阅读说明：本技术 一种大脑局部功能动态性实时测量系统 (Brain local function dynamic real-time measurement system ) 是由 温旭云 张道强 杨明 刘玉婷 于 2021-09-23 设计创作，主要内容包括：本发明公开了一种大脑局部功能动态性实时测量系统,包括：实验范式设计模块、核磁图像处理模块、脑区定位模块、脑区功能动态性评估模块和分析结果显示模块；实验范式设计模块用于确定实验对象参与的实验内容和流程,根据研究人员的实验目的进行设计；核磁图像处理模块对采集到的图像进行预处理并保存；脑区定位模块加载感兴趣的大脑研究区域；脑区功能动态性评估模块计算所定位脑区的功能系统和功能动态性；分析结果显示模块将大脑局部功能动态性以图像和数值方式反馈给研究人员和实验对象。本发明实现对关注脑区功能连接动态性的实时评估与记录。(The invention discloses a dynamic real-time measurement system for local functions of a brain, which comprises: the system comprises an experimental paradigm design module, a nuclear magnetic image processing module, a brain region positioning module, a brain region functional dynamic evaluation module and an analysis result display module; the experimental paradigm design module is used for determining experimental contents and processes of experimental subjects, and designing according to experimental purposes of researchers; the nuclear magnetic image processing module is used for preprocessing and storing the acquired image; loading a brain research region of interest by a brain region positioning module; the brain area functional dynamic evaluation module calculates the functional system and functional dynamic of the positioned brain area; and the analysis result display module feeds back the local brain function dynamics to researchers and experimental objects in an image and numerical mode. The invention realizes the real-time evaluation and recording of the function connection dynamics of the concerned brain area.)

1. A brain local function dynamic real-time measurement system is characterized by comprising: the system comprises an experimental paradigm design module, a nuclear magnetic image processing module, a brain region positioning module, a brain region functional dynamic evaluation module and an analysis result display module; the experimental paradigm design module is used for determining experimental contents and processes of experimental subjects, and designing according to experimental purposes of researchers; the nuclear magnetic image processing module is used for preprocessing and storing the acquired image; loading a brain research region of interest by a brain region positioning module; the brain area functional dynamic evaluation module calculates the functional system and functional dynamic of the positioned brain area; and the analysis result display module feeds back the local brain function dynamics to researchers and experimental objects in an image and numerical mode.

2. The system of claim 1, wherein the experimental paradigm design module employs a resting state scanning mode.

3. The brain local function dynamic real-time measuring system according to claim 1, wherein the nuclear magnetic image processing module comprises a parameter configuration unit, an image reading unit, a format conversion unit, an image preprocessing unit; an operator of the nuclear magnetic resonance scanner determines scanning parameters of the structural magnetic resonance image and the functional magnetic resonance image through a parameter configuration unit; acquiring three-dimensional structure magnetic resonance data and four-dimensional functional magnetic resonance data acquired by magnetic resonance equipment through an image reading unit; converting the read structural magnetic resonance image and the read functional magnetic resonance image from an original DICOM format to an NIFTY format at a format conversion unit; the magnetic resonance data is preprocessed by an image preprocessing unit.

4. The brain local function dynamic real-time measurement system according to claim 3, wherein the preprocessing of the magnetic resonance data by the image preprocessing unit comprises: head motion correction, registration of the functional magnetic resonance image to the structural magnetic resonance image, and filtering and smoothing preprocessing operation of the functional magnetic resonance image.

5. The brain partial function dynamic real-time measurement system according to claim 1, wherein the brain region selection of the brain region localization module is achieved by two ways, one is to select the brain region of interest by means of the brain partition template; another is to set the coordinates of the region of interest and then locate the brain region with a sphere.

6. The brain partial function dynamic real-time measuring system according to claim 1, wherein the brain region function dynamic evaluation module comprises a time sequence extraction module, a brain function network construction module, a brain partial function system detection module and a brain partial function dynamic evaluation module; the researcher generates time sequences of different voxels in the brain function magnetic resonance image through the time sequence extraction unit; a brain function network module is utilized to construct a brain function network with large-scale connection, and the functional connection between different nodes in the network is evaluated by calculating the Pearson correlation coefficient of the time sequence of the functional connection; the brain local function dynamic measurement unit detects a brain function system to which the selected brain area belongs by using a multilayer random walk method based on the constructed brain function network, and calculates the dynamic property of the function of the selected brain area in real time by using a brain local function dynamic evaluation module.

7. The system according to claim 6, wherein the means for measuring the dynamic local brain function in real time detects the brain function system of the selected brain area by using a multi-layer random walk method based on the constructed brain function network, and calculates the dynamic performance of the selected brain area in real time by using the module for evaluating the dynamic local brain function, specifically comprises the following steps:

(1) initializing; three random walkers are emitted from the selected brain area, wherein one random walker is defined as the brain function network G at the current time point^tTwo other brain function networks G defined at the first two time points^t-1And G^t-2The initial access probability of each random walker is defined asWherein e_qThe vector is a 1 XN-dimensional vector, the value of the corresponding position of the selected brain area is 1, and the values of other positions are 0;

(2) updating the transition probability; for network G^tCalculating the transition probability matrix of the nodes i and j in the step s by using the formula (1)

Wherein, P^t，P^t-1And P^t-2Respectively, the brain function network G^t，G^t-1And G^t-2Corresponding original transition probability matrix, for P^tWherein the transition probabilities of nodes i and j are For network G^tThe connection strength of the intermediate nodes i and j is calculated by the same method^t-1And P^t-2Each element of (1);whereinAndare respectively a network G^tAnd G^t-1The local access probability in the step s, cos represents cosine similarity, then R_s(t, t-1) measures the network G of step s of the random walk process^tAnd G^t-1Similarity of random walk visit histories; all in oneIn order to solve the problems that, represents G^tAnd G^t-2Local access probability similarity of the step s in the random walk process;

(3) determining a local brain function system; after the random walks of all the networks are converged, the conductance is adopted as a target function, and the node local access probability x based on convergence is obtained^TSDetermining a network G^tThe optimal brain function system to which the selected brain region belongs; considering that the brain function network is densely connected, a parameter M is introduced to constrain x^TSThe number of non-zero elements in (1), i.e. the size of the detected local brain function system; thus, M-1 potential local brain function systems are obtained, for each M ∈ [1, M-1 ]]Calculating the corresponding conductance value by adopting the formula (2)

In the above formula, the first and second carbon atoms are,for network G^tA brain function system consisting of^TSThe access probability of the middle node is formed by the top m nodes,for network G^tIn the removal ofThe collection of other nodes of the node involved,andare respectively asAndoverall connection strength between the intermediate nodes.

8. The system according to claim 6, wherein the real-time dynamic brain local function evaluation module defines the real-time dynamic brain local function as the difference between the brain function system at the time point and the brain function system at the previous time point, and the difference is defined as: 1 minus the percentage of the number of overlapping nodes of the brain function system at two time points to the sum of all the nodes, the functional dynamics is a numerical value between 0 and 1, and a larger value indicates that the selected brain region has higher functional variability in the time period.

9. The system according to claim 1, wherein the analysis result display module is configured to feed back the dynamic measurement indexes of the brain function system and the brain region function of the selected brain region for each time period to researchers and experimental subjects.

Technical Field

The invention relates to the technical field of medical image analysis, in particular to a brain local function dynamic real-time measurement system.

Background

The human brain is composed of billions of neurons, and the neurons performing the same function generate a common discharge due to direct or indirect axonal connection between the neurons, and further generate a synchronously fluctuating signal, which is called "functional connection". The functional connectivity of the brain is not static, and the functional network topology needs to be dynamically integrated and coordinated on multiple time scales according to the environmental requirements to respond to internal and external stimuli, thereby completing various complex cognitive behaviors. Furthermore, the dynamics of brain functional connectivity can shape the brain's steady-state connectivity pattern through hebry-type learning. More and more scientists are fully aware of the importance of brain functional link dynamics and have therefore developed a series of targeted research efforts with rich results. Research finds that the brain dynamic function connection is not only related to various cognition and behaviors of human beings, but also contributes to improving the diagnosis precision of mental diseases.

Functional magnetic resonance imaging (fMRI) is a main technical means for studying functional connections of human brain, and the technology indirectly measures the activity level of neurons in the brain by using a Blood oxygen level dependent technology (BOLD), and can show the activation condition of the neurons in a task state or a resting state of the brain with high spatial resolution under a non-invasive condition. At present, the dynamics of functional connection of human brain based on fMRI is generally measured and evaluated based on a brain functional network constructed by BOLD time series, and is mainly realized by two ways: 1) calculating the fluctuation of functional connection among brain regions along with time; 2) the two modes of measuring the conversion frequency of the brain area between different functional systems have certain defects. Specifically, the first method can only be obtained by image post-analysis after the acquisition of the brain image data is finished, so that the real-time monitoring of the dynamic property of the brain function connection cannot be realized; the second method needs to calculate the change of the function system of the brain region with time based on the community segmentation result of the whole brain network, so that the method cannot obtain a better evaluation result in a brain system with large-scale connection and cannot meet the real-time observation of researchers on the dynamics of the brain region of interest. Meanwhile, the research on the brain function dynamics only stays at the design and analysis stage of an experiential laboratory at present, and a human brain function dynamic real-time measurement system which is matched with an advanced functional magnetic resonance imaging technology and meets the requirements of actual cognitive research and clinical application is very lacking.

Disclosure of Invention

The invention aims to provide a brain local function dynamic real-time measurement system, which realizes real-time evaluation and recording of the function connection dynamic of a key attention brain area.

In order to solve the above technical problems, the present invention provides a system for measuring local brain function dynamics in real time, comprising: the system comprises an experimental paradigm design module, a nuclear magnetic image processing module, a brain region positioning module, a brain region functional dynamic evaluation module and an analysis result display module; the experimental paradigm design module is used for determining experimental contents and processes of experimental subjects, and designing according to experimental purposes of researchers; the nuclear magnetic image processing module is used for preprocessing and storing the acquired image; loading a brain research region of interest by a brain region positioning module; the brain area functional dynamic evaluation module calculates the functional system and functional dynamic of the positioned brain area; and the analysis result display module feeds back the local brain function dynamics to researchers and experimental objects in an image and numerical mode.

Preferably, the experimental paradigm design module employs a resting state scanning mode.

Preferably, the nuclear magnetic image processing module comprises a parameter configuration unit, an image reading unit, a format conversion unit and an image preprocessing unit; an operator of the nuclear magnetic resonance scanner determines scanning parameters of the structural magnetic resonance image and the functional magnetic resonance image through a parameter configuration unit; acquiring three-dimensional structure magnetic resonance data and four-dimensional functional magnetic resonance data acquired by magnetic resonance equipment through an image reading unit; converting the read structural magnetic resonance image and the read functional magnetic resonance image from an original DICOM format to an NIFTY format at a format conversion unit; the magnetic resonance data is preprocessed by an image preprocessing unit.

Preferably, the preprocessing the magnetic resonance data with the image preprocessing unit includes: head motion correction, registration of the functional magnetic resonance image to the structural magnetic resonance image, and filtering and smoothing preprocessing operation of the functional magnetic resonance image.

Preferably, the brain region selection of the brain region positioning module is realized by two modes, one mode is that the brain region of interest is selected by means of a brain partition template; another is to set the coordinates of the region of interest and then locate the brain region with a sphere.

Preferably, the local function dynamic evaluation module comprises a time sequence extraction module, a brain function network construction module, a brain local function system detection module and a brain local function dynamic evaluation module; the researcher generates time sequences of different voxels in the brain function magnetic resonance image through the time sequence extraction unit; a brain function network module is utilized to construct a brain function network with large-scale connection, and the functional connection between different nodes in the network is evaluated by calculating the Pearson correlation coefficient of the time sequence of the functional connection; the brain local function dynamic measurement unit detects a brain function system to which the selected brain area belongs by using a multilayer random walk method based on the constructed brain function network, and calculates the dynamic property of the function of the selected brain area in real time by using a brain local function dynamic evaluation module.

Preferably, the step of detecting the brain function system to which the selected brain region belongs by using a multilayer random walk method based on the constructed brain function network by the brain local function dynamic measurement unit, and calculating the dynamic property of the selected brain region function in real time by using the brain local function dynamic evaluation module specifically includes the following steps:

(1) initializing; three random walkers are emitted from the selected brain area, wherein one random walker is defined as the brain function network G at the current time point^tTwo other brain function networks G defined at the first two time points^t-1And G^t-2The initial access probability of each random walker is defined asWherein e_qThe vector is a 1 XN-dimensional vector, the value of the corresponding position of the selected brain area is 1, and the values of other positions are 0;

(2) updating the transition probability; for network G^tCalculating the nodes i and j in step s by using equation (1)Transition probability matrix

Wherein, P^t，P^t-1And P^t-2Respectively, the brain function network G^t，G^t-1And G^t-2Corresponding original transition probability matrix, for P^tWherein the transition probabilities of nodes i and j are For network G^tThe connection strength of the intermediate nodes i and j is calculated by the same method^t-1And P^t-2Each element of (1);whereinAndare respectively a network G^tAnd G^t-1The local access probability in the step s, cos represents cosine similarity, then R_s(t, t-1) measures the network G of step s of the random walk process^tAnd G^t-1Similarity of random walk visit histories; in the same way, the method for preparing the composite material, represents G^tAnd G^t-2Local access probability similarity of the step s in the random walk process;

(3) determining a local brain function system; after the random walks of all the networks are converged, the conductance is adopted as a target function, and the node local access probability x based on convergence is obtained^TSDetermining a network G^tThe optimal brain function system to which the selected brain region belongs; considering that the brain function network is densely connected, a parameter M is introduced to constrain x^TSThe number of non-zero elements in (1), i.e. the size of the detected local brain function system; thus, M-1 potential local brain function systems are obtained, for each M ∈ [1, M-1 ]]Calculating the corresponding conductance value by adopting the formula (2)

In the above formula, the first and second carbon atoms are,for network G^tA brain function system consisting of^TSThe access probability of the middle node is formed by the top m nodes,for network G^tIn the removal ofThe collection of other nodes of the node involved,andare respectively asAndoverall connection strength between the intermediate nodes.

Preferably, in the module for evaluating the dynamic property of the local brain function, the dynamic property of the local brain function at a time point is defined as a difference between the brain function system at the time point and the brain function system at a previous time point, and the difference is defined as: 1 minus the percentage of the number of overlapping nodes of the brain function system at two time points to the sum of all the nodes, the functional dynamics is a numerical value between 0 and 1, and a larger value indicates that the selected brain region has higher functional variability in the time period.

Preferably, the analysis result display section is configured to feed back dynamic measurement indexes of the brain function system and the brain function of the selected brain region for each time period to researchers and experimental subjects.

The invention has the beneficial effects that: (1) the function system of the brain region of interest can be acquired more accurately, quickly and in real time, and researchers can be facilitated to reveal the change rule of the functional connection of the key brain region along with the environmental requirements; (2) the real-time dynamics of the functions of the brain regions of interest are quantified, and researchers and clinicians can be helped to monitor the functional state of the human body in real time; (3) the dynamic real-time measurement system for the local brain function has potential application value in the fields of brain cognitive function research, brain function diagnosis and regulation, mental disease diagnosis and treatment and the like.

Drawings

FIG. 1 is a schematic diagram of the system of the present invention.

FIG. 2 is a schematic view of a nuclear magnetic image processing flow according to the present invention.

FIG. 3 is a schematic diagram of a brain region functional dynamics assessment process according to the present invention.

Fig. 4 is a schematic flow chart of a multilayer random walk detection method.

Detailed Description

As shown in fig. 1, a system for measuring the dynamic local function of the brain in real time comprises: the system comprises an experimental paradigm design module, a nuclear magnetic image processing module, a brain region positioning module, a brain region functional dynamic evaluation module and an analysis result display module; the experimental paradigm design module is used for determining experimental contents and processes of experimental subjects, and designing according to experimental purposes of researchers; the nuclear magnetic image processing module is used for preprocessing and storing the acquired image; loading a brain research region of interest by a brain region positioning module; the brain area functional dynamic evaluation module calculates the functional system and functional dynamic of the positioned brain area; and the analysis result display module feeds back the local brain function dynamics to researchers and experimental objects in an image and numerical mode. The experimental paradigm design module adopts a resting state scanning mode.

As shown in fig. 2, the nuclear magnetic image processing module includes a parameter configuration unit, an image reading unit, a format conversion unit, and an image preprocessing unit; an operator of the nuclear magnetic resonance scanner determines scanning parameters of the structural magnetic resonance image and the functional magnetic resonance image through a parameter configuration unit; acquiring three-dimensional structure magnetic resonance data and four-dimensional functional magnetic resonance data acquired by magnetic resonance equipment through an image reading unit; converting the read structural magnetic resonance image and the read functional magnetic resonance image from an original DICOM format to an NIFTY format at a format conversion unit; the magnetic resonance data is preprocessed by an image preprocessing unit. In order to measure brain dynamics, functional magnetic resonance images are output every 5 time points, each time 20 time points before the current time point.

The preprocessing of the magnetic resonance data with the image preprocessing unit comprises: pre-processing operations such as head movement correction, registration of a functional magnetic resonance image to a structural magnetic resonance image, filtering smoothing of the functional magnetic resonance image and the like.

Brain region selection is realized in two ways, one is to select the interested brain region by means of a brain partition template; another is to set the coordinates of the region of interest and then locate the brain region with a sphere. The system defaults to the second mode, and the operator finishes the brain area positioning by loading the coordinates of the region of interest and setting the radius of the sphere, wherein the brain area positioning is only performed once in the whole experiment process.

As shown in fig. 3, the local function dynamics evaluation module includes a time sequence extraction unit, a brain function network construction module, a brain local function system detection module, and a brain local function dynamics evaluation module; the researcher generates time sequences of different voxels in the brain function magnetic resonance image through the time sequence extraction unit; a brain function network module is utilized to construct a brain function network with large-scale connection, and the functional connection between different nodes in the network is evaluated by calculating the Pearson correlation coefficient of the time sequence of the functional connection; the brain local function system detection module detects a brain function system to which the selected brain area belongs by utilizing multilayer random walk based on the constructed brain function network, and calculates the dynamic property of the function of the selected brain area in real time by utilizing the brain local function dynamic property evaluation module.

As shown in fig. 4, in the local brain function system detection module, in the local brain function system detection method based on the multilayer random walk method, the influence of network noise on the detection of the brain function system is reduced by loading the brain function network at the current time point and the brain function networks at the first two time points, and meanwhile, the continuous variability of the detected brain function system over time is ensured. Assuming that the number of nodes contained in the network is N, the selected brain area is the ith node, the current time point is t, and the brain function network constructed at the current time point is G^tThe brain function network corresponding to the first two time points is G^t-1And G^t-2. The specific multilayer random walk method comprises three steps of initialization, transition probability updating and local function system determination:

the first step, initialization, is to send out three random walkers from the selected brain area, one of which is defined as the brain function network G at the current time point^tTwo other brain function networks G defined at the first two time points^t-1And G^t-2. The initial access probability of each random walker is defined asWherein e_qThe vector is a 1 × N-dimensional vector, the value of the ith position is 1, and the values of the other positions are 0.

Second step, transition probability update, for network G_tWe use equation (1) to calculate the s-th transition probability matrix in the random walk of nodes i and j

Wherein, P^t，P^t-1And P^t-2Respectively, the brain function network G^t，G^t-1And G^t-2Corresponding original transition probability matrix. For P^tWherein the transition probabilities of nodes i and j are For network G_tThe connection strength of the intermediate nodes i and j is calculated by the same method^t-1And P^t-2Each element of (1);whereinAndare respectively a network G^tAnd G^t-1The local access probability in the step s, cos represents cosine similarity, then R_s(t, t-1) measures the random walk step s network G^tAnd G^t-1Similarity of random walk visit histories; in the same way, the method for preparing the composite material,represents G^tAnd G^t-2And (5) local access probability similarity of the step s in the random walk process.

Thirdly, a local brain function system is determined, when random walk of all networks is converged, conductance is adopted as a target function, and node local access probability x based on convergence is determined^TSDetermining a network G^tThe optimal brain function system to which the selected brain region belongs. Considering that the brain functional network is densely connected, we introduce a parameter M to constrain x^TSMiddle nonzero partThe number of elements, i.e. the size of the detected local brain function system. Thus, M-1 potential local brain function systems can be obtained. For each M e [1, M-1]Calculating the corresponding conductance value by adopting the formula (2)

In the above formula, the first and second carbon atoms are,for network G^tA brain function system consisting of^TSThe access probability of the middle node is formed by the top m nodes,for network G^tIn the removal ofA collection of other nodes including a node.Andare respectively asAndoverall connection strength between the intermediate nodes.

The functional dynamics in the brain local functional dynamics evaluation module is defined as the difference between the brain functional system at the time point and the brain functional system at the previous time point, and the difference is defined as: 1 minus the percentage of the number of overlapped nodes of the brain function system at two time points to the sum of all the nodes.