阅读说明：本技术 一种基于状态转换动态脑网络算法的神经障碍诊断系统 (Neurological disorder diagnosis system based on state transition dynamic brain network algorithm ) 是由 邹凌 宋志伟 孔万增 周天彤 于 2021-01-13 设计创作，主要内容包括：本发明公开了一种基于状态转换动态脑网络算法的神经障碍诊断系统,包括范式设计模块、数据转换模块、网络构建模块、动态分析模块、数据检测模块以及结果示出模块。数据转换模块用于采集核磁共振脑功能成像BOLD信号并进行预处理；网络构建模块采用互信息算法构建全脑功能网络；动态分析模块根据空间标准差对全时段脑功能网络进行动态时间窗划分,在数据驱动下解码适当数量的脑部状态,使用曲率面积算法进行脑网络指标计算,获取模型训练分析结果,形成数据化诊断报告。本发明能够为一些神经障碍疾病提供一种新型的疾病诊断与预测思路,在脑功能连接分析领域具有一定的实际意义与应用价值。(The invention discloses a neurological disorder diagnosis system based on a state transition dynamic brain network algorithm, which comprises a paradigm design module, a data transition module, a network construction module, a dynamic analysis module, a data detection module and a result display module. The data conversion module is used for acquiring and preprocessing a BOLD signal of the nuclear magnetic resonance brain function imaging; the network construction module adopts a mutual information algorithm to construct a whole brain function network; the dynamic analysis module performs dynamic time window division on the full-time brain function network according to the space standard deviation, decodes a proper number of brain states under data driving, performs brain network index calculation by using a curvature area algorithm, obtains a model training analysis result, and forms a data diagnosis report. The invention can provide a novel disease diagnosis and prediction idea for certain neurological disorder diseases, and has certain practical significance and application value in the field of brain function connection analysis.)

1. A neurological disorder diagnosis system based on a state transition dynamic brain network algorithm is characterized by comprising a paradigm design module, a data transition module, a network construction module, a dynamic analysis module, a data detection module and a result display module;

the paradigm design module is used for carrying out experimental paradigm design on the neurological disorder diseases;

the data conversion module is used for acquiring a BOLD signal image of the nuclear magnetic resonance brain function imaging, and preprocessing and storing the BOLD signal image;

the network construction module is used for constructing a whole brain function network;

the dynamic analysis module is used for dividing a dynamic time-varying sliding window based on a BOLD signal image and dividing a whole brain function network into a plurality of dynamic time-varying brain networks; extracting curvature features of each dynamic time-varying brain network;

the data detection module is used for constructing a classification model based on the extracted curvature characteristics and predicting normal and neurological disorder patients;

and the result display module is used for synthesizing and outputting a neurological disorder disease detection analysis report according to the classification model result.

2. The neurological disorder diagnosis system based on state-transition dynamic brain network algorithm of claim 1 wherein said paradigm design module is specifically configured to,

and (4) designing a task state experimental scheme of a patient group and a control group by adopting visual graphic disorder paradigm setting.

3. The neurological disorder diagnosis system based on the state transition dynamic brain network algorithm according to claim 1, wherein the data transition module comprises an upper computer data acquisition unit, a format transition unit, a data screening unit and a data preprocessing unit;

the upper computer data acquisition unit is used for acquiring a BOLD signal image of nuclear magnetic resonance brain function imaging of each brain region;

the format conversion unit is used for forming a time sequence by sequencing the BOLD signal images of each brain area according to time;

the data screening unit is used for removing noise data and machine error data;

the data preprocessing unit is used for sequentially performing time layer correction, head motion correction, space standardization, space smoothing, image convolution and filtering operation on the screened data; and storing the preprocessed data in a static link library.

4. The neurological disorder diagnosis system based on state-transition dynamic brain network algorithm of claim 3 wherein said data preprocessing unit is further configured to,

and labeling the preprocessed data, labeling the prefix of the resting state data with 0, labeling the prefix of the task state data with 1, and uniformly storing the data in a static link library.

5. The neurological disorder diagnosis system based on state-transition dynamic brain network algorithm of claim 1 wherein said network construction module is specifically configured to,

a brain function network is constructed by adopting a mutual information method, and the correlation between any brain areas in the brain is expressed as follows:

wherein I (X; Y) is the correlation between brain regions X and Y, p (X, Y) is the joint probability distribution function of brain regions X and Y, and p (X) and p (Y) are the edge probability distribution functions of brain regions X and Y, respectively.

6. The neurological disorder diagnosis system based on the state transition dynamic brain network algorithm of claim 1, wherein the dynamic analysis module comprises a whole brain time analysis unit, a dynamic division unit and an index calculation unit;

the whole brain time analysis unit is used for evaluating the effective time period of the time sequence; the evaluation standard is that each BOLD signal image in the time sequence represents 1 time point, and the BOLD signal value difference of each time point in the effective time period is not more than 20% of the average value;

the dynamic division unit is used for dividing the whole brain area into a plurality of dynamic time-varying sliding windows based on the selected effective time period; constructing a dynamic time-varying brain network for each dynamic time-varying sliding window;

the index calculation unit is used for calculating the curvature area index of each dynamic time-varying brain network and extracting curvature characteristics.

7. The neurological disorder diagnosis system based on state-transition dynamic brain network algorithm according to claim 6, wherein said dynamic partitioning unit is specifically configured to,

the spatial standard deviation was calculated for each time point:

wherein s is_mIs the spatial standard deviation of time m, n is the number of brain regions, x_iIs the BOLD value in the ith brain region, x is the average of all BOLD values;

if the calculated spatial standard deviation of the time point exceeds a preset threshold, the point is a dynamic constraint point;

forming a dynamic time-varying sliding window based on each dynamic constraint point;

and respectively constructing dynamic time-varying brain networks for each dynamic time-varying sliding window by using a mutual information method, wherein each dynamic time-varying sliding window corresponds to one dynamic time-varying brain network.

8. The neurological disorder diagnosis system based on state-transition dynamic brain network algorithm according to claim 6, wherein said index calculation unit is specifically configured to,

calculating the curvature area index of each dynamic time-varying brain network:

wherein D represents a curvature area index, N represents the number of nodes in the dynamic time-varying brain network, one brain region is a node, and p is a network sparsity threshold;

and the number of the first and second groups,

setting the step length of a network sparsity threshold;

in the value range of the network sparsity threshold p, sequentially selecting threshold values to calculate curvature area points by taking the step length as an interval, constructing a dynamic time window brain network curvature area curve, and fitting;

and (3) solving the curvature of points on the dynamic time window brain network curvature area curve based on a fitting function:

where y represents the fitted function, y 'is the first derivative of y, y' is the second derivative of y, and K is the curvature of a point on the curve.

9. The neurological disorder diagnosis system based on the state transition dynamic brain network algorithm according to claim 8, wherein the network sparsity threshold value is 0.1-0.4;

the step size is 0.01 or 0.02.

10. The neurological disorder diagnosis system based on state-transition dynamic brain network algorithm of claim 8 wherein said data detection module is specifically configured to,

curvature of each point in each dynamic time window brain network is obtained in real time, and T inspection and FDR (fully drawn description) verification are carried out;

obtaining a prediction result of whether the patient with the neurological disorder is present or not based on the classification model;

and the number of the first and second groups,

carrying out T test and FDR (fully drawn description) verification according to the curvature of each point in each dynamic time window brain network of the training sample;

and (3) constructing a classification model by adopting a classification algorithm of an extreme learning machine or a support vector machine, and identifying whether the patient is suffering from the neurological disorder.

Technical Field

The invention relates to a neurological disorder diagnosis system based on a state transition dynamic brain network algorithm, and belongs to the technical field of medical diagnosis.

Background

Functional magnetic resonance imaging (fMRI) techniques utilize magnetic resonance imaging to measure hemodynamic changes induced by neuronal activity. With the mature development of neuroimaging technology, the use of fMRI technology for diagnosis and adjuvant therapy of cranial nerve diseases has become a professionally approved therapeutic method.

In recent years, brain function networks have been used in the detection of various types of neurological and brain diseases. For Attention Deficit and Hyperactivity Disorder (ADHD), alzheimer's disease and other neurological disorders, graph-theory-based brain function network analysis has become a hot spot. The brain function network can convert a complex brain into a brain network composed of nodes and edges in an abstract mode, and auxiliary diagnosis is provided for treatment of neurological disorder diseases by calculating related indexes of the brain network.

In brain function network research, the connections between brain regions are called functional connections, and the strength value of connectivity can reflect the change of the brain network strength. For the brain function network, the overall and local characteristics of the brain network can be measured through various network indexes in the graph theory, such as degree distribution, betweenness, average shortest path and the like. At present, in brain function networks, the research on dynamic brain function networks is mostly to divide a fixed equivalent time window into whole time periods and then to perform network index analysis in sequence. The method can divide the brain network in the experimental process into the dynamic networks with equal time intervals, but ignores that the transition of the brain network among various modes is possibly nonlinear, and the division of equal-length time windows can cause the splitting of dynamic information, cause the loss of important brain connection information and have certain influence on the diagnosis result.

Disclosure of Invention

The invention aims to provide a neurological disorder diagnosis system based on a state transition dynamic brain network algorithm, which divides a full-time brain network into various dynamic time-interval sub-networks and can dynamically and accurately reflect the abnormal conditions of a brain functional area of a patient, thereby providing an idea for information technology diagnosis of neurological disorder diseases.

In order to achieve the purpose, the invention adopts the following technical scheme:

the invention provides a neurological disorder diagnosis system based on a state transition dynamic brain network algorithm, which comprises a paradigm design module, a data transition module, a network construction module, a dynamic analysis module, a data detection module and a result display module, wherein the paradigm design module is used for carrying out state transition on a neural network;

the paradigm design module is used for carrying out experimental paradigm design on the neurological disorder diseases;

the data conversion module is used for acquiring a BOLD signal image of the nuclear magnetic resonance brain function imaging, and preprocessing and storing the BOLD signal image;

the network construction module is used for constructing a whole brain function network;

the dynamic analysis module is used for dividing a dynamic time-varying sliding window based on a BOLD signal image and dividing a whole brain function network into a plurality of dynamic time-varying brain networks; extracting curvature features of each dynamic time-varying brain network;

the data detection module is used for constructing a classification model based on the extracted curvature characteristics and predicting normal and neurological disorder patients;

and the result display module is used for synthesizing and outputting a neurological disorder disease detection analysis report according to the classification model result.

Further, the paradigm-design module is specifically configured to,

and (4) designing a task state experimental scheme of a patient group and a control group by adopting visual graphic disorder paradigm setting.

Furthermore, the data conversion module comprises an upper computer data acquisition unit, a format conversion unit, a data screening unit and a data preprocessing unit;

the upper computer data acquisition unit is used for acquiring a BOLD signal image of nuclear magnetic resonance brain function imaging of each brain region;

the format conversion unit is used for forming a time sequence by sequencing the BOLD signal images of each brain area according to time;

the data screening unit is used for removing noise data and machine error data;

the data preprocessing unit is used for sequentially performing time layer correction, head motion correction, space standardization, space smoothing, image convolution and filtering operation on the screened data; and storing the preprocessed data in a static link library.

Further, the data preprocessing unit is also used for,

and labeling the preprocessed data, labeling the prefix of the resting state data with 0, labeling the prefix of the task state data with 1, and uniformly storing the data in a static link library.

Further, the network construction module is specifically configured to,

a brain function network is constructed by adopting a mutual information method, and the correlation between any brain areas in the brain is expressed as follows:

wherein I (X; Y) is the correlation between brain regions X and Y, p (X, Y) is the joint probability distribution function of brain regions X and Y, and p (X) and p (Y) are the edge probability distribution functions of brain regions X and Y, respectively.

Further, the dynamic analysis module comprises a whole brain time analysis unit, a dynamic division unit and an index calculation unit;

the whole brain time analysis unit is used for evaluating the effective time period of the time sequence; the evaluation standard is that each BOLD signal image in the time sequence represents 1 time point, and the BOLD signal value difference of each time point in the effective time period is not more than 20% of the average value;

the dynamic division unit is used for dividing the whole brain area into a plurality of dynamic time-varying sliding windows based on the selected effective time period; constructing a dynamic time-varying brain network for each dynamic time-varying sliding window;

the index calculation unit is used for calculating the curvature area index of each dynamic time-varying brain network and extracting curvature characteristics.

Further, the dynamic partitioning unit is specifically configured to,

the spatial standard deviation was calculated for each time point:

wherein s is_mIs the spatial standard deviation of time m, n is the number of brain regions, x_iIs the BOLD value in the ith brain region,is the average of all BOLD values;

if the calculated spatial standard deviation of the time point exceeds a preset threshold, the point is a dynamic constraint point;

forming a dynamic time-varying sliding window based on each dynamic constraint point;

and respectively constructing dynamic time-varying brain networks for each dynamic time-varying sliding window by using a mutual information method, wherein each dynamic time-varying sliding window corresponds to one dynamic time-varying brain network.

Further, the index calculation unit is specifically configured to,

calculating the curvature area index of each dynamic time-varying brain network:

wherein D represents a curvature area index, N represents the number of nodes in the dynamic time-varying brain network, one brain region is a node, and p is a network sparsity threshold;

and the number of the first and second groups,

setting the step length of a network sparsity threshold;

in the value range of the network sparsity threshold p, sequentially selecting threshold values to calculate curvature area points by taking the step length as an interval, constructing a dynamic time window brain network curvature area curve, and fitting;

and (3) solving the curvature of points on the dynamic time window brain network curvature area curve based on a fitting function:

where y represents the fitted function, y 'is the first derivative of y, y' is the second derivative of y, and K is the curvature of a point on the curve.

Further, the value of the network sparsity threshold is 0.1-0.4;

the step size is 0.01 or 0.02.

Further, the data detection module is specifically configured to,

curvature of each point in each dynamic time window brain network is obtained in real time, and T inspection and FDR (fully drawn description) verification are carried out;

obtaining a prediction result of whether the patient with the neurological disorder is present or not based on the classification model;

and the number of the first and second groups,

carrying out T test and FDR (fully drawn description) verification according to the curvature of each point in each dynamic time window brain network of the training sample;

and (3) constructing a classification model by adopting a classification algorithm of an extreme learning machine or a support vector machine, and identifying whether the patient is suffering from the neurological disorder.

The invention has the following beneficial effects:

(1) the invention constructs the brain function network by a mutual information method, can decompose the complex brain network into an abstract model, and has the function of forming a graph network structure formed by the interaction of all brain areas.

(2) According to the invention, the brain time sequence is processed by the SSD method, the dynamic time-varying sliding window is obtained, the dynamic time-varying network is reconstructed secondarily, the related coordination degree between the networks is further embodied, the neural mechanism is better explained and analyzed from the aspect of network modal change, and an idea is provided for the information technology diagnosis of the neurological disorder diseases.

(3) The method selects the curvature area index as the characteristic, so that the characteristic of the dynamic brain area can be accurately positioned, and an optimized result is obtained.

(4) The invention can evaluate the patient and judge whether the subject has the neurological disorder diseases, is beneficial to the diagnosis and treatment of the neurological disorder diseases by the clinician, and plays an auxiliary role.

Drawings

FIG. 1 is a schematic diagram of a neurological disorder diagnostic system based on a state transition dynamic brain network algorithm according to the present invention;

FIG. 2 is a block diagram of a data conversion module according to the present invention;

FIG. 3 is a schematic diagram of a dynamic analysis module of the present invention;

fig. 4 is a flow chart of the neurological disorder diagnosis based on the state transition dynamic brain network algorithm of the present invention.

Detailed Description

The invention is further described below. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.

The embodiment of the invention provides a neurological disorder diagnosis system based on a state transition dynamic brain network algorithm, which comprises a paradigm design module, a data conversion module, a network construction module, a dynamic analysis module, a data detection module and a result display module, as shown in fig. 1.

Taking ADHD child data as an example, the application of the embodiment of the invention is as follows:

the paradigm design module is used for carrying out the design and the realization of experiment paradigm according to experiment ADHD children patient's characteristic.

In the implementation of the invention, experiment operators realize the experimental paradigm design corresponding to the neurological disorder diseases based on Visual Studio 2019 software, and consider the aspects of time length, immersion experience, comfort of head-mounted equipment and the like to realize the paradigm design. In this embodiment, the system adopts a visual pattern impairment paradigm setting to perform a task state experimental plan for a patient group and a control group.

Referring to fig. 2, the data conversion module includes an upper computer data acquisition unit, a format conversion unit, a data screening unit and a data preprocessing unit.

The upper computer data acquisition unit is used for acquiring a BOLD signal (blood oxygen level dependent signal) for nuclear magnetic resonance brain function imaging. Each nuclear magnetic resonance brain function picture corresponds to a BOLD signal.

The format conversion unit is used for sequencing the BOLD signal pictures collected by a single testee according to time to form a time sequence, one BOLD signal picture represents 1 time point, and each brain area of the testee has a time sequence.

The data screening unit is used for screening the data after format conversion and removing noise data and machine error data generated in the experiment.

The data preprocessing unit is used for sequentially performing time layer correction, head motion correction, space standardization, space smoothing, image convolution and filtering operation on the screened data; and labeling the preprocessed data, marking the static state data prefix with 0, marking the task state data prefix with 1, and uniformly storing the data in the static link library. And storing the data in the static link library for calling at any time every time the data is acquired.

The network construction module is used for constructing a brain function network by adopting a mutual information method, decomposing the complex brain network into an abstract model and carrying out subsequent analysis in a graph network mode.

The mutual information indicates whether a relationship exists between two variables and the strength of the relationship. In the brain, the correlation between any two brain areas can be effectively reflected by a mutual information algorithm, so that a whole brain function network is constructed according to the correlation between every two brain areas. For any brain region X and Y inside the brain, there are:

wherein I (X; Y) is the correlation between brain regions X and Y, p (X, Y) is the joint probability distribution function of brain regions X and Y, and p (X) and p (Y) are the edge probability distribution functions of brain regions X and Y, respectively.

Referring to fig. 3, the dynamic analysis module includes a whole brain time analysis unit, a dynamic division unit, and an index calculation unit.

Wherein the content of the first and second substances,

the whole brain time analysis unit is used for evaluating the effective time period of the time series. The evaluation criterion was that the differences in BOLD signal values at each time point did not exceed 20% of the mean.

The dynamic division unit is used for dividing the whole brain area into a plurality of dynamic time-varying sliding windows based on the selected effective time period. The specific operation is as follows:

each region of interest to be measured is compartmentalized using a Spatial Standard Deviation (SSD). Taking 0.2 as a threshold, if the SSD calculation result exceeds 0.2, it is considered that this time point can be used as a dynamic constraint point, and so on, the signal of the full time period is divided, one BOLD signal picture represents 1 time point, and the SSD calculation is as follows:

wherein s is_mThe spatial standard deviation of time m, n is the region of interest (ROI), i.e. the number of brain regions, x_iIs the BOLD value in the ith ROI,is the average of all BOLD values.

And constructing dynamic time-varying sliding windows based on the dynamic constraint points, and constructing dynamic time-varying brain networks for each dynamic time-varying sliding window by using a mutual information method, namely each dynamic time-varying sliding window corresponds to one dynamic time-varying brain network.

The index calculation unit is used for calculating the curvature area index of each dynamic time-varying brain network and extracting curvature characteristics. The specific operation is as follows:

the curvature area index of each dynamic time-varying brain network is calculated as:

wherein D represents a curvature area index, N represents the number of nodes in the dynamic time-varying brain network, one brain region is a node, p is a network sparsity threshold, and the threshold is generally controlled to be 0.1-0.4 in order to prevent the network from being excessively dense and sparse.

The curvature characteristic extraction process comprises the following steps:

setting the step size, generally 0.01 or 0.02;

in the value range of the threshold value p, sequentially selecting threshold values to calculate curvature area points by taking the step length as an interval, constructing a dynamic time window brain network curvature area curve, and fitting;

and (3) calculating the curvature of points on the curvature area curve of the dynamic time window brain network based on a fitting function, wherein the calculation formula of the curvature is as follows:

where y represents the fitted function, y' is its first derivative, y "is its second derivative, and K is the curvature of a point.

The data detection module is used for acquiring the brain network parameters of each dynamic time window in real time, namely the curvature of each point, performing T test and FDR (pulse discovery rate) verification, and under the condition of high significance, constructing a classification model by using classification algorithms such as an Extreme Learning Machine (ELM) and a Support Vector Machine (SVM), distinguishing normal persons from ADHD children, and storing the classification model locally in a form of a static link library.

And the result display module is used for synthesizing the classification prediction results in the data detection module into a children ADHD detection analysis report to assist a doctor in clinical diagnosis and treatment of ADHD.

In this embodiment, the system platform is implemented in a microsoft Visual studio development environment under a Windows operating system, the carrier for data acquisition, storage and preprocessing is a professional GPU computational power operation unit server, and the man-machine interface carrier is a dell liquid crystal touch display screen.

As shown in fig. 4, the present invention further provides a neurological disorder diagnosis method based on a state transition dynamic brain network algorithm, which specifically includes:

(1) according to the clinical change of the target brain area of the neurological disorder disease, the paradigm design of task state experiments is carried out, and the testees are recruited to carry out the experiments.

(2) Importing an fMRI image extracted from the head of an experimental subject, adjusting a format suffix which can be identified by processing software, and sequentially: data preprocessing, time sequence extraction, brain network construction, dynamic time-varying sliding window division, secondary reconstruction characteristic (curvature) extraction, data fitting and finally use of the formed classification model for clinical detection and diagnosis of children ADHD.

(3) And performing multiple fitting analysis on the formed clinical detection diagnosis model of the child ADHD, judging errors in the model, performing corresponding adjustment, and after the whole model is trained for 5 times continuously to reach a recognition rate of more than 99%, reintroducing the original experimental data for subsequent analysis.

(4) And matching degree integration is carried out on the defect detection of the key brain area by a clinician and the brain network curvature of the dynamic time window, if the integration height is consistent, the subject has higher risk of suffering from ADHD, so as to evaluate whether the risk of ADHD neurological disorder exists or not, if the risk exists, corresponding examination and treatment are carried out, and if the risk does not exist, a period of time is observed for carrying out next experimental analysis.

It is to be noted that the apparatus embodiment corresponds to the method embodiment, and the implementation manners of the method embodiment are all applicable to the apparatus embodiment and can achieve the same or similar technical effects, so that the details are not described herein.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.