CN113628167A - Individual structural brain network construction method and system, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses a method and a system for constructing an individual structural brain network, electronic equipment and a storage medium, and relates to the technical field of brain networks. The application discloses a method for constructing an individual structural brain network, which comprises the following steps: acquiring a plurality of structural magnetic resonance data of a brain structure, and obtaining a plurality of first morphological characteristic values according to the plurality of structural magnetic resonance data; dividing the brain structure to obtain a plurality of functional areas, and acquiring a first morphological characteristic value corresponding to each functional area as a second morphological characteristic value; generating a corresponding morphological vector according to each second morphological characteristic value, and obtaining the similarity of at least two morphological vectors according to the at least two morphological vectors; a brain network of the brain structure is constructed based on the similarity of the at least two morphology vectors. According to the method and the device, the optimal normalization distance among vectors with different lengths can be calculated according to the vectors with different lengths formed by the morphological characteristics of the brain structure, the voxel information of the brain is not lost, and the accuracy of the brain network construction is further improved.
Description
Technical Field
The present application relates to the field of brain network technologies, and in particular, to a method and a system for constructing an individualized structured brain network, an electronic device, and a storage medium.
Background
At present, there are many methods for constructing a collaborative brain network of an individualized structure, such as a method based on probability density plus KL divergence. The method does not consider the difference of the real structural morphology in the brain, and the KL divergence can be calculated only by sampling the distribution of the structural morphology features in the brain to obtain vectors with consistent lengths. However, because the sizes of each region in the brain are different, that is, the number of gray matter voxels is different, the lengths of the formed vectors are different, and obtaining the vectors with the same length through sampling can cause part of voxel information to be lost, so that the accuracy of brain network construction is not high.
Disclosure of Invention
The present application is directed to solving at least one of the problems in the prior art. Therefore, the application provides an individualized structure brain network construction method, an individualized structure brain network construction system, electronic equipment and a storage medium, which can directly calculate the optimal regular distance between vectors with different lengths formed according to the morphological characteristics of the brain structure, so that the problem of low accuracy of brain network construction caused by brain voxel information loss is solved.
An individualized structural brain network construction method according to an embodiment of the first aspect of the application, comprising:
acquiring a plurality of structural magnetic resonance data of a brain structure;
obtaining a plurality of first morphological characteristic values according to a plurality of structural magnetic resonance data;
dividing the brain structure into a plurality of functional areas;
acquiring the first morphological characteristic value corresponding to each functional area as a second morphological characteristic value;
generating a corresponding morphological vector according to each second morphological characteristic value;
obtaining the similarity of at least two morphological vectors according to the at least two morphological vectors;
and constructing a brain network of the brain structure according to the similarity of at least two morphological vectors.
The method for constructing the individualized structural brain network according to the embodiment of the application has at least the following beneficial effects:
acquiring a plurality of structural magnetic resonance data of a brain structure, and obtaining a plurality of first morphological characteristic values according to the plurality of structural magnetic resonance data; dividing the brain structure to obtain a plurality of functional areas, and acquiring a first morphological characteristic value corresponding to each functional area as a second morphological characteristic value; generating a corresponding morphological vector according to each second morphological characteristic value, and obtaining the similarity of at least two morphological vectors according to the at least two morphological vectors; a brain network of the brain structure is constructed based on the similarity of the at least two morphology vectors. According to the method and the device, the optimal normalization distance among vectors with different lengths can be calculated according to the vectors with different lengths formed by the morphological characteristics of the brain structure, the voxel information of the brain is not lost, and the accuracy of the brain network construction is further improved.
According to some embodiments of the application, the obtaining a plurality of first morphological feature values from a plurality of the structural magnetic resonance data comprises:
performing data processing on a plurality of the structural magnetic resonance data;
and registering each processed structural magnetic resonance data to a standard template space to obtain a first morphological characteristic value corresponding to each structural magnetic resonance data.
According to some embodiments of the application, the dividing of the brain structure into a plurality of functional regions comprises:
and acquiring a plurality of region voxels corresponding to each functional region and the number of the region voxels.
According to some embodiments of the present application, the deriving a similarity of at least two of the morphology vectors according to the at least two of the morphology vectors comprises:
constructing a structure covariant matrix according to the number of the region voxels corresponding to each functional region and the at least two morphological vectors corresponding to each functional region;
and obtaining the similarity of at least two morphological vectors according to the structure covariant matrix.
According to some embodiments of the present application, each first element in the structural covariant matrix represents a cumulative distance of at least two of the morphology vectors;
correspondingly, the obtaining the similarity of at least two morphological vectors according to the structural covariant matrix comprises:
acquiring an element path between each first element and the rest first elements in the structure covariant matrix;
selecting an element path with the shortest path as an optimal path;
selecting a plurality of first elements on the optimal path as a plurality of second elements according to the optimal path;
acquiring accumulated distances corresponding to a plurality of second elements;
and obtaining the similarity of at least two morphological vectors according to the accumulated distances corresponding to the second elements.
According to some embodiments of the application, said constructing a brain network of said brain structure according to a similarity of at least two of said morphology vectors comprises:
calculating network parameters of the brain structure according to the structure covariant matrix;
and constructing a brain network of the brain structure according to the network parameters.
According to some embodiments of the application, the calculating network parameters of the brain structure according to the structure covariant matrix comprises:
selecting the first element with the largest median value of a plurality of first elements of the structure covariant matrix to obtain the maximum value;
calculating each first element in the structure covariant matrix to obtain an updated structure covariant matrix;
and calculating network parameters of the brain structure according to the updated structure covariant matrix.
A brain network construction system according to an embodiment of the second aspect of the present application, includes:
a data acquisition module: acquiring a plurality of structural magnetic resonance data of a brain structure;
a similarity calculation module: obtaining a plurality of first morphological characteristic values according to a plurality of structural magnetic resonance data; obtaining a plurality of functional areas according to the brain structure division; acquiring the first morphological characteristic value corresponding to each functional area as a second morphological characteristic value; generating a corresponding morphological vector according to each second morphological characteristic value; acquiring the similarity of at least two morphological vectors;
a brain network construction module: and constructing a brain network of the brain structure according to the similarity of at least two morphological vectors.
The brain network construction system according to the embodiment of the application has at least the following beneficial effects:
the brain network construction system of the embodiment of the application comprises a data acquisition module, a similarity calculation module and a brain network construction module. A data acquisition module acquires a plurality of structural magnetic resonance data of the brain structure; the similarity calculation module obtains a plurality of first morphological characteristic values according to the plurality of structural magnetic resonance data; obtaining a plurality of functional areas according to brain structure division; acquiring a first morphological characteristic value corresponding to each functional area as a second morphological characteristic value; generating a corresponding morphological vector according to each second morphological characteristic value; acquiring the similarity of at least two morphological vectors; the brain network construction module constructs a brain network of the brain structure according to the similarity of the at least two morphological vectors.
According to the method and the device, the optimal normalization distance among vectors with different lengths can be calculated according to the vectors with different lengths formed by the morphological characteristics of the brain structure, the voxel information of the brain is not lost, and the accuracy of the brain network construction is further improved.
An electronic device according to an embodiment of a third aspect of the present application includes:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions for execution by the at least one processor to cause the at least one processor, when executing the instructions, to implement a method of personalized structured brain network construction according to any of the embodiments of the first aspect of the present application.
According to the electronic equipment of the embodiment of the application, at least the following beneficial effects are achieved: acquiring a plurality of structural magnetic resonance data of a brain structure by executing the individualized structural brain network construction method according to the embodiment of the first aspect, and obtaining a plurality of first morphological characteristic values according to the plurality of structural magnetic resonance data; dividing the brain structure to obtain a plurality of functional areas, and acquiring a first morphological characteristic value corresponding to each functional area as a second morphological characteristic value; generating a corresponding morphological vector according to each second morphological characteristic value, and obtaining the similarity of at least two morphological vectors according to the at least two morphological vectors; a brain network of the brain structure is constructed based on the similarity of the at least two morphology vectors. According to the method and the device, the optimal normalization distance among vectors with different lengths can be calculated according to the vectors with different lengths formed by the morphological characteristics of the brain structure, the voxel information of the brain is not lost, and the accuracy of the brain network construction is further improved.
A computer-readable storage medium according to a fourth aspect embodiment of the present application, comprising:
the computer-readable storage medium stores computer-executable instructions for performing the method for constructing a personalized structured brain network according to the embodiments of the first aspect of the present application.
The computer-readable storage instructions according to the embodiments of the present application have at least the following advantages: acquiring a plurality of structural magnetic resonance data of a brain structure by executing the individualized structural brain network construction method according to the embodiment of the first aspect, and obtaining a plurality of first morphological characteristic values according to the plurality of structural magnetic resonance data; dividing the brain structure to obtain a plurality of functional areas, and acquiring a first morphological characteristic value corresponding to each functional area as a second morphological characteristic value; generating a corresponding morphological vector according to each second morphological characteristic value, and obtaining the similarity of at least two morphological vectors according to the at least two morphological vectors; a brain network of the brain structure is constructed based on the similarity of the at least two morphology vectors. According to the method and the device, the optimal normalization distance among vectors with different lengths can be calculated according to the vectors with different lengths formed by the morphological characteristics of the brain structure, the voxel information of the brain is not lost, and the accuracy of the brain network construction is further improved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The present application is further described with reference to the following figures and examples, in which:
FIG. 1 is a first flowchart of a method for building an individualized structured brain network according to some embodiments of the present application;
FIG. 2 is a second flowchart of a method for constructing a personalized structured brain network, provided in accordance with some embodiments of the present application;
FIG. 3 is a third flowchart of a method for constructing an individualized structured brain network according to some embodiments of the present application;
FIG. 4 is a fourth flowchart of a method of personalized structured brain network construction provided by some embodiments of the present application;
FIG. 5 is a fifth flowchart of a method of personalized structured brain network construction provided by some embodiments of the present application;
fig. 6 is a sixth flowchart of a method of personalized structured brain network construction provided by some embodiments of the present application;
FIG. 7 is an overall flow diagram of a method for constructing an individualized structured brain network according to some embodiments of the present application;
fig. 8 is a block diagram of a block structure of a brain network construction system according to some embodiments of the present application.
Reference numerals:
the system comprises a data acquisition module 100, a similarity calculation module 200 and a brain network construction module 300.
Detailed Description
Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
In the description of the present application, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
At present, there are many methods for constructing a collaborative brain network of an individualized structure, such as a method based on probability density plus KL divergence. The method does not consider the difference of the real structural morphology in the brain, and the KL divergence can be calculated only by sampling the distribution of the structural morphology features in the brain to obtain vectors with consistent lengths. However, because the sizes of each region in the brain are different, that is, the number of gray matter voxels is different, the lengths of the formed vectors are different, and obtaining the vectors with the same length through sampling can cause part of voxel information to be lost, so that the accuracy of brain network construction is not high.
Based on the above, the application provides an individualized structure brain network construction method, system, electronic device and storage medium, which are used for acquiring a plurality of structure magnetic resonance data of a brain structure and obtaining a plurality of first morphological characteristic values according to the plurality of structure magnetic resonance data; dividing the brain structure to obtain a plurality of functional areas, and acquiring a first morphological characteristic value corresponding to each functional area as a second morphological characteristic value; generating a corresponding morphological vector according to each second morphological characteristic value, and obtaining the similarity of at least two morphological vectors according to the at least two morphological vectors; a brain network of the brain structure is constructed based on the similarity of the at least two morphology vectors. According to the method and the device, the optimal normalization distance among vectors with different lengths can be calculated according to the vectors with different lengths formed by the morphological characteristics of the brain structure, the voxel information of the brain is not lost, and the accuracy of the brain network construction is further improved.
In a first aspect, embodiments of the present application provide a method for constructing an individualized structural brain network.
Referring to fig. 1, fig. 1 is a first flowchart of a method for constructing an individualized structural brain network according to some embodiments of the present application, which specifically includes the steps of:
s100, acquiring a plurality of structural magnetic resonance data of a brain structure;
s200, obtaining a plurality of first morphological characteristic values according to the plurality of structural magnetic resonance data;
s300, dividing the brain structure to obtain a plurality of functional areas;
s400, acquiring a first morphological characteristic value corresponding to each functional area as a second morphological characteristic value;
s500, generating a corresponding morphological vector according to each second morphological characteristic value;
s600, obtaining the similarity of the at least two morphological vectors according to the at least two morphological vectors;
s700, according to the similarity of at least two morphological vectors, a brain network of the brain structure is constructed.
In step S100, a plurality of structural magnetic resonance data of the brain structure may be acquired by a nuclear magnetic resonance scanner.
In step S200, a plurality of first morphological feature values are obtained according to the plurality of structural magnetic resonance data acquired in step S100, where the first morphological feature values include structural morphological features such as gray matter volume and cortex thickness.
In some embodiments, as shown in fig. 2, step S200 specifically includes the steps of:
s210, carrying out data processing on the magnetic resonance data of the plurality of structures;
and S220, registering the processed magnetic resonance data of each structure to a standard template space to obtain a first morphological characteristic value corresponding to the magnetic resonance data of each structure.
In step S210, the multiple pieces of structural magnetic resonance data acquired in step S100 are processed, and since the data of the magnetic resonance scan is skull-bound, the skull needs to be removed to register the data to the template space for segmentation.
In step S220, the structural magnetic resonance data from which the skull has been removed in step S210 is registered to a standard template space based on software such as freesburger or VBM8, so as to obtain a first morphological feature value corresponding to each structural magnetic resonance data.
In some embodiments, the method for constructing an individualized structured brain network of the present application specifically further comprises: a plurality of region voxels and the number of region voxels corresponding to each functional region are obtained.
In step S300, the brain structure is divided into a plurality of functional regions, where the brain can be divided into different functional sub-regions by using a brain template, for example, an Anatomical Automated Labeling (AAL) template, a Harvard-Oxford template, or a brain template can be used. Wherein the ALL template comprises 90 cortical and subcortical regions and 26 cerebellar regions; the Harvard-Oxford template contained 48 cortical regions and 7 subcortical regions per hemisphere; the Brainnetome template contains 236 cortical and subcortical regions, etc. It should be noted that, those skilled in the art can select an appropriate template to partition the brain according to actual requirements, and details are not described herein.
In step S400, a first morphological feature value corresponding to each functional region is extracted as a second morphological feature value, where the first morphological feature value includes structural morphological features such as gray matter volume, cortex thickness, and the like.
In step S500, each second morphological feature value, such as thickness, gray matter volume, is extracted and then a morphological vector is constructed.
In step S600, similarity between at least two morphological vectors is obtained according to the at least two morphological vectors.
In some embodiments, as shown in fig. 3, step S600 specifically includes the steps of:
s610, constructing a structure covariant matrix according to the number of region voxels corresponding to each functional region and at least two morphological vectors corresponding to each functional region;
and S620, obtaining the similarity of at least two morphological vectors according to the structural covariant matrix.
In step S610, a Dynamic Time Warping (DTW) method is used to calculate the covariant similarity of morphological features of different regions. In order to calculate the optimal distance, firstly, the morphological characteristics of two regions with different voxel numbers, such as gray matter volume, thickness and the like, are converted into a vector, such as an A region 1m, wherein m is the number of voxels in the A region, and a B region 1n, wherein n is the number of voxels in the B region; then, a matrix C of mn is constructed, and the values C (i, j) at any position in the matrix are the Euclidean distances between the area A (i) and the area B (j), i.e. the similarity values. Each element in the matrix represents the cumulative distance of two vectors, with smaller distances indicating more similarity.
In step S620, the similarity of at least two morphological vectors can be obtained according to the matrix C.
In some embodiments, as shown in fig. 4, step S620 specifically includes the steps of:
s621, obtaining an element path between each first element and the rest first elements in the structure covariant matrix;
s622, selecting the element path with the shortest path as the optimal path;
s623, according to the optimal path, selecting a plurality of first elements on the optimal path as a plurality of second elements;
s624, acquiring accumulated distances corresponding to a plurality of second elements;
and S625, obtaining the similarity of at least two morphological vectors according to the accumulated distances corresponding to the plurality of second elements.
In step S621, each element in the matrix C is called a first element, and for each first element, an element path between the remaining first elements is calculated;
in step S622, finding the shortest path from C (1,1) point to C (m, n) point in the C matrix as the optimal path;
in step S623, an element on the shortest path from the C (1,1) point to the C (m, n) point in the C matrix is called a second element;
in step S624, obtaining cumulative distances corresponding to a plurality of second elements according to the coordinate values of the second elements;
in step S625, the cumulative distance corresponding to the second element is defined as similarity, and the similarity between at least two morphological vectors is obtained, and the similarity between at least two morphological vectors is used to construct a brain network.
In step S700, a brain network of the brain structure is constructed based on the similarity of the at least two morphology vectors.
In some embodiments, as shown in fig. 5, step S700 specifically includes the steps of:
s710, calculating network parameters of the brain structure according to the structure covariant matrix;
and S720, constructing a brain network of the brain structure according to the network parameters.
In step S710, calculating network parameters of the brain structure according to the structure covariant matrix;
in some embodiments, as shown in fig. 6, step S710 specifically includes steps;
s711, selecting a first element with the largest median value of a plurality of first elements of the structure covariant matrix to obtain a maximum value;
s712, calculating each first element in the structure covariant matrix to obtain an updated structure covariant matrix;
and S713, calculating network parameters of the brain structure according to the updated structure covariant matrix.
In step S711, selecting a maximum value in the structure covariant matrix;
in step S722, each value in the structural covariant matrix is inverted and then divided by the maximum value in the matrix.
In step S720, for the normalized covariant matrix, that is, a brain network matrix with different brain regions connected to each other is formed, and then network parameters of each individual, such as small world attributes, clustering coefficients, efficiency, degree, etc., are calculated by using a brain network index calculation tool, such as GRETNA, bran, CONN, etc., and a brain network can be constructed according to these indexes.
In the embodiment of the application, a plurality of first morphological characteristic values are obtained according to a plurality of structural magnetic resonance data of a brain structure by acquiring the plurality of structural magnetic resonance data; dividing the brain structure to obtain a plurality of functional areas, and acquiring a first morphological characteristic value corresponding to each functional area as a second morphological characteristic value; generating a corresponding morphological vector according to each second morphological characteristic value, and obtaining the similarity of at least two morphological vectors according to the at least two morphological vectors; a brain network of the brain structure is constructed based on the similarity of the at least two morphology vectors. According to the method and the device, the optimal normalization distance among vectors with different lengths can be calculated according to the vectors with different lengths formed by the morphological characteristics of the brain structure, the voxel information of the brain is not lost, and the accuracy of the brain network construction is further improved.
As shown in fig. 7, in the embodiment of the present application, a brain structure magnetic resonance image is first acquired, brain morphological features are extracted from the brain structure magnetic resonance image and an original covariant connection matrix is obtained by using an ALL brain template, then the covariant connection matrix is normalized, a structure covariant brain network is constructed according to the normalized covariant connection matrix, and then the brain network topology attribute is depicted according to the structure covariant brain network.
In a second aspect, the present application further provides a brain network construction system for executing the personalized structured brain network construction method mentioned in the first aspect.
As shown in fig. 8, in some embodiments, the brain network construction system includes a data acquisition module 100, a similarity calculation module 200, and a brain network construction module 300. The data acquisition module 100 acquires a plurality of structural magnetic resonance data of the brain structure; the similarity calculation module 200 obtains a plurality of first morphological characteristic values according to the plurality of structural magnetic resonance data; obtaining a plurality of functional areas according to brain structure division; acquiring a first morphological characteristic value corresponding to each functional area as a second morphological characteristic value; generating a corresponding morphological vector according to each second morphological characteristic value; acquiring the similarity of at least two morphological vectors; the brain network construction module 300 constructs a brain network of the brain structure based on the similarity of the at least two morphological vectors.
In the embodiment of the application, the optimal normalization distance among vectors with different lengths can be calculated according to the vectors with different lengths formed by the morphological characteristics of the brain structure, and the brain voxel information is not lost, so that the accuracy of constructing the brain network is improved.
In a third aspect, an embodiment of the present application further provides an electronic device.
In some embodiments, an electronic device includes: at least one processor, and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions that are executed by the at least one processor to cause the at least one processor to implement any of the personalized structured brain network construction methods of the embodiments of the present application when the instructions are executed.
The processor and memory may be connected by a bus or other means.
The memory, as a non-transitory computer readable storage medium, may be used to store a non-transitory software program and a non-transitory computer executable program, such as the personalized structured brain network construction method described in the embodiments of the present application. The processor implements the above-described individualized structured brain network construction method by executing a non-transitory software program and instructions stored in the memory.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area can store and execute the individualized structural brain network construction method. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software programs and instructions needed to implement the above described individualized structured brain network construction method are stored in a memory and, when executed by one or more processors, perform the individualized structured brain network construction method mentioned in the above described embodiments of the first aspect.
In a fourth aspect, the present application further provides a computer-readable storage medium.
In some embodiments, a computer-readable storage medium stores computer-executable instructions for performing the individualized structured brain network construction method mentioned in the embodiments of the first aspect.
In some embodiments, the storage medium stores computer-executable instructions that are executed by one or more control processors, e.g., by a processor in the electronic device, which cause the one or more processors to perform the individualized structured brain network construction method.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
The embodiments of the present application have been described in detail with reference to the drawings, but the present application is not limited to the embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present application. Furthermore, the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
Claims (10)
1. An individualized structural brain network construction method, characterized by comprising:
acquiring a plurality of structural magnetic resonance data of a brain structure;
obtaining a plurality of first morphological characteristic values according to a plurality of structural magnetic resonance data;
dividing the brain structure into a plurality of functional areas;
acquiring the first morphological characteristic value corresponding to each functional area as a second morphological characteristic value;
generating a corresponding morphological vector according to each second morphological characteristic value;
obtaining the similarity of at least two morphological vectors according to the at least two morphological vectors;
and constructing a brain network of the brain structure according to the similarity of at least two morphological vectors.
2. The method according to claim 1, wherein obtaining a plurality of first morphological feature values from a plurality of the structural magnetic resonance data comprises:
performing data processing on a plurality of the structural magnetic resonance data;
and registering each processed structural magnetic resonance data to a standard template space to obtain a first morphological characteristic value corresponding to each structural magnetic resonance data.
3. The method for constructing an individualized structure brain network according to claim 1, wherein the dividing the brain structure into a plurality of functional regions comprises:
and acquiring a plurality of region voxels corresponding to each functional region and the number of the region voxels.
4. The method according to claim 3, wherein the deriving the similarity of at least two morphological vectors according to the at least two morphological vectors comprises:
constructing a structure covariant matrix according to the number of the region voxels corresponding to each functional region and the at least two morphological vectors corresponding to each functional region;
and obtaining the similarity of at least two morphological vectors according to the structure covariant matrix.
5. The individualized structural brain network construction method according to claim 4, wherein each first element in the structural covariant matrix represents a cumulative distance of at least two of the morphology vectors;
correspondingly, the obtaining the similarity of at least two morphological vectors according to the structural covariant matrix comprises:
acquiring an element path between each first element and the rest first elements in the structure covariant matrix;
selecting an element path with the shortest path as an optimal path;
selecting a plurality of first elements on the optimal path as a plurality of second elements according to the optimal path;
acquiring accumulated distances corresponding to a plurality of second elements;
and obtaining the similarity of at least two morphological vectors according to the accumulated distances corresponding to the second elements.
6. The method according to claim 5, wherein said constructing a brain network of said brain structure based on similarities of at least two said morphological vectors comprises:
calculating network parameters of the brain structure according to the structure covariant matrix;
and constructing a brain network of the brain structure according to the network parameters.
7. The method according to claim 6, wherein the calculating network parameters of the brain structure according to the structure covariant matrix comprises:
selecting the first element with the largest median value of a plurality of first elements of the structure covariant matrix to obtain the maximum value;
calculating each first element in the structure covariant matrix to obtain an updated structure covariant matrix;
and calculating network parameters of the brain structure according to the updated structure covariant matrix.
8. A brain network construction system, comprising:
a data acquisition module: acquiring a plurality of structural magnetic resonance data of a brain structure;
a similarity calculation module: obtaining a plurality of first morphological characteristic values according to a plurality of structural magnetic resonance data; obtaining a plurality of functional areas according to the brain structure division; acquiring the first morphological characteristic value corresponding to each functional area as a second morphological characteristic value; generating a corresponding morphological vector according to each second morphological characteristic value; acquiring the similarity of at least two morphological vectors;
a brain network construction module: and constructing a brain network of the brain structure according to the similarity of at least two morphological vectors.
9. An electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein: the processor, when executing the computer program, implements the individualized structural brain network construction method according to any one of claims 1 to 7.
10. Computer-readable storage medium, characterized in that it stores computer-executable instructions for causing a computer to execute the individualized structured brain network construction method according to any one of claims 1 to 7.
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