Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure

• Published in: NeuroImage (Orlando, Fla.) Vol. 63; no. 1; pp. 365 - 380
• Main Authors: Groves, Adrian R., Smith, Stephen M., Fjell, Anders M., Tamnes, Christian K., Walhovd, Kristine B., Douaud, Gwenaëlle, Woolrich, Mark W., Westlye, Lars T.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.10.2012; Elsevier Limited
• Subjects: Adolescent; Adult; Age; Aged; Aged, 80 and over; Aging; Aging - pathology; Bayesian modeling; Brain; Brain research; Child; Cognition & reasoning; Data fusion; Data processing; Databases, Factual; Diffusion tensor imaging; Disease; Female; Humans; Independent component analysis; Longevity; Magnetic Resonance Imaging - methods; Male; Medical imaging; Microstructure; Middle Aged; Nerve Fibers, Myelinated - ultrastructure; Random variables; Reproducibility of Results; Sensitivity and Specificity; Studies; Subtraction Technique; Surface-based morphometry; Voxel-based morphometry; Young Adult; Surface-based morphometry; Bayesian modeling; Independent component analysis; Voxel-based morphometry; Aging; Data fusion; Diffusion tensor imaging

Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been...