Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s Disease and Autism Spectrum Disorder
© 2020 The Authors Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We argue that weak connections in bra...
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/1721.1/136283 |
| _version_ | 1810976483270721536 |
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| author | Gupta, Sukrit Rajapakse, Jagath C Welsch, Roy E |
| author2 | Statistics and Data Science Center (Massachusetts Institute of Technology) |
| author_facet | Statistics and Data Science Center (Massachusetts Institute of Technology) Gupta, Sukrit Rajapakse, Jagath C Welsch, Roy E |
| author_sort | Gupta, Sukrit |
| collection | MIT |
| description | © 2020 The Authors Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We argue that weak connections in brain functional networks lead to misclassification of brain regions as hubs. In order to resolve this, we propose a new measure called ambivert degree that considers the node's degree as well as connection weights in order to identify nodes with both high degree and high connection weights as hubs. Using resting-state functional MRI scans from the Human Connectome Project, we show that ambivert degree identifies brain hubs that are not only crucial but also invariable across subjects. We hypothesize that nodal measures based on ambivert degree can be effectively used to classify patients from healthy controls for diseases that are known to have widespread hub disruption. Using patient data for Alzheimer's Disease and Autism Spectrum Disorder, we show that the hubs in the patient and healthy groups are very different for both the diseases and deep feedforward neural networks trained on nodal hub features lead to a significantly higher classification accuracy with significantly fewer trainable weights compared to using functional connectivity features. Thus, the ambivert degree improves identification of crucial brain hubs in healthy subjects and can be used as a diagnostic feature to detect neurological diseases characterized by hub disruption. |
| first_indexed | 2024-09-23T08:55:59Z |
| format | Article |
| id | mit-1721.1/136283 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:55:59Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1362832023-09-01T19:08:23Z Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s Disease and Autism Spectrum Disorder Gupta, Sukrit Rajapakse, Jagath C Welsch, Roy E Statistics and Data Science Center (Massachusetts Institute of Technology) © 2020 The Authors Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We argue that weak connections in brain functional networks lead to misclassification of brain regions as hubs. In order to resolve this, we propose a new measure called ambivert degree that considers the node's degree as well as connection weights in order to identify nodes with both high degree and high connection weights as hubs. Using resting-state functional MRI scans from the Human Connectome Project, we show that ambivert degree identifies brain hubs that are not only crucial but also invariable across subjects. We hypothesize that nodal measures based on ambivert degree can be effectively used to classify patients from healthy controls for diseases that are known to have widespread hub disruption. Using patient data for Alzheimer's Disease and Autism Spectrum Disorder, we show that the hubs in the patient and healthy groups are very different for both the diseases and deep feedforward neural networks trained on nodal hub features lead to a significantly higher classification accuracy with significantly fewer trainable weights compared to using functional connectivity features. Thus, the ambivert degree improves identification of crucial brain hubs in healthy subjects and can be used as a diagnostic feature to detect neurological diseases characterized by hub disruption. 2021-10-27T20:34:42Z 2021-10-27T20:34:42Z 2020 2021-03-24T16:57:57Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136283 en 10.1016/J.NICL.2020.102186 NeuroImage: Clinical Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Elsevier |
| spellingShingle | Gupta, Sukrit Rajapakse, Jagath C Welsch, Roy E Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s Disease and Autism Spectrum Disorder |
| title | Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s Disease and Autism Spectrum Disorder |
| title_full | Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s Disease and Autism Spectrum Disorder |
| title_fullStr | Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s Disease and Autism Spectrum Disorder |
| title_full_unstemmed | Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s Disease and Autism Spectrum Disorder |
| title_short | Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s Disease and Autism Spectrum Disorder |
| title_sort | ambivert degree identifies crucial brain functional hubs and improves detection of alzheimer s disease and autism spectrum disorder |
| url | https://hdl.handle.net/1721.1/136283 |
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