Multi-modal brain fingerprinting: a manifold approximation based framework

Kuldeep Kumar 1 Laurent Chauvin 1 Matthew Toews 1 Olivier Colliot 2 Christian Desrosiers 1
2 ARAMIS - Algorithms, models and methods for images and signals of the human brain
SU - Sorbonne Université, Inria de Paris, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
Abstract : This work presents an efficient framework, based on manifold approximation , for generating brain fingerprints from multi-modal data. The proposed framework represents images as bags of local features, which are used to build a subject proximity graph. Compact fingerprints are obtained by projecting this graph in a low-dimensional manifold, using spectral embedding. Experiments using the T1/T2-weighted MRI, diffusion MRI, and resting state fMRI data of 945 Human Connectome Project subjects demonstrate the benefit of combining multiple modalities, with multi-modal fingerprints more discriminative than those generated from individual modalities. Results also highlight the link between fingerprint similarity and genetic proximity, monozygotic twins having more similar fingerprints than dizygotic or non-twin siblings. This link is also reflected in the differences of feature correspondences between twin/sibling pairs, occurring in major brain structures and across hemispheres. The robustness of the proposed framework to factors like image alignment and scan resolution, as well as the reproducibility of results on retest scans, suggest the potential of multi-modal brain fingerprinting for characterizing individuals in a large cohort analysis. In addition, taking inspiration from the computer vision community, the proposed rank retrieval evaluation based on the task of twin/sibling identification and using Mean Average Precision (MAP) can be used for a standardized comparison of future brain fingerprints.
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Kuldeep Kumar, Laurent Chauvin, Matthew Toews, Olivier Colliot, Christian Desrosiers. Multi-modal brain fingerprinting: a manifold approximation based framework. NeuroImage, Elsevier, 2018, 183, pp.212 - 226. ⟨10.1016/j.neuroimage.2018.08.006⟩. ⟨hal-01910367⟩

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