title: BrainPrint: a discriminative characterization of brain morphology.
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.
authors.affiliation: Computer Science and Artificial Intelligence Lab, MIT, USA; Massachusetts General Hospital, Harvard Medical School, USA. Electronic address: wachinger@csail.mit.edu.
authors.forename: Christian
authors.initials: C
authors.lastname: Wachinger
authors.affiliation: Computer Science and Artificial Intelligence Lab, MIT, USA.
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
authors.affiliation: University of California, San Diego, USA; VA San Diego, Center of Excellence for Stress and Mental Health, USA.
authors.forename: William
authors.initials: W
authors.lastname: Kremen
authors.affiliation: Computer Science and Artificial Intelligence Lab, MIT, USA; Massachusetts General Hospital, Harvard Medical School, USA.
authors.forename: Bruce
authors.initials: B
authors.lastname: Fischl
authors.affiliation: Computer Science and Artificial Intelligence Lab, MIT, USA; Massachusetts General Hospital, Harvard Medical School, USA.
authors.forename: Martin
authors.initials: M
authors.lastname: Reuter
mesh_terms: D000367:Age Factors; D000368:Aged; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D012737:Sex Factors; D012815:Signal Processing, Computer-Assisted; D014427:Twins / Q000235:genetics
keywords: Brain asymmetry; Brain shape; Brain similarity; Large brain datasets; Morphological heritability; Subject identification