Disentangling the heterogeneity of brain atrophy in Alzheimer’s Disease

Abstract Background Alzheimer’s disease (AD) is the most common cause of dementia. In AD patients, structural MRI analyses have identified various brain atrophy patterns, which are likely the result of differences in regional pathology distribution and variable presence of co‐pathologies. Method We included 496 ADNI participants and applied our robust collaborative clustering approach (RCC) to the MRI scans of the Aβ+ participants. RCC removes noise while simultaneously clustering subjects and features. Group comparisons were performed applying Kruskal‐Wallis analyses for quantitative variables and chi‐square tests for categorical variables (Table 1). Power transformations were used to normalize the distribution of quantitative variable for linear regression analyses. APOE ε4 presence and white matter hyperintensity (WMH) volume were included as predictors. P‐values <0.05 were considered statistically significant. Result We identified three distinct atrophy clusters among the Aβ+ participants (Table 1). A “limbic predominant” atrophy cluster showed greater cingulate and right hippocampus, inferior and middle temporal lobe atrophy than the cognitively unremarkable (CU) Aβ‐ reference group (Figure 1). The diffuse atrophy cluster showed greater parieto‐occipital‐temporal atrophy. The “hippocampus sparing” atrophy cluster showed a similar neocortical atrophy pattern except for less atrophy in the temporal lobe. The limbic predominant cluster participants had a higher APOE ε4 prevalence than the hippocampal sparing cluster participants (Table 1, 2). The three clusters had higher baseline ADAS‐Cog13 and flortaucipir SUVR binding in all the Braak staging‐defined areas than the CU Aβ‐ reference group (Table 3). The diffuse atrophy cluster had higher ADAS‐Cog13 scores, florbetapir composite scores, and flortaucipir SUVRs in all the Braak staging‐defined areas than the two other atrophy clusters. The hippocampal sparing atrophy cluster participants were older and had higher WMH volumes than the limbic predominant atrophy cluster and the CU Aβ‐ reference group participants. Conclusion Our clustering method discovered three reproducible atrophy patterns in AD, including typical and atypical presentations. This finding likely reflects different combinations of co‐pathology leading to the development of AD, some of which are captured with MRI. Mainly, WMH contributed to the heterogeneity of AD and the atypical neocortical variant.