Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

BackgroundThe AT(N) biological framework classifies Alzheimers disease (AD) pathology using CSF biomarkers, with the A+T+ profile defining biological AD and the A-T+ profile representing a biologically distinct entity consistent with suspected non-Alzheimers pathophysiology, such as primary age-related tauopathy. Functional assessment capable of differentiating these profiles non-invasively remains limited. This study investigates whether cognitive vergence and pupillary temporal dynamics during a visual oddball task can distinguish A-T+ from A+T+ biological profiles in individuals with mild cognitive impairment (MCI). MethodsThirty-eight participants with MCI (12 A-T+, 26 A+T+) classified by CSF biomarkers completed a visual oddball task (80% distractors, 20% targets) under continuous eye-tracking. Linear mixed-effects models examined profile x condition interactions on full time series and six trial-level temporal features. Participant-level differentiation was assessed using binomial logistic regression, adjusting for age, sex, and MMSE. ResultsBoth profiles showed comparable overall oculomotor response magnitudes but diverged markedly in temporal organization. Significant profile x condition interactions emerged for cognitive vergence global slope, time to peak, and pupillary time to peak. Logistic regression confirmed that timing features discriminated biological profiles at the participant level, with differentiation reversing direction between distractor and target conditions. A-T+ participants also maintained superior target detection accuracy (89.3% vs. 82.4%, p = 0.001). ConclusionCognitive Vergence and pupillary temporal dynamics during an oddball task provide condition-dependent functional oculomotor signatures that systematically differentiate AT(N) biological profiles in MCI, suggesting that oculomotor assessment may offer an accessible, non-invasive complement to CSF-based profile characterization.

INTRODUCTIONHealthful dietary patterns may attenuate dementia risk by preserving cerebrovascular health. Prior work has focused on systemic arterial stiffness, but cerebrovascular measures may be more sensitive to neuroprotective effects of diet. We examined associations between Mediterranean diet adherence, prefrontal cortex (PFC) arterial elasticity, and cognition in older adults. METHODSParticipants were 198 older adults (58% female; mean age 65.6 years) from the Newcastle ACTIVate cohort. Mediterranean Diet (MedDiet) scores were derived from the Australian Eating Survey food frequency questionnaire. Pulse Relaxation Function (PReFx), an index of PFC arterial elasticity, was measured using pulse Diffuse Optical Tomography. Cognition was assessed with CANTAB and a cued task-switching paradigm. RESULTSHigher MedDiet was associated with higher PFC arterial elasticity. MedDiet was not associated with cognition, and PReFx did not mediate diet-cognition associations. DISCUSSIONGreater Mediterranean diet alignment was cross-sectionally associated with PFC arterial elasticity, suggesting a pathway through which diet may influence brain health in ageing.

BackgroundOdor identification impairment is an early marker of Alzheimers disease (AD) that predicts memory decline, yet its underlying microstructural basis remains unclear. We hypothesized that mild cognitive impairment (MCI) involves early myelin and lipid disruption within olfactory-limbic circuits, detectable using a synthetic MRI-derived contrast that provides complementary sensitivity to myelin volume fraction (MVF). MethodsThirty-three older adults (healthy controls [HC], n = 16; mild cognitive impairment [MCI], n = 17) completed olfactory and cognitive testing and underwent 3T brain MRI using a QALAS sequence. An MVF map and synthetic FLAIR and DIR images were generated, and a FLAIR-DIR-derived metric (FD) was computed as FD = (FLAIR - DIR) / FLAIR. We investigated ROI-based group differences in olfactory-limbic gray-matter regions and associated white-matter tracts, voxel-wise regressions investigating FD-odor identification associations, and ROI-based MCI vs HC classification using cross-validated logistic regression models. ResultsCompared with HC, MCI showed significantly lower FD across olfactory-limbic gray-matter regions and white-matter pathways--including hippocampus, amygdala, orbitofrontal cortex, thalamus, and corpus callosum--whereas MVF differences were more limited. FD achieved moderate discrimination, with baseline performance comparable to MVF. Voxel-wise analyses revealed that better odor identification was associated with higher FD in the hippocampus/parahippocampal and insula; the association persisted after adjusting for voxel-wise MVF. MVF also showed significant positive voxel-wise associations with odor identification in the insula and genu of the corpus callosum. ConclusionFD is a practical, myelin- and lipid-sensitive contrast derived from routinely acquired synthetic FLAIR & DIR images that complement quantitative MVF. It captures behaviorally relevant variance beyond local myelin content and may improve detection of early olfactory-limbic microstructural changes in MCI. These findings support FD as a scalable candidate marker linking early network disruption to olfactory symptoms across the AD continuum.

BackgroundPlasma biomarkers demonstrate strong within-cohort performance for identifying cerebral amyloid pathology, but their real-world clinical utility depends on generalization across populations and assay platforms. The impact of cross-cohort deployment on clinically actionable metrics such as negative predictive value (NPV) remains poorly characterized. ObjectiveTo evaluate the performance and portability of plasma biomarker-based machine learning models for amyloid PET prediction across independent cohorts, with emphasis on calibration and clinically relevant predictive values. MethodsData from ADNI (n=885) and A4 (n=822) were analyzed. Machine learning models were trained within each cohort to predict amyloid PET status and continuous amyloid burden (centiloids). Performance was assessed using ROC AUC, accuracy, R{superscript 2}, and RMSE. Cross-cohort generalizability was evaluated using bidirectional transfer without retraining. Calibration, predictive values, and decision curve analysis were used to assess clinical utility. ResultsWithin-cohort discrimination was high (AUC up to 0.913 in ADNI and 0.870 in A4), with moderate performance for centiloid prediction (R{superscript 2} up to 0.628 and 0.535, respectively). Cross-cohort deployment resulted in modest attenuation of AUC ([~]4-7%) but substantially greater degradation in clinically actionable performance. NPV declined from 0.831 to 0.644 under ADNI[->]A4 transfer ([~]19 percentage points) despite preserved discrimination. Calibration analyses demonstrated systematic probability misestimation, and decision curve analysis showed reduced net clinical benefit. Biomarker distribution differences across cohorts were consistent with dataset shift. ConclusionPlasma biomarker models retain discrimination across cohorts but exhibit clinically meaningful degradation in predictive value under deployment. Calibration instability and prevalence differences critically affect NPV, highlighting the need for cross-cohort validation, calibration assessment, and assay harmonization before clinical implementation.

Introduction: Sleep-dependent memory consolidation differs by sex and maybe disrupted by Alzheimer disease (AD) risk. Whether sex moderates associations between apolipoprotein E {varepsilon}4(APOE {varepsilon}4) status, non-rapid eye movement (NREM) sleep, and memory remains unclear. Methods: Eighty cognitively unimpaired older adults completed a word-pair memory task with encoding and immediate testing occurring prior to overnight polysomnography with high-density electroencephalography (hdEEG) and delayed recall occurring after sleep. Sleep-memory associations were examined as a function of sex and APOE {varepsilon}4 status. Results: In this sample, a sex by APOE {varepsilon}4 interaction was associated with overnight memory retention, with female carriers exhibiting less overnight forgetting than female non-carriers and male {varepsilon}4 carriers. NREM sleep differed by sex and APOE {varepsilon}4 status and was associated with memory retention in {varepsilon}4 carriers. Discussion: These findings indicate sex-specific, sleep-dependent memory mechanisms associated with genetic AD risk, highlighting sleep as a potential early target for intervention, pending replication in larger samples. This study was not a clinical trial.

Importance: Adverse neighborhood conditions can lead to poorer health outcomes, potentially through accelerated biological aging. However, whether these relationships are explained by individual- or neighborhood-level factors remains unclear. Objective: To examine the association between neighborhood deprivation, measured by the Area Deprivation Index (ADI), and epigenetic age acceleration and assess whether individual- and neighborhood-level characteristics mediate or modify these associations. Design: Cross-sectional study using data from a Yale Stress Center study between 2008 and 2012. Data analysis was conducted from July 2025 to January 2026. Setting: Community-based sample from the greater New Haven, CT area. Participants: A total of 370 healthy adults aged 18 to 50 years without major psychiatric, medical, or cognitive disorders who provided blood samples for DNA methylation analysis. Main Outcomes and Measures: Epigenetic age acceleration measured from DNA methylation using four second-generation epigenetic clocks, with associations assessed among aging, neighborhood deprivation, and individual- and neighborhood-level factors. Results: Data were analyzed from 370 participants (212 women [57.3%], 158 men [42.7%]; mean [SEM] age, 29.3 [0.46] years). Greater neighborhood deprivation was associated with greater lifetime adversity ({beta}=0.112, p<.001) and lower educational attainment ({beta}=-0.019, p=.012), and accelerated epigenetic aging as measured by GrimAge ({beta}=0.037, p<.001), PCGrimAge ({beta}=0.019, p<.001), and PCPhenoAge ({beta}=0.041, p<.001), but not PhenoAge (p=.23). In multivariable models accounting for individual factors, neighborhood deprivation remained associated with these three clocks. Lifetime adversity partially mediated the association between ADI and accelerated GrimAge (20.3% of total effect) and PCGrimAge (23.3%). Race moderated the direct association between ADI and epigenetic aging, with stronger associations between neighborhood deprivation and accelerated GrimAge ({beta}=0.061, p=.004) and PCPhenoAge ({beta}=0.057, p=.02) observed among Black participants compared to White. Conclusions: Greater neighborhood deprivation was associated with accelerated epigenetic aging across multiple second-generation clocks, with lifetime adversity partially mediating these associations. Stronger effects were observed among Black participants. These findings suggest that neighborhood environments and cumulative stress may contribute to biological aging and racial disparities in aging trajectories.

BackgroundAlzheimers disease (AD) causes progressive decline in language and cognition. Automated speech analysis has emerged as a promising screening tool, yet clinical data scarcity limits progress. To address this, we generated a large-scale simulated speech dataset to model linguistic and acoustic deterioration across cognitive stages, Control, Mild Cognitive Impairment (MCI), and AD. MethodsUsing Monte Carlo simulations, we emulated the Pitt DementiaBank "Cookie Theft" narratives. Acoustic features (speech rate, pause duration, jitter, shimmer) and linguistic features (type-token ratio, unique-word count, filler usage) were synthetically sampled from real-world DementiaBank distributions. We trained an XGBoost classifier to distinguish diagnostic groups, and applied SHAP (Shapley Additive exPlanations) to assess feature importance. ResultsThe model achieved high discriminative performance (AUC {approx} 0.94; accuracy {approx} 85%). Compared to controls, simulated MCI and AD groups showed progressive declines in fluency and lexical diversity, and increases in disfluencies and voice instability. SHAP analysis revealed that key predictors included reduced type-token ratio, higher pause and filler rates, and elevated jitter/shimmer. Classification was most accurate for Control vs. AD; MCI misclassifications highlighted intermediate profiles. InterpretationOur framework, FMN (Forget Me Not), captures clinically relevant speech changes using simulated data, offering an explainable and scalable approach for cognitive screening. While not a substitute for real datasets, FMN validates a pipeline that mirrors known AD markers and can guide future real-world deployments. External validation remains a key next step for translational impact.

Background Autosomal dominant Alzheimer's disease (ADAD) serves as a model for presymptomatic biomarker discovery. Characterising the temporal profile of plasma biomarker levels in presymptomatic individuals may enhance understanding of disease pathogenesis, inform future clinical trials, and guide clinical interpretation. Methods We evaluated 124 proteins using a NUcleic acid-Linked Immuno-Sandwich Assay (NULISA) panel in 270 plasma samples from a longitudinal cohort study of ADAD, comprising 113 individuals (73 mutation carriers and 40 non-carriers). We determined the plasma proteomic changes that distinguished mutation carriers from non-carriers. We then used predicted age at symptom onset to determine the approximate timing of presymptomatic divergence in biomarker levels in carriers relative to non-carriers. Results Nine proteins (A{beta}42, BACE1, GFAP, pTau181, pTau231, pTau217, MAPT, NfL, and AChE) robustly differed between carriers and non-carriers, cross-sectionally. Longitudinal analyses showed A{beta}42 levels were elevated in carriers at least 26 years before expected symptom onset. Carriers diverged from non-carriers in phosphorylated tau markers at 21-24 years before expected symptoms, total-tau at 19 years, GFAP and BACE1 at 14 years, and NfL at 6 years. Differences in AChE were seen in symptomatic individuals, likely reflecting cholinesterase inhibitor use. Conclusion Multiple plasma proteins are elevated in presymptomatic and symptomatic autosomal dominant AD mutation carriers relative to non-carriers. Changes in eight biomarkers occur sequentially from 26 to 6 years prior to symptom onset. Combining biomarkers may help in staging presymptomatic AD and optimise clinical trial inclusion. Further work is needed to assess how these findings generalise to non-monogenic AD.

ObjectiveTo examine whether menopausal women who initiate systemic menopausal hormone therapy (MHT) around menopause (45-60 years old) have a different risk of developing dementia than those not taking MHT. DesignSystematic review and meta-analysis of randomised controlled trials and longitudinal observational studies. Risk of bias was assessed using ROB-2 and ROBINS I-V2. Data sourcesMEDLINE, Web of Science, EMBASE, and Cochrane Library to 27 March 2026. Eligibility criteria for selecting studiesStudies which measured dementia or cognitive decline in women who initiated systemic MHT between ages 45-60 or within 5 years of menopause, compared with placebo or no MHT. Authors contacted for additional details if needed. Main outcome measuresDementia, Alzheimers disease (AD), cognitive decline. Results10 studies totalling 213,678 participants (189,525 in studies with the primary population). There was no significant increased risk in women with a uterus for all cause dementia (pooled hazard ratio (HR): 1.12; 95% CI 0.91-1.31, N=78,613, I2 = 96.9%), but increased AD risk (HR: 1.14; 95% CI 1.02, 1.29, N=134,865, I2 = 35.6%). Results were similar in sensitivity analyses including women with or without a uterus. Results for cognitive decline were variable. ConclusionsMHT initiated around the age of menopause should not be prescribed for cognition or dementia prevention. It is not protective against dementia and may increase risk slightly. The magnitude of risk was similar in AD and dementia, but the latter with larger confidence intervals. Studies which followed up individuals rather than on health records lost people to follow up. This may account for difference in cognitive decline outcomes between studies, as people with cognitive impairment and dementia are more likely not to attend. MHT prescribing should balance benefits against risks, including evidence of a small increased dementia risk. There are few high-quality studies, so further research would inform recommendations. Systematic review registration Prospero CRD420251010663 What is already known on this topic?O_LIMenopausal hormone therapy (MHT) is effective for alleviating vasomotor symptoms. Contemporary guidelines recommend treatment should be initiated for such symptoms under age 60 and or within 10 years of menopause onset. C_LIO_LIA large randomised trial on the topic found increased risk of dementia in women initiating MHT after the age of 65. C_LIO_LIIt is unknown whether initiating MHT around the age of menopause impacts the risk of dementia or cognitive decline. C_LI What this study addsO_LIThere was no evidence that taking MHT around the time of menopause decreases the risk of dementia or cognitive impairment. C_LIO_LIThey should not be prescribed for these indications. C_LIO_LIWe were able to find more studies which examine this question by contacting authors for additional data. C_LIO_LIInitiating MHT in women with a uterus around the age of menopause increased the risk of Alzheimers disease slightly, by over 10%, and there is a similar but not significant effect in the fewer studies of all cause dementia. Women with or without a uterus show similar results. C_LIO_LIWe found no significant difference shown in cognitive decline, possibly due to loss to follow up. This may be because most studies of cognitive decline follow up C_LI

INTRODUCTION: Bilingualism is a proposed cognitive reserve factor that delays symptom onset in Alzheimer's disease (AD), though current evidence lacks biomarker confirmation. This retrospective study examined bilingualism's association with symptom onset across AD clinical stages, including biomarker-confirmed cases. METHODS: Participants from the Sant Pau Memory Unit spanning amnestic mild cognitive impairment (MCI), amnestic dementia, and biomarker-confirmed AD were analyzed, with balanced representation of active and passive Spanish-Catalan bilinguals. Linear regression models evaluated associations between bilingualism and reported age at symptom onset, controlling for education, sex, and disease severity. RESULTS: Active bilingualism was associated with delayed symptom onset in amnestic MCI (2.21 years), amnestic dementia (1.42 years), and biomarker-confirmed AD (1.45 years; ps < .05). Higher education was associated with earlier onset, likely representing healthcare seeking behavior. DISCUSSION: Bilingualism protects against earlier symptom manifestation in MCI and AD, supporting bilingualism as a contributor to cognitive reserve.

BackgroundIndividual clinical cognitive assessments (CCA) for Alzheimers disease (AD) provide broad disease stratification but are limited in sensitivity and specificity, requiring integration of multiple CCA for optimal disease staging. Recent work from our lab suggests that neuro-metabolic and vascular dysregulation (MVD) occurs early in AD, prior to clinical symptoms, and may provide higher sensitivity and specificity than CCA alone. In this study, we combined three widely accepted CCA with MVD readouts and developed a multimodal ensemble machine learning approach across the AD spectrum to predict disease stage and grade. MethodsAD subjects (N=372) across the disease spectrum with imaging (PET:18F-FDG, MRI:T1w, T2 FLAIR, ASL) and CCAs (ADAS-Cog, CDR, MoCA) data were analyzed from ADNI. Imaging data were registered to MNI152+, z-scored relative to cognitively normal controls, and processed for MVD. A clinical-set-enrichment analysis (CSEA) was developed to link regional brain changes with CCA scores, map changes to functional categories, project them into a 3D Cartesian space, and model trajectories, thus revealing at-risk and resilient regions. In addition, an ensemble machine-learning approach was utilized for disease stage classification, and a disease grading scheme across the AD spectrum was developed to further stratify within disease stages. FindingsRegional data followed an MVD pattern across AD stages stratified by CSEA scores. Females showed greater stage separation along the CCA axis within each region, indicating faster disease progression. Moreover, progression in at-risk brain regions (e.g., mid- and inf-temporal gyri, amygdala) was associated with longer disease path lengths, whereas progression in resilient brain regions (supramarginal gyrus) was not. Moreover, our classification and grading approach can predict AD stage and grade independent of amyloid-beta and tau with high precision and accuracy. InterpretationA framework was developed to evaluate MVD and CCA variations across the AD spectrum, thereby distinguishing at-risk and resilient brain regions. Distinct disease trajectories were identified, and a new data-driven grading scheme was proposed to highlight the potential for precision medicine and therapeutic evaluation. FundingNIH T32AG071444

Background: This study investigates whether proteomic aging clocks (PACs) are associated with cerebral small vessel disease (CSVD). Methods: We included participants from two US community-based cohorts: the Atherosclerosis Risk in Communities (ARIC) Study and the Multi-Ethnic Study of Atherosclerosis (MESA) Study. These analyses leveraged PACs that were developed in ARIC using proteomics measured by SomaScan in midlife (Visit 2; mean age 56 y; n=1,486) and late-life (Visit 5; mean age 76 y; n=1,496), trained on chronological age. Proteomic age acceleration (PAA) was calculated as residuals from regressing PACs on chronological age. 3T brain MRI data were collected in late-life. We examined associations of PAA with log-transformed white matter hyperintensity (WMH) volume using linear regression and with the presence of microbleeds, and subcortical, lacunar, and cortical infarcts using logistic regression. Associations of PACs with WMH volume and microbleeds were tested in MESA using proteins measured at Exam 1 (mean age 57 y; n=932) and Exam 5 (mean age 66 y; n=934). All associations were quantified per 5-year increase in PAA. All models were adjusted for demographics and cardiovascular risk factors. Results: In ARIC, higher midlife PAA was associated with greater WMH volume (percent difference: 25% [95% CI: 13%, 39%]) and higher odds of subcortical infarcts (OR: 1.24 [1.02, 1.51]). Late-life PAA was associated with all CSVD markers: WMH volume (percent difference: 20% [8%, 34%]), cerebral microbleeds (OR: 1.40 [1.15, 1.69]), subcortical (OR: 1.80 [1.47, 2.22]), lacunar (OR: 1.80 [1.46, 2.23]), and cortical infarcts (OR: 1.39 [1.07, 1.82]). In MESA, higher late-life PAA was associated with greater WMH volume (28% [3%, 58%]) but not with microbleeds. Conclusion: Accelerated proteomic aging is associated with a higher prevalence of MRI markers of CSVD, most predominantly in late-life. Understanding this relationship may help stratify those at higher risk of CSVD at an early stage.

BackgroundGenome-wide association studies have identified numerous variants associated with neuropsychiatric disorders. Although some significant loci can carry substantial risk, as in Alzheimers Disease, the remaining genetic variance is distributed across many small-effect loci. Polygenic risk scores (PRS) aggregate this risk but do not capture epistatic interactions, and offer limited biological interpretability and predictive accuracy. Computing gene level risk scores and integrating known or statistically validated gene-gene associations has the potential to increase interpretability and/or accuracy. Graph Neural Networks (GNNs) can leverage graph structured genetic data that models potential epistatic interactions to achieve these goals. MethodsWe developed a three-stage Graph Attention Network (GAT) classifier using individual-level GWAS data from 7,358 participants across seven Alzheimers Disease Center cohorts. Nodes were defined as genes, with risk scores from AD and 11 genetically correlated phenotypes serving as features. We evaluated two graph construction strategies: gene co-expression networks derived from hippocampal transcriptomic data and curated pathway-based graphs. Additionally, a bilinear context module was incorporated to capture global gene-gene interactions beyond the graph topology. In Stage 1, a GNN encoder was trained on the graphs; Stage 2 injected PRS for non-coding SNPs after the encoder to better capture genetic risk via transfer learning, and Stage 3 applied adversarial training with gradient reversal for ancestry debiasing. GNN predictions were ensembled with whole-genome PRS using elastic net regression. ResultsThe best-performing GNN model -- a GAT with bilinear context operating on the pathway graph -- achieved an AUROC of 0.78 (95% CI: 0.75-0.80). Ensemble models combining Stage 2 or 3 GNN logits with whole-genome PRS achieved an AUROC of 0.82 (0.79-0.84), outperforming PRS alone (0.80). GxI attribution and additional explainability analyses revealed stage-specific biological signals, some of which re-capitulated known gene-phenotype associations and others which may reflect potential new areas of inquiry. ConclusionA multi-stage GAT framework captures complementary, non-additive genetic signal that, when ensembled with PRS, improves the accuracy of AD classification. Post-hoc explainability analyses yield biologically interpretable gene networks, supporting the utility of graph-based deep learning for dissecting complex genetic architectures.

Chronic inflammation is a common feature of aging and is observed across various age-related neurodegenerative diseases, including Alzheimers disease (AD). It has, however, been challenging to develop measurements of brain structure directly linked to peripheral measures of neuroinflammation. This cross-sectional study examined whether plasma levels of markers related to inflammation are associated with diffusion magnetic resonance imaging (dMRI) measures of white matter microstructure: mean diffusivity (MD) and Neurite Orientation Dispersion and Density Imaging (NODDI) free water fraction (FWF) and orientation dispersion index (ODI). Participants included 457 dementia-free individuals (mean age=63.82, SD=7.63). Blood plasma markers related to inflammation included two measures of systemic inflammation, (1) high-sensitivity C-reactive protein (CRP), and (2) a composite of pro-inflammatory cytokines (IL-1, IL-1{beta}, IL-2, IL-6, IL-8, TNF-, TNF-{beta}), as well as (3) glial fibrillary acidic protein (GFAP), a measure of astrocytic activation. Higher cytokine composite levels were associated with higher values of all three measures (FWF, ODI, MD) in cerebral white matter, and with higher ODI in the cerebellar peduncles. Higher CRP levels were associated with higher ODI in cerebral and cerebellar white matter. Associations with GFAP were not significant after adjusting for multiple comparisons. Results were consistent after accounting for plasma biomarkers of AD pathology (p-tau181/A{beta}42). Thus, higher levels of peripheral pro-inflammatory markers are associated with white matter microstructure (higher FWF, ODI, and MD), supporting the view that these dMRI-based metrics are sensitive to inflammatory processes. Additionally, the sensitivity of dMRI-based measures to inflammation may differ by inflammatory marker types.

Methods: DigiCog is a single-center cross-sectional study conducted within the Luxembourgish Predi-COVID cohort (NCT04380987). Participants aged 25-65 years, with and without persistent COVID-19 symptoms, are invited to participate. Cognitive assessments are performed during face-to-face sessions by trained nurses and neuropsychologists using both the VMTech device and standardized neuropsychological tests. Additional data on PCC symptom status, CR, sociodemographic characteristics, fatigue, and psychological factors are also collected. Agreement between digital and standard cognitive assessments will be evaluated using Cohen's kappa coefficient, with sensitivity, specificity, and receiver operating characteristic analyses as secondary measures. Cognitive performance will be compared between participants with and without PCC, and associations with CR proxies will be explored.

BackgroundAlzheimers disease (AD) can be diagnosed using cerebrospinal fluid (CSF) biomarkers reflecting amyloid and tau pathology. However, it provides no information about functional network status. We aimed to determine whether CSF biomarkers (A{beta}42, p-Tau, t-Tau, and A{beta}42/p-Tau ratio) are associated with altered stimulus differentiation in vergence and pupil responses during an oddball task, and to evaluate oculomotor metrics as predictors of CSF core AD biomarkers in patients at mild cognitive impairment (MCI) stage. MethodsThirty-eight participants with abnormal CSF core AD biomarkers at MCI stage completed a visual oddball task while oculomotor responses were recorded. Linear mixed-effects models examined condition x biomarker interactions, controlling for sex, age, and MMSE. Temporal and magnitude features were tested as predictors using linear regression. ResultsHigher p-Tau levels were negatively associated with target-distractor differentiation in cognitive vergence ({beta} = -0.035, p < 0.001) and pupil responses ({beta} = - 0.060, p < 0.001). Higher A{beta}42 and A{beta}42/p-Tau showed positive associations with vergence differentiation but opposite effects on pupil responses. Oculomotor features predicted p-Tau levels (R2 = 0.20-0.21). ConclusionOculomotor differentiation metrics capture functional signatures of tau-related network dysfunction, positioning them as accessible biomarkers complementing CSF measures for detecting network disruption at MCI stage.

Understanding the time course of Alzheimer's disease biomarkers of amyloid and tau pathology and their temporal relation to clinical symptoms is key to identifying optimal windows for disease intervention and planning future drug trials. The goal of this work was to determine the extent to which Sampled Iterative Local Approximation (SILA), an algorithm extensively validated for amyloid PET, is capable of modeling longitudinal tau (T) PET trajectories and estimating person-level tau positivity onset ages in two commonly analyzed brain regions and two tracers from two different cohorts. Methods: 385 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI; mean (SD) age = 73.4 (7.3) years) with longitudinal flortaucipir tau PET and 288 participants from the Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center (collectively referred to as WISC; mean (SD) age = 67.4 (6.7) years) with longitudinal MK-6240 tau PET were included in the study. Standard uptake value ratios (SUVRs) in the entorhinal cortex and a meta-temporal ROI were modeled with SILA separately, for each cohort and region. Forward and backward SUVR and T+/- prediction were characterized with ten-fold cross-validation and in-sample validation techniques. Accuracy of estimated T+ onset ages (ETOA) was characterized in T- to T+ converters. Differences in ETOA were tested between APOE-e4 carriers and non-carriers, as well as differences in time T+ between levels of cognitive impairment. Results: SILA was able to accurately estimate retrospective change in tau SUVR in the meta-temporal region regardless of age, sex, APOE-e4 carriage, tau SUVR, and dementia (p >0.05) whereas dementia was associated with model residuals in entorhinal cortex (p [&le;] 0.05; ADNI). In subsets of observed T- to T+ converters, the difference between "observed" and estimated meta-temporal T+ onset age [95% CI] was 0.12 [-0.27, 0.52] years for ADNI and -0.09 [0.93, 0.74] years for WISC. ETOA was significantly earlier, and odds of SILA-estimated T+ status were higher amongst APOE-e4 carriers (p <0.05) and those with dementia (p <0.05). Conclusions: Our results suggest SILA can be used to accurately model longitudinal tau PET trajectories and retrospectively estimate individual T+ onset ages in the meta-temporal region. The accuracy of SILA time estimates in entorhinal cortex worsened amongst those with dementia in ADNI suggesting entorhinal cortex may only be suitable for studying the temporal progression of tau during the preclinical time frame.

BACKGROUND: Increasing evidence suggests that accurate prediction of Alzheimer disease (AD) symptom onset requires more than amyloid- and tau-centric biomarkers such as cerebrospinal fluid (CSF) A{beta}42/40, total tau and p-tau181 and plasma p-tau217. Autosomal dominant AD (ADAD), caused by pathogenic PSEN1, PSEN2 and APP mutations with predictable age at symptom onset, presents a unique opportunity to characterize the chronological changes in proteins beyond amyloid and tau and clarify them as early biomarkers of disease onset or as biomarkers related to disease staging and progression monitoring. METHODS: We measured 972 CSF samples corresponding to 484 participants of the Dominantly Inherited Alzheimer Disease Network (DIAN) using the NULISASeq 120 CNS Disease Panel. We first benchmarked the technology against gold-standard measurements followed by the identification of proteins that were differentially abundant in relation to mutation status and symptomatology. Next, we determined the chronological emergence of protein changes in relation to the estimated years to onset (EYO). Finally, we assessed whether specific protein measures improved the prediction of EYO in the ADAD. FINDINGS: NULISA measurements were comparable to those previously published. We demonstrated that known early alterations in CSF amyloid and tau were followed by inflammatory and neurodegenerative responses suggesting that clinical manifestation of AD happens before the inflammatory processes is fully developed. Finally, we found a multi-protein composite approach for predicting EYO that outperformed single biomarker values. INTERPRETATION: Our results suggest that the main CSF proteomic landscape changes in ADAD are due to the presence of a pathogenic mutation and occur prior to symptom onset. Improved performance of multi-protein composite to predict EYO compared to single biomarker values highlights the added value of multiplex proteomic signatures for biomarker panel development. FUNDING: National Institute on Aging, Alzheimers Association, German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, Japan Agency for Medical Research and Development, Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea, Spanish Institute of Health Carlos III.

INTRODUCTION: Bilingualism may confer resilience via enhanced neural integrity. However, evidence for bilingualism's neuroprotective effect is mixed, and studies across Alzheimer's disease (AD) variants are scarce. This study examined gray matter volume (GMV) differences between bilinguals and monolinguals with amnestic AD and logopenic variant primary progressive aphasia (lvPPA). METHODS: In 136 amnestic AD and 88 lvPPA participants with neuropsychological assessments and structural MRI, we analyzed differences between monolinguals and bilinguals within each variant, controlling for demographic covariates. RESULTS: Amnestic AD bilinguals exhibited less GMV in hippocampal, fusiform, and occipital regions compared to monolinguals. LvPPA bilinguals had less temporal and occipital volumes, but they had greater volumes in inferior parietal regions, which are considered a disease epicenter in lvPPA. Cognitive performance in monolinguals and bilinguals was comparable within variants. DISCUSSION: Bilingualism may support cognitive reserve (preserved cognition despite reduced GMV) in both AD variants, with additional brain reserve in lvPPA.

This analysis evaluates the feasibility and psychometric properties of daily digital cognitive assessments (DCAs) delivered on smartphones using data from the large, international Identifying Digital Endpoints to Assess FAtigue, Sleep and acTivities of daily living in Neurodegenerative disorders and Immune-mediated inflammatory diseases (IDEA-FAST) study. The data we analyse were collected from patients with neurodegenerative diseases (NDDs) and immune-mediated inflammatory diseases (IMIDs), and healthy controls (a subset who participated in all phases of the study, total N=977) in their own homes. These data were obtained alongside data from other devices that monitored physiology, kinematics, and sleep quality. Following a baseline visit, participants were remotely monitored via three scheduled daily sessions for 6-7 days in each of 4 active assessment phases (APs). APs were separated by 6-week intervals. Daily schedules comprised a morning psychomotor vigilance task (PVT) with eDiary, afternoon session (eDiary only), and an evening digit symbol substitution task (DSST) with eDiary. We evaluated session coverage using logistic mixed effects, test-retest reliability using ICCs, disease impacts on performance using linear mixed effect ANCOVA, and familiarisation using linear mixed effects. Overall coverage was 67.5% for the PVT and 77.0% for the DSST, with no significant differences between the healthy volunteers and disease cohorts. Coverage varied significantly by time-of-day (Evening > Morning > Afternoon), and improved with age, with an interaction revealing session time-of-day affected older participants less, all p < .001. Coverage was highest in AP 1 and reduced in subsequent APs. AP-day effects on coverage interacted significantly with AP, with a modest decline over AP 1, and the pattern reversed in APs 2-4. Baseline reliability was good (> .70) for both PVT mean reaction time and DSST total correct across all cohorts, and the movement-based measure from the DSST ranged [.55, .75], with lower values in the Parkinson's Disease and Primary Sjogren's Syndrome cohorts. Both tasks showed significant cohort effects, with performance in IMID cohorts intermediate between healthy controls and NDD. Longitudinal analysis revealed significant familiarisation effects in DSST. This was greatest in healthy controls, with significant attenuation of these effects in disease cohorts. No effect of familiarisation was seen in the PVT. Collectively, these results support the usefulness of at-home cognitive assessment on smartphones. Brief measures of cognition can be captured remotely in disease as well as controls with good adherence and sensitivity to distinguish known patient groups from healthy controls.