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Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders
A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of th...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170931/ https://www.ncbi.nlm.nih.gov/pubmed/32313006 http://dx.doi.org/10.1038/s41398-020-0798-6 |
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author | Scarpazza, C. Ha, M. Baecker, L. Garcia-Dias, R. Pinaya, W. H. L. Vieira, S. Mechelli, A. |
author_facet | Scarpazza, C. Ha, M. Baecker, L. Garcia-Dias, R. Pinaya, W. H. L. Vieira, S. Mechelli, A. |
author_sort | Scarpazza, C. |
collection | PubMed |
description | A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of the variance in clinical outcome. Building on these findings, a number of imaging-based clinical tools have been developed to make diagnostic and prognostic inferences about individual patients from their structural Magnetic Resonance Imaging scans. This systematic review describes and compares the technical characteristics of the available tools, with the aim to assess their translational potential into real-world clinical settings. The results reveal that a total of eight tools. All of these were specifically developed for neurological disorders, and as such are not suitable for application to psychiatric disorders. Furthermore, most of the tools were trained and validated in a single dataset, which can result in poor generalizability, or using a small number of individuals, which can cause overoptimistic results. In addition, all of the tools rely on two strategies to detect brain abnormalities in single individuals, one based on univariate comparison, and the other based on multivariate machine-learning algorithms. We discuss current barriers to the adoption of these tools in clinical practice and propose a checklist of pivotal characteristics that should be included in an “ideal” neuroimaging-based clinical tool for brain disorders. |
format | Online Article Text |
id | pubmed-7170931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-71709312020-04-29 Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders Scarpazza, C. Ha, M. Baecker, L. Garcia-Dias, R. Pinaya, W. H. L. Vieira, S. Mechelli, A. Transl Psychiatry Review Article A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of the variance in clinical outcome. Building on these findings, a number of imaging-based clinical tools have been developed to make diagnostic and prognostic inferences about individual patients from their structural Magnetic Resonance Imaging scans. This systematic review describes and compares the technical characteristics of the available tools, with the aim to assess their translational potential into real-world clinical settings. The results reveal that a total of eight tools. All of these were specifically developed for neurological disorders, and as such are not suitable for application to psychiatric disorders. Furthermore, most of the tools were trained and validated in a single dataset, which can result in poor generalizability, or using a small number of individuals, which can cause overoptimistic results. In addition, all of the tools rely on two strategies to detect brain abnormalities in single individuals, one based on univariate comparison, and the other based on multivariate machine-learning algorithms. We discuss current barriers to the adoption of these tools in clinical practice and propose a checklist of pivotal characteristics that should be included in an “ideal” neuroimaging-based clinical tool for brain disorders. Nature Publishing Group UK 2020-04-20 /pmc/articles/PMC7170931/ /pubmed/32313006 http://dx.doi.org/10.1038/s41398-020-0798-6 Text en © The Author(s) 2020 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/. |
spellingShingle | Review Article Scarpazza, C. Ha, M. Baecker, L. Garcia-Dias, R. Pinaya, W. H. L. Vieira, S. Mechelli, A. Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders |
title | Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders |
title_full | Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders |
title_fullStr | Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders |
title_full_unstemmed | Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders |
title_short | Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders |
title_sort | translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170931/ https://www.ncbi.nlm.nih.gov/pubmed/32313006 http://dx.doi.org/10.1038/s41398-020-0798-6 |
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