We disclosed amyloid positron emission tomography (PET) results in individuals with subjective cognitive decline (SCD) and studied patient experiences and outcomes over a 6-month period. Fifty-seven participants from the Subjective Cognitive Impairment Cohort (SCIENCe) (66±8 years, 21 [37%] F, Mini-Mental State Examination 29±1, 15 [26%] amyloid positive [A+]) completed questionnaires 1 week prior (T0), 1 day after (T1), and 6 months after amyloid PET disclosure (T2). Questionnaires addressed patient-reported experiences and outcomes. Independent of amyloid status, participants were satisfied with the consultation (scale 1-10; 7.9±1.7) and information provided (scale 1-4; T1: 3.3±0.9, T2: 3.2±0.8). After 6 months, A+ participants reported more information needs (45%vs. 12%, p=0.02). Independent of amyloid status, decision regret (scale 1-5; A+: 1.5±0.9, A-: 1.4±0.6, p=0.53) and negative emotions (negative affect, uncertainty, anxiety) were low (all p>0.15 and Pinteraction>0.60). Participants with SCD valued amyloid PET disclosure positively, regardless of amyloid status. The need for information after 6 months, which was stronger in A+ individuals, underscores the importance of follow-up. Participants with subjective cognitive decline (SCD) positively valued amyloid positron emission tomography (PET) disclosure. Participants with SCD experienced low levels of decision regret. We did not observe an increase in negative emotions. After 6 months, amyloid-positive individuals wanted more information.

Subjective cognitive decline (SCD) in amyloid-positive (Aβ+) individuals was proposed as a clinical indicator of Stage 2 in the Alzheimer's disease (AD) continuum, but this requires further validation across cultures, measures, and recruitment strategies. Eight hundred twenty-one participants from SILCODE and DELCODE cohorts, including normal controls (NC) and individuals with SCD recruited from the community or from memory clinics, underwent neuropsychological assessments over up to 6 years. Amyloid positivity was derived from positron emission tomography or plasma biomarkers. Global cognitive change was analyzed using linear mixed-effects models. In the combined and stratified cohorts, Aβ+ participants with SCD showed steeper cognitive decline or diminished practice effects compared with NC or Aβ- participants with SCD. These findings were confirmed using different operationalizations of SCD and amyloid positivity, and across different SCD recruitment settings. Aβ+ individuals with SCD in German and Chinese populations showed greater global cognitive decline and could be targeted for interventional trials. SCD in amyloid-positive (Aβ+) participants predicts a steeper cognitive decline. This finding does not rely on specific SCD or amyloid operationalization. This finding is not specific to SCD patients recruited from memory clinics. This finding is valid in both German and Chinese populations. Aβ+ older adults with SCD could be a target population for interventional trials.

Medial Amygdalar Tau Is Associated With Mood Symptoms in Preclinical Alzheimer’s Disease

Amyloid positron emission tomography (PET) acquisition timing impacts quantification. In florbetaben (FBB) PET scans of 245 adults with and without cognitive impairment, we investigated the impact of post-injection acquisition time on Centiloids (CLs) across five reference regions. CL equations for FBB were derived using standard methods, using FBB data collected between 90 and 110 min with paired Pittsburgh compound B data. Linear mixed models and t-tests evaluated the impact of acquisition time on CL increases. CL values increased significantly over the scan using the whole cerebellum, cerebellar gray matter, and brainstem as reference regions, particularly in amyloid-positive individuals. In contrast, CLs based on white matter-containing reference regions decreased across the scan. The quantification of CLs in FBB PET imaging is influenced by both the overall scan acquisition time and the choice of reference region. Standardized acquisition protocols or the application of acquisition time-specific CL equations should be implemented in clinical protocols. Acquisition timing affects florbetaben positron emission tomography (PET) scan quantification, especially in amyloid-positive participants. The impact of acquisition timing on quantification varies across common reference regions. Consistent acquisitions and/or appropriate post-injection adjustments are needed to ensure comparability of PET data.

Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN. Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.

BACKGROUND:While the amygdala receives early tau deposition in Alzheimer’s disease (AD) and is involved in social and emotional processing, the relationship between amygdalar tau and early neuropsychiatric symptoms in AD is unknown. We sought to determine whether focal tau binding in the amygdala and abnormal amygdalar connectivity were detectable in a preclinical AD cohort and identify relationships between these and self-reported mood symptoms.METHODS:We examined n=598 individuals (n=347 amyloid-positive (58% female), n=251 amyloid-negative (62% female); subset into tau PET and fMRI cohorts) from the A4 Study. In our tau PET cohort, we used amygdalar segmentations to examine representative nuclei from three functional divisions of the amygdala. We analyzed between-group differences in division-specific tau binding in the amygdala in preclinical AD. We conducted seed-based functional connectivity analyses from each division in the fMRI cohort. Finally, we conducted exploratory post-hoc correlation analyses between neuroimaging biomarkers of interest and anxiety and depression scores.RESULTS:Amyloid-positive individuals demonstrated increased tau binding in medial and lateral amygdala (F(4,442)=14.61, p=0.00045; F(4,442)=5.83, p=0.024, respectively). Across amygdalar divisions, amyloid-positive individuals had relatively increased regional connectivity from amygdala to other temporal regions, insula, and orbitofrontal cortex. There was an interaction by amyloid group between tau binding in the medial and lateral amygdala and anxiety. Medial amygdala to retrosplenial connectivity negatively correlated with anxiety symptoms (rs=−0.103, p=0.015).CONCLUSIONS:Our findings suggest that preclinical tau deposition in the amygdala may result in meaningful changes in functional connectivity which may predispose patients to mood symptoms.

BackgroundMaximizing the efficiency to screen amyloid-positive individuals in asymptomatic and non-demented aged population using blood-based biomarkers is essential for future success of clinical trials in the early stage of Alzheimer’s disease (AD). In this study, we elucidate the utility of combination of plasma amyloid-β (Aβ)-related biomarkers and tau phosphorylated at threonine 217 (p-tau217) to predict abnormal Aβ-positron emission tomography (PET) in the preclinical and prodromal AD.MethodsWe designed the cross-sectional study including two ethnically distinct cohorts, the Japanese trial-ready cohort for preclinica and prodromal AD (J-TRC) and the Swedish BioFINDER study. J-TRC included 474 non-demented individuals (CDR 0: 331, CDR 0.5: 143). Participants underwent plasma Aβ and p-tau217 assessments, and Aβ-PET imaging. Findings in J-TRC were replicated in the BioFINDER cohort including 177 participants (cognitively unimpaired: 114, mild cognitive impairment: 63). In both cohorts, plasma Aβ(1-42) (Aβ42) and Aβ(1-40) (Aβ40) were measured using immunoprecipitation-MALDI TOF mass spectrometry (Shimadzu), and p-tau217 was measured with an immunoassay on the Meso Scale Discovery platform (Eli Lilly).ResultsAβ-PET was abnormal in 81 participants from J-TRC and 71 participants from BioFINDER. Plasma Aβ42/Aβ40 ratio and p-tau217 individually showed moderate to high accuracies when detecting abnormal Aβ-PET scans, which were improved by combining plasma biomarkers and by including age, sex and APOE genotype in the models. In J-TRC, the highest AUCs were observed for the models combining p-tau217/Aβ42 ratio, APOE, age, sex in the whole cohort (AUC = 0.936), combining p-tau217, Aβ42/Aβ40 ratio, APOE, age, sex in the CDR 0 group (AUC = 0.948), and combining p-tau217/Aβ42 ratio, APOE, age, sex in the CDR 0.5 group (AUC = 0.955), respectively. Each subgroup results were replicated in BioFINDER, where the highest AUCs were seen for models combining p-tau217, Aβ42/40 ratio, APOE, age, sex in cognitively unimpaired (AUC = 0.938), and p-tau217/Aβ42 ratio, APOE, age, sex in mild cognitive impairment (AUC = 0.914).ConclusionsCombination of plasma Aβ-related biomarkers and p-tau217 exhibits high performance when predicting Aβ-PET positivity. Adding basic clinical information (i.e., age, sex, APOE ε genotype) improved the prediction in preclinical AD, but not in prodromal AD. Combination of Aβ-related biomarkers and p-tau217 could be highly useful for pre-screening of participants in clinical trials of preclinical and prodromal AD.

Differentiating clinical stages based solely on positive findings from amyloid PET is challenging. We aimed to investigate the neuroanatomical characteristics at the whole-brain level that differentiate prodromal Alzheimer’s disease (AD) from cognitively unimpaired amyloid-positive individuals (CU A+) in relation to amyloid deposition and regional atrophy. We included 45 CU A+ participants and 135 participants with amyloid-positive prodromal AD matched 1:3 by age, sex, and education. All participants underwent 18F-florbetaben positron emission tomography and 3D structural T1-weighted magnetic resonance imaging. We compared the standardized uptake value ratios (SUVRs) and volumes in 80 regions of interest (ROIs) between CU A+ and prodromal AD groups using independent t-tests, and employed the least absolute selection and shrinkage operator (LASSO) logistic regression model to identify ROIs associated with prodromal AD in relation to amyloid deposition, regional atrophy, and their interaction. After applying False Discovery Rate correction at < 0.1, there were no differences in global and regional SUVR between CU A+ and prodromal AD groups. Regional volume differences between the two groups were observed in the amygdala, hippocampus, entorhinal cortex, insula, parahippocampal gyrus, and inferior temporal and parietal cortices. LASSO logistic regression model showed significant associations between prodromal AD and atrophy in the entorhinal cortex, inferior parietal cortex, both amygdalae, and left hippocampus. The mean SUVR in the right superior parietal cortex (beta coefficient = 0.0172) and its interaction with the regional volume (0.0672) were also selected in the LASSO model. The mean SUVR in the right superior parietal cortex was associated with an increased likelihood of prodromal AD (Odds ratio [OR] 1.602, p = 0.014), particularly in participants with lower regional volume (OR 3.389, p < 0.001). Only regional volume differences, not amyloid deposition, were observed between CU A+ and prodromal AD. The reduced volume in the superior parietal cortex may play a significant role in the progression to prodromal AD through its interaction with amyloid deposition in that region.

Alzheimer's disease is strongly linked to metabolic abnormalities. We aimed to distinguish amyloid-positive people who progressed to cognitive decline from those who remained cognitively intact. We performed untargeted metabolomics of blood samples from amyloid-positive individuals, before any sign of cognitive decline, to distinguish individuals who progressed to cognitive decline from those who remained cognitively intact. A plasma-derived metabolite signature was developed from Supercritical Fluid chromatography coupled with high-resolution mass spectrometry (SFC-HRMS) and nuclear magnetic resonance (NMR) metabolomics. The 2 metabolomics data sets were analyzed by Data Integration Analysis for Biomarker discovery using Latent approaches for Omics studies (DIABLO), to identify a minimum set of metabolites that could describe cognitive decline status. NMR or SFC-HRMS data alone cannot predict cognitive decline. However, among the 320 metabolites identified, a statistical method that integrated the 2 data sets enabled the identification of a minimal signature of 9 metabolites (3-hydroxybutyrate, citrate, succinate, acetone, methionine, glucose, serine, sphingomyelin d18:1/C26:0 and triglyceride C48:3) with a statistically significant ability to predict cognitive decline more than 3 years before decline. This metabolic fingerprint obtained during this exploratory study may help to predict amyloid-positive individuals who will develop cognitive decline. Due to the high prevalence of brain amyloid-positivity in older adults, identifying adults who will have cognitive decline will enable the development of personalized and early interventions.

Alzheimer’s disease biomarkers are crucial to understanding disease pathophysiology, aiding accurate diagnosis and identifying target treatments. Although the number of biomarkers continues to grow, the relative utility and uniqueness of each is poorly understood as prior work has typically calculated serial pairwise relationships on only a handful of markers at a time. The present study assessed the cross-sectional relationships among 27 Alzheimer’s disease biomarkers simultaneously and determined their ability to predict meaningful clinical outcomes using machine learning. Data were obtained from 527 community-dwelling volunteers enrolled in studies at the Charles F. and Joanne Knight Alzheimer Disease Research Center at Washington University in St Louis. We used hierarchical clustering to group 27 imaging, CSF and plasma measures of amyloid beta, tau [phosphorylated tau (p-tau), total tau t-tau)], neuronal injury and inflammation drawn from MRI, PET, mass-spectrometry assays and immunoassays. Neuropsychological and genetic measures were also included. Random forest-based feature selection identified the strongest predictors of amyloid PET positivity across the entire cohort. Models also predicted cognitive impairment across the entire cohort and in amyloid PET-positive individuals. Four clusters emerged reflecting: core Alzheimer’s disease pathology (amyloid and tau), neurodegeneration, AT8 antibody-associated phosphorylated tau sites and neuronal dysfunction. In the entire cohort, CSF p-tau181/Aβ40lumi and Aβ42/Aβ40lumi and mass spectrometry measurements for CSF pT217/T217, pT111/T111, pT231/T231 were the strongest predictors of amyloid PET status. Given their ability to denote individuals on an Alzheimer’s disease pathological trajectory, these same markers (CSF pT217/T217, pT111/T111, p-tau/Aβ40lumi and t-tau/Aβ40lumi) were largely the best predictors of worse cognition in the entire cohort. When restricting analyses to amyloid-positive individuals, the strongest predictors of impaired cognition were tau PET, CSF t-tau/Aβ40lumi, p-tau181/Aβ40lumi, CSF pT217/217 and pT205/T205. Non-specific CSF measures of neuronal dysfunction and inflammation were poor predictors of amyloid PET and cognitive status. The current work utilized machine learning to understand the interrelationship structure and utility of a large number of biomarkers. The results demonstrate that, although the number of biomarkers has rapidly expanded, many are interrelated and few strongly predict clinical outcomes. Examining the entire corpus of available biomarkers simultaneously provides a meaningful framework to understand Alzheimer’s disease pathobiological change as well as insight into which biomarkers may be most useful in Alzheimer’s disease clinical practice and trials.

BackgroundThe primary criteria for diagnosing mild cognitive impairment (MCI) due to Alzheimer’s Disease (AD) or probable mild AD dementia rely partly on cognitive assessments and the presence of amyloid plaques. Although these criteria exhibit high sensitivity in predicting AD among cognitively impaired patients, their specificity remains limited. Notably, up to 25% of non-demented patients with amyloid plaques may be misdiagnosed with MCI due to AD, when in fact they suffer from a different brain disorder. The introduction of anti-amyloid antibodies complicates this scenario. Physicians must prioritize which amyloid-positive MCI patients receive these treatments, as not all are suitable candidates. Specifically those with non-AD amyloid pathologies are not primary targets for amyloid-modifying therapies. Consequently there is an escalating medical necessity for highly specific blood biomarkers that can accurately detect pre-dementia AD, thus optimizing amyloid antibody prescription.ObjectivesThe objective of this study was to evaluate a predictive model based on peripheral biomarkers to identify MCI and mild dementia patients who will develop AD dementia symptoms in cognitively impaired population with high specificity.DesignPeripheral biomarkers were identified in a gene transfer-based animal model of AD and then validated during a retrospective multi-center clinical study.SettingParticipants from 7 retrospective cohorts (US, EU and Australia).ParticipantsThis study followed 345 cognitively impaired individuals over up to 13 years, including 193 with MCI and 152 with mild dementia, starting from their initial visits. The final diagnoses, established during their last assessments, classified 249 participants as AD patients and 96 as having non-AD brain disorders, based on the specific diagnostic criteria for each disorder subtype. Amyloid status, assessed at baseline, was available for 82.9% of the participants, with 61.9% testing positive for amyloid. Both amyloid-positive and negative individuals were represented in each clinical group. Some of the AD patients had co-morbidities such as metabolic disorders, chronic diseases, or cardiovascular pathologies.MeasurementsWe developed targeted mass spectrometry assays for 81 blood-based biomarkers, encompassing 45 proteins and 36 metabolites previously identified in AAV-AD rats.MethodsWe analyzed blood samples from study participants for the 81 biomarkers. The B-HEALED test, a machine learning-based diagnostic tool, was developed to differentiate AD patients, including 123 with Prodromal AD and 126 with mild AD dementia, from 96 individuals with non-AD brain disorders. The model was trained using 70% of the data, selecting relevant biomarkers, calibrating the algorithm, and establishing cutoff values. The remaining 30% served as an external test dataset for blind validation of the predictive accuracy.ResultsIntegrating a combination of 19 blood biomarkers and participant age, the B-HEALED model successfully distinguished participants that will develop AD dementia symptoms (82 with Prodromal AD and 83 with AD dementia) from non-AD subjects (71 individuals) with a specificity of 93.0% and sensitivity of 65.4% (AUROC=81.9%, p<0.001) during internal validation. When the amyloid status (derived from CSF or PET scans) and the B-HEALED model were applied in association, with individuals being categorized as AD if they tested positive in both tests, we achieved 100% specificity and 52.8% sensitivity. This performance was consistent in blind external validation, underscoring the model’s reliability on independent datasets.ConclusionsThe B-HEALED test, utilizing multiomics blood-based biomarkers, demonstrates high predictive specificity in identifying AD patients within the cognitively impaired population, minimizing false positives. When used alongside amyloid screening, it effectively identifies a nearly pure prodromal AD cohort. These results bear significant implications for refining clinical trial inclusion criteria, facilitating drug development and validation, and accurately identifying patients who will benefit the most from disease-modifying AD treatments.

BackgroundSecondary prevention clinical trials for Alzheimer’s disease (AD) target amyloid accumulation in asymptomatic, amyloid-positive individuals, but it is unclear to what extent other pathophysiological processes, such as small vessel cerebrovascular disease, account for participant performance on the primary cognitive outcomes in those trials. White matter hyperintensities are areas of increased signal on T2-weighted magnetic resonance imaging (MRI) that reflect small vessel cerebrovascular disease. They are associated with cognitive functioning in older adults and with clinical presentation and course of AD, particularly when distributed in posterior brain regions. The purpose of this study was to examine to what degree regional WMH volume is associated with performance on the primary cognitive outcome measure in the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) study, a secondary prevention trial.MethodsData from 1791 participants (59.5% women, mean age (SD) 71.6 (4.74)) in the A4 study and the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) companion study at the screening visit were used to quantify WMH volumes on T2-weighted fluid-attenuated inversion recovery (FLAIR) MR images. Cognition was assessed with the preclinical Alzheimer cognitive composite (PACC). We tested the association of total and regional WMH volumes with PACC performance, adjusting for age, education, and amyloid positivity status, with general linear models. We also considered interactions between WMH and amyloid positivity status.ResultsIncreased frontal and parietal lobe WMH volume was associated with poorer performance on the PACC. While amyloid positivity was also associated with lower cognitive test scores, WMH volumes did not interact with amyloid positivity status.ConclusionThese results highlight the potential of small vessel cerebrovascular disease to drive AD-related cognitive profiles. Measures of small vessel cerebrovascular disease should be considered when evaluating outcome in trials, both as potential effect modifiers and as a possible target for intervention or prevention.

It is unclear to what extent cognitive outcome measures are sensitive to capture decline in Alzheimer disease (AD) prevention trials. We aimed to analyze the sensitivity to changes over time of a range of neuropsychological tests in several cognitively unimpaired, biomarker-defined patient groups. Cognitively unimpaired individuals from the Amsterdam Dementia Cohort and the SCIENCe project with available AD biomarkers, obtained from CSF, PET scans, and plasma at baseline, were followed over time (4.5 ± 3.1 years, range 0.6-18.9 years). Based on common inclusion criteria for clinical trials, we defined groups (amyloid, phosphorylated tau [p-tau], APOE ε4). Linear mixed models, adjusted for age, sex, and education, were used to estimate change over time in neuropsychological tests, a functional outcome, and 2 cognitive composite measures. Standardized regression coefficients of time in years (βtime) were reported as outcome of interest. We analyzed change over time with full follow-up, as well as with follow-up limited to 1.5 and 3 years. We included 387 individuals (aged 61.7 ± 8.6 years; 44% female) in the following (partly overlapping) biomarker groups: APOE ε4 carriers (n = 212), amyloid-positive individuals (n = 109), amyloid-positive APOE ε4 carriers (n = 66), CSF p-tau-positive individuals (n = 127), plasma p-tau-positive individuals (n = 71), and amyloid and CSF p-tau-positive individuals (n = 50), or in a control group (normal biomarkers; n = 65). An executive functioning task showed most decline in all biomarker groups (βtime range -0.30 to -0.71), followed by delayed word list recognition (βtime range -0.18 to -0.50). Functional decline (βtime range -0.17 to -0.63) was observed in all, except the CSF and plasma tau-positive groups. Both composites showed comparable amounts of change (βtime range -0.12 to -0.62) in all groups, except plasma p-tau-positive individuals. When limiting original follow-up duration, many effects disappeared or even flipped direction. In conclusion, functional, composite, and neuropsychological outcome measures across all cognitive domains detect changes over time in various biomarker-defined groups, with changes being most evident among individuals with more AD pathology. AD prevention trials should use sufficiently long follow-up duration and/or more sensitive outcome measures to optimally capture subtle cognitive changes over time.

Alzheimer's disease (AD) is characterized by amyloid beta (Aβ) plaques and hyperphosphorylated tau in the brain. Aβ plaques precede cognitive impairments and can be detected through amyloid-positron emission tomography (PET) or in cerebrospinal fluid (CSF). Assessing the plasma Aβ42/Aβ40 ratio seems promising for non-invasive and cost-effective detection of brain Aβ accumulation. This approach involves some challenges, including the accuracy of blood-based biomarker measurements and the establishment of clear, standardized thresholds to categorize the risk of developing brain amyloid pathology. Plasma Aβ42/Aβ40 ratio was measured in 277 volunteers without dementia, 70 AD patients and 18 non-AD patients using single-molecule array. Patients (n = 88) and some volunteers (n = 66) were subject to evaluation of amyloid status by CSF Aβ quantification or PET analysis. Thresholds of plasma Aβ42/Aβ40 ratio were determined based on a Gaussian mixture model, a decision tree, and the Youden's index. The 0.0472 threshold, the one with the highest sensitivity, was retained for general population without dementia screening, and the 0.0450 threshold was retained for research and clinical trials recruitment, aiming to minimize the need for CSF or PET analyses to identify amyloid-positive individuals. These findings offer a promising step towards a cost-effective method for identifying individuals at risk of developing AD.

Disruption of the default mode network is a hallmark of Alzheimer's disease, which has not been extensively examined in atypical phenotypes. We investigated cross-sectional and 1-year longitudinal changes in default mode network sub-systems in the visual and language variants of Alzheimer's disease, in relation to age and tau. Sixty-one amyloid-positive Alzheimer's disease participants diagnosed with posterior cortical atrophy (n = 33) or logopenic progressive aphasia (n = 28) underwent structural MRI, resting-state functional MRI and [18F]flortaucipir PET. One-hundred and twenty-two amyloid-negative cognitively unimpaired individuals and 60 amyloid-positive individuals diagnosed with amnestic Alzheimer's disease were included as controls and as a comparison group, respectively, and had structural and resting-state functional MRI. Forty-one atypical Alzheimer's disease participants, 26 amnestic Alzheimer's disease participants and 40 cognitively unimpaired individuals had one follow-up functional MRI ∼1-2 years after the baseline scan. Default mode network connectivity was calculated using the dual regression method for posterior, ventral, anterior ventral and anterior dorsal sub-systems derived from independent component analysis. A global measure of default mode network connectivity, the network failure quotient, was also calculated. Linear mixed-effects models and voxel-based analyses were computed for each connectivity measure. Both atypical and amnestic Alzheimer's disease participants had lower cross-sectional posterior and ventral and higher anterior dorsal connectivity and network failure quotient relative to cognitively unimpaired individuals. Age had opposite effects on connectivity in Alzheimer's disease participants and cognitively unimpaired individuals. While connectivity declined with age in cognitively unimpaired individuals, younger Alzheimer's disease participants had lower connectivity than the older ones, particularly in the ventral default mode network. Greater baseline tau-PET uptake was associated with lower ventral and anterior ventral default mode network connectivity in atypical Alzheimer's disease. Connectivity in the ventral default mode network declined over time in atypical Alzheimer's disease, particularly in older participants, with lower tau burden. Voxel-based analyses validated the findings of higher anterior dorsal default mode network connectivity, lower posterior and ventral default mode network connectivity and decline in ventral default mode network connectivity over time in atypical Alzheimer's disease. Visuospatial symptoms were associated with default mode network connectivity disruption. In summary, default mode connectivity disruption was similar between atypical and amnestic Alzheimer's disease variants, and discriminated Alzheimer's disease from cognitively unimpaired individuals, with decreased posterior and increased anterior connectivity and with disruption more pronounced in younger participants. The ventral default mode network declined over time in atypical Alzheimer's disease, suggesting a shift in default mode network connectivity likely related to tau pathology.

Plasma glial fibrillary acidic protein (GFAP) is an emerging biomarker of Alzheimer’s disease (AD), with higher blood GFAP levels linked to faster cognitive decline, particularly among individuals with high brain amyloid burden. However, few studies have examined brain GFAP expression to clarify if peripheral associations reflect brain changes. This study aimed to correlate region-specific GFAP mRNA expression (n = 917) and protein abundance (n=386) with diverse neuropathological measures at autopsy in the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) and to characterize the interaction between brain GFAP and brain amyloid burden on downstream outcomes. We assessed GFAP gene expression in the dorsolateral prefrontal cortex, caudate nucleus, and posterior cingulate cortex with respect to core AD pathology (amyloid-β and tau), cerebrovascular (microinfarcts, macroinfarcts, and cerebral amyloid angiopathy [CAA]), proteinopathic (TDP-43, Lewy bodies), and cognitive outcomes. These associations were further examined at the protein level using tandem-mass tag proteomic measurements from the dorsolateral prefrontal cortex. We also assessed GFAP interactions with AD neuropathology on downstream outcomes. Cortical GFAP gene and protein expression were significantly upregulated in participants with a neuropathologically confirmed AD diagnosis at autopsy (all PFDR < 3.5e−4), but not in individuals positive for tau pathology and negative for amyloid pathology (all PFDR > 0.05). Higher cortical GFAP levels were associated with increased amyloid pathology, CAA pathology, and faster cognitive decline (all PFDR < 3.3e−3). GFAP’s associations with phosphorylated tau burden and cognition were influenced by amyloid burden, being most pronounced among amyloid-positive individuals, confirming previous in vivo biomarker observations. No associations were observed between GFAP gene expression and outcomes in the caudate nucleus. Our results support previous biomarker findings and suggest that higher brain GFAP levels are associated with higher brain amyloid burden and faster cognitive decline among amyloid-positive individuals.

Sleep and physical activity measures are associated with resting-state network segregation in non-demented older adults

Abstract BackgroundUnderstanding the earliest pathophysiological changes in Alzheimer’s disease (AD) is important for development of therapeutic intervention strategies. Studying genetically identical twins provides unique information on genetic and environmental influences on development of AD. Here we studied change in AD biomarkers amyloid β1‐42 (aβ42), amyloid β1‐40 (aβ40), phosphorylated tau181 (p‐tau) and total tau (t‐tau) in CSF over time in a cohort of older cognitively normal monozygotic twins and tested the genetic contribution to biomarker concentrations at baseline and to change of biomarker concentrations over time.MethodWe included 103 twins (npairs = 42) from the EMIF‐AD preclinAD study with 2‐4 repeated CSF sampling available across a time period of 4.47±2 years (mean±SD age: 68.8±7 years, 54 (52%) female). We measured aβ42, aβ40, p‐tau and t‐tau using the Lumipulse platform (Fujirebio Diagnostics, Inc.). We used linear mixed models to investigate change in biomarker concentrations over time. We used twin‐pair intraclass correlation (ICC) analysis to estimate the genetic contribution to biomarker concentrations at baseline and at repeated measurements, and to annual change in biomarker concentrations.ResultAt baseline, 25 (24%) individuals had an abnormal aβ42/aβ40 ratio, 27 (26%) abnormal p‐tau and 35 (34%) abnormal t‐tau, and at last measurement respectively 34 (33%), 38 (37%) and 45 (44%). Across the group, aβ42/aβ40 decreased (β±SE ‐0.001±0, p‐value&lt;0.001), and p‐tau and t‐tau increased over time (respectively 1.37±0.3, p‐value&lt;0.001; 6.58±2.3, p‐value = 0.004). Increases in p‐tau levels were most pronounced in amyloid positive individuals (2.72±0.38, p‐value&lt;0.001) compared to amyloid negative individuals (0.65±0.24, p‐value = 0.007; p‐value interaction&lt;0.001). At baseline, the genetic contribution to biomarker concentrations was lowest for t‐tau (ICC = 0.5, p&lt;0.001) and highest for aβ40 (ICC = 0.79, p&lt;0.001). ICC’s remained similar at repeated measurements (table 1). Annual changes of aβ40 and p‐tau showed significant twin‐pair correlations (resp. ICC’s: 0.58 and 0.42; p‐values &lt;0.01; Figure 1), indicating a genetic contribution to change in these concentrations over time.ConclusionIn this sample of older cognitively normal monozygotic twins, we found that AD biomarkers became increasingly abnormal over time. Our unique twin design further indicates that these very early pathophysiological changes are under moderate to high genetic influence, which also indicates a role of environmental factors.

Abstract BackgroundDysfunctional excitation‐inhibition balance (EIB) is hypothesized to precede cognitive impairment in Alzheimer’s disease (AD); however, overlapping neuropathology (like amyloid plaques) can develop in cognitively normal (CN) and impaired (CI) individuals. We investigate EIB trajectories for individuals with significant amyloid (Aβ+).MethodMultimodal imaging was used to construct hybrid resting‐state structural connectomes (Fortel, Igor, et al. Network Neuroscience 6.2 (2022): 420‐444.), computing whole‐brain EIB based on 105 ROIs. This cohort (N = 185, 94 Female) aged 55‐90 (μ FEMALES = 74.9, and μ MALES 73) was limited to Aβ+ individuals with APOE genotyping (ε3/ε3, ε3/ε4, ε4/ε4); grouped further by clinical dementia rating (CDR), with CDR &gt; 0 indicating CI. Imaging, Aβ+ classifications, CDR, and APOE were obtained from OASIS‐3: Longitudinal Multimodal Neuroimaging: Principal Investigators: T. Benzinger, D. Marcus, J. Morris; NIH P30 AG066444, P50 AG00561, P30 NS09857781, P01 AG026276, P01 AG003991, R01 AG043434, UL1 TR000448, R01 EB009352. AV‐45 doses were provided by Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly.ResultResults presented in Figure 1 suggest whole‐brain EIB in both CN sexes with Aβ+ decreases with increasing age (APOE‐ε4 agnostic). However, sex differences are highlighted with CI; regression analyses produce a marginally significant 4‐way interaction of age, sex, cognitive status, and APOE (P = 0.085, β = ‐0.013). Partial correlation of EIB with age, controlling for sex, APOE and cognitive status is also significant (P = 0.016, r = ‐0.18). Results indicate sex‐mediated EIB differences in individuals with CI, dominated by APOE‐ε4 in women rather than amyloid.ConclusionWe’ve demonstrated EIB deviations in cognitively impaired men and women that may be associated with AD neuropathology. Our findings are consistent with known AD sex differences and suggest novel multimodal connectomics may capture subtle disruptions in brain dynamics that traditional methods may not. Figure. 1. Whole‐brain excitation‐Inhibition balance (EIB) trajectories for amyloid positive (Aβ+) men and women. The cognitively normal (CN) group (rows A‐B) suggests no sex differences in EIB trajectories with age, while the cognitively impaired (CI) group (rows C‐D) begins to reveal distinct patterns of dysfunction (more significantly in women with APOE‐ε4). Note a value of 0.5 indicates perfect balance.

Abstract Background[18F]AV45 (florbetapir) based Positron emission tomography (PET) imaging of brain amyloid load is one of the core biomarkers for Alzheimer’s disease (AD). However, with the rise in the trend of combining data from multicenter studies, the need to correct substantial technical variability associated with image intensity scale due to multi‐center effect continues to rise. While smoothing methods remove small variability, data‐driven feature harmonization methods can adjust for scanner settings and patient motion without over‐blurring the scans. Here we aimed to investigate the effect of ComBat harmonization on multi‐site florbetapir studies to reduce variance across diagnosis groups.MethodWe assessed 163 scans from the Alzheimer’s disease Neuroimaging Initiative (ADNI3) database. These scans were acquired from 14 different sites. T1‐weighted MRI images were processed using the ADNI pipeline. PET images had 20 min (4×5min frames) acquisition at 50‐70 min post‐injection of 370 MBq (10.0 mCi) ± 10% florbetapir. Raw PET images from all sites were downloaded for quality control at the University of Michigan where the rest of the preprocessing took place using ADNI guidelines. These preprocessed scans were then used to extract SUVR maps using the cerebellar gray matter as the reference region. Based on the literature, the global SUVR for each subject was estimated from the averaged frontal, parietal, temporal, and cingulate cortices. ComBat harmonization was then performed using multicenter data, preserving diagnosis group as the covariate. A paired t‐test and logistic regression were performed to analyze the effect of the harmonization method on the SUVR.ResultA paired t‐test confirms the significant difference between the pre‐and post‐ComBat SUVR, especially for AD samples. Although the variance only decreases by 7.1% for CN as opposed to 26.9% (MCI) and 55.4% (AD), this may be explained by the large variance in the age in the sample as well as the possibility of amyloid positive individuals in the CN population. The logistic regression shows an accuracy of 87.9% for AD.ConclusionEven though ComBat harmonization significantly reduces the variance in AD and MCI population, more studies need to be performed to check its use across all PET derived features.