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- W2000619457 endingPage "905" @default.
- W2000619457 startingPage "892" @default.
- W2000619457 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. Here our focus is on the disruption in neuropsychiatric disorders (pathoconnectomics) and how whole-brain computational models can help generate and predict the dynamical interactions and consequences of brain networks over many timescales. We review methods and emerging results that exhibit remarkable accuracy in mapping and predicting both spontaneous and task-based healthy network dynamics. 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." @default.
- W2000619457 created "2016-06-24" @default.
- W2000619457 creator A5031782595 @default.
- W2000619457 creator A5043559110 @default.
- W2000619457 date "2014-12-01" @default.
- W2000619457 modified "2023-10-10" @default.
- W2000619457 title "Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders" @default.
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