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Abstract INTRODUCTION Combining Alzheimer's disease blood biomarkers may improve prediction of amyloid positivity (A+) compared with single biomarkers. METHODS Logistic regression models using plasma phosphorylated tau 217 (p‐tau217), amyloid beta (Aβ) 42, and Aβ40 were developed in cognitively impaired (CI) participants from Mayo Clinic Rochester (MCR) and validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) and Mayo Clinic Florida (MCF) cohorts. Performance was measured by area under the curve (AUC) and Probability of Amyloid Positivity Score (PAPS). RESULTS Plasma p‐tau217 alone showed high AUC (95% confidence intervals) across cohorts MCR, 0.928 (0.900–0.953); ADNI, 0.938 (0.915–0.959); MCF, 0.935 (0.893–0.970). Adding Aβ42 to regression models yielded marginal model improvement MCR, 0.940 (0.915–0.963); ADNI, 0.945 (0.922–0.965); MCF, 0.960 (0.925–0.986). Utilization of two cutpoints (≥ 90% sensitivity and specificity), yielded ≤ 10% intermediate results for p‐tau217, and ≤ 8% for p‐tau217+Aβ42. Addition of Aβ40 did not improve models’ performance. DISCUSSION An immunoassay‐based p‐tau217 PAPS model accurately predicted amyloid positivity across independent CI cohorts and achieved confirmatory‐test performance with two cutpoints. The added value of Aβ42 was modest and cohort dependent, supporting the potential of use of p‐tau217–based PAPS in clinical workflows.

Abstract INTRODUCTION Cognitive characterization is increasingly important for Alzheimer’s disease treatment decisions, highlighting the need for scalable cognitive assessment. We evaluated the diagnostic accuracy of a remote self-administered cognitive screening battery, Mayo Test Drive (MTD), across clinically and biologically defined groups. METHODS In this prospectively designed study, 2,131 cognitively unimpaired (CU) and 118 cognitively impaired participants, including 96 with mild cognitive impairment and 22 with mild dementia (MCI/DEM), were recruited from observational cohort studies and completed MTD remotely (mean age=70 years, SD=12; 48% male; mean education=16 years, SD=2). Clinical diagnoses were determined by multidisciplinary consensus independently of MTD. Amyloid- and tau-PET were available for 1,224 participants. Logistic regression estimated associations between MTD and CU versus MCI/DEM, CU A-versus MCI/DEM A+, and A-T-versus A+T+. MTD diagnostic accuracy was compared with person-administered cognitive measures. Sensitivity and specificity across score types and comparison groups were examined. RESULTS MTD showed excellent discrimination of CU versus MCI/DEM (AUC=.89; 95% CI=.87-.92), and discriminability remained high for CU versus MCI (AUC=.88; 95% CI=.85-.92). Discriminability was highest when comparing CU A- and MCI/DEM A+ groups (AUC=.95; 95% CI=.92-.97). MTD outperformed estimated MMSE (p=.02) and was comparable to Mayo-PACC (p=.71) for A-T-versus A+T+ discrimination (AUC=.76; 95% CI=.71-.81). Combining both raw score and normative score cutoffs helped balance sensitivity and specificity relative to use of either cutoff in isolation. A two-stage interpretation framework further improved sensitivity while maintaining clinically useful specificity and informed development of an interpretation guide. DISCUSSION A remote self-administered digital cognitive assessment showed strong diagnostic accuracy for detecting MCI and mild dementia in a large, primarily population-based cohort. The remote, self-administered MTD composite also showed comparable performance to established in-person cognitive composites for differentiating amyloid and tau-defined biomarker groups. Remote self-administered digital cognitive assessment may complement existing cognitive evaluation approaches and support scalable cognitive characterization. Highlights Remote self-administered cognitive assessment showed strong differentiation of cognitively unimpaired and cognitively impaired participants. Diagnostic accuracy improved when amyloid PET biomarker status was incorporated. Remote assessment demonstrated sensitivity to amyloid and tau PET-defined Alzheimer’s disease. Performance was comparable to established in-person composites and better than an in-person mental status screening measure. Digital cognitive screening may support scalable treatment and clinical trial workflows.

Importance Classifying disease based on underlying pathobiology rather than clinical phenotype has implications for the development of biomarkers and therapy development. Observations Transactive response DNA-binding protein 43 kDa (TDP-43) pathology is observed across a range of clinically defined neurodegenerative disorders including limbic predominant age-related encephalopathy (LATE), most cases of amyotrophic lateral sclerosis (ALS), inclusion body myositis, multisystem proteinopathy, and approximately half the cases of frontotemporal dementia (FTD). Despite this shared biology, the current nosology for these neurodegenerative disorders is based on their distinct clinical phenotypes. An alternative approach recognizes the central role of TDP-43 pathology in disease pathogenesis, reserving the use of clinical terms like ALS, FTD, or LATE to describe phenotypic manifestations of underlying pathobiology. This approach also recognizes the converging biomarker and neuropathological data indicating that pathology begins presymptomatically, before the overt clinical manifestations of disease appear. Conclusions and Relevance In proposing a pathobiological definition of disease, the goal is to provide a road map for developing biomarkers that accurately reflect the underlying pathobiology of disease and for advancing therapeutic candidates that effectively target fundamental disease mechanisms.

Abstract INTRODUCTION Plasma p‐tau217 and p‐tau217/Aβ42 reliably identify non‐Hispanic White individuals with symptomatic and pre‐clinical AD. However, performance across ethnoracially diverse groups remains unknown. METHODS Cognitively normal Black, Hispanic, and non‐Hispanic White participants completed cognitive evaluations, brain magnetic resonance imaging (MRI), amyloid and tau positron emission tomography (PET), and measures of kidney function. Plasma p‐tau217 and Aβ42 were measured using Fujirebio Lumipulse assays. Pre‐clinical AD detection was evaluated using symptomatic and cohort‐derived optimized cutoffs. RESULTS Among 191 participants (57 Black, 46 Hispanic White, 88 non‐Hispanic White), age‐adjusted plasma p‐tau217 and p‐tau217/Aβ42 performed similarly in detecting amyloid‐positive individuals (≥ 25 Centiloids), regardless of race/ethnicity. Both measures independently correlated with amyloid deposition, whereas estimated glomerular filtration rate (eGFR) correlated solely with p‐tau217. Pre‐clinical AD optimized cutoffs (p‐tau217 ≥ 0.132 pg/ml; p‐tau217/Aβ42 ≥ 0.0059) outperformed cutoffs established in symptomatic individuals (sensitivity: 83% vs. 57%; 77% vs. 63%; negative predictive value [NPV] > 93%). DISCUSSION Plasma biomarkers performed similarly across ethnoracially diverse cohorts. Optimized cutoffs may improve early detection and trial enrollment.

BACKGROUND: White matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease but reflect late-stage injury. Diffusion magnetic resonance imaging (MRI) biomarkers have been proposed to capture earlier small vessel disease-related microstructural damage but their temporal progression relative to WMH and risk factors associated with progression remain unexplored. METHODS: We identified 2077 participants from the population-based cohort study of the Mayo Clinic Study of Aging in Olmsted County, Minnesota (aged 50–101 years) collected between 05/2005 and 09/2024 with longitudinal neuroimaging. Using multioutput nonlinear mixed-effects models in those with at least 2 fluid-attenuated inversion recovery-MRI and diffusion MRI scans, we characterized the temporal progression of WMH and 4 diffusion MRI biomarkers: fractional anisotropy of the genu of the corpus callosum, peak width of skeletonized mean diffusivity, free water, and Arteriolosclerosis-score, which were automatically estimated. Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE ε4 status, cardiometabolic conditions). RESULTS: The study population had a mean age of 78 years, 47% were women, 28% were APOE ε4 allele carriers, and 80% were cognitively unimpaired, with an average follow-up of 5.2 years (SD, 4.3 years) for fluid-attenuated inversion recovery-MRI and 4.3 years (SD, 3.9 years) for diffusion MRI. Arteriolosclerosis-score, fractional anisotropy of the genu of the corpus callosum, free water, and peak width of skeletonized mean diffusivity became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH become abnormal (half-width of CI <1 year), respectively. Global markers (Arteriolosclerosis-score, free water, peak width of skeletonized mean diffusivity, and WMH) were correlated, indicating shared substrates of widespread white matter injury. Fractional anisotropy of the genu of the corpus callosum, a vascular risk microstructural injury biomarker, was weakly coupled with WMH and had an earlier but more linear worsening across adulthood. Cardiometabolic conditions predicted earlier worsening of all biomarkers. Female participants showed earlier WMH, fractional anisotropy of the genu of the corpus callosum, and Arteriolosclerosis-score abnormalities, whereas male participants exhibited earlier peak width of skeletonized mean diffusivity and free water abnormalities. CONCLUSIONS: Diffusion MRI biomarkers were abnormal at least a decade before WMH become abnormal in the population, revealing a prolonged phase of early small vessel disease and highlighting their potential for small vessel disease prevention.

Abstract INTRODUCTION Although cerebrospinal fluid (CSF) biomarkers reflect neurodegeneration in Alzheimer's disease (AD), it remains unclear whether these biomarkers track neurodegeneration in early‐onset Alzheimer's disease (EOAD). METHODS In 80 EOAD patients, we examined correlations between eight CSF biomarker levels and cortical thickness decreases within the EOAD cortical atrophy signature. Significant correlations were then entered into a multiple regression analysis. We also examined contributions of EOAD atrophy and CSF biomarkers to cognitive impairment. RESULTS The levels of four CSF biomarkers correlated to EOAD signature atrophy. Stepwise regression analyses revealed that CSF neurofilament light chain (NfL) levels best predicted EOAD signature atrophy. Multiple regression showed EOAD signature atrophy combined with CSF NfL levels explained more variance in cognitive impairment than either factor alone. DISCUSSION Within EOAD patients, CSF NfL levels relate to the magnitude of cortical atrophy, and a combination of EOAD signature atrophy and CSF NfL levels most robustly predict cognitive impairment.

Objective Magnetic resonance imaging (MRI)‐visible enlarged perivascular spaces (PVS) are markers of cerebral small vessel disease (SVD) and aging, processes implicated in both neurodegenerative and cerebrovascular pathologies. However, longitudinal positron emission tomography (PET) studies examining PVS as a mechanism underlying Alzheimer's disease (AD) pathophysiological progression are lacking. Our overall objective was to investigate the associations of baseline PVS burden with white matter hyperintensity (WMH) volume, amyloid‐PET, and tau‐PET, both cross‐sectionally and longitudinally. Methods We examined 2,229 participants across the aging‐AD spectrum from the Mayo Clinic Study of Aging (MCSA) and the Mayo Alzheimer's Disease Research Center (ADRC) (mean age = 68.59 ± 12.70 and 48.94% women). PVS and WMH were quantified using a novel in‐house algorithm. Amyloid on [ 11 C]Pittsburgh Compound B (PiB)‐PET and tau on AV1451‐PET were measured. Cross‐sectional regional and global associations were examined using canonical correlation analysis (CCA). Longitudinal associations were examined using age‐ and sex‐adjusted linear mixed effect (LME) models with natural splines and subject‐wise 10‐fold cross‐validation. Result Three distinct associations were observed: (1) PVS in the basal ganglia (PVS‐BG) was associated with greater WMH volume both cross‐sectionally and longitudinally; (2) PVS‐BG covaried with higher PiB standardized uptake value ratio (SUVR) cross‐sectionally, with longitudinal trajectory associations limited to amyloid‐negative individuals; (3) PVS in the centrum semiovale (PVS‐CSO) was not associated with amyloid or tau, but was associated with occipital WMH, a pattern characteristic of CAA. Interpretation Findings lend support for PVS‐BG as an early indicator of small vessel dysfunction and WMH pathogenesis. Individuals with higher PVS‐BG burden exhibited higher WMH burden and faster WMH progression, suggesting that PVS‐BG may identify individuals at increased risk of progressive SVD. Evidence linking PVS to downstream AD biomarker progression was weaker. Findings support the relevance of PVS‐CSO to CAA, suggesting that they may reflect changes occurring early in the disease process. ANN NEUROL 2026

Diffusion MRI (dMRI) enables assessment of white matter microstructural abnormalities in Alzheimer's disease (AD), and multisite datasets enable more robust modeling of non-biological variation that can confound analyses. The Alzheimer's Disease Neuroimaging Initiative (ADNI) includes over 10 dMRI protocols, necessitating robust methods to model protocol-related variability when pooling data. Here, we compared three harmonization approaches: (1) mixed-effects models, (2) ComBat-GAM, and (3) eHarmonize, a reference-based lifespan method. We assessed their ability to reduce protocol-related variability in diffusion tensor imaging fractional anisotropy (FA) and mean diffusivity (MD) while preserving associations with cognitive impairment (CI), and amyloid-beta (Aβ) and tau PET burden in 1,086 ADNI3/4 participants. All approaches yielded more closely aligned FA/MD distributions across protocols. Associations with clinical indicators of CI were highly consistent across approaches, whereas PET associations were less widespread and more variable. Overall, multiple strategies effectively modeled protocol-related variability while preserving AD-related associations.

Abstract INTRODUCTION Face identification algorithms have increased the risk of study participants being identified through different neuroimaging modalities. De‐facing algorithms can de‐identify neuroimages; however, none are validated in individuals with Down syndrome (DS) – a population at high risk of Alzheimer's disease and the focus of numerous Alzheimer's disease cohort studies. METHODS Overall, 37 adults with DS were parcellated using FreeSurfer before and after de‐facing with mri_reface . A group of neurotypically developed individuals balanced on age, sex, and magnetic resonance imaging scanner manufacturer served as controls. RESULTS De‐facing produced no effect on cortical thickness or volumetrics in adults with DS compared to controls or regional discrimination based on an area under the receiver operating characteristic curve analysis after correcting for multiple comparisons. DISCUSSION FreeSurfer‐derived volumetric and cortical thickness measures changed minimally following de‐facing with mri_reface in adults with DS and are unlikely to influence study outcomes. De‐facing should be considered prior to data sharing in studies of adults with DS.