title: Functional brain segmentation using inter-subject correlation in fMRI.
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.
authors.affiliation: Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland.|Department of Computer Science and HIIT, University of Helsinki, Helsinki, Finland.
authors.forename: Jukka-Pekka
authors.identifier: 0000-0003-2864-5583
authors.initials: JP
authors.lastname: Kauppi
authors.affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|VTT Technical Research Centre of Finland, Tampere, Finland.
authors.forename: Juha
authors.initials: J
authors.lastname: Pajula
authors.affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
authors.forename: Jari
authors.initials: J
authors.lastname: Niemi
authors.affiliation: Department of Art, Aalto University, Helsinki, Finland.
authors.forename: Riitta
authors.initials: R
authors.lastname: Hari
authors.affiliation: AI Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
authors.forename: Jussi
authors.initials: J
authors.lastname: Tohka
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D008959:Models, Neurological; D010100:Oxygen / Q000097:blood; D013997:Time Factors; D055815:Young Adult
chemical_list: D010100:Oxygen
keywords: Gaussian mixture model; functional magnetic resonance imaging; functional segmentation; human brain; inter-subject correlation; inter-subject variability; naturalistic stimulation; shared nearest-neighbor graph