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Functional brain segmentation using inter-subject correlation in fMRI

Jukka-Pekka Kauppi, Juha Pajula, Jari Niemi, Riitta Hari, Jussi Tohka
doi: https://doi.org/10.1101/057620
Jukka-Pekka Kauppi
aDepartment of Mathematical Information Technology, University of Jyväskylä, Finland
bDepartment of Computer Science and HIIT, University of Helsinki, Finland
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Juha Pajula
cDepartment of Signal Processing, Tampere University of Technology, Finland
fVTT Technical Research Centre of Finland, Tampere, Finland
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Jari Niemi
cDepartment of Signal Processing, Tampere University of Technology, Finland
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Riitta Hari
dDepartment of Art, Aalto University, Helsinki, Finland
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Jussi Tohka
eAI Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
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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++.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted February 17, 2017.
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Functional brain segmentation using inter-subject correlation in fMRI
Jukka-Pekka Kauppi, Juha Pajula, Jari Niemi, Riitta Hari, Jussi Tohka
bioRxiv 057620; doi: https://doi.org/10.1101/057620
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Functional brain segmentation using inter-subject correlation in fMRI
Jukka-Pekka Kauppi, Juha Pajula, Jari Niemi, Riitta Hari, Jussi Tohka
bioRxiv 057620; doi: https://doi.org/10.1101/057620

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