Detection of Brain Activity in Functional Magnetic Resonance Imaging Data using Matrix Factorization

Amir A Khaliq,Jawad A Shah,Ihsanulhaq Ihsanulhaq,I.M Qureshi

doi:10.19026/rjaset.5.4237

Abstract

Non-negative matrix factorization (NMF) is becoming a popular tool for decomposition of data in the field of signal and image processing like Independent Component Analysis (ICA). In this study we are relaxing the requirement of non-negative data for NMF making the update equations simple and thus making it Matrix Factorization (MF) and implementing it on simulated Functional Magnetic Resonance Imaging (fMRI) data for detection of neuronal activity. Simulated fMRI data is processed to detect the hidden sources of task related activity, functional activity and artifacts using the proposed MF technique. Performance of the proposed scheme is better than NMF in terms of average correlation results of the extracted sources/time courses with the actual sources/time courses. Similarly proposed MF is computationally cost effective and converges fast as compared to NMF. Also extracted sources obey no permutation which is the limitation of ICA and NMF.

Highlights

Functionality of brain can be detected by many techniques like Positron Emission Tomography (PET), Single Photon Emission ComputedTomography (SPECT) and Functional Magnetic Resonance Imaging (fMRI) etc. fMRI has the advantage of being non-invasive
We need the experimental model of the fMRI test as in the case of time frequency analysis (Mitra et al, 1997), Statistical Parametric Mapping (SPM) (Friston, 1996), Canonical Correlation Analysis (CCA) (Friman et al, 2001) etc
We have proposed Matrix Factorization (MF) which is a modified version of negative matrix factorization (NMF), with the advantage of fast convergence, quality of extracted sources and no permutations

Summary

Introduction

Functionality of brain can be detected by many techniques like Positron Emission Tomography (PET), Single Photon Emission ComputedTomography (SPECT) and fMRI etc. fMRI has the advantage of being non-invasive. If it is the case in fMRI data, one needs to look for other methods which do not require the independence of sources, like NMF further relaxes the requirement and it only needs that the data contains no negative elements (Lee and Seung, 1999; Liu et al, 2006). Algorithm for decomposition of fMRI data into sources and corresponding time courses.

Results

Conclusion