Disentangling biologically distinct subgroups of Alzheimer’s disease (AD) may facilitate a deeper understanding of the neurobiology underlying clinical heterogeneity. We employed longitudinal [18F]FDG-PET standardized uptake value ratios (SUVRs) to map hypometabolism across cognitively-defined AD subgroups. Participants were 384 amyloid-positive individuals with an AD dementia diagnosis from ADNI who had a total of 1028 FDG-scans (mean time between first and last scan: 1.6 ± 1.8 years). These participants were categorized into subgroups on the basis of substantial impairment at time of dementia diagnosis in a specific cognitive domain relative to the average across domains. This approach resulted in groups of AD-Memory (n = 135), AD-Executive (n = 8), AD-Language (n = 22), AD-Visuospatial (n = 44), AD-Multiple Domains (n = 15) and AD-No Domains (for whom no domain showed substantial relative impairment; n = 160). Voxelwise contrasts against controls revealed that all AD-subgroups showed progressive hypometabolism compared to controls across temporoparietal regions at time of AD diagnosis. Voxelwise and regions-of-interest (ROI)-based linear mixed model analyses revealed there were also subgroup-specific hypometabolism patterns and trajectories. The AD-Memory group had more pronounced hypometabolism compared to all other groups in the medial temporal lobe and posterior cingulate, and faster decline in metabolism in the medial temporal lobe compared to AD-Visuospatial. The AD-Language group had pronounced lateral temporal hypometabolism compared to all other groups, and the pattern of metabolism was also more asymmetrical (left < right) than all other groups. The AD-Visuospatial group had faster decline in metabolism in parietal regions compared to all other groups, as well as faster decline in the precuneus compared to AD-Memory and AD-No Domains. Taken together, in addition to a common pattern, cognitively-defined subgroups of people with AD dementia show subgroup-specific hypometabolism patterns, as well as differences in trajectories of metabolism over time. These findings provide support to the notion that cognitively-defined subgroups are biologically distinct.

Trajectories of decline in cognitively complex everyday activities across the Alzheimer’s disease continuum

Alzheimer's disease (AD) studies are increasingly targeting earlier (pre)clinical populations, in which the expected degree of observable cognitive decline over a certain time interval is reduced as compared to the dementia stage. Consequently, endpoints to capture early cognitive changes require refinement. We aimed to determine the sensitivity to decline of widely applied neuropsychological tests at different clinical stages of AD as outlined in the National Institute on Aging - Alzheimer's Association (NIA-AA) research framework. Amyloid-positive individuals (as determined by positron emission tomography or cerebrospinal fluid) with longitudinal neuropsychological assessments available were included from four well-defined study cohorts and subsequently classified among the NIA-AA stages. For each stage, we investigated the sensitivity to decline of 17 individual neuropsychological tests using linear mixed models. 1103 participants (age = 70.54 ± 8.7, 47% female) were included: n = 120 Stage 1, n = 206 Stage 2, n = 467 Stage 3 and n = 309 Stage 4. Neuropsychological tests were differentially sensitive to decline across stages. For example, Category Fluency captured significant 1-year decline as early as Stage 1 (β = -.58, p < .001). Word List Delayed Recall (β = -.22, p < .05) and Trail Making Test (β = 6.2, p < .05) became sensitive to 1-year decline in Stage 2, whereas the Mini-Mental State Examination did not capture 1-year decline until Stage 3 (β = -1.13, p < .001) and 4 (β = -2.23, p < .001). We demonstrated that commonly used neuropsychological tests differ in their ability to capture decline depending on clinical stage within the AD continuum (preclinical to dementia). This implies that stage-specific cognitive endpoints are needed to accurately assess disease progression and increase the chance of successful treatment evaluation in AD.

ObjectiveTo develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability.MethodsThree thousand twenty-seven individuals (1,763 cognitively unimpaired [CU], 658 impaired, 467 with Alzheimer disease [AD] dementia, 111 with non-AD dementia, and 28 with missing diagnosis) from 6 cohorts (European Medical Information Framework for AD, Alzheimer's and Family, Alzheimer's Biomarkers in Daily Practice, Amsterdam Dementia Cohort, Open Access Series of Imaging Studies [OASIS]-3, Alzheimer’s Disease Neuroimaging Initiative [ADNI]) who underwent amyloid PET were retrospectively included; 1,049 individuals had follow-up scans. With application of dataset-specific cutoffs to global standard uptake value ratio (SUVr) values from 27 regions, single-tracer and pooled multitracer regional rankings were constructed from the frequency of abnormality across 400 CU individuals (100 per tracer). The pooled multitracer ranking was used to create a staging model consisting of 4 clusters of regions because it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables, and longitudinal cognitive decline were investigated.ResultsSUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices and then the associative, temporal, and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of individuals in stage 0 to 4, respectively. Baseline stage predicted decline in Mini-Mental State Examination (MMSE) score (ADNI: n = 867, F = 67.37, p < 0.001; OASIS: n = 475, F = 9.12, p < 0.001) and faster progression toward an MMSE score ≤25 (ADNI: n = 787, hazard ratio [HR]stage1 2.00, HRstage2 3.53, HRstage3 4.55, HRstage4 9.91, p < 0.001; OASIS: n = 469, HRstage4 4.80, p < 0.001).ConclusionThe pooled multitracer staging model successfully classified the level of amyloid burden in >3,000 individuals across cohorts and radiotracers and detects preglobal amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive individuals.

Amyloid Positivity as a Risk Factor for Memory Decline and Lower Memory Performance as an Indicator of Conversion to Amyloid Positivity: Chicken and Egg

IntroductionThe molecular mechanism of neurodegeneration, including tau and neurite complexity, is an important topic in Alzheimer's disease (AD) research.MethodsWe recruited 27 amyloid‐positive individuals identified through 11C‐Pittsburgh compound B (PiB) positron emission tomography (PET) and 31 amyloid‐negative individuals with normal cognition. All participants underwent 11C‐PiB and 18F‐THK5351 PET and magnetic resonance imaging (MRI) with neurite orientation dispersion and density imaging (NODDI) protocol. The neurite density index (NDI), orientation dispersion index (ODI), and PET images were analyzed to calculate voxel‐wise correlations among the imaging modalities and correlations with cognitions.ResultsIn the amyloid‐positive participants, there were significant negative correlations between 18F‐THK5351 and NDI and between 18F‐THK5351 and ODI. The bilateral mesial and lateral temporal lobes were mainly involved. Regarding cognition, 18F‐THK5351 showed more marked associations with all cognitive domains than the other modalities.DiscussionTau and neuroinflammation in AD may reduce the neurite density and orientation dispersion, particularly in the mesial and lateral temporal lobes.

Although amyloid-β (Aβ) and microstructural brain changes are both effective biomarkers of Alzheimer's disease, their independent or synergistic effects on cognitive decline are unclear. To examine associations of Aβ and brain microstructure with cognitive decline in amnestic mild cognitive impairment and dementia. Restriction spectrum imaging, cerebrospinal fluid Aβ, and longitudinal cognitive data were collected on 23 healthy controls and 13 individuals with mild cognitive impairment or mild to moderate Alzheimer's disease. Neurite density (ND) and isotropic free water diffusion (IF) were computed in fiber tracts and cortical regions of interest. We examined associations of Aβ with regional and whole-brain microstructure, and assessed whether microstructure mediates effects of Aβ on cognitive decline. Lower ND in limbic and association fibers and higher medial temporal lobe IF predicted baseline impairment and longitudinal decline across multiple cognitive domains. ND and IF predicted cognitive outcomes after adjustment for Aβ or whole-brain microstructure. Correlations between microstructure and cognition were present for both amyloid-positive and amyloid-negative individuals. Aβ correlated with whole-brain, rather than regional, ND and IF. Aβ correlates with widespread microstructural brain changes, whereas regional microstructure correlates with cognitive decline. Microstructural abnormalities predict cognitive decline regardless of amyloid, and may inform about neural injury leading to cognitive decline beyond that attributable to amyloid.

Plasma neurofilament light associates with Alzheimer's disease metabolic decline in amyloid-positive individuals

BackgroundMagnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer’s disease (AD) pathophysiologic continuum constituting what has been established as “AD signature”. To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration.MethodLongitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), and 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cutoffs (< 192 pg/mL) to establish diagnostic categories. Cognitively unimpaired individuals were defined as Ctrls if were amyloid negative and PreAD otherwise. The MCI/AD group was amyloid positive. Only subjects with the same diagnostic category at baseline and follow-up visits were considered for the study. Longitudinal morphometric analysis was performed using SPM12 to calculate Jacobian determinant maps. Statistical analysis was carried out on these Jacobian maps to identify structural changes that were significantly different between diagnostic categories. A machine learning classifier was applied on Jacobian determinant maps to predict the presence of abnormal amyloid levels in cognitively unimpaired individuals. The performance of this classifier was evaluated using receiver operating characteristic curve analysis and as a function of the follow-up time between MRI scans. We applied a cost function to assess the benefit of using this classifier in the triaging of individuals in a clinical trial-recruitment setting.ResultsThe optimal follow-up time for classification of Ctrls vs PreAD was Δt > 2.5 years, and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC = 0.87 (95%CI 0.72–0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior, and lateral temporal lobes; precuneus; caudate heads; basal forebrain; and lateral ventricles.ConclusionsOur work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, the presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid-positive individuals, this longitudinal voxel-wise classifier is expected to avoid 55% of unnecessary CSF and/or PET scans and reduce economic cost by 40%.

C-03 The sensitivity and specificity of the Montreal Cognitive Assessment is Age Dependent for Amyloid Positivity in a Mixed Clinical Sample

The beta-site APP cleaving enzyme 1 (BACE1) is known primarily for its initial cleavage of the amyloid precursor protein (APP), which ultimately leads to the generation of Aβ peptides. Here, we provide evidence that altered BACE1 levels and activity impact the degradation of Aβ40 and Aβ42 into a common Aβ34 intermediate. Using human cerebrospinal fluid (CSF) samples from the Amsterdam Dementia Cohort, we show that Aβ34 is elevated in individuals with mild cognitive impairment who later progressed to dementia. Furthermore, Aβ34 levels correlate with the overall Aβ clearance rates in amyloid positive individuals. Using CSF samples from the PREVENT-AD cohort (cognitively normal individuals at risk for Alzheimer’s disease), we further demonstrate that the Aβ34/Aβ42 ratio, representing Aβ degradation and cortical deposition, associates with pre-clinical markers of neurodegeneration. We propose that Aβ34 represents a marker of amyloid clearance and may be helpful for the characterization of Aβ turnover in clinical samples.

Amyloid-β (Aβ) pathology in Alzheimer's disease (AD) is characterized by the formation of polymorphic deposits comprising diffuse and cored plaques. Because diffuse plaques are predominantly observed in cognitively unaffected, amyloid-positive (CU-AP) individuals, pathogenic conversion into cored plaques appears to be critical to AD pathogenesis. Herein, we identified the distinct Aβ species associated with amyloid polymorphism in brain tissue from individuals with sporadic AD (s-AD) and CU-AP. To this end, we interrogated Aβ polymorphism with amyloid conformation–sensitive dyes and a novel in situ MS paradigm for chemical characterization of hyperspectrally delineated plaque morphotypes. We found that maturation of diffuse into cored plaques correlated with increased Aβ1–40 deposition. Using spatial in situ delineation with imaging MS (IMS), we show that Aβ1–40 aggregates at the core structure of mature plaques, whereas Aβ1–42 localizes to diffuse amyloid aggregates. Moreover, we observed that diffuse plaques have increased pyroglutamated Aβx-42 levels in s-AD but not CU-AP, suggesting an AD pathology–related, hydrophobic functionalization of diffuse plaques facilitating Aβ1–40 deposition. Experiments in tgAPPSwe mice verified that, similar to what has been observed in human brain pathology, diffuse deposits display higher levels of Aβ1–42 and that Aβ plaque maturation over time is associated with increases in Aβ1–40. Finally, we found that Aβ1–40 deposition is characteristic for cerebral amyloid angiopathy deposition and maturation in both humans and mice. These results indicate that N-terminal Aβx-42 pyroglutamation and Aβ1–40 deposition are critical events in priming and maturation of pathogenic Aβ from diffuse into cored plaques, underlying neurotoxic plaque development in AD.

We propose a method for recruiting asymptomatic Amyloid positive individuals in clinical trials, using a two-step process. We first select during a pre-screening phase a subset of individuals which are more likely to be amyloid positive based on the automatic analysis of data acquired during routine clinical practice, before doing a confirmatory PET-scan to these selected individuals only. This method leads to an increased number of recruitments and to a reduced number of PET-scans, resulting in a decrease in overall recruitment costs. We validate our method on three different cohorts, and consider five different classification algorithms for the pre-screening phase. We show that the best results are obtained using solely cognitive, genetic and socio-demographic features, as the slight increased performance when using MRI or longitudinal data is balanced by the cost increase they induce. We show that the proposed method generalizes well when tested on an independent cohort, and that the characteristics of the selected set of individuals are identical to the characteristics of a population selected in a standard way. The proposed approach shows how Machine Learning can be used effectively in practice to optimize recruitment costs in clinical trials.

The strongest genetic risk factor for Alzheimer disease (AD), the apolipoprotein E (APOE) gene, has a stronger association among women compared with men. Yet limited work has evaluated the association between APOE alleles and markers of AD neuropathology in a sex-specific manner. To evaluate sex differences in the association between APOE and markers of AD neuropathology measured in cerebrospinal fluid (CSF) during life or in brain tissue at autopsy. This multicohort study selected data from 10 longitudinal cohort studies of normal aging and AD. Cohorts had variable recruitment criteria and follow-up intervals and included population-based and clinic-based samples. Inclusion in our analysis required APOE genotype data and either CSF data available for analysis. Analyses began on November 6, 2017, and were completed on December 20, 2017. Biomarker analyses included levels of β-amyloid 42, total tau, and phosphorylated tau measured in CSF. Autopsy analyses included Consortium to Establish a Registry for Alzheimer's Disease staging for neuritic plaques and Braak staging for neurofibrillary tangles. Of the 1798 patients in the CSF biomarker cohort, 862 were women, 226 had AD, 1690 were white, and the mean (SD) age was 70 [9] years. Of the 5109 patients in the autopsy cohort, 2813 were women, 4953 were white, and the mean (SD) age was 84 (9) years. After correcting for multiple comparisons using the Bonferroni procedure, we observed a statistically significant interaction between APOE-ε4 and sex on CSF total tau (β = 0.41; 95% CI, 0.27-0.55; P < .001) and phosphorylated tau (β = 0.24; 95% CI, 0.09-0.38; P = .001), whereby APOE showed a stronger association among women compared with men. Post hoc analyses suggested this sex difference was present in amyloid-positive individuals (β = 0.41; 95% CI, 0.20-0.62; P < .001) but not among amyloid-negative individuals (β = 0.06; 95% CI, -0.18 to 0.31; P = .62). We did not observe sex differences in the association between APOE and β-amyloid 42, neuritic plaque burden, or neurofibrillary tangle burden. We provide robust evidence of a stronger association between APOE-ε4 and CSF tau levels among women compared with men across multiple independent data sets. Interestingly, APOE-ε4 is not differentially associated with autopsy measures of neurofibrillary tangles. Together, the sex difference in the association between APOE and CSF measures of tau and the lack of a sex difference in the association with neurofibrillary tangles at autopsy suggest that APOE may modulate risk for neurodegeneration in a sex-specific manner, particularly in the presence of amyloidosis.

Brain amyloid deposition is a marker of Alzheimer disease (AD) pathology. The population-based prevalence and outcomes of amyloid positivity in a population without dementia are important for understanding the trajectory of amyloid positivity to clinically significant outcomes and for designing AD prevention trials. To determine prevalence and outcomes of amyloid positivity in a population without dementia. In the prospective, population-based Mayo Clinic Study of Aging in Olmsted County, Minnesota, participants without dementia were randomly selected from the county population and were clinically and cognitively evaluated at baseline and every 15 months from August 1, 2008, to September 18, 2018. They were also invited to undergo carbon11-Pittburgh compound B positron emission tomography (PET) imaging. Amyloid positivity (defined as a standardized uptake value ratio >1.42 on PET). Prevalence of amyloid positivity in the Olmsted County population without dementia and risk of progression from no cognitive impairment (ie, normal cognition for age) to incident amnestic MCI (aMCI) and from MCI or aMCI to incident AD dementia. Of 3894 participants, 1671 underwent PET imaging and were included in the study; 2198 did not undergo imaging, and 25 were excluded for other reasons. The mean (SD) age of participants was 71.3 (9.8) years; 892 (53.4%) were men, and 179 (10.7%) had prevalent MCI. The prevalence of amyloid positivity without cognitive impairment in the population without dementia increased from 2.7% (95% CI, 0.5% to 4.9%) in persons aged 50 to 59 years to 41.3% (95% CI, 33.4% to 49.2%) in those aged 80 to 89 years at baseline. Prevalence of amyloid-positive MCI in the population without dementia increased from 0% in persons aged 50 to 59 years to 16.4% (95% CI, 10.3% to 22.5%) in those aged 80 to 89 years. The incident aMCI risk increased more than 2-fold in participants without cognitive impairment who were amyloid positive vs those who were amyloid negative (hazard ratio [HR], 2.26; 95% CI, 1.52 to 3.35; P < .001). The risk of AD dementia was 1.86 (95% CI, 0.89 to 3.88; P = .10) for amyloid-positive participants with MCI vs amyloid-negative participants with MCI, 1.63 (95% CI, 0.78 to 3.41; P = .20) for participants with aMCI who were amyloid positive vs amyloid negative, and 2.56 (95% CI, 1.35 to 4.88; P = .004) for amyloid-positive participants who were either without cognitive impairment or had aMCI vs those who were amyloid negative. Global cognitive and memory domain z scores declined significantly in amyloid-positive individuals during follow-up. The mean (SD) follow-up time from baseline was 3.7 (1.9) years to incident aMCI and 3.8 (2.0) years to incident AD dementia. Population-based prevalence of amyloid-positive status and progression rates of amyloid positivity provide valid information for designing AD prevention trials and assessing the public health outcomes of AD prevention and interventions.

Animal models of Alzheimer’s disease (AD) have suggested that tau pathology propagation, facilitated by amyloid pathology, may occur along connected pathways. To investigate these ideas in humans, we combined amyloid scans with longitudinal data on white matter connectivity, hippocampal volume, tau positron emission tomography and memory performance in 256 cognitively healthy older individuals. Lower baseline hippocampal volume was associated with increased mean diffusivity of the connecting hippocampal cingulum bundle (HCB). HCB diffusivity predicted tau accumulation in the downstream-connected posterior cingulate cortex (PCC) in amyloid positive, not in amyloid negative individuals. Furthermore, HCB diffusivity predicted memory decline in amyloid positive individuals with high PCC tau binding. Our results provide in vivo evidence that higher amyloid pathology strengthens the association between HCB diffusivity and tau accumulation in the down-stream PCC and, facilitates memory decline. This confirms amyloid’s crucial role in potentiating neural vulnerability and cognitive decline marking the onset of preclinical AD.

Early affective changes and increased connectivity in preclinical Alzheimer's disease

IntroductionRelationship between self– and informant memory concerns and tau aggregation was assessed in adults at risk for Alzheimer's disease (AD). MethodsRegional mean standardized uptake value ratios were extracted from [18F]flortaucipir positron emission tomography (PET) scans of 82 at-risk adults in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Associations between self– and informant ECog memory scores and tau aggregation were analyzed on both regional and voxelwise bases. Analyses were completed both on the whole sample and restricted to amyloid-positive individuals only. ResultsMemory concerns were associated with tau aggregation. Self-perception was more associated with frontal tau. In contrast, informant scores were more associated with parietal tau. This source-by-region interaction was more prominent in amyloid-positive participants and observed in both regional and voxelwise analyses. DiscussionQuantitative assessment of perceived memory functioning may be useful for screening older adults at risk for Alzheimer's disease. Individuals and their informants may provide complementary information relating to the anatomical distribution of tau.

BETA-AMYLOID ACCUMULATION HURTS AND CRYSTALLIZED KNOWLEDGE HELPS BRAIN MODULATORY CAPACITY: AN FMRI STUDY

A COMPARISON OF TECHNIQUES FOR QUANTIFYING AMYLOID BURDEN ON A COMBINED PET/MR SCANNER