CN115099369A - Network fusion method based on functional connection and structural connection - Google Patents
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Abstract
The invention discloses a network fusion method based on functional connection and structural connection, which comprises the following steps: step one, dividing cerebral cortex into different brain areas and networks based on an AAL template and a Yeo brain network template; step two, preprocessing fMRI and DTI data; thirdly, constructing a large-scale brain connection matrix for the preprocessed fMRI and DTI data; and step four, calculating the network horizontal weighting probability between the function-structure connections. The invention fuses structural connection and functional connection at a brain network level, explores the cooperative relationship among different networks on the basis of a large-scale brain network, and quantitatively describes the brain network cooperative action based on the structural connection and the functional connection according to the obvious correlation among the networks for the first time.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a network fusion method based on functional connection and structural connection.
Background
The human brain is known as the most complex system in nature, and how to understand and research the highly complex system is always the difficulty and hotspot of brain science research. Magnetic Resonance Imaging (MRI) is a common imaging technique for observing internal tissue structures of the body, and is particularly effective for examination of the brain, muscles, and most tumor tissues. Magnetic resonance has no ionizing radiation, so the magnetic resonance is widely used in researches such as neurophysiology, cognitive neuroscience, neuropsychiatric diseases and the like besides clinical application, and has achieved great research results. Among them, functional magnetic resonance imaging (fMRI) and Diffusion Tensor Imaging (DTI) are particularly favored by researchers due to their imaging principles.
fMRI based on Blood Oxygen Level Dependent (BOLD) imaging is used to indirectly reflect spontaneous fluctuations in neuronal signals. Because of the advantages of non-invasiveness and high spatial resolution, fMRI has been widely used in many fields of neuroscience research, such as the study of advanced neurophysiological and psychological patterns, the exploration of functional interrelations between cortex, and the like. fMRI generally divides brain areas according to an existing brain area template (e.g., AAL template, etc.), and uses the brain areas as nodes of a network to find node relationships between the brain areas, so as to reflect dynamic coordination among neural information. However, with the progress of research, researchers gradually find that the human brain has the characteristic of functional integration. Functional integration indicates that different brain regions of the brain interact and cooperate with each other, and the brain regions which may be spatially independent from each other functionally show a synergistic characteristic, indicating that the brain has the characteristics of a network. The functional network can well depict the information interaction between the brain areas, but the real connection condition between the brain areas is difficult to depict.
The DTI can track white matter fiber bundles through the whole dispersion level and direction of water molecules, and obtain the specific running form of white matter fibers in a brain area by a non-invasive means, so that richer brain anatomical information is provided. According to the connection condition of the fiber bundles between the two brain areas, the real connection structure of the brain is depicted more intuitively, so that a brain structure network is constructed. Researchers use DTI to construct a weighted anatomical structure network, and find that the core region of brain network transmission is mainly located on the inner sides of cortex such as anterior cuneiform lobe and posterior cingulum. This finding further supports that structural connectivity is an intrinsic basis for brain functional connectivity. However, the imaging principle of DTI determines that DTI technology is more suitable for analyzing the integrity of the orientation of white matter fibers in the brain, and the exploration of the synergistic link between brain region functions is of limited help. Therefore, the fusion analysis of the two modal data, which makes up for the deficiency, is a need to further examine the brain activity.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a network fusion method for fusing structural connection and functional connection at a brain network level, researching the cooperative relationship between different networks on the basis of a large-scale brain network, and quantitatively depicting the brain network cooperative action based on the structural connection and the functional connection for the first time according to the remarkable correlation between the networks.
The purpose of the invention is realized by the following technical scheme: a network fusion method based on functional connection and structural connection comprises the following steps:
step one, dividing a cerebral cortex into different brain areas and networks based on an AAL template and a Yeo brain network template;
step two, preprocessing fMRI and DTI data;
step three, constructing a large-scale brain connection matrix for the preprocessed fMRI and DTI data;
and step four, calculating the network level weighting probability between the functional network-structure connection.
Further, the first specific implementation method of the step is as follows: dividing the cerebral cortex into 90 brain areas and 8 brain networks based on the AAL template and the Yeo brain network template; when the network is divided, the brain areas which belong to a plurality of networks at the same time are defined by adopting a 'winner takes all' mode, namely the most voxel points in the brain areas belong to a certain network, and then the brain areas are defined as the network; the 8 brain networks are as follows: visual network, edge network, sensorimotor network, default mode network, forehead network, ventral attention network, dorsal attention network, and subcortical network.
Further, in the second step, a specific method of data preprocessing is as follows:
preprocessing fMRI data with an SPM12 toolbox, comprising the steps of: (1) removing the first five time points; (2) correcting a time layer; (3) correcting the head movement; (4) normalization to MNI templates; (5) removing the influence of cephalic motion, white matter, cerebrospinal fluid signals and linear trend by utilizing linear regression; (6) band-pass filtering, the frequency band is 0.01-0.1 Hz;
preprocessing DTI data by using FSL software, and performing head movement and eddy current correction on the DTI data by using affine transformation registration; then, on an individual level, tracking white matter fiber tracts of the whole brain by using a deterministic tracing algorithm on the corrected DTI data, and calculating an FA parameter; the termination condition of the tracking path is as follows: the FA value is below 0.2 or the tracking angle deflection exceeds 35 °.
Further, in the third step, the specific implementation method for constructing the brain connection matrix is as follows:
for fMRI data, extracting BOLD time sequences of all voxels in each tested brain area according to an AAL template, solving the average value of BOLD signals of all voxels in the current brain area at each time point, taking the average time sequence as the time sequence of the current brain area, and calculating the Pearson correlation value between any two average time sequences of the brain areas to obtain a brain area-brain area functional connection matrix; performing Fisher-z transformation on the connection matrix to obtain a functional connection matrix conforming to normal distribution, wherein an element V (i.j) in the matrix represents the functional connection strength between the ith brain area and the jth brain area, and the size of the matrix is 90 multiplied by 90;
for DTI data, when the DTI data is processed by using FSL software, FA values between each voxel of a brain and any other voxel are generated, and the FA values between the voxels belonging to two brain areas are averaged to obtain an average FA matrix which is used as a structural connection matrix; and detecting which elements in the structure connection matrix are obviously present on the group and have obvious statistical difference by using single-tail symbol test, and reserving the side of the obvious difference, namely reserving and setting the connection with the statistical difference as 1, and setting other connections as 0, thereby generating a binary structure connection network mask; each element v (i, j) in mask with a value of 1 represents that there is a significant connection between brain region i and brain region j in the group.
Further, the fourth specific implementation method of the step is as follows: extracting the functional connection and the structural connection between each tested brain area i and each tested brain area j, storing the extracted functional connection and the structural connection into a column of matrixes, and respectively obtaining group-level functional connection vectors FC (i,j) And a structural connection vector SC (i,j) Computing a functional connection vector FC (i,j) And a structural connection vector SC (i,j) Pearson correlation between two groups of data is calculated by a Pearson correlation method, and two parameters are simultaneously calculated when the correlation between two groups of data is calculated by the Pearson correlation method: correlation r and significance p; repeatedly calculating correlation between any two brain areas to respectively obtain a 90 multiplied by 90 functional connection and structural connection coupling matrix C and a 90 multiplied by 90 significance matrix P; extracting the network N from the coupling matrix C and the significance matrix P according to the brain network division i Brain region m and network N j Of brain regions n (m,n) And a coupling matrix C (m,n) Using a threshold value p<0.05 general procedure of P (m,n) Matrix binarization, reserving only the parts with significant connections and limiting the coupling matrix | C tintafter attenuation m，n Obtaining a significant coupling connection matrix
Computing a significant coupling connection matrix
And a coupling matrix | C m，n And (3) solving the ratio of the sum of all element values in each matrix, wherein the ratio is the network level weighting probability between the functional structure connections, and the formula is as follows:
wherein N is i And N j For the ith and jth brain networks, m and n represent twoThe m and n brain regions in the brain network; calculating a correlation | C ¬ of functional and structural connections between m-brain and n-brain regions on a group level using Pearson correlation m，n To is that
Are statistically based significant correlations.
The beneficial effects of the invention are: the invention integrates structural connection and functional connection on the brain network level, explores the cooperative relationship among different networks on the basis of a large-scale brain network, and quantitatively depicts the brain network cooperative action based on the structural connection and the functional connection according to the obvious correlation among the networks for the first time. The method provides a new idea for understanding brain network information interaction and deepens the understanding of the epileptic brain network abnormality.
Drawings
FIG. 1 is a flow chart of a network convergence method based on functional connection and structural connection according to the present invention;
FIG. 2 is a graph showing the results of the experiment in this example.
Detailed Description
As shown in fig. 1, a network convergence method based on functional connection and structural connection of the present invention includes the following steps:
step one, dividing a cerebral cortex into different brain areas and networks based on an AAL template and a Yeo brain network template; the specific implementation method comprises the following steps: based on an AAL template and a Yeo brain network template, the cerebral cortex is divided into 90 brain areas (the AAL template comprises 90 brain areas of the cerebral cortex and 26 brain areas of the cerebellum, the cerebral cortex brain areas are used in the invention) and 8 brain networks; when the network is divided, the brain areas which belong to a plurality of networks at the same time are defined by adopting a 'winner takes all' mode, namely the most voxel points in the brain areas belong to a certain network, and then the brain areas are defined as the network; the 8 brain networks are as follows: visual network, edge network, sensorimotor network, default mode network, frontal network, ventral attention network, dorsal attention network, and subcortical network.
Step two, preprocessing fMRI and DTI data; the specific method comprises the following steps:
preprocessing fMRI data with an SPM12 toolbox, comprising the steps of: (1) removing the first five time points; (2) correcting a time layer; (3) correcting the head movement; (4) normalization to MNI templates; (5) removing the influence of the tested cephalic motion, cerebral white matter, cerebrospinal fluid signals and linear trend during magnetic resonance scanning by a linear regression method; (6) band-pass filtering, the frequency range is 0.01-0.1 Hz.
Preprocessing DTI data by using FSL software, and performing head movement and eddy current correction on the DTI data by using affine transformation registration; secondly, tracking white matter fiber tracts of the whole brain by using a deterministic tracing algorithm on the corrected DTI data on an individual level, and calculating an FA parameter; the termination condition of the tracking path is as follows: the FA value is below 0.2 or the tracking angle deflection exceeds 35 °.
This example used the actual data set as a test, and a total of 41 epileptic patients (20 medication groups, mean age: 24.7 + -10.5 years; 21 medication groups, mean age: 26.5 + -14.2 years) and 29 young subjects (mean age 25.3 + -8.9 years) were tested. In the experiment, a plane echo imaging sequence is adopted to obtain fMRI data and DTI data of the head in a resting state, and the data are preprocessed by using the method.
Step three, constructing a large-scale brain connection matrix for the preprocessed fMRI and DTI data; the specific implementation method comprises the following steps:
for fMRI data, extracting BOLD time sequences of all voxels in each tested brain area according to an AAL template, solving the average value of BOLD signals of all voxels in the current brain area at each time point, taking the average time sequence as the time sequence of the current brain area, and calculating the Pearson correlation value between any two average time sequences of the brain areas to obtain a brain area-brain area functional connection matrix; performing Fisher-z transformation on the connection matrix to obtain a functional connection matrix conforming to normal distribution, wherein an element V (i.j) in the matrix represents the functional connection strength between the ith brain area and the jth brain area, and the size of the matrix is 90 multiplied by 90;
for DTI data, when the DTI data is processed by using FSL software, FA values between each voxel of a brain and any other voxel are generated, and the FA values between the voxels belonging to two brain areas are averaged to obtain an average FA matrix which is used as a structural connection matrix; to reduce false connections in the structural connection matrix, a single-tailed symbolic test is used to detect in a single group of subjects which elements in the structural connection matrix are significantly present on the group, with the statistical assumption that no structural connection exists; in order to avoid false positive results due to multiple statistical tests, multiple comparison corrections are performed on all statistical comparison results p values by using the Bonferroni method, wherein the statistical significance threshold p is set to 0.05; when p is less than 0.05, the significant statistical difference is considered to exist, the edges with the significant difference are reserved, namely the connections with the statistical difference are reserved and set as 1, and other connections are set as 0, so that a binaryzation structure connection network mask is generated for each group; and taking a union set from the structure connection network masks of the three groups of data in the experiment, and limiting the subsequent functional structure network coupling analysis in the union set mask, wherein each element v (i, j) with the value of 1 in the mask represents that the brain area i and the brain area j have obvious connection on the groups.
Step four, calculating the network horizontal weighting probability between the function-structure connections; the method is used for inspecting the correlation among different neuroimaging modal data, and the method is used for realizing multi-mode data fusion and exploring the correlation among brain networks under different neuroimaging modal data. The method can explore the cooperative change degree between brain structures and functional networks, and the higher the network level weighted probability value is, the higher the cooperative consistency between the networks is. The specific implementation method comprises the following steps: extracting the functional connection and the structural connection between each tested brain area i and each tested brain area j in the group, storing the extracted functional connection and the structural connection as a column of matrix, and respectively obtaining group-level functional connection vectors FC (i,j) And a structural connection vector SC (i,j) Computing a functional connection vector FC (i,j) And a structural connection vector SC (i,j) Pearson correlation between two groups of data is calculated by a Pearson correlation method, and two parameters are simultaneously calculated when the correlation between two groups of data is calculated by the Pearson correlation method: correlation r and significance p; repeatedly calculating correlation between any two brain areas to respectively obtain a 90 multiplied by 90 functional connection and structural connection coupling matrix C and a 90 multiplied by 90 significance matrix P; extracting the network N from the coupling matrix C and the significance matrix P according to the brain network division i Brain region m and network N j Of brain regions n (m,n) And a coupling matrix C (m,n) Using a threshold value p<0.05 mixing of P (m,n) Matrix binarization, reserving only the parts with significant connections and limiting the coupling matrix | C tintafter attenuation m，n Obtaining a significant coupling connection matrix
Computing a significant coupling connection matrix
And a coupling matrix
And (3) summing all element values in each matrix, and solving the ratio of the element values to the element values, wherein the ratio is the network level weighting probability between the function-structure connections, and the formula is expressed as follows:
wherein N is i And N j The ith brain network and the jth brain network, m and n respectively represent the mth brain region and the nth brain region in the two brain networks; calculating a correlation | C Ybetween functional and structural connections between m and n brain regions at the group level using Pearson's correlation m，n To is that
Are statistically based significant correlations.
Carrying out statistical tests: since the data of this example do not fit a normal distribution, a permutation test is used here. Three groups of data are used in the experiment, and a mode of comparing any two groups of results is adopted in statistical test. The specific operation is as follows: assume that there are two sets of data (set a and set B) for which statistical differences need to be calculated. When comparing the group A and the group B, mixing and disordering the tested data of the group A and the group B between the groups, maintaining the corresponding relation between the functional data and the structural data of each tested data, dividing the data into two groups, keeping the tested number of each group the same as the original number of the group A and the group B, repeating the steps of disordering, redistributing and calculating the network horizontal weighting probability between the functional-structural connection for 5000 times, and constructing the null distribution. The statistical threshold P was set to 0.05. The statistical difference between any two groups of the used medicine group, the unused medicine group and the normal control group is calculated by the method.
Results of the experiment
The results on the real experimental set are shown in fig. 2. It was found that the non-drug group showed a reduced probability of network level weighting within the default mode network and between it and the ventral attention network, and that the patient group had a consistent reduced probability of network level weighting between the limbic network and the sensorimotor network, compared to the healthy controls. In addition, the medicated group exhibits a reduced probability of network level weighting between the default mode network and the edge network, but increased within the default mode network, as compared to the non-medicated group.
The experimental result shows that the epilepsy brain network abnormal model based on the magnetic resonance construction brain function and structure connection network fusion analysis effectively reveals that the benefit of medication on the epilepsy seizure is probably the influence on the functional network rather than the structural network, but the medication can improve the abnormal brain structure network by influencing the coupling relationship. The network abnormal coupling model can be a means for evaluating the curative effect of medication.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (5)
1. A network convergence method based on functional connection and structural connection is characterized by comprising the following steps:
step one, dividing a cerebral cortex into different brain areas and networks based on an AAL template and a Yeo brain network template;
step two, preprocessing fMRI and DTI data;
step three, constructing a large-scale brain connection matrix for the preprocessed fMRI and DTI data;
and step four, calculating the network horizontal weighting probability between the function-structure connections.
2. The method for network convergence based on functional connection and structural connection according to claim 1, wherein the step one specific implementation method is as follows: dividing the cerebral cortex into 90 brain areas and 8 brain networks based on the AAL template and the Yeo brain network template; when the network is divided, the brain areas which belong to a plurality of networks at the same time are divided by adopting a 'winner takes all' mode, namely the brain areas are determined as the network when the maximum number of voxel points in the brain areas belong to a certain network; the 8 brain networks are as follows: visual network, edge network, sensorimotor network, default mode network, frontal network, ventral attention network, dorsal attention network, and subcortical network.
3. The network convergence method based on functional connection and structural connection according to claim 1, wherein in the second step, a specific method for data preprocessing is as follows:
preprocessing fMRI data with an SPM12 toolbox, comprising the steps of: (1) removing the first five time points; (2) correcting a time layer; (3) correcting the head movement; (4) normalization to MNI templates; (5) removing the influence of cephalic motion, white matter, cerebrospinal fluid signals and linear trend by utilizing linear regression; (6) band-pass filtering is carried out, and the frequency band is 0.01-0.1 Hz;
preprocessing DTI data by using FSL software, and performing head movement and eddy current correction on the DTI data by using affine transformation registration; then, on an individual level, tracking white matter fiber tracts of the whole brain by using a deterministic tracing algorithm on the corrected DTI data, and calculating an FA parameter; the termination condition of the tracking path is as follows: the FA value is below 0.2 or the tracking angle deflection exceeds 35 °.
4. The network fusion method based on functional connection and structural connection according to claim 1, wherein in step three, the specific implementation method for constructing the brain connection matrix is as follows:
for fMRI data, extracting BOLD time sequences of all voxels in each tested brain area according to an AAL template, solving the average value of BOLD signals of all voxels in the current brain area at each time point, taking the average time sequence as the time sequence of the current brain area, and calculating the Pearson correlation value between any two average time sequences of the brain areas to obtain a brain area-brain area functional connection matrix; performing Fisher-z transformation on the connection matrix to obtain a functional connection matrix conforming to normal distribution, wherein an element V (i.j) in the matrix represents the functional connection strength between the ith brain area and the jth brain area, and the size of the matrix is 90 multiplied by 90;
for DTI data, when the DTI data is processed by using FSL software, FA values between each voxel of a brain and any other voxel are generated, and the FA values between the voxels belonging to two brain areas are averaged to obtain an average FA matrix which is used as a structural connection matrix; detecting which elements in the structural connection matrix are obviously present on the group and have obvious statistical difference by using single-tail symbol test, and reserving the edges with the obvious difference, namely reserving and setting the connections with the statistical difference as 1 and setting other connections as 0, thereby generating a binaryzation structural connection network mask; each element v (i, j) in mask with a value of 1 represents that there is a significant connection between brain region i and brain region j in the group.
5. The network convergence method based on functional connection and structural connection according to claim 1, wherein the fourth specific implementation method is as follows: extracting the functional connection and the structural connection between each tested brain area i and each tested brain area j, storing the extracted functional connection and the structural connection into a column of matrixes, and respectively obtaining group-level functional connection vectors FC (i,j) Sum structure connecting vector SC (i,j) Computing a functional connection vector FC (i,j) And a structural connection vector SC (i,j) The Pearson correlation method calculates the correlation time of two groups of data, and simultaneously calculatesTwo parameters were obtained: correlation r and significance p; repeatedly calculating correlation between any two brain areas to respectively obtain a 90 multiplied by 90 functional connection and structural connection coupling matrix C and a 90 multiplied by 90 significance matrix P; extracting the belonging network N from the coupling matrix C and the significance matrix P according to the brain network division i Brain region m and network N j Of the brain region n (m,n) And a coupling matrix C (m,n) Using a threshold value p<0.05 mixing of P (m,n) Matrix binarization, reserving only the parts with significant connections and limiting the coupling matrix | C tintafter attenuation m，n Obtaining a significant coupling connection matrix
Computing a significant coupling connection matrix
And a coupling matrix | C $ m，n And (3) summing all element values in each matrix, and solving the ratio of the element values to the element values, wherein the ratio is the network level weighting probability between the function-structure connections, and the formula is expressed as follows:
wherein, N i And N j The ith brain network and the jth brain network, m and n respectively represent the mth brain area and the nth brain area in the two brain networks; calculating a correlation | C ¬ of functional and structural connections between m-brain and n-brain regions on a group level using Pearson correlation m，n To do so
Are statistically based significant correlations.
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