54: A Principal Manifold-based Framework for Comparisons of Hierarchical Manifold Estimates

Music City Center 

Magnetic resonance imaging (MRI) data is frequently used to monitor brain regions for the effects of neurodegenerative conditions like Alzheimer's disease (AD). AD exhibits substantial heterogeneity between patients, with this variability frequently described using disease subtypes defined by distinct pathological characteristics. Thus, MRI data from studies of AD often has a nested structure, with several images collected for each participant, who in turn are grouped by disease subtype. Statistical methods that do not account for this structure may be unable to fully capture the relationships present in the data. To address this problem, we adapt the principal manifold estimation algorithm using an additive spline model to obtain manifold estimates of brain region structure that vary at each level of a nested hierarchy. A hypothesis testing framework allows testing for significant differences between group- or individual-level manifold estimates. The proposed method is compared to existing approaches using simulated data and applied to estimate the surfaces of hippocampi of participants in the Alzheimer's Disease Neuroimaging Initiative study.

Magnetic resonance imaging

Alzheimer's disease

Principal manifold estimation 

Section on Statistics in Imaging