Iterative consensus spectral clustering improves detection of subject and group level brain functional modules
Specialized processing in the brain is performed by multiple groups of brain regions organized as functional modules. Although, in vivo studies of brain functional modules involve multiple functional Magnetic Resonance Imaging (fMRI) scans, the methods used to derive functional modules from function...
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| Format: | Journal Article |
| Language: | English |
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2021
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| Online Access: | https://hdl.handle.net/10356/146113 |
| _version_ | 1811679599946366976 |
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| author | Gupta, Sukrit Rajapakse, Jagath Chandana |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Gupta, Sukrit Rajapakse, Jagath Chandana |
| author_sort | Gupta, Sukrit |
| collection | NTU |
| description | Specialized processing in the brain is performed by multiple groups of brain regions organized as functional modules. Although, in vivo studies of brain functional modules involve multiple functional Magnetic Resonance Imaging (fMRI) scans, the methods used to derive functional modules from functional networks of the brain ignore individual differences in the functional architecture and use incomplete functional connectivity information. To correct this, we propose an Iterative Consensus Spectral Clustering (ICSC) algorithm that detects the most representative modules from individual dense weighted connectivity matrices derived from multiple scans. The ICSC algorithm derives group-level modules from modules of multiple individuals by iteratively minimizing the consensus-cost between the two. We demonstrate that the ICSC algorithm can be used to derive biologically plausible group-level (for multiple subjects) and subject-level (for multiple subject scans) brain modules, using resting-state fMRI scans of 589 subjects from the Human Connectome Project. We employed a multipronged strategy to show the validity of the modularizations obtained from the ICSC algorithm. We show a heterogeneous variability in the modular structure across subjects where modules involved in visual and motor processing were highly stable across subjects. Conversely, we found a lower variability across scans of the same subject. The performance of our algorithm was compared with existing functional brain modularization methods and we show that our method detects group-level modules that are more representative of the modules of multiple individuals. Finally, the experiments on synthetic images quantitatively demonstrate that the ICSC algorithm detects group-level and subject-level modules accurately under varied conditions. Therefore, besides identifying functional modules for a population of subjects, the proposed method can be used for applications in personalized neuroscience. The ICSC implementation is available at https://github.com/SCSE-Biomedical-Computing-Group/ICSC. |
| first_indexed | 2024-10-01T03:11:44Z |
| format | Journal Article |
| id | ntu-10356/146113 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:11:44Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1461132021-01-27T01:28:31Z Iterative consensus spectral clustering improves detection of subject and group level brain functional modules Gupta, Sukrit Rajapakse, Jagath Chandana School of Computer Science and Engineering Engineering::Computer science and engineering Network Models Neural Circuits Specialized processing in the brain is performed by multiple groups of brain regions organized as functional modules. Although, in vivo studies of brain functional modules involve multiple functional Magnetic Resonance Imaging (fMRI) scans, the methods used to derive functional modules from functional networks of the brain ignore individual differences in the functional architecture and use incomplete functional connectivity information. To correct this, we propose an Iterative Consensus Spectral Clustering (ICSC) algorithm that detects the most representative modules from individual dense weighted connectivity matrices derived from multiple scans. The ICSC algorithm derives group-level modules from modules of multiple individuals by iteratively minimizing the consensus-cost between the two. We demonstrate that the ICSC algorithm can be used to derive biologically plausible group-level (for multiple subjects) and subject-level (for multiple subject scans) brain modules, using resting-state fMRI scans of 589 subjects from the Human Connectome Project. We employed a multipronged strategy to show the validity of the modularizations obtained from the ICSC algorithm. We show a heterogeneous variability in the modular structure across subjects where modules involved in visual and motor processing were highly stable across subjects. Conversely, we found a lower variability across scans of the same subject. The performance of our algorithm was compared with existing functional brain modularization methods and we show that our method detects group-level modules that are more representative of the modules of multiple individuals. Finally, the experiments on synthetic images quantitatively demonstrate that the ICSC algorithm detects group-level and subject-level modules accurately under varied conditions. Therefore, besides identifying functional modules for a population of subjects, the proposed method can be used for applications in personalized neuroscience. The ICSC implementation is available at https://github.com/SCSE-Biomedical-Computing-Group/ICSC. Ministry of Education (MOE) Published version This work was partially supported by AcRF Tier 1 grant RG 149/17 of Ministry of Education, Singapore. We would like to thank Dr. Jiang Qiu and Dr. Dongtao Wei from the Creativity and Affective Neuroscience Lab, Brain Imaging Center of Southwest University for providing us the validation dataset used in the study. 2021-01-27T01:28:31Z 2021-01-27T01:28:31Z 2020 Journal Article Gupta, S., & Rajapakse, J. C. (2020). Iterative Consensus Spectral Clustering improves detection of subject and group level brain functional modules. Scientific Reports, 10(1), 7590-. doi:10.1038/s41598-020-63552-0 2045-2322 0000-0002-8974-8482 0000-0001-7944-1658 https://hdl.handle.net/10356/146113 10.1038/s41598-020-63552-0 32371990 2-s2.0-85084221646 1 10 en RG 149/17 Scientific Reports © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Network Models Neural Circuits Gupta, Sukrit Rajapakse, Jagath Chandana Iterative consensus spectral clustering improves detection of subject and group level brain functional modules |
| title | Iterative consensus spectral clustering improves detection of subject and group level brain functional modules |
| title_full | Iterative consensus spectral clustering improves detection of subject and group level brain functional modules |
| title_fullStr | Iterative consensus spectral clustering improves detection of subject and group level brain functional modules |
| title_full_unstemmed | Iterative consensus spectral clustering improves detection of subject and group level brain functional modules |
| title_short | Iterative consensus spectral clustering improves detection of subject and group level brain functional modules |
| title_sort | iterative consensus spectral clustering improves detection of subject and group level brain functional modules |
| topic | Engineering::Computer science and engineering Network Models Neural Circuits |
| url | https://hdl.handle.net/10356/146113 |
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