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Diagnostic and Statistical Manual of Mental Disorders (DSM)

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The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...

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Related Experiment Video

Updated: Jan 23, 2026

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

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Conceptualizing mental disorders as deviations from normative functioning.

Andre F Marquand^1,2,3, Seyed Mostafa Kia^4,5, Mariam Zabihi^4,5

¹Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands. a.marquand@donders.ru.nl.

Molecular Psychiatry

|June 16, 2019

Summary

This summary is machine-generated.

Normative models analyze individual biological data to understand mental disorders, moving beyond group averages. These statistical tools help identify unique patient variations, paving the way for precision medicine in psychiatry.

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Area of Science:

Psychiatry
Statistical modeling
Neuroscience

Background:

Mental disorders exhibit significant biological heterogeneity.
Current approaches often average data, masking individual differences.
Normative models offer a framework to analyze individual variations within clinical cohorts.

Purpose of the Study:

To provide a unifying review of normative modeling theory and applications in psychiatry.
To propose a conceptual framework linking diverse normative modeling methodologies.
To guide future applications of normative modeling in mental health research.

Main Methods:

Review of existing literature on normative modeling.
Focus on applications using quantitative neuroimaging biomarkers.
Development of a conceptual framework for normative modeling approaches.

Main Results:

Normative models provide statistically grounded inferences at the individual participant level.
These models effectively parse heterogeneity in clinical cohorts.
Applications in neuroimaging demonstrate utility for psychiatric biomarkers.

Conclusions:

Normative modeling is crucial for analyzing individual differences in heterogeneous mental disorders.
It bridges the gap between theoretical importance and practical data analysis.
Normative modeling represents a promising avenue for precision medicine in psychiatry.