CN115294084A - Brain region homology comparison method based on white matter fiber tracts - Google Patents
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Abstract
The invention discloses a brain region homology comparison method based on white matter fiber tracts, which mainly solves the problem that the prior homologous brain region information is lost in cross-species comparison in the prior art. The method comprises the following implementation steps: 1. reading DTI images and T1 images, 2, preprocessing the images, 3, registering the images and extracting regions of interest, 4, constructing a cross-species reference system, 5, constructing a structural connectivity relation, 6, and performing intra-species comparison and inter-species comparison.
Description
Technical Field
The invention belongs to the technical field of homology comparison methods, and particularly relates to a brain region homology comparison method based on white matter fiber tracts.
Background
Brain science is a scientific study that is closely related to our lives. The development of brain science is helpful for people to better understand the brain, and the function of the brain can be analyzed from the neural basis, so that the brain science is valuable in clinic. In addition, the brain-like subject inspired by brain science can promote the development of new-generation artificial intelligence and novel information industry.
The macaque is a natural transition model for researching human beings, has unique resource advantages in China, and along with the successive breakthrough of somatic cell cloning monkeys and transgenic macaque models, the position of the macaque as a focus model experimental animal is further strengthened, and the cross-species research on the human beings and the macaque is more and more important. Exploring the working mechanism and pathological mechanism of human brain through macaque is an important means for human brain research, so the comparison of human brain and monkey brain has become a hotspot and difficulty of current research. In recent years, the continuous accumulation and disclosure of human and macaque brain image data provides support for direct comparison of the two. Research on new techniques and methods for cross-species comparison neuroimaging is also of increasing importance and is becoming a hot issue for international leading-edge fundamental research.
Cross-species studies are constrained and supported by the necessity of prior homology information, as opposed to comparisons between individuals or groups of single species. At present, the most important cross-species comparison method is to use the existing homologous brain regions as a reference system, and some scholars compare the functional connection modes of prefrontal cortex loci of human beings and rhesus macaques by using the known information of the prior homologous brain regions and the like, but the method depends on the information of the existing homologous brain regions, and has certain limitation under the condition of lacking the information of the homologous brain regions. It has been shown that in higher primates, white matter tissue exhibits a higher commonality among different species. Some co-existing white matter fiber tracts have been identified in humans, chimpanzees and rhesus monkeys to date, their connecting backbones are relatively similar, and these commonalities can be used for cross-species comparison studies.
Disclosure of Invention
Aiming at the technical problem that the accuracy of registering a fiber bundle atlas to an individual is low in the prior art, the invention provides the brain region homology comparison method based on the white matter fiber bundle, which has high accuracy, strong reliability and wide application range.
In order to solve the technical problems, the invention adopts the technical scheme that:
a brain region homology comparison method based on white matter fiber tracts comprises the following steps:
s1, reading a diffusion tensor magnetic resonance imaging DTI image and a structural magnetic resonance imaging T1 image: reading two groups of data, wherein one group of data is a magnetic resonance image of a human, the other group of data is a magnetic resonance image of a macaque, the magnetic resonance image of the human comprises a brain DTI image and a T1 image which are in the same format as the nii.gz, and the magnetic resonance image of the macaque comprises a brain DTI image and a T1 image which are in the same format as the nii.gz;
s2, preprocessing the image in the first step: respectively carrying out preprocessing operation on DTI images and T1 images of human beings and macaques by using a data preprocessing method;
s3, image registration and region of interest extraction: registering standard maps of human beings and macaques to the preprocessed DTI images by using an image registration method and combining the T1 images, and extracting the region of interest;
s4, constructing a cross-species reference system: respectively tracking 32 white matter fiber bundles on human and macaque individuals by utilizing probability fiber bundle tracking as a cross-species reference system;
s5, constructing a structural connectivity relation at an individual level: respectively tracking probability fiber bundles from white matter fiber bundles of human beings and macaques to the region of interest to respectively generate structural connectivity relations of the human beings and the macaques;
s6, cross-species comparison: and (3) performing intra-species consistency analysis on the constructed human and macaque structure connectivity relation by using a cross-species comparison formula, wherein the intra-species consistency coefficient is greater than 0.6, and performing inter-species homology similarity measurement on the constructed human and macaque structure connectivity relation.
The image registration method in the step S3 comprises the following steps: comprises the following steps:
s3.1, linearly registering the individual DTI image to an individual T1 structural image, storing a deformation matrix generated in the registration process, and transposing the deformation matrix to obtain a deformation matrix from the individual T1 structural image to the individual DTI image;
s3.2, linearly registering the individual T1 structural image to an MNI standard space to obtain a deformation matrix file, registering the individual T1 structural image to an MNI standard image space according to the deformation matrix to obtain a deformation field file, and reversing the deformation field file to obtain a deformation field file from the MNI standard image space to the individual T1 structural image;
s3.3, combining the deformation field file from the MNI standard image space to the individual T1 structural image with the deformation matrix from the individual T1 structural image to the individual DTI image to obtain a deformation field file from the individual MNI standard space to the individual DTI image;
and S3.4, registering the brain atlas to an individual DTI image diffusion space through a deformation field file from the individual MNI standard space to the individual B0 image to obtain the brain atlas based on the individual diffusion space.
The method for constructing the cross-species reference frame in the S4 comprises the following steps: comprises the following steps:
s4.1.1, respectively registering the regions of interest of the white matter fiber tracts, such as Seed Mask, target Mask, extract Mask and the like, onto the individuals according to the image registration result;
s4.1.2, tracking probability fiber bundles from a Seed Mask to a Target Mask, wherein probability fiber streamlines of human beings and macaques are respectively set to be 5000 times and 50000 times, and white matter fiber bundles based on individual levels are obtained;
s4.1.3, in order to ensure the reliability of white matter fiber bundles and reduce false positive connections, thresholding the obtained tracking results by using an empirical value with P >0.04%, and finally obtaining the connection trunk of 32 white matter fiber bundles as a common reference system of the two species.
The method for constructing the structure communication relationship in the S4 comprises the following steps: comprises the following steps:
s4.2.1, carrying out probability fiber bundle tracking on the DTI image of the human brain from a white matter fiber bundle reference system to a brain area, setting a fiber streamline to be 5000 times, and obtaining a voxel-level connectivity matrix of the main white matter fiber bundle of the human and the brain area; carrying out probability fiber bundle tracking on a DTI image of the brain of the macaque from a white matter fiber bundle reference system to a brain area, setting a fiber streamline to be 50000 times, and obtaining a voxel-level connectivity matrix of the main white matter fiber bundles of the macaque and the brain area;
s4.2.2, respectively carrying out dimension reduction operation on the voxel-level connectivity matrixes of human beings and macaques, namely averaging the voxel-level connectivity matrixes row by row and column by column, and taking the obtained value as the connectivity intensity value of the fiber bundles to the brain area;
and S4.2.3, splicing the connection strength values of the fiber bundles of the human and the macaque to the brain area to obtain the connection relation of the human and the macaque structures.
The specific calculation formula of the dimensionality reduction operation of the voxel-level connectivity matrix in the S5 is as follows:
the i and j represent two voxels of white matter fiber tracts and brain regions, the X ij And the connecting intensity value between two voxels is represented, and m and n represent the number of the voxels of the fiber bundle and the brain area.
The equation for the mid-span species comparison in S6 is:
k represents the number of individuals to be compared, p, q represent two fingerprint maps to be compared, and σ represents i Representing the internal variance in the structural connectivity of the I-th brain region homowhite matter fiber tracts, the σ x The variance of all individual connectivity relations is represented, n is the number of target areas in the fingerprint image, and pi isAnd qi respectively represent the connection values of the ith target area in the p and q connected fingerprint images to be compared.
Compared with the prior art, the invention has the following beneficial effects:
1. aiming at the problem that prior homologous brain region information is lost possibly in cross-species research, the invention provides a brain region homology comparison method based on white matter fiber bundles, which selects the white matter fiber bundles shared by human beings and macaques as cross-species reference systems and respectively constructs the structural connectivity relations between the human beings and the macaque brain regions and the reference systems; to demonstrate the reliability of the present invention, the consistency of structural connectivity relationships between individuals within a single species was verified from an intraspecies perspective, and the homology of structural connectivity relationships of known homologous brain regions was verified from an interspecies perspective.
2. The method of the invention adopts the tracking generation of the white matter fiber bundle cross-species reference system on the individual level, thus overcoming the defect of low accuracy of registering the fiber bundle atlas to the individual in the prior art and improving the accuracy of constructing the structural connectivity relation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so that those skilled in the art can understand and read the present invention, and do not limit the conditions for implementing the present invention, so that the present invention has no technical significance, and any structural modifications, changes in the ratio relationship, or adjustments of the sizes, without affecting the functions and purposes of the present invention, should still fall within the scope of the present invention.
FIG. 1 is a flow chart of the method steps of the present invention;
FIG. 2 is a diagram of the steps of an image registration method provided by the present invention;
FIG. 3 is a cross-species frame of reference constructed in accordance with the present invention;
FIG. 4 is a structural connectivity graph constructed in accordance with the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below, obviously, the described embodiments are only a part of the embodiments of the present application, but not all embodiments, and the description is only for further explaining the features and advantages of the present invention, and not for limiting the claims of the present invention; all other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
A cross-species comparison method based on white matter fiber tracts is provided for the situation that homologous brain region deletion may exist in cross-species research. Selecting a fiber bundle which commonly exists in two species of human beings and macaques and has a homologous relation as a cross-species comparison reference system, constructing a structural communication relation between a brain region and the fiber bundle by a probability fiber bundle tracking technology, and finally measuring the similarity of the structural communication relation of the two species by cosine similarity, wherein a flow chart of the specific invention is shown in fig. 1.
(1) Reading a diffusion tensor magnetic resonance imaging DTI image and a structural magnetic resonance imaging T1 image:
reading two groups of data, wherein one group of data is a magnetic resonance image of a human, the other group of data is a magnetic resonance image of a macaque, the magnetic resonance image of the human comprises a brain DTI image and a T1 image which are in the same format as the nii.gz, and the magnetic resonance image of the macaque comprises a brain DTI image and a T1 image which are in the same format as the nii.gz;
(2) Carrying out data preprocessing on the image in the first step:
and respectively carrying out preprocessing operation on DTI images and T1 images of human beings and macaques by using a data preprocessing method.
The data preprocessing method comprises the following steps:
the method comprises the following steps of firstly, checking data quality, checking basic parameters of an image, including resolution, dimension information and the like, checking the number of gradient directions and b values, checking the signal-to-noise ratio of data, checking artifacts and the like;
second, data format conversion, converting the data from DICOM format to NIfTI format;
thirdly, correcting the head moving eddy current to eliminate the head moving in the scanning process to a certain extent, the deformation caused by the head moving and the eddy current and the like;
fourthly, correcting the gradient direction, and adjusting the original gradient direction according to the change of the eddy current correction;
and fifthly, respectively acquiring a Mask of a DTI image and a Mask of a T1 image of the brain, acquiring a Mask of the DTI image, acquiring a B0 image from the 4D data by using a fslroi command, and removing an extracerebral image of the B0 image by using a BET tool to acquire the Mask. Acquiring a Mask of the T1 image, and removing the extracerebral image of the T1 image by using a beta 2 command to obtain the Mask;
sixthly, tensor calculation, namely calculating tensor by using the FSL toolset;
and seventhly, constructing a cross fiber model, and performing voxel model fitting on the diffusion direction by using the cross fiber model limited to three fiber directions.
(3) Image registration and region of interest extraction:
and registering the standard maps of the human and the macaque to the preprocessed DTI image by using an image registration method and combining the T1 image, and extracting the region of interest.
The image registration method comprises the following steps:
the method comprises the steps that firstly, an individual DTI image is linearly registered to an individual T1 structural image by using FLIRT, a deformation matrix generated in the registration process is saved, and the deformation matrix is transposed to obtain a deformation matrix from the individual T1 structural image to an individual B0 image;
secondly, linearly registering the individual T1 structural image to an MNI standard space by using FLIRT and obtaining a deformation matrix file, registering the individual T1 structural image to the MNI standard image space by using FNIRT according to the deformation matrix and obtaining a deformation field file, and reversing the deformation field file to obtain a deformation field file from the MNI standard image space to the individual T1 structural image;
thirdly, combining the deformation field file from the MNI standard image space to the individual T1 structural image with the deformation matrix from the individual T1 structural image to the individual B0 image to obtain the deformation field file from the individual MNI standard space to the individual B0 image;
and fourthly, registering the brain atlas to the individual B0 diffusion space through a deformation field file from the individual MNI standard space to the individual B0 image to obtain the brain atlas based on the individual diffusion space.
(4) Construction of cross-species frame of reference:
(4a) Respectively registering regions of interest such as Seed Mask, target Mask, extract Mask and the like of 32 white matter fiber tracts on an individual according to the result of image registration;
(4b) Tracking probability fiber bundles from the Seed Mask to the Target Mask, wherein probability fiber streamlines of a human and a macaque are respectively set to be 5000 times and 50000 times, and white matter fiber bundles based on individual levels are obtained;
(4c) To ensure the reliability of white matter fiber tracts and reduce false positive connections, the resulting tracking results were thresholded using an empirical value of P >0.04%, and the connected stems of 32 white matter fiber tracts were finally obtained as a common frame of reference for both species.
(5) Constructing a structural connectivity relation:
(5a) Performing probability fiber tract tracking from a white matter fiber tract reference system to a brain region, wherein a human fiber streamline is set to be 5000 times, a macaque fiber streamline is set to be 50000 times, and voxel-level connectivity matrixes of main white matter fiber tracts of human beings and macaques and the brain region are respectively obtained by using a matrix2 mode;
(5b) Performing dimension reduction operation on the voxel-level connectivity matrix, namely averaging the voxel-level connectivity matrix according to rows and columns, and taking the obtained value as a connectivity intensity value from the fiber bundle to the brain area, wherein a specific calculation formula is as follows:
wherein i and j represent two voxels of white matter fiber tracts and a brain region, xij represents a communication intensity value between the two voxels, and m and n represent the number of the voxels of the fiber tracts and the brain region;
(5c) And splicing the connection strength values of the fiber bundles of the human and the macaque to the brain area to obtain the connection relation of the human and the macaque structures.
(6) Intra-species and inter-species comparisons were performed:
and (3) performing intraspecific consistency analysis on the structural connectivity relationship between the human beings and the macaques constructed by using the Krenbach alpha coefficient (5), and performing interspecies homology similarity measurement on the structural connectivity relationship between the human beings and the macaques constructed by using cosine similarity.
The formula of the kronebach α coefficient is as follows:
where k represents the number of individuals to be compared, σ i represents the internal variance in the structural connectivity of the ith brain region homowhite matter fiber tract, and σ x represents the variance of the connectivity of all individuals.
The formula of the cosine similarity is as follows:
wherein, p and q represent two fingerprint images to be compared; n is the number of target areas in the fingerprint image, pi and qi respectively represent the connection values of the ith target area in the two connected fingerprint images to be compared, p and q.
In order to prove that the method provided by the invention has higher reliability, the brain regions of Broca44, S1, hippocampus are randomly selected for cross-species comparative analysis, and early cross-species studies have proved that the 3 brain regions have a homologous relationship between human and macaque. In order to demonstrate that the structural connectivity of a single brain region has consistency between individuals within a single species, the internal consistency of 3 known homologous brain regions in two species was calculated respectively, and when the internal consistency coefficient was greater than 0.6, interspecies homology analysis was performed. The results show that the coefficients of the 3 brain regions in both human and cynomolgus monkeys are greater than 0.6; the homology of 3 brain regions is mainly verified among species, and the result shows that the homology relation of the 3 brain regions can be verified by the invention.
TABLE 1 in-species identity analysis
| Intraspecies consistency analysis | Human being | Kiwi fruit |
| Region Broca44 | 0.781 | 0.636 |
| Region S1 | 0.726 | 0.780 |
| Hippocampus region | 0.607 | 0.977 |
TABLE 2 analysis of homology between species
| Brain region | Cosine similarity |
| Broca44 | 0.979 |
| S1 | 0.994 |
| Hippocampus | 0.995 |
Although only the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art, and all changes are encompassed in the scope of the present invention.
Claims (6)
1. A brain region homology comparison method based on white matter fiber tracts is characterized in that: comprises the following steps:
s1, reading a diffusion tensor magnetic resonance imaging DTI image and a structural magnetic resonance imaging T1 image: reading two groups of data, wherein one group of data is a magnetic resonance image of a human, the other group of data is a magnetic resonance image of a macaque, the magnetic resonance image of the human comprises a brain DTI image and a T1 image which are in the same format as the nii.gz, and the magnetic resonance image of the macaque comprises a brain DTI image and a T1 image which are in the same format as the nii.gz;
s2, preprocessing the image in the first step: respectively carrying out preprocessing operation on DTI images and T1 images of human beings and macaques by using a data preprocessing method;
s3, image registration and region of interest extraction: registering standard maps of human beings and macaques to the preprocessed DTI images by using an image registration method and combining the T1 images, and extracting the region of interest;
s4, constructing a cross-species reference system: respectively tracking 32 common white matter fiber bundles on human and macaque individuals by utilizing probability fiber bundle tracking as a cross-species reference system;
s5, constructing a structural connectivity relation at an individual level: respectively tracking probability fiber bundles from white matter fiber bundles of human beings and macaques to the region of interest to respectively generate structural connectivity relations of the human beings and the macaques;
s6, cross-species comparison: and (3) performing intra-species consistency analysis on the constructed human and macaque structure connectivity relation by using a cross-species comparison formula, wherein the intra-species consistency coefficient is greater than 0.6, and performing inter-species homology similarity measurement on the constructed human and macaque structure connectivity relation.
2. The method for brain region homology comparison based on white matter fiber tracts according to claim 1, wherein: the image registration method in the step S3 comprises the following steps: comprises the following steps:
s3.1, linearly registering the individual DTI image to an individual T1 structural image, storing a deformation matrix generated in the registration process, and transposing the deformation matrix to obtain a deformation matrix from the individual T1 structural image to the individual DTI image;
s3.2, linearly registering the individual T1 structural image to an MNI standard space to obtain a deformation matrix file, registering the individual T1 structural image to the MNI standard image space by referring to a deformation matrix to obtain a deformation field file, and reversing the deformation field file to obtain a deformation field file from the MNI standard image space to the individual T1 structural image;
s3.3, combining the deformation field file from the MNI standard image space to the individual T1 structural image with the deformation matrix from the individual T1 structural image to the individual DTI image to obtain a deformation field file from the individual MNI standard space to the individual DTI image;
and S3.4, registering the brain atlas to the individual DTI image diffusion space through the deformation field file from the individual MNI standard space to the individual B0 image to obtain the brain atlas based on the individual diffusion space.
3. The method for brain region homology comparison based on white matter fiber tracts according to claim 1, wherein: the method for constructing the cross-species reference system in the S4 comprises the following steps: comprises the following steps:
s4.1.1, respectively registering regions of interest such as Seed Mask, target Mask, exclude Mask and the like of white matter fiber bundles to individuals according to the result of image registration;
s4.1.2, tracking probability fiber bundles from a Seed Mask to a Target Mask, wherein probability fiber streamlines of human beings and macaques are respectively set to be 5000 times and 50000 times, and white matter fiber bundles based on individual levels are obtained;
s4.1.3, in order to ensure the reliability of white matter fiber bundles and reduce false positive connections, thresholding the obtained tracking results by using an empirical value with P >0.04%, and finally obtaining the connection trunk of 32 white matter fiber bundles as a common reference system of the two species.
4. The method for brain region homology comparison based on white matter fiber tracts according to claim 1, wherein: the method for constructing the structure communication relationship in the S4 comprises the following steps: comprises the following steps:
s4.2.1, carrying out probability fiber bundle tracking on the DTI image of the human brain from a white matter fiber bundle reference system to a brain area, setting a fiber streamline to be 5000 times, and obtaining a voxel-level connectivity matrix of the main white matter fiber bundle of the human and the brain area; carrying out probability fiber bundle tracking on the DTI image of the brain of the macaque from a white matter fiber bundle reference system to a brain region, setting a fiber streamline to be 50000 times, and obtaining a voxel-level connectivity matrix of the main white matter fiber bundle of the macaque and the brain region;
s4.2.2, respectively carrying out dimension reduction operation on the voxel-level connectivity matrixes of human beings and macaques, namely averaging the voxel-level connectivity matrixes in rows and columns, and taking the obtained value as a connectivity intensity value from the fiber bundle to the brain area;
and S4.2.3, splicing the connection strength values of the fiber bundles of the human and the macaque to the brain area to obtain the connection relation of the human and the macaque structures.
5. The method for brain region homology comparison based on white matter fiber tracts according to claim 1, wherein: the specific calculation formula of the dimension reduction operation of the voxel-level connectivity matrix in the step S5 is as follows:
the i and j represent two voxels of white matter fiber tracts and brain regions, the X ij And the connecting intensity value between two voxels is represented, and m and n represent the number of the voxels of the fiber bundle and the brain area.
6. The method for brain region homology comparison based on white matter fiber tracts according to claim 1, wherein: the equation for the mid-span species comparison in S6 is:
k denotes the number of individuals to be compared, p, q denote two fingerprint maps to be compared, σ denotes i Representing the internal variance in the structural connectivity of the I-th brain region homowhite matter fiber tracts, the σ x And representing the variance of all individual connectivity relations, wherein n is the number of target areas in the fingerprint image, and pi and qi respectively represent the connection values of the ith target area in the p and q connected fingerprint images to be compared.
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