Search Results (485)

Traumatic brain injury (TBI) represents a major public health problem, being a leading cause of disability and mortality among young people in developed countries. Head trauma occurs across all age groups, each experiencing consistently high rates of mortality and disability. This review aims to present an overview of TBI epidemiology and its socioeconomic impact, alongside data valuable for prevention, clinical management, and research efforts. Methods: A narrative review of TBI was performed with a particular focus on forensic pathology and public health. In fact, this review highlighted the economic and epidemiological aspects of TBI, as well as autopsy, histology, immunohistochemistry, and miRNA. Results: These data, together with immunohistochemical markers, are crucial for histopathological diagnosis and to determine the timing of injury onset, a fundamental aspect in forensic pathology practice. There is compelling evidence that brain injury biomarkers may enhance predictive models for clinical and prognostic outcomes. By clarifying the cause of death and providing details on survival time after trauma, forensic tools offer valuable information to improve the clinical management of TBI and guide preventive interventions. Conclusions: TBI is one of the most common causes of death today, with high costs for health care spending. Knowing the different mechanisms of TBI, reduces health care costs and helps improve prognosis. Full article

Background: The utility of single-pulse TMS (transcranial magnetic stimulation)-evoked EEG (electroencephalograph) potentials (TEPs) has been extensively studied in the past three decades. TEPs have been shown to provide insights into features of cortical excitability and connectivity, reflecting mechanisms of excitatory/inhibitory balance, in various neurological and psychiatric conditions. In the present study, we sought to review and summarize the most studied neurological and psychiatric clinical indications utilizing single-pulse TEP and describe its promise as an informative novel tool for the evaluation of brain physiology. Methods: A thorough search of PubMed, Embase, and Google Scholar for original research utilizing single-pulse TMS-EEG and the measurement of TEP was conducted. Our review focused on the indications and outcomes most clinically relevant, commonly studied, and well-supported scientifically. Results: We included a total of 55 publications and summarized them by clinical application. We categorized these publications into seven sub-sections: healthy aging, Alzheimer’s disease (AD), disorders of consciousness (DOCs), stroke rehabilitation and recovery, major depressive disorder (MDD), Parkinson’s disease (PD), as well as prediction and monitoring of treatment response. Conclusions: TEP is a useful measurement of mechanisms underlying neuronal networks. It may be utilized in several clinical applications. Its most prominent uses include monitoring of consciousness levels in DOCs, monitoring and prediction of treatment response in MDD, and diagnosis of AD. Additional applications including the monitoring of stroke rehabilitation and recovery, as well as a diagnostic aid for PD, have also shown encouraging results but require further evidence from randomized controlled trials (RCTs). Full article

Background/Objectives: Neuronal Ceroid Lipofuscinosis type 2 is a rare pathology affecting mainly the central nervous system (CNS) and retina, and is caused by variants in the gene encoding the lysosomal enzyme tripeptidyl peptidase 1. Therapy with enzyme replacement through the brain infusion of the orphan drug cerliponase alfa, a recombinant human tripeptidyl peptidase 1 enzyme replacement therapy delivered via intracerebroventricular infusion, has been approved for Neuronal Ceroid Lipofuscinosis type 2 disease. The safety profile of cerliponase alfa has been established based on pre-authorization studies; currently, no post-marketing investigation has been performed to confirm it. Here, a descriptive analysis of real-world spontaneous reporting data of suspected adverse reactions (SARs) to cerliponase alfa in the EudraVigilance database was performed to compile clear information on the safety profile. Methods: Suspected adverse reactions to cerliponase alfa reported in the data system EudraVigilance were analyzed for age, sex of the patient, adverse reactions, and the indication for use. Results: Cases with suspected adverse reactions to cerliponase alfa were found to be more frequent in female patients (58.1%) and in children aged 3–11 years. The most common adverse reactions were, in decreasing order, fever/pyrexia, device-related infection, vomiting, seizures/convulsions, pleocytosis, irritability, ventriculitis, and respiratory disorders. Conclusions: The results confirm the safety profile of cerliponase alfa established with pre-registration clinical studies but suggest the need for further studies to investigate the occurrence of adverse reactions, as possible predictive prognostic markers, in more depth. Full article

Background/Objectives: The COVID-19 pandemic reduced in-person pediatric visits in the United States by over 50%, while telehealth visits increased significantly. The national use of telehealth for children and the factors influencing their use have been rarely studied. This study aimed to investigate the prevalence of telehealth use during the COVID-19 pandemic and explore the potential factors linked to its use at the state level. Methods: A cross-sectional study of the National Survey of Children’s Health (2021–22) sponsored by the federal Maternal and Child Health Bureau was performed. We used the least absolute shrinkage and selection operator (LASSO) regression to predict telehealth use during the pandemic. A bar map showing the significant factors from the multivariable regression was created. Results: Of the 101,136 children, 15.25% reported using telehealth visits due to COVID-19, and 3.67% reported using telehealth visits due to other health reasons. The Northeast states showed the highest telehealth use due to COVID-19. In the Midwest and Southern states, children had a lower prevalence of telehealth visits due to other health reasons. The LASSO regressions demonstrated that telehealth visits were associated with age, insurance type, household income, usual source of pediatric preventive care, perceived child health, blood disorders, allergy, brain injury, seizure, ADHD, anxiety, depression, and special needs. Conclusions: This study demonstrated significant variability in the use of telehealth among states during the COVID-19 pandemic. Understanding who uses telehealth and why, as well as identifying access barriers, helps maximize telehealth potential and improve healthcare outcomes for all. Full article

Background/Objective: It is difficult to predict perinatal brain injury, and performing brain magnetic resonance imaging (MRI) based on suspected injury remains a clinical challenge. Therefore, we aimed to develop a reliable method for predicting perinatal brain injury using a machine learning model with early birth data. Methods: Neonates admitted to our institution from January 2017 to June 2024 with a gestational age of ≥36 weeks, a birth weight of ≥1800 g, admission within 6 h of birth, and who underwent brain MRI to confirm perinatal brain injury were included. Various machine learning models, including gradient boosting, were trained using early birth data to predict perinatal brain injury. Synthetic minority over-sampling and adaptive synthetic sampling (ADASYN) were applied to address class imbalance. Model performance was evaluated using accuracy, F1 score, and ROC curves. Feature importance scores and Shapley additive explanations (SHAP) values were also calculated. Results: Among 179 neonates, 39 had perinatal brain injury. There were significant differences between the injury and non-injury groups in mode of delivery, Apgar scores, capillary pH, lactate dehydrogenase (LDH) levels, and whether therapeutic hypothermia was performed. The gradient boosting model with the ADASYN method achieved the best performance. In terms of feature importance scores, the 1 min Apgar score was the most influential predictor. Additionally, SHAP analysis showed that LDH levels had the highest SHAP values. Conclusion: the gradient boosting model with ADASYN oversampling effectively predicts perinatal brain injury, potentially improving early detection for predicting long-term outcomes, reducing unnecessary MRI scans, and lowering healthcare costs. Full article

Krill oil (KO) has been described as having the potential to ameliorate the detrimental consequences of a high-fat diet (HFD) on the aging brain, though the magnitude and mechanism of this benefit is unclear. We thus hypothesized that dietary KO supplementation could counteract the effects of cognitive aging and an HFD on spatial learning, neuroinflammation, neurogenesis, and synaptic density in the cortex and hippocampus of aged rats. Sixteen-month-old Sprague Dawley rats were fed for 12 weeks while being divided into four groups: control (CON); control with KO supplementation (CONKO); high-fat diet (HF); and high-fat diet with KO supplementation (HFKO). We measured food consumption, body mass, spatial memory (Morris water maze), microglia, neurogenesis, cytokine concentrations, and synaptic markers (post-synaptic density-95 and synaptophysin). Predictably, an HFD did induce significant differences in body weights, with the high-fat groups gaining more weight than the low-fat controls. However, KO supplementation did not produce significant changes in the other quantified parameters. Our results demonstrate that the dietary KO dose provided in the current study does not benefit hippocampal or cortical functions in an aging model. Our results provide a benchmark for future dosing protocols that may eventually prove to be beneficial. Full article

Background: The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications. Methods: Data came from 30 original studies in PubMed with the following search terms: “neuroimaging” (title) together with “machine learning” (title) or ”deep learning” (title). The 30 original studies were eligible according to the following criteria: the participants with the dependent variable of brain image or associated disease; the interventions/comparisons of artificial intelligence; the outcomes of accuracy, the area under the curve (AUC), and/or variable importance; the publication year of 2019 or later; and the publication language of English. Results: The performance outcomes reported were within 58–96 for accuracy (%), 66–97 for sensitivity (%), 76–98 for specificity (%), and 70–98 for the AUC (%). The support vector machine and the convolutional neural network registered the best performance (AUC 98%) for the classifications of low- vs. high-grade glioma and brain conditions, respectively. Likewise, the random forest delivered the best performance (root mean square error 1) for the regression of brain conditions. The following factors were discovered to be major predictors of brain image or associated disease: (demographic) age, education, sex; (health-related) alpha desynchronization, Alzheimer’s disease stage, CD4, depression, distress, mild behavioral impairment, RNA sequencing; (neuroimaging) abnormal amyloid-β, amplitude of low-frequency fluctuation, cortical thickness, functional connectivity, fractal dimension measure, gray matter volume, left amygdala activity, left hippocampal volume, plasma neurofilament light, right cerebellum, regional homogeneity, right middle occipital gyrus, surface area, sub-cortical volume. Conclusion: Predictive and explainable artificial intelligence provide an effective, non-invasive decision support system for neuroimaging applications. Full article

Existing reports on the association between oxidative stress and pulmonary hemodynamics in congenital heart disease (CHD) are limited, and the relationship remains inadequately understood. To address this, we evaluated the link between oxidative stress and hemodynamics in children with left-to-right shunt CHD. We analyzed the derivatives of reactive oxygen metabolites (d-ROMs) in a cohort of 60 children with left-to-right shunt CHD and compared them to 60 healthy, age- and sex-matched controls. In the CHD group, hemodynamics measured by cardiac catheterization were evaluated in relation to d-ROMs. We also assessed the diagnostic performance of the d-ROMs for a pulmonary-to-systemic blood flow ratio (Qp/Qs) of >1.5. We found that the blood d-ROM levels in the CHD group were significantly higher than those in the control group (p < 0.001). A significant positive correlation was observed between d-ROMs and Qp/Qs (p < 0.001), d-ROMs and the ratio of the right ventricular end-diastolic volume (p < 0.001), d-ROMs and the mean pulmonary arterial pressure (p < 0.001), and d-ROMs and the ratio of the left ventricular end-diastolic volume (p = 0.007). In the receiver operating characteristic curve analysis, the area under the curve for d-ROMs in predicting Qp/Qs > 1.5 was 0.806 (p < 0.001), which, although not statistically significant, was higher than that of the plasma N-terminal pro-brain natriuretic peptide (0.716). These findings indicate that d-ROM levels are closely associated with hemodynamics and the disease severity in patients with left-to-right shunt CHD and may serve as a valuable marker for determining the need for surgical intervention. Full article

Natriuretic peptides (NPs) are polypeptide hormones involved in the homeostasis of the cardiovascular system. They are produced by cardiomyocytes and regulate circulating blood volume and sodium concentration. Clinically, measurements of brain natriuretic peptide (BNP) and N-terminal pro-BNP (NT-proBNP) are recommended by international guidelines as evidence is accumulating on their usefulness. They have a high negative predictive value, and in the setting of low NPs, a diagnosis of heart failure (HF) can be safely excluded in both emergency (BNP < 100 pg/mL, NT-proBNP < 300 pg/mL) and outpatient settings (BNP < 35 pg/mL and NT-proBNP < 125 pg/mL). Moreover, the 2023 consensus from the European Society of Cardiology suggests threshold values for inclusion diagnosis. These values are also associated with increased risks of major cardiovascular events, cardiovascular mortality, and all-cause mortality whether measured in inpatient or outpatient settings. Among patients without known HF, but at high risk of developing it (e.g., in the setting of diabetes mellitus, hypertension, or atherosclerotic cardiovascular disease), NPs may be useful in stratifying cardiovascular risk, optimizing therapy, and reducing the risk of developing overt HF. In the diabetes setting, risk stratification with the use of these peptides can guide the physician to a more informed and appropriate therapeutic choice as recommended by guidelines. Notably, NP levels should be carefully interpreted in light of certain conditions that may affect their reliability, such as chronic kidney disease and obesity, as well as demographic variables, including age and sex. In conclusion, NPs are useful in the diagnosis and prognosis of HF, but they also offer advantages in the primary prevention setting. Full article

(1) Background: In 2022, the World Stroke Organization said there were more than 12.2 million new cases of stroke each year, between all ages and sexes. Six and a half million people die each year from stroke. Ischemic stroke accounts for 7.6 million (62%) cases, with 3.3 million (51%) deaths. Stroke is mainly linked to the atherosclerosis of a large artery. (2) Objective: Since the carotid artery directly supplies the brain, we used age-dependent mechanical mapping on the healthy common carotid artery (CCA) with the aim of being able to predict and thus potentially prevent ischemic stroke. (3) Methods: We assessed the CCA stiffness of 95 healthy control (CTL) females (2.23–39.46 years) and 107 healthy CTL males (2.85–40 years). Cine-loops of B-mode CCA data were digitally recorded with conventional medical ultrasound devices. Arterial wall elastic moduli were estimated offline using a proprietary non-invasive imaging-based biomarker algorithm (ImBioMark). Statistical analyzes were carried out with Excel software. (4) Results: Females showed a linear regression profile of CCA elastic moduli ranging from 41 ± 2 kPa to 54 ± 17 kPa (R² = 0.88), while males showed one ranging from 38 ± 5 kPa to 63 ± 22 kPa (R² = 0.83). For qualitative and quantitative illustrations, the elastic modulus data of CTLs were compared with those of subjects with Kawasaki disease and subjects born prematurely, respectively. (5) Conclusions: This study introduced some fundamental features of the mechanical evolution of the CCA as a function of age (2–40 years). Since atherosclerotic arteriopathy starts early in life, this gives the ability to predict risks of stroke and other cardiovascular diseases with the possibility of applying a more comprehensive range of potential preventive measures early in life. This is consistent with preventive medicine objectives which aim to be more predictive to implement pre-emptive measures as opposed to diagnostic and curative approaches. Full article

Background/Objectives: Alzheimer’s disease (AD) is a leading cause of death worldwide, affecting millions of older Americans and resulting in a substantial economic burden. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) aims to investigate and develop treatments for AD. Methods: This study included 60 participants, divided equally into AD and control cohorts, and utilized magnetic resonance imaging (MRI) scans to detect gray matter volumetric alterations, a key biomarker of AD. The participants’ cortical volume and surface area were quantified using an automated pipeline in MIMICS (Materialise Interactive Medical Imaging Control System). Results: A multivariate regression analysis was conducted to explore the relationship between cortical measurements and potential factors influencing AD susceptibility. The study found that both cortical volume and surface area were statistically significant predictors of AD (p = 0.0004 and p = 0.011, respectively). Age was also a significant factor, with the 65–70 age group showing the strongest association (p < 0.001). The model achieved an accuracy of 0.68 in predicting AD. Conclusions: While voxel-based morphometry (VBM) using MIMICS showed promise, further development of the automated pipeline could enhance accuracy and correlation indices. These findings contribute to our understanding of brain atrophy in AD pathophysiology and highlight the potential of MRI morphometry as a tool for AD biomarker development. Full article

Background/Objectives: High-grade gliomas are the most common primary malignant brain tumors in adults. These neoplasms remain predominantly incurable due to the genetic diversity within each tumor, leading to varied responses to specific drug therapies. With the advent of new targeted and immune therapies, which have demonstrated promising outcomes in clinical trials, there is a growing need for image-based techniques to enable early prediction of treatment response. This study aimed to evaluate the potential of radiomics and artificial intelligence implementation in predicting progression-free survival (PFS) in patients with highest-grade glioma (CNS WHO 4) undergoing a standard treatment plan. Methods: In this retrospective study, prediction models were developed in a cohort of 51 patients with pathologically confirmed highest-grade glioma (CNS WHO 4) from the authors’ institution and the repository of the Cancer Imaging Archive (TCIA). Only patients with confirmed recurrence after complete tumor resection with adjuvant radiotherapy and chemotherapy with temozolomide were included. For each patient, 109 radiomic features of the tumor were obtained from a preoperative magnetic resonance imaging (MRI) examination. Four clinical features were added manually—sex, weight, age at the time of diagnosis, and the lobe of the brain where the tumor was located. The data label was the time to recurrence, which was determined based on follow-up MRI scans. Artificial intelligence algorithms were built to predict PFS in the training set (n = 75%) and then validate it in the test set (n = 25%). The performance of each model in both the training and test datasets was assessed using mean absolute percentage error (MAPE). Results: In the test set, the random forest model showed the highest predictive performance with 1-MAPE = 92.27% and a C-index of 0.9544. The decision tree, gradient booster, and artificial neural network models showed slightly lower effectiveness with 1-MAPE of 88.31%, 80.21%, and 91.29%, respectively. Conclusions: Four of the six models built gave satisfactory results. These results show that artificial intelligence models combined with radiomic features could be useful for predicting the progression-free survival of high-grade glioma patients. This could be beneficial for risk stratification of patients, enhancing the potential for personalized treatment plans and improving overall survival. Further investigation is necessary with an expanded sample size and external multicenter validation. Full article

Alzheimer’s disease (AD) is a neurodegenerative disorder that progressively involves brain regions with an often-predictable pattern. Damage to the brain appears to spread and worsen with time, but the molecular mechanisms underlying the region-specific distribution of AD pathology at different stages of the disease are still under-investigated. In this study, a whole-transcriptome analysis was carried out on brain samples from the hippocampus (HI), temporal and parietal cortices (TC and PC, respectively), cingulate cortex (CG), and substantia nigra (SN) of six subjects with a definite AD diagnosis and three healthy age-matched controls in duplicate. The transcriptomic results showed a greater number of differentially expressed genes (DEGs) in the TC (1571) and CG (1210) and a smaller number of DEGs in the HI (206), PC (109), and SN (60). Furthermore, the GSEA showed a difference between the group of brain areas affected early (HI and TC) and the group of areas that were subsequently involved (PC, CG, and SN). Notably, in the HI and TC, there was a significant downregulation of shared DEGs primarily involved in synaptic transmission, while in the PC, CG, and SN, there was a significant downregulation of genes primarily involved in protein folding and trafficking. The course of AD could follow a definite time- and severity-related pattern that arises from protein misfolding, as observed in the PC, CG, and SN, and leads to synaptic impairment, as observed in the HI and TC. Therefore, a map of the molecular and biological processes involved in AD pathogenesis may be traced. This could aid in the discovery of novel biological targets in order to develop effective and well-timed therapeutic approaches. Full article

Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder, and numerous functional and structural differences have been identified in the brains of individuals with ADHD compared to controls. This study uses data from the baseline sample of the large, epidemiologically informed Adolescent Brain Cognitive Development Study of children aged 9–10 years old (N = 7979). Cross-validated Poisson elastic net regression models were used to predict a dimensional measure of ADHD symptomatology from within- and between-network resting-state correlations and several known risk factors, such as biological sex, socioeconomic status, and parental history of problematic alcohol and drug use. We found parental history of drug use and biological sex to be the most important predictors of attention problems. The connection between the default mode network and the dorsal attention network was the only brain network identified as important for predicting attention problems. Specifically, we found that reduced magnitudes of the anticorrelation between the default mode and dorsal attention networks relate to increased attention problems in children. Our findings complement and extend recent studies that have connected individual differences in structural and task-based fMRI to ADHD symptomatology and individual differences in resting-state fMRI to ADHD diagnoses. Full article

Background/Objectives: Human brain aging is a complex process that affects various aspects of brain function and structure, increasing susceptibility to neurological and psychiatric disorders. A number of nongenetic (e.g., environmental and lifestyle) and genetic risk factors are found to contribute to the varying rates at which the brain ages among individuals. Methods: In this paper, we conducted both an exposome-wide association study (XWAS) and a genome-wide association study (GWAS) on white matter brain aging in the UK Biobank, revealing the multifactorial nature of brain aging. We applied a machine learning algorithm and leveraged fractional anisotropy tract measurements from diffusion tensor imaging data to predict the white matter brain age gap (BAG) and treated it as the marker of brain aging. For XWAS, we included 107 variables encompassing five major categories of modifiable exposures that potentially impact brain aging and performed both univariate and multivariate analysis to select the final set of nongenetic risk factors. Results: We found current tobacco smoking, dietary habits including oily fish, beef, lamb, cereal, and coffee intake, length of mobile phone use, use of UV protection, and frequency of solarium/sunlamp use were associated with the BAG. In genetic analysis, we identified several SNPs on chromosome 3 mapped to genes IP6K1, GMNC, OSTN, and SLC25A20 significantly associated with the BAG, showing the high heritability and polygenic architecture of human brain aging. Conclusions: The critical nongenetic and genetic risk factors identified in our study provide insights into the causal relationship between white matter brain aging and neurodegenerative diseases. Full article