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Related Experiment Video

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Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Published on: April 23, 2015

Computational Phenotyping in Psychiatry: A Worked Example.

Philipp Schwartenbeck¹, Karl Friston²

¹The Wellcome Trust Centre for Neuroimaging, UCL, London WC1N 3BG, UK; Centre for Cognitive Neuroscience, University of Salzburg, 5020 Salzburg, Austria; Neuroscience Institute, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, A-5020 Salzburg, Austria; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK.

|August 13, 2016

Summary

This summary is machine-generated.

Computational psychiatry uses computational models to understand brain abnormalities in psychopathology. This approach offers mechanistic insights for diagnosis, treatment, and relapse prediction.

Keywords:

Markov decision process active inference computational psychiatry generative model model inversion

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Area of Science:

Neuroscience
Psychiatry
Computational Science

Background:

Computational psychiatry leverages model-based approaches to investigate psychopathology.
It aims to provide mechanistic insights into brain function and psychiatric disorders.
This field seeks a quantitative framework for diagnosis, treatment, and relapse prediction.

Purpose of the Study:

To provide an illustrative overview of the computational psychiatry workflow.
To demonstrate the process of modeling choice behavior, simulating data, and parameter inference.
To showcase cross-validation for assessing diagnostic group recovery.

Main Methods:

Formalizing behavioral or neuronal processes using computational models.
Estimating model parameters from measured behavioral or neuronal responses.
Utilizing a two-step maze task with a choice behavior model based on active inference and Markov decision processes.

Main Results:

Successful simulation and inversion of a computational model for choice behavior.
Estimation of group-level effects from simulated data.
Demonstration of cross-validation for recovering between-subject variables like diagnosis.

Conclusions:

The illustrated computational psychiatry workflow is broadly applicable across various research domains.
Model-based inference provides a quantitative approach to understanding psychopathology.
This methodology supports mechanistic insights into brain function and psychiatric nosology.