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BackgroundWhite matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research.MethodsWe used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS).ResultsAcross tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions.ConclusionTo conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.

Several Alzheimer's disease (AD) atrophy subtypes were identified, but their brain network properties are unclear. We analyzed data from two independent datasets, including 166 participants (103 AD/63 controls) from the DZNE-longitudinal cognitive impairment and dementia study and 151 participants (121 AD/30 controls) from the AD neuroimaging initiative cohorts, aiming to identify differences between AD atrophy subtypes in resting-state functional magnetic resonance imaging intra-network connectivity (INC) and global and nodal network properties. Using a data-driven clustering approach, we identified four AD atrophy subtypes with differences in functional connectivity, accompanied by clinical and biomarker alterations, including a medio-temporal-predominant (S-MT), a limbic-predominant (S-L), a diffuse (S-D), and a mild-atrophy (S-MA) subtype. S-MT and S-D showed INC reduction in the default mode, dorsal attention, visual and limbic network, and a pronounced reduction of "global efficiency" and decrease of the "clustering coefficient" in parietal and temporal lobes. Despite severe atrophy in limbic areas, the S-L exhibited only marginal global network but substantial nodal network failure. S-MA, in contrast, showed limited impairment in clinical and cognitive scores but pronounced global network failure. Our results contribute toward a better understanding of heterogeneity in AD with the detection of distinct differences in functional connectivity networks accompanied by CSF biomarker and cognitive differences in AD subtypes., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: The hippocampus and its subfields (HippSub) are reported to be diminished in patients with Alzheimer's disease (AD), bipolar disorder (BD), and major depressive disorder (MDD). We examined these groups vs healthy controls (HC) to reveal HippSub alterations between diseases. Methods: We segmented 3T-MRI T2-weighted hippocampal images of 67 HC, 58 BD, and MDD patients from the AFFDIS study and 137 patients from the DELCODE study assessing cognitive decline, including subjective cognitive decline (SCD), amnestic mild cognitive impairment (aMCI), and AD, via Free Surfer 6.0 to compare volumes across groups. Results: Groups differed significantly in several HippSub volumes, particularly between patients with AD and mood disorders. In comparison to HC, significant lower volumes appear in aMCI and AD groups in specific subfields. Smaller volumes in the left presubiculum are detected in aMCI and AD patients, differing from the BD group. A significant linear regression is seen between left hippocampus volume and duration since the first depressive episode. Conclusions: HippSub volume alterations were observed in AD, but not in early-onset MDD and BD, reinforcing the notion of different neural mechanisms in hippocampal degeneration. Moreover, duration since the first depressive episode was a relevant factor explaining the lower left hippocampal volumes present in groups., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Hansen, Singh, Bartels, Brosseron, Buerger, Cetindag, Dobisch, Dechent, Ertl-Wagner, Fliessbach, Haynes, Heneka, Janowitz, Kilimann, Laske, Metzger, Munk, Peters, Priller, Roy, Scheffler, Schneider, Spottke, Spruth, Teipel, Tscheuschler, Vukovich, Wiltfang, Duezel, Jessen and Goya-Maldonado.)

Background: Amyloid-β accumulation was found to alter precuneus-based functional connectivity (FC) in mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia, but its impact is less clear in subjective cognitive decline (SCD), which in combination with AD pathologic change is theorized to correspond to stage 2 of the Alzheimer's continuum in the 2018 NIA-AA research framework., Objective: This study addresses how amyloid pathology relates to resting-state fMRI FC in SCD, especially focusing on the precuneus., Methods: From the DELCODE cohort, two groups of 24 age- and gender-matched amyloid-positive (SCDAβ+) and amyloidnegative SCD (SCDβ-) patients were selected according to visual [18F]-Florbetaben (FBB) PET readings, and studied with resting-state fMRI. Local (regional homogeneity [ReHo], fractional amplitude of low-frequency fluctuations [fALFF]) and global (degree centrality [DC], precuneus seed-based FC) measures were compared between groups. Follow-up correlation analyses probed relationships of group differences with global and precuneal amyloid load, as measured by FBB standard uptake value ratios (SUVR=⫖FBB)., Results: ReHo was significantly higher (voxel-wise p < 0.01, cluster-level p < 0.05) in the bilateral precuneus for SCDAβ+patients, whereas fALFF was not altered between groups. Relatively higher precuneus-based FC with occipital areas (but no altered DC) was observed in SCDAβ+ patients. In this latter cluster, precuneus-occipital FC correlated positively with global (SCDAβ+) and precuneus SUVRFBB (both groups)., Conclusion: While partial confounding influences due to a higher APOE ε4 carrier ratio among SCDAβ+ patients cannot be excluded, exploratory results indicate functional alterations in the precuneus hub region that were related to amyloid-β load, highlighting incipient pathology in stage 2 of the AD continuum.