Multi-modal brain fingerprinting: A manifold approximation based framework

• Published in: NeuroImage (Orlando, Fla.) Vol. 183; pp. 212 - 226
• Main Authors: Kumar, Kuldeep, Toews, Matthew, Chauvin, Laurent, Colliot, Olivier, Desrosiers, Christian
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2018; Elsevier Limited; Elsevier
• Subjects: Adult; Bag-of-Features; Bioengineering; Brain; Brain - anatomy & histology; Brain - diagnostic imaging; Brain - physiology; Brain fingerprinting; Brain mapping; Cerebral hemispheres; Cohort Studies; Computer Science; Computer Vision and Pattern Recognition; Connectome - methods; Diffusion Magnetic Resonance Imaging - methods; Embedding; Engineering Sciences; Female; Fingerprinting; Functional magnetic resonance imaging; Functional Neuroimaging - methods; HCP Twin data; Humans; Identification; Image Processing; Imaging; Individuality; Life Sciences; Magnetic Resonance Imaging - methods; Male; Manifold approximation; Medical Imaging; Multi-modal data; Neurobiology; Neurons and Cognition; Schizophrenia; Siblings; Signal and Image processing; sMRI-dMRI-rfMRI; Twins; Young Adult; United Kingdom > UK; Manifold approximation; Brain fingerprinting; Multi-modal data; Bag-of-Features; sMRI-dMRI-rfMRI; HCP Twin data; Brain fingerprint; Structural MRI; Hemisphere asymmetry; Manifold; Multi-modal; rfMRI netmats; Diffusion MRI; MAP

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 t...