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Towards a brain‐based predictome of mental illness

Neuroimaging‐based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for indivi...

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Detalles Bibliográficos
Autores principales: Rashid, Barnaly, Calhoun, Vince
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375108/
https://www.ncbi.nlm.nih.gov/pubmed/32374075
http://dx.doi.org/10.1002/hbm.25013
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author Rashid, Barnaly
Calhoun, Vince
author_facet Rashid, Barnaly
Calhoun, Vince
author_sort Rashid, Barnaly
collection PubMed
description Neuroimaging‐based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term “predictome” to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network‐based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject‐level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention‐deficit hyperactivity disorder, obsessive–compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging‐based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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spelling pubmed-73751082020-07-22 Towards a brain‐based predictome of mental illness Rashid, Barnaly Calhoun, Vince Hum Brain Mapp Review Articles Neuroimaging‐based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term “predictome” to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network‐based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject‐level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention‐deficit hyperactivity disorder, obsessive–compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging‐based predictomic approaches, current trends, and common shortcomings and share our vision for future directions. John Wiley & Sons, Inc. 2020-05-06 /pmc/articles/PMC7375108/ /pubmed/32374075 http://dx.doi.org/10.1002/hbm.25013 Text en © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Articles
Rashid, Barnaly
Calhoun, Vince
Towards a brain‐based predictome of mental illness
title Towards a brain‐based predictome of mental illness
title_full Towards a brain‐based predictome of mental illness
title_fullStr Towards a brain‐based predictome of mental illness
title_full_unstemmed Towards a brain‐based predictome of mental illness
title_short Towards a brain‐based predictome of mental illness
title_sort towards a brain‐based predictome of mental illness
topic Review Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375108/
https://www.ncbi.nlm.nih.gov/pubmed/32374075
http://dx.doi.org/10.1002/hbm.25013
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