Journal
NEUROIMAGE
Volume 199, Issue -, Pages 351-365Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2019.05.082
Keywords
Machine learning; Neuroimaging; Regression algorithms; Reproducibility
Funding
- Irish Research Council [GOIPG/2014/418, EPSPG/2017/277]
- European Union [LSHM-CT-2007-037286]
- ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) [PR-ST-0416-10004]
- BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) [MR/N027558/1]
- FP7 project IMAGE-MEND [602450]
- FP7 project (MATRICS) [603016]
- Innovative Medicine Initiative Project EU-AIMS [115300-2]
- Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions) [MR/N000390/1]
- Swedish Research Council FORMAS
- Medical Research Council [MR/R00465X/1]
- Maudsley NHS Foundation Trust, King's College London
- Bundesministerium fur Bildung und Forschung (BMBF) [01GS08152, 01EV0711, eMED SysAlc01ZX1311A, Forschungsnetz AERIAL 01EE1406A, 01EE1406B]
- Deutsche Forschungsgemeinschaft (DFG) [SM 80/7-2, SFB 940/2]
- Medical Research Foundation
- ANR [AF12-NEUR0008-01-WM2NA, ANR-12-SAMA-0004]
- Fondation de France
- Fondation pour la Recherche Medicale
- Mission Interministerielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA)
- Assistance-Publique-Hopitaux-de-Paris
- INSERM (interface grant), Paris Sud University IDEX 2012
- Science Foundation Ireland [16/ERCD/3797]
- U.S.A. (Axon, Testosterone and Mental Health during Adolescence) [RO1 MH085772-01A1]
- NIH [U54 EB020403]
- National Institute on Drug Abuse [T32DA043593, R01DA047119]
- Horizon 2020 [695313]
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London
- National Institutes of Health
- Irish Research Council (IRC) [GOIPG/2014/418, EPSPG/2017/277] Funding Source: Irish Research Council (IRC)
- MRC [MR/N000390/1, MR/R00465X/1] Funding Source: UKRI
Ask authors/readers for more resources
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.
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