Tools
Tools
Doborjeh, M, Doborjeh, Z, Merkin, A, Bahrami, H, Sumich, A 
ORCID: https://orcid.org/0000-0003-4333-8442, Krishnamurthi, R, Medvedev, ON, Crook-Rumsey, M, Morgan, C, Kirk, I, Sachdev, PS, Brodaty, H, Kang, K, Wen, W, Feigin, V and Kasabov, N,
2021.
Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia.
Neural Networks, 144, pp. 522-539.
ISSN 0893-6080
Preview |
Text
1481497_Sumich.pdf - Post-print Download (3MB) | Preview |
Abstract
Background: Longitudinal neuroimaging provides spatiotemporal brain data (STBD) measurement that can be utilised to understand dynamic changes in brain structure and/or function underpinning cognitive activities. Making sense of such highly interactive information is challenging, given that the features manifest intricate temporal, causal relations between the spatially distributed neural sources in the brain.
Methods: The current paper argues for the advancement of deep learning algorithms in brain-inspired spiking neural networks (SNN), capable of modelling structural data across time (longitudinal measurement) and space (anatomical components). The paper proposes a methodology and a computational architecture based on SNN for building personalised predictive models from longitudinal brain data to accurately detect, understand, and predict the dynamics of an individual’s functional brain state. The methodology includes finding clusters of similar data to each individual, data interpolation, deep learning in a 3-dimensional brain-template structured SNN model, classification and prediction of individual outcome, visualisation of structural brain changes related to the predicted outcomes, interpretation of results, and individual and group predictive marker discovery.
Results: To demonstrate the functionality of the proposed methodology, the paper presents experimental results on a longitudinal magnetic resonance imaging (MRI) dataset derived from 175 older adults of the internationally recognised community-based cohort Sydney Memory and Ageing Study (MAS) spanning 6 years of follow-up.
Significance: The models were able to accurately classify and predict 2 years ahead of cognitive decline, such as mild cognitive impairment (MCI) and dementia with 95% and 91% accuracy, respectively. The proposed methodology also offers a 3-dimensional visualisation of the MRI models reflecting the dynamic patterns of regional changes in white matter hyperintensity (WMH) and brain volume over 6 years.
Conclusion: The method is efficient for personalised predictive modelling on a wide range of neuroimaging longitudinal data, including also demographic, genetic, and clinical data. As a case study, it resulted in finding predictive markers for MCI and dementia as dynamic brain patterns using MRI data.
| Item Type: | Journal article |
|---|---|
| Publication Title: | Neural Networks |
| Creators: | Doborjeh, M., Doborjeh, Z., Merkin, A., Bahrami, H., Sumich, A., Krishnamurthi, R., Medvedev, O.N., Crook-Rumsey, M., Morgan, C., Kirk, I., Sachdev, P.S., Brodaty, H., Kang, K., Wen, W., Feigin, V. and Kasabov, N. |
| Publisher: | Elsevier BV |
| Date: | December 2021 |
| Volume: | 144 |
| ISSN: | 0893-6080 |
| Identifiers: | Number Type 10.1016/j.neunet.2021.09.013 DOI 1481497 Other |
| Divisions: | Schools > School of Social Sciences |
| Record created by: | Jeremy Silvester |
| Date Added: | 03 Nov 2021 12:12 |
| Last Modified: | 20 Sep 2022 03:00 |
| URI: | https://irep.ntu.ac.uk/id/eprint/44597 |
Actions (login required)
 |
Edit View |
Statistics
Views
Views per month over past year
Downloads
Downloads per month over past year
