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Neuron. 2018 Jan 3;97(1):14-31. doi: 10.1016/j.neuron.2017.11.007.

From Maps to Multi-dimensional Network Mechanisms of Mental Disorders.

Author information

1
Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, 68159 Mannheim, Germany.
2
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
3
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: dsb@seas.upenn.edu.

Abstract

The development of advanced neuroimaging techniques and their deployment in large cohorts has enabled an assessment of functional and structural brain network architecture at an unprecedented level of detail. Across many temporal and spatial scales, network neuroscience has emerged as a central focus of intellectual efforts, seeking meaningful descriptions of brain networks and explanatory sets of network features that underlie circuit function in health and dysfunction in disease. However, the tools of network science commonly deployed provide insight into brain function at a fundamentally descriptive level, often failing to identify (patho-)physiological mechanisms that link system-level phenomena to the multiple hierarchies of brain function. Here we describe recently developed techniques stemming from advances in complex systems and network science that have the potential to overcome this limitation, thereby contributing mechanistic insights into neuroanatomy, functional dynamics, and pathology. Finally, we build on the Research Domain Criteria framework, highlighting the notion that mental illnesses can be conceptualized as dysfunctions of neural circuitry present across conventional diagnostic boundaries, to sketch how network-based methods can be combined with pharmacological, intermediate phenotype, genetic, and magnetic stimulation studies to probe mechanisms of psychopathology.

KEYWORDS:

brain imaging; control theory; dynamic networks; generative models; graph theory; mental disorders; multilayer networks; network neuroscience; psychiatry; schizophrenia

PMID:
29301099
PMCID:
PMC5757246
[Available on 2019-01-03]
DOI:
10.1016/j.neuron.2017.11.007

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