Convex analysis based blind separation algorithm for analyzing brain functional magnetic resonance imaging data
FENG Bao
Abstract：Functional magnetic resonance imaging(fMRI)data analysis is of great challenge for its characteristics of high dimensionality and low signal noise ratio.Independent component analysis(ICA)is a classical approach for analysis of fMRI data.ICA based methods exploit independence assumption while extracting consistent task related(CTR)component from fMRI data. However,recent studies show that independence assumption of ICA based method is sometime violated in practice due to the principle of"functional integration"of human brain. In this paper,we proposed a new fMRI data analysis method based on blind source separation(BSS)technique.The proposed method does not emphasize independence assumption but sparsity and non-negativity,which is considered more realistic to fMRI data.With convex optimization,we constructed a closed convex set on the observed fMRI data.Then,the task of estimating the source component is converted into the task of geometrically determining the extreme points of the convex set. While determining the extreme points, alternative volume maximum(AVM)is used to find a simplex with maximum volume to approximate the obtained convex set to achieve robustness against fMRI modelling errors. Numerical results in this paper show that proposed method may localize brain activations with higher accuracy rate.
Keywords：functional magnetic resonance imaging(fMRI)convex optimizationblind source separationbrain acti-vation localization
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 232-238 )