Neuroinformatics

, Volume 13, Issue 1, pp 31–46

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

  • Mert R. Sabuncu
  • Ender Konukoglu
  • for the Alzheimer’s Disease Neuroimaging Initiative
Original Article

DOI: 10.1007/s12021-014-9238-1

Cite this article as:
Sabuncu, M.R., Konukoglu, E. & for the Alzheimer’s Disease Neuroimaging Initiative Neuroinform (2015) 13: 31. doi:10.1007/s12021-014-9238-1

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.

Keywords

Image-based prediction Computer aided diagnosis Machine learning MRI 

Supplementary material

12021_2014_9238_MOESM1_ESM.docx (682 kb)
ESM 1(DOC 681 kb)

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Mert R. Sabuncu
    • 1
    • 2
  • Ender Konukoglu
    • 1
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownUSA
  2. 2.Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyCambridgeUSA

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