Functional brain segmentation using inter-subject correlation in fMRI

Jukka-Pekka Kauppi, Juha Pajula, Jari Niemi, Riitta Hari, Jussi Tohka

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)

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 ++.
Original languageEnglish
Pages (from-to)2643-2665
Number of pages23
JournalHuman Brain Mapping
Volume38
Issue number5
DOIs
Publication statusPublished - 1 May 2017
MoE publication typeA1 Journal article-refereed

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Magnetic Resonance Imaging
Brain
Atlases
Neuroimaging
Cluster Analysis
Magnetic Resonance Spectroscopy
Datasets

Keywords

  • functional magnetic resonance imaging
  • functional segmentation
  • gaussian mixture model
  • human brain
  • inter-subject correlation
  • inter-subject variability
  • naturalistic stimulation
  • shared nearest-neighbor graph

Cite this

Kauppi, Jukka-Pekka ; Pajula, Juha ; Niemi, Jari ; Hari, Riitta ; Tohka, Jussi. / Functional brain segmentation using inter-subject correlation in fMRI. In: Human Brain Mapping. 2017 ; Vol. 38, No. 5. pp. 2643-2665.
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Functional brain segmentation using inter-subject correlation in fMRI. / Kauppi, Jukka-Pekka; Pajula, Juha; Niemi, Jari; Hari, Riitta; Tohka, Jussi.

In: Human Brain Mapping, Vol. 38, No. 5, 01.05.2017, p. 2643-2665.

Research output: Contribution to journalArticleScientificpeer-review

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