Brain Age Prediction With Morphological Features Using Deep Neural Networks

Results From Predictive Analytic Competition 2019

Bibliographic Data

ID15523239
AuthorsAngela Lombardi (0000-0002-2013-3009, University of Bari Aldo Moro), A Monaco (0000-0002-5968-8642, Istituto Nazionale di Fisica Nucleare), Giacinto Donvito (0000-0002-0628-1080, Istituto Nazionale di Fisica Nucleare), Nicola Amoroso (0000-0003-0211-0783, University of Bari Aldo Moro), R Bellotti (0000-0003-3198-2708, Istituto Nazionale di Fisica Nucleare), Sabina Tangaro (0000-0002-1372-3916, University of Bari Aldo Moro, corresponding author)
Year2021
Volume11
Pages619629-619629
Publication date2021-01-20
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2020.619629
PMID33551880
OpenAlexW3121408759
LanguageEN
Citations received1
References cited67

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 MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results

Artificial neural network · Deep learning · Deep neural networks · Feature (linguistics · Interpretability · Lasso (programming language · Machine learning · Neuroimaging · Pattern recognition (psychology · Predictive modelling · Advanced Neuroimaging Techniques and Applications · Computer Science · Functional Brain Connectivity Studies · Health, Environment, Cognitive Aging · Neuroscience · Psychology · Artificial Intelligence