based algorithms show that there is a trend in the NM levels in LC to decrease with increasing disease severity compared to healthy controls (right panel).

Fig. 20 shows the application of voxel-based and segmentation-based algorithms in cocaine usage barriers. Voxel-based algorithms showed that NM increase in SNc was significantly correlated with cocaine usage impairment compared to healthy controls (left panel). Segmentation-based algorithms show that there is a trend of NM reduction in LC compared to healthy controls (right panel).

Detailed Description

Before the present disclosure is described in more detail, it is to be understood that this disclosure is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.

Definition of the definition

The particulars shown herein are by way of example and for purposes of illustrative discussion of the embodiments of the present disclosure only and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the present disclosure. In this regard, no attempt is made to show structural details of the present disclosure in more detail than is necessary for a fundamental understanding of the present disclosure, the description taken with the drawings making apparent to those skilled in the art how the forms of the present disclosure may be embodied in practice.

As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

Unless otherwise indicated, all numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term "about". Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the present disclosure. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

In addition, disclosure of a numerical range within this specification is to be considered as disclosure of all numbers and ranges within that range. For example, if a range is from about 1 to about 50, it is considered to include, for example, 1, 7, 34, 46.1, 23.7, or any other value or range within the range. Furthermore, the term includes at least the number, e.g. "at least 50" includes 50.

The term "MR" refers to magnetic resonance and is the physical principle underlying a variety of experimental procedures known in the art and/or described herein, including magnetic resonance imaging ("MRI"), magnetic resonance spectroscopy ("MRs"), and the like. The term neuromelanin sensitive MRI or neuromelanin-MRI refers to the study of neuromelanin in the brain using MRI. In this context, the generic term magnetic resonance image, magnetic resonance imaging or MRI includes variants of the sensitivity of neuromelanin.

As used herein, the term "NM-MRI" and similar designations refer to each MRI scan and corresponding voxel-by-voxel analysis independently, i.e., individually and in combination.

The terms "T1" and "T2" as used herein refer to conventional meanings (i.e., "spin-lattice relaxation time" and "spin-spin relaxation time", respectively) as are well known in the art.

In the case of MRI images, the term "T1 weighted" refers to images made with a pulse spin echo sequence or an inversion recovery sequence, with appropriately shortened TR and TE, which may exhibit contrast between tissues with different T1 values, as known in the art. In this case, the term "TR" refers to the repetition time between excitation pulses. The term "excitation pulse" is understood to mean a 90 degree Radio Frequency (RF) excitation pulse. The term "TE" refers to the echo time between the excitation pulse and the MR signal sampling.

The term "subject" may be a mammalian subject, such as a murine, a domestic murine, an equine, a bovine, an ovine, a canine, a feline, or a human. In some embodiments of the methods described herein, the subject is a mouse, while in other embodiments, the subject is a human. Herein, the term "patient" refers to a human subject.

As used herein, the term "alleviating" is intended to describe a method of reducing the severity of a sign or symptom of a disorder. Importantly, signs or symptoms can be reduced, rather than eliminated. In a preferred embodiment, signs or symptoms are eliminated using the methods of treatment disclosed herein, although elimination is not required. An effective dose directed by the present disclosure is expected to reduce the severity of signs or symptoms.

Dosage and administration are adjusted to provide adequate levels of active agent or to maintain the desired effect. Factors that may be considered include the severity of the disease state, the general health of the subject, the age, weight and social gender (gender) of the subject, diet, time and frequency of administration, drug interactions, response sensitivity, and tolerance/response to therapy. An effective amount of the agent is an amount that provides an objectively identifiable improvement.

The term "neurological disorder" is used interchangeably with "neurological disorder" and "neurological disease" and is intended to encompass disorders/conditions known in the art, at least some of which have been enumerated herein.

As used herein, "stable" means that the measurement is reproducible. In one embodiment, "stable levels of neuromelanin" refers to the levels of neuromelanin remaining relatively constant over successive scans. In some cases, a "stable nerve melanin level" will be maintained for one or more hours, one or more days, one or more weeks, or one or more treatment cycles.

In the case of a disease, the terms "treatment", "treatment" and the like refer to ameliorating, inhibiting, eradicating and/or delaying the onset of the disease being treated. In some embodiments, the methods described herein are performed on a subject in need of treatment. As used herein, the term "in need of treatment" and the like refers to a subject at risk of developing a disease, suffering from a condition understood by those skilled in the medical or veterinary arts to be likely to result in a disease, and/or actually suffering from a disease. Alzheimer's disease treatment includes currently approved treatments and research therapies. Conventional MRI lacks the spatial and quantitative data required to predict clinical outcome. However, the methods discussed herein detect levels of neuromelanin in the brain, whereby clinical progression, severity, and response of Alzheimer's disease can be predicted from changes in neuromelanin in the brain or loss of neurons containing neuromelanin.

NM-MRI of the present disclosure may monitor the efficacy of Alzheimer's disease treatment. NM-MRI of the present disclosure may determine the efficacy of a research treatment. A non-exhaustive list of alzheimer's disease treatments that may be monitored according to one embodiment of the present disclosure includes one or more of the following:

alzheimer's disease treatment includes disease modifying therapies. These therapies aim to prevent, slow or stop the overall progression of alzheimer's disease (PD). They target different proteins and pathways thought to play a role in the disease.

In some embodiments, NM-MRI provides a dose setting method for treating Alzheimer's disease while avoiding and currently approved adverse or side effects of therapeutic or research treatments. In particular, the administration of a treatment, while monitoring NM signals using the voxel-by-voxel method described herein to guide the dosage regimen, may increase efficacy.

Furthermore, the side effects that may be associated with administration may be reduced according to the particular variable dose regimen using NM-MRI guidance to administer the treatment. For example, administration of a treatment according to a particular dosage regimen guided by NM-MRI voxel analysis using the present disclosure may significantly reduce, or even completely eliminate, treatment-related side effects.

In one embodiment, the region of interest is a voxel associated with the symptoms of Alzheimer's disease. Dose variation will increase patient compliance, improve therapy and reduce unwanted and/or adverse effects. In certain embodiments, the methods of treatment of the present disclosure provide improved overall therapy relative to administration of the therapeutic agent alone.

In certain embodiments, the dosages of existing therapeutic agents can be reduced or administered less frequently when using the guided interventions of the present disclosure, thereby increasing patient compliance, improving therapy and reducing unwanted or adverse effects. In one embodiment, the use of NM-MRI monitoring treatment of the present disclosure allows patients to benefit from the treatment over a longer period of time.

The neuromelanin sensitive MRI data can be used as biomarkers for alzheimer's disease or risk of developing alzheimer's disease, severity, disease progression, therapeutic response, and/or clinical outcome. The neuromelanin sensitive MRI method satisfies the need for objective biomarkers for tracking alzheimer's disease, severity, or risk of developing it. Neuromelanin sensitive MRI can be used as a safe alternative to invasive/radiation imaging measures (e.g., PET). Neuromelanin sensitive MRI can also be used to monitor progression, which is currently not possible due to the risk of repeated exposure to radiation. Neuromelanin sensitive MRI is non-invasive, cheaper, safer, and easier to perform in a clinical setting. It greatly improves (5-10 times) anatomical resolution and can resolve anatomical details in the relevant brain structures.

In certain embodiments, the neuromelanin sensitive magnetic resonance images are obtained periodically, for example every 1, 2, 3, 4, 5, 6, or 7 days; every 1, 2, 3 or 4 weeks; every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 months; or every 1, 2, 3, 4 or 5 years. In certain embodiments, the first magnetic resonance image is obtained before symptoms appear. In certain embodiments, the first magnetic resonance image is obtained before the occurrence of symptoms associated with alzheimer's disease. The second magnetic resonance image may be obtained before or after the symptoms appear. In other embodiments, the second magnetic resonance image may be acquired 1 year after the first magnetic resonance image is acquired.

In some embodiments, neuromelanin sensitive magnetic resonance imaging ("NM-MRI") techniques are effective in diagnosing Alzheimer's disease, measuring the effects of Alzheimer's disease, and/or providing a prognosis for Alzheimer's disease in a non-invasive manner.

In some embodiments, NM-MRI techniques are used as a tool for diagnosing Alzheimer's disease before symptoms appear. In some embodiments, NM-MRI techniques will be effective in distinguishing Alzheimer's disease from other neurological disorders, including but not limited to Parkinson's disease and/or dementia with Lewy bodies. In other embodiments, NM-MRI techniques are effective in selecting and/or monitoring the course of treatment, optionally such treatment is effective in treating Alzheimer's disease.

In some embodiments, NM-MRI techniques are used as a tool to monitor the progression of Alzheimer's disease. In some embodiments, NM-MRI techniques are effective for longitudinal assessment of Alzheimer's disease progression.

In some embodiments, this technique directly or indirectly measures neuromelanin. In other embodiments, this technique directly or indirectly measures dopamine function. In some embodiments, there is a link between the neuromelanin sensitive MRI (NM-MRI) signal and the severity of alzheimer's disease.

In some embodiments, NM-MRI techniques are capable of determining the concentration of neuromelanin in all parts of brain tissue. In other embodiments, NM-MRI techniques are capable of determining local concentrations of neuromelanin. In other embodiments, NM-MRI techniques are capable of determining local levels of neuromelanin. In other embodiments, NM-MRI techniques are capable of determining local signal intensities of neuromelanin.

In other embodiments, NM-MRI techniques determine the concentration of neuromelanin in the Locus Coeruleus (LC) sub-region. In other embodiments, the NM-MRI technique directly or indirectly determines dopamine release in the dorsal striatum and resting blood flow within the blue patch.

In some embodiments, the NM-MRI signal is directly related to the severity of Alzheimer's disease. In some embodiments, the NM-MRI signal is inversely related to the severity of Alzheimer's disease. In other embodiments, NM-MRI exhibits lower signals in the striatal pathway of the substantia nigra in humans with Alzheimer's disease. In some embodiments, NM-MRI captures dopamine dysfunction. In other embodiments, NM-MRI may be used as a biomarker for Alzheimer's disease. In other embodiments, NM-MRI may be used to determine the severity of Alzheimer's disease. In other embodiments, NM-MRI may be used to diagnose and/or provide a prognosis for Alzheimer's disease.

In some embodiments, the analysis is performed in comparison to a previous NM-MRI. In other embodiments, the analysis is performed in comparison to a reference value and/or range. In some embodiments, the reference values and/or ranges are generated using a compilation of neuromelanin data from healthy humans. In some embodiments, the reference values and/or ranges are generated using a compilation of neuromelanin data from a person with alzheimer's disease. In some embodiments, the reference values and/or ranges are generated using a compilation of neuromelanin data from people with alzheimer's disease and people not with alzheimer's disease.

In some embodiments, the NM-MRI signal is obtained from substantia nigra or blue-spots. In some embodiments, the NM-MRI signal is obtained from SN and blue spots.

Certain embodiments of the present disclosure may provide objective tests to improve diagnostic accuracy, advance knowledge of Alzheimer's disease to the pre-symptomatic stage, and serve as monitors for therapy. In general, embodiments of the present disclosure can be used to diagnose neuromelanin, differentiate between a variety of different conditions or diseases, and monitor a subject over a period of time using stored templates.

In one embodiment, the present disclosure is used with a second imaging method, wherein the second imaging method is Positron Emission Tomography (PET). In one embodiment, the present disclosure is used with a second imaging method, wherein the second imaging method is structural MRI. In one embodiment, the present disclosure is used with a second imaging method, wherein the second imaging method is functional MRI (fMRI). In one embodiment, the present disclosure is used with a second imaging method, wherein the second imaging method is blood oxygen level-dependent (BOLD) fMRI. In one embodiment, the present disclosure is used with a second imaging method, wherein the second imaging method is iron-sensitive MRI. In one embodiment, the present disclosure is used with a second imaging method, wherein the second imaging method is quantitative susceptibility imaging (QSM). In one embodiment, the present disclosure is used with a second imaging method, wherein the second imaging method is diffusion tensor imaging DTI. In one embodiment, the present disclosure is used with a second imaging method, wherein the second imaging method is Single Photon Emission Computed Tomography (SPECT). In one embodiment, the present disclosure is used with a second imaging method, wherein the second imaging method is DaTscan. In one embodiment, the present disclosure is used with a second imaging method, wherein the second imaging method is datquat.

In some embodiments, the concentration and/or level of neuromelanin is measured relative to a control, and diagnosis of alzheimer's disease is supported if the concentration and/or level of neuromelanin is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90% lower than the control. In some embodiments, the change in neuromelanin is assessed as a net change in concentration or level per year. In some embodiments, the change in neuromelanin is assessed as a percentage of change per year. In some embodiments, the concentration and/or level of neuromelanin is measured relative to a control, and the concentration and/or level of neuromelanin is about 1%, about 2%, about 3%, about 4%, about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 11%, about 12%, about 13%, about 14%, or about 15% lower than the control. In some embodiments, the concentration and/or level of neuromelanin is measured relative to a control, and is reduced by about 1%, about 2%, about 3%, about 4%, about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 11%, about 12%, about 13%, about 14%, or about 15% per year as compared to the control.

In one embodiment, the control is a NM-MRI scan prior to the patient. In one embodiment, the concentration and/or level of neuromelanin is measured relative to a control, and the concentration and/or level of neuromelanin is measured once per year, once per 2 years, once per 3 years, once per 4 years, once per 5 years, once per 6 years, once per 7 years, once per 8 years, once per 9 years, once per 10 years, once per 20 years. In one embodiment, the second time point is about 3 months, about 6 months, about 9 months, about 12 months, about 2 years, about 3 years, about 4 years, about 5 years, about,

About 6 years, about 7 years, about 8 years, about 9 years, about 10 years, about 15 years, about 20 years, about 25 years, or about 30 years. In certain embodiments, the patient is diagnosed with alzheimer's disease when the concentration and/or level of neuromelanin is measured to be lower than the control. In certain embodiments, the patient is diagnosed with alzheimer's disease when the measured annual or net overall change in nerve melanin concentration and/or level is less than the control by a predetermined amount. In other embodiments, the measured neuromelanin is more than about 20% lower than the control. In other embodiments, the measured neuromelanin is more than about 25% lower than the control. In other embodiments, the measured neuromelanin is more than about 30% lower than the control. In other embodiments, the measured neuromelanin is more than about 35% lower than the control. In other embodiments, the measured neuromelanin is more than about 45% lower than the control. In other embodiments, the measured neuromelanin is more than about 40% lower than the control. In other embodiments, the measured neuromelanin is more than about 50% lower than the control. In certain embodiments, the control is optionally a previous neuromelanin MRI scan of the same patient. In other embodiments, the control comprises a reference value optionally determined from a database of neuromelanin MRI scans from at least one other person suffering from the disease.

In one embodiment, diagnosis of alzheimer's disease is provided if the change in the level, signal, and/or concentration of neuromelanin at the second time point is more than about 5% lower or more than about 10% lower than the level, signal, and/or concentration of neuromelanin at the first time point, wherein the first time point is about 1 year, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, or about 10 years apart from the second time point.

In one embodiment, diagnosis of alzheimer's disease is provided if the change in the level, signal, and/or concentration of neuromelanin at the second time point is more than about 35% lower, more than about 40% lower, more than about 45% lower, or more than about 50% lower than the level, signal, and/or concentration of neuromelanin at the first time point, wherein the first time point is about 1 year, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, or about 10 years apart from the second time point.

In one embodiment, the degree of decrease in nerve melanin volume, signal, or concentration in a given patient as compared to a control is proportional to the progression and/or severity of alzheimer's disease.

In one embodiment, the extent to which the volume, signal or concentration of neuromelanin is increased in a given patient, as compared to a control, is proportional to the improvement and/or efficacy of the progression and/or treatment of alzheimer's disease.

To illustrate the use of extended NM-MRI in such applications, a series of validation studies are shown. The first procedure provided shows that NM-MRI may have sufficient sensitivity to detect local variability in tissue NM concentration, which may depend on inter-individual and inter-regional differences in dopamine function (including, for example, synthesis and storage capacity), not just on the loss of neurons containing NM. To test this, MRI measurements were compared with neurochemical measurements of NM concentration in post mortem tissue without alzheimer's disease. Since changes in dopamine function may not occur uniformly in all SN layers, the next procedure is to show that NM-MRI with high anatomical resolution compared to standard molecular imaging procedures has sufficient anatomical specificity. NM-MRI is used to test the ability of novel voxel-by-voxel methods to capture known topological patterns of cell loss in SN in Alzheimer's disease conditions. Next, the procedure was to provide direct evidence for the relationship between NM-MRI and Alzheimer's disease using segmentation methods.

As discussed in WO 2020/077098, which is incorporated herein by reference in its entirety, the NM-MRI signal is related to the release of fully validated positron emission tomography ("PET") measured dopamine into the striatum, which is the primary projection site of SN neurons, and the local blood flow in SN measured by functional MRI, which is an indirect measure of activity in SN neurons. The increased levels of neuromelanin (SNc concentration, volume of NM in SNc) when measured with the methods of the present disclosure, resulted in an improvement in UPDRS with levodopa therapy.

In one embodiment, a representative treatment for any Alzheimer's disease is used. In one embodiment, the treatment is gene therapy. In one embodiment, if the concentration of neuromelanin remains stable, constant, or constant, the dosage of treatment remains constant. In one embodiment, the dosage of the treatment is increased if the concentration of neuromelanin remains stable. In one embodiment, the dose of the treatment of alzheimer's disease is increased if the concentration of neuromelanin is reduced by more than about 1%, more than about 2%, more than about 3%, more than about 5%, more than about 10%, more than 15%, more than about 20%, or more than about 25%.

In one embodiment, the neuromelanin is monitored. In one embodiment, the control sets from other patients are age-matched. In one embodiment, the control sets from other patients are socially gender matched.

In some embodiments, the neuromelanin is measured at least every other day, weekly, monthly, yearly, every 6 months, yearly, every 2 years, every 3 years, every 4 years, every 5 years, every 6 years, every 7 years, every 8 years, every 9 years, every 10 years, every 15 years, every 20 years, every 25 years, every 30 years. In certain embodiments, the second therapeutic agent is administered once a week or once every 2 weeks. In certain embodiments, the therapeutic agent is administered every 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 8 hours, 10 hours, 12 hours, 14 hours, 16 hours, 18 hours, 20 hours, 24 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, or at least 14 days.

In one embodiment, the treatment period (initial or subsequent) or monitoring period discussed herein is daily, every other day, every 28 days, weekly, every 2 weeks, every 3 weeks, every 4 weeks, every 5 weeks, every 6 weeks, every 7 weeks, every 8 weeks, every 9 weeks, every 10 weeks, every 11 weeks, every 12 weeks, every 13 weeks, every 14 weeks, every 15 weeks, every 16 weeks, every 17 weeks, every 18 weeks, every 19 weeks, or every 20 weeks, about monthly, about every month, about every 3 months, about every 6 months, or about yearly.

In some embodiments, the degree of decrease in nerve melanin volume, signal, or concentration in a given patient as compared to a control is proportional to the progression and/or severity of Alzheimer's Disease (AD) and neuropsychiatric symptoms (NPS).

In some embodiments, the extent to which the volume, signal, or concentration of neuromelanin is increased in a given patient as compared to a control is proportional to the improvement and/or efficacy of AD and/or NPS progression and/or treatment.

In some embodiments, the standard control is a level of neuromelanin present at about the same level in the subject population, or the standard control is an approximate average level of neuromelanin present in the subject population.

The present disclosure relates specific LC segments to AD and/or NPS symptoms measured using Clinician-managed AD and/or NPS Scale (CAPS); it was demonstrated that applying a segmentation-based analysis method would find specific LC segments (termed AD segments and/or NPS segments) unique to each patient or consistent among patient populations with the same disease, which segments are associated with specific symptoms of the patient on CAPS; determining a correlation between changes in the measurement of neuromelanin and CAPS score improvement following the initial therapy; determining differences in neuromelanin measurements (e.g., total NM concentration in blue spot (LC) (microgram neuromelanin/microgram wet tissue), NM concentration in LC subregion, volume of neuromelanin in total LC, volume of LC subregion) in AD and/or NPS patients relative to the normal range of the control group; determining differences in neuromelanin levels relative to control groups, which would warrant diagnosis of AD and/or NPS; correlating changes in the neuromelanin measurements after initiation of therapy with improvement in CAPS scores; determining an increased level of neuromelanin that results in an improvement in CAPS score to verify that NM levels can be used to monitor response to treatment; associating AD and/or NPS segments with AD and/or NPS symptoms measured by CAPS scores; applying a segment-based analysis method to find specific segments (termed AD and/or NPS segments) unique to each patient or consistent among patient populations with the same disease, which segments are associated with specific symptoms of the patient on CAPS; correlation between NM-MRI scan and τ -PET or p- τ181 or p- τ217 blood test and CAPS score.

In one embodiment, a region of interest is determined and a section covering the region is measured to determine the volume of neuromelanin in the region.

In one embodiment, the region of interest is subdivided and the segments covering each sub-region are measured to determine the volume of neuromelanin in that region.

In one embodiment, the segments are compared to a reference dataset and used to calculate the concentration of neuromelanin in the region of interest or a sub-region within the region of interest.

In one embodiment, these segments are compared to a reference dataset and used to calculate the total amount of neuromelanin in the region of interest or sub-regions within the region of interest.

In one embodiment, multiple comparisons are performed of all segments identified in the region of interest with a particular symptom or symptom severity scale, or disease state, or demographic information, or other patient or disease specific information, and associations between individual segment subgroups and particular symptoms or symptom severity levels on the disease monitoring scale are found. These are called symptom-specific segments.

In one embodiment, multiple comparisons are performed of all segments identified in a region of interest with specific disease diagnosis or demographic information, or other patient or disease specific information, and associations between individual segment subsets and conditions diagnosed with a specific disease are found. These are referred to as disease-specific segments and may in one example comprise AD and/or NPS disease-specific segments.

In one embodiment, these symptom-specific or disease-specific segments have similarities between multiple patients with the same symptoms in the same disease context, and can be used to make comparisons between multiple patients with the same disease (e.g., two patients with AD and/or NPS disease who have excessive arousal, sleep disorder, or nightmare symptoms). In this case, similarities between patients can be compared, and symptom-specific segments can be used as diagnostic biomarkers for symptom specificity, while disease-specific segments can be used as diagnostic biomarkers for specific diseases.

In one embodiment, these symptom-specific or disease-specific segments have differences between patients presenting with the same symptoms in different disease contexts. In this case, the difference between symptom-specific segments can be used to distinguish between two different disorders sharing the same symptom.

In one embodiment, symptom-specific or disease-specific segments, or concentrations of neuromelanin, or volumes of neuromelanin, of a particular region or sub-region can be used as non-invasive biomarkers to determine diagnostic information, diagnosing the presence of a particular disease (in this case AD and/or NPS disease or related stress disorder, such as acute stress disorder ASD).

In one embodiment, this may be accomplished by comparing a baseline measurement of the symptom-specific segment or disease-specific segment, or the concentration of neuromelanin, or the volume of neuromelanin for a particular region or sub-region of a particular patient with future measurements of these values for the same patient.

In one embodiment, this can be accomplished by comparing a measured value of the symptom-specific segment or disease-specific segment, or the concentration of neuromelanin, or the volume of neuromelanin of a particular region or sub-region of a particular patient to a standard control value.

In one embodiment, symptom-specific or disease-specific segments, or concentrations of neuromelanin, or volumes of neuromelanin, of a particular region or sub-region can be used as non-invasive biomarkers to determine diagnostic information, to exclude the presence of related disorders or to distinguish related disorders, such as AD and/or NPS from ASD.

In one embodiment, a symptom-specific segment or disease-specific segment, or a concentration of neuromelanin, or a volume of neuromelanin, of a particular region or sub-region can be used as a non-invasive biomarker to stage or rank a particular disease or symptom and to distinguish or classify this information for a patient. For example, this may be used to determine the stage of AD and/or NPS or stress disorder for a particular patient.

In one embodiment, a symptom-specific segment or disease-specific segment, or a concentration of neuromelanin, or a volume of neuromelanin of a particular region or sub-region can be used as a non-invasive biomarker to determine the severity of a patient's current symptoms.

In one embodiment, a symptom-specific segment or disease-specific segment, or a concentration of neuromelanin, or a volume of neuromelanin of a particular region or sub-region can be used as a non-invasive biomarker to predict the development of new symptoms that a patient has not developed yet.

In one embodiment, a symptom-specific segment or disease-specific segment, or concentration of neuromelanin, or volume of neuromelanin, of a particular region or sub-region can be used as a non-invasive biomarker to predict the severity of a current symptom, predict the future development of a disease process, or predict the response of a particular symptom to treatment or the overall response of a disease to treatment, and as a non-invasive prognostic biomarker.

In one embodiment, symptom-specific segments or disease-specific segments, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as a non-invasive biomarker to monitor the response to treatment for a particular symptom or entire disease state.

In one embodiment, symptom-specific segments or disease-specific segments, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as a non-invasive biomarker to guide the selection of the correct treatment for a particular symptom or entire disease state.

In one embodiment, a symptom-specific segment or disease-specific segment, or a concentration of neuromelanin, or a volume of neuromelanin, of a particular region or sub-region can be used as a non-invasive biomarker to determine the treatment status and to determine whether treatment for a particular symptom or the entire disease status is properly responded to.

In one embodiment, symptom-specific segments or disease-specific segments, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as a non-invasive biomarker to predict future responses to treatment for a particular symptom or entire disease state.

In any embodiment, the comparison may be made between:

a symptom-specific segment or disease-specific segment, or a nerve melanin concentration, or a baseline measurement of nerve melanin volume of a particular region or sub-region of a particular patient, and future measurements of these values for the same patient.

A measured value of a symptom-specific segment or a disease-specific segment, or a concentration of neuromelanin, or a volume of neuromelanin, of a particular region or sub-region of a particular patient is compared to a standard control value.

Other PTSD and MDD embodiments

In one embodiment, the degree of decrease in nerve melanin volume, signal, or concentration in a given patient as compared to a control is proportional to the progression and/or severity of PTSD.

In some embodiments, the extent to which the volume, signal, or concentration of neuromelanin is increased in a given patient as compared to a control is proportional to the improvement and/or efficacy of PTSD and/or MDD progression and/or treatment.

The present disclosure associates specific LC segments with PTSD symptoms measured using a clinician managed PTSD scale (CAPS) and/or MDD measured using a DSM-5 diagnostic standard or a MINI standard; it was demonstrated that applying a segmentation-based analysis approach found specific LC segments (termed MDD segments or PTSD segments) unique to each patient or consistent in patient populations with the same disease, which segments were associated with specific symptoms on CAPS for the patient populations; determining a correlation between changes in the measurement of neuromelanin and CAPS score improvement after initiation of therapy; determining differences in the measurement of neuromelanin in PTSD and/or MDD patients (e.g., total NM concentration in blue spots (LC) (micrograms of neuromelanin/micrograms of wet tissue), NM concentration in LC subregions, volume of neuromelanin in total LC, volume of LC subregions) relative to the normal range of the control group; determining differences in nerve melanin levels relative to control, which would warrant diagnosis of PTSD; correlating changes in the neuromelanin measurements after initiation of therapy with improvement in CAPS scores; determining an increased level of neuromelanin that results in an improvement in CAPS score to verify that NM levels can be used to monitor response to treatment; associating PTSD and/or MDD segments with PTSD symptoms measured by CAPS score and/or MDD symptoms measured by BDI-II total score or HAMD or MADRS scale; segment-based analysis methods are applied to find specific segments (termed PTSD and/or MDD segments) that are unique to each patient or consistent among patient populations with the same disease, which segments are associated with specific symptoms on CAPS for the patient population.

In one embodiment, multiple comparisons are performed of all segments identified in the region of interest with specific symptoms or symptom severity scales including CAPS, or disease states including PTSD and/or MDD, or demographic information, or other patient or disease specific information, and associations are found between individual segment subgroups and specific symptoms or symptom severity levels on the disease monitor scale. These are called symptom-specific segments.

In one embodiment, multiple comparisons are performed of all segments identified in a region of interest with specific disease diagnosis or demographic information, or other patient or disease specific information, and associations between individual segment subsets and conditions diagnosed with a specific disease are found. These are referred to as disease-specific segments, and in one example may comprise PTSD disease-specific segments or MDD disease-specific segments.

In one embodiment, these symptom-specific or disease-specific segments have similarities between multiple patients with the same symptoms in the same disease context and can be used to make comparisons between multiple patients with the same disease (e.g., two PTSD patients with excessive arousal, sleep disorder, or nightmare symptoms and/or MDD patients with a lack of pleasure symptom). In this case, similarities between patients can be compared, and the symptom-specific segment can be used as a diagnostic biomarker for symptom specificity, while the disease-specific segment can be used as a diagnostic biomarker for specific disease.

In one embodiment, these symptom-specific or disease-specific segments have differences between patients who develop the same symptoms in different disease contexts. In this case, the difference between symptom-specific segments can be used to distinguish between two different disorders sharing the same symptom.

In one embodiment, the symptom-specific segment or disease-specific segment of a particular region or sub-region, or the concentration of neuromelanin, or the volume of neuromelanin, can be used as a non-invasive biomarker to determine diagnostic information to diagnose the presence of a particular disease (in this case PTSD or related stress disorders, such as acute stress disorder ASD, or panic disorder and/or MDD or related depression, including dysthymia, circulatory mental disorder, bipolar I and II disorder, adaptation disorder, or loss of affinity).

In one embodiment, the symptom-specific segment or disease-specific segment of a particular region or sub-region, or the concentration of neuromelanin, or the volume of neuromelanin, can be used as a non-invasive biomarker to determine diagnostic information, to exclude the presence of a related disorder, or to distinguish between related disorders (in this case PTSDs or related stress disorders, such as acute stress disorder ASD, or panic disorder and/or MDD or related depression, including dysthymia, circulatory mental disorder, bipolar I and II disorder, adaptation disorder, or loss of affinity).

In one embodiment, a symptom-specific segment or disease-specific segment, or concentration of neuromelanin, or volume of neuromelanin of a particular region or sub-region can be used as a non-invasive biomarker to stage or rank a particular disease or symptom and to distinguish or classify this information in a patient. For example, this may be used to determine the stage of a PTSD, ASD, panic disorder or related stress disorder in a particular patient.

In one embodiment, a symptom-specific segment or disease-specific segment, or concentration of neuromelanin, or volume of neuromelanin of a particular region or sub-region can be used as a non-invasive biomarker to determine the severity of a patient's current symptoms, including excessive arousal, sleep disorders, and nightmares.

In one embodiment, a symptom-specific segment or disease-specific segment, or concentration of neuromelanin, or volume of neuromelanin, of a particular region or sub-region can be used as a non-invasive biomarker to predict the severity of a current symptom, predict the future development of a disease process, or predict the response of a particular symptom to treatment or the overall response of a disease to treatment, and as a non-invasive prognostic biomarker. These treatments may include stellate ganglion block, vagal nerve stimulation, venlafaxine, beta blockers, prazosin (prazosin), epipiprazole (bripiprazole) and aripiprazole, iloperidone (iloperidone) and 3, 4-methylenedioxymethamphetamine (MDMA), selective Serotonin Reuptake Inhibitors (SSRI), SNRI, NMDA antagonists including ketamine (ketamine).

In one embodiment, symptom-specific segments or disease-specific segments, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as a non-invasive biomarker to monitor the response to treatment for a particular symptom or entire disease state. These treatments may include stellate ganglion block, vagal nerve stimulation, venlafaxine, beta blockers, prazosin, eppiprazole and aripiprazole, iloperidone and 3, 4-methylenedioxymethamphetamine (MDMA), selective Serotonin Reuptake Inhibitors (SSRI), SNRI, NMDA antagonists including ketamine.

In one embodiment, symptom-specific segments or disease-specific segments, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as non-invasive biomarkers to direct the selection of the correct treatment for a particular symptom or entire disease state. These treatments may include stellate ganglion block, vagal nerve stimulation, venlafaxine, beta blockers, prazosin, eppiprazole and aripiprazole, iloperidone and 3, 4-methylenedioxymethamphetamine (MDMA), selective Serotonin Reuptake Inhibitors (SSRI), SNRI, NMDA antagonists including ketamine.

In one embodiment, symptom-specific segments or disease-specific segments or nerve melanin concentrations, or nerve melanin volume, of a particular region or sub-region can be used as a non-invasive biomarker to determine the treatment status and to determine whether an appropriate response is obtained to treatment of a particular symptom or the entire disease status. These treatments may include stellate ganglion block, vagal nerve stimulation, venlafaxine, beta blockers, prazosin, eppiprazole and aripiprazole, iloperidone and 3, 4-methylenedioxymethamphetamine (MDMA), selective Serotonin Reuptake Inhibitors (SSRI), SNRI, NMDA antagonists including ketamine.

In one embodiment, symptom-specific segments or disease-specific segments or nerve melanin concentrations, or nerve melanin volumes, of a particular region or sub-region can be used as non-invasive biomarkers to predict future responses to treatment for a particular symptom or entire disease state. These treatments may include stellate ganglion block, vagal nerve stimulation, venlafaxine, beta blockers, prazosin, eppiprazole and aripiprazole, iloperidone and 3, 4-methylenedioxymethamphetamine (MDMA), selective Serotonin Reuptake Inhibitors (SSRI), SNRI, NMDA antagonists including ketamine.

In any embodiment, the comparison may be made between:

The measurement of symptom-specific or disease-specific segments, or nerve melanin concentration, or nerve melanin volume, of a particular region or sub-region of a particular patient can be combined with information from a second imaging test, including PET imaging, fMRI, and BOLD, in our algorithm to obtain a more accurate diagnosis.

In some embodiments, the NM level in SNc is measured by a voxel-based algorithm, while the NM level in LC is measured by a segmentation-based algorithm. Together, these two readings enable a more accurate diagnosis than if either reading alone was used.

In some embodiments, the concentration, volume, signal, and/or level of neuromelanin in the SNc is measured using a voxel-based algorithm.

In some embodiments, the concentration, volume, signal and/or level of neuromelanin in the LC is measured by a segmentation-based algorithm.

In some embodiments, the concentration, volume, signal and/or level of neuromelanin in SNc is measured using a voxel-based algorithm, while the NM level in LC is measured by a segmentation-based algorithm.

In some embodiments, combining the two measurement methods enables a more accurate diagnosis than using only either measurement algorithm. In some embodiments, combining the two measurement methods enables discrimination of the diagnoses as compared to using only either measurement algorithm. In some embodiments, combining the two measurements enables discrimination of similar diagnoses and selection of more useful treatment regimens than using only either measurement algorithm.

In some embodiments, the absolute difference between the volume, signal, or concentration of neuromelanin in SNc and LC of a given patient as compared to the decrease in control is proportional to the progression and/or severity of parkinson's disease.

In some embodiments, the extent to which the volume, signal, or concentration of neuromelanin in SNc and LC of a given patient is increased compared to a control is proportional to the improvement and/or efficacy of the progression and/or treatment of parkinson's disease.

In some embodiments, the present disclosure relates voxels of parkinson's disease to symptoms of parkinson's disease measured by UPDRS; it was demonstrated that applying a voxel-based analysis method would locate specific voxels (called PD voxels) unique to each patient that are associated with a specific symptom of the patient on the UPDRS; determining a correlation between a change in a neuromelanin measurement after initiation of levodopa therapy and an improvement in UPDRS score; determining differences in the measured value of neuromelanin (e.g., total NM concentration (micrograms of neuromelanin/micrograms of wet tissue) in substantia nigra pars compacta (SNc), NM concentration in the SNc subregion, volume of neuromelanin in total SNc, volume of the SNc subregion) in PD patients relative to the normal range of the control group; determining the difference in the level of neuromelanin compared to a control group that warrants diagnosis of PD; correlating the change in neuromelanin measurements following initiation of levodopa therapy with an improvement in UPDRS score; determining an increased level of neuromelanin that causes an improvement in UPDRS to verify that NM levels are available to monitor response to treatment; correlating the parkinsonian voxels with parkinsonian symptoms measured by UPDRS score; applying a voxel-based analysis method to find specific voxels (called PD voxels) unique to each patient, which voxels are associated with a specific symptom of the patient on the UPDRS; correlation between NM-MRI scan and DaTscan and UPDRS scores.

Among the voxels discussed herein, the diagnostic or prognostic value of a particular voxel is enhanced when combined with data concerning NM segments in LC obtained using segmentation-based algorithms.

In one embodiment, a region of interest is determined and voxels covering the region are measured to determine the volume of neuromelanin in the region.

In one embodiment, the region of interest is subdivided and voxels covering each sub-region are measured to determine the volume of neuromelanin in the region.

In one embodiment, these voxels are compared to a reference dataset and used to calculate the concentration of neuromelanin in the region of interest or a sub-region within the region of interest.

In one embodiment, these voxels are compared to a reference dataset and used to calculate the total amount of neuromelanin in the region of interest or sub-regions within the region of interest.

In one embodiment, multiple comparisons are performed on all voxels identified in the region of interest with a particular symptom or symptom severity scale, or disease state, or demographic information, or other patient or disease specific information, and associations between individual voxel subgroups and particular symptoms or symptom severity levels on the disease monitoring scale are found. These are called symptom-specific voxels. In some embodiments, this capability is enhanced when combined with information about NM levels in LC segments determined using a segmentation-based algorithm.

In one embodiment, multiple comparisons are performed of all segments identified in the region of interest with specific disease diagnosis or demographic information, or other patient or disease specific information, and associations between individual voxel subgroups and conditions diagnosed with a specific disease are found. These are referred to as disease-specific voxels and in one example may comprise parkinson's disease-specific voxels. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, these symptom-specific or disease-specific voxels have similarities between patients with the same symptoms in the same disease context and can be used to make comparisons between patients with the same disease (e.g., two parkinsonian patients with symptoms of bradykinesia). In this case, similarities between patients can be compared, and symptom-specific voxels can be used as diagnostic biomarkers. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, these symptom-specific or disease-specific voxels have differences between patients who develop the same symptoms in different disease contexts. In this case, the difference between symptom-specific voxels can be used to distinguish between two different diseases sharing the same symptom. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume, of a particular region or sub-region can be used as non-invasive biomarkers to determine diagnostic information, diagnosing the presence of a particular disease (in this case parkinson's disease or related disorder, such as MSA, PSP, parkinsonian symptoms, dyskinesia, dystonia, or essential tremors). This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, this may be accomplished by comparing a baseline measurement of symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume, of a particular region or sub-region of a particular patient with future measurements of these values for the same patient. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, this may be accomplished by comparing a measured value of symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume, of a particular region or sub-region of a particular patient to a standard control value. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as non-invasive biomarkers to determine diagnostic information, exclude the presence of a particular disorder, or distinguish between related disorders such as parkinson's disease and MSA, PSP, parkinsonism, dyskinesia, dystonia, or essential tremors. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as a non-invasive biomarker to stage or classify a particular disease or symptom and to differentiate or classify this information for a patient. This may be used, for example, to determine the stage of PD or related dyskinesias for a particular patient. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as a non-invasive biomarker to determine the severity of a patient's current symptoms. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as a non-invasive biomarker to predict the development of new symptoms that the patient has not developed yet. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume of a specific region or sub-region can be used as a non-invasive biomarker to predict the severity of a current symptom, predict the future development of a disease process, or predict the response of a specific symptom to treatment or the overall response of a disease to treatment, and as a non-invasive prognostic biomarker. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as a non-invasive biomarker to monitor the response to treatment for a particular symptom or entire disease state. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as non-invasive biomarkers to direct the selection of the correct treatment for a particular symptom or entire disease state. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as non-invasive biomarkers to determine the treatment status and to determine whether treatment for a particular symptom or the entire disease status is getting an appropriate response. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In one embodiment, symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or nerve melanin volume of a particular region or sub-region can be used as non-invasive biomarkers to predict future responses to treatment for a particular symptom or entire disease state. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In any embodiment, the comparison may be made between:

symptom-specific voxels or disease-specific voxels, or nerve melanin concentration, or baseline measurements of nerve melanin volume for a particular region or sub-region of a particular patient, and future measurements of these values for the same patient. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

Symptom-specific voxels or disease-specific voxels, or neuromelanin concentration, or neuromelanin volume measurements of a particular region or sub-region of a particular patient are compared to standard control values. This capability is enhanced when combined with information about NM levels in LC sections determined using segmentation-based algorithms.

In some embodiments, any of the methods discussed herein with respect to a single nervous system disorder can be applied to any other nervous system disorder.

LC and SNc double analysis

In one embodiment, prognosis and/or diagnosis of one or more neurological disorders may be determined using any of the methods discussed herein according to the following table:

in some embodiments, determining a change in NM level, volume or concentration in LC and SNc will provide diagnostic or prognostic information, where LC neuromelanin is determined by a segmentation method and SNc neuromelanin is determined by a voxel-by-voxel method.

In some embodiments, the neurological disorder is diagnosed according to the table above using detected changes between NM-MRI scans or relative to a standard control according to any of the methods described herein.

In some embodiments, the detected changes between NM-MRI scans or relative to a standard control are used according to any of the methods described herein to diagnose neurological disorders according to the table above.

Computer-based analysis

The example programs according to the disclosure described herein may be executed by a cloud-based processing arrangement and/or a computing arrangement (e.g., a computer hardware arrangement). Such a processing/computing arrangement may be, for example, all or a portion of, or include, but is not limited to, a computer/processor, which may include, for example, one or more microprocessors, and uses instructions stored on a computer-accessible medium (e.g., RAM, ROM, hard drive, or other storage device).

For example, a computer accessible medium (e.g., as described above, storage device such as an encrypted cloud file, hard disk, floppy disk, memory stick, CD-ROM, RAM, ROM, etc., or a collection thereof) may be provided (e.g., in communication with a processing arrangement). The computer-accessible medium may have executable instructions embodied thereon. Additionally or alternatively, the storage device may be provided separately from the computer-accessible medium, which may provide instructions to the processing arrangement for configuring the processing arrangement to, for example, cause it to perform certain exemplary programs, procedures, and methods, as described above.

Further, exemplary processing arrangements may be provided with or include input/output ports, which may include, for example, wired networks, wireless networks, the internet, intranets, data collection probes, sensors, etc. The exemplary processing arrangement may be in communication with an exemplary display arrangement, which may be, for example, a touch screen configured to input information to and output information from the processing arrangement, according to certain exemplary embodiments of the present disclosure. Further, the exemplary display arrangement and/or storage arrangement may be used to display and/or store data in a user-accessible format and/or a user-readable format.

Examples

The present disclosure will be further illustrated with the following examples, which should not be construed as limiting the scope or spirit of the disclosure to the specific procedures described herein. It should be understood that the examples are provided for the purpose of illustrating certain embodiments and are not intended to limit the scope of the present disclosure thereby. It is also to be understood that various other embodiments, modifications, and equivalents of the invention can be employed as would occur to one skilled in the art without departing from the spirit of the disclosure and/or the scope of the appended claims.

Example 1: phase of neuromelanin-sensitive MRI signals in blue spots with cortical tau proliferation and neuropsychiatric symptoms Concerns of the relationship

Neuropsychiatric symptoms (NPS) are a common and annoying aspect of Alzheimer's Disease (AD). Management of these symptoms generally requires hospitalization more than management of cognitive deficits alone. While some of these symptoms may not appear until the later stages of the disease, others may appear in latency or even before onset. Effective NPS treatment at the earliest stage may slow its progression and minimize related complications. The most common existing treatments, namely antidepressants or antipsychotics, may have different therapeutic effects due to the inability to target the neurobiological cause of a given patient's symptoms.

AD pathophysiology and NPS: little is known about the physiological mechanisms behind these symptoms. They may be associated with key pathophysiological changes occurring in AD, including accumulation of β -amyloid and phosphorylated tau. Tau and amyloid burden in AD patients has been found to be associated with aggressiveness, psychosis, and other NPSs. LC is the major site of norepinephrine neurons, beginning to degenerate early in AD and accumulating hyperphosphorylated tau in the first brain region of Braak 0 phase. Compensatory changes in the noradrenergic system to restore equilibrium may even lead to excessive activity of the remaining LC neurons. The noradrenergic system is becoming a major target of interest for the treatment of AD, especially for NPS. Noradrenergic disorders are associated with NPS in AD and may have causal relationships because the symptoms of agitation/attack and depression are responsive to noradrenergic medication. In the direction of this relationship, depressive symptoms in AD are associated with LC degeneration and low noradrenergic function, while overall, aggressive and psychotic symptoms in AD are associated with high or retained noradrenergic function. Our study examining these characteristic pathophysiological AD features will strongly focus on expanding the existing model, i.e. how these lesions interact at early disease stages to promote NPS.

Advanced neuroimaging measurements of AD pathophysiology: advances in MRI and PET imaging allow for the measurement of LC degeneration and β -amyloid and tau burden in anatomical details within the living human brain. Our team has a rich experience in utilizing all of these tools. 300 PET scans were successfully collected using the tracers [18F ] AZD4694 (for amyloid) and [18F ] MK6240 (for τ; see FIG. 1). These validated tracers allow for in vivo AD diagnosis and Braak staging. Our team has verified that NM-MRI can be used as a measure of catecholamine neuron structure and function. As shown in fig. 1, NM-MRI will capture the degradation of the noradrenergic system in AD with loss of signal in LC (secondary to loss of neuromelanin). An additional illustration of LC NM-MRI is the small size of this structure (1-2 mm in cross-sectional diameter, fig. 1), which is the limit that can be detected using high-field (3 tesla) MRI. Recent developments have developed ultra-high field (7T) NM-MRI sequences that can increase image resolution (5.5×inour example) and thereby reduce measurement noise (fig. 3). While no studies have been made to test the advantages of 7T NM-MRI in AD, at 3T this approach not only reveals the degenerative status of LC in AD, but in other populations it is associated with symptoms like NPS, including depression, sleep disorders and autonomic neuromodulation.

Combining these advanced neuroimaging methods with the assessment of NPS using a high sensitivity instrument (MBI) designed to be used prior to the onset of dementia will enable us to simulate the mechanism by which AD-related brain changes lead to the appearance of NPS at the disease stage.

Participants and clinical measures

Study participants from community or outpatient clinics at the university of mchx aging study center (McGill University Research Centre for Studies in Aging) were enrolled into the canadian university of mchx aging and dementia transition biomarker (Translational Biomarkers of Aging and Dementia, TRIAD) cohort. The cohort participants performed detailed clinical evaluations, including clinical dementia assessment (Clinical Dementia Rating, CDR) and simple mental state examination (MMSE). Participants with unimpaired cognition had no objective cognitive impairment and CDR scores of 0. Mild Cognitive Impairment (MCI) individuals have subjective and objective cognitive impairment, maintain mobility in daily life and have CDR scores of 0.5. Patients with mild to moderate sporadic Alzheimer's disease have a CDR score of between 0.5 and 2 and conform to the United states national institute of aging (National Institute on Aging) and the Alzheimer's disease Association's criteria for possible Alzheimer's disease as determined by physicians (McKhann et al, 2011). Sporadic early onset Alzheimer's dementia is an individual whose dementia has been onset before age 65 (Snowden et al, 2011). Participants were excluded if they had inappropriately treated disorders, active substance abuse, recent head trauma or major surgery, or they had MRI/PET safety contraindications. Patients with Alzheimer's disease do not stop taking medications for this study.

NPS severity was assessed using the mild behavioural barrier checklist (MBI-C, http:// www.MBItest.org). MBI-C is done by the participants' primary information provider, most commonly their spouse. MBI-C consists of 34 problems, divided into five areas: (1) motor and motivation decline (apathy), (2) affective disorders (mood and anxiety symptoms), (3) impulsive runaway (shock, impulsive and abnormal rewarding significance), (4) social inadaptation (social cognitive impairment) and (5) abnormal perception and thought content (psychotic symptoms). Each question was answered with either "yes" or "no" and the severity of the question was rated 1=mild, 2=moderate or 3=severe according to each answer "yes". To obtain a "yes" assessment, the symptoms must last for at least 6 months. This study was approved by the institute of mental health, university of douglas, research ethics committee (Douglas Mental Health University Institute Research Ethics Board) and the montreal neurological institute, PET working committee (Montreal Neurological Instituted PET working committee), and written informed consent was obtained for all participants.

TABLE 1 clinical and demographic measurements

MRI acquisition

All neuroimaging data were acquired at the montreal neurological institute (Montreal Neurological Institute). Magnetic Resonance (MR) images were acquired on a 3T prism scanner. NM-MRI images were collected by a fast spin echo (TSE) sequence using the following parameters: repetition Time (TR) =600 ms; echo Time (TE) =10 ms; flip angle = 120 °; in-plane resolution=0.7×0.7mm ² The method comprises the steps of carrying out a first treatment on the surface of the Partial brain coverage of field of view (FoV) =165×220; number of layers = 20; layer thickness = 1.8mm; average = 7; acquisition time = 8.45min. The layer specification (slice-description) scheme consists of: the image stack was oriented along the anterior commissure-posterior commissure line and the top layer was placed 3mm above the third ventricle floor, viewed on the sagittal plane along the middle of the brain. Full brain high resolution T1 weighted MRI images were also acquired, pre-processing M-MRI and PET data using MPRAGE sequences (inversion time=105ms, tr=2500, te=1.69 ms, flip angle=7°, fov=192×192, matrix=192×256, number of layers=256, isotropic voxel size=1 mm, acquisition time=5.47 minutes). Immediately after acquisition, the artifacts are visually inspected to determine the quality of the MRI image and, if necessary, the scan is repeated as time permits.

Preprocessing of NM-MRI images

The initial preprocessing step is performed using SPM12 to examine NM-MRI signals from the substantia nigra (5) as in the previous work. Although the final analysis of the LC signals is performed on the raw spatial NM-MRI images, it is necessary to spatially normalize the NM-MRI images in order to register a generic LC search space from MNI space to raw space for each participant. The NM-MRI scan is first co-registered with the participant's T1 weighted scan. Then, tissue segmentation is performed using the T1 weighted image. NM-MRI scans were normalized for MNI space using the DARTEL routine and using gray matter and white matter templates generated by all study participants. The resampled voxel size of these normalized NM-MRI scans is 1mm, with isotropy. After each of these steps, all images were visually inspected. The visualization template is created by averaging spatially normalized NM-MRI images from all participants.

The subsequent steps were developed using custom Matlab scripts to specifically examine LC signals. An overly inclusive LC mask was drawn over the visualization template to cover the high density voxels along the caudal axis of the beak spanning 15mm of the anterior edge of the 4 th ventricle (MNI spatial coordinates z= -16 to-31, see fig. 1). The coracoid tail margin is set by cross-referencing the distance to anatomical landmarks in the brain atlas (inferior colliculus on the coracoid end and posterior recess of the 4 th ventricle on the caudal end). A subdivided version of the mask was made by dividing into 5 extended beak tail sections of equal length. The over inclusion full LC mask and the subdivided mask are then twisted to the original space using the inverse of the flow field generated in the spatial normalization step and resampled to NM-MRI image space. A twisted over inclusion full LC mask may then be used to define a search space within which the LCs of each participant are found. LC is segmented in this space using a cluster formation algorithm, defined as the 4 adjacent voxels (1.96 mm) with the highest average signal ² ). This operation is repeated for the right and left LC. The contrast to noise ratio (CNR) of each voxel v in a given axial layer is calculated as the relative difference of NM-MRI signal intensity I with respect to the reference region RR in the same layer: CNR (CNR) _v ＝(I _v -mode(I _RR ))/mode(I _RR ). The reference value from the central bridge of the area known to have a low NM concentration, which is defined by a circle of radius 11.6mm, and the centre distance connects the axes of the left and right LC by 32.6mm, is used. Each axial layer in the original space is identified as belonging to one of the 5 beak-tailed LC segments based on which of the 5 subdivided LC masks is present on that layer (if 2 of these masks are present on the same layer, the LC segment is defined by the mask covering the most LC voxels). Five is calculated by averaging NM-MRI CNR values from brightest voxels determined to fall on each side and layer within this segmentLC signals for each segment. The brightest voxels per layer are chosen instead of the average value of all LC voxels to minimize partial volume effects.

PET acquisition and analysis

All individuals experience ¹⁸ F-AZD4694 ¹⁸ F-MK-6240PET scans were acquired with a brain-specific Siemens high resolution study tomographic scanner. For more details on PET methods, please refer to previous studies. Tau entanglement ¹⁸ F-MK-6240 images were acquired 90-110 minutes after intravenous bolus injection of the tracer and reconstructed using the OSEM algorithm on 4D volumes with four frames (4 times, 300 seconds) (Pascal et al, 2018 b). Amyloid-b ¹⁸ F-AZD4694 images were acquired 40-70 minutes after intravenous bolus injection of the tracer and a scan was reconstructed using the same OSEM algorithm on a 4D volume with three frames (3 times, 600 seconds) (Cselenyi et al 2012). At the end of each PET acquisition, a 6 minute transmission scan was performed using a rotating 137Cs point source to perform attenuation correction. These images are also corrected for motion, dead time, decay, and random and scatter coincidence. Jian Shandian the T1 weighted MRI is inhomogeneous and the field distortion is corrected. The PET image is then automatically registered with the T1 weighted image space, and the T1 weighted image is linearly and non-linearly registered with the MNI reference space (Mazziotta et al, 1995). Using the transformation from the T1 weighted image to the MNI space and from the PET image to the T1 weighted image space, the meninges and cranium in the PET image are removed and non-linearly registered with the MNI space. ¹⁸ F-MK-6240 normalized uptake value ratio (SUVR) and ¹⁸ F-AZD4694 SUVR uses the lower and full cerebellum grey matter as reference regions, respectively (Cselengyi et al 2012; pascal et al 2018 b). The PET image was spatially smoothed to achieve a final full width half maximum resolution of 8 mm. Estimating the whole of the whole cortex ¹⁸ F-AZD4694 SUVR value (Pascal et al, 2018 a). The Braak phase-shift regions proposed by Braak were estimated (Braak and Braak,1991,1997; braak et al, 2006; braak et al, 2011; braak I (transentorhinal)), braak II (entorhinal and hippocampus), braak III (amygdala, parahippocampal, clostridial, lingual), braak IV (brain island, temporal inferior), and the likeLeaf, temporal lateral, posterior cingulate and inferior parietal), braak V (orbito-frontal, temporal superior, frontal inferior, cuneiform, anterior cingulate, limbic superior, occipital lateral, cuneiform anterior, superior parietal, superior frontal, coracoid medial prefrontal), and Braak VI (paracentral, posterior central, anterior central and parabular) ¹⁸ F-MK-6240SUVR value. Subjects were divided into 3 groups according to Braak stage: tau negative [ ] ¹⁸ F-MK-6240SUVR in Braak region 1<1.2 A) is provided; braak phase 1 positive ¹⁸ F-MK-6240SUVR in the Braak1 phase region>1.2; these cases will be in Braak1 or 2) and Braak 3 positive ¹⁸ F-MK-6240SUVR in the Braak 3 phase region>1.5; these cases will be in Braak stage 3 or higher). There is no case where τ is above the threshold in the phase 3 region, but there is no inconsistency that exceeds the threshold in the phase 1 region.

Statistical analysis

The statistical tests correlating the final imaging measurements with clinical measurements were performed on Matlab software. These include ANCOVA, linear regression analysis, and partial Spearman correlation using Tukey post hoc test. For detailed information about the particular model used, please refer to the results. Matlab software version 9.2 (http:// www.mathworks.com) pair with the VoxelStats software package was used ¹⁸ F-MK-6240SUVR atlas was used for voxel-by-voxel statistics (Mathotaarachchi et al 2016).

Results

LC NM-MRI signals and Braak staging

First, the fact that LCNM-MRI signals are attenuated in AD was confirmed. LC NM-MRI signals were examined in 191 elderly individuals, grouped according to cognitive status (2 levels: cognition normal and cognition impaired (AD and MCI individuals)) and tau status (3 levels: tau negative, tau positive in Braak zone 1 (threshold suv=1.2 of zone 1 ROI) and tau positive in Braak zone 3 (threshold suv=1.5 of zone 3 ROI; definition of ROI see fig. 3)), averaged over LC. Two-factor analysis of variance for full LC NM-MRI signals controlling age and physiological gender showed significant effects of tau status (F2,184 =4.53, p=0.012) while cognitive status did not (F1,184 =0.34, p=0.56; model controlling age and physiological gender; more complex model showed no significant tau status x cognitive status interactions, p=0.48). Post hoc testing found a significant difference between tau negative and Braak 3 positive individuals (p=0.012), but no difference between Braak 1 (only) positive individuals and either tau negative individuals (p=0.17) or Braak 3 positive individuals (p=0.27, tukey's) HSD.

Next, the structure of τ state-related signal loss is checked within the partition of LC. With the exception of the extended beak and tail ends of the LC, there was a significant difference in all segments between each τ group, and the middle segment showed the strongest effect (fig. 1). The middle LC segment (average of NM-MRI signals from left and right of this segment) was examined and found to be very closely related to τ status (F2,184 =15.3, p < 0.00001), where the signal of Braak3 positive individuals was attenuated relative to that of τ negative individuals (p < 0.00001) and Braak 1 positive individuals (p=0.0045), but the signal of Braak 1 positive individuals was not attenuated relative to τ negative individuals (p=0.075, tukins HSD; fig. 1. LC middle segment signals of all study groups are shown). Given these strong effects in the intermediate LC section, this was retained as LC NM-MRI measurements for all subsequent analyses.

These results indicate that LC signal loss is minimal during Braak 1 and becomes apparent from Braak 3. To determine evidence of whether LC signals gradually lost from the beginning of Braak3 phase, cognitive dysfunction and dementia phases were correlated with LC signals in Braak3 positive individuals. Both measurements are significantly correlated with LC signal loss (MMSE error: t23= -2.95, p=0.0072, robust linear regression; clinical dementia rating scale: spearman ρ= -0.52, p=0.012, n=25; bias correlation; both analyses control age and physiological gender; see fig. 1).

LC NM-MRI signals and AD pathophysiology

To more fully investigate the relationship of LC NM-MRI signals to tau proliferation, LC signals were combined with the whole brain [ ¹⁸ F]MK-6240 ingests the associated voxel-by-voxel analysis. Obvious clusters were found in the multiple regions (see fig. 6). To investigate how direct there is a link between LC signal and tau burden, cortical beta amyloid burden and cortical gray were also controlledThe mass volume is subjected to a subsequent voxel-by-voxel analysis.

To examine which aspects of AD pathophysiology can independently predict loss of LC NM-MRI signals, linear regression analysis was performed on cognitively impaired individuals (n=75); due to the extremely high collinearity (r=0.91), the aggregated measurement of τ and amyloid are not included together in the same model. By using the τ load (t ₇₀ = -2.36, p=0.021, linear regression of CDR scores, age and physiological gender was controlled) and cortical amyloid burden (t ₇₀ = -2.50, p=0.015, linear regression with the same covariates) clearly predicts LC NM-MRI signals. However, when cortical gray matter volume is incorporated into any of these models, it becomes an important predictor of LC NM-MRI signals (for the τ model: t ₆₈ =2.13, p=0.037, linear regression controlling τload, cortical gray matter volume, total intracranial volume, CDR score, age and physiological gender in Braak zone 3), and τ and amyloid are not important predictors.

LC NM-MRI signals and clinical manifestations

Finally, the clinical relevance of LC NM-MRI signal loss was investigated. In particular, cognitive disorders and neuropsychiatric symptoms associated with LC signals, respectively, are examined while controlling key pathophysiological measurements. In cognitively impaired individuals (n=72), there was no significant correlation between LC signal and extent of cognitive impairment (MMSE score: t ₆₄ =0.83; linear regression of τ burden, cortical gray matter volume, total intracranial volume, CDR scores, age, and physiologic gender in Braak zone 3 was controlled.

Next, neuropsychiatric symptoms were considered as measured with the total score on the mild behavioral disorder checklist (MBI). This measurement was significantly correlated with LC NM-MRI signals of cognitively impaired individuals (n=73), regardless of which pathophysiological measurements were included as covariates (table xx). In a preferred model, the LC NM-MRI signal and τ burden in Braak zone 3 both predict the MBI total score (t, respectively ₆₅ =3.48, p=0.0009, and t ₆₅ =2.48, p=0.016; also control the volume and total volume of cortical ashLinear regression of intracranial volume, CDR score, age, and physiological gender; fig. 4). The correlation of LC NM-MRI signals with the total MBI score was confirmed in non-parametric tests using the same covariates as linear regression (table xx, spearman ρ=0.40 for the preferred model). This positive correlation indicates that LC retention is associated with poor NPS and notably that this effect becomes stronger when any cortical pathology measurement is included in the model. Post hoc analysis examined the MBI subdomain and found that the domain most strongly correlated with LC NM-MRI signals was pulse out of control (Spearman p=0.36, p=0.003, partial correlation, control τ burden, cortical gray matter volume, total intracranial volume, CDR scores, age and physiological gender).

When predicting the total MBI score of a cognitively normal elderly, neither LC NM-MRI signal (t92= -0.19, p=0.85) nor τ load in Braak zone 1 (t92=1.91, p=0.059; controlling linear regression of cortical gray matter volume, total intracranial volume, CDR score, age and physiological gender) are important predictors.

Table 2: prediction of neuropsychiatric symptom severity (MBI total score) in cognitively impaired individuals

Analysis included age, social gender, and CDR scores as covariates (analysis including cortical gray matter volume also included total intracranial volume as covariates). * p <0.05, < p <0.01, < p < 0.001%

Example 2: for determining the impending neuropsychiatric symptom progression in MCI and AD patients and in CN elderly Longitudinal multi-modal neuroimaging studies of LC, amyloid and tau markers.

LC NM-MRI signals, amyloid and tau burden in critical brain regions predict the progression of NPS after 18 months. This will be examined in cognitively impaired (AD and MCI) and in intact elderly, respectively, and the prediction can be determined even at the earliest stage of the disease.

The data obtained show that NPS is associated with tau accumulation, beta-amyloid accumulation and LC integrity, as measured in vivo by PET [18F ] MK6240, [18F ] AZD4694 and neuromelanin sensitive MRI (NM-MRI), respectively.

Without being bound by any theory, NPS reflects an imbalance of key pathophysiological changes occurring in AD: on the one hand the integrity of LC and on the other hand the accumulation of amyloid and τ in the cortex. The combined effects of these processes may lead to imbalance in cortical and subcortical behavioral regulation, leading to the appearance of NPS. Our preliminary data reveals a pattern associated with NPS in early stages of disease: cortical tau accumulation, as well as LC retention, may reflect cortical control disorders of intact or even overactive LC and lead to expression of NPS, including impulsivity loss of control and mood disorders. This is also consistent with reports on elevated noradrenergic activity and tau pathology (measured in CSF), both associated with poor NPS and noradrenergic blocking treatment NPS. The prediction of NPS using a linear regression model, including all neuroimaging measurements, in particular LC NM-MRI signals, tau burden and amyloid burden will all be positively correlated with the progression/appearance of NPS, and the combined prediction in a model including all measurements will be superior to the prediction using only any one measurement. Before targeted NPS treatment becomes realistic, it is a critical step to determine which neuropathological process is most relevant to a given patient.

Based on LC NM-MRI signals, τ burden (in Braak 3-phase ROI [ in ] ¹⁸ F]SUVR of MK 6240) and beta-amyloid burden (in cortical ROI [ in ¹⁸ F]SUVR of AZD 4694) determines NPS severity (MBI total score, n=73). For all measurements, multivariate predictions outperform univariates.

All analyses control age, social gender and dementia severity (clinical dementia rating scale scores).

*p<0.05，**p<0.01，***p<0.001

The heterogeneity of NPS is an important consideration. Of our samples, the MBI overall score correlated most with the impulse runaway sub-domain (r=0.85), a symptom type with strong theoretical correlation to the noradrenergic system. On the other hand, depression symptoms are associated with reduced noradrenergic function (reduced LC NM-MRI signal); thus, analysis will take into account depression symptoms alone.

Study design and timetable

The study consisted of the following operations performed in n=70 CN elderly, n=35 patients with MCI and n=35 AD patients: baseline clinical and neuropsychological assessment, amyloid and tau PET scans, and MRI scans. The participants returned to another clinical and neuropsychological assessment after 18 months. The participants will have been involved in a longitudinal regimen, namely senescence and dementia transforming biomarkers (TRIAD), to facilitate follow-up. Schedule: month 1-6, ethical approval, staff training, and optimization of 7T NM-MRI sequences. Participants were recruited on months 7-30. To obtain n=140 samples (after the deluge), we recruit 77 people each year. Similar to the TRIAD cohort, 100-130 participants completed MRI and PET imaging procedures each year. When collecting data, performing quality control and preprocessing of the neuroimage data; when half of the sample is collected, the preliminary analysis is complete. Clinical and cognitive assessments were followed 24-48 months.

Recruitment and consent:

all participants were recruited from the longitudinal PET biomarker cohort: aging and dementia transforming biomarkers (TRIAD) from the MCSA, mcgil research center for aging. MCSA had a clinical database containing 4000 patients from which TRIAD cohorts were mainly derived, which had recruited more than 1000 people willing to participate in the study.

Inclusion criteria

Participants aged 55 to 90 years were enrolled with academic achievement history to rule out mental disabilities. Participants will be excluded if they cannot provide informed consent, or if they cannot provide consent during the study. Their ability to provide consent will be determined using a screening kit comprising MMSE and MoCA. Senile individuals with normal Cognition (CN) are defined by a clinical dementia assessment (CDR) of 0. MCI is defined by CDR 0.5, subjective and objective memory loss, and normal activities of daily living. There was no dementia in the CN group and MCI group according to the standards of the institute for aging (Petersen and National Institute of Ageing) -alzheimer's disease, peterson and american national institute. AD cases will have mild severity, defined as CDRs 0.5-1.0, and are diagnosed using national institute of aging-alzheimer's disease association criteria.

Exclusion criteria:

(i) Does not read or write (not caused by cognitive decline); (ii) use of recreational medicaments (recreational drug); (iii) MRI examination found severe structural abnormalities or severe vascular pathology; (iv) Dry pre-tests were run over the past 4 weeks or exposure to ionizing radiation (by research studies or radiological examination) over the past 12 months; (v) contraindications for MRI/PET scanning; (vi) chronic and recurrent mental health conditions; that is, there is a past history of mental diseases (e.g., schizophrenia, major depression, PTSD). People with new psychotic symptoms will be included unless the severity prevents study participation (e.g., violence or aggression).

Neuropsychiatric symptoms, as well as other clinical and cognitive measurements:

NPS measurements provide a comprehensive assessment of numerous types of NPS and are sensitive to detecting their performance at different disease stages. These measurements include the standard questionnaires used in AD: neuropsychiatric symptom questionnaires (Neuropsychiatric Inventory, NPI), apathy Inventory, and Epworth sleep questionnaires. In addition, a specialized questionnaire designed to be sensitive to the assessment of NPS in prodromal AD, a mild behavioral impairment checklist (developed by Ismail doctor), was also employed. Clinical features and cognitive assessment followed the standard protocol used in the TRIAD cohort. Cognitive measures include the Rey auditory language learning test (Rey Auditory Verbal Learning Test, RAVLT), the digital breadth and digital symbols of WAIS-III, and IQ (WASI-II; matrix reasoning, vocabulary; part of a 3 hour set of tests administered by neuropsychologists). These measurements were recorded every 24 months in the TRIAD regimen and were not repeated for participants tested within 60 days of baseline or follow-up assessment. The measurement of primary concern is the change in MBI over 18 months. Preliminary follow-up data shows that this measurement can capture changes over 1 or 2 years.

7T NM-MRI acquisition

Participants scanned on a Siemens Terra 7T scanner (Nova Medical) equipped with an 8-channel transmit and 32-channel receive head coil. LC was imaged using a magnetization transfer preparation ultrafast gradient echo sequence developed by Christine Tardif laboratories (magnetization transfer-prepared turbo-flash sequence, MTw-TFL). MT preparation consisted of a series of 15 pulses of duration 1ms (interval 2 ms), offset frequency 10kHz and B1 root mean square value 9 μt. The polarity of the frequency offset alternates between pulses. Each MT prepared block was followed by one TFL readout from the center to the periphery (TE/tr=4.3/505 ms; acceleration factor=29; flip angle=8°; grappa=2), scan time=4:31 minutes, resolution 0.4×0.4×1.0mm3. The sequence was repeated twice and averaged to increase the SNR. This sequence is further optimized to maximize the SNR and reliability of the LC signal, thus improving this prior art sequence. High resolution (0.65 mm, isotropic) T1 weighted anatomic scan using MP2RAGE sequence: TI 1/ti2=1000/32000 ms, tr=43000 ms, te=2.46, α=4°, echo spacing=7.5 ms, slice partial fourier (slice partial Fourier) 6/8, grappa=3, scan time=11 minutes. For subjects willing to tolerate longer periods of time, resting state Blood Oxygen Level Dependent (BOLD) functional MRI data was also collected for exploratory analysis of functional link changes associated with NPS. For this, a 1.8mm isotropic 2D gradient echo EPI sequence was used: te=25 ms, tr=2010 ms, α=70°, grappa=2, phase portion fourier=6/8, and scan time=10.5 minutes. If all scans are completed, the total scan time will be about 40 minutes.

NM-MRI pre-treatment and analysis

Similar to the previously used method, the LC is segmented in raw space using custom scripts and SPM12 tools to measure LC NM-MRI signals on the unprocessed NM-MRI image. It is advantageous to analyze the signals from this small structure in the original space, compared to conventional methods that operate in standardized space for MRI analysis. NM-MRI pre-treatment procedure was developed by Cassidy doctor.

PET acquisition

PET scans were performed on Siemens HRRT. The radiotracer is produced by the radiochemistry laboratory of centrex and cyclotron. The PET scan and the MRI scan will be performed on the same day. The next day, another PET scan is performed.

A [18F ] AZD4694 or [18F ] MK6240PET scan was obtained after 185MBq tracer administration. The scan using 18F MK6240 was 20 minutes long starting from 90-110 minutes post injection. The scan with [18F ] AZD4694 was 30 minutes starting from 40-70 minutes after injection. The subject wears specialized glasses to correct head movements. The moving image is acquired using the list mode file. The transmission image was acquired using a Ge-68 source. The tissue radiological images will be regrouped using 4 frames and reconstructed using OSM3 method, and scatter and attenuation corrected. Then, motion correction is applied.

PET analysis

Quantification of [18F ] AZD4694 or [18F ] MK6240 PET will be performed for anatomical regions of interest (ROI) and individual voxel maps. In both cases, as a first step, the MRI volume (T1 weighted image) will be segmented to obtain a gray map, which is then co-registered with the PET image by rigid transformation using a MINC tool. The corresponding [18F]AZD4694 SUVR50-70 or [18F ] MK6240SUVR90-110 will be analyzed using the cortex as a reference area. The MRI scan will be converted to standard MNI space using nonlinear registration. In the case of ROI analysis, the inverse transformation of the registration parameters is used to map the probabilistic anatomical atlas back to the PET image. A local time-radioactivity curve (TAC) is calculated from a mask obtained by convolution of the segmented gray matter image and the atlas of the subject. The region of interest (ROI) and voxel TAC are then entered into an appropriate quantification procedure to obtain a SUVR for the anatomical ROI or BP parameter set for voxel-by-voxel analysis. All images will be subjected to Partial Volume Correction (PVC). Voxel-based analysis will be performed by: the parametric map is first twisted into MNI space using the nonlinear registration procedure described above. Then, the parameter map is smoothed (6 mm) to reduce noise. Voxel level univariate tests using Generalized Linear Model (GLM) will then be applied, as well as post-hoc multiple comparison corrections derived from random field theory, implemented in voxel-stat, a suite of voxel-based generalized linear models developed at the university of mcgill.

Statistical analysis: the primary analysis for the hypothesis testing was linear regression to predict changes in NPS severity 18 months after baseline based on baseline LC NM-MRI signals, amyloid burden in the ROI where amyloid and τ occur in early disease stages (τ in Braak 3 phase region and amyloid in the entire cerebral cortex) and τ burden. The effect of interest is an independent effect of LC NM-MRI signals and an increase in variance explained by the inclusion of additional amyloid and/or τ burden into the model. CN case analysis uses MBI total score as a result metric (high sensitivity tool for cases with minimal symptom burden). In CI individuals (MCI and AD), these analyses will predict the total scores of MBI and NPI. Post hoc testing includes voxel-by-voxel analysis using the same predictors and results (within a mask involving regions of early tau/amyloid accumulation to minimize the penalty of multiple comparisons) and ROI analysis using more specific types of NPS as outcome metrics (e.g., out of impulse, sleep problems, aggressive behavior). For all assays, the LC NM-MRI signal was examined for possible interactions with amyloid/τ in NPS severity prediction. Analysis included covariate age, physiological gender, dementia severity (clinical dementia rating scale) and depression severity (NPI depression program). Controlling the severity of depression is necessary because noradrenergic function may have an inverse relationship to depression (the latter being highly correlated with the overall score of the primary index MBI of interest) compared to symptoms such as aggression and impulsivity. In contrast, analysis of predicted depressive symptoms (NPI depression) will control the severity of other NPSs.

Analysis based on physiological gender (sex) and social gender (gender): physiological sex effects were observed in late-year depression; for example, regarding its association with cognitive disorders, functional effects and brain structures. Furthermore, animal studies report the effect of physiological sex-type on the relationship between LC injury and tau phosphorylation, consistent with a female's predisposition to norepinephrine-related disorders. Thus, physiological gender may be one factor in these relationships. Equal numbers of men and women were recruited (63% of women previously recruited from the TRIAD cohort). The primary analytical model was performed in men and women, respectively, and it was determined whether the intensity of the influence was significantly different by physiological gender.

The result has a long lasting effect on dementia patients and individuals at risk. About 75% of AD patients and 50% of individuals presenting with Mild Cognitive Impairment (MCI) experience NPS, while only 25% of individuals show normal cognitive aging. The presence of these symptoms is associated with faster cognitive and functional decline, lower quality of life, earlier admission to nursing homes, and greater caregiver burden. Because of the limited efficacy of existing treatment of neuropsychiatric symptoms of AD in many patients at significant risk of injury, there is an urgent need to understand their neurobiology to discover and monitor improved methods of treatment. The biomarkers described herein help guide the development of new NPS treatments and optimization of existing treatments, ultimately supporting accurate medical methods to identify patients most likely to respond to certain NPS treatments. One NPS therapeutic target is the norepinephrine system, the integrity of which is measured by the LC NM-MRI signals described herein. In fact, since NM-MRI is a practical non-invasive measure of the neurochemical changes associated with the underlying AD pathology, this may prove to be a useful tool, for example as a potential regulator of the NPS therapeutic response or as a marker of the response to LC neuroprotective drugs. Both applications have promising evidence: the aggressive behavior of AD patients is likely to respond to noradrenergic drug therapy in patients exhibiting noradrenergic dysfunction and direct LC neuroprotective drugs that will reduce NPS-like behavior in AD animal models.

Example 3: NM for assessment of Post Traumatic Stress Disorder (PTSD) and major depressive disorder MRI

Introduction to the invention

Post-traumatic stress disorder (PTSD) is a heterogeneous condition that reduces the quality of life of the refurbished soldiers and causes serious suicide risk. In view of the complex manifestations of the disease, treatments targeting specific neurobiological disruptions in a patient-specific manner may be needed. However, finding biomarkers that support PTSD targeted therapies has been a challenge. Recent studies have shown that deregulation of the neuromodulator Norepinephrine (NE) may cause PTSD symptoms. Blue spots (LC) are central nuclei in the human brain that release NE, and the LC-NE system plays an important role in regulating stress, autonomic nervous function, emotional memory, sleep and wakefulness. These LC-regulated behaviors have a very high correlation with the field of excessive arousal symptoms of PTSD, which is defined by DSM-5 as exaggerated startle response, excessive vigilance and sleep disorders. For example, individuals with PTSD have been observed to exhibit higher LC BOLD fMRI activation for stimulation than controls. It has also been shown that there is a relationship between autonomic nervous system disorders and the severity of excessive arousal symptoms. For example, in one study conducted by Blechert et al in 2007, individuals with PTSD showed "decreased parasympathetic control and increased sympathetic control that exhibited low respiratory sinus arrhythmia (a measure of cardiac vagal control) and high skin electrical activity". Given the evidence that NE systems play a role in PTSD, there has been an increasing effort to deploy drug therapies targeting this system. Several drugs acting on this system have shown benefits, including venlafaxine as a mixed NE/serotonin reuptake inhibitor, a commonly used treatment for PTSD, and have been shown to be superior to certain serotonin reuptake inhibitors and beta-receptor blockers when used in combination with wound recurrence psychotherapy (traumare-experiencing psychotherapy). Furthermore, the NE alpha-1 receptor antagonist prazosin shows evidence of inconsistent efficacy in PTSD, underscores the potential advantages of biomarkers for tracking noradrenergic imbalances, and may be pre-selected for likely responders to noradrenergic drugs such as prazosin, and thus also support trials on experimental noradrenergic therapies.

Neuromelanin sensitive magnetic resonance imaging (NM-MRI) is a novel non-invasive neuroimaging method, which can be used to image structures containing NM in the human brain due to the paramagnetic nature of NM: LC and dopaminergic substantia nigra. We have previously shown that NM signals in the substantia nigra can provide an indirect measure of PET imaging index of dopamine function, with the practical advantages of being inexpensive, non-invasive and available at high resolution. We propose here that the LC NM signal can provide a similar understanding of the function of the NE system. Although the LC NM-MRI signal in PTSD has not been studied, there is evidence that this signal tracks the measurement of NE or autonomic function: it is associated with heart rate variability, alpha amylase secretion and anxiety-wakefulness symptoms in anxiety disorders. We are particularly interested here in tail LC because this region sends a down-projection to the autonomic system, enhancing PTSD activity and correlating with autonomic measurements.

We examined here the LC NM-MRI signal in a sample of 24 armed Canada (CAF) refunds seeking help with a combat deployment history, as a dimensional measure of PTSD psychopathology, with symptoms of excessive arousal. We assume that the NM-MRI signal in tail LC is positively correlated with the severity of excessive wakefulness symptoms measured using the clinician-administered DSM-5PTSD scale (CAPS-5).

Method

Participants (participants)

Twenty three CAF refund soldiers with a history of combat deployment were recruited from a combat stress injury clinic (Operational Stress Injury Clinic) located at the royal mental health center (Royal Mental Health Center) of Ottawa, ontario. As determined by the clinician-administered DSM-5PTSD scale (CAPS-5), 18 of these persons met the criteria for PTSD in DSM-5. CAPS interviews are conducted by trained raters. See table 1 for all clinical and demographic measurements. The severity of depressive symptoms was assessed using the beck depression questionnaire (Beck Depression Inventory) -II (version 21). Other clinical evaluations included the life event list of DSM-5, the Pittsburgh sleep quality index (Pittsburgh Sleep Quality Index) and the Columbia suicide severity rating scale (Columbia Suicide Severity Rating Scale). Inclusion criteria included: CAF refunds with a history of operational deployment after year 2000, aged between 18 and 65 years. Exclusion criteria included mania/hypomania or a history of mental illness, diagnosis of Substance Use Disorder (SUD) in the last 6 months, suffering from major medical diseases, neurological disorders, traumatic brain injury (or head trauma with loss of consciousness for at least 5 minutes), inability to abstain from alcohol, nicotine, cannabis or caffeine within 24 hours, and current use of stimulants (as may affect NM-MRI signals). The study was approved by the ethical review board located in the mental health center of Royal in Ottawa, and participants provided written informed consent.

MRI acquisition

Magnetic Resonance (MR) images of all study participants were acquired on a Siemens 3T PET BIOGRAPH mMR scanner using a 12-channel head coil. NM-MRI images were collected by using a 2D gradient response echo sequence (2D GRE-MT) of magnetization transfer contrast, using the following parameters: repetition Time (TR) =337 ms; echo Time (TE) =3.97 ms; flip angle = 50 °; in-plane resolution=0.43×0.43mm ² The method comprises the steps of carrying out a first treatment on the surface of the Partial brain coverage of field of view (FoV) =165×220; matrix = 384 x 512; number of layers = 10; layer thickness = 3mm; layer gap = 0mm; magnetization transfer frequency offset = 1200Hz; number of shots (NEX) =6; acquisition time = 7.24min. The layer specification scheme consists of: the image stack was oriented along the anterior commissure-posterior commissure line and the top layer was placed 3mm above the third ventricle floor, viewed on the sagittal plane along the middle of the brain.

Full brain high resolution T1 weighted MRI images were also acquired, NM-MRI data were preprocessed using memrage sequences (inversion time=105ms, tr=2500, te=1.69 ms, flip angle=7 °, fov=192×192, matrix=192×256, number of layers=256, isotropic voxel size=1 mm, acquisition time=5.47 min.

Preprocessing of NM-MRI images

The initial pre-processing step is performed using SPM12 to examine the NM-MRI signal from the substantia nigra (5) as in the previous operation. Although the final analysis of the LC signals is performed on the raw spatial NM-MRI images, it is necessary to spatially normalize the NM-MRI images in order to register a generic LC search space from MNI space to raw space for each participant. The NM-MRI scan is first co-registered with the participant's T1 weighted scan. Then, tissue segmentation is performed using the T1 weighted image. NM-MRI scans were normalized for MNI space using the DARTEL formula and using gray matter and white matter templates generated from all study participants. The resampled voxel size of these normalized NM-MRI scans is 1mm, with isotropy. After each of these steps, all images were visually inspected. The visualization template is created by averaging spatially normalized NM-MRI images from all participants.

The subsequent steps were developed using custom Matlab scripts to specifically examine the LC signal. An overly inclusive LC mask was drawn over the visualization template to cover high density voxels along the coracoid axis that span the 4 th ventral-lateral edge of z=xx-xy (see fig. 1). The coracoid tail margin is set by cross-referencing the distance to anatomical landmarks in the brain atlas (inferior colliculus on the head end and posterior recess of the 4 th ventricle on the tail end). A subdivided version of the mask was made by dividing into 3 extended beak tail sections of equal length. The inverse of the flow field generated in the spatial normalization step is then used to twist the over-inclusive full LC mask and the subdivided mask to the original space, and the twisted over-inclusive full LC mask can then be used to define a search space within which the LCs of each participant are found. LC is segmented in this space using a cluster formation algorithm, defined as the 6 adjacent voxels (2.58 mm) with the highest average signal ² ). This operation is repeated for the right and left LC. The contrast to noise ratio (CNR) of each voxel v in a given axial layer is determined by NM-MRI signalsThe relative difference of intensity I with respect to the reference region RR in the same layer is calculated: CNR (CNR) _v ＝(I _v -mode(I _RR ))/mode(I _RR ). We use a central bridge of the reference area known to have a low NM concentration, which is defined by a circle of radius xx mm and whose centre distance connects the axes x mm of the left and right LCs. Each axial layer in the original space is identified as belonging to LC section 1 (beak-extending section), section 2 (middle section) or section 3 (tail section) based on which of the 3 subdivided LC masks is present on that layer (LC section is defined as matching the mask covering the brighter LC voxels if 2 of these masks are present on the same level). LC signals for each of the three segments are calculated by averaging NM-MRI CNR values for all voxels determined to fall within the segment (e.g., if one segment covers 2 axial layers on the right and left in the original space, this would be the average of CNR from 24 voxels, 24 voxels = 6 voxels/LC x 2 layers).

Statistical analysis

Final statistical analysis was calculated using Matlab. Partial correlation examined the relationship between LC NM-MRI signals and clinical measurements including covariate age, physiological gender and PTSD diagnosis. According to the Lilliefors test, key measurements (LC NM-MRI signal, excessive wake severity and depression severity) were found to be normally distributed, thus supporting us to use parameter statistics.

Results

This samples of the refurbished soldiers with a history of combat deployment showed relatively high levels of excessive arousal and depression symptoms (present in those diagnosed with and not with PTSD, see table 3). As hypothesized, the NM-MRI signal in tail-end LC correlated significantly positively with the severity of CAPS-5 over-awakening symptoms (r=0.52, p=0.019, control of the depression severity, PTSD diagnosis, age and physiological gender bias correlations; see fig. 2). Consistent with studies conducted in other populations, we observed a significant negative correlation between tail-end LC NM signal and depression severity (BDI-II overall severity score, r= -0.48, p=0.033, controlling excessive arousal severity, age, physiological gender and PTSD diagnosis)Is a partial correlation of (a). Finally, although there are very few samples of participants that do not meet the PTSD standard (n=5), we tested whether there is evidence that PTSD diagnosis has an effect on NM-MRI signals (fig. 4). We found that there was a trend degree effect of trend significance in this analysis (t ₂₀ -1.0, p=0.32, controlling linear regression of excessive arousal severity, depression severity, age and physiological gender). The efficacy of this final analysis is severely deficient and we hypothesize that our trending effect will play an important role in future larger scale studies.

Taken together, the above results indicate that LC activation is altered in mental diseases such as PTSD and depression. In particular, LC NM-MRI signals are significantly positively correlated with excessive wakefulness symptoms in individuals with PTSD. Our current study also correlates increased LC-NE activity and its correlation with excessive arousal symptoms

Furthermore, in studies directed to pharmacological intervention by PTSD, many adrenergic drugs have been shown to be somewhat useful in treating symptoms associated with a population of excessive arousal symptoms. In particular, there is some evidence supporting nightmares for treating individuals with PTSD with prazosin as an alpha-1 adrenergic receptor antagonist. In particular, it has proven helpful to the refurbished soldiers suffering from excessive wakefulness symptoms associated with PTSD. However, prazosin has also proven ineffective in other individuals with PTSD, further demonstrating the complexities associated with this situation. For example, in a meta-analysis of soldiers, clinical studies involving multiple drugs have been analyzed, focusing specifically on the efficacy of each drug. Here, only 6% of individuals had a complete, successful response to prazosin in 106 trials. 51% of individuals do not respond at all to the drug. PTSD is well known as a heterogeneous disease, and thus requires more research to further identify biomarkers associated with this disease.

In terms of a negative correlation between LC NM-MRI signals and depression severity, major depression is also assumed to have altered LC-NE system activity, and our results support the use of NM-MRI as a biomarker for MDD. In pharmacological studies focused on the innervation targets of major depressive disorder, NE receptors for blue spots have been studied. Here, when the selective NE reuptake inhibitor reboxetine (reboxetine) is administered to an individual suffering from depression, drug s=has an efficacy very similar to tricyclic antidepressants. Other drugs targeting the serotonin system and the norepinephrine system (serotonin and norepinephrine reuptake inhibitors) also show utility in treating symptoms associated with depression. Furthermore, in postmortem studies on individuals with depression, a significant change in NE neuron density and a decrease in NE transporter binding were observed in the blue spots of individuals with depression. Taken together, our results are consistent with the current literature, indicating that individuals suffering from depression have reduced NE and thus cause a change in LC-NE activity.

Concerning the method we used in this study, we have previously demonstrated the utility and effectiveness of this method in capturing alterations in the dopamine system. In this study we could further verify the semi-automated method for extracting LC NM-MRI signals in LC. Here, our method provides a unique approach for NM image analysis of LCs. With this and our observed results in capturing and analyzing NM data, we are confident about our methods and their ability to capture LC-NE system changes, particularly in clinical settings.

Furthermore, by utilizing novel neuroimaging methods within the field of psychiatry, the challenges faced by researchers in the past who have studied dopamine and norepinephrine neurotransmitter systems can be overcome. In particular, using this approach we can increase the in-plane resolution of the LC and thus better capture the changes associated with LC activity. NM-MRI is also a novel imaging technique that has not been used in PTSD studies before, thereby adding a unique aspect to our current study.

Limitations of our current study include small sample size, lack of defined healthy controls, which led to a significant trend in our findings in LC NM-MRI signals and overall PTSD diagnostics. To address these limitations in the future, the recruitment of our PTSD group and established control groups should be increased. In PTSD-related studies, it is difficult to find large healthy control groups, since many individuals who have experienced traumatic events but have not continued to develop PTSD have other potential mental health problems, we compared our PTSD group with individuals who have not had PTSD but had depression. Furthermore, with respect to the correlation between LC NM-MRI signals and PTSD diagnostics, an increase in sample size may make this correlation more pronounced, and we hypothesize that the significance trend we observe here will reach significance if a truly healthy control is incorporated.

In summary, the results of this study demonstrate that NM is a biomarker for PTSD and depression and supports the use of our segmentation-based algorithm to measure NM in patients with these diseases. The correlation between LC NM-MRI signals and PTSD and depression provides clinical evidence supporting altered NE activity and thus provides further evidence to support the role of the NE system in both disorders. This study may also provide insight into the noradrenergic targets for future treatment of both disorders.

Table 3: participant demographic and clinical data

Example 4: NM as a biomarker for PTSD using segmentation-based methods。

SUMMARY

Current studies propose neuromelanin sensitive MRI (NM-MRI) as a novel biomarker capable of targeted treatment against the population of excessive wakefulness symptoms in PTSD. These symptoms lead to severe dysfunction and suicidal tendencies, and no specific neuroscience-based treatment is currently available. NM-MRI is a short, non-invasive MRI scan that can provide a practical and reliable marker for excess Norepinephrine (NE) in PTSD, and thus make neurobiological informed therapeutic decisions. We propose to test this approach by correlating the symptoms of excessive arousal with NM-MRI signals in the current regimen and using NM-MRI to predict the response of treatment in subsequent clinical trials.

Neuromelanin is a pigment that causes NE neurons in the Locus Cerulosa (LC) to appear blue. It is formed by NE metabolism and slowly accumulates throughout the life cycle. Verification work by our team has established that unlike most neurochemicals, NM content can be measured at high resolution using a specialized MRI sequence NM-MRI. This approach is practical for numerous clinical applications: it is non-invasive and can run on any MRI scanner within 10 minutes. Work by our team and others suggests that NM-MRI can provide a neurochemical basis for critical PTSD intrinsic biological phenotypes, excessive sympathetic nerves, and excessive wakefulness. Thus, this technique can provide a stable measurement of NE imbalance in PTSD, but the method has not been tested in PTSD and is very innovative and novel.

Excessive NE function may be a key component of PTSD; however, this may only be applicable to certain individuals, such as those who exhibit excessive wakefulness. One of the main functions of the central NE system is to promote arousal, and clinical and preclinical work has linked excessive arousal to excessive NE activity. Thus, NM-MRI can guide treatment decisions, consistent with future clinical practices where treatment is selected based on objective neurobiological measurements rather than subjective clinical measurements removed from neurobiological underlying pathology.

We propose to recruit 60 individuals with a history of trauma (mainly from combat stress injury clinics), 30 of which would meet CAPS-5PTSD criteria. This trauma exposure panel will be a representative sample of a study covering the complete PTSD phenotype and promoting neurobiological relevance for specific symptoms inspired by RDoC. All participants will receive MRI scans and clinical evaluations. MRI work will consist of NM-MRI scans and functional MRI scans performed during fear conditioning. The LC NM-MRI signal will be measured by an automated LC segmentation procedure.

For functional MRI scans, fear-related activation of LCs will be measured in the original space using segmented LCs as localizers. The analysis will test whether clinical and physiological measurements of wakefulness correlate with NM signals in LC and LC activation during fear conditioning as shown by our experimental data.

Exploratory analysis will investigate the relationship of LC NM signal to activation of structures like amygdala and prefrontal cortex within the fear circuit. This study will lay the foundation for subsequent testing of pharmacological and non-pharmacological interventions to address the symptoms of excessive wakefulness and their association with neurobiology. It would therefore provide a novel and useful disease and therapeutic biomarker, which we propose to introduce into the clinical care of PTSD.

Background

PTSD is a heavy and common mental health problem for armed forces to refund troops. Of the regular army refunds who participated in the combat deployment between 1998 and 2015, 16.4% reported to have PTSD. Excessive arousal is one of the underlying symptoms of post-traumatic stress disorder (PTSD). This group of symptoms is characterized by excessive alertness, excessive startle, irritability or reckless behavior, and sleep disorders. Excessive arousal is common in PTSDs and can be very detrimental, leading to disability, physical health problems and suicide. Early and relatively effective treatment of military-related PTSDs would help mitigate these deleterious downstream effects. While typing of PTSDs is currently dependent on clinical assessment, an increasing understanding of the neurobiological mechanisms behind PTSD etiology opens the door to the future where biometrics will become the method of choice to distinguish between different pathologies within PTSDs. While there is currently a peripheral physiological assessment for excessive arousal, this group of symptoms may depend on disorders within the central nervous system, while it may be best, but more difficult to find, to track biomarkers of excessive arousal from sources.

Furthermore, to be useful for treatment, biomarkers must be practical in order to be widely implemented in a clinical setting, and must help indicate optimal treatment strategies.

In the present proposal, we will test the utility of a novel putative biomarker neuromelanin sensitive MRI (NM-MRI), which is practical, reliable and can be associated with drug treatment strategies. NM-MRI is an imaging method that can provide a practical and targeted assay for the central Norepinephrine (NE) system that can identify a sub-population of PTSD patients with an NE imbalance, thereby allowing targeted therapy using existing or experimental drugs (targeted to the NE system) or using targeted psychotherapy strategies to address this imbalance.

The NE system in the brain is thought to be a critical site of PTSD imbalance, which is related to its role in stress response, wakefulness and fear memory consolidation. A recent influential study showed convincing evidence that excessive arousal of individuals with PTSD was related to the activity of blue spots (LC), where NE neurons were located in the brain. This demonstrates a decades of history of theory that links the NE system to excessive arousal based on preclinical work as well as human PET imaging and genetic studies. The NE system is also a common important target for PTSD treatment, including NE reuptake inhibitors (SNRI and SNDRI) and the α1-adrenergic receptor antagonist drug prazosin, which can be effective in treating excessive arousal in some individuals. Furthermore, the development of drugs for PTSD has an effect on NE receptors: an ongoing clinical trial on epipiprazole (brixpiprazole) is testing the target engagement (in pupil diameter) of LC NE neurons, iloperidone (iloperidone) is a drug of interest due to its high affinity for NE receptors, and recent findings indicate that treatment with propranolol prior to wound memory reactivation is useful. Thus, a specific biomarker of NE imbalance in PTSD would be a very useful tool for characterizing afflictions, guiding treatments, and assessing the efficacy of these new experimental treatments on target patients. Neuromelanin sensitive MRI (NM-MRI; see FIG. 3) may provide such novel tools.

The Neuromelanin (NM) is a dark pigment formed by the decomposition of catecholamine neurotransmitters NE and dopamine, and is present only in catecholamine neurons in the brain (NE neurons in LC and dopamine neurons in substantia nigra). This pigment has unique properties that make it the only one of the neurochemicals that can be quantified using MR imaging at high spatial resolution, thus allowing interrogation of the function of the NE system without the invasive and costly aspects of PET imaging. It has similar advantages of measuring brain chemistry to inform drug therapy, and it is relatively suitable for large scale applications because it is inexpensive, short (< 10 minutes), non-invasive, and available on any 3T MRI scanner. Since NM accumulates gradually over the life cycle and does not decompose, NM-MRI has the additional advantage that it is a very stable measurement method with high retest reliability. In LC, this signal may provide an alternative measure of persistent NE imbalance. This beneficial property ensures that the signal does not change (as opposed to some putative biomarkers) in response to transient fluctuations in mental state or symptom severity.

We have gained a wealth of expertise in the acquisition and analysis of such imaging methods. Our validation and development work demonstrated that NM-MRI is indeed sensitive to NM and is a marker of catecholamine neuronal function and is associated with excessive arousal symptoms in PTSD, consistent with the correlation of this signal with anxiety and autonomic nerve function measurements in other populations. Although this evidence supports its correlation with PTSD, no PTSD NM-MRI study has yet been published.

While stability of NM-MRI signals over time allows their use as a measure of long term NE system function, their utility may be enhanced when combined with information from state dependent NE system function measurements that are able to measure recent NE functions. One such measurement is LC activity measured during the fear conditioned reflex paradigm using BOLD fMRI. This paradigm will provide additional information for LC NM-MRI signals, as LC activity promotes fear conditioning and generalization, which is a model of PTSD pathophysiology. Another supplementary measurement here is pupillometry, and pupil dilation assessment is a sensitive measurement of reflex, autonomic responses mediated by neural activity generated within the locus blue.

NM-MRI signals in LC are biomarkers of NE imbalance in the central nervous system. Given the link between the symptoms of excessive arousal and NE function in PTSD, we evaluated this signal in Canadian Armed Forces (CAF) refund soldiers with PTSD. This supports an attempt to shift from clinical typing of PTSD to neurobiological subtypes, thereby facilitating targeted therapy [38] and accelerating the discovery of new therapies by pre-selecting persons likely to respond to experimental therapies. NM-MRI signals in LC are correlated with clinical and physiological measurements of NE function in trauma exposed individuals.

The utility of NM-MRI signals in LC as biomarkers of long term NE function in individuals exposed to trauma (and possible modulation of physiological sex in this relationship) was evaluated.

LC NM-MRI signals are positively correlated with the severity of CAPS-5 excessive arousal symptoms, skin conductance response during fear conditioning, and pupil dilation rates of both physiological sexes.

The effectiveness of the BOLD fMRI activation of LC during fear conditioning as a surrogate biomarker of immediate NE function was evaluated as a supplement to NM-MRI signals associated with excessive wakefulness. Exploratory objective 1b: the correlation of LC measurements (NM-MRI and BOLD) with fear-related BOLD activation of brain structures in classical fear loops was assessed [40]. Exploratory goal 1c: LC NM-MRI signals from trauma-exposed individuals who met CAPS-5PTSD criteria were compared to trauma-exposed individuals who did not meet PTSD criteria.

Preliminary data

Verification of NM-MRI as a measure of catecholamine system function

NM-MRI is actually sensitive to neuromelanin: the NM-MRI signal corresponds to the local tissue concentration of NM in the midbrain after death of the human (β=0.87, t114=5.05, p=10-6, mixed effect model, 116 measurements, 7 samples). Our in vivo PET imaging studies demonstrated a link between NM-MRI and catecholamine neurotransmitter function (p=0.69, p=0.004, n=18; in this study, the catecholamine detected was not NE, but rather dopamine, which produced a dopamine-related NM-MRI signal in the substantia nigra, in contrast to the NE-related signal in LC). NE and its precursor dopamine enter the same metabolic pathways as lead to NM, so we have validated from the black NM-MRI signal of the dopamine system that we find that it should be well converted to the LC NM-MRI signal of the NE system.

Correlation of NM-MRI in blue spots with excessive arousal symptoms in PTSD

We examined LC NM-MRI signals in the dataset of 24 individuals treated in the battle stress injury (OSI) clinic of the royal wortmax mental health center, 19 of which met CAPS PTSD criteria. LC NM-MRI signals were measured using established methods: the percent signal change in LC was calculated relative to a reference area without neuromelanin.

The psychopathology of this sample was measured using CAPS-5. According to this scale, we examined the population of excessive arousal symptoms. Consistent with our hypothesis, LC NM-MRI signals correlated positively with the severity of CAPS-5 over-awakening symptoms population (r=0.52, p=0.019, control age, physiological gender, PTSD diagnosis, bias correlation of depression severity [ BDI total score ], see fig. 1). Similar to previous reports, LC NM-MRI signals are inversely related to depression severity (r= -0.48, p=0.033, a partial correlation controlling age, physiological gender, PTSD diagnosis and excessive arousal severity).

Efficacy calculation

From our data, we expect that the magnitude of the effect of the relationship between LC NM-MRI signals and excessive arousal symptoms is close to r=0.52. Even with slightly lower effect amounts (r=0.4) for conservation, we will have 90% efficacy to detect a significant correlation with a sample size of n=60. Therefore, we propose to recruit 60 participants with a history of trauma. Based on preliminary data from the OSI clinic for wound exposure of the unwarped soldier (fig. 1), as well as internal data from the clinic, we expected that a wide range of overwake symptom severity would be observed in this population, thus supporting our analysis of this symptom dimension, a method consistent with the RDoC method [9].

Study participants

Study participants were male and female armed forces in Canada, who had a history of combat deployment (representing trauma exposure), aged between 18-55 years. Individuals over 55 years of age are not enrolled to minimize confusion due to early LC degeneration in some elderly individuals. In agreement with the objectives of the RDoC initiative [9], our study also examined a group of trauma-exposed individuals representing all trauma-related symptoms. In view of the heterogeneity of PTSD, this approach maximizes our ability to identify neurobiological correlations by recruiting individuals who experience similar wounds but different areas of symptoms of interest (excessive arousal). We maximized the variability of this measurement of interest, but minimized the variability of other clinical measurements (e.g., wound exposure, complications, lifestyle factors specifically matched in PTSD control). N=66 participants need to be recruited to meet our goal of 60 participants having available data. Individuals co-with mental disorders will be eligible to participate in the study. The exclusion criteria included: active suicidal intent, major unstable medical conditions, agonist treatment (> 1 month time), pregnancy, neurological disorders, and the presence of any MRI scan contraindications. Are not excluded by material usage or by medical history (except for agonists, which may affect NM-MRI signals). Inclusion criteria such as these are consistent with many PTSD studies, attempting to capture representative samples based on the prevalence of substance use disorders and heterogeneity of prescribed agents in this population (see reverts to previous reviews for further discussion of these issues).

The refund of the military recruiting a combat deployment history in the community will proceed in parallel with the oral phase of delivery (e.g., participants recruited from the OSI clinic) through classified advertising. To facilitate a secondary analysis comparing wound-exposed individuals with and without PTSD, we will ensure that n=20 (total samples from 60 wound-exposed refund soldiers) of individuals who did not meet CAPS-5PTSD criteria were enrolled. This participant classification matched exactly the proportion of individuals diagnosed with PTSD at the OSI clinic (66% in 2017). To correctly determine both physiological sexes and support physiological sex effect analysis, we will ensure that at least 40% of the samples are females (we now collect neuroimaging data from this clinic including 7/24 females, 29%).

Clinical measurement

After screening and consent, all study participants underwent a 3-4 hour test session at the Royal wortmannin mental health center consisting of MRI scans, physiological measurements and clinical interviews. The following clinical measurements will be collected for all participants by interview or self-report: interview measurement: the clinician-administered PTSD scale (CAPS-5, the primary clinical measurement of interest), DSM 5 structural clinical interview (SCID-5); self-reporting measurements: PTSD list-5 (PCL-5), pittsburgh sleep quality index, life event list, beck depression questionnaire (BDI-II), beck anxiety questionnaire (BAI), dissociation experience scale, mood adjustment difficulty scale, chemical abuse and dependency scale (CUAD), columbia suicide severity rating scale.

Magnetic Resonance Imaging (MRI) and physiological measurements

All subjects will be subjected to MRI scans using a 3T MR-PET Siemens Biograph scanner at the royal wortmannin mental health center. This would include structural scans (T1 and T2 weighted scans), NM-MRI scans, and BOLD functional MRI scans performed during fear conditioning. The total scan time for each participant was about 50 minutes. All scans used 32 channel head coils. NM-MRI scanning is 2D-GRE scanning using magnetization transfer contrast and the following parameters: tr=260 ms, te=2.68 ms, flip angle=40°, in-plane resolution=0.39×0.39mm, fov=162×200, matrix=416×512, number of layers=10, layer thickness=3.0 mm, magnetization transfer frequency offset=1200 Hz, excitation number=8, acquisition time=8.04 minutes. BOLD functional MRI images were acquired at high temporal and anatomical resolution with the following sequence parameters: 66 layers; tr=864 ms; te=34.8 ms; flip angle = 52 °; matrix = 88 x 90; fov=208×97.8mm2; voxel size = 2.3mm, isotropic; multiband acceleration factor=6. Spin echo sequences and B0 field patterns are also collected to help correct for distortion and field inhomogeneities in BOLD images.

BOLD imaging occurs during a fear conditioned reflex paradigm consisting of 3 different aversive conditioned reflex tasks, each task lasting 7 minutes. During each task, the participants see two computer-generated neutral faces (created using FaceGen; www.facegen.com). Each task has a different face. In each task, a mild shock (unconditional stimulus) was applied to the tibia after presentation of a face (conditioned stimulus, cs+) in 33% of the trials. The other face (control stimulus, CS-) was presented without electric shock. Skin Conductance Response (SCR) was calculated using the Ledalab in Matlab using a method called Continuous Decomposition Analysis (CDA). CDA breaks down SCR data into continuous signals of temporal activity (peak, i.e., after cs+ event) and stressor activity ("baseline"). In practice, pharmaceutical Max (maximum peak after a single event) is the average of each event type (cs+, CS-). The final value of the comparison is cs±contrast. The positive ratio indicates successful conditional reflection for cs+.

Pupil response measurements were obtained using a neurotics PLR-3000 hand-held pupillometer, a validated instrument that can produce highly repeatable measurements [48]. The soft cup of the pupillometer is placed against the eye to minimize external light. The subject kept the eyes open without testing and gazed at a point on the wall 10 feet away. The protocol for using this device was adapted from another study on PTSD.

The measurement is completed after 5-6 seconds during which the pupil diameter at rest and in response to light pulse stimulation is measured. This procedure was repeated under 3 ambient light conditions (bright, dim and dark: 350 lux, 5 lux and 0 lux, respectively) with 4 minutes intervals therebetween to adjust the light intensity. The light pulse is characterized as follows: positive pulse stimulation, pulse intensity=50 uW, background intensity=0 uW, measurement duration=6.02 s, pulse duration=0.30 s, pulse s (for "bright" conditions); or positive pulse stimulation, pulse intensity=10 uW, background intensity=0 uW, measurement duration=12.03 s, pulse duration=0.17 s, pulse s (for "dim" and "dark" conditions). Resting blood pressure will also be measured prior to clinical evaluation.

Statistical analysis

NM-MRI signals in the LC were measured directly from NM-MRI images using custom automated methods [49] (FIG. 3). The primary analysis is linear regression, predicting CAPS hypersomnia score, temporal skin conductance response to fear condition stimulation, or pupil dilation rate from LC NM-MRI signals, including age, physiological gender, and BDI severity as covariates. The quadratic linear regression analysis test predicts a model of the severity of excessive arousal symptoms from the LC NM-MRI signal and also includes physiological measurements (skin conductance, blood pressure and pupil diameter) as covariates to determine if the independent contribution of the LC NM-MRI signal to the predicted severity of symptoms exceeds the contribution of the more convenient peripheral measurements. Secondary analysis the refund soldiers (n=40) meeting CAPS-5PTSD criteria were compared to the wound-exposed refund soldiers without PTSD (n=20) using a linear regression analysis to control age, physiological gender and severity of depression. Additional secondary analysis will take into account physiological gender effects.

Analysis of functional MRI data will provide segmentation of LCs using NM-MRI images as subject-specific LC localizers and thereby allow examination of LC activity during fear conditioning. This method improves the estimation of BOLD fMRI activity in LC compared to the standard fMRI method [47] (fig. 2). The final linear regression analysis will include LC NM-MRI signals and LC BOLD activation (comparison of conditional stimulus minus unconditional stimulus) to determine if they are complementary measures of long-term and short-term NE system conditions and predict excessive arousal symptoms and physiological measurements independently. We will also explore the correlation of our LC measurements with activation of fear-related brain structures in classical fear circuits like amygdala, hypothalamus and prefrontal cortex (fig. 2), thus developing an integrated model of brain mechanisms that correlate NE dysfunction with clinical symptoms.

Analysis based on physiological and social gender

There are important physiological sex differences among the fear system and the physiological sex-specific risk factors of PTSD [50], and there is limited study of autonomic dysfunction in women with PTSD [51]. Therefore, we will check if physiological gender is the regulator of the relationship between LC NM-MRI signals and excessive arousal. We will do this by including physiological gender LC signal interactions in a linear regression model that predicts the measure of excessive arousal. We will recruit at least 40% of women in our samples to support this analysis. We will also perform our preliminary analysis specifically for males and females to ensure that the effect values of the two groups are similar, thereby supporting their use in both physiological sexes.

Example 5 verification of the algorithm in all indications

Different neurological and psychiatric disorders are associated with changes in neuromelanin in two major areas, the substantia nigra pars compacta (SNc) and the blue spot (LC). It is quite difficult to distinguish between different diseases with similar clinical manifestations based on the main symptoms alone, as these symptoms often overlap between related disorders.

The present disclosure describes the combined use of two fully automated algorithms to measure the concentration and volume of Neuromelanin (NM) in two different brain regions (SNc and LC) to improve the ability to differentiate between related disorders. In the present disclosure, voxel-based analysis algorithms (previously invented and patented at university of Columbia) are used to measure NM in SNc. However, since LC is much smaller and may not be well suited for voxel-based analysis on 3T MRI (the most clinically used scanner), the university of wortmannin invents a new algorithm to measure NM in LC. Such LC algorithms are referred to as segmentation-based analysis algorithms. The present disclosure describes a combination of two algorithms in a software package that can be used to help diagnose and differentiate between neuropsychiatric disorders that are difficult to differentiate based on symptoms alone.

In the present disclosure, voxel-based analysis algorithm software is used to measure NM in SNc, and segmentation-based analysis algorithms are used to measure NM changes in LC. The software will report to the physician the NM levels and volumes in both brain regions. The combination of these two algorithms may increase the ability to distinguish between related neurological conditions. Inclusion of these algorithms in fully automated software enables them to be widely used clinically.

An unmet medical need to be addressed herein is the ability to distinguish between related conditions such as parkinson's disease, multiple system atrophy and progressive supranuclear palsy, and different dementias such as, for example, alzheimer's disease and lewis body dementia. An increase in the ability to distinguish between related conditions should increase the clinical effect of the software and ultimately will drive the wider use of NM software as a medical device, rather than using either algorithm alone.

Results and support data from indications

Fig. 13. Software automatically applies one mask to select brain regions for SNc voxel based algorithms and automatically applies a second mask to select brain regions for LC segmentation based algorithms.

Parkinson's disease

Both algorithms were validated by analyzing SNc and NM in LC in parkinson patients compared to healthy controls. When compared to healthy controls, voxel-based analysis algorithms found significant differences in NM contrast to noise ratio (CNR) in SNc of Parkinson's patients, whereas segmentation-based algorithms did not find any differences in LC of the same patient population (FIG. 14). This is consistent with previous literature, indicating that the major change in NM in Parkinson's disease occurs in SNc, but this was done for the first time in a single brain scan using a dual fully automatic algorithm.

Diagnosis of Alzheimer's disease

These algorithms were validated in patients with alzheimer's disease compared to healthy controls. LC is analyzed using a segmentation-based analysis algorithm, while SNc is analyzed using a voxel-based algorithm. In contrast to parkinson's disease patients, the segmentation-based analysis algorithm found significant differences in NM in the LCs of alzheimer's disease patients when compared to healthy controls, whereas the voxel-based algorithm did not find any differences in SNc of the same patient population (fig. 15). This is also consistent with previous literature, indicating that the major NM changes in AD occur in LC. The combination of these two data sets shows that voxel-based algorithms and segmentation-based algorithms are able to detect significant changes in NM in different brain regions simultaneously when used together.

Prediction of neuropsychiatric symptoms of Alzheimer's disease

A combination of both algorithms is used to help determine that a neuropsychiatric symptom is present in an alzheimer's patient. LC was analyzed using a segmentation-based analysis algorithm, while SNc was analyzed using a voxel-based algorithm (fig. 16). Segmentation analysis for LC found a significant increase in NM in LC compared to healthy controls. Voxel-based algorithms showed a significant reduction in NM compared to healthy controls. This is the first indication that NM levels in SNc can significantly predict the presence of neuropsychiatric symptoms.

Schizophrenia (schizophrenia)

The algorithm was validated in schizophrenic patients. LC was analyzed using a segmentation-based analysis algorithm, while SNc was analyzed using a voxel-based algorithm (fig. 17). It has been previously published that NM levels in SNc vary in schizophrenic patients, but it is not clear whether NM levels in LC vary. We found that NM levels in SNc were significantly altered compared to healthy controls, and that higher levels were associated with increased severity of psychosis as measured by PANSS scale. Importantly, we did not find any significant change in NM levels in LC. This is important because Alzheimer's disease patients develop psychotic symptoms (hallucinations and delusions) that overlap with schizophrenic symptoms. Importantly, this is the first data to suggest that NM levels measured in two brain regions may be helpful in diagnosing these diseases. Importantly, the psychotic symptoms of schizophrenia are associated with an increase in NM in SNc only, while the neuropsychiatric symptoms of alzheimer's disease are associated with a decrease in NM in SNc and an increase in NM in LC.

PTSD

The combination of algorithms was validated in patients with post-traumatic stress disorder (PTSD).

Voxel-based algorithms showed that disease severity was not significantly correlated with NM levels in SNc compared to healthy controls (fig. 18). Segmentation-based algorithms showed significant changes in LC compared to healthy controls, and increased levels of NM correlated significantly with disease severity (right panel).

Major depressive disorder

Algorithms were validated in major depressive patients compared to healthy controls. LC was analyzed using a segmentation-based analysis algorithm, while SNc was analyzed using a voxel-based algorithm (fig. 19). Voxel-based algorithms showed no significant difference in NM levels in SNc compared to healthy controls (left panel). Segmentation-based algorithms showed that NM levels in LC showed a trend to decrease with increasing disease severity compared to healthy controls (right panel).

Cocaine dysuse

Algorithms were validated in patients with cocaine dysuse as compared to healthy controls. LC was analyzed using a segmentation-based analysis algorithm, while SNc was analyzed using a voxel-based algorithm (fig. 20). Use of voxel-based and segmentation-based algorithms in cocaine usage barriers. Voxel-based algorithms showed that NM increase in SNc was significantly correlated with cocaine usage impairment compared to healthy controls (left panel). Segmentation-based algorithms show that there is a trend of NM reduction in LC compared to healthy controls (right panel).

In one embodiment, the following summary table indicates various NM levels in SN and LC, and how this directs the diagnosis of a patient's particular disease or symptom severity.

Example 6 clinical trial verification of all disease indications

One of the difficulties faced in developing new therapies is the need to increase the recruitment and exclusion of patients that are unlikely to respond to the patients that are most likely to benefit from treatment. Accidental recruitment of the wrong patient is a contributor to the failure of the clinical trial of the new therapy and may lead to delays in the development of other effective therapies. This is particularly necessary in PD because it is well known that it is clinically difficult to distinguish Parkinson's disease from conditions in which Parkinson's disease may be a major symptom, including Parkinson's multisystemic atrophy (MSA-P) and Progressive Supranuclear Palsy (PSP) or similar conditions, such as Essential Tremor (ET) and Idiopathic Normal Pressure Hydrocephalus (iNPH). A retrospective analysis showed that the diagnostic accuracy of the general neurologist with respect to PD was 75%, whereas for atypical parkinson's disease including PSP and MSA, the accuracy of the general neurologist was only 61% and the accuracy of the dyskinesia expert was 71%. The authors concluded that high misdiagnosis rates increased noise in PD clinical trials. One study with ET showed that 25% of patients initially diagnosed with PD were later found to have ET. Similarly, censored reports on iinph state that iinph may be difficult to distinguish from PD when gait dysfunction is present.

Imaging modalities that show promise in differential diagnosis of PD have a number of limitations that reduce their utility as biomarkers in therapeutic clinical trials. These include tau positron emission tomography (T-PET), which may be helpful in diagnosing PD and PSP, and DaTscan, which may be helpful in differentially diagnosing PD and ET and PD and iNPH [16]. Both of these methods are expensive, require IV indwelling, expose the patient to the radiotracer, require lengthy preparation and scanning times, and require the use of a PET scanner and SPECT scanner, respectively. In summary, these limitations make widespread deployment in large clinical trials impractical.

This study assessed NM-MRI as a biomarker to aid in the differential diagnosis of PD from patients with PSP, MSA, ET and iNPH. A total of fifty subjects (10 per group) diagnosed with PD (pre-treatment), PSP, MSA-P, ET and innph (pre-bypass intervention) and 10 healthy controls will undergo NM-MRI scans and clinical evaluations, including medical history, neurological examinations, including unified parkinson's disease rating scale (Movement Disorder Society's Unified Parkinson's Disease Rating Scale, MDS-UPDRS) of the dyskinesia association. The absolute concentration and volume of neuromelanin in SNc and LC of each brain hemisphere will be determined by Terrans NM-SAMD. In addition, a Terran unique voxel-based analysis will also be applied to determine the voxel-based pattern of each condition. The main result will be the absolute NM concentration and volume differences in SNc and LC. The secondary result will be a local specificity voxel-based NM pattern specific for each disorder in SNc and LC. Recruitment will take 12 months. Specifically, PD, MSA, PSP and ET patients underwent recruitment of the iinph patients prior to bypass intervention.

Studies have verified that biomarker NM can distinguish between parkinson's disease lineage disorders. NM-MRI may improve the design and performance of future clinical trials by helping to distinguish PD, PSP, MSA, ET from iNPH. This is an inexpensive, rapid, readily available, non-invasive biomarker that is widely used in clinical trials to increase the chance of success of future treatments targeting PD or related conditions by reducing the likelihood of recruiting misdiagnosed patients that would otherwise impair the potentially valuable therapeutic effect. Finally, a rich patient population can reduce the cost of future clinical trials by reducing the number of patients required to reach statistical significance.

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Equivalent content

The foregoing merely illustrates the principles of the disclosure. Various modifications and alternatives to the described implementations will be apparent to those skilled in the art in light of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise various systems, arrangements and procedures which, although not explicitly shown or described herein, embody the principles of the disclosure and are thus included within its spirit and scope. Those of ordinary skill in the art will appreciate that the various exemplary embodiments may be used with each other and interchangeably. Furthermore, certain terms used in the present disclosure, including the description, drawings, and claims, may be used synonymously in certain circumstances, including, but not limited to, data and information, for example. It will be understood that although these terms and/or other terms that may be synonymous with each other may be used synonymously herein, there may be circumstances when these terms are not intended to be synonymously used. Furthermore, to the extent that prior art knowledge is not expressly incorporated by reference above, the entire contents of which are expressly incorporated herein. All publications cited are incorporated by reference in their entirety.

Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the stated limits, ranges excluding either or both of those included limits are also included in the disclosure.

Claims

1. An in vivo method of determining the progression of alzheimer's disease in a subject over time, the method comprising:

(ii) After step (i), obtaining a second NM-MRI scan at a second point in time;

(iii) Comparing the first neuro-melanin magnetic resonance image with the second neuro-melanin magnetic resonance image, thereby determining whether a change in the level, signal, and/or concentration of neuro-melanin occurs between the first time point and the second time point.

2. The method of claim 1, wherein alzheimer's disease is progressing if the change in the level, signal, and/or concentration of neuromelanin at the second time point is more than about 1%, more than about 2%, more than about 3%, more than about 4%, more than about 5%, more than about 6%, more than about 7%, more than about 8%, more than about 9%, more than about 10%, more than about 11%, more than about 12%, more than about 13%, more than about 14%, more than about 15%, more than about 20%, or more than about 25% less than the level, signal, and/or concentration of neuromelanin at the first time point.

3. An in vivo method of diagnosing alzheimer's disease, the method comprising:

4. The method of any one of the preceding claims, wherein diagnosis of alzheimer's disease is provided if the change in the level, signal, and/or concentration of neuromelanin at the second time point is more than about 1%, more than about 2%, more than about 3%, more than about 4%, more than about 5%, more than about 6%, more than about 7%, more than about 8%, more than about 9%, more than about 10%, more than about 11%, more than about 12%, more than about 13%, more than about 14%, more than about 15%, more than about 20%, or more than about 25% less than the signal and/or concentration of neuromelanin of the alzheimer's disease at the first time point.

5. A method of diagnosing a patient with alzheimer's disease, the method comprising:

(i) Measuring the level of neuromelanin;

(ii) Comparing the level of neuromelanin to a standard control value,

(iii) Diagnosis of Alzheimer's disease is optionally provided if the measured levels of neuromelanin are below the standard control value.

6. The method of any of the preceding claims, further comprising determining a first signal intensity from the first neuro-melanin magnetic resonance image and a second signal intensity from the second neuro-melanin magnetic resonance image, wherein the comparing the first magnetic resonance image to the second magnetic resonance image comprises comparing the first signal intensity to the second signal intensity.

7. The method of any one of the preceding claims, wherein the standard control value is a level of neuromelanin present at about the same level in the population of subjects, or the standard control value is an approximate mean level of neuromelanin present in the population of subjects.

8. The method of any one of the preceding claims, wherein the level, signal and/or concentration of neuromelanin is measured using a neuromelanin gradient phantom.

9. The method of any one of the preceding claims, wherein the neuro-melanin phantom concentration gradient is scanned about once an hour, about once a day, about once a week, or about once a month, about once per patient.

10. The method of any one of the preceding claims, wherein the neuro-melanin phantom gradient is scanned daily.

11. The method of any one of the preceding claims, wherein a neuromelanin phantom gradient is scanned for each patient.

12. The method of claims 5-11, wherein diagnosis of alzheimer's disease is provided if the change in the level, signal, and/or concentration of neuromelanin at the second time point is more than about 5% lower or more than about 10% lower than the level, signal, and/or concentration of neuromelanin at the first time point, wherein the first time point is about 1 year, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, or about 10 years apart from the second time point.

13. The method of any one of the preceding claims, wherein diagnosis of alzheimer's disease is provided if the change in the level, signal, and/or concentration of neuromelanin at the second time point is more than about 35%, more than about 40%, more than about 45%, or more than about 50% lower than the signal and/or concentration of neuromelanin at the first time point, wherein the first time point is spaced from the second time point by about 1 year, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, or about 10 years.

14. The method of any one of the preceding claims, wherein the second time point is about 3 months, about 6 months, about 9 months, about 12 months, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, about 10 years, about 15 years, about 20 years, about 25 years, or about 30 years after the first time point.

15. A method of assessing the concentration of neuromelanin in a brain region of interest of a subject, the method comprising:

performing a neuromelanin-magnetic resonance imaging (NM-MRI) scan on the subject;

obtaining a neuromelanin dataset from the NM-MRI scan;

Optionally encrypting the neuromelanin dataset;

uploading the neuromelanin dataset to a remote server;

optionally decrypting the data set;

(ii) Comparing the neuromelanin dataset to a control dataset;

(iv) Generating a report comprising a neuromelanin analysis;

(v) Optionally encrypting the report;

(vi) Uploading the report to a remote server; and

(vii) Optionally decrypting the report.

16. A method of determining whether a subject has or is at risk of developing alzheimer's disease, the method comprising analyzing one or more neuro-melanin magnetic resonance imaging (NM-MRI) scans of a region of interest of the brain of the subject, wherein the analyzing comprises:

(1) If the one or more NM-MRI scans have reduced NM signals compared to one or more control scans performed in the absence of Alzheimer's disease, the subject has or is at risk of developing Alzheimer's disease; or alternatively

17. A method of treating a subject with alzheimer's disease, the method comprising analyzing a neuromelanin magnetic resonance imaging (NM-MRI) scan of a region of interest of the brain of the subject, wherein the analyzing comprises:

(i) Receiving imaging information of a region of interest of the brain at a first point in time;

(ii) Receiving imaging information of the brain region of interest at a second point in time;

(iii) Determining a NM concentration in the brain region of interest at the first and second time points using segmentation analysis based on the imaging information; and is also provided with

(iv) Comparing the NM concentration at the first time point with the NM concentration at the second time point,

wherein the method of treatment further comprises:

(1) If the NM-MRI scan has a reduced NM signal at the second time point compared to the NM signal at the first time point, the method comprises administering one or more Alzheimer's disease therapeutic agents; or alternatively

(2) If the NM-MRI scan has an increased NM signal at the second point in time compared to the NM signal at the first point in time, the method comprises:

(a) Refusing to administer one or more therapeutic agents for Alzheimer's disease; and is also provided with

(b) Repeating steps (i) to (iv).

18. The method of any one of the preceding claims, wherein the MRI scan is neuromelanin sensitive.

19. A method of providing a treatment regimen to a patient, the method comprising performing a NM-MRI scan; acquiring NM signals in a region of interest from the NM-MRI scan; comparing NM signal data from the NM-MRI scan in a region of interest with an age-matched database number; if the NM signal is less than a predetermined value, a corresponding treatment regimen is administered.

20. A method according to any of the preceding claims, wherein the NM-MRI is compared to a standard control.

21. The method of any one of the preceding claims, wherein the patient exhibits symptoms of parkinson's disease or dementia with lewy bodies.

22. A method according to any one of the preceding claims, wherein the NM-MRI scan distinguishes between alzheimer's disease and parkinson's disease and between alzheimer's disease and lewis body dementia.

23. The method of any one of the preceding claims, wherein the subject or patient exhibits one or more symptoms of alzheimer's disease.

24. The method of any one of the preceding claims, wherein the patient is diagnosed with alzheimer's disease without exhibiting symptoms.

25. The method of any one of the preceding claims, further comprising diagnosing the patient as having or not having alzheimer's disease; and informing the user of the diagnosis through the user interface.

26. The method of any one of the preceding claims, wherein the analysis is a segmentation analysis.

27. The method according to any of the preceding claims, wherein the segmentation analysis comprises determining at least one topology pattern within the brain region of interest.

28. The method of any one of the preceding claims, wherein the method further comprises calculating using a value representative of the volume of the nerve melanin segment.

29. The method of any preceding claim, wherein the segmented analysis region of interest is substantia nigra.

30. The method of any preceding claim, wherein the segmented analysis region of interest is a blue patch.

31. A diagnostic system for providing diagnostic information for alzheimer's disease, the diagnostic system comprising:

an MRI system configured to generate and acquire a neuromelanin sensitive MRI scan of voxels or segments located within a region of interest of a brain of a subject and a neuromelanin data series;

a signal processor configured to process the series of neuro-melanin data to produce a processed neuro-melanin MRI spectrum; and

extracting measured values in the region of interest corresponding to neuromelanin at a certain point in time,

comparing the measurement with one or more control measurements taken prior to the certain point in time;

If the measurement is more than about 25% lower than the control measurement, an Alzheimer's disease diagnosis is provided.

32. A method for treating a patient suffering from alzheimer's disease, the method comprising:

a) Administering an initial amount of an Alzheimer's disease therapeutic agent to a patient;

b) Performing successive NM-MRI scans on the patient, monitoring a concentration of neuromelanin in a region of interest of the brain of the patient, and assessing treatment-related adverse events during initial treatment;

c) If during the initial treatment the patient exhibits

i) A decrease in nerve melanin concentration in the brain region of interest of the patient;

ii) no treatment-related adverse or side effects;

increasing the dosage of the therapeutic agent for Alzheimer's disease during the subsequent treatment period;

wherein the Alzheimer's disease therapeutic treatment results in an improvement in Alzheimer's disease symptoms in the patient.

33. The method of claim 32, comprising the steps of:

d) Repeating steps a) -c) until the patient does not exhibit one or more of i) -ii) in step c).

34. The method of any of the preceding claims, wherein the method is used with a second imaging method, wherein the second imaging method is selected from the group consisting of: positron Emission Tomography (PET); structural MRI, including functional MRI (fMRI), blood Oxygen Level Dependent (BOLD) fMRI, iron sensitive MRI; quantitative susceptibility imaging (QSM); diffusion tensor imaging DTI; single Photon Emission Computed Tomography (SPECT), daTscan, and datquat.

35. The method of any one of the preceding claims, wherein the second imaging method comprises Positron Emission Tomography (PET).

36. The method of any of the preceding claims, wherein the second imaging method comprises structural MRI.

37. The method of any of the preceding claims, wherein the second imaging method comprises functional MRI (fMRI).

38. The method of any one of the preceding claims, wherein the second imaging method comprises Blood Oxygen Level Dependence (BOLD) fMRI.

39. The method according to any of the preceding claims, wherein the segmentation analysis comprises determining at least one topology pattern within the brain region of interest, wherein the brain region of interest is one or more alzheimer's disease-like relevant segments.

40. The method according to any of the preceding claims, wherein the segmentation analysis comprises determining at least one topological pattern within the brain region of interest, wherein the brain region of interest is one or more patient-specific alzheimer's disease-like related segments.

41. The method of any one of the preceding claims, wherein the brain region of interest is substantia nigra or locus blurri.

42. The method of claims 1-41, wherein the brain region of interest is ventral substantia nigra.

43. The method of claims 1-41, wherein the brain region of interest is lateral substantia nigra.

44. The method of claims 1-41, wherein the brain region of interest is the ventral substantia nigra.

45. The method of claims 1-41, wherein the brain region of interest is substantia nigra pars compacta (SNpc).

46. The method of claims 1-41, wherein the brain region of interest is the substantia nigra reticula (SNpr).

47. The method of claims 1-41, wherein the brain region of interest is a Ventral Tegmental Area (VTA).

48. The method of claims 1-41, wherein the brain region of interest is a locus bluish.

49. A method of diagnosing a neurological disorder, determining the progression of a neurological disorder over time, or providing a prognosis of a neurological disorder in a subject, the method comprising:

(ii) After step (i), obtaining a second NM-MRI scan at a second point in time;

(v) Comparing a first neuro-melanin magnetic resonance image with a second neuro-melanin magnetic resonance image, thereby determining whether a change in the level, signal, and/or concentration of the neuro-melanin occurs in the SNc determined using the voxel-based algorithm and the LC determined using the segmentation-based algorithm between the first time point and the second time point;

(vi) Providing a diagnosis, progression over time, or prognosis of the neurological disorder based on the difference in NM levels in the SNc between the first scan and the second scan and the difference in NM levels in the LC between the first scan and the second scan.

50. An in vivo method of selecting a treatment regimen for preventing or treating a neurological disorder in a subject, the method comprising:

(ii) After step (i), obtaining a second NM-MRI scan at a second point in time;

(vi) Providing a diagnosis, progression or prognosis of the neurological disorder based on the difference in NM levels in the SNc between the first scan and the second scan and the difference in NM levels in the LC between the first scan and the second scan;

51. A method for distinguishing between motion disorders having similar cardinal symptoms, the method comprising:

(i) Performing a check to determine a unified parkinson's disease rating scale score;

(iii) Obtaining a second NM-MRI scan at a second point in time after steps (i) and (ii);

(vi) Comparing the first neuro-melanin magnetic resonance image with the second neuro-melanin magnetic resonance image, thereby determining whether a change in the level, signal, and/or concentration of the neuro-melanin occurs in the SNc and the LC between the first time point and the second time point;

52. A method of diagnosing a patient having a neurological disorder, the method comprising:

(ii) Comparing the level of neuromelanin in the SNc to a standard control level of neuromelanin in the SNc, and comparing the level of neuromelanin in the LC to a standard control level of neuromelanin in the LC,

(iii) Providing a diagnosis of said neurological disorder if the magnitude or ratio of said SNc and said LC in their respective corresponding regions is lower or higher than said standard control.

53. The method of any one of claims 48 to 52, wherein the method is used with a second imaging method, wherein the second imaging method is selected from the group consisting of: positron Emission Tomography (PET); tau-PET; structural MRI, including functional MRI (fMRI), blood Oxygen Level Dependent (BOLD) fMRI, iron sensitive MRI; quantitative susceptibility imaging (QSM); diffusion tensor imaging DTI; single Photon Emission Computed Tomography (SPECT), daTscan, and datquat.

54. The method of claim 53, wherein said second imaging method comprises Positron Emission Tomography (PET).

55. The method of claim 53, wherein the second imaging method comprises structural MRI.

56. The method of claim 53, wherein the second imaging method comprises functional MRI (fMRI).

57. The method of claim 53, wherein said second imaging method comprises Blood Oxygen Level Dependence (BOLD) fMRI.

58. The method of any one of claims 1 to 57, wherein the analysis focuses on a level, concentration, volume or pattern of neuromelanin within symptom-specific voxels and/or disease-specific voxels in the SNc.

59. The method of any one of claims 1-57, wherein the analysis focuses on a level, concentration, volume, or pattern of neuromelanin within a symptom-specific segment and/or a disease-specific segment in the LC.

60. The method of any one of claims 1 to 57, wherein the analysis focuses on a level, concentration, volume or pattern of neuromelanin within symptom-specific voxels and/or disease-specific voxels in the SNc and a level, concentration, volume or pattern of neuromelanin within disease-specific segments and/or symptom-specific segments in the LC.

61. The method of any one of claims 1-57, wherein the analysis focuses on a level, concentration, or volume of neuromelanin within the SNc and a level, concentration, volume, or pattern of neuromelanin within a disease-specific segment and/or symptom-specific segment in the LC.

62. The method of any one of claims 1 to 57, wherein the analysis focuses on a level, concentration, volume or pattern of neuromelanin within symptom-specific voxels and/or disease-specific voxels in the SNc and a level, concentration or volume of neuromelanin within the LC.

63. The method of any one of the preceding claims, wherein the neurological disorder is selected from schizophrenia, cocaine usage disorder, parkinson's disease, neuropsychiatric symptoms of alzheimer's disease, major depression, and/or post-traumatic stress disorder.