Open AccessJoint cortical surface and structural connectivity analysis of Alzheimer’s disease
Author(s) -
Leon Y. Cai,
Cailey I. Kerley,
Changqiu Yu,
Katherine S. Aboud,
Lori L. BeasonHeld,
Andrea T. Shafer,
Susan M. Resnick,
Lori C. Jordan,
Adam W. Anderson,
Kurt G. Schilling,
Ilwoo Lyu,
Bennett A. Landman
Publication year - 2021
Publication title -
pubmed central
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
pISSN - 0277-786X
DOI - 10.1117/12.2580956
Subject(s) - computer science , principal component analysis , joint (building) , neuroimaging , diffusion mri , artificial intelligence , independent component analysis , pattern recognition (psychology) , tractography , component (thermodynamics) , neuroscience , biology , magnetic resonance imaging , architectural engineering , medicine , engineering , physics , radiology , thermodynamics
Prior neuroimaging studies have demonstrated isolated structural and connectivity changes in the brain due to Alzheimer's Disease (AD). However, how these changes relate to each other is not well understood. We present a preliminary study to begin to fill this gap by leveraging joint independent component analysis (jICA). We explore how jICA performs in an analysis of T1 and diffusion weighted MRI by characterizing the joint changes of complex cortical surface and structural connectivity metrics in AD in subjects from the Baltimore Longitudinal Study of Aging. We calculate 588 region-based cortical metrics and 4,753 fractional anisotropy-based connectivity metrics and project them into a low-dimensional manifold with principal component analysis. We perform jICA on the manifold and subsequently backproject the independent components to the original data space. We demonstrate component stability with 3-fold cross validation and find differential component loadings between 776 cognitively unimpaired control subjects and 23 with AD that generalizes across folds. In addition, we perform the same analysis on the surface and connectivity metrics separately and find that the joint approach identifies both novel and similar components to the separate approaches. To illustrate the joint approach's primary utility, we provide an example hypothesis for how surface and connectivity components may vary together with AD. These preliminary results suggest jointly varying independent cortical surface and structural connectivity components can be consistently extracted from MRI data and provide a data-driven way for generating novel hypotheses about AD that may not be captured by separate analyses.