CN110491518A - A kind of transcranial magnetic stimulation modeling and simulating method for task state - Google Patents
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Abstract
The invention discloses a kind of transcranial magnetic stimulation modeling and simulating methods for task state, pass through tranquillization state MRI T1 picture construction cranium brain institutional framework, it is connected by the MRI DTI image under tranquillization state with the fMRI BOLD picture construction encephalic neural network under task state, then anisotropic conductivity head model is established according to neural network connection, finally the TMS encephalic current density distribution under task state is emulated.The encephalic neuroelectricity action message constructing function neural network that the present invention reflects according to blood oxygen level, it is combined with structural network for describing the connection of encephalic neural network using functional network, there to be better correspondence than individually using structural network to show more effective neural network connection, and with specific task state.TMS modeling and simulating under task state is of great significance for simulating the effect of stimulation, optimization TMS coil and stimulation parameter of TMS combination specific tasks.
Description
Technical field
The present invention relates to a kind of transcranial magnetic stimulation modeling and simulating methods.More particularly to it is a kind of for task state through cranium magnetic
Stimulate modeling and simulating method.
Background technique
Repetitive transcranial magnetic stimulation (repetitive transcranial magnetic stimulation, rTMS) is one
Kind brain stimulation technology is widely used in the research such as brain function, brain network and gyrus road.
TMS emulation at present passes through the magnetic resonance of individuation primarily directed to the modeling and simulating of tranquillization state cranium brain feature
(magnetic resonance imaging, MRI) T1 image description cranium brain fold structural information, passes through MRI dispersion tensor
The neural network structure information that (diffusion tensor imaging, DTI) image description encephalic fibre bundle is formed.For appointing
TMS encephalic current density distribution under business state, there is no specific modeling and simulating method at present.
Summary of the invention
The object of the present invention is to provide a kind of transcranial magnetic stimulation modeling and simulating methods for task state, solve the prior art
In can not to the TMS under task state carry out modeling and simulating the shortcomings that.
In order to solve the above technical problems, the present invention adopts the following technical scheme:
A kind of transcranial magnetic stimulation modeling and simulating method for task state, includes the following steps:
The fMRI BOLD figure under MRI DTI image and task state under S1, acquisition tranquillization state MRI T1 image, tranquillization state
Picture, and it is all matched to standard form, it is under tranquillization state MRI T1 image, tranquillization state according to the space coordinate of standard form
MRI DTI image and task state under fMRI BOLD image establish unified space nodes, number of nodes N；
S2, image flame detection is carried out to tranquillization state MRI T1 image, tissue segmentation and cortex are rebuild, obtain comprising scalp, skull,
Cerebrospinal fluid, ectocinerea, white matter of brain structure head model, and retaining space nodal information；
S3, dispersion tensor reconstruction is carried out to the MRI DTI image under tranquillization state, obtains encephalic ectocinerea and each node of white matter of brain
Dispersion tensor characteristic value, be denoted as λij, i=1,2 ... ..., n, n be ectocinerea and white matter of brain in number of nodes, n < N, j=1,
2,3, λi1For maximum dispersion coefficient, λi2For intermediate dispersion coefficient, λi3For minimum dispersion coefficient, λi1Representative is parallel to machine direction
Dispersion coefficient, λi2And λi3Lateral dispersion coefficient is represented, fiber bundle structure link information is characterized；
S4, the fMRI BOLD image under task state is pre-processed, obtains the time of ectocinerea and each node of white matter of brain
Sequence data measures the relationship between network node using time correlation analysis, determines to whether there is connection between node by threshold value
Side obtains the node degree of each node, is expressed as Ki, i=1,2 ... ..., n characterize weight of each node in functional network
The property wanted；
S5, the functional conductivity tensor that ectocinerea and white matter of brain are established in conjunction with dispersion tensor feature and nodal function feature；
S6, integrated structure head model and functional conductivity tensor establish the anisotropic electric comprising functional network link information
Conductance head model carries out conductivity assignment to structure head model, wherein for ectocinerea and white matter of brain, using functional conductance
Rate tensor σijTo each node valuation, for scalp, skull, cerebrospinal fluid, using isotropic conductivity rate assignment, respectivelyObtain the anisotropic conductivity head model comprising functional network link information；
S7, the TMS encephalic current density distribution under task state is emulated.
Further, the S5 specifically comprises the following steps:
S5.1, the structural conductivity tensor for establishing characterization fiber bundle structure link information, are denoted as dij, according to body normalization method,
dijIt can be acquired by formula (1), whereinFor ectocinerea or the isotropic conductivity rate of white matter of brain,
S5.2, functional conductivity tensor is established in conjunction with nodal function feature, using the node degree of each node as structural electricity
The weight coefficient of conductance tensor, functional conductivity tensor σijIt can be acquired by formula (2):
σij=Ki·dij (2)。
Further, the S7 specifically comprises the following steps:
S7.1, uncoupling, face subnetting, body subnetting behaviour are carried out to anisotropic conductivity head model using finite element analysis software
Make, obtains corresponding finite element model；
S7.2, according to the law of electromagnetic induction and interface charge build-up effect, simulation calculating, encephalic electric field are carried out to encephalic electric field
It is denoted asConcrete operation such as formula (3), whereinFor TMS coil magnetic vector potential,The mark generated for the electrostatic charge of interface accumulation
Gesture is measured,
S7.3, according to the current density of each node of encephalic electric Field Calculation encephalic, The respectively tissue current density of scalp, skull and cerebrospinal fluid,For the tissue current density of ectocinerea and white matter of brain.
Further, MRI T1 and MRI the DTI image, specifically using T1 three-dimensional brain volume scan sequence and
DTI dispersion tensor scanning sequence carries out the image that full brain structural scan obtains.
Further, the fMRI BOLD image under the task state, specifically using magnetic resonance radiography in the task of execution
While scanning function image, detect encephalic nervous activity information when execution task, i.e. BOLD signal.
Further, the isotropic conductivity rate is specifically each of inquiry Daniele Andreuccetti et al. foundation
Frequency range biological tissue electromagnetic property public database obtains the corresponding each Conductivity of Brain of TMS pulse frequency.
Compared with prior art, advantageous effects of the invention:
The present invention is directed to the transcranial magnetic stimulation modeling and simulating under task state, proposes the functional MRI under acquisition tasks state
(functional magnetic resonance imaging, fMRI) Blood oxygen level dependence (Blood Oxygenation
Level Dependent, BOLD) image, the encephalic neuroelectricity action message constructing function nerve reflected according to blood oxygen level
Network is combined with structural network for describing the connection of encephalic neural network using functional network, will be than individually using Structure Network
Network shows more effective neural network connection, and has better correspondence with specific task state.TMS under task state is built
It imitates and is very of great significance for simulating the effect of stimulation, optimization TMS coil and stimulation parameter of TMS combination specific tasks, have
There are good development and application prospect.
Detailed description of the invention
The invention will be further described for explanation with reference to the accompanying drawing.
Fig. 1 is flow diagram of the present invention for the transcranial magnetic stimulation modeling and simulating method of task state.
Specific embodiment
As shown in Figure 1, a kind of transcranial magnetic stimulation modeling and simulating method for task state, includes the following steps:
The fMRI BOLD image under MRI DTI image and task state under S1, acquisition tranquillization state MRI T1 image, tranquillization state, and
It is all matched to standard form, is the MRI under tranquillization state MRI T1 image, tranquillization state according to the space coordinate of standard form
FMRI BOLD image under DTI image and task state establishes unified space nodes, number of nodes N；
S2, image flame detection is carried out to tranquillization state MRI T1 image, tissue segmentation and cortex are rebuild, obtain comprising scalp, skull,
Cerebrospinal fluid, ectocinerea, white matter of brain structure head model, and retaining space nodal information；
S3, dispersion tensor reconstruction is carried out to the MRI DTI image under tranquillization state, obtains encephalic ectocinerea and each node of white matter of brain
Dispersion tensor characteristic value, be denoted as λij, i=1,2 ... ..., n, n be ectocinerea and white matter of brain in number of nodes, n < N, j=1,
2,3, λi1For maximum dispersion coefficient, λi2For intermediate dispersion coefficient, λi3For minimum dispersion coefficient, λi1Representative is parallel to machine direction
Dispersion coefficient, λi2And λi3Lateral dispersion coefficient is represented, fiber bundle structure link information is characterized；
S4, the fMRI BOLD image under task state is pre-processed, obtains the time of ectocinerea and each node of white matter of brain
Sequence data measures the relationship between network node using time correlation analysis, determines to whether there is connection between node by threshold value
Side obtains the node degree of each node, is expressed as Ki, i=1,2 ... ..., n characterize weight of each node in functional network
The property wanted；
S5, the functional conductivity tensor that ectocinerea and white matter of brain are established in conjunction with dispersion tensor feature and nodal function feature, tool
Body includes the following steps:
S5.1, the structural conductivity tensor for establishing characterization fiber bundle structure link information, are denoted as dij, according to body normalization method,
dijIt can be acquired by formula (1), whereinFor ectocinerea or the isotropic conductivity rate of white matter of brain,
S5.2, functional conductivity tensor is established in conjunction with nodal function feature, using the node degree of each node as structural electricity
The weight coefficient of conductance tensor, functional conductivity tensor σijIt can be acquired by formula (2):
σij=Ki·dij(2)；
S6, integrated structure head model and functional conductivity tensor establish the anisotropic electric comprising functional network link information
Conductance head model carries out conductivity assignment to structure head model, wherein for ectocinerea and white matter of brain, using functional conductance
Rate tensor σijTo each node valuation, for scalp, skull, cerebrospinal fluid, using isotropic conductivity rate assignment, respectivelyObtain the anisotropic conductivity head model comprising functional network link information；
S7, the TMS encephalic current density distribution under task state is emulated, is specifically comprised the following steps:
S7.1, uncoupling, face subnetting, body subnetting behaviour are carried out to anisotropic conductivity head model using finite element analysis software
Make, obtains corresponding finite element model；
S7.2, according to the law of electromagnetic induction and interface charge build-up effect, simulation calculating, encephalic electric field are carried out to encephalic electric field
It is denoted asConcrete operation such as formula (3), whereinFor TMS coil magnetic vector potential,The mark generated for the electrostatic charge of interface accumulation
Gesture is measured,
S7.3, according to the current density of each node of encephalic electric Field Calculation encephalic, The respectively tissue current density of scalp, skull and cerebrospinal fluid,For the tissue current density of ectocinerea and white matter of brain.
MRI T1 and MRI the DTI image specifically applies T1 three-dimensional brain volume scan sequence and DTI dispersion tensor
Scanning sequence carries out the image that full brain structural scan obtains.FMRI BOLD image under the task state specifically utilizes magnetic
Resonate radiography scanning function image while the task of execution, detects encephalic nervous activity information when execution task, i.e. BOLD
Signal.The isotropic conductivity rate is specifically each frequency range biological tissue inquiring Daniele Andreuccetti et al. and establishing
Electromagnetic property public database, obtains the corresponding each Conductivity of Brain of TMS pulse frequency, which is that electromagnetic field modeling is imitative
Very generally acknowledged authentic data library.
Embodiment described above is only that preferred embodiment of the invention is described, and is not carried out to the scope of the present invention
It limits, without departing from the spirit of the design of the present invention, those of ordinary skill in the art make technical solution of the present invention
Various changes and improvements, should all fall into claims of the present invention determine protection scope in.
Claims (5)
1. a kind of transcranial magnetic stimulation modeling and simulating method for task state, which comprises the steps of:
The fMRI BOLD image under MRI DTI image and task state under S1, acquisition tranquillization state MRI T1 image, tranquillization state, and
It is all matched to standard form, is the MRI under tranquillization state MRI T1 image, tranquillization state according to the space coordinate of standard form
FMRI BOLD image under DTI image and task state establishes unified space nodes, number of nodes N；
S2, image flame detection, tissue segmentation and cortex reconstruction are carried out to tranquillization state MRI T1 image, obtain comprising scalp, skull, brain
Spinal fluid, ectocinerea, white matter of brain structure head model, and retaining space nodal information；
S3, dispersion tensor reconstruction is carried out to the MRIDTI image under tranquillization state, obtains encephalic ectocinerea and each node of white matter of brain
Dispersion tensor characteristic value, is denoted as λij, i=1,2 ... ..., n, n be ectocinerea and white matter of brain in number of nodes, n < N, j=1,2,3,
λi1For maximum dispersion coefficient, λi2For intermediate dispersion coefficient, λi3For minimum dispersion coefficient, λi1Representative is parallel to machine direction more
Dissipate coefficient, λi2And λi3Lateral dispersion coefficient is represented, fiber bundle structure link information is characterized；
S4, the fMRI BOLD image under task state is pre-processed, obtains the time sequence of ectocinerea and each node of white matter of brain
Column data measures the relationship between network node using time correlation analysis, determines to whether there is connection side between node by threshold value,
The node degree for obtaining each node, is expressed as Ki, i=1,2 ... ..., n characterize importance of each node in functional network；
S5, the functional conductivity tensor that ectocinerea and white matter of brain are established in conjunction with dispersion tensor feature and nodal function feature；
S6, integrated structure head model and functional conductivity tensor establish the anisotropy conductance comprising functional network link information
Rate head model carries out conductivity assignment to structure head model, wherein for ectocinerea and white matter of brain, using functional electric conductance
Tensor σijTo each node valuation, for scalp, skull, cerebrospinal fluid, using isotropic conductivity rate assignment, respectivelyWithObtain the anisotropic conductivity head model comprising functional network link information；
S7, the TMS encephalic current density distribution under task state is emulated.
2. the transcranial magnetic stimulation modeling and simulating method according to claim 1 for task state, which is characterized in that the S5
Specifically comprise the following steps:
S5.1, the structural conductivity tensor for establishing characterization fiber bundle structure link information, are denoted as dij, according to body normalization method, dij
It can be acquired by formula (1), whereinFor ectocinerea or the isotropic conductivity rate of white matter of brain,
S5.2, functional conductivity tensor is established in conjunction with nodal function feature, using the node degree of each node as structural electricity
The weight coefficient of conductance tensor, functional conductivity tensor σijIt can be acquired by formula (2):
σij=Ki·dij (2)。
3. the transcranial magnetic stimulation modeling and simulating method according to claim 1 for task state, which is characterized in that the S7
Specifically comprise the following steps:
S7.1, uncoupling, face subnetting, body subnetting behaviour are carried out to anisotropic conductivity head model using finite element analysis software
Make, obtains corresponding finite element model；
S7.2, according to the law of electromagnetic induction and interface charge build-up effect, simulation calculating, encephalic electric field note are carried out to encephalic electric field
ForConcrete operation such as formula (3), whereinFor TMS coil magnetic vector potential,The scalar generated for the electrostatic charge of interface accumulation
Gesture,
S7.3, according to the current density of each node of encephalic electric Field Calculation encephalic, The respectively tissue current density of scalp, skull and cerebrospinal fluid,For the tissue current density of ectocinerea and white matter of brain.
4. the transcranial magnetic stimulation modeling and simulating method according to claim 1 for task state, which is characterized in that described
MRI T1 and MRI DTI image is specifically carried out using T1 three-dimensional brain volume scan sequence and DTI dispersion tensor scanning sequence complete
The image that brain structural scan obtains.
5. the transcranial magnetic stimulation modeling and simulating method according to claim 1 for task state, which is characterized in that described
FMRI BOLD image under task state specifically utilizes magnetic resonance radiography scanning function image while the task of execution, detection
Encephalic nervous activity information when execution task, i.e. BOLD signal.
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