Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia. (December 2021)

• Record Type: Journal Article
• Authors: Doborjeh, Maryam; Doborjeh, Zohreh; Merkin, Alexander; Bahrami, Helena; Sumich, Alexander; Krishnamurthi, Rita; Medvedev, Oleg N.; Crook-Rumsey, Mark; Morgan, Catherine; Kirk, Ian; Sachdev, Perminder S.; Brodaty, Henry; Kang, Kristan; Wen, Wei; Feigin, Valery; Kasabov, Nikola
• Is Part Of: Neural networks. Volume 144(2021)
• Page Start: 522
• Page End: 539
• Publication Date: 2021-12
• Subjects: Personalised modelling -- Spiking neural networks -- Longitudinal MRI data -- Dementia -- Classification -- Prediction; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2021.09.013
• Languages: English
• ISSNs: 0893-6080

Abstract

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. Highlights: Personalised modelling of longitudinal MRI data to predict cognitive outcome. Spiking neural networks for modelling of integrated spatial and temporal MRI data. 3D brain-shaped visualisation of the changes in MRI anatomical components over 6 years. Spike-time dependent learning rules (supervised and unsupervised) in spiking neural networks. Personalised profiling of an individual to improve interpretability.