Abstract
The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily-life situations. We propose a new exploratory data-analysis approach, functional segmentation intersubject correlation analysis (FuSeISC), to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is highly variable.
We tested FuSeISC using fMRI data sets collected during traditional block-design stimuli (37 subjects) as well as naturalistic auditory narratives (19 subjects). The method identified spatially local and/or bilaterally symmetric clusters in several cortical areas, many of which are known to be processing the types of stimuli used in the experiments. The method is not only prominent for spatial exploration of large fMRI data sets obtained using naturalistic stimuli, but has other potential applications such as generation of a functional brain atlases including both lower-and higher-order processing areas.
Finally, as a part of FuSeISC, we propose a criterion-based sparsification of the shared nearest-neighbor graph for detecting clusters in noisy data. In our tests with synthetic data, this technique was superior to well-known clustering methods, such as Ward's method, affinity propagation and K-means++.
