Dynamic brain function connection mode dividing method based on PAC and K-means clustering

A connection mode, dynamic function technology, applied in the field of image processing, can solve the problem of ignoring dynamic information

Active Publication Date: 2016-12-07
XI AN JIAOTONG UNIV
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Problems solved by technology

[0004] At present, most studies only focus on static functional connectivity, and the assu...

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  • Dynamic brain function connection mode dividing method based on PAC and K-means clustering
  • Dynamic brain function connection mode dividing method based on PAC and K-means clustering
  • Dynamic brain function connection mode dividing method based on PAC and K-means clustering

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Embodiment Construction

[0024] The present invention is described in detail below in conjunction with accompanying drawing.

[0025] The present invention decomposes the functional connection mode of the dynamic functional connection in the resting state based on PCA and K-means clustering.

[0026] 1. First, preprocess the original resting-state magnetic resonance data collected. Due to the influence of various noises during the magnetic resonance scanning process, there are differences in the scale and position of the individual itself. It is very necessary to analyze the data before analyzing the data. The data do some preprocessing. In the data acquisition of the whole experiment, the main sources of noise information include: (1) physical head movement; (2) difference in scanning time between layers in the image; (3) inhomogeneity of the external magnetic field. Brain function image preprocessing is to use the brain function image and standard templates to perform affine registration transforma...

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Abstract

The invention discloses a dynamic brain function connection mode dividing method based on PAC and K-means clustering. The method divides the dynamic function into basic connection modes. The method includes the following steps: conducting pre-processing on resting state magnetic resonance data, extracting the time sequences of brain zones, then adopting the sliding window method in calculating related coefficients between each two of the brain zones, constructing corresponding function connection matrixes, and finally conducting PCA and K-means clustering on the function connection matrixes so as to obtain basic connection modes. According to the invention, the dynamic function connection mode dividing algorithm can effectively and accurately obtain the divided basic function connection modes, not only can capture transition among different basic connection modes, and can provide strategies for further research and prevention and treatment of clinic neurological diseases, such as Schizophrenia, Alzheimer's disease, depression, and etc.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a method for decomposing dynamic brain function connection patterns based on PCA and K-means clustering, and in particular to using Principal Component Analysis (Principal, Component Aanlysis, PCA) and K-means clustering algorithms for default patterns Decomposition of dynamic brain functional connectivity patterns of networks, executive control networks, and sensory networks. Background technique [0002] fMRI is one of the main non-invasive methods to study brain activity and brain function. With millimeter-level spatial resolution, fMRI has become an important tool for neuroscience to explore the neural mechanism of the human brain. fMRI indirectly measures neuronal activity based on blood oxygen level-dependent contrast (BOLD), which reflects brain activity by measuring changes in magnetic resonance signals caused by changes in cerebral blood flow and cerebral blood oxygen cau...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06F19/00A61B5/00A61B5/055
CPCA61B5/055G06T7/0012A61B5/7203G06T2207/10088G06T2207/30016G06F18/23213
Inventor 林盘王雪丽徐进窦顺阳
Owner XI AN JIAOTONG UNIV
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