A Theoretical Framework for Evaluating Psychiatric Research Strategies

One of the major goals of basic studies in psychiatry is to find etiological mechanisms or biomarkers of mental disorders. A standard research strategy to pursue this goal is to compare observations of potential factors from patients with those from healthy controls. Classifications of individuals i...

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Main Authors: Kentaro Katahira, Yuichi Yamashita
Format: Article
Language:English
Published: The MIT Press 2017-12-01
Series:Computational Psychiatry
Subjects:
Online Access:https://www.mitpressjournals.org/doi/pdf/10.1162/CPSY_a_00008
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spelling doaj-5a610fcbe17e4ae09da8f53da4a2f46e2020-11-25T02:34:04ZengThe MIT PressComputational Psychiatry2379-62272017-12-01118420710.1162/CPSY_a_00008CPSY_a_00008A Theoretical Framework for Evaluating Psychiatric Research StrategiesKentaro Katahira0Yuichi Yamashita1Department of Psychology, Graduate School of Informatics, Nagoya University, Nagoya, Aichi, JapanDepartment of Functional Brain Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, JapanOne of the major goals of basic studies in psychiatry is to find etiological mechanisms or biomarkers of mental disorders. A standard research strategy to pursue this goal is to compare observations of potential factors from patients with those from healthy controls. Classifications of individuals into patient and control groups are generally based on a diagnostic system, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases (ICD). Several flaws in these conventional diagnostic-based approaches have been recognized. The flaws are primarily due to the complexity in the relation between the pathogenetic factors (causes) and disorders: The current diagnostic categories may not reflect the underlying etiological mechanisms. To overcome this difficulty, the National Institute of Mental Health initiated a novel research strategy called Research Domain Criteria (RDoC), which encourages studies to focus on the neurobiological mechanisms and core aspects of behavior rather than to rely on traditional diagnostic categories. However, how RDoC can improve research in psychiatry remains a matter of debate. In this article, we propose a theoretical framework for evaluating psychiatric research strategies, including the conventional diagnostic category-based approaches and the RDoC approach. The proposed framework is based on the statistical modeling of the processes of how the disorder arises from pathogenetic factors. This framework provides the statistical power to quantify how likely relevant pathogenetic factors are to be detected under various research strategies. On the basis of the proposed framework, we can discuss which approach performs better in different types of situations. We present several theoretical and numerical results that highlight the advantages and disadvantages of the strategies. We also demonstrate how a computational model is incorporated into the proposed framework as a generative model of behavioral observations. This demonstration highlights how the computational models contribute to designing psychiatric studies.https://www.mitpressjournals.org/doi/pdf/10.1162/CPSY_a_00008psychiatric researchstatistical powergenerative modelresearch domain criteriadiagnostic category
collection DOAJ
language English
format Article
sources DOAJ
author Kentaro Katahira
Yuichi Yamashita
spellingShingle Kentaro Katahira
Yuichi Yamashita
A Theoretical Framework for Evaluating Psychiatric Research Strategies
Computational Psychiatry
psychiatric research
statistical power
generative model
research domain criteria
diagnostic category
author_facet Kentaro Katahira
Yuichi Yamashita
author_sort Kentaro Katahira
title A Theoretical Framework for Evaluating Psychiatric Research Strategies
title_short A Theoretical Framework for Evaluating Psychiatric Research Strategies
title_full A Theoretical Framework for Evaluating Psychiatric Research Strategies
title_fullStr A Theoretical Framework for Evaluating Psychiatric Research Strategies
title_full_unstemmed A Theoretical Framework for Evaluating Psychiatric Research Strategies
title_sort theoretical framework for evaluating psychiatric research strategies
publisher The MIT Press
series Computational Psychiatry
issn 2379-6227
publishDate 2017-12-01
description One of the major goals of basic studies in psychiatry is to find etiological mechanisms or biomarkers of mental disorders. A standard research strategy to pursue this goal is to compare observations of potential factors from patients with those from healthy controls. Classifications of individuals into patient and control groups are generally based on a diagnostic system, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases (ICD). Several flaws in these conventional diagnostic-based approaches have been recognized. The flaws are primarily due to the complexity in the relation between the pathogenetic factors (causes) and disorders: The current diagnostic categories may not reflect the underlying etiological mechanisms. To overcome this difficulty, the National Institute of Mental Health initiated a novel research strategy called Research Domain Criteria (RDoC), which encourages studies to focus on the neurobiological mechanisms and core aspects of behavior rather than to rely on traditional diagnostic categories. However, how RDoC can improve research in psychiatry remains a matter of debate. In this article, we propose a theoretical framework for evaluating psychiatric research strategies, including the conventional diagnostic category-based approaches and the RDoC approach. The proposed framework is based on the statistical modeling of the processes of how the disorder arises from pathogenetic factors. This framework provides the statistical power to quantify how likely relevant pathogenetic factors are to be detected under various research strategies. On the basis of the proposed framework, we can discuss which approach performs better in different types of situations. We present several theoretical and numerical results that highlight the advantages and disadvantages of the strategies. We also demonstrate how a computational model is incorporated into the proposed framework as a generative model of behavioral observations. This demonstration highlights how the computational models contribute to designing psychiatric studies.
topic psychiatric research
statistical power
generative model
research domain criteria
diagnostic category
url https://www.mitpressjournals.org/doi/pdf/10.1162/CPSY_a_00008
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