CN110491518B - Transcranial magnetic stimulation modeling simulation method for task state - Google Patents
Transcranial magnetic stimulation modeling simulation method for task state Download PDFInfo
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Abstract
The invention discloses a transcranial magnetic stimulation modeling simulation method aiming at a task state, which comprises the steps of constructing a craniocerebral tissue structure through an MRI T1 image in a resting state, constructing intracranial neural network connection through an MRI DTI image in the resting state and an fMRI BOLD image in the task state, then establishing an anisotropic conductivity head model according to the neural network connection, and finally simulating TMS intracranial current density distribution in the task state. The functional neural network is constructed according to the intracranial neuroelectric activity information reflected by the blood oxygen level, the functional network and the structural network are combined for describing the intracranial neural network connection, the more effective neural network connection is shown compared with the singly adopted structural network, and the functional neural network has better correspondence with a specific task state. TMS modeling simulation in a task state has important significance for simulating the stimulation effect of combining TMS with a specific task and optimizing a TMS coil and stimulation parameters.
Description
Technical Field
The invention relates to a transcranial magnetic stimulation modeling simulation method. In particular to a transcranial magnetic stimulation modeling simulation method aiming at a task state.
Background
Repeated transcranial magnetic stimulation (rTMS) is a brain stimulation technique that is widely used in studies of brain function, brain network, brain circuits, and the like.
Currently, TMS simulation mainly aims at modeling simulation of resting state craniocerebral features, and describes craniocerebral sulcus structure information through an individualized Magnetic Resonance (MRI) T1 image, and describes neural network structure information formed by intracranial fiber bundles through an MRI Diffusion Tensor Imaging (DTI) image. For TMS intracranial current density distribution in a task state, no clear modeling simulation method exists at present.
Disclosure of Invention
The invention aims to provide a transcranial magnetic stimulation modeling simulation method aiming at a task state, and solves the defect that TMS in the task state cannot be modeled and simulated in the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
a transcranial magnetic stimulation modeling simulation method aiming at a task state comprises the following steps:
s1, collecting an MRI T1 image in a rest state, an MRI DTI image in the rest state and an fMRI BOLD image in a task state, matching all the images to a standard template, and establishing a uniform spatial node for the MRI T1 image in the rest state, the MRI DTI image in the rest state and the fMRI BOLD image in the task state according to the spatial coordinate of the standard template, wherein the number of the nodes is N;
s2, carrying out image correction, tissue segmentation and cortical reconstruction on the static MRI T1 image to obtain a structural head model containing scalp, skull, cerebrospinal fluid, grey brain matter and white brain matter, and reserving space node information;
s3, performing diffusion tensor reconstruction on the MRI DTI image in the resting state to obtain diffusion tensor characteristic values of each node of intracranial grey brain matter and white brain matter, and recording the diffusion tensor characteristic values as lambda ij I =1,2, … …, n, n is the number of nodes in grey and white brain matter, n<N，j＝1,2,3，λ i1 Is maximum diffusion coefficient, lambda i2 Is a medium diffusion coefficient, λ i3 At the lowest diffusion coefficient, λ i1 Representing the diffusion coefficient, λ, parallel to the fibre direction i2 And λ i3 Representing a transverse dispersion coefficient, and representing the connection information of the fiber bundle structure;
s4, preprocessing the fMRI BOLD image in the task state to obtain time sequence data of each node of the grey brain matter and the white brain matter, measuring the relation between network nodes by adopting time correlation analysis, determining whether a connecting edge exists between the nodes through a threshold value, and obtaining the node degree of each node, wherein the node degree is represented as K i I =1,2, … …, n, characterizing the importance of each node in the functional network;
s5, establishing functional conductivity tensors of grey brain matter and white brain matter by combining diffusion tensor features and node functional features;
s6, establishing an anisotropic conductivity head model containing functional network connection information by combining the structural head model and the functional conductivity tensor, and carrying out conductivity assignment on the structural head model, wherein the functional conductivity tensor sigma is adopted for the gray matter and the white matter of the brain ij Assigning values to each node, and assigning values to scalp, skull and cerebrospinal fluid by using isotropic conductivity
Obtaining an anisotropic conductivity head model containing functional network connection information; />
And S7, simulating TMS intracranial current density distribution in a task state.
Further, the step S5 specifically includes the following steps:
s5.1, establishing a structural conductivity tensor for representing the fiber bundle structure connection information, and recording the structural conductivity tensor as d ij According to the method of body normalization, d ij Can be obtained from the formula (1), wherein,
is the isotropic conductivity of grey or white brain matter,
s5.2, establishing a functional conductivity tensor by combining the functional characteristics of the nodes, and taking the node degree of each node as a weight coefficient of the structural conductivity tensor, wherein the functional conductivity tensor sigma ij The following can be obtained from equation (2):
σ ij ＝K i ·d ij (2)。
further, the step S7 specifically includes the following steps:
s7.1, decoupling, surface net separating and body net separating operations are carried out on the anisotropic conductivity head model by adopting finite element analysis software to obtain a corresponding finite element model;
s7.2, according to the electromagnetic induction law and the interface charge accumulation effect, carrying out simulation operation on the intracranial electric field, and recording the intracranial electric field as
The specific operation is as shown in formula (3), wherein>
Is the magnetic vector potential of the TMS coil>
A scalar potential generated for static charge build up at the interface,
s7.3, calculating the current density of each intracranial node according to the intracranial electric field,
tissue current densities of the scalp, skull and cerebrospinal fluid respectively,
tissue current densities of grey and white brain matter.
Further, the MRI T1 and MRI DTI images are images obtained by scanning a whole brain structure by using a T1 three-dimensional brain volume scanning sequence and a DTI diffusion tensor scanning sequence.
Further, the fMRI BOLD image in the task state is to scan a functional image while performing the task by using magnetic resonance imaging, and detect intracranial nerve activity information, i.e., a BOLD signal, while performing the task.
Further, the isotropic conductivity is to specifically query a public database of electromagnetic properties of biological tissues of each frequency band established by Daniele Andreuccetti et al to obtain the conductivity of each brain tissue corresponding to the TMS pulse frequency.
Compared with the prior art, the invention has the beneficial technical effects that:
aiming at transcranial magnetic stimulation modeling simulation in a task state, the invention provides a method for acquiring functional magnetic resonance (fMRI) Blood Oxygen Level Dependent (BOLD) images in the task state, a functional neural network is constructed according to intracranial neuroelectric activity information reflected by the Blood oxygen Level, the functional neural network is combined with a structural network for describing intracranial neural network connection, more effective neural network connection is shown than that of the structural network, and the functional neural network connection has better correspondence with the specific task state. TMS modeling simulation in a task state has important significance for simulating the stimulation effect of combining TMS with a specific task, optimizing a TMS coil and stimulation parameters, and has good development and application prospects.
Drawings
The invention is further illustrated in the following description with reference to the drawings.
FIG. 1 is a schematic flow chart of a task-state transcranial magnetic stimulation modeling simulation method according to the present invention.
Detailed Description
As shown in fig. 1, a transcranial magnetic stimulation modeling simulation method for a task state includes the following steps:
s1, collecting an MRI T1 image in a rest state, an MRI DTI image in the rest state and an fMRI BOLD image in a task state, matching all the images to a standard template, and establishing a uniform spatial node for the MRI T1 image in the rest state, the MRI DTI image in the rest state and the fMRI BOLD image in the task state according to the spatial coordinate of the standard template, wherein the number of the nodes is N;
s2, carrying out image correction, tissue segmentation and cortical reconstruction on the static MRI T1 image to obtain a structural head model containing scalp, skull, cerebrospinal fluid, grey brain matter and white brain matter, and reserving space node information;
s3, performing diffusion tensor reconstruction on the MRI DTI image in the resting state to obtain intracranial brain ashAnd (3) recording diffusion tensor eigenvalues of each node of the texture and the white matter as lambda ij I =1,2, … …, n, n is the number of nodes in the grey and white brain matter, n<N，j＝1,2,3，λ i1 Is maximum diffusion coefficient, lambda i2 Is a medium diffusion coefficient, λ i3 At the lowest diffusion coefficient, λ i1 Representing the diffusion coefficient, λ, parallel to the fibre direction i2 And λ i3 Representing a transverse dispersion coefficient, and representing the connection information of the fiber bundle structure;
s4, preprocessing the fMRI BOLD image in the task state to obtain time sequence data of each node of the grey brain matter and the white brain matter, measuring the relation between network nodes by adopting time correlation analysis, determining whether a connecting edge exists between the nodes through a threshold value, and obtaining the node degree of each node, wherein the node degree is represented as K i I =1,2, … …, n, characterizing the importance of each node in the functional network;
s5, establishing functional conductivity tensors of the grey brain matter and the white brain matter by combining diffusion tensor features and node functional features, and specifically comprising the following steps:
s5.1, establishing a structural conductivity tensor which represents the fiber bundle structure connection information and is recorded as d ij According to the method of body normalization, d ij Can be obtained from the formula (1), wherein,
is the isotropic conductivity of grey or white brain matter,
s5.2, establishing a functional conductivity tensor by combining the node functional characteristics, taking the node degree of each node as a weight coefficient of the structural conductivity tensor, wherein the functional conductivity tensor sigma is ij The following can be obtained from equation (2):
σ ij ＝K i ·d ij (2)；
s6, establishing anisotropic conductivity containing functional network connection information by combining the structure head model and the functional conductivity tensorA head model, conductivity assignment is carried out on the structure head model, wherein, for the grey brain matter and the white brain matter, functional conductivity tensor sigma is adopted ij Assigning values to each node, and assigning values to scalp, skull and cerebrospinal fluid by using isotropic conductivity
Obtaining an anisotropic conductivity head model containing functional network connection information;
s7, simulating TMS intracranial current density distribution in a task state, and specifically comprising the following steps:
s7.1, decoupling, surface net separating and body net separating operations are carried out on the anisotropic conductivity head model by adopting finite element analysis software to obtain a corresponding finite element model;
s7.2, according to the electromagnetic induction law and the interface charge accumulation effect, carrying out simulation operation on the intracranial electric field, and recording the intracranial electric field as
The specific operation is as in formula (3), wherein>
Is the magnetic vector potential of the TMS coil>
A scalar potential generated for static charge build up at the interface,
s7.3, calculating the current density of each intracranial node according to the intracranial electric field,
the tissue current densities of the scalp, skull and cerebrospinal fluid respectively,
tissue current densities of grey and white brain matter.
The MRI T1 and MRI DTI images are images obtained by scanning a whole brain structure by using a T1 three-dimensional brain volume scanning sequence and a DTI diffusion tensor scanning sequence. The fMRI BOLD image in the task state is used for scanning a functional image while performing a task by using magnetic resonance imaging, and detecting intracranial nerve activity information, namely a BOLD signal, during the task. The isotropic conductivity is specifically to query a public database of electromagnetic properties of biological tissues in each frequency band established by Daniele Andrewcetti et al to obtain the conductivity of each brain tissue corresponding to the TMS pulse frequency, and the database is a reliable database acknowledged by electromagnetic field modeling simulation.
The above-described embodiments are only intended to illustrate the preferred embodiments of the present invention, and not to limit the scope of the present invention, and various modifications and improvements made to the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.
Claims (3)
1. A transcranial magnetic stimulation modeling simulation method aiming at a task state is characterized by comprising the following steps:
s1, collecting an MRI T1 image in a rest state, an MRIDTI image in the rest state and an fMRI BOLD image in a task state, matching all the images to a standard template, and establishing a uniform spatial node for the MRI T1 image in the rest state, the MRIDTI image in the rest state and the fMRI BOLD image in the task state according to the spatial coordinate of the standard template, wherein the number of the nodes is N;
s2, carrying out image correction, tissue segmentation and cortical reconstruction on the static MRI T1 image to obtain a structural head model containing scalp, skull, cerebrospinal fluid, grey brain matter and white brain matter, and reserving space node information;
s3, performing diffusion tensor reconstruction on the MRIDTI image in the resting state to acquire diffusion tensor characteristics of each node of intracranial grey brain matter and white brain matterValue of λ ij I =1,2, … …, n, n is the number of nodes in the grey and white brain matter, n<N，j＝1,2,3，λ i1 Is maximum diffusion coefficient, lambda i2 Is a medium diffusion coefficient, λ i3 At the lowest diffusion coefficient, λ i1 Representing the diffusion coefficient, λ, parallel to the fibre direction i2 And λ i3 Representing a transverse dispersion coefficient, and representing the connection information of the fiber bundle structure;
s4, preprocessing the fMRI BOLD image in the task state to obtain time sequence data of each node of the grey brain matter and the white brain matter, measuring the relation between network nodes by adopting time correlation analysis, determining whether a connecting edge exists between the nodes through a threshold value, and obtaining the node degree of each node, wherein the node degree is represented as K i I =1,2, … …, n, characterizing the importance of each node in the functional network;
s5, establishing functional conductivity tensors of grey brain matter and white brain matter by combining diffusion tensor features and node functional features;
s6, establishing an anisotropic conductivity head model containing functional network connection information by combining the structural head model and the functional conductivity tensor, and carrying out conductivity assignment on the structural head model, wherein the functional conductivity tensor sigma is adopted for the gray matter and the white matter of the brain ij Assigning values to each node, for scalp, skull, cerebrospinal fluid, respectively, using isotropic conductivity assignment
And &>
Obtaining an anisotropic conductivity head model containing functional network connection information;
s7, simulating TMS intracranial current density distribution in a task state;
the S5 specifically comprises the following steps:
s5.1, establishing a structural conductivity tensor which represents the fiber bundle structure connection information and is recorded as d ij According to the method of body normalization, d ij Can be obtained from the formula (1), wherein,
is the isotropic conductivity of grey or white brain matter,
s5.2, establishing a functional conductivity tensor by combining the node functional characteristics, taking the node degree of each node as a weight coefficient of the structural conductivity tensor, wherein the functional conductivity tensor sigma is ij The following can be obtained from equation (2):
σ ij ＝K i ·d ij (2)；
the S7 specifically comprises the following steps:
s7.1, decoupling, surface net separating and body net separating operations are carried out on the anisotropic conductivity head model by adopting finite element analysis software to obtain a corresponding finite element model;
s7.2, according to the electromagnetic induction law and the interface charge accumulation effect, carrying out simulation operation on the intracranial electric field, and recording the intracranial electric field as
The specific operation is as shown in formula (3), wherein>
For the magnetic vector potential of TMS coils>
Scalar potential created for static charge accumulated at the interface->
S7.3, calculating the current density of each intracranial node according to the intracranial electric field,
the tissue current densities of the scalp, skull and cerebrospinal fluid respectively,
tissue current density of grey and white brain matter.
2. The transcranial magnetic stimulation modeling simulation method for the task state according to claim 1, wherein the MRI T1 and MRIDTI images are images obtained by performing whole brain structure scanning using a T1 three-dimensional brain volume scanning sequence and a DTI diffusion tensor scanning sequence.
3. The method for modeling and simulating transcranial magnetic stimulation according to claim 1, wherein the fMRI BOLD image in the task state is a BOLD signal, which is information of intracranial neural activity when the task is performed, and is detected by scanning a functional image while the task is performed by magnetic resonance imaging.
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