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Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry

Computational modeling has been applied for data analysis in psychology, neuroscience, and psychiatry. One of its important uses is to infer the latent variables underlying behavior by which researchers can evaluate corresponding neural, physiological, or behavioral measures. This feature is especia...

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Detalles Bibliográficos
Autores principales: Katahira, Kentaro, Toyama, Asako
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899379/
https://www.ncbi.nlm.nih.gov/pubmed/33561125
http://dx.doi.org/10.1371/journal.pcbi.1008738
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author Katahira, Kentaro
Toyama, Asako
author_facet Katahira, Kentaro
Toyama, Asako
author_sort Katahira, Kentaro
collection PubMed
description Computational modeling has been applied for data analysis in psychology, neuroscience, and psychiatry. One of its important uses is to infer the latent variables underlying behavior by which researchers can evaluate corresponding neural, physiological, or behavioral measures. This feature is especially crucial for computational psychiatry, in which altered computational processes underlying mental disorders are of interest. For instance, several studies employing model-based fMRI—a method for identifying brain regions correlated with latent variables—have shown that patients with mental disorders (e.g., depression) exhibit diminished neural responses to reward prediction errors (RPEs), which are the differences between experienced and predicted rewards. Such model-based analysis has the drawback that the parameter estimates and inference of latent variables are not necessarily correct—rather, they usually contain some errors. A previous study theoretically and empirically showed that the error in model-fitting does not necessarily cause a serious error in model-based fMRI. However, the study did not deal with certain situations relevant to psychiatry, such as group comparisons between patients and healthy controls. We developed a theoretical framework to explore such situations. We demonstrate that the parameter-misspecification can critically affect the results of group comparison. We demonstrate that even if the RPE response in patients is completely intact, a spurious difference to healthy controls is observable. Such a situation occurs when the ground-truth learning rate differs between groups but a common learning rate is used, as per previous studies. Furthermore, even if the parameters are appropriately fitted to individual participants, spurious group differences in RPE responses are observable when the model lacks a component that differs between groups. These results highlight the importance of appropriate model-fitting and the need for caution when interpreting the results of model-based fMRI.
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spelling pubmed-78993792021-03-02 Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry Katahira, Kentaro Toyama, Asako PLoS Comput Biol Research Article Computational modeling has been applied for data analysis in psychology, neuroscience, and psychiatry. One of its important uses is to infer the latent variables underlying behavior by which researchers can evaluate corresponding neural, physiological, or behavioral measures. This feature is especially crucial for computational psychiatry, in which altered computational processes underlying mental disorders are of interest. For instance, several studies employing model-based fMRI—a method for identifying brain regions correlated with latent variables—have shown that patients with mental disorders (e.g., depression) exhibit diminished neural responses to reward prediction errors (RPEs), which are the differences between experienced and predicted rewards. Such model-based analysis has the drawback that the parameter estimates and inference of latent variables are not necessarily correct—rather, they usually contain some errors. A previous study theoretically and empirically showed that the error in model-fitting does not necessarily cause a serious error in model-based fMRI. However, the study did not deal with certain situations relevant to psychiatry, such as group comparisons between patients and healthy controls. We developed a theoretical framework to explore such situations. We demonstrate that the parameter-misspecification can critically affect the results of group comparison. We demonstrate that even if the RPE response in patients is completely intact, a spurious difference to healthy controls is observable. Such a situation occurs when the ground-truth learning rate differs between groups but a common learning rate is used, as per previous studies. Furthermore, even if the parameters are appropriately fitted to individual participants, spurious group differences in RPE responses are observable when the model lacks a component that differs between groups. These results highlight the importance of appropriate model-fitting and the need for caution when interpreting the results of model-based fMRI. Public Library of Science 2021-02-09 /pmc/articles/PMC7899379/ /pubmed/33561125 http://dx.doi.org/10.1371/journal.pcbi.1008738 Text en © 2021 Katahira, Toyama http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Katahira, Kentaro
Toyama, Asako
Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry
title Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry
title_full Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry
title_fullStr Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry
title_full_unstemmed Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry
title_short Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry
title_sort revisiting the importance of model fitting for model-based fmri: it does matter in computational psychiatry
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899379/
https://www.ncbi.nlm.nih.gov/pubmed/33561125
http://dx.doi.org/10.1371/journal.pcbi.1008738
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