There is increasing evidence showing that brain atrophy varies between patients with Alzheimer's disease (AD), suggesting that different anatomical patterns might exist within the same disorder. We investigated AD heterogeneity based on cortical and subcortical atrophy patterns in 299 AD subjects from 2 multicenter cohorts. Clusters of patients and important discriminative features were determined using random forest pairwise similarity, multidimensional scaling, and distance-based hierarchical clustering. We discovered 2 typical (72.2%) and 3 atypical (28.8%) subtypes with significantly different demographic, clinical, and cognitive characteristics, and different rates of cognitive decline. In contrast to previous studies, our unsupervised random forest approach based on cortical and subcortical volume measures and their linear and nonlinear interactions revealed more typical AD subtypes with important anatomically discriminative features, while the prevalence of atypical cases was lower. The hippocampal-sparing and typical AD subtypes exhibited worse clinical progression in visuospatial, memory, and executive cognitive functions. Our findings suggest there is substantial heterogeneity in AD that has an impact on how patients function and progress over time.

Biological subtypes in Alzheimer’s disease, originally identified on neuropathological data, have been translated to in vivo biomarkers such as structural magnetic resonance imaging (sMRI) and positron emission tomography (PET), to disentangle the heterogeneity within Alzheimer’s disease. Although there is methodological variability across studies, comparable characteristics of subtypes are reported at the group level. In this study, we investigated whether group-level similarities translate to individual-level agreement across subtyping methods, in a head-to-head context. We compared five previously published subtyping methods. Firstly, we validated the subtyping methods in 89 amyloid-beta positive Alzheimer’s disease dementia patients (reference group: 70 amyloid-beta negative healthy individuals) using sMRI. Secondly, we extended and applied the subtyping methods to 53 amyloid-beta positive prodromal Alzheimer’s disease and 30 amyloid-beta positive Alzheimer’s disease dementia patients (reference group: 200 amyloid-beta negative healthy individuals) using sMRI and tau PET. Subtyping methods were implemented as outlined in each original study. Group-level and individual-level comparisons across methods were performed. Each individual subtyping method was replicated, and the proof-of-concept was established. At the group level, all methods captured subtypes with similar patterns of demographic and clinical characteristics, and with similar cortical thinning and tau PET uptake patterns. However, at the individual level large disagreements were found in subtype assignments. Although characteristics of subtypes are comparable at the group level, there is a large disagreement at the individual level across subtyping methods. Therefore, there is an urgent need for consensus and harmonization across subtyping methods. We call for establishment of an open benchmarking framework to overcome this problem.

ObjectiveTo investigate the association between longitudinal changes in proton magnetic resonance spectroscopy (MRS) metabolites and amyloid pathology in individuals without dementia, and to explore the relationship between MRS and cognitive decline.MethodsIn this longitudinal multiple time point study (a subset of the Swedish BioFINDER), we included cognitively healthy participants, individuals with subjective cognitive decline, and individuals with mild cognitive impairment. MRS was acquired serially in 294 participants (670 individual spectra) from the posterior cingulate/precuneus. Using mixed-effects models, we assessed the association between MRS and baseline β-amyloid (Aβ), and between MRS and the longitudinal Mini-Mental State Examination, accounting for APOE, age, and sex.ResultsWhile baseline MRS metabolites were similar in Aβ positive (Aβ+) and negative (Aβ−) individuals, in the Aβ+ group, the estimated rate of change was +1.9%/y for myo-inositol (mI)/creatine (Cr) and −2.0%/y for N-acetylaspartate (NAA)/mI. In the Aβ− group, mI/Cr and NAA/mI yearly change was −0.05% and +1.2%; however, this was not significant across time points. The mild cognitive impairment Aβ+ group showed the steepest MRS changes, with an estimated rate of +2.93%/y (p = 0.07) for mI/Cr and −3.55%/y (p < 0.01) for NAA/mI. Furthermore, in the entire cohort, we found that Aβ+ individuals with low baseline NAA/mI had a significantly higher rate of cognitive decline than Aβ+ individuals with high baseline NAA/mI.ConclusionWe demonstrate that the longitudinal change in mI/Cr and NAA/mI is associated with underlying amyloid pathology. MRS may be a useful noninvasive marker of Aβ-related processes over time. In addition, we show that in Aβ+ individuals, baseline NAA/mI may predict the rate of future cognitive decline.

Understanding Alzheimer’s disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.

Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer’s disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clustering that simultaneously: 1) incorporates whole brain data, 2) leverages unequal visits per individual, 3) compares clusters with a control group, 4) allows for study confounding effects, 5) provides cluster visualization, 6) measures clustering uncertainty. We used amyloid-β positive AD and negative healthy subjects, three longitudinal structural magnetic resonance imaging scans (cortical thickness and subcortical volume) over two years. We found three distinct longitudinal AD brain atrophy patterns: one typical diffuse pattern (n=34, 47.2%), and two atypical patterns: minimal atrophy (n=23 31.9%) and hippocampal sparing (n=9, 12.5%). We also identified outliers (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters differed not only in regional distributions of atrophy at baseline, but also longitudinal atrophy progression, age at AD onset, and cognitive decline. A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed. We believe this framework may aid in disentangling distinct subtypes of AD from disease staging.

Dementia with Lewy bodies (DLB) is a neurodegenerative disorder with a wide heterogeneity of symptoms, which suggests the existence of different subtypes. We used data-driven analysis of magnetic resonance imaging (MRI) data to investigate DLB subtypes. We included 165 DLB from the Mayo Clinic and 3 centers from the European DLB consortium and performed a hierarchical cluster analysis to identify subtypes based on gray matter (GM) volumes. To characterize the subtypes, we used demographic and clinical data, as well as β-amyloid, tau, and cerebrovascular biomarkers at baseline, and cognitive decline over three years. We identified 3 subtypes: an older subtype with reduced cortical GM volumes, worse cognition, and faster cognitive decline (n = 49, 30%); a subtype with low GM volumes in fronto-occipital regions (n = 76, 46%); and a subtype of younger patients with the highest cortical GM volumes, proportionally lower GM volumes in basal ganglia and the highest frequency of cognitive fluctuations (n = 40, 24%). This study shows the existence of MRI subtypes in DLB, which may have implications for clinical workout, research, and therapeutic decisions.

Cross-sectional studies have indicated potential for positron emission tomography (PET) in imaging tau pathology in Alzheimer’s disease (AD); however, its prognostic utility remains unproven. In a longitudinal, multi-modal, prognostic study of cognitive decline, 20 patients with a clinical biomarker-based diagnosis in the AD spectrum (mild cognitive impairment or dementia and a positive amyloid-beta PET scan) were recruited from the Cognitive Clinic at Karolinska University Hospital. The participants underwent baseline neuropsychological assessment, PET imaging with [18F]THK5317, [11C]PIB and [18F]FDG, magnetic resonance imaging, and in a subgroup cerebrospinal fluid (CSF) sampling, with clinical follow-up after a median 48 months (interquartile range = 32:56). In total, 11 patients declined cognitively over time, while 9 remained cognitively stable. The accuracy of baseline [18F]THK5317 binding in temporal areas was excellent at predicting future cognitive decline (area under the receiver operating curve 0.84–1.00) and the biomarker levels were strongly associated with the rate of cognitive decline (β estimate −33.67 to −31.02, p < 0.05). The predictive accuracy of the other baseline biomarkers was poor (area under the receiver operating curve 0.58–0.77) and their levels were not associated with the rate of cognitive decline (β estimate −4.64 to 15.78, p > 0.05). Baseline [18F]THK5317 binding and CSF tau levels were more strongly associated with the MMSE score at follow-up than at baseline (p < 0.05). These findings support a temporal dissociation between tau deposition and cognitive impairment, and suggest that [18F]THK5317 predicts future cognitive decline better than other biomarkers. The use of imaging markers for tau pathology could prove useful for clinical prognostic assessment and screening before inclusion in relevant clinical trials.

Deterioration in white matter health plays a role in cognitive aging. Our goal was to discern heterogeneity of white matter tract vulnerability in aging using longitudinal imaging data (2-5 imaging and cognitive assessments per participant) from a population-based sample of 553 elderly participants (aged ≥60 years). We found that different clusters (healthy white matter, fast white matter decliners, and intermediate white matter group) were heterogeneous in the spatial distribution of white matter integrity, systemic health, and cognitive trajectories. White matter health of specific tracts (genu of corpus callosum, posterior corona radiata, and anterior internal capsule) informed about cluster assignments. Not surprisingly, brain amyloidosis was not significantly different between clusters. Clusters had differential white matter tract vulnerability to aging (commissural fibers&gt;association/brainstem fibers). Identification of vulnerable white matter tracts is a valuable approach to assessing risk for cognitive decline.