Ethological computational psychiatry: Challenges and opportunities

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over sh...

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Published inCurrent opinion in neurobiology Vol. 86; p. 102881
Main Authors Monosov, Ilya E., Zimmermann, Jan, Frank, Michael J., Mathis, Mackenzie W., Baker, Justin T.
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.06.2024
Subjects
Animals
Artificial Intelligence
Ethology - methods
Humans
Mental Disorders - therapy
Psychiatry - methods
Psychiatry - trends
Online AccessGet full text
ISSN0959-4388
1873-6882
1873-6882
DOI10.1016/j.conb.2024.102881

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Abstract Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring. •Psychiatric disorders are non stationary, and evolve over long time scales.•They are associated with aberrant behavioral hierarchies.•New neurotechnology and analytic methods to study behavior continuously could facilitate precision neuropsychiatry.•Reverse translation across species and models is necessary to discover the disease related breakpoints in neural circuits.
AbstractList Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring. •Psychiatric disorders are non stationary, and evolve over long time scales.•They are associated with aberrant behavioral hierarchies.•New neurotechnology and analytic methods to study behavior continuously could facilitate precision neuropsychiatry.•Reverse translation across species and models is necessary to discover the disease related breakpoints in neural circuits.
Studying the intricacies of individual subjects’ moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring.
Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring.Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring.
ArticleNumber 102881
Author Zimmermann, Jan
Mathis, Mackenzie W.
Baker, Justin T.
Monosov, Ilya E.
Frank, Michael J.
AuthorAffiliation 2 Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
3 Carney Center for Computational Brain Science, Brown University, Providence, RI, USA
5 McLean Hospital, Harvard Medical School, Boston, MA, USA
1 Departments of Neuroscience, Biomedical Engineering, Electrical Engineering, and Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
4 Brain Mind Institute & NeuroX, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
AuthorAffiliation_xml – name: 1 Departments of Neuroscience, Biomedical Engineering, Electrical Engineering, and Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/38696972$$D View this record in MEDLINE/PubMed
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SubjectTerms Animals
Artificial Intelligence
Ethology - methods
Humans
Mental Disorders - therapy
Psychiatry - methods
Psychiatry - trends
Title Ethological computational psychiatry: Challenges and opportunities
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