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Beyond neophrenology: neuroimaging and whole-brain computational connectomics
28.01.2016 |
| Dato | ons 03 feb |
| Tid | 13:30 — 15:00 |
| Sted | DNC Auditorium Aarhus University Hospital, Nørrebrogade 44, Building 10G |
Abstract:
The study of human brain networks with in vivo neuroimaging has given rise to the field of connectomics, furthered by advances in network science and graph theory informing our understanding of the topology and function of the healthy brain. Whole-brain computational models can help generate and predict the dynamical interactions and consequences of brain networks over many timescales, which may help provide the tools to make probabilistic causal inferences and move beyond neophrenology. I review methods and emerging results that exhibit remarkable accuracy in mapping and predicting both spontaneous and task-based network dynamics in health and disease. This raises great expectations that whole-brain modeling and computational connectomics may provide an entry point for understanding brain disorders at a causal mechanistic level, and that computational neuropsychiatry can ultimately be leveraged to provide novel, more effective therapeutic interventions, e.g., through drug discovery and new targets for deep brain stimulation.
Refs:
Deco G. & Kringelbach M.L. (2014) Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders.
Neuron, 84(3): 892-905.
Deco G., Tononi G, Boly M. & Kringelbach M.L. (2015) Rethinking segregation and integration: contributions of whole-brain modelling.
Nature Reviews Neuroscience 16:430-439.
Kringelbach M.L., McIntosh A.R., Ritter P., Jirsa V. & Deco G. (2015) The rediscovery of slowness: exploring the timing of cognition. Trends in Cognitive Sciences 19(10):616-28.