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Great expectations: Using whole-brain computational connectomics for understanding neuropsychiatric disorders

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  • Gustavo Deco, Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain. Electronic address: tristan.nakagawa@upf.edu., Spain
  • Morten L. Kringelbach

The study of human brain networks with invivo 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. Video Abstract: In this Perspective, Deco and Kringelbach discuss the methods and emerging results of combining connectomics with generative whole-brain computational models to understand neuropsychiatric disorders.

Original languageEnglish
JournalNeuron
Volume84
Issue5
Pages (from-to)892-905
Number of pages14
ISSN0896-6273
DOIs
Publication statusPublished - 1 Jan 2014

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