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Method for determining threshold value of dynamic brain function network

A brain function network and threshold technology, applied in the field of signal processing, can solve the problems of inability to guarantee the brain network and the sparseness of the brain function network, and achieve the effect of reducing blindness, facilitating research, and avoiding randomness.

Active Publication Date: 2019-01-25
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

However, when using the sliding time window technique to study the dynamic evolution of the brain functional network, one fMRI sample will form multiple time windows, and when using the correlation analysis method to calculate the correlation sparse matrix in each time window, the matrix will not be processed by thresholding. The constructed network is a fully connected network, which obviously does not conform to the sparse characteristics of the real brain function network.
Moreover, in different resting-state fMRI scanning time periods, the information interaction between brain regions will change, and the specific characteristics of brain functional networks will also change over time. If you randomly use a fixed threshold to sparse the correlation coefficient matrix During binarization, although the brain network is already sparse, it cannot be guaranteed that the brain network satisfies other characteristics

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  • Method for determining threshold value of dynamic brain function network
  • Method for determining threshold value of dynamic brain function network
  • Method for determining threshold value of dynamic brain function network

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Embodiment 1

[0017] Embodiment 1: as Figure 1-4 As shown, a method for determining the dynamic brain function network threshold, the method steps are as follows:

[0018] Step 1: Use the Python / FSL Resting State Pipline platform to preprocess 20 resting-state fMRI raw data samples, in which the resting-state fMRI raw image data has 265 sampling time points. Using one of the samples for illustration, the whole brain was divided into 90 brain regions using Automated Anatomical Labeling (AAL). After removing the first 4 time points, time layer correction, head movement correction, skull removal and band-pass filter preprocessing steps, the BOLD signal time series of 90 brain regions at 261 sampling points were obtained. Then, taking the sampling time axis of BOLD signals in 90 brain regions as a benchmark, the sliding time window technique is used to traverse the entire time series in turn with a step size of 1 and a time window size of 20, and the time series of BOLD signals in each small ...

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Abstract

The invention discloses a method for determining a threshold value of a dynamic brain function network, and belongs to the field of signal processing. The invention combines the characteristics of thesliding time window technology, the correlation coefficient matrix constructed in each time window is sparsely binarized with different thresholds, the rationality of the threshold can be judged by the small-world and integrity of the brain function network, so the blindness in constructing the dynamic brain function network can be reduced through the determined threshold by the correlation analysis method, and then a reasonable brain function network can be constructed. At the same time, it can not only avoid the randomness in the process of threshold selection, but also facilitate the studyof the subsequent dynamic brain function network in a reasonable situation.

Description

technical field [0001] The invention relates to a method for determining the threshold value of a dynamic brain function network, which belongs to the field of signal processing. Background technique [0002] When using correlation analysis to construct human brain functional network, different thresholds will have a greater impact on the topological properties of the constructed brain functional network. However, there is no unified method for how to select the threshold. The currently popular methods for determining thresholds generally perform threshold analysis based on the existing characteristics of brain functional networks, such as small-world, sparsity, and completeness, or construct and analyze brain functional networks by selecting different thresholds. different topological properties. However, when using the sliding time window technique to study the dynamic evolution of the brain functional network, one fMRI sample will form multiple time windows, and when us...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/055A61B5/00
CPCA61B5/00A61B5/055A61B5/4064A61B5/72
Inventor 王彬龙雨涵杜芬刘畅郭子洋
Owner KUNMING UNIV OF SCI & TECH
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