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Principal component analysis-based key encephalic region measurement method

A technology of principal component analysis and measurement method, applied in the field of neuroimaging data analysis, which can solve the problems of inconsistency and failure to consider the proportion of nodes

Active Publication Date: 2017-06-06
ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, these traditional single measurement indicators do not consider the proportion of the nodes themselves in the brain, and they are measured from different angles, often resulting in inconsistent results

Method used

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  • Principal component analysis-based key encephalic region measurement method
  • Principal component analysis-based key encephalic region measurement method
  • Principal component analysis-based key encephalic region measurement method

Examples

Experimental program
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Embodiment 2

[0206] Embodiment two: a kind of measurement method of key brain region based on principal component analysis of the present invention, comprises the following steps:

[0207] Step 1: Collect fMRI data of M patients with functional diarrhea in a resting state;

[0208] Step 2: Preprocess each fMRI data; the specific operation is as follows:

[0209] Step 2.1: remove the first 10 time points from the collected fMRI data;

[0210] Step 2.2: Perform time layer correction on the fMRI data after removing the time points;

[0211] Step 2.3: Perform head movement correction on the time layer corrected fMRI data;

[0212] Step 2.4: Spatial normalization of the head movement corrected fMRI data;

[0213] Step 2.5: de-linearize the spatially normalized fMRI data;

[0214] Step 2.6: band-pass filter the fMRI data after de-linear drift, the frequency range of the band-pass filter is 0.01-0.08 Hz;

[0215] Step 3: Based on the preprocessed fMRI data, construct a brain network under Q ...

Embodiment 3

[0311] Embodiment three: this embodiment selects a group of healthy people as subjects, the number is 20, as a healthy control group of patients with functional diarrhea, and obtains the measurement results of the criticality of brain regions; the present invention in embodiment three In this method, only a group of subjects was exchanged, and the operation steps were exactly the same as those in Example 2, which will not be repeated here; the centrality scores of the 90 brain regions finally obtained are shown in Table 2.

[0312] Table 2 Centrality scores of 90 brain regions of healthy control population obtained by different measurement methods

[0313]

[0314]

[0315] Table 3 The top 10 key brain regions obtained by different measurement methods

[0316]

[0317] According to the data obtained in Table 1 and Table 2 in Example 2 and Example 3, the different scoring results of functional diarrhea patients and healthy control groups were counted, and the top 10 br...

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Abstract

The invention belongs to the technical field of neuroimaging data analysis and discloses a principal component analysis-based key encephalic region measurement method. The method comprises collecting and preprocessing fMRI (functional magnetic resonance imaging) data of M persons in a resting state; structuring an encephalic network under Q sparseness; calculating the weighted node degree, the node efficiency and the betweenness centrality of the encephalic network at every sparseness; through principal component analysis, performing comprehensive dimension reduction on the obtained node degrees, the node efficiency and the betweenness centralities to obtain a new index, namely, the centrality score H of every node of every person at every sparseness; integrating the Q sparseness and the centrality scores of the M persons, acquiring the centrality score of every node, and according to the centrality score of every node, determining key encephalic regions. The principal component analysis-based key encephalic region measurement method is applied to measuring the importance of encephalic regions, overcomes the problem of active selection of determining the sparseness of functional encephalic networks, and through combination of various classical encephalic region importance measurement methods, achieves more objective and more accurate key encephalic region measurement.

Description

technical field [0001] The invention belongs to the technical field of neuroimage data analysis, in particular to a method for measuring key brain regions in an fMRI human brain network in a resting state, in particular to a method for measuring key brain regions based on principal component analysis. Background technique [0002] The principle of functional magnetic resonance imaging is based on the level of blood oxygen dependence. When neurons in a certain area of ​​the brain are excited, the amount of oxygen required by this area will increase significantly, which will cause an increase in blood flow in this area, and oxygen needs red blood cell protein transport. When oxyhemoglobin comes to a certain tissue in the brain, the magnetic sensitivity of the tissue will decrease. At this time, the magnetic resonance imaging shows a high local signal. When the tissue deprives oxygen, the oxyhemoglobin will become deoxygenated hemoglobin , leading to enhanced magnetic sensitivi...

Claims

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Application Information

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IPC IPC(8): A61B5/055A61B5/00
CPCA61B5/0042A61B5/055A61B5/4064A61B5/7225A61B5/725A61B5/7264A61B2576/026
Inventor 南姣芬陈启强朱颢东藤瑛珏夏永泉张亮亮张金华郑倩
Owner ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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