Magnetic resonance imaging subtypes in subjective cognitive decline: Neuroimaging / Optimal neuroimaging measures for early detection. (7th December 2020)

• Record Type: Journal Article
• Authors: Diaz‐Galvan, Patricia; Poulakis, Konstantinos; Grothe, Michel J.; Fripp, Jurgen; Maruff, Paul T.; Rowe, Christopher C.; Dore, Vincent; Barroso, Jose; Ferreira, Daniel; Westman, Eric
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• 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.042439
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Older adults who do not meet clinical criteria for mild cognitive impairment (MCI) or dementia, but who report progressive change in their cognitive abilities (i.e. subjective cognitive decline, SCD) are at increased risk for developing Alzheimer's disease (AD). However, SCD can be associated to other conditions and its heterogeneity needs to be investigated further. In patients with AD dementia, data‐driven approaches to biological information have been useful in disentangling heterogeneity. We therefore utilized a data‐driven clustering method on regional structural magnetic resonance imaging (MRI) data to identify subtypes of SCD. Method: Cognitively normal adults with SCD (n=86) and without SCD (n=68) from the AIBL cohort were studied. Clusters of SCD were determined based on 82 cortical and subcortical grey matter volumes using random forest pairwise similarity, multidimensional scaling, and distance‐based hierarchical clustering. The number of clusters was decided according to Carlinski‐Harabasz index. We also studied neuroanatomical, clinical, cognitive, and genetic characteristics at baseline, and clinical and cognitive progression over 7.5 years of follow‐up. Results: The data driven analyses yielded four SCD subtypes. The largest proportion (49%) showed regional volumes equivalent to those without SCD. Nonetheless, this group could be divided into two subtypes depending on the volume of the thalamus ( no atrophy + low thalamic volume, 35%; and no atrophy + high thalamic volume, 15%). We also identified a Hippocampal‐sparing SCD subtype (32%), which displayed reduced cortical volumes but normal volume of the hippocampus; and a Diffuse SCD subtype (16%), which displayed reduced volume in both the hippocampus and cortical regions. Diffuse, Hippocampal‐sparing, and no atrophy + low Thalamic volume SCD subtypes were each associated with increased risk for MCI or dementia over 7.5 years, and showed faster decline in MMSE. The Diffuse SCD subtype was also characterized by an increased amyloid‐PET burden, small vessel disease (white matter hypointensities), and worse CDR scores at baseline. Conclusions: Data‐driven analyses of structural MRI data revealed that SCD characterized by a Diffuse, Hippocampal‐sparing, or no atrophy with a low Thalamic volume pattern were at increased risk for MCI or dementia. Thus, these data‐driven methods show great potential for disentangling biological heterogeneity within SCD.