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.
Original language | English |
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Pages (from-to) | 522-539 |
Number of pages | 18 |
Journal | Neural Networks |
Volume | 144 |
Early online date | 20 Sept 2021 |
DOIs | |
Publication status | Published (in print/issue) - Dec 2021 |
Bibliographical note
Funding Information:The data from the Memory and Ageing Study (MAS) were provided by the MAS Management Committee. This research was supported by a research grant from the Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand and by the AUT SRIF Interact funding of the Knowledge Engineering & Discovery Research Institute (KEDRI) in collaboration with the National Institute for Stroke and Applied Neurosciences (NISAN) of Auckland University of Technology, New Zealand. The authors would like to sincerely acknowledge the support from Donna Rose Addis (Rotman Research Institute, University of Toronto), Anbupalam Thalamuthu (Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia) and Lynette Tippett (School of Psychology, Centre for Brain Research, University of Auckland, and Brain Research New Zealand). All authors approved the version of the manuscript to be published.
Funding Information:
The data from the Memory and Ageing Study (MAS) were provided by the MAS Management Committee. This research was supported by a research grant from the Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand and by the AUT SRIF Interact funding of the Knowledge Engineering & Discovery Research Institute (KEDRI) in collaboration with the National Institute for Stroke and Applied Neurosciences (NISAN) of Auckland University of Technology, New Zealand. The authors would like to sincerely acknowledge the support from Donna Rose Addis (Rotman Research Institute, University of Toronto), Anbupalam Thalamuthu (Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia) and Lynette Tippett (School of Psychology, Centre for Brain Research, University of Auckland, and Brain Research New Zealand). All authors approved the version of the manuscript to be published. The data recording was approved by Committees of the University of New South Wales and the South Eastern Sydney and Illawarra Area Health Service. In New Zealand, the Ethics Approval for the study was reviewed by the Auckland University of Technology Ethics Committee (AUTEC), and ethical approval has been granted for three years until 1 March 2021.
Publisher Copyright:
© 2021 Elsevier Ltd
Keywords
- Classification
- Dementia
- Longitudinal MRI data
- Personalised modelling
- Prediction
- Spiking neural networks