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Neuroimage Clin. 2018 Apr 20;19:240-251. doi: 10.1016/j.nicl.2018.04.017. eCollection 2018.

Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models.

Author information

1
Baycrest Health Sciences, Rotman Research Institute, 3560 Bathurst St, Toronto, Ontario M6A 2E1, Canada. Electronic address: jzimmermann@research.baycrest.org.
2
Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Program of Mental Health Research, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany.
3
Program of Mental Health Research, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Metro North Mental Health Service, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia.
4
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, Chariteplatz 1, Berlin 13353, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
5
Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
6
UC Irvine Health School of Medicine, Irvine Hall, 1001 Health Sciences Road, Irvine, CA 92697-3950, USA.
7
Baycrest Health Sciences, Rotman Research Institute, 3560 Bathurst St, Toronto, Ontario M6A 2E1, Canada.

Abstract

Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, "The Virtual Brain (TVB)", based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

PMID:
30035018
PMCID:
PMC6051478
DOI:
10.1016/j.nicl.2018.04.017
[Indexed for MEDLINE]
Free PMC Article

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