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Activity Number: 132 - Statistical Advances in Dimension Reduction and Feature Interpretability in Neuroimaging
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #322224
Title: Longitudinal Canonical Correlation Analysis
Author(s): seonjoo lee* and Jongwoo Choi and zhiqian Fang and F Dubois Bowman
Companies: columbia university/NYSPI and NYSPI and NYSPI and Univeristy of Michigan
Keywords: canonical correlation analysis; dimension reduction; longitudinal data analysis; Alzheimer's disease

This talk considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modeled trajectories of the multi- variate variables using random effects and found the most correlated sets of linear combinations in the latent space. Our numerical simulations showed that the longitudinal canonical correlation analysis (LCCA) effectively recovers underlying correlation patterns between two high-dimensional longitudinal data sets. We applied the proposed LCCA to data from the Alzheimer’s Disease NeuroImaging Initia- tive and identified the longitudinal profiles of morphological brain changes and amyloid cumulation.

Authors who are presenting talks have a * after their name.

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