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Neuroinformatics

Erschienen in:

01.01.2015 | Original Article

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

verfasst von: Mert R. Sabuncu, Ender Konukoglu, for the Alzheimer’s Disease Neuroimaging Initiative

Erschienen in: Neuroinformatics | Ausgabe 1/2015

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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 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.

Vorheriger Artikel POAS4SPM: A Toolbox for SPM to Denoise Diffusion MRI Data

Nächster Artikel Quantitative Arbor Analytics: Unsupervised Harmonic Co-Clustering of Populations of Brain Cell Arbors Based on L-Measure

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Titel: Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study
verfasst von: Mert R. Sabuncu
Ender Konukoglu
for the Alzheimer’s Disease Neuroimaging Initiative
Publikationsdatum: 01.01.2015
Verlag: Springer US
Erschienen in: Neuroinformatics / Ausgabe 1/2015
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-014-9238-1

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