Journal
NEUROIMAGE-CLINICAL
Volume 19, Issue -, Pages 240-251Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.nicl.2018.04.017
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Funding
- Weston Brain Institute
- NSERC [RGPIN-2017-06793]
- German Ministry of Education and Research (US-German Collaboration in Computational Neuroscience) [01GQ1504A]
- German Ministry of Education and Research (Bernstein Focus State Dependencies of Learning) [01GQ0971-5]
- European Union Horizon 2020 (ERC) [683049]
- Stiftung Charite/Private Exzellenzinitiative Johanna Quandt
- Berlin Instititute of Health (BIH Johanna Quandt Professorship for Brain Simulation)
- Australian Research Council [CE140100007]
- National Health and Medical Research Council (NHMRC) Australia [APP1093083]
- Julich Supercomputing Centre (JSC) [8344, 10276]
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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.
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