Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements <i>Running title: Functional parcellation of the cerebral cortex</i>

To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similari...

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Autores principales:	Tien-Wen Lee, Gerald Tramontano
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	cortex functional connectivity functional magnetic resonance imaging (fmri) functional parcellation resting state fmri (rs-fmri) Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/bdd7afa050584ae899b3ceb49f992a27
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spelling	oai:doaj.org-article:bdd7afa050584ae899b3ceb49f992a272021-12-02T01:21:47ZAutomatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements <i>Running title: Functional parcellation of the cerebral cortex</i>10.3934/Neuroscience.20210282373-7972https://doaj.org/article/bdd7afa050584ae899b3ceb49f992a272021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/Neuroscience.2021028?viewType=HTMLhttps://doaj.org/toc/2373-7972To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similarity measurements (MOSI) to fulfill functional parcellation (FP) of the cortex. MOSI is carried out by iteratively dividing a module into sub-modules (via the Louvain community detection method) and unifying similar neighboring sub-modules into a new module (adjacent sub-modules with a similarity index <0.05) until the brain modular structures of successive runs become constant. By adjusting the gamma value, a parameter in the Louvain algorithm, MOSI may segment the cortex with different resolutions. rs-fMRI scans of 33 healthy subjects were selected from the dataset of the Rockland sample. MOSI was applied to the rs-fMRI data after standardized pre-processing steps. The results indicate that the parcellated modules by MOSI are more homogeneous in content. After reducing the grouped voxels to representative neural nodes, the network structures were explored. The resultant network components were comparable with previous reports. The validity of MOSI in achieving data reduction has been confirmed. MOSI may provide a novel starting point for further investigation of the network properties of rs-fMRI data. Potential applications of MOSI are discussed.Tien-Wen Lee Gerald Tramontano AIMS Pressarticlecortexfunctional connectivityfunctional magnetic resonance imaging (fmri)functional parcellationresting state fmri (rs-fmri)Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENAIMS Neuroscience, Vol 8, Iss 4, Pp 526-842 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cortex functional connectivity functional magnetic resonance imaging (fmri) functional parcellation resting state fmri (rs-fmri) Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	cortex functional connectivity functional magnetic resonance imaging (fmri) functional parcellation resting state fmri (rs-fmri) Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Tien-Wen Lee Gerald Tramontano Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements <i>Running title: Functional parcellation of the cerebral cortex</i>
description	To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similarity measurements (MOSI) to fulfill functional parcellation (FP) of the cortex. MOSI is carried out by iteratively dividing a module into sub-modules (via the Louvain community detection method) and unifying similar neighboring sub-modules into a new module (adjacent sub-modules with a similarity index <0.05) until the brain modular structures of successive runs become constant. By adjusting the gamma value, a parameter in the Louvain algorithm, MOSI may segment the cortex with different resolutions. rs-fMRI scans of 33 healthy subjects were selected from the dataset of the Rockland sample. MOSI was applied to the rs-fMRI data after standardized pre-processing steps. The results indicate that the parcellated modules by MOSI are more homogeneous in content. After reducing the grouped voxels to representative neural nodes, the network structures were explored. The resultant network components were comparable with previous reports. The validity of MOSI in achieving data reduction has been confirmed. MOSI may provide a novel starting point for further investigation of the network properties of rs-fMRI data. Potential applications of MOSI are discussed.
format	article
author	Tien-Wen Lee Gerald Tramontano
author_facet	Tien-Wen Lee Gerald Tramontano
author_sort	Tien-Wen Lee
title	Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements <i>Running title: Functional parcellation of the cerebral cortex</i>
title_short	Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements <i>Running title: Functional parcellation of the cerebral cortex</i>
title_full	Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements <i>Running title: Functional parcellation of the cerebral cortex</i>
title_fullStr	Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements <i>Running title: Functional parcellation of the cerebral cortex</i>
title_full_unstemmed	Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements <i>Running title: Functional parcellation of the cerebral cortex</i>
title_sort	automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements <i>running title: functional parcellation of the cerebral cortex</i>
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/bdd7afa050584ae899b3ceb49f992a27
work_keys_str_mv	AT tienwenlee automaticparcellationofrestingstatecorticaldynamicsbyiterativecommunitydetectionandsimilaritymeasurementsirunningtitlefunctionalparcellationofthecerebralcortexi AT geraldtramontano automaticparcellationofrestingstatecorticaldynamicsbyiterativecommunitydetectionandsimilaritymeasurementsirunningtitlefunctionalparcellationofthecerebralcortexi
_version_	1718403159426072576

Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements <i>Running title: Functional parcellation of the cerebral cortex</i>

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