Quantifying performance of machine learning methods for neuroimaging data
Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the...
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| Published in: | NeuroImage (Orlando, Fla.) Vol. 199; pp. 351 - 365 |
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| Main Authors: | , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Elsevier Inc
01.10.2019
Elsevier Limited |
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| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
| Online Access: | Get full text |
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| Abstract | Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques – embedded feature selection and bootstrap aggregation (bagging) – to model performance was also quantified. Five machine learning regression methods – Gaussian Process Regression, Multiple Kernel Learning, Kernel Ridge Regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge Regression and Gaussian Process Regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size.
•The choice of machine learning algorithm influenced prediction accuracy.•Sample size was important: prediction accuracy generally increased once N ≥ 400.•The Elastic Net performed well at a range of effect sizes, relative to other methods.•Random Forest performed well at small effect sizes.•Gaussian Process Regression performed well at large effect sizes. |
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| AbstractList | Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques – embedded feature selection and bootstrap aggregation (bagging) – to model performance was also quantified. Five machine learning regression methods – Gaussian Process Regression, Multiple Kernel Learning, Kernel Ridge Regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge Regression and Gaussian Process Regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size. Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques – embedded feature selection and bootstrap aggregation (bagging) – to model performance was also quantified. Five machine learning regression methods – Gaussian Process Regression, Multiple Kernel Learning, Kernel Ridge Regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge Regression and Gaussian Process Regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size. •The choice of machine learning algorithm influenced prediction accuracy.•Sample size was important: prediction accuracy generally increased once N ≥ 400.•The Elastic Net performed well at a range of effect sizes, relative to other methods.•Random Forest performed well at small effect sizes.•Gaussian Process Regression performed well at large effect sizes. Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques - embedded feature selection and bootstrap aggregation (bagging) - to model performance was also quantified. Five machine learning regression methods - Gaussian Process Regression, Multiple Kernel Learning, Kernel Ridge Regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge Regression and Gaussian Process Regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size.Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques - embedded feature selection and bootstrap aggregation (bagging) - to model performance was also quantified. Five machine learning regression methods - Gaussian Process Regression, Multiple Kernel Learning, Kernel Ridge Regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge Regression and Gaussian Process Regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size. |
| Author | Jollans, Lee Whelan, Robert Boyle, Rory Martinot, Jean-Luc Smolka, Michael N. Garavan, Hugh Schumann, Gunter Artiges, Eric Banaschewski, Tobias Walter, Henrik Desrivières, Sylvane Paus, Tomáš Grigis, Antoine |
| AuthorAffiliation | 6 NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France 4 Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany 2 Max-Planck Institute of Psychiatry, Munich, Germany 7 Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Maison de Solenn, Paris, France 9 Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany 11 Department of Psychiatry, University of Vermont, Burlington, USA 3 Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France 1 School of Psychology, Trinity College Dublin, Dublin, Ireland 12 Global |
| AuthorAffiliation_xml | – name: 10 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany – name: 3 Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France – name: 11 Department of Psychiatry, University of Vermont, Burlington, USA – name: 12 Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland – name: 6 NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France – name: 9 Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany – name: 4 Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany – name: 8 Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital and Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, M6A 2E1, Canada – name: 7 Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Maison de Solenn, Paris, France – name: 1 School of Psychology, Trinity College Dublin, Dublin, Ireland – name: 2 Max-Planck Institute of Psychiatry, Munich, Germany – name: 5 Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom |
| Author_xml | – sequence: 1 givenname: Lee surname: Jollans fullname: Jollans, Lee organization: School of Psychology, Trinity College Dublin, Dublin, Ireland – sequence: 2 givenname: Rory surname: Boyle fullname: Boyle, Rory organization: School of Psychology, Trinity College Dublin, Dublin, Ireland – sequence: 3 givenname: Eric surname: Artiges fullname: Artiges, Eric organization: Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité, and Psychiatry Department 91G16, Orsay Hospital, France – sequence: 4 givenname: Tobias surname: Banaschewski fullname: Banaschewski, Tobias organization: Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany – sequence: 5 givenname: Sylvane surname: Desrivières fullname: Desrivières, Sylvane organization: Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom – sequence: 6 givenname: Antoine surname: Grigis fullname: Grigis, Antoine organization: NeuroSpin, CEA, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France – sequence: 7 givenname: Jean-Luc surname: Martinot fullname: Martinot, Jean-Luc organization: Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité, and Maison de Solenn, Paris, France – sequence: 8 givenname: Tomáš surname: Paus fullname: Paus, Tomáš organization: Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital and Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, M6A 2E1, Canada – sequence: 9 givenname: Michael N. surname: Smolka fullname: Smolka, Michael N. organization: Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany – sequence: 10 givenname: Henrik surname: Walter fullname: Walter, Henrik organization: Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany – sequence: 11 givenname: Gunter surname: Schumann fullname: Schumann, Gunter organization: Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom – sequence: 12 givenname: Hugh surname: Garavan fullname: Garavan, Hugh organization: Department of Psychiatry, University of Vermont, Burlington, USA – sequence: 13 givenname: Robert surname: Whelan fullname: Whelan, Robert email: robert.whelan@tcd.ie organization: School of Psychology, Trinity College Dublin, Dublin, Ireland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31173905$$D View this record in MEDLINE/PubMed |
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| Copyright | 2019 Elsevier Inc. Copyright © 2019 Elsevier Inc. All rights reserved. 2019. Elsevier Inc. |
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| Title | Quantifying performance of machine learning methods for neuroimaging data |
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