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Predicting the future of neuroimaging predictive models in mental health

Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora of literature reviewing published studies, the mathematics underlying machine lea...

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Autores principales: Tejavibulya, Link, Rolison, Max, Gao, Siyuan, Liang, Qinghao, Peterson, Hannah, Dadashkarimi, Javid, Farruggia, Michael C., Hahn, C. Alice, Noble, Stephanie, Lichenstein, Sarah D., Pollatou, Angeliki, Dufford, Alexander J., Scheinost, Dustin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708554/
https://www.ncbi.nlm.nih.gov/pubmed/35697759
http://dx.doi.org/10.1038/s41380-022-01635-2
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author Tejavibulya, Link
Rolison, Max
Gao, Siyuan
Liang, Qinghao
Peterson, Hannah
Dadashkarimi, Javid
Farruggia, Michael C.
Hahn, C. Alice
Noble, Stephanie
Lichenstein, Sarah D.
Pollatou, Angeliki
Dufford, Alexander J.
Scheinost, Dustin
author_facet Tejavibulya, Link
Rolison, Max
Gao, Siyuan
Liang, Qinghao
Peterson, Hannah
Dadashkarimi, Javid
Farruggia, Michael C.
Hahn, C. Alice
Noble, Stephanie
Lichenstein, Sarah D.
Pollatou, Angeliki
Dufford, Alexander J.
Scheinost, Dustin
author_sort Tejavibulya, Link
collection PubMed
description Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora of literature reviewing published studies, the mathematics underlying machine learning, and the best practices for using these approaches. As our knowledge of mental health and machine learning continue to evolve, we instead aim to look forward and “predict” topics that we believe will be important in current and future studies. Some of the most discussed topics in machine learning, such as bias and fairness, the handling of dirty data, and interpretable models, may be less familiar to the broader community using neuroimaging-based predictive modeling in psychiatry. In a similar vein, transdiagnostic research and targeting brain-based features for psychiatric intervention are modern topics in psychiatry that predictive models are well-suited to tackle. In this work, we target an audience who is a researcher familiar with the fundamental procedures of machine learning and who wishes to increase their knowledge of ongoing topics in the field. We aim to accelerate the utility and applications of neuroimaging-based predictive models for psychiatric research by highlighting and considering these topics. Furthermore, though not a focus, these ideas generalize to neuroimaging-based predictive modeling in other clinical neurosciences and predictive modeling with different data types (e.g., digital health data).
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spelling pubmed-97085542022-12-01 Predicting the future of neuroimaging predictive models in mental health Tejavibulya, Link Rolison, Max Gao, Siyuan Liang, Qinghao Peterson, Hannah Dadashkarimi, Javid Farruggia, Michael C. Hahn, C. Alice Noble, Stephanie Lichenstein, Sarah D. Pollatou, Angeliki Dufford, Alexander J. Scheinost, Dustin Mol Psychiatry Review Article Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora of literature reviewing published studies, the mathematics underlying machine learning, and the best practices for using these approaches. As our knowledge of mental health and machine learning continue to evolve, we instead aim to look forward and “predict” topics that we believe will be important in current and future studies. Some of the most discussed topics in machine learning, such as bias and fairness, the handling of dirty data, and interpretable models, may be less familiar to the broader community using neuroimaging-based predictive modeling in psychiatry. In a similar vein, transdiagnostic research and targeting brain-based features for psychiatric intervention are modern topics in psychiatry that predictive models are well-suited to tackle. In this work, we target an audience who is a researcher familiar with the fundamental procedures of machine learning and who wishes to increase their knowledge of ongoing topics in the field. We aim to accelerate the utility and applications of neuroimaging-based predictive models for psychiatric research by highlighting and considering these topics. Furthermore, though not a focus, these ideas generalize to neuroimaging-based predictive modeling in other clinical neurosciences and predictive modeling with different data types (e.g., digital health data). Nature Publishing Group UK 2022-06-13 2022 /pmc/articles/PMC9708554/ /pubmed/35697759 http://dx.doi.org/10.1038/s41380-022-01635-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Review Article
Tejavibulya, Link
Rolison, Max
Gao, Siyuan
Liang, Qinghao
Peterson, Hannah
Dadashkarimi, Javid
Farruggia, Michael C.
Hahn, C. Alice
Noble, Stephanie
Lichenstein, Sarah D.
Pollatou, Angeliki
Dufford, Alexander J.
Scheinost, Dustin
Predicting the future of neuroimaging predictive models in mental health
title Predicting the future of neuroimaging predictive models in mental health
title_full Predicting the future of neuroimaging predictive models in mental health
title_fullStr Predicting the future of neuroimaging predictive models in mental health
title_full_unstemmed Predicting the future of neuroimaging predictive models in mental health
title_short Predicting the future of neuroimaging predictive models in mental health
title_sort predicting the future of neuroimaging predictive models in mental health
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708554/
https://www.ncbi.nlm.nih.gov/pubmed/35697759
http://dx.doi.org/10.1038/s41380-022-01635-2
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