Functional brain segmentation using inter-subject correlation in fMRI

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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, 05.2017, p. 2643–2665.

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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. pp. 2643–2665.

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@article{10c68c32f8674993a084821b6cf5aa33,
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 nearestneighbor 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++",
keywords = "fMRI, inter-subject correlation, human, brain, movies, analysis, brain segmentation, FuSeISC",
author = "Jukka-Pekka Kauppi and Juha Pajula and Jari Niemi and Riitta Hari and Jussi Tohka",
year = "2017",
month = "5",
doi = "10.1002/hbm.23549",
language = "English",
volume = "38",
pages = "2643–2665",
journal = "Human Brain Mapping",
issn = "1065-9471",
publisher = "Wiley",

}

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TY - JOUR

T1 - Functional brain segmentation using inter-subject correlation in fMRI

AU - Kauppi, Jukka-Pekka

AU - Pajula, Juha

AU - Niemi, Jari

AU - Hari, Riitta

AU - Tohka, Jussi

PY - 2017/5

Y1 - 2017/5

N2 - 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 nearestneighbor 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++

AB - 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 nearestneighbor 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++

KW - fMRI

KW - inter-subject correlation

KW - human

KW - brain

KW - movies

KW - analysis

KW - brain segmentation

KW - FuSeISC

U2 - 10.1002/hbm.23549

DO - 10.1002/hbm.23549

M3 - Article

VL - 38

SP - 2643

EP - 2665

JO - Human Brain Mapping

JF - Human Brain Mapping

SN - 1065-9471

ER -

ID: 12111663