Functional brain segmentation using inter‐subject correlation in fMRI. Issue 5 (10th March 2017)

• Record Type: Journal Article
• Authors: Kauppi, Jukka‐Pekka; Pajula, Juha; Niemi, Jari; Hari, Riitta; Tohka, Jussi
• Is Part Of: Human brain mapping. Volume 38:Issue 5(2017)
• Page Start: 2643
• Page End: 2665
• Publication Date: 2017-03-10
• Subjects: functional magnetic resonance imaging -- functional segmentation -- inter‐subject correlation -- inter‐subject variability -- naturalistic stimulation -- Gaussian mixture model -- shared nearest‐neighbor graph -- human brain; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.23549
• Languages: English
• ISSNs: 1065-9471

Abstract

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. A new exploratory data‐analysis approach, functional segmentation inter‐subject correlation analysis (FuSeISC), was proposed 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. FuSeISC was tested 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 useful for spatial exploration of large fMRI data sets obtained using naturalistic stimuli, but also 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, a criterion‐based sparsification of the shared nearest‐neighbor graph was proposed for detecting clusters in noisy data. In the tests with synthetic data, this technique was superior to well‐known clustering methods, such as Ward's method, affinity propagation, and K ‐means + + . Hum Brain Mapp 38:2643–2665, 2017 . ©2017 Wiley Periodicals, Inc.