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

Maryam Doborjeh; Zohreh Doborjeh; Alexander Merkin; Helena Bahrami; Alexander Sumich; Rita Krishnamurthi; Oleg N Medvedev; Mark Crook-Rumsey; Catherine Morgan; Ian Kirk; Perminder S Sachdev; Henry Brodaty; Kristan Kang; Wei Wen; Valery Feigin; Nikola Kasabov

doi:10.1016/j.neunet.2021.09.013

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

Neural Netw. 2021 Dec:144:522-539. doi: 10.1016/j.neunet.2021.09.013. Epub 2021 Sep 20.

Authors

Maryam Doborjeh¹, Zohreh Doborjeh², Alexander Merkin³, Helena Bahrami⁴, Alexander Sumich⁵, Rita Krishnamurthi³, Oleg N Medvedev⁶, Mark Crook-Rumsey⁷, Catherine Morgan⁸, Ian Kirk⁸, Perminder S Sachdev⁹, Henry Brodaty¹⁰, Kristan Kang¹⁰, Wei Wen⁹, Valery Feigin¹¹, Nikola Kasabov¹²

Affiliations

¹ Computer Science and Software Engineering Department, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand. Electronic address: mgholami@aut.ac.nz.
² Department of Audiology, School of Population Health, Faculty of Medical and Health Sciences, The University of Auckland, New Zealand.
³ The National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, New Zealand.
⁴ School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand.
⁵ NTU Psychology, Nottingham Trent University, Nottingham, United Kingdom.
⁶ University of Waikato, School of Psychology, Hamilton, New Zealand.
⁷ NTU Psychology, Nottingham Trent University, Nottingham, United Kingdom; School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand.
⁸ School of Psychology and Centre for Brain Research, University of Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand.
⁹ Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia; Neuropsychiatric Institute, the Prince of Wales Hospital, Sydney, Australia.
¹⁰ Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia.
¹¹ The National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, New Zealand; Research Center of Neurology, Moscow, Russia.
¹² School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand; George Moore Chair, Ulster University, Londonderry, United Kingdom.

PMID: 34619582
DOI: 10.1016/j.neunet.2021.09.013

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.

Keywords: Classification; Dementia; Longitudinal MRI data; Personalised modelling; Prediction; Spiking neural networks.

MeSH terms

Aged
Brain / diagnostic imaging
Cognitive Dysfunction*
Dementia* / diagnostic imaging
Humans
Magnetic Resonance Imaging
Neural Networks, Computer
Neuroimaging