The heterogeneous amyloid‐positive brain: mapping individualised patterns of atrophy in amyloid‐positive Alzheimer's disease patients using neuroanatomical normative models. (20th December 2022)

• Record Type: Journal Article
• Authors: Verdi, Serena; Loreto, Flavia; Kia, Seyed Mostafa; Duvnjak, Aleksandar; Hakeem, Haneen; Perry, Richard J; Win, Zarni; Patel, Neva; Schott, Jonathan M; Marquand, Andre F; Malhotra, Paresh A; Cole, James H
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 6
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.065489
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Patterns of atrophy in the brain highly differ between individual Alzheimer's Disease (AD) patients, however, most clinical research focuses on group‐level differences (Verdi et al., 2021). Here we used a novel technique, neuroanatomical normative modelling, to reveal individual patterns of cortical thickness by quantifying deviations from the normative range. We applied a hierarchical Bayesian regression (HBR) normative model (Kia et al., 2021), to amyloid‐positive AD patients who had clinical amyloid PET imaging at the Imperial Memory Clinic (IMC). Patients had either AD dementia or mild cognitive impairment due to AD. We hypothesised that there would be individual differences in patterns of cortical atrophy in these patients. Method: Cortical thickness across 148 regions of interest (ROIs) was generated using FreeSurfer from n=130 (102 AD patients and 28 healthy controls) T1‐weighted MRIs acquired from the IMC. A reference HBR normative model was trained on a separate dataset of n=34, 490 healthy individuals to index population variability, which predicted cortical thickness at each ROI using age and sex. This generated cortical thickness z‐scores for each ROI, per patient (z‐score < ‐1.96 = outlier). Result: The patterns of cortical atrophy outliers were highly varied in amyloid‐positive AD patients. For instance, the largest proportion of outliers in a region was 60% within the superior temporal sulci, if atrophy were homogenous we would expect 100% of outliers to be here (Fig.1) . This heterogeneity was also seen when comparing how similar outlier patterns were between patients (Fig.2) . We also found that the proportions of outliers differ according to disease severity, e.g. the highest percentage of outliers was 70% within superior temporal sulci within the AD dementia subgroup, and 50% in superior temporal sulci in the mild cognitive impairment due to AD subgroup (Fig.3) . Conclusion: Amyloid‐positivity results in heterogenous patterns of cortical atrophy. This is more pronounced in AD dementia, though still present in people with mild cognitive impairment due to AD. Neuroanatomical normative maps have the potential to be individualised markers of disease, and with application to longitudinal data could track individual disease progression.