Prediction of East Asian Brain Age using Machine Learning Algorithms Trained With Community-based Healthy Brain MRI.
Asians
Machine Learning
Magnetic Resonance Imaging
Regression Analysis
Journal
Dementia and neurocognitive disorders
ISSN: 2384-0757
Titre abrégé: Dement Neurocogn Disord
Pays: Korea (South)
ID NLM: 101600298
Informations de publication
Date de publication:
Oct 2022
Oct 2022
Historique:
received:
31
08
2022
revised:
20
10
2022
accepted:
31
10
2022
entrez:
21
11
2022
pubmed:
22
11
2022
medline:
22
11
2022
Statut:
ppublish
Résumé
Magnetic resonance imaging (MRI) helps with brain development analysis and disease diagnosis. Brain volumes measured from different ages using MRI provides useful information in clinical evaluation and research. Therefore, we trained machine learning models that predict the brain age gap of healthy subjects in the East Asian population using T1 brain MRI volume images. In total, 154 T1-weighted MRIs of healthy subjects (55-83 years of age) were collected from an East Asian community. The information of age, gender, and education level was collected for each participant. The MRIs of the participants were preprocessed using FreeSurfer(https://surfer.nmr.mgh.harvard.edu/) to collect the brain volume data. We trained the models using different supervised machine learning regression algorithms from the scikit-learn (https://scikit-learn.org/) library. The trained models comprised 19 features that had been reduced from 55 brain volume labels. The algorithm BayesianRidge (BR) achieved a mean absolute error (MAE) and r squared (R The MAE and R
Sections du résumé
Background and Purpose
UNASSIGNED
Magnetic resonance imaging (MRI) helps with brain development analysis and disease diagnosis. Brain volumes measured from different ages using MRI provides useful information in clinical evaluation and research. Therefore, we trained machine learning models that predict the brain age gap of healthy subjects in the East Asian population using T1 brain MRI volume images.
Methods
UNASSIGNED
In total, 154 T1-weighted MRIs of healthy subjects (55-83 years of age) were collected from an East Asian community. The information of age, gender, and education level was collected for each participant. The MRIs of the participants were preprocessed using FreeSurfer(https://surfer.nmr.mgh.harvard.edu/) to collect the brain volume data. We trained the models using different supervised machine learning regression algorithms from the scikit-learn (https://scikit-learn.org/) library.
Results
UNASSIGNED
The trained models comprised 19 features that had been reduced from 55 brain volume labels. The algorithm BayesianRidge (BR) achieved a mean absolute error (MAE) and r squared (R
Conclusions
UNASSIGNED
The MAE and R
Identifiants
pubmed: 36407289
doi: 10.12779/dnd.2022.21.4.138
pmc: PMC9644058
doi:
Types de publication
Journal Article
Langues
eng
Pagination
138-146Informations de copyright
© 2022 Korean Dementia Association.
Déclaration de conflit d'intérêts
Conflicts of Interest: The authors have no financial conflicts of interest.
Références
IEEE Trans Biomed Eng. 2003 Oct;50(10):1136-42
pubmed: 14560766
Front Aging Neurosci. 2018 Aug 22;10:252
pubmed: 30186151
Front Neurol. 2020 Oct 19;11:584682
pubmed: 33193046
Sci Rep. 2021 Jul 1;11(1):13639
pubmed: 34211058
Eur J Neurosci. 2022 Sep;56(5):4619-4641
pubmed: 35799402
Hum Brain Mapp. 2021 Apr 15;42(6):1626-1640
pubmed: 33314530
Neurobiol Aging. 2021 Sep;105:78-85
pubmed: 34049061
Am J Orthod Dentofacial Orthop. 2016 Mar;149(3):431-4
pubmed: 26926032
Nat Rev Neurol. 2019 Oct;15(10):565-581
pubmed: 31501588
IEEE J Biomed Health Inform. 2022 Apr;26(4):1432-1440
pubmed: 34029201
Neurobiol Aging. 2020 Mar;87:44-48
pubmed: 31843257
Genes (Basel). 2018 Aug 21;9(9):
pubmed: 30134623
Brain Sci. 2022 Apr 29;12(5):
pubmed: 35624966
Proc Natl Acad Sci U S A. 2019 Oct 15;116(42):21213-21218
pubmed: 31575746
Int J Environ Res Public Health. 2020 Feb 24;17(4):
pubmed: 32102277
Front Aging Neurosci. 2018 Feb 12;10:28
pubmed: 29483870
J Gerontol B Psychol Sci Soc Sci. 2022 Mar 3;77(3):589-591
pubmed: 33788932
Brain Nerve. 2018 Jul;70(7):841-848
pubmed: 29997280
SN Comput Sci. 2021;2(3):160
pubmed: 33778771
Semin Perinatol. 2015 Mar;39(2):73-104
pubmed: 25743582
Trends Neurosci. 2017 Dec;40(12):681-690
pubmed: 29074032