title: A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli.
abstract: This paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.
authors.affiliation: JSPM's Rajarshi Shahu College of Engineering, Pune, India.
authors.forename: Savita V
authors.initials: SV
authors.lastname: Raut
authors.affiliation: College of Engineering, Malegaon, Maharashtra, India.
authors.forename: Dinkar M
authors.initials: DM
authors.lastname: Yadav
mesh_terms: D000465:Algorithms; D001931:Brain Mapping; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results
keywords: decomposition; empirical mean curve decomposition (EMCD); functional magnetic resonance imaging (fMRI); geometric mean curve decomposition (GMCD); mean; mutual information (MI); voxel