Tools
Tools
Wadhera, T. and Mahmud, M. 
ORCID: 0000-0002-2037-8348,
2023.
Computational model of functional connectivity distance predicts neural alterations.
IEEE Transactions on Cognitive and Developmental Systems.
ISSN 2379-8920
|
Text
1810478_Mahmud.pdf - Post-print Download (678kB) | Preview |
Abstract
Modelling brain signals play a crucial role in analysing the brain’s architecture, functions and associated disorders. This paper aims to model the brain topology by exploring the relationship between complex neural correlates and functional connectivity-based distances. A computational model inspired by multivariate visibility graphs (VG) algorithm and Euclidean distance is proposed to analyse quantitatively the brain network data. When applied to resting-state EEG signals from three groups (typically developing (TD), autism spectrum disorder (ASD), and epilepsy (E)), the network topological properties (e.g., global efficiency, modularity, small-worldness, and betweenness centrality) demonstrate variations in connectivity distance probabilities among brain regions (e.g., frontal, temporal, parietal, and occipital) via the model’s delay and connection distance parameters. The results showed a higher delay and skewed distribution towards short functional connections in ASD than in TD, while a lower delay in E than in ASD and TD. Additionally, ASD had more short-distance connections, while E had more long-distance connections compared to TD. ASD and E significantly overlapped over short-distance connections within the temporal lobe. In summary, the proposed model illustrates that delay parameter and connection distance obtained from brain network data have the potential to objectively identify and associate co-occurring neurological conditions (e.g., ASD and E)
| Item Type: | Journal article | ||||||
|---|---|---|---|---|---|---|---|
| Publication Title: | IEEE Transactions on Cognitive and Developmental Systems | ||||||
| Creators: | Wadhera, T. and Mahmud, M. | ||||||
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | ||||||
| Date: | 28 September 2023 | ||||||
| ISSN: | 2379-8920 | ||||||
| Identifiers: |
|
||||||
| Rights: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Divisions: | Schools > School of Science and Technology | ||||||
| Record created by: | Laura Ward | ||||||
| Date Added: | 02 Oct 2023 08:52 | ||||||
| Last Modified: | 02 Oct 2023 08:52 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/49837 |
Actions (login required)
 |
Edit View |
Views
Views per month over past year
Downloads
Downloads per month over past year
