A distance-function-based structure-functional connectivity coupling method for white matter fiber bundles
By using a distance function-based method, the structural and functional connections of white matter fiber bundles are quantified, which solves the problem that traditional methods cannot adapt to multi-dimensional spaces. This enables a more comprehensive quantification of structural and functional interactions and improves the accuracy of connection coupling.
Patent Information
- Application Number
- CN202510009237.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-03
- Publication Date
- 2025-11-14
- Estimated Expiration
- 2045-01-03
AI Technical Summary
Traditional methods cannot accurately quantify the relationship between structural and functional connections in white matter fiber bundles, and cannot adapt to the multidimensional space of structure and function. Pearson correlation methods can only measure linear relationships and cannot adapt to multidimensional features.
By employing a distance function-based approach, brain imaging data is collected and preprocessed to extract structural attributes and functional signals of white matter fiber tracts. A structural and functional connectivity matrix is constructed, and various distance functions, such as Euclidean distance, are used to measure the similarity of connectivity weights, thereby achieving multi-dimensional quantification of structure and function.
It provides a more comprehensive physiological meaning, explores the complex interactions between structure and function, improves the method of quantifying structure-function connectivity coupling, and reflects the correlation between diffusion tensor imaging and functional magnetic resonance imaging.
Smart Images
Figure CN119961687B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of medical image processing, and in particular to a method for coupling structure-function connectivity of white matter fiber bundles based on distance functions. Background Technology
[0002] Currently, neuroscience is increasingly focused on the structural and functional networks of the brain. Traditional structural networks, based on fiber tract imaging, are defined as the average fractional anisotropy of voxels traversed by connecting fibers between two regions. Two regions are considered structurally connected when at least one streamlined fiber exists and both endpoints lie within those regions. Traditional methods focus on only a single structural property. In reality, various structural properties have different neural and genetic bases, and multiple structural properties may offer further insights for the field of neuroscience.
[0003] Meanwhile, research indicates that structural and functional connectivity are interrelated, providing comprehensive insights into subtle changes in brain activity. Traditional methods for quantifying structure-functional connectivity coupling use diffusion tractography based on diffusion tensors reconstructed in white matter to measure structural features, while functional connectivity is estimated separately using functional magnetic resonance imaging (fMRI) in gray matter. Since structural connectivity based on diffusion tractography and functional connectivity between gray matter regions are inherently very different, direct integration of these features offers limited insights into the structural connectivity that underpins functional interactions. Accurately quantifying the relationship between white matter fiber tract structural connectivity and white matter functional connectivity remains a challenge.
[0004] Furthermore, traditional methods for quantifying the relationship between structure and function mainly utilize Pearson correlation, but this method can only measure the linear relationship between two variables and cannot adapt to the multidimensional space of structure and function. Summary of the Invention
[0005] To address the aforementioned shortcomings in existing technologies, this invention provides a white matter fiber bundle structure-function connection coupling method based on distance functions, which solves the problem that traditional connection coupling methods cannot intuitively measure the spatial distance between points and cannot adapt to multi-dimensional spaces of structure and function.
[0006] To achieve the aforementioned objectives, the technical solution adopted by this invention is: a white matter fiber bundle structure-functional connectivity coupling method based on a distance function, comprising:
[0007] S1. Collect brain imaging data and perform preprocessing;
[0008] S2. Extract the structural properties and functional signals of white matter fiber bundles from the preprocessed brain imaging data;
[0009] S3. Based on the structural properties and functional signals of white matter fiber bundles, construct the structural connectivity matrix and the individualized functional connectivity matrix;
[0010] S4. Measure the similarity of connection weights between corresponding columns of the structural connection matrix and the functional connection matrix using a distance function;
[0011] S5. Complete the structure-functional connection coupling of white matter fiber bundles based on the similarity metric of connection weights.
[0012] Furthermore: In S1, the brain imaging data includes diffusion tensor imaging data and functional magnetic resonance imaging data.
[0013] Furthermore, for diffusion tensor imaging data, the preprocessing steps include: denoising, B1 field correction, eddy current correction, DTI-T1 weighted alignment, model fitting, and inter-regional probabilistic fiber bundle tracking.
[0014] Furthermore, for functional magnetic resonance imaging data, the preprocessing steps include: slice time correction, motion correction, normalization, delinearization, time bandpass filtering, interference regression, and smoothing.
[0015] Furthermore: S2 includes:
[0016] S21. Divide the whole brain into N brain regions;
[0017] S22. Extract the anisotropy fraction, axial diffusion rate, radial diffusion rate, average diffusion rate and local diffusion uniformity of the preprocessed diffusion tensor imaging data as structural properties of white matter fiber bundles.
[0018] S23. Extract the mean blood oxygenation level dependent signal time series of each brain region from the preprocessed functional magnetic resonance imaging data as the functional signal.
[0019] Furthermore, in S3, the methods for constructing the structure connection matrix include:
[0020] S301. Using whole-brain white matter fiber tracts as network nodes and white matter fiber tract structural properties as structural feature vectors;
[0021] S303. Construct a structural connectivity matrix using Spearman correlations of structural feature vectors from any two brain regions.
[0022] Furthermore: In S3, the method for constructing the individualized functional connectivity matrix includes:
[0023] S311. Using whole-brain white matter fiber bundles as network nodes, calculate the Pearson correlation coefficient of the mean blood oxygenation level of N brain regions in the whole brain dependent on the paired mean of the signal time series.
[0024] S312. Construct an individual-level functional network based on the Pearson correlation coefficient of the pairwise average values of the mean blood oxygenation levels of N brain regions throughout the whole brain dependent on the signal time series.
[0025] Furthermore, in S4, the distance functions include Euclidean distance, normalized Euclidean distance, Manhattan distance, Minkowski distance, Chebyshev distance, and Mahalanobis distance.
[0026] The beneficial effects of this invention are as follows:
[0027] 1. The structural similarity of white matter fiber bundles is evaluated based on five structural properties: fractional anisotropy, axial diffusivity, radial diffusivity, average diffusivity, and local diffusion uniformity. This integrates multiple property information and provides a more comprehensive physiological meaning.
[0028] 2. Evidence of structure-function interaction at the white matter level is provided: This invention quantifies the relationship between structure and function based on the division of white matter fiber bundles as both structural and functional network nodes, and can explore the complex interaction between structure and function at the white matter level;
[0029] 3. Improved the current method for quantifying structure-function connection coupling, measuring six distance functions: Euclidean distance, normalized Euclidean distance, Manhattan distance, Minkowski distance, Chebyshev distance, and Mahalanobis distance, and weighted average quantizing the relationship between structure and function, more accurately reflecting the correlation between signals from two modalities: diffusion tensor imaging and functional magnetic resonance imaging. Attached Figure Description
[0030] Figure 1 This is a flowchart of a distance function-based structure-functional connectivity coupling method for white matter fiber bundles. Detailed Implementation
[0031] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.
[0032] like Figure 1 As shown, in one embodiment of the present invention, a method for coupling structure-functional connectivity of white matter fiber bundles based on a distance function is provided, comprising:
[0033] S1. Collect brain imaging data and perform preprocessing;
[0034] Brain imaging data includes diffusion tensor imaging data and functional magnetic resonance imaging data.
[0035] S2. Extract the structural properties and functional signals of white matter fiber bundles from the preprocessed brain imaging data;
[0036] S3. Based on the structural properties and functional signals of white matter fiber bundles, construct the structural connectivity matrix and the individualized functional connectivity matrix;
[0037] S4. Measure the similarity of connection weights between corresponding columns of the structural connection matrix and the functional connection matrix using a distance function;
[0038] S5. Complete the structure-functional connection coupling of white matter fiber bundles based on the similarity metric of connection weights.
[0039] For diffusion tensor imaging data, the preprocessing steps include: denoising, B1 field correction, eddy current correction, DTI-T1 weighted alignment, model fitting, and inter-regional probability fiber bundle tracking; for functional magnetic resonance imaging data, the preprocessing steps include: slice time correction, motion correction, normalization, delinearization, time bandpass filtering, interference regression, and smoothing.
[0040] Specifically, S2 includes:
[0041] S21. Divide the whole brain into N brain regions;
[0042] S22. Extract the anisotropy fraction, axial diffusion rate, radial diffusion rate, average diffusion rate and local diffusion uniformity of the preprocessed diffusion tensor imaging data as structural properties of white matter fiber bundles.
[0043] S23. Extract the mean blood oxygenation level dependent signal time series of each brain region from the preprocessed functional magnetic resonance imaging data as the functional signal.
[0044] Methods for constructing structural connection matrices include:
[0045] S301. Using whole-brain white matter fiber tracts as network nodes and white matter fiber tract structural properties as structural feature vectors;
[0046] S303. Construct a structural connectivity matrix using Spearman correlations of structural feature vectors from any two brain regions.
[0047] Methods for constructing individualized functional connectivity matrices include:
[0048] S311. Using whole-brain white matter fiber bundles as network nodes, calculate the Pearson correlation coefficient of the mean blood oxygenation level of N brain regions in the whole brain dependent on the paired mean of the signal time series.
[0049] S312. Construct an individual-level functional network based on the Pearson correlation coefficient of the pairwise average values of the mean blood oxygenation levels of N brain regions throughout the whole brain dependent on the signal time series.
[0050] In S4, the distance functions include Euclidean distance, normalized Euclidean distance, Manhattan distance, Minkowski distance, Chebyshev distance, and Mahalanobis distance.
[0051] In one embodiment of the present invention, the distance function-based white matter fiber bundle structure-functional connectivity coupling method described in this application is used for depression and healthy controls;
[0052] Data on diffusion tensor (DTI) imaging and functional magnetic resonance imaging (fMRI) of depressed patients and healthy controls were acquired using MRI equipment such as the GE 750W.
[0053] Throughout the scan, participants were instructed to keep their eyes closed, remain awake, and stay still as much as possible. In order to rule out disease, routine axial T2-weighted brain imaging was performed; the brain structure and functional data were further preprocessed.
[0054] The DTI images were registered to correct for motion artifacts and distortion. Fiber paths were reconstructed using a deterministic fiber tracing algorithm. The fiber tracing process started in the deep white matter (WM) region and terminated if the intersection angle between two consecutive directions of movement exceeded 35°, or if the fractional anisotropy (FA) exceeded the range (0.1–1).
[0055] Resting-state fMRI data preprocessing included discarding the first 10 time points to eliminate magnetization disequilibrium effects and allow subjects to adapt to the scanning environment. The remaining data were corrected for time-point and head motion artifacts. Participants with mean inter-frame displacement (FD) values greater than 2 mm and deviation angles greater than 2° were excluded from the study. Functional images were normalized to the Montreal Neurological Institute (MNI), resampled to a 3 × 3 × 3 mm³ voxel size, and subjected to linear trend removal and temporal bandpass filtering (0.01–0.08) to remove high-frequency noise. Cerebrospinal fluid and head motion noise signals were regressed; white matter and global brain signals were not removed to avoid eliminating important neural signals. Finally, a 6 mm full-width smoothing kernel was applied to spatially smooth the images, thereby reducing inter-individual variability.
[0056] Extraction of structural attributes and functional signals at the white matter level: Forty-eight regions of interest in white matter were defined based on the JHU ICBM-DTI-81 white matter map. Fractional anisotropy, axial diffusion rate, radial diffusion rate, mean diffusion rate, and local diffusion homogeneity were extracted from the preprocessed DTI data, resulting in a 48×5 feature vector representing the structural feature vector for each subject. Functional signal extraction involved extracting mean oxygenation level-dependent (BOLD) time series signals from brain regions from the preprocessed fMRI data.
[0057] Using 48 fiber bundles from the whole brain white matter as network nodes, Spearman correlations of structural feature vectors between any two brain regions were calculated to construct an individual-level structural similarity network. This yielded a symmetric 48×48 connectivity matrix representing the brain structural network of each subject. Subsequently, sparsity (threshold) was used to measure the significance of the correlation between any two brain regions. If the correlation between two brain regions exceeds a specific threshold (the threshold ranges from -1 to 1, and can also be set based on statistical results), then the two regions are considered structurally connected. Conversely, if the correlation between two brain regions is below the threshold, they are considered not connected, thus obtaining a weighted symmetric 48×48 structural connectivity matrix for each subject.
[0058] Using 48 fiber bundles of the whole brain white matter as network nodes, the Pearson correlation coefficient of the pairwise average of the BOLD time series of the 48 white matter regions of the brain was calculated to construct an individual-level functional network. The network was then converted into z-scores using Fisher's r-to-z transformation, thereby obtaining a standardized 48×48 functional connectivity matrix for each subject.
[0059] The similarity of the structural and functional network connection weights of each brain region is calculated based on six distance functions: Euclidean distance, standardized Euclidean distance, Manhattan distance, Minkowski distance, Chebyshev distance, and Mahalanobis distance.
[0060] For each distance, the distance between each column of the structural connectivity matrix and the corresponding column of the functional connectivity matrix represents the regional structural-functional connectivity coupling for each individual. Then, each individual receives a vector of length 48, where each element represents the structural-functional connectivity coupling value between that region and all other regions within the entire brain region of interest. Finally, based on the objective, a weight is assigned to each distance metric, and the weighted average yields the final structural-functional connectivity coupling.
[0061] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A method for coupling structure-functional connectivity of white matter fiber bundles based on distance functions, characterized in that, include: S1. Collect brain imaging data and perform preprocessing; S2. Extract the structural properties and functional signals of white matter fiber bundles from the preprocessed brain imaging data; S3. Based on the structural properties and functional signals of white matter fiber bundles, construct the structural connectivity matrix and the individualized functional connectivity matrix; S4. Measure the similarity of connection weights between corresponding columns of the structural connection matrix and the individualized functional connection matrix using a distance function; S5. Complete the structure-function connection coupling of white matter fiber bundles based on the similarity metric of connection weights; S2 includes: S21. Divide the whole brain into N brain regions; S22. Extract the anisotropy fraction, axial diffusion rate, radial diffusion rate, average diffusion rate and local diffusion uniformity of the preprocessed diffusion tensor imaging data as structural properties of white matter fiber bundles. S23. Extract the mean blood oxygenation level dependent signal time series of each brain region from the preprocessed functional magnetic resonance imaging data as the functional signal. In S3, the methods for constructing the structure connection matrix include: S301. Using whole-brain white matter fiber tracts as network nodes and white matter fiber tract structural properties as structural feature vectors; S302. Construct a structural connectivity matrix by Spearman correlation of structural feature vectors of any two brain regions. In S3, the methods for constructing the individualized functional connectivity matrix include: S311. Using whole-brain white matter fiber bundles as network nodes, calculate the Pearson correlation coefficient of the mean blood oxygenation level of N brain regions in the whole brain dependent on the paired mean of the signal time series. S312. Construct an individualized functional connectivity matrix based on the Pearson correlation coefficient of the pairwise average values of the mean blood oxygenation levels of N brain regions throughout the whole brain dependent on the signal time series. In S4, the distance functions include Euclidean distance, normalized Euclidean distance, Manhattan distance, Minkowski distance, Chebyshev distance, and Mahalanobis distance.
2. The method for structure-functional connectivity coupling of white matter fiber bundles based on distance function according to claim 1, characterized in that, In S1, brain imaging data includes diffusion tensor imaging data and functional magnetic resonance imaging data.
3. The method for structure-functional connectivity coupling of white matter fiber bundles based on distance function according to claim 2, characterized in that, For diffusion tensor imaging data, the preprocessing steps include: denoising, B1 field correction, eddy current correction, DTI-T1 weighted alignment, model fitting, and inter-region probabilistic fiber bundle tracking.
4. The method for structure-functional connectivity coupling of white matter fiber bundles based on distance function according to claim 2, characterized in that, For functional magnetic resonance imaging (fMRI) data, the preprocessing steps include: slice time correction, motion correction, normalization, delinearization, time bandpass filtering, interference regression, and smoothing.