Citations

Abstract Alzheimer’s disease (AD) emerges from multi-scale interactions between molecular pathology and disruptions in large-scale brain network dynamics. Understanding how these processes co-evolve and relate to disease stages is essential for advancing complex systems models of aging and AD, and for developing system-informed interventions. However, progress has been limited by a lack of large-scale longitudinal data. To address this, we examined the longitudinal relationship between subsystems of the default mode network (DMN) (posterior DMN, ventral DMN, anterior dorsal DMN) using task-free functional MRI (fMRI) and amyloid positron emission tomography (PET) imaging in a large longitudinal cohort spanning the clinico-biological spectrum of AD (n = 1,451; 2,763 time points) using mixed-effect models. We also assessed whether patterns of DMN connectivity predicted conversion to amyloid positivity, mild cognitive impairment (MCI), and dementia using Cox proportional hazards models. Our findings reveal a dynamic interplay between amyloid accumulation and connectivity within and between DMN subsystems, with both hyper- and hypoconnectivity emerging across DMN subsystems in association with increasing amyloid burden. Importantly, survival models showed that DMN connectivity patterns predicted conversion to critical stages of the disease, including not only conversion to MCI and dementia, but also conversion to amyloid positivity in otherwise clinically unimpaired individuals who were amyloid negative at baseline. These associations were independent of age, APOE4 status, sex, education, and in-scanner motion. These results support a model in which breakdowns in tightly regulated feedback loops governing DMN physiology represent a core systems-level pathophysiology of AD. Notably, this functional dyshomeostasis precedes detectable amyloidosis on imaging. Future studies should focus on the development of robust biomarkers of brain function that can be applied at the individual level, which could in turn help support the development of therapeutic approaches targeting system-level pathophysiology.

Abstract INTRODUCTION Anti—amyloid‐β (Aβ) therapies are reshaping Alzheimer's disease (AD) management. Understanding changes in real‐world patterns of diagnostic testing and infusion chair usage is essential for optimizing access to care. METHODS Retrospective analysis of Mayo Clinic enterprise electronic health records (Jan 2019–Mar 2025) assessed trends in AD‐relevant brain imaging, fluid biomarkers, apolipoprotein E (APOE) testing, and lecanemab infusions. Rates of amyloid‐beta (Aβ) positivity by sex and age, APOE genotype frequencies, and lecanemab treatment initiation and discontinuation were evaluated. RESULTS Following national insurance coverage changes, lecanemab infusions grew by 110 infusions per quarter to 605 in Q1 2025. Aβ positron emission tomography scans increased (+22/quarter), cerebrospinal fluid biomarker orders declined (−25/quarter), and plasma p‐tau 217 orders rapidly increased (+238/quarter). Females were more likely to be Aβ positive ( p < 0.006). APOE ‐ε4 homozygotes were less likely to initiate lecanemab (HR = 0.11, p < 0.001). DISCUSSION The adoption of anti‐Aβ therapies coincided with a rapid shift in diagnostic workflows.

Abstract INTRODUCTION We examined when Alzheimer's disease biomarkers become informative by identifying age‐related breakpoints with slope‐changing trajectories. METHODS In 2082 Mayo Clinic Study of Aging participants, we modeled plasma amyloid beta (Aβ)42/40, phosphorylated tau (p‐tau)181, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid/tau positron emission tomography (PET), hippocampal volume (HVa), and global cognition. Generalized additive models described age trends; Davies' test and piecewise linear regression estimated breakpoints. A C2N subsample ( n = 462) provided mass‐spectrometry plasma markers (p‐tau181, p‐tau217, their ratios, Aβ42/40). RESULTS In the full cohort, Aβ42/40, HVa, and cognition declined with age; p‐tau181, NfL, GFAP, and amyloid/tau PET increased. We observed single breakpoints (years, 95% CI): GFAP 68.1 (63.5–71.8), NfL 70.7 (65.9–75.6), p‐tau181 67.2 (60.3–70.3), amyloid PET 62.3 (56.2–69.3), HVa 68.1 (63.1–71.9), cognition 59.8 (55.4–66.0); tau PET showed none. In the mass‐spectrometry subset, p‐tau217 and p‐tau181 broke at 72.6; their ratios and Aβ42/40 showed no breakpoints. DISCUSSION Breakpoints cluster near late midlife, suggesting windows for screening and monitoring.

Abstract INTRODUCTION Visual perceptual difficulties occur in dementia with Lewy bodies (DLB). Normative data and cut scores for DLB and mild cognitive impairment with Lewy bodies (MCI‐LB) are needed for the 20‐item Noise Pareidolia Task. METHODS Participants included 344 cognitively unimpaired (CU), 112 DLB, and 105 MCI‐LB. Cumulative percentile ranking was used for normative classification. Receiver operating characteristic analyses, convergent validity, and associations with visual hallucinations (VH) were assessed. RESULTS In CU participants, a perfect score was average, one error was low average, and two or more errors fell below average. A cut score of one or more errors optimally distinguished CU participants from DLB (Total score area under the curve [AUC]: 0.82, Noise score AUC: 0.77) and MCI‐LB (Total score AUC: 0.75, Noise score AUC: 0.74). A cut score of two or more errors lowered sensitivity but increased specificity to 94%. Performance was associated with VH in DLB, but not MCI‐LB. DISCUSSION Results establish normative data for the 20‐item Noise Pareidolia Task and support its use in DLB and MCI‐LB.

Abstract INTRODUCTION The tau positron emission tomography (PET) overlap index (OI) has shown promise in maximizing signal‐to‐noise for longitudinal tau PET imaging, particularly for early tau pathology, but requires validation against neuropathology. METHODS Fifty‐seven participants who underwent serial tau PET imaging (flortaucipir) and subsequent autopsy were included. Tau PET OI and standardized uptake value ratios (SUVRs) were compared across neuropathological diagnoses. RESULTS Tau PET OI showed greater concordance with neurofibrillary tangle (NFT) severity in the entorhinal cortex (a key region for Alzheimer's disease [AD] tauopathy) than SUVR, particularly in early Braak tangle stages (positivity: 52.2% for OI vs. 13.0% for SUVR). OI detected overlapping tau voxels that exhibited spatial correspondence with immunohistochemical and autoradiography measures of tau deposition across both AD and non‐AD tauopathies. DISCUSSION These findings demonstrate the enhanced capacity of OI in serial tau PET to robustly detect early and spatially localized tau pathology, supporting its application as a sensitive imaging metric in AD and select non‐AD tauopathies.

Abstract Background White matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease (SVD) but reflect late-stage injury. Diffusion MRI based biomarkers have been proposed to capture earlier SVD-related microstructural damage but their temporal progression relative to WMH and the risk factors associated with this progression have not been explored. Methods We analyzed longitudinal neuroimaging data from 2,047 participants from a population-based cohort study (aged 49-101 years, 47% female). Using multi-output nonlinear mixed-effects models, we characterized the temporal progression of WMH and four diffusion MRI based biomarkers: fractional anisotropy of the genu of the corpus callosum (Genu-FA), peak width of skeletonized mean diffusivity (PSMD), free water (FW), and Arteriolosclerosis-score (ARTS). Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE ε4 status, and cardiometabolic conditions). Results ARTS, Genu-FA, FW, and PSMD became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH, respectively. Global markers (ARTS, FW, PSMD, WMH) were correlated, indicating shared substrates of widespread white matter injury. Genu-FA, 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. Females showed earlier WMH, Genu-FA, and ARTS abnormalities whereas males exhibited earlier PSMD and FW abnormalities. Conclusions Diffusion MRI based biomarkers capture microstructural injury at least a decade before appearance of WMH, revealing a prolonged phase of early SVD and highlighting their potential for SVD prevention.

BACKGROUND: Cerebral microbleeds (CMBs) are associated with dementia, but the impact of specific microbleed patterns (with distinct pathophysiologies) is unclear. Lobar CMBs commonly result from cerebral amyloid angiopathy (sometimes with cortical superficial siderosis [cSS]), while subcortical microbleeds indicate a hypertensive cause. This study investigates the association of CMB presence, patterns, and frequency with dementia risk. METHODS: Participants from the ARIC-NCS (Atherosclerosis Risk in Communities-Neurocognitive Study), a community-based longitudinal cohort, with a 3T research magnetic resonance imaging scan at visit 5 (2011–2013) and without prior intracerebral hemorrhage or dementia were included. CMB and cSS presence and location were evaluated using a T2* gradient-recalled echo sequence. Each individual’s pattern of CMBs was classified as no CMBs, subcortical only, mixed (subcortical and lobar±cSS), lobar±cSS only, or cSS. Dementia adjudication was determined by blinded review of cognitive testing, informant interviews, and hospital discharge codes or death certificates (from follow-up visits and ongoing surveillance). Cox proportional hazards models assessed associations between CMBs and incident dementia through 2020, adjusted for demographics, vascular risk factors, and white matter hyperintensities. RESULTS: Among 1583 participants (mean age, 76 years; 24.7% cumulative dementia incidence over a median of 23.6 follow-up years), 361 had CMBs. Compared with individuals without CMBs, those with mixed (hazard ratio, 1.99 [95% CI, 1.40–2.83]) or lobar±cSS (hazard ratio, 1.96 [95% CI, 1.30–2.97]) CMB patterns had increased dementia risk, whereas individuals with a subcortical-only pattern did not (hazard ratio, 1.04 [95% CI, 0.78–1.39]). cSS presence (n=21; versus absence) showed an increased risk of dementia (hazard ratio, 2.57 [95% CI, 1.36–4.89]). Greater numbers of microbleeds across all CMB subtypes exhibited an increased risk of dementia. CONCLUSIONS: Lobar and mixed CMB patterns and increasing numbers of CMBs are associated with incident dementia. Certain CMB patterns have differential associations with dementia, but severe enough disease burden, regardless of CMB distribution, is associated with increased dementia risk.

Abstract Associations of Alzheimer’s disease biomarker progression with cognitive decline are important to inform patient prognosis. Of particular interest is how newly available plasma biomarkers evolve relative to cognitive decline. The goals of this work are to measure how much earlier versus later an individual’s progression on plasma and PET Alzheimer’s disease biomarkers is associated with earlier versus later cognitive progression and to estimate the average timeline of progression of these processes in the population. In this cohort study of 2369 Mayo Clinic Study of Aging (MCSA) and 1591 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants, we fit non-linear mixed-effects models to estimate how much earlier versus later each individual progresses on plasma phosphorylated tau (p-tau)217, amyloid PET, tau PET and auditory verbal learning test (AVLT) sum of trials relative to the population mean (individual adjustment), the associations of these individual adjustments among biomarker pairs and how covariates affect the timing of biomarker progression. The association of individual adjustments implies mechanistic associations and the amount of variability in cognitive decline accounted for by each biomarker. By applying cut-off points, we also estimated the relative timing that these biomarkers become abnormal in the population. Associations of individual adjustments were moderate between all biomarkers and AVLT (R = 0.38–0.47) in the MCSA and stronger (R = 0.74–0.81) in ADNI; plasma p-tau217 accounted for 16% of the variability in timing of AVLT decline in the MCSA and 64% in ADNI. APOE ɛ4 carriership was associated with earlier biomarker progression. AVLT became abnormal after the biomarkers up to age 90, after which AVLT was estimated to become abnormal prior to tau biomarkers. The association of the timing of plasma and PET Alzheimer’s disease biomarker progression with cognitive decline was modest in the MCSA population-based sample and stronger in the Alzheimer’s disease-enriched ADNI cohort. The timing of plasma p-tau217 progression explained a similar degree of variability in AVLT progression as amyloid PET, supporting its utility as a marker of disease progression. The estimated temporal ordering of biomarkers and cognitive abnormality was as anticipated (amyloid, tau, cognition) up to the age of 90, beyond which AVLT was estimated to become abnormal prior to tau biomarkers, likely related to the effects of non-Alzheimer’s disease co-pathologies.