Clinical Prediction from Structural Brain MRI Scan

Authors: Mert R Sabuncu Ender Konukoglu for the Alzheimer’s Disease Neuroimaging Initiative

Publish Date: 2014/07/22

Volume: 13, Issue: 1, Pages: 31-46

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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 stateoftheart 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 amyloidβ levels and minimental state exam score We analyzed data from over 2800 subjects compiled from six publicly available datasets The employed data and computational tools are freely distributed https//wwwnmrmghharvardedu/lab/mripredict making this the largest most comprehensive reproducible benchmark imagebased 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 crossvalidation estimates of performance while generally optimistic correlate well with generalization accuracy on a new datasetData 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//tinyurlcom/ADNImainThis 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 1K25EB01364901 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 NS05258501 1R21NS07265201 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 5U01MH093765 part of the multiinstitutional Human Connectome Project

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