Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019.
FreeSurfer
MRI
aging biomarker
brain aging
deep neural networks
machine learning
morphological features
Journal
Frontiers in psychiatry
ISSN: 1664-0640
Titre abrégé: Front Psychiatry
Pays: Switzerland
ID NLM: 101545006
Informations de publication
Date de publication:
2020
2020
Historique:
received:
20
10
2020
accepted:
18
12
2020
entrez:
8
2
2021
pubmed:
9
2
2021
medline:
9
2
2021
Statut:
epublish
Résumé
Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with
Identifiants
pubmed: 33551880
doi: 10.3389/fpsyt.2020.619629
pmc: PMC7854554
doi:
Types de publication
Journal Article
Langues
eng
Pagination
619629Informations de copyright
Copyright © 2021 Lombardi, Monaco, Donvito, Amoroso, Bellotti and Tangaro.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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