Multi-subject fMRI data analysis method combining ICA and shift invariant CPD

Abstract

The invention relates to a multi-subject fMRI data analysis method combining ICA and shift invariant CPD, and belongs to the field of medical signal processing. The method comprises: taking the CPD as a center, and the ICA as a main and auxiliary combined way in a pre-processing step; pre-processing the ICA to provide a joint mixing matrix to the shift invariant CPD; gradually decomposing the joint mixing matrix to obtain multi-subject shared TC, and each-subject delay and each-subject intensity corresponding to the shared TC by a rank-one estimation method through the shift invariant CPD; and reconstructing the joint mixing matrix by using the output of the shift invariant CPD, and estimating a multi-subject shared SM by using a least square method . With the adoption of the method, stable and more effective analysis can be performed on multi-subject fMRI data. When between-subject SM difference and between-subject TC difference are greater, the advantage of the method is more obvious, estimated shared SM component and shared TC component have higher correlation with a prior reference signal, and TC large time delay has high estimation accuracy and a small amount of calculation.

Description

technical field [0001] The invention relates to the field of medical signal processing, in particular to an analysis method for multi-subject functional magnetic resonance imaging (functional magnetic resonance imaging, fMRI) data. Background technique [0002] fMRI data is the brain function data collected by scanning the brain of the subject (healthy person or patient) with a magnetic resonance imaging scanner, and has the advantages of no damage and high spatial resolution. By using the multi-subject fMRI data analysis method, people can extract the shared brain spatial activation area (spatial map, SM), shared time course (time course, TC) of each subject, and the intensity information of each subject. This information is of great value for brain function research and clinical diagnosis. [0003] Multi-subject fMRI data are high-dimensional data with three dimensions: space, time and subjects. The most effective analysis methods will utilize both statistical and struct...

AI Technical Summary

Problems solved by technology

[0004] However, there are still two problems in the method of Beckmann and Smith: First, it does not consider the TC differences among multiple subjects

However, the direct application of the shift-invariant CPD method to the analysis of multi-subject fMRI data is even inferior to the Beckmann and Smith methods because the spatial differences of SMs are not considered.

[0005] Second, the Beckmann and Smith method does not have an overall objective function. In the process of combining ICA and CPD, the two objective functions of ICA and CPD are completely independent and parallel, regardless of primary or secondary

As a result, the iterative operation between ICA and CPD is prone to divergence, which in turn leads to failure of decomposition

Therefore, the analysis results of the Beckmann and Smith method for multi-subject fMRI data are not yet satisfactory

Images