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Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry

It is now possible to estimate an individual’s brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neurops...

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Detalles Bibliográficos
Autores principales: Sone, Daichi, Beheshti, Iman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695293/
https://www.ncbi.nlm.nih.gov/pubmed/36579560
http://dx.doi.org/10.3390/jpm12111850
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author Sone, Daichi
Beheshti, Iman
author_facet Sone, Daichi
Beheshti, Iman
author_sort Sone, Daichi
collection PubMed
description It is now possible to estimate an individual’s brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.
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spelling pubmed-96952932022-11-26 Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry Sone, Daichi Beheshti, Iman J Pers Med Review It is now possible to estimate an individual’s brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions. MDPI 2022-11-05 /pmc/articles/PMC9695293/ /pubmed/36579560 http://dx.doi.org/10.3390/jpm12111850 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Sone, Daichi
Beheshti, Iman
Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry
title Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry
title_full Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry
title_fullStr Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry
title_full_unstemmed Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry
title_short Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry
title_sort neuroimaging-based brain age estimation: a promising personalized biomarker in neuropsychiatry
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695293/
https://www.ncbi.nlm.nih.gov/pubmed/36579560
http://dx.doi.org/10.3390/jpm12111850
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