Robust Identification of Alzheimer’s Disease subtypes based on cortical atrophy patterns

Abstract

Accumulating evidence suggests that Alzheimer’s disease (AD) is heterogenous and can be classified into several subtypes. Here, we propose a robust subtyping method for AD based on cortical atrophy patterns and graph theory. We calculated similarities between subjects in their atrophy patterns throughout the whole brain, and clustered subjects with similar atrophy patterns using the Louvain method for modular organization extraction. We applied our method to AD patients recruited at Samsung Medical Center and externally validated our method by using the AD Neuroimaging Initiative (ADNI) dataset. Our method categorized very mild AD into three clinically distinct subtypes with high reproducibility (>90%); the parietal-predominant (P), medial temporal-predominant (MT), and diffuse (D) atrophy subtype. The P subtype showed the worst clinical presentation throughout the cognitive domains, while the MT and D subtypes exhibited relatively mild presentation. The MT subtype revealed more impaired language and executive function compared to the D subtype.

Distinct tau PET patterns in atrophy‐defined subtypes of Alzheimer's disease

“…Although this study-consistently with previous studies 18, 25 -showed that typical patients with AD had the greatest WMH volume burden, it is unknown whether this originates from Wallerian degeneration as a response to hyperphosphorylated tau and/or A or is due to small vessel disease. However, based on the observation that both WHM volumes and tau load were most pronounced in the temporal cortex in typical AD versus the other subtypes, one may speculate that the observed temporal WMH lesions were primarily due to Wallerian degeneration.…”

Tracking Cognitive Decline in Amnestic Mild Cognitive Impairment and Early-Stage Alzheimer Dementia: Mini-Mental State Examination versus Neuropsychological Battery

“…Second, as our study population was limited to aMCI and mild AD (global CDR score ≤1.0), our findings cannot be generalized or extrapolated to more severely impaired subjects. Third, when considering that AD-driven cortical atrophy is not uniformly distributed throughout the cortices [34], and that high heterogeneity exists across individuals [35,36], it is possible that the percent change in total cGMV may not perfectly serve as a valid standard of disease progression. Fourth, the follow-up interval was not equal among participants.…”

Fully Bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

“…The two main benefits of mean centering and unit variance scaling of the patients' data are: 1) after the transformation, each ROI value will represent how many standard deviations below the average CU an AD subject's value is; 2) since we have volume and thickness data, after transformation, all the variables will have the same unit and fair statistical comparisons will be possible (without affecting the kurtosis or skewness of the distributions). This transformation has previously been applied for cross-sectional assessment of AD subtypes (Park et al, 2017;Westman, Muehlboeck, & Simmons, 2012;Zhang et al, 2016). In this study, we adapted this procedure to longitudinal data in order to account for the atrophy that is caused by the normal ageing process in the CU group over time , = , −̂, / ̂, ,where is the original measurement of subject , in the time point for the region , while ̂ and ̂ are the mean and standard deviation of the CU group at time and region .…”

“…When samples include only one diagnosis, a common use of clustering methods is to investigate whether the neuroimaging measures of interest show heterogeneous patterns within that same diagnostic label. Several studies have investigated the heterogeneity in AD with the aim to define disease specific subtypes (Byun et al, 2015(Byun et al, , 2015Corlier et al, 2018;Park et al, 2017;Poulakis et al, 2018;Schwarz et al, 2018;Whitwell et al, 2012;Young et al, 2017). When samples include more than one diagnosis, the main aim of unsupervised clustering methods is to investigate whether neuroimaging markers can be used to distinguish between the diagnostic classes without specifying them with a label.…”

“…Previous clustering studies grouped AD patients based on sMRI features from a single time-point (Dong et al, 2016(Dong et al, , 2017Ferreira et al, 2017;Noh et al, 2014;Park et al, 2017;Poulakis et al, 2018;Varol et al, 2017). Their conclusions were based on a single observation in time and the chance that those clusters reflect different stages of the disease and not particular patterns of atrophy (distinct AD subtypes) cannot be excluded.…”