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Method of determining FMRI dynamic brain function time window

A technology of sliding time window and time window, which is applied in the field of biomedical image pattern recognition to achieve the effect of efficient experiment

Active Publication Date: 2020-12-11
CHINA JILIANG UNIV
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Problems solved by technology

While sliding time windows are popular, results depend heavily on the length of the window, however, no study has convincingly determined the optimal window length for dynamic functional analysis

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  • Method of determining FMRI dynamic brain function time window
  • Method of determining FMRI dynamic brain function time window
  • Method of determining FMRI dynamic brain function time window

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Embodiment Construction

[0022] In order to illustrate the specific flow of the present invention, the following will be described in detail in conjunction with the accompanying drawings.

[0023] The present invention proposes a method for determining the time window of fMRI dynamic brain function, which belongs to the technical field of biomedical image pattern recognition. The method includes functional magnetic resonance data preprocessing, and the total time is 200TR. TR is 2s preprocessing and discards the first 5 time points. , the frequency is 0.01-0.1, and the minimum sliding time window is 1 / fmin, so the sliding time window is divided into 50TR, 100TR, 150TR, and 195TR at the beginning. Under each sliding time window, calculate the mean and variance of the small-world parameters under ten sparsity degrees with a sparsity of 0.05-0.5 (step size is 0.05). Then use the dichotomy method to continuously narrow the range to find the best small-world parameters, and finally select the appropriate s...

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Abstract

The invention discloses a method for determining an FMRI dynamic brain function time window. The method mainly comprises the following steps of obtaining functional magnetic resonance imaging data byusing magnetic resonance imaging and preprocessing the data, obtaining a single tested function connection network according to the sizes of 50TR, 100TR, 150TR and 195TR sliding time windows in the experiment, processing the obtained windows according to the sparsity of 0.05-0.5 (the step length is 0.05), and finally acquiring ten sparse matrixes for each window, solving small-world parameters forthe sparse matrix obtained by each window, solving a mean value and a variance, calculating a total mean value and a mean value of the variance, and comparing the attribute strength and robustness ofthe small-world network with different sliding time window sizes, and according to the attributes of the small-world network under different sliding time windows, using a similar binary search methodfor narrowing the range, and searching for an optimal sliding window. According to the method, a relatively reliable sliding time window in dynamic brain analysis is obtained by taking small-world parameter strength and attribute stability as standards, and support is provided for related research on brain function connection networks.

Description

technical field [0001] The invention belongs to the technical field of biomedical image pattern recognition, in particular to a method for determining the size of the time window in FMRI dynamic brain analysis Background technique [0002] People can analyze and deal with problems in various environments, thanks to the human brain. Numerous nerve cells in the brain are connected to each other through synaptic transmitters to form a complex network, which is the key to processing information. Resting state functional magnetic resonance (rsfMRI) With great success as a tool to study normal and disordered brain function, initially, resting-state fMRI analysis studies were based on the a priori assumption that networks in the resting brain are fixed across the scan length, but more recently , a recent development in the study of brain function is the dynamic analysis of resting-state fMRI, as the functional connectivity network changes during the scan (within seconds), revealing...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R33/48G01R33/54
CPCG01R33/48G01R33/546
Inventor 包祖鹏张艳陈俊
Owner CHINA JILIANG UNIV
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