CN112741613A

CN112741613A - Resting human brain default network function and structure coupling analysis method

Info

Publication number: CN112741613A
Application number: CN202110042211.1A
Authority: CN
Inventors: 宗小芬; 胡茂林; 翁深宏; 郑俊杰; 何长春; 刘忠纯
Original assignee: Wuhan University WHU
Current assignee: Wuhan University WHU
Priority date: 2021-01-13
Filing date: 2021-01-13
Publication date: 2021-05-04

Abstract

The invention relates to a neural signal processing technology, in particular to a resting state human brain default network function and structure coupling analysis method, which takes brain function magnetic resonance fMRI and structure magnetic resonance DTI data as research objects, carries out data preprocessing after data acquisition, ensures the consistency of the data, firstly extracts each component of the brain default network, calculates the function connection value and the structure connection value between every two components, then calculates the coupling value between the function connection and the structure connection, and adopts independent sample t test to compare the difference between a schizophrenia patient and a healthy person to research the abnormal change of the brain function and the structure coordination of the schizophrenia patient. The analysis method can reflect the fit degree and coordination degree of functions and structures between two components of the human brain default network, so that the connection characteristics, the function and the structure coordination characteristics of each brain area of the default network under different activities, stimulation or diseases can be researched, and the analysis method has high application value.

Description

Resting human brain default network function and structure coupling analysis method

Technical Field

The invention belongs to the technical field of neural signal processing, and particularly relates to a resting state human brain default network function and structure coupling analysis method.

Background

The brain is a complex network composed of spatially independent regions connected in a special manner^[1]. At present, researchers at home and abroad have detected various brain networks by means of modern 'in-vivo' brain image technology and image and signal data processing methods of computer information subject, and one of them is a default network (default mode network) which is widely researched^[2]. The hypothesis of the human brain default network was originally made by Raichle et al^[3]It was first proposed in 2001 based on Positron Emission Tomography (PET) data that the hypothesis considers that certain brain regions have regular functional activity while the human brain is in a resting state without performing any task, and that the functional network formed by these brain regions is called the default network. The default mode herein refers to the intrinsic functional mode of the brain in a resting (or resting) state. By "resting state" is meant an alert or awake state of the human brain in which the human brain does not have to perform tasks that require attention. The main components of the default network include two important nodes, namely the cingulate and anterior cuneiform (PCC/PCUN) and medial prefrontal cortex (mPFC), as well as other important areas such as bilateral gyrus (AG), medial temporal lobe (mTL)^[4]. These important areas constituting the default network are all found to be related to human neurological and psychiatric diseases^[5-7]. In addition, a number of studies have shown that default networks are associated with certain specific cognitive functions of the human brain, such as working memory^[8]. Therefore, once the default network hypothesis is proposed, it becomes a hot spot for neuropsychiatric disease research^[2]。

Human brain default netFunctional connection inside collaterals^[9-13]And fibrous anatomical connections, i.e. structural connection patterns^[14]Have been characterized separately, but prior studies have lacked characterization of the coupling mode between the functional and structural connections.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a method for analyzing and researching the connection characteristics of the internal functions and the multi-modes of the structure of a brain default network after the function and the structure connection of the brain default network are constructed in a coupling mode.

In order to solve the technical problems, the invention adopts the following technical scheme: a resting state human brain default network function and structure coupling analysis method comprises the following steps:

step 1, acquiring magnetic resonance brain images of a healthy subject and a schizophrenia subject; the brain image comprises a functional image fMRI and a structural image DTI;

step 2, preprocessing the acquired brain image;

step 3, extracting and identifying each component of the default network by adopting an Independent Component Analysis (ICA);

step 4, calculating functional connection values among all components of the default network;

step 5, extracting white matter fiber bundles connected among all components of the default network;

step 6, calculating an average FA value of the fiber bundles between every two components as a structural connection value between every two components of a default network;

step 7, calculating the coupling values of the functional connection and the structural connection;

and 8, adopting independent sample t test to compare the difference between the functional connection and structural connection coupling values of all the components of the default network of the healthy subject and the schizophrenia subject, and judging whether the fitness between the brain function and the structure of the schizophrenia patient is damaged.

In the above method for analyzing the function and structure coupling of the default network of the brain of the resting state, the step 2 of preprocessing the acquired brain image includes:

step 2.1, preprocessing functional image fMRI data;

step 2.1.1, functional images fMRI at the first 10 time points are removed, and the remaining functional images at 230 time points enter the following preprocessing flow;

step 2.1.2, correcting a time layer;

step 2.1.3, correcting head movement: functional images fMRI with translation amplitude of any direction of head movement of a subject in head movement detection being more than or equal to 1.5mm or rotation angle being more than or equal to 1.5 degrees are removed;

step 2.1.4, spatial normalization to echo planar template EPIT in Montreal space MNI, followed by resampling voxels to 3 × 3 × 3mm³；

Step 2.1.5, performing Gaussian smoothing, and taking a full width at half maximum FWHM of 8 mm;

step 2.1.6, removing baseline drift and carrying out band-pass filtering treatment, wherein the band-pass cut-off frequency is as follows: 0.0-0.08 Hz; to remove the signal interference caused by respiration, heartbeat physiological noise and machine signal drift;

step 2.1.7, regression is carried out by taking Friston 24 cephalic motion parameters, cerebrospinal fluid average signals and white matter average signals as covariates, and influences of the covariates are removed;

step 2.2, preprocessing the DTI data of the structural image:

step 2.2.1, calibrating 64 weighted diffusion images to unweighted B0 diffusion images to remove head movement;

step 2.2.2, correction of the eddy current distortion of the SS-SE EPI sequence by affine transformation to B0 image.

In the resting state human brain default network function and structure coupling analysis method, step 3, the default network components include: medial prefrontal cortex mPFC, posterior cingulate gyrus and anterior cuneiform PCC/PCUN, bilateral canthus return AG, bilateral medial temporal lobe mTL and bilateral temporal subreturn ITG.

In the resting state human brain default network function and structure coupling analysis method, the step 4 is realized by: extracting a time sequence of each component of a default network, calculating a correlation coefficient r between the time sequences of two components by adopting a Pearson correlation method, and then carrying out Fisher's Z change to obtain a functional connection value between the two components; and by analogy, calculating the functional connection values among all the components of the default network.

In the resting state human brain default network function and structure coupling analysis method, the implementation of the step 5 comprises the following steps:

step 5.1, tracking all cerebral fiber bundles in the DTI local space of the structural image of each subject by adopting interactive software Trackvis, reconstructing fiber bundle tracking through a difference streamline propagation algorithm, and tracking each fiber bundle according to the following standards: the tracking of the fiber bundle is finished when the FA value is lower than 0.15 or the angle between the current fiber and the previous fiber is more than 35 degrees; after the whole brain fiber is traced, the fiber bundle with the fiber length shorter than 20 mm and obvious false positive fiber passage is eliminated;

step 5.2, taking the default network component as a template, and dividing the DTI space of each subject structural image into corresponding component areas;

step 5.3, extracting white matter fiber bundles connected between two components; and extracting white matter fiber bundles among all the components of the default network by analogy.

In the resting state human brain default network function and structure coupling analysis method, the implementation of the step 7 comprises the following steps:

step 7.1, firstly, calculating a correlation coefficient r between a functional connection value and a structural connection value between a component A and a component B of the default network by using Pearson correlation, and taking the r value as a coefficient value reflecting the coupling between the function and the structure;

step 7.2, performing Fisher Z transformation on the r value to obtain a Z value, and taking the Z value as a coupling value between the functional connection and the structural connection between the component A and the component B;

and 7.3, calculating the coupling values of the functional connection and the structural connection among all the components of the default network.

Compared with the prior art, the invention has the beneficial effects that: (1) the fit degree and coordination degree of functions and structures between two components of the human brain default network can be reflected, so that the connection characteristics, the function and the structure coordination characteristics of each brain area of the default network under different activities, stimulation or diseases can be researched.

(2) The invention has certain application value in the fields of brain function connection analysis, brain structure connection analysis, mental disease diagnosis and treatment and the like. Provides an important theoretical basis for researching the brain development pattern change of human serious neuropsychiatric diseases, and has higher application value.

Drawings

FIG. 1 is a flow chart of one embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the following embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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 invention.

It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.

The present invention is further illustrated by the following examples, which are not to be construed as limiting the invention.

In the embodiment, the connection characteristics of the internal functions and the multi-mode structures of the brain default network are analyzed and researched by respectively constructing the functions and the structural connections of the brain default network of the subject and analyzing and constructing the coupling mode between the brain default network structure and the functional connections of the subject. Provides an important theoretical basis for researching the brain development pattern change of human serious neuropsychiatric diseases, and has higher application value.

The method comprises the steps of taking brain functional magnetic resonance (fMRI) and structural magnetic resonance (DTI) data as research objects, preprocessing the data after collecting the data to ensure the consistency of the data, firstly extracting each component of a brain default network, calculating a functional connection value and a structural connection value between every two components, calculating a coupling value between the functional connection and the structural connection, and comparing the difference between a schizophrenic patient and a healthy patient by adopting independent sample t inspection to research the abnormal change of the brain function and the structural coordination of the schizophrenic patient.

The embodiment is realized by the following technical scheme, and the resting state human brain default network function and structure coupling analysis method comprises the following steps:

s1, acquiring a group of magnetic resonance brain images (including a functional image fMRI and a structural image DTI) of healthy subjects in a resting state and a group of magnetic resonance brain images (including a functional image fMRI and a structural image DTI) of subjects suffering from schizophrenia;

s2, preprocessing each acquired magnetic resonance brain image;

s3, extracting and identifying each component of the default network by adopting an Independent Component Analysis (ICA) method: mainly comprises 8 components: medial prefrontal cortex (mPFC), posterior cingulate and anterior cuneiform lobes (PCC/PCUN), bilateral canthus (AG), bilateral medial temporal lobes (mTL), and bilateral temporal Infraversion (ITG);

s4, calculating the function connection values among all components of the default network;

s5, extracting white matter fiber bundles connected among all components of the default network;

s6, calculating the average FA value of the fiber bundles between every two components as the structural connection value between every two components of the default network;

s7, calculating the coupling values of the functional connection and the structural connection;

and S8, comparing the differences between the functional connection and structural connection coupling values of all the components of the default network of the healthy subject and the schizophrenia subject by adopting an independent sample t test, and judging whether the fitness between the brain function and the structure of the schizophrenia patient is damaged or not.

In specific implementation, as shown in fig. 1, a resting state human brain default network function and structure coupling analysis method includes the following steps:

acquiring a group of magnetic resonance brain images (including a functional image fMRI and a structural image DTI) of healthy subjects in a resting state and a group of magnetic resonance brain images (including a functional image fMRI and a structural image DTI) of subjects suffering from schizophrenia;

secondly, preprocessing each acquired magnetic resonance brain image; the method specifically comprises the following steps:

(1) fMRI data preprocessing:

firstly, in view of the adaptation of a subject to the environment in a magnetic resonance machine and the fact that a certain time is required for the magnetic field of the magnetic resonance machine to reach a steady state, functional images of the first 10 time points are removed, and the remaining 230 time points of the functional images enter the following preprocessing process;

correcting a time layer;

③ correcting the head movement: in the head movement detection, if the translational movement amplitude of a subject in any direction of the head movement is more than or equal to 1.5mm or the rotation angle is more than or equal to 1.5 degrees, the subject is rejected;

-echo-planar imaging template (EPIT) spatially normalized to Montreal Neurological Institute (MNI), followed by resampling of voxels to 3X 3mm³；

Fifthly, performing Gaussian smoothing, and taking a full-width half maximum (FWHM) of 8 mm;

removing baseline drift and band-pass filtering (0.0-0.08 Hz) to remove physiological noise such as breath and heartbeat and signal interference caused by machine signal drift;

and seventhly, performing regression by taking Friston 24 head movement parameters, cerebrospinal fluid average signals and white matter average signals as covariates, and removing the influence of the covariates.

(2) And (3) DTI data preprocessing:

this embodiment removes the head motion by calibrating all 64 weighted diffusion images onto a non-weighted B0 diffusion image. The eddy current distortion of the SS-SE EPI sequence is corrected by affine transformation to B0 image.

Thirdly, extracting and identifying each component of the default network by adopting an Independent Component Analysis (ICA) method: mainly comprises 8 components: medial prefrontal cortex (mPFC), posterior cingulate and anterior cuneiform lobes (PCC/PCUN), bilateral canthus (AG), bilateral medial temporal lobes (mTL), and bilateral temporal Infraversion (ITG);

fourthly, calculating functional connection values among all components of the default network, specifically: extracting a time sequence of each component of a default network, calculating a correlation coefficient r between the time sequences of two components by adopting a Pearson correlation method, and then changing Fisher's Z to obtain a value which is a functional connection value between the two components; and by analogy, calculating the functional connection values among all the components of the default network.

Fifthly, extracting white matter fiber bundles connected among all components of the default network; the method comprises the following specific steps:

(1) the interactive software Trackvis is used to track all brain fiber bundles in DTI local space of each subject, the fiber tracking is mainly reconstructed by an interpolant streamline propagation algorithm (Interpolated streamlining algorithm), and the tracking of each fiber bundle is based on the following criteria: the tracking of the fiber bundle will be ended when the FA value is below 0.15 or the angle between the current fiber and the previous fiber is greater than 35 °. After the whole brain fiber is traced, the fiber bundle with the fiber length shorter than 20 mm and obvious false positive fiber passage is eliminated;

(2) dividing DTI space of each subject into 8 component areas by using 8 components in the default network identified by ICA method in the previous default network function connection part as templates;

(3) extracting white matter fiber bundles connected between certain two components, such as the fiber bundles connected between component a and component B: all the fiber bundles connected to component a are extracted first, then all the fiber bundles connected to component B are extracted, and then the fiber bundles connecting both a and B, that is the white matter fiber bundles connected between component a and component B, are extracted. And by analogy, white matter fiber bundles among all the components of the default network are extracted.

Calculating the average FA value of the fiber bundles between every two components as the structural connection value between every two components of the default network;

seventhly, calculating coupling values of the functional connection and the structural connection; the method specifically comprises the following steps: first, a correlation coefficient r between a functional connection value and a structural connection value between the component a and the component B of the default network is calculated by using Pearson correlation, and the r value is taken as a coefficient value reflecting the coupling between the function and the structure. And then performing Fisher Z transformation on r to obtain a Z value, and using the Z value as a coupling value between a functional connection and a structural connection between the component A and the component B. And in the same way, calculating the coupling values of the functional connection and the structural connection among all the components of the default network.

And eighthly, adopting independent sample t test to compare the difference between the coupling values of the functional connection and the structural connection between all the components of the default network of the healthy subject and the schizophrenia subject, and judging whether the fitness between the brain function and the structure of the schizophrenia patient is damaged.

Example 1

The mri brain image data set used in this example was obtained by scanning resting-state functional images and structural images of each subject in a 16-channel head coil using the 3.0T high-field medical mri system verio from Siemens, Germany. Among 80 subjects, 42 patients with schizophrenia and 38 normal controls were included.

First, raw image data needs to be pre-processed using DPARSF software:

(1) fMRI data preprocessing:

firstly, considering the adaptation of a subject to the environment in a magnetic resonance machine and the fact that a certain time is required for the magnetic field of the magnetic resonance machine to reach a steady state, functional images of the first 10 time points are removed, and the remaining 230 time points are subjected to the following preprocessing process;

correcting the time layer: because the MRI scan is a barrier scan, we need to rearrange the separated layers according to the normal sequence of the structure to construct a normal complete anatomical image of the brain, the MRI sequence of this embodiment 1 has thirty layers, and the barrier scan is rearranged according to the following sequence: 1,3,5,7,9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29,2,4,6,8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30.

-flat space normalized to Montreal Neurological Institute (MNI)

Surface-echo map template (EPIT), followed by resampling of voxels to 3 × 3 × 3mm³；

(2) And (3) DTI data preprocessing:

the head motion was removed by calibrating all 64 weighted diffusion images to non-weighted B0 diffusion images. The eddy current distortion of the SS-SE EPI sequence is corrected by affine transformation to B0 image.

On the basis of the preprocessed magnetic resonance brain image, the following analysis is completed:

the method comprises the following steps of (I) extracting a default network space mode and analyzing functional connection:

first, fMRI data was obtained from each subject in the group of dimension-reduced patients and healthy volunteers using the ICA method. The ICA algorithm will operate in the GIFT software (version 1.3 e) (http:// icatb. source for. net /), and extract and identify the spatial functional anatomical pattern of the default network based on the Infmax ICA algorithm. Based on the MDL rule for dimension evaluation provided in the GIFT software, the optimal number of degradation components was calculated, and in example 1, the number of patient component major was 27 and the number of healthy volunteers major was 28. The principal components of the default network are extracted primarily by correlating all spatially independent components with a default network template extracted by the MarsBar toolkit (http:// MarsBar. This example 1 uses xjView to detect 8 default network space independent components in the normal control group, namely: mPF, PCC/PCUN, bilateral circumflex AG, bilateral medial temporal lobe mTL, and bilateral temporal suborbital ITG.

The functional connectivity of the patient groups was compared to the groups of the normal control group using a statistical approach of two independent sample t-tests (individual horizontal voxels | t | >1.96, P <0.05, FDR correction, differential area over 20 voxels). This example 1 uses paired t-tests to detect longitudinal changes in the individual component values before and after treatment in a patient group (P <0.05, FDR corrected, differential area over 20 voxels).

To investigate the correlation between the severity of a patient's clinical symptoms and its default network-internal functional connection strength, a functional connection map analysis based on the voxel levels of seed points is taken here. The seed point mask is mainly selected from a space independent component map with significant difference of independent component values of a patient group compared with a normal control group. A functional connection map between seed points and voxels inside the default network is created by computing continuous-time correlation coefficients. Then, a multiple regression model was used to calculate the correlation between the functional connectivity graph and the PANSS score.

Patient symptom severity and default network internal abnormal functional connectivity, detailed in table 1:

TABLE 1 inter-group comparative details of Default network internal functional connection Strength

In the context of table 1, the following,^※P<0.05, FDR correction, two independent sample t-tests (FESP vs HC), paired t-tests (FESP _8W vs FESP _ 0W); MNI: montreal Neurological Institute; PCC/PCUN (stereosr cingulate cortix and preconeuus), posterior cingulate gyrus/anterior cuneiform; mpfc (medial prefrontal cortex), medial prefrontal cortex; mtl (medial temporal lobe), medial temporal lobe; ag (angulargyrus), hornbeam; ITG (interferon temporal gyrus), temporomandibular gyrus FESP (first epidemic schizochrysene patients), patients with first schizophrenia; hc (health controls), healthy controls; 0W, before treatment; 8W, after treatment.

As can be seen from table 1, before treatment, the patients showed a decrease in the strength of functional connection between the two nodes PCC/PCUN (P <0.05, FDR corrected, minimum voxel level of 20 voxels) and mPFC (P <0.05, FDR corrected, minimum voxel level of 20 voxels) and the rest of the internal area of the default network compared to healthy volunteers.

The treatment resulted in a significant increase in the strength of the connection between the PCC/PCUN of the patient and the rest of the default network (P <0.05, FDR corrected, voxel level of 20 voxels minimum), but no significant change in the strength of the functional connection between the mPFC region of the patient and the rest of the default network (P > 0.05).

And (II) default network internal structure connection extraction:

extracting corresponding white matter fiber bundles between each area of the default network, wherein the specific process is as follows: first, the interactive software Trackvis is used to track all brain fiber bundles in DTI local space of each subject, the fiber tracking is mainly reconstructed by the differential streamline propagation algorithm (Interpolated streamline propagation algorithm), and the tracking of each fiber bundle is based on the following criteria: the tracking of the fiber bundle will be ended when the FA value is below 0.15 or the angle between the current fiber and the previous fiber is greater than 35 °. After the whole brain fiber tracking was completed, the fiber bundle with a fiber length of 20 mm and a fiber passage with obvious false positive was eliminated.

Secondly, 8 composition region templates in the default network which is identified by the ICA method in the previous default network function connection part and carries MNI space information are subjected to inverse transformation parameters T-1 to be converted into individual DTI spaces, so that 8 interested regions are obtained in each test subject DTI space. The mask of interest for each individual subject will extend 3mm over the individual DTI space to ensure that they can attach to the brain fiber bundles. Through such a process, the first region of interest can be selected, and the fiber bundle connected to this region of interest will be selected from all the fiber bundles of the entire brain to enter the next process.

Thirdly, after the second region of interest is found from the rest of the regions of interest by the same method as the second step to obtain any two regions of interest in each individual space of the subject, the embodiment 1 can extract the information of the white matter fiber connecting bundle passing through the two regions of interest to the FA data of each fiber bundle by combining the tracking condition of the whole brain white matter fiber connecting bundle in the first step.

After extracting white matter fiber bundles inside the default network through a three-step method, the PCC/PCUN and the mPF are connected, and three fiber bundles connected between the PCC/PCUN and the left side mTL and the right side can be detected in almost all subjects. The remaining additional fiber connecting strands can only be traced in a very small fraction of subjects, and therefore these fiber strands will not be in the process of entering into the next statistical analysis or the like. The average FA value of the three fiber bundles of each group of subjects PCC/PCUN and mPF C, PCC/PCUN and the left side mTL is used as an evaluation index of the connection strength of the default network structure to enter the following statistical analysis and the like.

In the comparison between groups connecting the PCCs/PCUNs to mPFCs and FAs from the three fiber bundles between the PCCs/PCUNs and the right and left mTL, two independent sample t-tests (P <0.05, FDR corrected) were used for the patient group compared to the normal control group. Longitudinal comparisons before and after treatment in the patient groups were performed using paired t-tests (P <0.05, FDR corrected).

This example 1 found that the mean FA values of the three white matter fiber ligated tracts between PCC/PCUN and mPFC before treatment, and between PCC/PCUN and bilateral mTL, were not any significant statistical difference from the normal control group (Ps > 0.05). The mean FA values of the three white matter fiber junction bundles between PCC/PCUN and mPFC, and between PCC/PCUN and bilateral mTL in the patient group after treatment were not any significant statistical difference from their pre-treatment values (Ps > 0.05).

Thirdly, calculating the coupling of default network internal structure and function connection:

considering that only PCC/PCUN and mPFC exist inside the default network, the structural connection of the fiber bundle between PCC/PCUN and left and right mTL can be detected in almost all subjects, therefore, in the structural and functional coupling analysis, only the coupling between PCC/PCUN and mPFC, PCC/PCUN and left and right mTL pairs of structural and functional connections is detected.

The structural connection strength is reflected by the average FA value obtained in the previous step. Regarding the functional connection values between the three pairs of connections, analysis of the region of interest was used to evaluate the strength of the three pairs of functional connections, PCC/PCUN and mPFC, PCC/PCUN and the left and right sides mTL. Specifically, first, a spherical region of interest mask with a radius of 5mm is selected and a ball is drawn with the peak point of the MNI coordinate of the individual component map of the significant default network of all the subjects in the healthy control group as the center. The time correlation coefficient between the interested regions can reflect the function connection strength between two brain regions in the default network after being changed by Fisher's Z. It should be noted that, in this embodiment 1, only the functional connections between the PCC/PCUN and the mPFC, and between the PCC/PCUN and the left and right sides mTL are detected, but it does not mean that there is no functional connection between other brain areas in the default network, because the research in the prior art has confirmed that there are functional connections between brain areas in the default network.

Subsequently, regarding the coupling of the functional connection and the structural connection, correlation coefficients r between the functional connection values and the structural connection values between the PCC/PCUN and mPFC within the default network, and between the PCC/PCUN and the left and right sides mTL are first calculated using Pearson correlation, and the r values are taken as coefficient values reflecting the coupling between the function and the structure. And then performing Fisher Z transformation on r to obtain a Z value, and then incorporating the Z value into Z test statistical analysis. Specifically, the Pearson correlation coefficient r for each of the three connections is calculated, and then the correlation coefficient is subjected to Fisher's Z transformation. The comparison of the difference between groups of the coupling strength between the function and the structure of the three connections mainly adopts a Z test (P <0.05, FDR correction), and the multiple test correction mainly adopts an FDR method, and takes the P <0.05 after the FDR correction as a statistical significance level.

For inter-group comparison details of default network internal function and structural connection coupling values, see table 2:

TABLE 2 results of the comparison between the default network internal function and the structural connection coupling value between groups

In the context of Table 2, the following examples are,^※fisher Z test; PCC/PCUN (stereosr cingulate cortix and preconeuus), posterior cingulate gyrus/anterior cuneiform; mpfc (medial prefrontal cortex), medial prefrontal cortex; mtl (medial temporal lobe), medial temporal lobe; FESP (first epoxy schizochrysia Patients).

As can be seen from table 2, for this edge between the connecting PCC/PCUN and mPFC, the coupling values of the default network functional connection and the structural connection were not statistically significantly different (P >0.05, FDR corrected) compared to healthy volunteers for the patient group; for this edge between the connecting PCC/PCUN and the left side mTL, the coupling values of the default network functional connection to the structural connection were not statistically significantly different for the patient group compared to healthy volunteers (P >0.05, FDR corrected); for the edge between the connecting PCC/PCUN and the right side mTL, the coupling values for the default network functional connections and structural connections were also not statistically significantly different (P >0.05, FDR corrected) for the patient group treatment compared to healthy volunteers.

After treatment, the coupling values for the default network functional connections and structural connections were not statistically significantly different for the edge connecting PCC/PCUN to mPFC compared before and after treatment in the patient group (P >0.05, FDR corrected); for the edge connecting between PCC/PCUN and left side mTL, the coupling of default network functional connections to structural connections decreased after treatment and the difference was statistically significant (P0.03, FDR corrected) compared to before treatment in the patient group; for the border between the PCC/PCUN and the right side mTL, the coupling of the default network functional and structural links was also reduced after treatment compared to before treatment in the patient group, and the difference was statistically significant (P0.03, FDR corrected).

With this example 1 it was found that patient treatment may cause a change in the dynamic level of the default network internal connections and we speculate that this change would mean an increase in the dynamic level within the network. Suggesting default network functional connectivity and coupling patterns between functional and structural connectivity may become biological markers for antipsychotic treatment and efficacy, providing hopes for future target search for individualized treatment of schizophrenia.

While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.

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Claims

1. a resting state human brain default network function and structure coupling analysis method is characterized by comprising the following steps:

step 2, preprocessing the acquired brain image;

2. The method for analyzing the functional and structural coupling of the default network of the brain in the resting state as claimed in claim 1, wherein the step 2 of preprocessing the collected brain image comprises:

step 2.1, preprocessing functional image fMRI data;

step 2.1.2, correcting a time layer;

step 2.2, preprocessing the DTI data of the structural image:

3. The resting state human brain default network function and structure coupling analysis method of claim 1, wherein the default network component of step 3 comprises: medial prefrontal cortex mPFC, posterior cingulate gyrus and anterior cuneiform PCC/PCUN, bilateral canthus return AG, bilateral medial temporal lobe mTL and bilateral temporal subreturn ITG.

4. The resting state human brain default network function and structure coupling analysis method of claim 1, wherein the implementation of step 4 comprises: extracting a time sequence of each component of a default network, calculating a correlation coefficient r between the time sequences of two components by adopting a Pearson correlation method, and then carrying out Fisher's Z change to obtain a functional connection value between the two components; and by analogy, calculating the functional connection values among all the components of the default network.

5. The resting state human brain default network function and structure coupling analysis method as claimed in claim 3, characterized in that the implementation of step 5 comprises the following steps:

6. The resting state human brain default network function and structure coupling analysis method as claimed in claim 1, wherein the implementation of step 7 comprises the following steps: