Spatial and temporal joint, partially-joint and individual sources in independent component analysis: Application to...

absectionBackground Three types of sources can be considered in the analysis of multi-subject datasets: (i) joint sources which are common among all subjects, (ii) partially-joint sources which are common only among a subset of subjects, and (iii) individual sources which belong to each subject and represent the specific conditions of that subject. Extracting spatial and temporal joint, partially-joint, and individual sources of multi-subject datasets is of significant importance to analyze common and cross information of multiple subjects.

We present a new framework to extract these three types of spatial and temporal sources in multi-subject functional magnetic resonance imaging (fMRI) datasets. In this framework, temporal and spatial independent component analysis are utilized, and a weighted sum of higher-order cumulants is maximized.

We evaluate the presented algorithm by analyzing simulated data and one real multi-subject fMRI dataset. Our results on the real dataset are consistent with the existing meta-analysis studies. We show that spatial and temporal jointness of extracted joint and partially-joint sources in the theory of mind regions of brain increase with the age of subjects.

In Richardson et al. (2018), predefined regions of interest (ROI) have been used to analyze the real dataset, whereas our unified algorithm simultaneously extracts activated and uncorrelated ROIs, and determines their spatial and temporal jointness without additional computations.

Extracting temporal and spatial joint and partially-joint sources in a unified algorithm improves the accuracy of joint analysis of the multi-subject fMRI dataset.