Personalized information on anatomic connectivity (structural connectivity; SC) or coordinated resting state activation patterns (functional connectivity; FC) is a source of powerful neuromarkers to detect and track the development of neurodegenerative diseases. However, there are often “gaps” in the available information, with only SC (or FC) being known but not FC (or SC). In our eNeuro paper, exploiting whole-brain modeling, we show that gap in databases can be filled by inferring the other connectome through computational simulations. The generated virtual connectomic data carry information analogous to the one of empirical connectomes, so that machine learning algorithms can be trained on them. This opens the way to the release in the future of cohorts of “virtual patients,” complementing traditional datasets in data-driven predictive medicine.
To know more:
- Arbabyazd, D.M., Shen, K., Wang, Z., Hofman-Apitius, M., Mcintosh, A.R., Battaglia, D.*, Jirsa, V.*, (2021). Completion and augmentation of connectomic datasets in dementia and Alzheimer’s Disease using Virtual Patient Cohorts. eNeuro, 0475, 1-33 [* Shared last authorship].