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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.

Delay discounting (DD) refers to the phenomenon that individuals discount future consequences. Previous studies showed that future imagination reduces DD, which was mediated by functional connectivity between medial prefrontal valuation areas and a key region for episodic memory (hippocampus). Future imagination involves an initial period of construction and a later period of elaboration, with the more elaborative latter period recruiting more cortical regions. This study examined whether elaborative future imagination modulated DD, and if so, what are the underlying neural substrates. It was assumed that cortical areas contribute to the modulation effect during the later period of imagination. Since future imagination is supported by episodic memory capacity, we additionally hypothesize that the neural network underlying the modulation effect is related to individual episodic memory capacity. Twenty-two subjects received an extensive interview on personal future events, followed by an fMRI DD experiment with and without the need to perform elaborative future imagination simultaneously. Subjects' episodic memory capacity was also assessed. Behavioral results replicate previous findings of a reduced discount rate in the DD plus imagination condition compared to the DD only condition. The behavioral effect positively correlated with: (i) subjective value signal changes in midline brain structures during the initial imagination period; and (ii) signal changes in left prefrontoparietal areas during the later imagination period. Generalized psychophysiological interaction (gPPI) analyses reveal positive correlations between the behavioral effect and functional connectivity among the following areas: right anterior cingulate cortex (ACC) and left hippocampus; left inferior parietal cortex (IPC) and left hippocampus; and left IPC and bilateral occipital cortices. These changes in functional connectivity are also associated with episodic memory capacity. A hierarchical multiple regression indicates that the model with both the valuation related signal changes in the right ACC and the imagination related signal changes in the left IPC best predicts the reduction in DD. This study illustrates interactions between the left hippocampus and multiple cortical regions underlying the modulation effect of elaborative episodic future imagination, demonstrating, for the first time, empirical support for a relation to individual episodic memory capacity. [ABSTRACT FROM AUTHOR]

Objective: To determine the nature and extent of minor neuropsychological deficits in patients with subjective cognitive decline (SCD) and their association with CSF biomarkers of Alzheimer disease (AD)., Method: We analyzed data from n = 449 cognitively normal participants (n = 209 healthy controls, n = 240 patients with SCD) from an interim data release of the German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study (DELCODE). An extensive neuropsychological test battery was applied at baseline for which we established a latent, 5 cognitive domain factor structure comprising learning and memory, executive functions, language abilities, working memory, and visuospatial functions. We compared groups in terms of global and domain-specific performance and correlated performance with different CSF markers of AD pathology., Results: We observed worse performance (Cohen d = ≈0.25-0.5, adjusted for age, sex differences with analysis of covariance) in global performance, memory, executive functions, and language abilities for the SCD group compared to healthy controls. In addition, worse performance in these domains was moderately ( r = ≈0.3) associated with lower CSF β-amyloid 42/40 and CSF β-amyloid 42 /phosphorylated tau181 in the whole sample and specifically in the SCD subgroup., Conclusions: Within the spectrum of clinically unimpaired (i.e., before mild cognitive impairment) cognitive performance, SCD is associated with minor deficits in memory, executive function, and language abilities. The association of these subtle cognitive deficits with AD CSF biomarkers speaks to their validity and potential use for the early detection of underlying preclinical AD., (© 2020 American Academy of Neurology.)

Background: Alterations of intrinsic networks from resting state fMRI (rs-fMRI) have been suggested as functional biomarkers of Alzheimer's disease (AD)., Objective: To determine the diagnostic accuracy of multicenter rs-fMRI for prodromal and preclinical stages of AD., Methods: We determined rs-fMRI functional connectivity based on Pearson's correlation coefficients and amplitude of low-frequency fluctuation in people with subjective cognitive decline, people with mild cognitive impairment, and people with AD dementia compared with healthy controls. We used data of 247 participants of the prospective DELCODE study, a longitudinal multicenter observational study, imposing a unified fMRI acquisition protocol across sites. We determined cross-validated discrimination accuracy based on penalized logistic regression to account for multicollinearity of predictors., Results: Resting state functional connectivity reached significant cross-validated group discrimination only for the comparison of AD dementia cases with healthy controls, but not for the other diagnostic groups. AD dementia cases showed alterations in a large range of intrinsic resting state networks, including the default mode and salience networks, but also executive and language networks. When groups were stratified according to their CSF amyloid status that was available in a subset of cases, diagnostic accuracy was increased for amyloid positive mild cognitive impairment cases compared with amyloid negative controls, but still inferior to the accuracy of hippocampus volume., Conclusion: Even when following a strictly harmonized data acquisition protocol and rigorous scan quality control, widely used connectivity measures of multicenter rs-fMRI do not reach levels of diagnostic accuracy sufficient for a useful biomarker in prodromal stages of AD.