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  Complexity and Heterogeneity in Psychiatric Disorders: Opportunities for Computational Psychiatry

Totah, N., Akil, H., Huys, Q., Krystal, J., MacDonald, A., Maia, T., et al. (2016). Complexity and Heterogeneity in Psychiatric Disorders: Opportunities for Computational Psychiatry. In A. Redish, & J. Gordon (Eds.), Computational Psychiatry: New Perspectives on Mental Illness (pp. 33-59). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-1148-A Version Permalink: http://hdl.handle.net/21.11116/0000-0002-1163-B
Genre: Book Chapter

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 Creators:
Totah, NK1, 2, Author              
Akil, H, Author
Huys, QJM, Author
Krystal, JH, Author
MacDonald, AW, Author
Maia, TV, Author
Malenka, RC, Author
Pauli, WG, Author
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Psychiatry faces numerous challenges: the reconceptualization of symptoms and diagnoses, disease prevention, treatment development and monitoring of its effects, and the provision of individualized, precision medicine. To confront the complexity and heterogeneity intrinsic to brain disorders, psychiatry needs better biological, quantitative, and theoretical grounding. This chapter seeks to identify the sources of complexity and heterogeneity, which include the interplay between genetic and epigenetic factors with the environment and their impact on neural circuits. Computational approaches provide a framework to address complexity and heterogeneity, which cannot be seen as noise to be eliminated from diagnosis and treatment of disorders. Complexity and heterogeneity arise from intrinsic features of brain function, and thus present opportunities for computational models to provide a more accurate biological foundation for diagnosis and treatment of psychiatric disorders. Challenges to be addressed by a computational framework include: (a) improving the search for risk factors and biomarkers, which can be used toward primary prevention of disease; (b) representing the biological ground truth of psychiatric disorders, which will improve the accuracy of diagnostic categories, assist in discovering new treatments, and aid in precision medicine; (c) representing how risk factors, biomarkers, and the underlying biology change through the course of development, disease progression, and treatment process.

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 Dates: 2016
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
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Source 1

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Title: Computational Psychiatry: New Perspectives on Mental Illness
Source Genre: Book
 Creator(s):
Redish, AD, Editor
Gordon, JA, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: 3 Start / End Page: 33 - 59 Identifier: ISBN: 978-0-262-03542-2

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Title: Strüngmann Forum Reports
Source Genre: Series
 Creator(s):
Lupp, J, Editor
Affiliations:
-
Publ. Info: -
Pages: - Volume / Issue: 20 Sequence Number: - Start / End Page: - Identifier: -