BrainPrint: A discriminative characterization of brain morphology
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BrainPrint.pdf
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2.14 MB
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Author(s)
Kremen, William
Wachinger, Christian
Golland, Polina
Fischl, Bruce
Reuter, Klaus Martin
Date Issued
January 2015
Journal
NeuroImage
Publisher
Elsevier
Citation
Wachinger, Christian et al. “BrainPrint: A Discriminative Characterization of Brain Morphology.” NeuroImage 109 (April 2015): 232–248 © 2015 Elsevier Inc
Version
Author's final manuscript
Abstract
We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace–Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/j.neuroimage.2015.01.032
