Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 67,143 bioRxiv papers from 295,510 authors.
Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models
Posted 08 Mar 2018
bioRxiv DOI: 10.1101/277624 (published DOI: 10.1016/j.nicl.2018.04.017)
Posted 08 Mar 2018
Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting on 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 modelled individual resting-state functional activity of 124 individuals across the behavioral 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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