Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements

Tien-Wen Lee; Gerald Tramontano; Tien-Wen Lee; Gerald Tramontano

doi:10.3934/Neuroscience.2021028

AIMS Neuroscience

2021, Volume 8, Issue 4: 526-542. doi: 10.3934/Neuroscience.2021028

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Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements

Running title: Functional parcellation of the cerebral cortex

Tien-Wen Lee ^{1,2
,
,},
Gerald Tramontano ¹

1.
The Neuro Cognitive Institute (NCI) Clinical Research Foundation, NJ 07856, US
2.
Department of Psychiatry, Dajia Lee's General Hospital, Lee's Medical Corporation, Taichung 43748, Taiwan

Received: 30 April 2021 Accepted: 01 September 2021 Published: 10 September 2021

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.
- cortex,
- functional connectivity,
- functional magnetic resonance imaging (fMRI),
- functional parcellation,
- resting state fMRI (rs-fMRI)
Citation: Tien-Wen Lee, Gerald Tramontano. Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements[J]. AIMS Neuroscience, 2021, 8(4): 526-542. doi: 10.3934/Neuroscience.2021028

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Abstract

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.

Acknowledgments

This work was supported by NeuroCognitive Institute (NCI) and NCI Clinical Research Foundation Inc. Financial support: N/A.

Author contributions

TW Lee is the author that developed the methodology. Note: The main code of MOSI is available from the link: https://github.com/dwleeibru/MOSI.

Conflict of interest

The authors declare no conflict of interest.

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