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Neuron. 2014 Dec 3;84(5):892-905. doi: 10.1016/j.neuron.2014.08.034.

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

Author information

1
Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain. Electronic address: gustavo.deco@upf.edu.
2
Department of Psychiatry, University of Oxford, OX3 7JX Oxford, UK; Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, 8000 Aarhus C, Denmark.

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.

PMID:
25475184
DOI:
10.1016/j.neuron.2014.08.034
[Indexed for MEDLINE]
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