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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.

Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer’s disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD (N = 293) and control subjects (N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious.

Alzheimer’s disease (AD) is associated with atrophy of the cornu ammonis (CA) 1 and the subiculum subfield of the hippocampus (HC), and with deficits in episodic memory and spatial orientation. These deficits are mainly associated with the functionality of the posterior HC. We therefore hypothesized that key AD pathologies, i.e., β-amyloid and tau pathology would be particularly associated with the volume of the posterior subiculum in non-demented individuals. In our study we included 302 cognitively normal elderly participants (CN), 183 patients with subjective cognitive decline (SCD) and 171 patients with amnestic mild cognitive impairment (MCI), all of whom underwent 3T magnetic resonance images (MRI). The subicular subfield was segmented using Freesurfer 5.3 and divided into 10 volumetric segments moving from the most posterior (segment 1) to the most anterior part along the axis of the hippocampal head and body (segment 10). Cerebrospinal fluid (CSF) Aβ42 and phosphorylated tau (P-tau) were quantified using ELISA and were used as biomarkers for β-amyloid and tau pathology, respectively. In the total sample, tau-pathology and Aβ-pathology and (measured by elevated P-tau and low Aβ42 levels in CSF) and mild memory dysfunction were mostly associated with the volume changes of the posterior subiculum. Both SCD and MCI patients with elevated P-tau or low Aβ42 levels displayed predominantly posterior subicular atrophy in comparisons to control subjects with normal CSF biomarker levels. Finally, there was no main effect of Aβ42 or P-tau when comparing SCD with abnormal P-tau or Aβ42 with SCD with normal levels of these CSF-biomarkers. However, in the left subiculum there was a significant interaction revealing atrophy in the left posterior but not the anterior subiculum in participants with low Aβ42 levels. The same pattern was observed on the contralateral side in participants with elevated P-tau levels. In conclusion, AD pathologies and mild memory dysfunction are mainly associated with atrophy of the posterior parts of the subicular subfields of the HC in non-demented individuals. In light of these findings we suggest that segmentation of the HC subfields may benefit from considering the volume of the different anterior-posterior subsections of each subfield.