A Brain Network Analysis Method and System Based on Multi-Network Collaborative Topology Analysis

Abstract

This invention belongs to the field of data processing technology. To address the instability of analysis results caused by the subjectivity of threshold selection in existing Alzheimer's disease brain network analysis, this invention proposes a brain network analysis method and system based on multi-network collaborative topology analysis. It extracts brain regions corresponding to the default mode network, saliency network, and executive control network, calculates correlation coefficients between different brain regions to construct a functional connectivity matrix, and converts the functional connectivity matrix into a distance matrix that meets the requirements for complex construction. Based on the distance matrix, a Vietoris-Rips complex is constructed, and homology groups are calculated at each scale of the filtered sequence to extract 0-dimensional and 1-dimensional Betty number topological features. A curve showing the Betty number changing with a threshold is plotted, and the differences in topological features between the two groups are compared to identify specific topological biomarkers related to Alzheimer's disease, providing richer biomarker information for the early diagnosis of Alzheimer's disease.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a brain network analysis method and system based on multi-network collaborative topology analysis. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Alzheimer's disease (AD) is a neurodegenerative disease of the central nervous system and the most common type of dementia in the elderly. It is characterized by progressive cognitive decline and behavioral impairment, particularly memory deficits and cognitive impairment. Because the pathogenesis of AD is not fully understood and the disease course is irreversible, early diagnosis plays a crucial role in slowing disease progression and improving patients' quality of life.

[0004] In the field of neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) has become an important tool for studying Alzheimer's disease (AD). By detecting blood oxygenation-dependent signals, it captures spontaneous neural activity in the brain and can reveal functional connectivity patterns closely related to cognitive processes. In existing techniques, researchers typically use graph theory methods to construct and analyze brain functional networks. By setting specific thresholds on weighted connectivity matrices, they construct binary networks and analyze the connectivity characteristics between different brain regions based on these networks.

[0005] However, traditional graph theory methods have significant technical limitations: their analysis results heavily depend on the choice of threshold. Since a unified standard for threshold selection has not yet been established in academia, researchers often need to rely on experience or simulation methods to determine the threshold. This not only introduces subjective bias but also affects the reproducibility and reliability of network analysis results. Especially in the early stages of Alzheimer's disease, this threshold dependence may mask subtle but important changes in functional connectivity, thereby reducing the accuracy of early diagnosis.

[0006] To overcome the threshold selection problem, brain network methods based on topological data analysis have emerged in recent years. These methods can analyze the topological features of brain networks at different scales, including topological invariants such as connected components and loop structures, without relying on a specific threshold. Existing research has shown that this method has good robustness in brain network feature extraction and can effectively avoid the problems caused by inappropriate threshold selection in traditional graph theory methods.

[0007] However, existing topological data analysis methods still have limitations in Alzheimer's disease research. In particular, they are insufficient for systematic joint analysis of multiple core functional networks (including the default mode network (DMN), saliency network (SN), and executive control network (ECN), and also lack a quantitative identification framework for AD-specific topological biomarkers. Therefore, there is an urgent need for an analytical method that is threshold-independent and can reliably capture topological anomalies in the Alzheimer's brain network. Summary of the Invention

[0008] To overcome the shortcomings of the prior art, this invention provides a brain network analysis method and system based on multi-network collaborative topology analysis, which provides more objective and reliable topological biomarkers for the early auxiliary diagnosis of Alzheimer's disease, while deepening the understanding of the pathological mechanism of Alzheimer's disease.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] In a first aspect, the present invention provides a brain network analysis method based on multi-network collaborative topology analysis, comprising:

[0011] Brain regions corresponding to the default mode network, saliency network, and executive control network are extracted. Correlation coefficients between different brain regions are calculated to construct a functional connectivity matrix. The functional connectivity matrix is ​​then converted into a distance matrix that meets the requirements for Vietoris-Rips complex construction.

[0012] Based on the distance matrix, a Vietoris-Rips complex is constructed, and the homology group is calculated at each scale of the filtered sequence to extract 0-dimensional Betti number and 1-dimensional Betti number topological features.

[0013] Plot the Betty number as a function of a threshold, compare the differences in topological features between the Alzheimer's disease group and the healthy control group, and identify specific topological biomarkers related to Alzheimer's disease.

[0014] Secondly, the present invention provides a brain network analysis system based on multi-network collaborative topology analysis, comprising:

[0015] The construction module is configured to: extract the brain regions corresponding to the default mode network, saliency network and executive control network, calculate the correlation coefficients between different brain regions to construct a functional connectivity matrix, and convert the functional connectivity matrix into a distance matrix that meets the requirements of Vietoris-Rips complex construction;

[0016] The extraction module is configured to: construct a Vietoris-Rips complex based on the distance matrix, calculate the homology group at each scale of the filtered sequence, and extract 0-dimensional Betti number and 1-dimensional Betti number topological features;

[0017] The analysis module is configured to: plot the curve of the Betty number as a function of a threshold, compare the differences in topological features between the Alzheimer's disease group and the healthy control group, and identify specific topological biomarkers related to Alzheimer's disease.

[0018] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0019] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0020] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0021] The above one or more technical solutions have the following beneficial effects:

[0022] In this invention, the three core functional systems of the default mode network, saliency network, and executive control network are analyzed together to accurately locate specific topologically abnormal brain regions related to Alzheimer's disease. At the same time, 0-dimensional and 1-dimensional Betty number features are extracted to quantify the connected components and loop structures of the brain network, respectively, achieving a comprehensive description of the brain network topology. This two-dimensional analysis method can capture subtle topological changes that are difficult to detect by traditional methods, providing richer biomarker information for the early diagnosis of Alzheimer's disease.

[0023] In this invention, multi-network collaborative topological analysis is employed to analyze brain networks, completely avoiding the subjectivity and uncertainty of empirical threshold selection. Multi-scale topological feature extraction eliminates biases caused by inappropriate threshold selection, significantly improving the reliability and reproducibility of the analysis results. The established topological biomarker framework provides an objective and quantitative assessment tool for the early diagnosis of Alzheimer's disease. By identifying specific topological abnormal patterns, it is expected to screen high-risk individuals before the onset of clinical symptoms, providing a valuable time window for early intervention and treatment.

[0024] In this invention, a nonparametric statistical method combining the Kolmogorov-Smirnov test and the Mann-Whitney U test is used, and the results are corrected for false discovery rate to ensure statistical significance of the analysis results.

[0025] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0026] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0027] Figure 1 This is an example diagram of a Vietoris-Rips complex in an embodiment of the present invention. Different Vietoris-Rips complex filtering subsets are obtained by using different thresholds: where A is the filtering parameter. B is the filtering parameter. C is the filtering parameter. D is the filtering parameter. ;

[0028] Figure 2 These are persistent barcodes and persistent images in embodiments of the present invention; wherein, A is a persistent barcode; and B is a persistent image.

[0029] Figure 3 This is a comparison diagram of 0-dimensional and 1-dimensional Betty number curves in an embodiment of the present invention; where A is the 0-dimensional Betty number curve; and B is the 1-dimensional Betty number curve.

[0030] Figure 4 This is a comparative analysis diagram of the average number of neural circuits in 46 brain regions in an embodiment of the present invention;

[0031] Figure 5 Box plots showing significant differences in brain regions in this embodiment of the invention are shown below; where A is the left superior frontal gyrus of the orbitofrontal region; B is the right olfactory cortex; C is the right superior frontal gyrus of the orbitofrontal region; D is the right anterior cingulate cortex and paracingulate cortex gyrus; and E is the right caudate nucleus.

[0032] Figure 6 This is a flowchart of the Alzheimer's disease brain network analysis method based on multi-network collaborative topology analysis in an embodiment of the present invention. Detailed Implementation

[0033] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0034] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0035] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0036] Example 1

[0037] This embodiment discloses a method for analyzing Alzheimer's disease brain networks based on multi-network collaborative topology analysis, including:

[0038] Brain regions corresponding to the default mode network, saliency network, and executive control network are extracted. Correlation coefficients between different brain regions are calculated to construct a functional connectivity matrix. The functional connectivity matrix is ​​then converted into a distance matrix that meets the requirements for Vietoris-Rips complex construction.

[0039] Based on the distance matrix, a Vietoris-Rips complex is constructed, and the homology group is calculated at each scale of the filtered sequence to extract 0-dimensional Betti number and 1-dimensional Betti number topological features.

[0040] Plot the Betty number as a function of a threshold, compare the differences in topological features between the Alzheimer's disease group and the healthy control group, and identify specific topological biomarkers related to Alzheimer's disease.

[0041] The core of this embodiment lies in using a continuous cohomology method to extract and analyze the topological features of multiple specific brain functional networks originating from the whole brain in a targeted and comparable manner. Specific features include:

[0042] Targeted data extraction for specific networks: Based on the AAL brain map template, nodes in brain regions covered by the default mode network, salience network, and executive control network are accurately extracted. A functional connectivity matrix focusing on the connections within and between these three networks is constructed solely based on the blood oxygenation level-dependent signals of these nodes. This step ensures that subsequent analysis directly targets the target network system, eliminating interference from irrelevant brain regions.

[0043] Functional connectivity to topological distance conversion: The functional connectivity matrix obtained above is uniformly converted into a functional distance matrix to provide standardized input for subsequent topological analysis based on Vietoris-Rips complexes.

[0044] Unified multi-scale topology modeling based on Vietoris-Rips complex: The Vietoris-Rips complex construction method is used to filter the obtained distance matrix within the same scale parameter range to generate its persistent homology barcode and persistent graph, thereby realizing multi-scale characterization of the topology of these three networks.

[0045] Collaborative extraction and comparison of robust topological features: From the continuous homology results, topological features such as 0-dimensional and 1-dimensional Betti numbers were extracted to quantify the connected components and ring structures of the network, respectively. Finally, by comparing the statistical differences of these features between the Alzheimer's disease group and the healthy control group, multi-network topological biomarkers with high sensitivity and specificity for disease identification were screened.

[0046] The core of this embodiment lies in proposing a multi-network collaborative topology analysis framework for specific brain functional systems. This framework does not simply apply topological data analysis to the whole brain, but rather precisely targets the three core systems working collaboratively: the default mode network, the salience network, and the executive control network, and characterizes and compares them within a unified, multi-scale topological space. This method overcomes the instability of results caused by reliance on a single threshold in traditional network analysis. By extracting robust multi-scale topological features, it aims to reveal multi-network collaborative topological disorder patterns that better reflect the essence of diseases, thereby significantly improving the reliability and interpretability of biomarker discovery.

[0047] The brain network analysis method based on multi-network collaborative topology analysis proposed in this embodiment includes the following steps:

[0048] I. Data Acquisition and Preprocessing.

[0049] Acquire resting-state functional magnetic resonance imaging (fMRI) data and perform preprocessing as follows:

[0050] Step 11: Magnetic field stabilization: The scan data of the first 10 time points are discarded from the original resting-state functional magnetic resonance imaging data to eliminate magnetic field inhomogeneity and subject adaptation period interference.

[0051] Step 12: Slice Time Correction: Corrects the time differences in the acquisition of different slices.

[0052] Step 13: Head movement correction: Data with large head movements are excluded, and subjects with translation >3mm or rotation >3° are excluded.

[0053] Step 14: Spatial normalization: Register individual T1 images to the MNI152 standard space using nonlinear registration.

[0054] Step 15: Spatial smoothing: Smooth the surface using a 6mm half-height full-width Gaussian kernel.

[0055] Step 16: Time filtering: Apply a 0.01-0.1Hz bandpass filter to eliminate low-frequency drift and high-frequency noise.

[0056] II. Brain Network Construction.

[0057] Based on preprocessed resting-state functional magnetic resonance imaging data, a brain network was constructed and features were extracted according to the following steps:

[0058] Step 21: Brain Region Definition: Regions of interest were defined using an automated anatomical labeling (AAL) template. This template divides the brain into 116 regions, including 90 cerebral regions and 26 cerebellar regions.

[0059] This embodiment accurately extracts and analyzes the brain regions corresponding to the three core networks: the default mode network, the saliency network, and the executive control network.

[0060] (1) Default mode network: It contains 20 brain regions, mainly covering the prefrontal cortex, cingulate gyrus and parietal lobe regions;

[0061] (2) Saliency network: It includes 16 brain regions, involving the olfactory cortex, hippocampus and basal ganglia, etc.

[0062] (3) Executive control network: It contains 22 brain regions, including the prefrontal and parietal lobes.

[0063] After deduplication, a total of 46 non-overlapping brain regions were retained for subsequent topological feature analysis.

[0064] Step 22: Functional connectivity matrix construction: Calculate the Pearson correlation coefficients between different brain regions and construct the functional connectivity matrix.

[0065] make Indicates the first The time series of average blood oxygenation levels in each brain region, among which n Represents the total number of time points. Calculate the function connection matrix. ,in The formula for calculating the Pearson correlation coefficient is as follows:

[0066] (1)

[0067] in, , These represent the time-series averages of brain regions i and j, respectively. The Pearson correlation coefficient ranges from -1 to 1, and its absolute value reflects the strength of functional connectivity between brain regions. A value close to 0 indicates a weak correlation between brain regions, while a value close to 1 indicates a strong correlation.

[0068] Given that in resting-state functional magnetic resonance imaging (fMRI) studies, the strength of functional connectivity is often more physiologically significant than its directionality (positive or negative), this embodiment uses the absolute value of the Pearson correlation coefficient. This is used to characterize the strength of functional connections for subsequent analysis.

[0069] III. Vietoris-Rips complex construction and multi-network collaborative topology feature extraction.

[0070] The core of this embodiment lies in using topology data analysis technology to extract topological features from the three major networks mentioned above. Topology data analysis is a technique that utilizes computational topology to extract multi-scale shape features from data. Its main idea is to introduce the concept of persistence on the basis of homology, representing a changing and dynamic process. Topology data analysis primarily focuses on the generation, disappearance time, and duration of k-dimensional holes, using this information to reflect important information about the topological space.

[0071] The basic steps of topology data analysis include:

[0072] Step 31: Distance matrix transformation.

[0073] To analyze the topological characteristics of functional networks and meet the metric space requirements for Vietoris-Rips complex construction, the correlation-based adjacency matrix is ​​transformed into a metric space.

[0074] The distance metric between two brain regions i and j is defined as:

[0075] (2)

[0076] Step 32: Construct a simple complex: Generate a simple complex from the preprocessed data above, such as the Vietoris-Rips complex constructed in this embodiment.

[0077] like Figure 1 As shown, the Vietoris-Rips complex is constructed by using the distance matrix between nodes in the brain network as a basis and adjusting the filtering parameter ε to build a multi-scale Vietoris-Rips complex, thereby capturing topological features (such as connected components and ring structures) at different scales. The construction rule is that a simplex is formed if and only if the distance between any two points in a set of nodes is ≤ ε. As ε increases, new simplexes are formed, and existing structures may be merged or filled, thus recording the persistence of topological features.

[0078] Step 33: Generate the filter sequence: Gradually increase the size of the simplex based on the filtering parameters to form a series of filter sequences: in , representing the sequence of filtering parameters that satisfy .

[0079] Step 34: Calculate homology: Calculate homology groups at each scale, identify and track the appearance and disappearance of topological features, and calculate 0-dimensional Betti number and 1-dimensional Betti number topological features.

[0080] IV. Statistical Analysis Module.

[0081] Step 41: Plot the curve of the Betty number as a function of the threshold;

[0082] Step 42: Use the Kolmogorov-Smirnov test to compare the differences in the distribution of Betty numbers between the two groups. Use the Mann-Whitney U test to compare the differences in the number of circuits in specific brain regions, and perform multiple comparison correction: employ the false discovery rate method to control the false positive rate.

[0083] Key parameter range:

[0084] Distance threshold range: 0-0.7 (optimal analysis range: 0.1-0.6);

[0085] Step size setting: 0.005-0.05 (preferably 0.01);

[0086] Filtering frequency band: 0.008-0.12Hz (standard 0.01-0.1Hz);

[0087] Space smooth core: 4-8mm FWHM (standard 6mm);

[0088] Statistical significance: p < 0.05, q < 0.05 after FDR correction;

[0089] The Pearson correlation coefficient ranges from -1 to 1.

[0090] Sample size requirement: no less than 50 cases per group to ensure statistical validity.

[0091] Through the systematic integration of the above-mentioned technical means, this embodiment establishes a complete AD brain network analysis process based on multi-network collaborative topology analysis, realizing stable and quantitative identification of AD-related topological features, and providing a reliable objective basis for early diagnosis.

[0092] The multi-network collaborative topology analysis proposed in this embodiment has the following characteristics:

[0093] (1) Targeted analysis of core systems: Unlike whole-brain topology analysis, this model focuses precisely on the default mode network, saliency network and executive control network that are closely related to the pathology of Alzheimer's disease. By extracting their specific brain regions to construct a joint functional connectivity matrix, it achieves targeted detection of key targets of the disease.

[0094] (2) Global Topology Analysis under a Unified Network Framework: The core of the model lies in treating the three major networks mentioned above as a unified system, and constructing a single Vietoris-Rips complex on this integrated network for multi-scale topology analysis. A functional connectivity matrix is ​​constructed based on the Pearson correlation coefficient and converted into a distance matrix for filtering the Vietoris-Rips complex. This method can simultaneously capture the global topology structure generated by interactions within the network and between different network regions.

[0095] Extraction and interpretation of collaborative topological features: From the continuous cohomology analysis of this unified network, 0-dimensional and 1-dimensional Betti numbers are extracted as topological features to characterize the connected components and ring structures in the network, respectively.

[0096] (6) Compare the differences in topological characteristics between the AD group and the healthy control group using statistical tests (such as the Mann-Whitney U test and the Kolmogorov-Smirnov test). Figure 2 As shown, the construction of the barcode image and persistence image is as follows: In the bar chart, each horizontal bar represents the lifecycle of a topological feature, from its birth to its extinction threshold. The horizontal axis corresponds to the filtering parameters, and the vertical axis sorts the features according to the homology dimension (e.g., Dim0 corresponds to Dim1, Dim1 to Dim0). The longer the bar, the more persistent the topological feature, and therefore the more significant it is. For example, in... Figure 2 The barcode A, Dim0 illustrates the situation where connecting components are added and merged.

[0097] like Figure 2 In the persistence graph, each point (b, d) represents a topological feature that forms at the generation threshold b and disappears at the disappearance threshold d. All points lie on the y=x line because disappearance necessarily occurs after generation. Figure 2 In the B-map, different colors and markers are used to distinguish homologous dimensions, with blue asterisks and red diamonds representing homologous dimensions. The persistence of topological features is quantified by their vertical distance (db) from the diagonal, serving as an indicator of the feature's robustness at different scales. Barcode maps clearly display the time series and duration of features, while persistence maps visually present the differences in topological structure at different scales through the distribution of points along the diagonal.

[0098] like Figure 3 As shown, topological data analysis was performed: nested brain complexes were constructed at multiple scales, and the 0-dimensional and 1-dimensional Betty numbers were calculated at each scale. The persistence of topological features was visualized using persistent barcodes and persistent graphs. 0-dimensional and 1-dimensional Betty number curves were extracted, and a comparison curve of the Betty numbers between the AD and NC groups was plotted. The Betty number curves reflect the changes in the topological structure of the brain network at different scales. Significant differences were observed in the Betty number curves of the AD and NC groups at different filtering scales.

[0099] like Figures 4-5 As shown, topological feature comparison: the average circuit count of 46 brain regions was statistically analyzed, and the results were summarized in... Figure 4 In the histogram, the horizontal axis corresponds to the brain regions defined by the Automatic Anatomical Labelling (AAL) atlas. For example... Figure 4 As shown, there were significant differences in mean circuit counts in multiple brain regions between the Alzheimer's disease group (AD) and the healthy control group (NC). To determine which specific brain regions showed statistically significant differences, the Mann-Whitney U test was performed separately for each region. Multiple comparisons were then corrected using the false discovery rate (FDR) method, revealing significant differences in several brain regions between the groups, including the left superior frontal gyrus, right olfactory cortex, right intraorbital superior frontal gyrus, right anterior cingulate cortex, and right caudate nucleus. Significantly different brain regions were identified by comparing the distribution of Betty's number and mean circuit length between the AD group and the healthy control group using statistical tests. Figure 5 Box plots were drawn for comparison of AD and NC data in five brain regions. The box plot on the left of each subplot represents the NC group, and the box plot on the right represents the AD group. It is clear that there are significant differences between the two groups of data.

[0100] This embodiment employs a multi-network collaborative topology analysis method to analyze brain networks, avoiding the threshold selection problem in traditional graph theory methods and improving the stability and reproducibility of the analysis results. By jointly analyzing the three major functional networks, DMN, SN, and ECN, and extracting 0-dimensional and 1-dimensional Betty number curves, it comprehensively captures the topological anomalies of AD brain networks at multiple scales, improving the specificity of biomarkers. Through statistical tests, it identifies the changes in the average circuit length of AD-related brain regions, quantitatively locates AD-specific brain regions, and provides potential topological biomarkers for early diagnosis.

[0101] Compared to traditional graph theory methods, this embodiment employs a multi-network collaborative topology analysis approach, completely avoiding the subjectivity and uncertainty of empirical threshold selection. Through multi-scale topological feature extraction, biases caused by inappropriate threshold selection are eliminated, significantly improving the reliability and repeatability of the analysis results.

[0102] This embodiment extracts both 0-dimensional and 1-dimensional Betty number features to quantify the connected components and loop structures of the brain network, achieving a comprehensive description of the brain network topology. This dual-dimensional analysis method can capture subtle topological changes that are difficult to detect using traditional methods, providing richer biomarker information for the early diagnosis of Alzheimer's disease.

[0103] This embodiment, through joint analysis of three core functional systems—the default mode network, the saliency network, and the executive control network—can accurately locate specific topologically abnormal brain regions associated with Alzheimer's disease. Experimental results show that regions with altered neural circuit lengths include: the left orbitofrontal gyrus, the right olfactory cortex, the orbitofrontal gyrus, the anterior cingulate cortex and paracingulate cortex, and the right caudate nucleus. These findings are highly consistent with the pathological mechanisms of Alzheimer's disease.

[0104] This embodiment employs a nonparametric statistical method combining the Kolmogorov-Smirnov test and the Mann-Whitney U test, and after error detection rate correction, ensures the statistical significance of the analysis results. Large-sample validation shows that there are highly significant differences (p<0.001) between the Alzheimer's disease group and the healthy control group in both 0-dimensional and 1-dimensional Betti number distributions, demonstrating the discriminative ability of this method.

[0105] The topological biomarker framework established in this embodiment provides an objective and quantitative assessment tool for the early diagnosis of Alzheimer's disease. By identifying specific topological abnormality patterns, it is hoped that high-risk individuals can be screened before the onset of clinical symptoms, providing a valuable time window for early intervention and treatment.

[0106] The method described in this embodiment is not only applicable to research on Alzheimer's disease, but can also be extended to brain network analysis of other neurological diseases (such as Parkinson's disease, mild cognitive impairment, etc.), and has broad application prospects and promotional value.

[0107] In summary, this embodiment combines topological data analysis technology with brain network research to establish a complete and reliable biomarker identification system for Alzheimer's disease. It has significant advantages in both methodological innovation and clinical application value, providing new technical support for the early diagnosis of neurodegenerative diseases.

[0108] Example 2

[0109] The purpose of this embodiment is to provide an Alzheimer's disease brain network analysis system based on multi-network collaborative topology analysis, including:

[0110] The construction module is configured to: extract the brain regions corresponding to the default mode network, saliency network and executive control network, calculate the correlation coefficients between different brain regions to construct a functional connectivity matrix, and convert the functional connectivity matrix into a distance matrix that meets the requirements of Vietoris-Rips complex construction;

[0111] The extraction module is configured to: construct a Vietoris-Rips complex based on the distance matrix, calculate the homology group at each scale of the filtered sequence, and extract 0-dimensional Betti number and 1-dimensional Betti number topological features;

[0112] The analysis module is configured to: plot the curve of the Betty number as a function of a threshold, compare the differences in topological features between the Alzheimer's disease group and the healthy control group, and identify specific topological biomarkers related to Alzheimer's disease.

[0113] In further embodiments, the following is also provided:

[0114] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When executed by the processor, the computer instructions perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0115] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0116] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0117] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0118] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0119] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0120] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0121] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0122] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0123] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0124] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A brain network analysis method based on multi-network collaborative topology analysis, characterized in that, include: Brain regions corresponding to the default mode network, saliency network, and executive control network are extracted. Correlation coefficients between different brain regions are calculated to construct a functional connectivity matrix. The functional connectivity matrix is ​​then converted into a distance matrix that meets the requirements for Vietoris-Rips complex construction. Based on the distance matrix, a Vietoris-Rips complex is constructed, and the homology group is calculated at each scale of the filtered sequence to extract 0-dimensional Betti number and 1-dimensional Betti number topological features. Plot the Betty number as a function of a threshold, compare the differences in topological features between the Alzheimer's disease group and the healthy control group, and identify specific topological biomarkers related to Alzheimer's disease.

2. The brain network analysis method based on multi-network collaborative topology analysis as described in claim 1, characterized in that, Based on the distance matrix, a Vietoris-Rips complex is constructed. Homology groups are calculated at each scale of the filtered sequence, and 0-dimensional and 1-dimensional Betti number topological features are extracted. Specifically: Based on the distance matrix, a multi-scale Vietoris-Rips complex is constructed by adjusting the distance threshold. For each distance threshold in the filtered sequence, calculate the homology group of the Vietoris-Rips complex at the corresponding scale; Extract 0-dimensional and 1-dimensional Betty number topological features from homology groups.

3. The brain network analysis method based on multi-network collaborative topology analysis as described in claim 1, characterized in that, The Mann-Whitney U test was used to compare the differences in the distribution of Betty numbers between the disease group and the healthy control group; the Kolmogorov-Smirnov test was used to compare the differences in the number of circuits in specific brain regions.

4. The brain network analysis method based on multi-network collaborative topology analysis as described in claim 1, characterized in that, Also includes: When calculating homology groups at each scale, topological features with 0-dimensional Betti numbers and 1-dimensional Betti numbers are extracted, and the generation and extinction thresholds for each topological feature are determined. A persistent graph is constructed using the generation threshold of each topological feature as the horizontal axis and the extinction threshold as the vertical axis. Features are sorted according to the homology dimension using the distance threshold as the horizontal axis and the vertical axis, and a barcode image is constructed by drawing horizontal bars for each feature.

5. The brain network analysis method based on multi-network collaborative topology analysis as described in claim 4, characterized in that, The persistence of topological features is visualized using barcode images and persistent graphs. 0-dimensional and 1-dimensional Betty number curves are extracted, and a comparison curve of the Betty number between the Alzheimer's disease group and the healthy control group is plotted.

6. The brain network analysis method based on multi-network collaborative topology analysis as described in claim 1, characterized in that, Resting-state functional magnetic resonance imaging (fMRI) data were acquired and preprocessed. Regions of interest were defined in the preprocessed resting-state fMRI data, and brain regions corresponding to the default mode network, saliency network, and executive control network were extracted. The preprocessing included magnetic field stabilization, slice time correction, head motion correction, spatial normalization, spatial smoothing, and temporal filtering.

7. A brain network analysis system based on multi-network collaborative topology analysis, characterized in that, include: The construction module is configured to: extract the brain regions corresponding to the default mode network, saliency network and executive control network, calculate the correlation coefficients between different brain regions to construct a functional connectivity matrix, and convert the functional connectivity matrix into a distance matrix that meets the requirements of Vietoris-Rips complex construction; The extraction module is configured to: construct a Vietoris-Rips complex based on the distance matrix, calculate the homology group at each scale of the filtered sequence, and extract 0-dimensional Betti number and 1-dimensional Betti number topological features; The analysis module is configured to: plot the Betty number as a function of a threshold, compare the differences in topological features between the Alzheimer's disease group and the healthy control group, and identify specific topological biomarkers related to Alzheimer's disease.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method described in any one of claims 1-6.

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