Mediterranean Diet, Alzheimer Disease Biomarkers, and Brain Atrophy in Old Age

Objective: To determine whether following a Mediterranean-like diet (MeDi) relates to cognitive functions and in vivo biomarkers for Alzheimer disease (AD), we analyzed cross-sectional data from the German DZNE-Longitudinal Cognitive Impairment and Dementia Study. Method: The sample (n = 512, mean age 69.5 ± 5.9 years) included 169 cognitively normal participants and individuals at higher AD risk (53 with relatives with AD, 209 with subjective cognitive decline, and 81 with mild cognitive impairment). We defined MeDi adherence according to the food frequency questionnaire. Brain volume outcomes were generated via voxel-based morphometry on T1-MRI, and cognitive performance was assessed with an extensive neuropsychological battery. AD-related biomarkers (β-amyloid42/40 [Aβ42/40] ratio, phosphorylated tau 181 [pTau181]) in CSF were assessed in n = 226 individuals. We analyzed the associations between MeDi and outcomes with linear regression models controlling for several covariates. In addition, we applied hypothesis-driven mediation and moderation analysis. Results: Higher MeDi adherence related to larger mediotemporal gray matter volume (p < 0.05 family-wise error corrected), better memory (β ± SE = 0.03 ± 0.02; p = 0.038), and less amyloid (Aβ42/40 ratio, β ± SE = 0.003 ± 0.001; p = 0.008) and pTau181 (β ± SE = −1.96 ± 0.68; p = 0.004) pathology. Mediotemporal volume mediated the association between MeDi and memory (40% indirect mediation). Finally, MeDi favorably moderated the associations among Aβ42/40 ratio, pTau181, and mediotemporal atrophy. Results were consistent correcting for APOE-ε4 status. Conclusion: Our findings corroborate the view of MeDi as a protective factor against memory decline and mediotemporal atrophy. They suggest that these associations might be explained by a decrease of amyloidosis and tau pathology. Longitudinal and dietary intervention studies should further examine this conjecture and its treatment implications

Resting-State Network Alterations Differ between Alzheimer's Disease Atrophy Subtypes

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

Neuropsychiatric symptoms in at-risk groups for AD dementia and their association with worry and AD biomarkers—results from the DELCODE study

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

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status

Assessment of perfusion deficit with early phases of [18F]PI-2620 tau-PET versus [18F]flutemetamol-amyloid-PET recordings

Purpose Characteristic features of amyloid-PET (A), tau-PET (T), and FDG-PET (N) can serve for the A/T/N classification of neurodegenerative diseases. Recent studies showed that the early, perfusion-weighted phases of amyloid- or tau-PET recordings serve to detect cerebrometabolic deficits equally to FDG-PET, therefore providing a surrogate of neuronal injury. As such, two channels of diagnostic information can be obtained in the setting of a single PET scan. However, there has hitherto been no comparison of early-phase amyloid- and tau-PET as surrogates for deficits in perfusion/metabolism. Therefore, we undertook to compare [18F]flutemetamol-amyloid-PET and [18F]PI-2620 tau-PET as “one-stop shop” dual purpose tracers for the detection of neurodegenerative disease. Methods We obtained early-phase PET recordings with [18F]PI-2620 (0.5–2.5 min p.i.) and [18F]flutemetamol (0–10 min p.i.) in 64 patients with suspected neurodegenerative disease. We contrasted global mean normalized images (SUVr) in the patients with a normal cohort of 15 volunteers without evidence of increased pathology to β-amyloid- and tau-PET examinations. Regional group differences of tracer uptake (z-scores) of 246 Brainnetome volumes of interest were calculated for both tracers, and the correlations of the z-scores were evaluated using Pearson’s correlation coefficient. Lobar compartments, regions with significant neuronal injury (z-scores <  − 3), and patients with different neurodegenerative disease entities (e.g., Alzheimer’s disease or 4R-tauopathies) served for subgroup analysis. Additionally, we used partial regression to correlate regional perfusion alterations with clinical scores in cognition tests. Results The z-scores of perfusion-weighted images of both tracers showed high correlations across the brain, especially in the frontal and parietal lobes, which were the brain regions with pronounced perfusion deficit in the patient group (R = 0.83 ± 0.08; range, 0.61–0.95). Z-scores of individual patients correlated well by region (R = 0.57 ± 0.15; range, 0.16–0.90), notably when significant perfusion deficits were present (R = 0.66 ± 0.15; range, 0.28–0.90). Conclusion The early perfusion phases of [18F]PI-2620 tau- and [18F]flutemetamol-amyloid-PET are roughly equivalent indices of perfusion defect indicative of regional and lobar neuronal injury in patients with various neurodegenerative diseases. As such, either tracer may serve for two diagnostic channels by assessment of amyloid/tau status and neuronal activity

Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease

Background: Although convolutional neural networks (CNN) 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. 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 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. Results: Across three independent datasets, group separation showed high accuracy for AD dementia vs. controls (AUC$\geq$0.92) and moderate accuracy for MCI vs. controls (AUC$\approx$0.75). Relevance maps indicated that hippocampal atrophy was considered as the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r$\approx$-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.Comment: 24 pages, 9 figures/tables, supplementary material, source code available on GitHu

Frequency and Longitudinal Course of Motor Signs in Genetic Frontotemporal Dementia

Appendix 1: Authors. Appendix 2: Coinvestigators: Coinvestigators are listed at https://cdn-links.lww.com/permalink/wnl/c/wnl_2022_07_12_levin_1_sdc1.pdf . Supplement at https://cdn-links.lww.com/permalink/wnl/c/wnl_2022_06_26_levin_1_sdc2.pdf .Copyright © 2022 The Author(s). Background and Objectives: Frontotemporal dementia (FTD) is a highly heritable disorder. The majority of genetic cases are caused by autosomal dominant pathogenic variants in the chromosome 9 open reading frame 72 (c9orf72), progranulin (GRN), and microtubule-associated protein tau (MAPT) gene. As motor disorders are increasingly recognized as part of the clinical spectrum, the current study aimed to describe motor phenotypes caused by genetic FTD, quantify their temporal association, and investigate their regional association with brain atrophy. Methods: We analyzed baseline visit data of known carriers of a pathogenic variant in the c9orf72, GRN, or MAPT gene from the Genetic Frontotemporal Dementia Initiative cohort study. Principal component analysis with varimax rotation was performed to identify motor sign clusters that were compared with respect to frequency and severity between groups. Associations with cross-sectional atrophy patterns were determined using voxel-wise regression. We applied linear mixed effects models to assess whether groups differed in the association between motor signs and estimated time to symptom onset. Results: A total of 322 pathogenic variant carriers were included in the analysis: 122 c9orf72 (79 presymptomatic), 143 GRN (112 presymptomatic), and 57 MAPT (43 presymptomatic) pathogenic variant carriers. Principal component analysis revealed 5 motor clusters, which we call progressive supranuclear palsy (PSP)-like, bulbar amyotrophic lateral sclerosis (ALS)-like, mixed/ALS-like, Parkinson disease (PD) like, and corticobasal syndrome–like motor phenotypes. There was no significant group difference in the frequency of signs of different motor phenotypes. However, mixed/ALS-like motor signs were most frequent, followed by PD-like motor signs. Although the PSP-like phenotype was associated with mesencephalic atrophy, the mixed/ALS-like phenotype was associated with motor cortex and corticospinal tract atrophy. The PD-like phenotype was associated with widespread cortical and subcortical atrophy. Estimated time to onset, genetic group and their interaction influenced motor signs. In c9orf72 pathogenic variant carriers, motor signs could be detected up to 25 years before expected symptom onset. Discussion: These results indicate the presence of multiple natural clusters of motor signs in genetic FTD, each correlated with specific atrophy patterns. Their motor severity depends on time and the affected gene. These clinicogenetic associations can guide diagnostic evaluations and the design of clinical trials for new disease-modifying and preventive treatments.This work is cofunded by the UK Medical Research Council (MR/M023664/1), Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy–ID 390857198), the Italian Ministry of Health, and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, a Canadian Institutes of Health Research operating grant and the Bluefield Project, as well as a JPND grant GENFIprox. Nonfinancial support was also provided through the European Reference Network for Rare Neurological Diseases (ERN-RND), 1 of 24 ERNs funded by the European Commission (ERN-RND: 3HP 767231). J.-M. Gorriz Saez is supported by the Ministerio de Ciencia e Innovación (España)/FEDER under the RTI2018-098913-B100 project and the Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under the CV20-45250 and A-TIC-080-UGR18 projects. M. Masellis was also funded by a Canadian Institutes of Health Research operating grant (MOP 327387) and funding from the Weston Brain Institute. J. Rowe is supported by the Medical Research Council (SUAG/051 G101400) and NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care

Associations between sex, body mass index and the individual microglial response in Alzheimer’s disease

Background and objectives 18-kDa translocator protein position-emission-tomography (TSPO-PET) imaging emerged for in vivo assessment of neuroinflammation in Alzheimer’s disease (AD) research. Sex and obesity effects on TSPO-PET binding have been reported for cognitively normal humans (CN), but such effects have not yet been systematically evaluated in patients with AD. Thus, we aimed to investigate the impact of sex and obesity on the relationship between β-amyloid-accumulation and microglial activation in AD. Methods 49 patients with AD (29 females, all Aβ-positive) and 15 Aβ-negative CN (8 female) underwent TSPO-PET ([18F]GE-180) and β-amyloid-PET ([18F]flutemetamol) imaging. In 24 patients with AD (14 females), tau-PET ([18F]PI-2620) was additionally available. The brain was parcellated into 218 cortical regions and standardized-uptake-value-ratios (SUVr, cerebellar reference) were calculated. Per region and tracer, the regional increase of PET SUVr (z-score) was calculated for AD against CN. The regression derived linear effect of regional Aβ-PET on TSPO-PET was used to determine the Aβ-plaque-dependent microglial response (slope) and the Aβ-plaque-independent microglial response (intercept) at the individual patient level. All read-outs were compared between sexes and tested for a moderation effect of sex on associations with body mass index (BMI). Results In AD, females showed higher mean cortical TSPO-PET z-scores (0.91 ± 0.49; males 0.30 ± 0.75; p = 0.002), while Aβ-PET z-scores were similar. The Aβ-plaque-independent microglial response was stronger in females with AD (+ 0.37 ± 0.38; males with AD − 0.33 ± 0.87; p = 0.006), pronounced at the prodromal stage. On the contrary, the Aβ-plaque-dependent microglial response was not different between sexes. The Aβ-plaque-independent microglial response was significantly associated with tau-PET in females (Braak-II regions: r = 0.757, p = 0.003), but not in males. BMI and the Aβ-plaque-independent microglial response were significantly associated in females (r = 0.44, p = 0.018) but not in males (BMI*sex interaction: F(3,52) = 3.077, p = 0.005). Conclusion While microglia response to fibrillar Aβ is similar between sexes, women with AD show a stronger Aβ-plaque-independent microglia response. This sex difference in Aβ-independent microglial activation may be associated with tau accumulation. BMI is positively associated with the Aβ-plaque-independent microglia response in females with AD but not in males, indicating that sex and obesity need to be considered when studying neuroinflammation in AD

Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission

Purpose Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer’s disease, Parkinson’s disease, multiple system atrophy) at individual patient level via principal component analysis (PCA). Methods FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level. Results Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912). Conclusion Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset