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Translating Research Findings into Clinical Practice: A Systematic and Critical Review of Neuroimaging-based Clinical Tools for Brain Disorders

Research output: Contribution to journalArticle

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Translating Research Findings into Clinical Practice : A Systematic and Critical Review of Neuroimaging-based Clinical Tools for Brain Disorders. / Scarpazza, Cristina; Ha, Minji; Baecker, Lea; Garcia Dias, Rafael; Pinaya, Walter; Vieira, Sandra; Mechelli, Andrea.

In: Translational psychiatry, 25.03.2020.

Research output: Contribution to journalArticle

Harvard

Scarpazza, C, Ha, M, Baecker, L, Garcia Dias, R, Pinaya, W, Vieira, S & Mechelli, A 2020, 'Translating Research Findings into Clinical Practice: A Systematic and Critical Review of Neuroimaging-based Clinical Tools for Brain Disorders', Translational psychiatry.

APA

Scarpazza, C., Ha, M., Baecker, L., Garcia Dias, R., Pinaya, W., Vieira, S., & Mechelli, A. (Accepted/In press). Translating Research Findings into Clinical Practice: A Systematic and Critical Review of Neuroimaging-based Clinical Tools for Brain Disorders. Translational psychiatry.

Vancouver

Scarpazza C, Ha M, Baecker L, Garcia Dias R, Pinaya W, Vieira S et al. Translating Research Findings into Clinical Practice: A Systematic and Critical Review of Neuroimaging-based Clinical Tools for Brain Disorders. Translational psychiatry. 2020 Mar 25.

Author

Scarpazza, Cristina ; Ha, Minji ; Baecker, Lea ; Garcia Dias, Rafael ; Pinaya, Walter ; Vieira, Sandra ; Mechelli, Andrea. / Translating Research Findings into Clinical Practice : A Systematic and Critical Review of Neuroimaging-based Clinical Tools for Brain Disorders. In: Translational psychiatry. 2020.

Bibtex Download

@article{f3dd1ce28a384bef85520508d1b56237,
title = "Translating Research Findings into Clinical Practice: A Systematic and Critical Review of Neuroimaging-based Clinical Tools for Brain Disorders",
abstract = "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 8 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.",
author = "Cristina Scarpazza and Minji Ha and Lea Baecker and {Garcia Dias}, Rafael and Walter Pinaya and Sandra Vieira and Andrea Mechelli",
year = "2020",
month = "3",
day = "25",
language = "English",
journal = "Translational psychiatry",
issn = "2158-3188",
publisher = "Nature Publishing Group",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Translating Research Findings into Clinical Practice

T2 - A Systematic and Critical Review of Neuroimaging-based Clinical Tools for Brain Disorders

AU - Scarpazza, Cristina

AU - Ha, Minji

AU - Baecker, Lea

AU - Garcia Dias, Rafael

AU - Pinaya, Walter

AU - Vieira, Sandra

AU - Mechelli, Andrea

PY - 2020/3/25

Y1 - 2020/3/25

N2 - 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 8 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.

AB - 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 8 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.

M3 - Article

JO - Translational psychiatry

JF - Translational psychiatry

SN - 2158-3188

ER -

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