  Normative modeling has emerged as a pivotal approach for characterizing
heterogeneity and individual variance in neurodegenerative diseases, notably
Alzheimer's disease(AD). One of the challenges of cortical normative modeling
is the anatomical structure mismatch due to folding pattern variability.
Traditionally, registration is applied to address this issue and recently many
studies have utilized deep generative models to generate anatomically align
samples for analyzing disease progression; however, these models are
predominantly applied to volume-based data, which often falls short in
capturing intricate morphological changes on the brain cortex. As an
alternative, surface-based analysis has been proven to be more sensitive in
disease modeling such as AD, yet, like volume-based data, it also suffers from
the mismatch problem. To address these limitations, we proposed a novel
generative normative modeling framework by transferring the conditional
diffusion generative model to the spherical non-Euclidean domain. Additionally,
this approach generates normal feature map distributions by explicitly
conditioning on individual anatomical segmentation to ensure better geometrical
alignment which helps to reduce anatomical variance between subjects in
analysis. We find that our model can generate samples that are better
anatomically aligned than registered reference data and through ablation study
and normative assessment experiments, the samples are able to better measure
individual differences from the normal distribution and increase sensitivity in
differentiating cognitively normal (CN), mild cognitive impairment (MCI), and
Alzheimer's disease (AD) patients.
