Automatic Identification of Functional Clusters in fMRI Data using Spatial Information

In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a more refined functional segmentation of brain. However, such segmentation does not per se establish the relationships among different brain networks, and also selecting and classifying components can be challenging. In this work, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In this MICA framework, stable components are hierarchically grouped into clusters based on spatial information and higher-order statistics, instead of typically used temporal information and second-order correlation. The final cluster membership is determined using a statistical hypothesis testing method. The experimental results from both simulated and real fMRI data sets show that the use of only spatial information with higher-order statistics leads to physiologically meaningful dependence structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid (CSF), arteries, and large draining veins, demonstrate a higher degree of dependence among them and encouragingly distinguished from other components of interest using our MICA approach.