Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 57,506 bioRxiv papers from 264,779 authors.

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

By Joelle Zimmermann, Alistair Perry, Michael Breakspear, Michael Schirner, Perminder Sachdev, Wei Wen, Nicole A Kochan, Michael Mapstone, Petra Ritter, Anthony R McIntosh, Ana Solodkin

Posted 08 Mar 2018
bioRxiv DOI: 10.1101/277624 (published DOI: 10.1016/j.nicl.2018.04.017)

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.

Download data

  • Downloaded 572 times
  • Download rankings, all-time:
    • Site-wide: 14,738 out of 57,506
    • In neuroscience: 2,419 out of 10,103
  • Year to date:
    • Site-wide: 32,309 out of 57,506
  • Since beginning of last month:
    • Site-wide: 31,473 out of 57,506

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide

Sign up for the Rxivist weekly newsletter! (Click here for more details.)