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Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019
As we age, our brain structure changes and our cognitive capabilities decline. Although brain aging is universal, rates of brain aging differ markedly, which can be associated with pathological mechanism of psychiatric and neurological diseases. Predictive models have been applied to neuroimaging da...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7738323/ https://www.ncbi.nlm.nih.gov/pubmed/33343431 http://dx.doi.org/10.3389/fpsyt.2020.604478 |
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author | Da Costa, Pedro F. Dafflon, Jessica Pinaya, Walter H. L. |
author_facet | Da Costa, Pedro F. Dafflon, Jessica Pinaya, Walter H. L. |
author_sort | Da Costa, Pedro F. |
collection | PubMed |
description | As we age, our brain structure changes and our cognitive capabilities decline. Although brain aging is universal, rates of brain aging differ markedly, which can be associated with pathological mechanism of psychiatric and neurological diseases. Predictive models have been applied to neuroimaging data to learn patterns associated with this variability and develop a neuroimaging biomarker of the brain condition. Aiming to stimulate the development of more accurate brain-age predictors, the Predictive Analytics Competition (PAC) 2019 provided a challenge that included a dataset of 2,640 participants. Here, we present our approach which placed between the top 10 of the challenge. We developed an ensemble of shallow machine learning methods (e.g., Support Vector Regression and Decision Tree-based regressors) that combined voxel-based and surface-based morphometric data. We used normalized brain volume maps (i.e., gray matter, white matter, or both) and features of cortical regions and anatomical structures, like cortical thickness, volume, and mean curvature. In order to fine-tune the hyperparameters of the machine learning methods, we combined the use of genetic algorithms and grid search. Our ensemble had a mean absolute error of 3.7597 years on the competition, showing the potential that shallow methods still have in predicting brain-age. |
format | Online Article Text |
id | pubmed-7738323 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77383232020-12-17 Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019 Da Costa, Pedro F. Dafflon, Jessica Pinaya, Walter H. L. Front Psychiatry Psychiatry As we age, our brain structure changes and our cognitive capabilities decline. Although brain aging is universal, rates of brain aging differ markedly, which can be associated with pathological mechanism of psychiatric and neurological diseases. Predictive models have been applied to neuroimaging data to learn patterns associated with this variability and develop a neuroimaging biomarker of the brain condition. Aiming to stimulate the development of more accurate brain-age predictors, the Predictive Analytics Competition (PAC) 2019 provided a challenge that included a dataset of 2,640 participants. Here, we present our approach which placed between the top 10 of the challenge. We developed an ensemble of shallow machine learning methods (e.g., Support Vector Regression and Decision Tree-based regressors) that combined voxel-based and surface-based morphometric data. We used normalized brain volume maps (i.e., gray matter, white matter, or both) and features of cortical regions and anatomical structures, like cortical thickness, volume, and mean curvature. In order to fine-tune the hyperparameters of the machine learning methods, we combined the use of genetic algorithms and grid search. Our ensemble had a mean absolute error of 3.7597 years on the competition, showing the potential that shallow methods still have in predicting brain-age. Frontiers Media S.A. 2020-12-02 /pmc/articles/PMC7738323/ /pubmed/33343431 http://dx.doi.org/10.3389/fpsyt.2020.604478 Text en Copyright © 2020 Da Costa, Dafflon and Pinaya. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Da Costa, Pedro F. Dafflon, Jessica Pinaya, Walter H. L. Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019 |
title | Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019 |
title_full | Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019 |
title_fullStr | Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019 |
title_full_unstemmed | Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019 |
title_short | Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019 |
title_sort | brain-age prediction using shallow machine learning: predictive analytics competition 2019 |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7738323/ https://www.ncbi.nlm.nih.gov/pubmed/33343431 http://dx.doi.org/10.3389/fpsyt.2020.604478 |
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