Functional brain segmentation using inter-subject correlation in fMRI Articles uri icon

authors

  • KAUPPI, JUKKA-PEKKA
  • PAJULA, JUHA
  • NIEMI, JARI
  • HARI, RIITTA
  • TOHKA, JUSSI

publication date

  • March 2017

start page

  • 2643

end page

  • 2665

issue

  • 5

volume

  • 38

International Standard Serial Number (ISSN)

  • 1065-9471

Electronic International Standard Serial Number (EISSN)

  • 1097-0193

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 math formula. Hum Brain Mapp 38:2643&-2665, 2017. © 2017 Wiley Periodicals, Inc.

keywords

  • naturalistic stimulation; functional segmentation; human brain; gaussian mixture model; shared nearest‐neighbor graph; inter‐subject correlation; inter‐subject variability; functional magnetic resonance imaging; neuroimaging; inter-subject correlation; responses; shared nearest-neighbor graph; networks; variability; inter-subject variability; model; synchronization; individual-differences; stimulation; neurosciences; parcellation; resting-state fmri; radiology; nuclear medicine & medical imaging; temporal cortex; human brain; naturalistic stimulation; inter-subject variability; functional magnetic resonance imaging; inter-subject correlation; functional segmentation; shared nearest-neighbor graph