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Alzheimer's disease (AD) is associated with alterations in functional connectivity (FC) of the brain. The FC underpinnings of CR, that is, lifelong experiences, are largely unknown. Resting-state FC and structural MRI were performed in 76 CSF amyloid-β (Aβ) negative healthy controls and 152 Aβ positive individuals as an AD spectrum cohort (ADS; 55 with subjective cognitive decline, SCD; 52 with mild cognitive impairment; 45 with AD dementia). Following a region-of-interest (ROI) FC analysis, intrinsic network connectivity within the default-mode network (INC-DMN) and anti-correlation in INC between the DMN and dorsal attention network (DMN:DAN) were obtained as composite scores. CR was estimated by education and Lifetime Experiences Questionnaire (LEQ). The association between INC-DMN and MEM was attenuated by higher LEQ scores in the entire ADS group, particularly in SCD. In ROI analyses, higher LEQ scores were associated with higher FC within the DMN in ADS group. INC-DMN remains relatively intact despite memory decline in individuals with higher lifetime activity estimates, supporting a role for functional networks in maintaining cognitive function in AD., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Neuroinflammation constitutes a pathological hallmark of Alzheimer's disease (AD). Still, it remains unresolved if peripheral inflammatory markers can be utilized for research purposes similar to blood-based beta-amyloid and neurodegeneration measures. We investigated experimental inflammation markers in serum and analyzed interrelations towards AD pathology features in a cohort with a focus on at-risk stages of AD., Methods: Data of 74 healthy controls (HC), 99 subjective cognitive decline (SCD), 75 mild cognitive impairment (MCI), 23 AD relatives, and 38 AD subjects were obtained from the DELCODE cohort. A panel of 20 serum biomarkers was determined using immunoassays. Analyses were adjusted for age, sex, APOE status, and body mass index and included correlations between serum and CSF marker levels and AD biomarker levels. Group-wise comparisons were based on screening diagnosis and routine AD biomarker-based schematics. Structural imaging data were combined into composite scores representing Braak stage regions and related to serum biomarker levels. The Preclinical Alzheimer's Cognitive Composite (PACC5) score was used to test for associations between the biomarkers and cognitive performance., Results: Each experimental marker displayed an individual profile of interrelations to AD biomarkers, imaging, or cognition features. Serum-soluble AXL (sAXL), IL-6, and YKL-40 showed the most striking associations. Soluble AXL was significantly elevated in AD subjects with pathological CSF beta-amyloid/tau profile and negatively related to structural imaging and cognitive function. Serum IL-6 was negatively correlated to structural measures of Braak regions, without associations to corresponding IL-6 CSF levels or other AD features. Serum YKL-40 correlated most consistently to CSF AD biomarker profiles and showed the strongest negative relations to structure, but none to cognitive outcomes., Conclusions: Serum sAXL, IL-6, and YKL-40 relate to different AD features, including the degree of neuropathology and cognitive functioning. This may suggest that peripheral blood signatures correspond to specific stages of the disease. As serum markers did not reflect the corresponding CSF protein levels, our data highlight the need to interpret serum inflammatory markers depending on the respective protein's specific biology and cellular origin. These marker-specific differences will have to be considered to further define and interpret blood-based inflammatory profiles for AD research., (© 2023. The Author(s).)

There is an urgent need to improve the understanding of neuroinflammation in Alzheimer's disease (AD). We analyzed cerebrospinal fluid inflammatory biomarker correlations to brain structural volume and longitudinal cognitive outcomes in the DELCODE study and in a validation cohort of the F.ACE Alzheimer Center Barcelona. We investigated whether respective biomarker changes are evident before onset of cognitive impairment. YKL-40; sTREM2; sAXL; sTyro3; MIF; complement factors C1q, C4, and H; ferritin; and ApoE protein were elevated in pre-dementia subjects with pathological levels of tau or other neurodegeneration markers, demonstrating tight interactions between inflammation and accumulating neurodegeneration even before onset of symptoms. Intriguingly, higher levels of ApoE and soluble TAM receptors sAXL and sTyro3 were related to larger brain structure and stable cognitive outcome at follow-up. Our findings indicate a protective mechanism relevant for intervention strategies aiming to regulate neuroinflammation in subjects with no or subjective symptoms but underlying AD pathology profile., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Inflammation has been described as a key pathogenic event in Alzheimer's disease (AD), downstream of amyloid and tau pathology. Preclinical and clinical data suggest that the cholinergic basal forebrain may moderate inflammatory response to different pathologies., Objective: To study the association of cholinergic basal forebrain volume and functional connectivity with measures of neuroinflammation in people from the AD spectrum., Methods: We studied 261 cases from the DELCODE cohort, including people with subjective cognitive decline, mild cognitive impairment, AD dementia, first degree relatives, and healthy controls. Using Bayesian ANCOVA, we tested associations of MRI indices of cholinergic basal forebrain volume and functional connectivity with cerebrospinal fluid (CSF) levels of sTREM2 as a marker of microglia activation, and serum levels of complement C3. Using Bayesian elastic net regression, we determined associations between basal forebrain measures and a large inflammation marker panel from CSF and serum., Results: We found anecdotal to moderate evidence in favor of the absence of an effect of basal forebrain volume and functional connectivity on CSF sTREM2 and serum C3 levels both in Aβ42/ptau-positive and negative cases. Bayesian elastic net regression identified several CSF and serum markers of inflammation that were associated with basal forebrain volume and functional connectivity. The effect sizes were moderate to small., Conclusion: Our data-driven analyses generate the hypothesis that cholinergic basal forebrain may be involved in the neuroinflammation response to Aβ42 and phospho-tau pathology in people from the AD spectrum. This hypothesis needs to be tested in independent samples.

Background: Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge., Methods: We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection., Results: Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r ≈ -0.86, p < 0.001)., Conclusion: The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia., (© 2021. The Author(s).)

While some individuals age without pathological memory impairments, others develop age-associated cognitive diseases. Since changes in cognitive function develop slowly over time in these patients, they are often diagnosed at an advanced stage of molecular pathology, a time point when causative treatments fail. Thus, there is great need for the identification of inexpensive and minimal invasive approaches that could be used for screening with the aim to identify individuals at risk for cognitive decline that can then undergo further diagnostics and eventually stratified therapies. In this study, we use an integrative approach combining the analysis of human data and mechanistic studies in model systems to identify a circulating 3-microRNA signature that reflects key processes linked to neural homeostasis and inform about cognitive status. We furthermore provide evidence that expression changes in this signature represent multiple mechanisms deregulated in the aging and diseased brain and are a suitable target for RNA therapeutics., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)

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: Cognitive decline has been found to be associated with gray matter atrophy and disruption of functional neural networks in Alzheimer's disease (AD) in structural and functional imaging (fMRI) studies. Most previous studies have used single test scores of cognitive performance among monocentric cohorts. However, cognitive domain composite scores could be more reliable than single test scores due to the reduction of measurement error. Adopting a multicentric resting state fMRI (rs-fMRI) and cognitive domain approach, we provide a comprehensive description of the structural and functional correlates of the key cognitive domains of AD., Method: We analyzed MRI, rs-fMRI and cognitive domain score data of 490 participants from an interim baseline release of the multicenter DELCODE study cohort, including 54 people with AD, 86 with Mild Cognitive Impairment (MCI), 175 with Subjective Cognitive Decline (SCD), and 175 Healthy Controls (HC) in the AD-spectrum. Resulting cognitive domain composite scores (executive, visuo-spatial, memory, working memory and language) from the DELCODE neuropsychological battery (DELCODE-NP), were previously derived using confirmatory factor analysis. Statistical analyses examined the differences between diagnostic groups, and the association of composite scores with regional atrophy and network-specific functional connectivity among the patient subgroup of SCD, MCI and AD., Result: Cognitive performance, atrophy patterns and functional connectivity significantly differed between diagnostic groups in the AD-spectrum. Regional gray matter atrophy was positively associated with visuospatial and other cognitive impairments among the patient subgroup in the AD-spectrum. Except for the visual network, patterns of network-specific resting-state functional connectivity were positively associated with distinct cognitive impairments among the patient subgroup in the AD-spectrum., Conclusion: Consistent associations between cognitive domain scores and both regional atrophy and network-specific functional connectivity (except for the visual network), support the utility of a multicentric and cognitive domain approach towards explicating the relationship between imaging markers and cognition in the AD-spectrum., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Early identification of individuals at risk of dementia is mandatory to implement prevention strategies and design clinical trials that target early disease stages. Subjective cognitive decline (SCD) and neuropsychiatric symptoms (NPS) have been proposed as potential markers for early manifestation of Alzheimer's disease (AD). We aimed to investigate the frequency of NPS in SCD, in other at-risk groups, in healthy controls (CO), and in AD patients, and to test the association of NPS with AD biomarkers, with a particular focus on cognitively unimpaired participants with or without SCD-related worries., Methods: We analyzed data of n = 687 participants from the German DZNE Longitudinal Cognitive Impairment and Dementia (DELCODE) study, including the diagnostic groups SCD (n = 242), mild cognitive impairment (MCI, n = 115), AD (n = 77), CO (n = 209), and first-degree relatives of AD patients (REL, n = 44). The Neuropsychiatric Inventory Questionnaire (NPI-Q), Geriatric Depression Scale (GDS-15), and Geriatric Anxiety Inventory (GAI-SF) were used to assess NPS. We examined differences of NPS frequency between diagnostic groups. Logistic regression analyses were carried out to further investigate the relationship between NPS and cerebrospinal fluid (CSF) AD biomarkers, focusing on a subsample of cognitively unimpaired participants (SCD, REL, and CO), who were further differentiated based on reported worries., Results: The numbers of reported NPS, depression scores, and anxiety scores were significantly higher in subjects with SCD compared to CO. The quantity of reported NPS in subjects with SCD was lower compared to the MCI and AD group. In cognitively unimpaired subjects with worries, low Aß42 was associated with higher rates of reporting two or more NPS (OR 0.998, 95% CI 0.996-1.000, p < .05)., Conclusion: These findings give insight into the prevalence of NPS in different diagnostic groups, including SCD and healthy controls. NPS based on informant report seem to be associated with underlying AD pathology in cognitively unimpaired participants who worry about cognitive decline., Trial Registration: German Clinical Trials Register DRKS00007966 . Registered 4 May 2015.

Background: Dysfunction of the cholinergic basal forebrain (cBF) is associated with cognitive decline in Alzheimer's disease (AD). Multimodal MRI allows for the investigation of cBF changes in-vivo. In this study we assessed alterations in cBF functional connectivity (FC), mean diffusivity (MD), and volume across the spectrum of AD. We further assessed effects of amyloid pathology on these changes., Methods: Participants included healthy controls, and subjects with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or AD dementia (ADD) from the multicenter DELCODE study. Resting-state functional MRI (rs-fMRI) and structural MRI data was available for 477 subjects, and a subset of 243 subjects also had DTI data available. Differences between diagnostic groups were investigated using seed-based FC, volumetric, and MD analyses of functionally defined anterior (a-cBF) and posterior (p-cBF) subdivisions of a cytoarchitectonic cBF region-of-interest. In complementary analyses groups were stratified according to amyloid status based on CSF Aβ42/40 biomarker data, which was available in a subset of participants., Results: a-cBF and p-cBF subdivisions showed regional FC profiles that were highly consistent with previously reported patterns, but there were only minimal differences between diagnostic groups. Compared to controls, cBF volumes and MD were significantly different in MCI and ADD but not in SCD. The Aβ42/40 stratified analyses largely matched these results., Conclusions: We reproduced subregion-specific FC profiles of the cBF in a clinical sample spanning the AD spectrum. At least in this multicentric cohort study, cBF-FC did not show marked changes along the AD spectrum, and multimodal MRI did not provide more sensitive measures of AD-related cBF changes compared to volumetry., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Subjective cognitive decline (SCD) can represent a preclinical stage of Alzheimer's disease. Diffusion tensor imaging (DTI) could aid an early diagnosis, yet only few monocentric DTI studies in SCD have been conducted, reporting heterogeneous results. We investigated microstructural changes in SCD in a larger, multicentric cohort., Methods: 271 participants with SCD, mild cognitive impairment (MCI) or Alzheimer's dementia (AD) and healthy controls (CON) were included, recruited prospectively at nine centers of the observational DELCODE study. DTI was acquired using identical protocols. Using voxel-based analyses, we investigated fractional anisotropy (FA), mean diffusivity (MD) and mode (MO) in the white matter (WM). Discrimination accuracy was determined by cross-validated elastic-net penalized regression. Center effects were explored using variance analyses., Results: MO and FA were lower in SCD compared to CON in several anterior and posterior WM regions, including the anterior corona radiata, superior and inferior longitudinal fasciculus, cingulum and splenium of the corpus callosum (p < 0.01, uncorrected). MD was higher in the superior and inferior longitudinal fasciculus, cingulum and superior corona radiata (p < 0.01, uncorrected). The cross-validated accuracy for discriminating SCD from CON was 67% (p < 0.01). As expected, the AD and MCI groups had higher MD and lower FA and MO in extensive regions, including the corpus callosum and temporal brain regions. Within these regions, center accounted for 3-15% of the variance., Conclusions: DTI revealed subtle WM alterations in SCD that were intermediate between those in MCI and CON and may be useful to detect individuals with an increased risk for AD in clinical studies.

Diffusion changes as determined by diffusion tensor imaging are potential indicators of microstructural lesions in people with mild cognitive impairment (MCI), prodromal Alzheimer's disease (AD), and AD dementia. Here we extended the scope of analysis toward subjective cognitive complaints as a pre-MCI at risk stage of AD. In a cohort of 271 participants of the prospective DELCODE study, including 93 healthy controls and 98 subjective cognitive decline (SCD), 45 MCI, and 35 AD dementia cases, we found reductions of fiber tract integrity in limbic and association fiber tracts in MCI and AD dementia compared with controls in a tract-based analysis (p < 0.05, family wise error corrected). In contrast, people with SCD showed spatially restricted white matter alterations only for the mode of anisotropy and only at an uncorrected level of significance. DTI parameters yielded a high cross-validated diagnostic accuracy of almost 80% for the clinical diagnosis of MCI and the discrimination of Aβ positive MCI cases from Aβ negative controls. In contrast, DTI parameters reached only random level accuracy for the discrimination between Aβ positive SCD and control cases from Aβ negative controls. These findings suggest that in prodromal stages of AD, such as in Aβ positive MCI, multicenter DTI with prospectively harmonized acquisition parameters yields diagnostic accuracy meeting the criteria for a useful biomarker. In contrast, automated tract-based analysis of DTI parameters is not useful for the identification of preclinical AD, including Aβ positive SCD and control cases.

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.