Detection method for causal connection strength of magnetic resonance brain imaging based on PCA (Principal component analysis) and GCA (Granger causality analysis)

A detection method and connection strength technology, applied in the field of image processing, can solve problems such as loss, and achieve the effect of improving accuracy and robustness

Active Publication Date: 2012-03-07
INST OF AUTOMATION CHINESE ACAD OF SCI
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In addition, in the past, for the time series of multiple voxels contained in the same brain activation area, a simple average process was used to obtain an average time series, which would lose a lot of useful brain activity signal information

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  • Detection method for causal connection strength of magnetic resonance brain imaging based on PCA (Principal component analysis) and GCA (Granger causality analysis)
  • Detection method for causal connection strength of magnetic resonance brain imaging based on PCA (Principal component analysis) and GCA (Granger causality analysis)
  • Detection method for causal connection strength of magnetic resonance brain imaging based on PCA (Principal component analysis) and GCA (Granger causality analysis)

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[0016] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0017] refer to figure 1 , a method for detecting causal connection strength of magnetic resonance brain imaging involved in the present invention, particularly involving the use of principal component analysis and Granger causality analysis for the detection of magnetic resonance brain imaging causal connection strength. The specific implementation steps are as follows:

[0018] Step Sa, extracting the multi-voxel time series in the activation area from the brain function image that has undergone data preprocessing, and obtaining the time series matrix of multiple voxels in the activation area;

[0019] 1. Data preprocessing

[0020] Due to the influence of various noises during the MRI scanning process, there are diffe...

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Abstract

The invention discloses a detection method for causal connection strength of magnetic resonance brain imaging based on PCA (Principal component analysis) and GCA (Granger causality analysis). The method comprises the following steps: extracting a multi-voxel time sequence in an activation region of brain function image subjected to data preprocessing to obtain time sequence matrixes of multiple voxels in the activation region; carrying out space dimensionality reduction on the multiple time sequence matrixes in each activation region by using PCA to obtain principal components, and averaging the activation values of the principal components to obtain a time sequence; constructing a multivariable autoregression model among the time sequences in all the activation regions; calculating the partial correlation coefficient among the time sequences; calculating dDTF value through a directed transfer function (DTF) method to obtain the causal connection strength and direction in an activation brain interval; and carrying out statistical checking on the significance of the connection strength by using a surrogate data method and displaying the result on a directed network graph. The experiments on an actual data set show that the method disclosed by the invention is an effective detection method for causal connection strength of magnetic resonance brain imaging.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a method for detecting causal connection strength of magnetic resonance brain imaging. In particular, it relates to the detection of causal connection strength in magnetic resonance brain imaging using principal component analysis (PCA) and Granger causality analysis (GCA). Background technique [0002] Functional Magnetic Resonance Imaging (fMRI) has been widely used in the diagnosis and treatment of neurological diseases due to its high spatial and temporal resolution and non-invasive features. fMRI generally refers to magnetic resonance imaging based on blood oxygen level-dependent (BOLD), which reflects the changes of magnetic resonance signals caused by changes in cerebral blood flow and cerebral blood oxygen caused by neural activities to reflect the Activity. The human brain is a complex system, and the interactions among various brain regions also change when...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/055
Inventor 田捷白丽君钟崇光
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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