Search

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 DELCODEcohort, 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 A beta(42)/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 A beta(42) and phospho-tau pathology in people from the AD spectrum. This hypothesis needs to be tested in independent samples.

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-beta (A beta) negative healthy controls and 152 A beta positive individ-uals 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 ed-ucation 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 in-tact despite memory decline in individuals with higher lifetime activity estimates, supporting a role for functional networks in maintaining cognitive function in AD.(c) 2022 Elsevier Inc. All rights reserved.

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.

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 approximate to 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 approximate to -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 relev

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.

BackgroundEarly 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.MethodsWe 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.ResultsThe 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 A ss 42 was associated with higher rates of reporting two or more NPS (OR 0.998, 95% CI 0.996-1.000, p<.05).ConclusionThese 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 wit

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 A beta positive MCI cases from A beta negative controls. In contrast, DTI parameters reached only random level accuracy for the discrimination between A beta positive SCD and control cases from A beta negative controls. These findings suggest that in prodromal stages of AD, such as in A beta positive MCI, multicenter DTI with prospectively harmonized acquisition parameters yields diagnostic accuracy meeting the criteria for a useful biomarker. In contrast, automated tractbased analysis of DTI parameters is not useful for the identification of preclinical AD, including A beta positive SCD and control cases.

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.

Mnemonic discrimination, the ability to distinguish similar events in memory, relies on subregions in the human medial temporal lobes (MTLs). Tau pathology is frequently found within the MTL of older adults and therefore likely to affect mnemonic discrimination, even in healthy older individuals. The MTL subregions that are known to be affected early by tau pathology, the perirhinal-transentorhinal region (area 35) and the anterior-lateral entorhinal cortex (alEC), have recently been implicated in the mnemonic discrimination of objects rather than scenes. Here we used an object-scene mnemonic discrimination task in combination with fMRI recordings and analyzed the relationship between subregional MTL activity, memory performance, and levels of total and phosphorylated tau as well as A beta 42/40 ratio in CSF. We show that activity in alEC was associated with mnemonic discrimination of similar objects but not scenes in male and female cognitively unimpaired older adults. Importantly, CSF tau levels were associated with increased fMRI activity in the hippocampus, and both increased hippocampal activity as well as tau levels were associated with mnemonic discrimination of objects, but again not scenes. This suggests that dysfunction of the alEC-hippocampus object mnemonic discrimination network might be a marker for tau-related cognitive decline.

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.