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Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study

Abstract

Multivariate pattern analysis (MVPA) methods have become an important tool in neuroimaging, revealing complex associations and yielding powerful prediction models. Despite methodological developments and novel application domains, there has been little effort to compile benchmark results that researchers can reference and compare against. This study takes a significant step in this direction. We employed three classes of state-of-the-art MVPA algorithms and common types of structural measurements from brain Magnetic Resonance Imaging (MRI) scans to predict an array of clinically relevant variables (diagnosis of Alzheimer’s, schizophrenia, autism, and attention deficit and hyperactivity disorder; age, cerebrospinal fluid derived amyloid-β levels and mini-mental state exam score). We analyzed data from over 2,800 subjects, compiled from six publicly available datasets. The employed data and computational tools are freely distributed (https://www.nmr.mgh.harvard.edu/lab/mripredict), making this the largest, most comprehensive, reproducible benchmark image-based prediction experiment to date in structural neuroimaging. Finally, we make several observations regarding the factors that influence prediction performance and point to future research directions. Unsurprisingly, our results suggest that the biological footprint (effect size) has a dramatic influence on prediction performance. Though the choice of image measurement and MVPA algorithm can impact the result, there was no universally optimal selection. Intriguingly, the choice of algorithm seemed to be less critical than the choice of measurement type. Finally, our results showed that cross-validation estimates of performance, while generally optimistic, correlate well with generalization accuracy on a new dataset.

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Notes

  1. 1.

    http://fcon_1000.projects.nitrc.org/indi/adhd200/general/ADHD-200_PhenotypicKey.pdf.

  2. 2.

    https://www.nmr.mgh.harvard.edu/lab/mripredict

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Acknowledgments

This research was carried out in whole or in part at the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital, using resources provided by the Center for Functional Neuroimaging Technologies, P41EB015896, a P41 Biotechnology Resource Grant supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health. Dr. Sabuncu received support from an AHAF (BrightFocus) Alzheimer’s Disease pilot grant (AHAF A2012333), and an NIH K25 grant (NIBIB 1K25EB013649-01). Further support for this research was provided in part by the National Center for Research Resources (U24 RR021382), the National Institute for Biomedical Imaging and Bioengineering (R01EB006758), National Institute for Neurological Disorders and Stroke (R01 NS052585-01, 1R21NS072652-01, 1R01NS070963, R01NS083534), and was made possible by the resources provided by Shared Instrumentation Grants 1S10RR023401, 1S10RR019307, and 1S10RR023043. Additional support was provided by the NIH Blueprint for Neuroscience Research (5U01-MH093765), part of the multi-institutional Human Connectome Project.

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Both authors are responsible for manuscript preparation and review and data analysis.

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No conflicts of interest exist for any of the named authors in this study.

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Correspondence to Mert R. Sabuncu.

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Mert R. Sabuncu and Ender Konukoglu contributed equally.

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators is available at http://tinyurl.com/ADNI-main.

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Sabuncu, M.R., Konukoglu, E. & for the Alzheimer’s Disease Neuroimaging Initiative. Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study. Neuroinform 13, 31–46 (2015). https://doi.org/10.1007/s12021-014-9238-1

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Keywords

  • Image-based prediction
  • Computer aided diagnosis
  • Machine learning
  • MRI