Revealing Individual Neuroanatomical Heterogeneity in Alzheimer Disease Using Neuroanatomical Normative Modeling

Abstract Alzheimer’s disease is clinically heterogeneous, in symptom profiles, progression rates and outcomes. This clinical heterogeneity is linked to underlying neuroanatomical heterogeneity. To explore this, we employed the emerging technique of neuroanatomical normative modelling to index regional patterns of variability in cortical thickness in individual patients from the large multi-site Alzheimer’s Disease Neuroimaging Initiative. We aimed to characterise individual differences and outliers in cortical thickness in patients with Alzheimer’s disease, people with mild cognitive impairment and cognitively normal controls. Furthermore, we assessed the relationships between cortical thickness heterogeneity and cognitive function, amyloid-beta, tau, ApoE genotype. Finally, we examined whether individual neuroanatomical normative maps were predictive of conversion from mild cognitive impairment to diagnosed Alzheimer’s disease. Data on cortical thickness from the 148 brain regions of the Destrieux FreeSurfer atlas was obtained from T1-weighted MRI scans of 1492 participants scanned at 62 different sites. A neuroanatomical normative model was developed to index normal cortical thickness distributions using a separate healthy reference dataset (n= 33,072), employing hierarchical Bayesian regression to predict cortical thickness per region using age and sex. These regional normative models were then fine-tuned to the ADNI dataset after which cortical thickness z-scores per region were calculated, resulting in a z-score ‘map’ for each participant. Regions with z-scores < -1.96 were classified as outliers. Patients with Alzheimer’s disease had a median of 12 outlier regions out of a possible 148. Individual patterns of outlier regions were highly variable, with the highest overlap in the parahippocampal gyrus at only 47% of patients. For 62 regions, over 90% of these patients had cortical thicknesses within the normal range. Patients with Alzheimer’s disease had significantly more outlier regions than people with mild cognitive impairment or controls [ F (2, 1022) = 95.39), P = 2.0 x ×10 −16 ]. They were also statistically more dissimilar to each other than were people with mild cognitive impairment or cognitive normal controls [ F (2, 1024) = 209.42, P = 2.2×10 −16 ]. Having a greater number of outlier regions was associated with worse cognitive function, CSF protein concentrations and an increased risk of converting from mild cognitive impairment to Alzheimer’s disease within three years (HR =1.028, 95% CI[1.016,1.039], P =1.8 ×10 −16 ). Individualised normative maps of cortical thickness highlight the heterogeneity of Alzheimer’s effects on the brain. Regional outlier estimates have the potential to be a marker of disease and could be used to track an individual’s disease progression or treatment response in clinical trials.

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