Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder
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 netwo...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/10356/147369 |
| _version_ | 1811692167184252928 |
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| author | Gupta, Sukrit Rajapakse, Jagath Chandana Welsch, Roy E. |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Gupta, Sukrit Rajapakse, Jagath Chandana Welsch, Roy E. |
| author_sort | Gupta, Sukrit |
| collection | NTU |
| description | 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 | 2024-10-01T06:31:29Z |
| format | Journal Article |
| id | ntu-10356/147369 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:31:29Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1473692021-03-31T02:58:32Z Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder Gupta, Sukrit Rajapakse, Jagath Chandana Welsch, Roy E. School of Computer Science and Engineering Engineering::Computer science and engineering Alzheimer’s Disease Ambivert Degree 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. Ministry of Education (MOE) Published version This work was partially supported by AcRF Tier 1 grant RG 149/17 of Ministry of Education, Singapore. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). 2021-03-31T02:58:32Z 2021-03-31T02:58:32Z 2020 Journal Article Gupta, S., Rajapakse, J. C. & Welsch, R. E. (2020). Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder. NeuroImage: Clinical, 25. https://dx.doi.org/10.1016/j.nicl.2020.102186 2213-1582 0000-0002-8974-8482 0000-0001-7944-1658 https://hdl.handle.net/10356/147369 10.1016/j.nicl.2020.102186 32000101 2-s2.0-85078236472 25 en RG 149/17 NeuroImage: Clinical © 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). application/pdf |
| spellingShingle | Engineering::Computer science and engineering Alzheimer’s Disease Ambivert Degree Gupta, Sukrit Rajapakse, Jagath Chandana 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 |
| topic | Engineering::Computer science and engineering Alzheimer’s Disease Ambivert Degree |
| url | https://hdl.handle.net/10356/147369 |
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