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Curr Top Behav Neurosci. 2018;40:79-109. doi: 10.1007/7854_2018_41.

Network Neuroscience: A Framework for Developing Biomarkers in Psychiatry.

Author information

1
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
2
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA. dsb@seas.upenn.edu.
3
Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA. dsb@seas.upenn.edu.
4
Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. dsb@seas.upenn.edu.

Abstract

Psychiatric disorders are disturbances of cognitive and behavioral processes mediated by the brain. Emerging evidence suggests that accurate biomarkers for psychiatric disorders might benefit from incorporating information regarding multiple brain regions and their interactions with one another, rather than considering local perturbations in brain structure and function alone. Recent advances in the field of applied mathematics generally - and network science specifically - provide a language to capture the complexity of interacting brain regions, and the application of this language to fundamental questions in neuroscience forms the emerging field of network neuroscience. This chapter provides an overview of the use and utility of network neuroscience for building biomarkers in psychiatry. The chapter begins with an overview of the theoretical frameworks and tools that encompass network neuroscience before describing applications of network neuroscience to the study of schizophrenia and major depressive disorder. With reference to work on genetic, molecular, and environmental correlates of network neuroscience features, the promises and challenges of network neuroscience for providing tools that aid in the diagnosis and the evaluation of treatment response in psychiatric disorders are discussed.

KEYWORDS:

Cognitive neuroscience; Depression; Graph theory; Network neuroscience; Schizophrenia

PMID:
29626337
DOI:
10.1007/7854_2018_41

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