Schwarz, Christopher G

Objective To characterize regional brain atrophy patterns, evaluate their association with regional leucine‐rich glioma‐inactivated‐1 ( LGI1 ) gene expression, and identify clinical predictors of hippocampal atrophy in LGI1 autoimmune encephalitis (AE). Methods This study compared regional brain volumes in LGI1‐AE patients versus age‐ and sex‐matched healthy controls (HC) and Alzheimer's disease (AD) patients. LGI1 gene expression was derived from the Allen Human Brain Atlas. Multivariable linear regression and generalized linear models were utilized. Results A total of 55 LGI1‐AE patients were included. Median age was 68 years (IQR 62.5–74.2 years) at volumetric analysis, after 19 months (IQR 11–45 months) from symptom onset. Compared with HC ( n = 55) and AD patients ( n = 55), LGI1‐AE patients had greater regional atrophy of the hippocampus, insula, caudate, and putamen ( p < 0.002). Longitudinal analyses demonstrated hippocampal and pallidal atrophy after covariate adjustment. Among those who did not fulfill limbic encephalitis criteria and/or had non‐lesional magnetic resonance imaging, limbic and striatal atrophy were identified compared with HC ( p < 0.01). Regions with the greatest differential atrophy corresponded to higher LGI1 gene expression (Spearman ρ = 0.7, p = 0.005). Severe cognitive deficits at diagnosis ( β = −0.56; p = 0.002) and longer disease duration ( β = −0.01; p = 0.001) independently predicted total hippocampal volume ( R 2 = 0.31). Interpretation LGI1‐AE demonstrates a distinct pattern of limbic and striatal atrophy that differs from AD and aligns with regional LGI1 expression, supporting a model of selective network vulnerability. Greater baseline cognitive impairment and longer disease duration independently associated with hippocampal atrophy, underscoring the potential importance of early diagnosis and treatment to limit structural brain injury. ANN NEUROL 2026

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

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 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.

Abstract A workgroup assembled by the Alzheimer’s Association recently described a conceptual framework for Alzheimer’s disease biological staging based on amyloid and tau PET imaging. However, specific tau PET cut points were left to be determined, a step necessary prior to clinical application. We sought to operationalize and evaluate Alzheimer’s disease biological staging by identifying meaningful tau PET cut points to define the four biological stages in a well-characterized participant cohort and describe the features of individuals placed into the different biological stages. The primary analysis included 896 participants in the Mayo Clinic Study of Aging or the Mayo Clinic Alzheimer’s Disease Research Center longitudinal cohorts. A validation cohort consisted of 328 participants in the Alzheimer’s Disease Neuroimaging Initiative. Both cognitively normal and impaired individuals with positive amyloid PET and evaluable tau PET imaging were included. Tau PET cut points were identified with Gaussian mixture models to characterize Alzheimer’s disease biological stage in study participants with different clinical diagnoses and objective degrees of cognitive impairment as measured by Mini-Mental State Examination. A tau PET cut point in the medial temporal region and two cut points in the temporoparietal region were identified to produce, collectively, the four Alzheimer’s disease biological stages described in the revised criteria. Increasing stage was associated with greater likelihood of mild cognitive impairment and dementia diagnosis and worsening cognitive performance on Mini-Mental State Examination and Clinical Dementia Rating Sum of Boxes, a result that was reproduced in the independent Alzheimer’s Disease Neuroimaging Initiative cohort. This study provided empirical validation for the concept of using amyloid PET and tau PET to separate subjects with biomarker-proven Alzheimer’s disease into four biological stages with distinct characteristics.

Objective The objective of this study was to determine the predictive value of amyloid‐positron emission tomography (PET) versus the plasma ratio of phosphorylated tau at threonine 217 (p‐tau217) to non‐phosphorylated tau217 (%p‐tau217) for tau‐PET transitions (T− to T+). The added value of combining plasma amyloid‐β 42 and amyloid‐β 40 (Aβ42/40) and %p‐tau217 into an amyloid probability score (APS2) was also assessed. Methods Mayo Clinic Study of Aging (MCSA) participants had plasma markers measured at via mass spectrometry (MS), an amyloid‐PET scan, and a tau‐PET (meta‐temporal region of interest [ROI]) negative scan (standardized uptake value ratio [SUVR] <1.29) at the index (baseline) date, along with one or more follow‐up tau‐PET scans. The BioFINDER‐2 cohort was used for validation. Cox proportional hazards models adjusted for age, sex, and apolipoprotein ( APOE ) ε4 were used to assess predictors, with scaling to the interquartile range (IQR) for comparability of hazard ratios (HR). Results Among 255 tau‐PET negative MCSA participants (median age: 71.9 years), 37 converted to tau‐PET positive (median follow‐up time: 3.81 years). Higher %p‐tau217 (HR: 1.52 [95% CI: 1.28–1.80]), amyloid‐PET centiloid (HR: 1.47 [95% CI: 1.20–1.79]), and APS2 (HR: 1.62 [95% CI: 1.22–2.16]) predicted tau‐PET conversion. However, Aβ42/40 (HR: 0.94 [95% CI: 0.54–1.66]) was not associated with tau‐PET conversion. In the BioFINDER‐2 cohort (605 tau‐negative, median age: 70.2), 33 converted to tau‐positive (median follow‐up time: 2 years), with higher %p‐tau217 (HR: 1.80 [95% CI: 1.50–2.17]), amyloid‐PET centiloid (HR: 2.29 [95% CI: 1.77–2.97]), and lower Aβ42/40 (HR: 2.38 [95% CI: 1.17–4.83]) predicting conversion. Interpretation In two cohorts, %p‐tau217 was associated with tau‐PET conversion, comparable to amyloid‐PET. APS2 also predicted conversion in the MCSA cohort, whereas Aβ42/40 predicted conversion in the BioFINDER‐2 cohort, which had more individuals with cognitive impairment. ANN NEUROL 2025;98:1249–1260

Abstract The Centiloid scale is the standard for Amyloid (Aβ) PET quantification, widely used in research, clinical settings, and trial stratification. However, variability between tracers and scanners remains a challenge. This study introduces DeepSUVR, a deep learning method to correct Centiloid quantification, by penalising implausible longitudinal trajectories during training. The model was trained using data from 2,098 participants (6,762 Aβ PET scans) in AIBL/ADNI and validated using 15,806 Aβ PET scans from 10,543 participants across 10 external datasets. DeepSUVR increased correlation between tracers, and reduced variability in the Aβ-negatives. It showed the strongest association with cognition, highest AUC against visual reads and best longitudinal consistency between studies. DeepSUVR also increased the effect size for detecting lower Centiloid increase per year in the A4 study. DeepSUVR advances Aβ PET quantification, outperforming standard approaches, which is particularly important for consistent decision making and to detect subtle and early changes in clinical interventions.

Background Studies suggest that plasma Alzheimer's disease (AD) biomarkers may aid in the overall diagnosis of AD, but their utility among patients with atypical clinical presentations of AD are unknown. Objective The main objective of this study was to determine the relationship between amyloid-β (Aβ) and tau plasma biomarkers and PET measures of both Aβ and tau in atypical AD. The secondary objective was to determine if plasma biomarkers could differentiate patients with different atypical AD phenotypes and whether they were related to measures of disease severity. Methods We assessed whether plasma p-tau 181 and Aβ 42/40 were associated with Aβ and tau PET uptake, clinical phenotype and severity in 77 patients with PET biomarker-confirmed atypical AD. Results Plasma Aβ 42/40 ratio showed positive associations with tau PET uptake, with higher (more normal) Aβ 42/40 ratio associated with higher tau uptake; the ratio was not associated with Aβ PET. No associations were noted with plasma p-tau 181. Plasma Aβ 42/40 ratio and p-tau 181 concentrations were not associated with AD phenotype or cognitive severity. Conclusion Plasma Aβ 42/40 ratio and p-tau 181 concentrations are not associated with amyloid or tau PET or with clinical severity among individuals presenting with atypical AD.