Hyperphosphorylated tau tangles are essential hallmarks of Alzheimer's disease (AD) and their propagation across brain regions was often considered to follow the classic Braak stages. Recent post-mortem and in vivo tau positron emission tomography (PET) studies, however, revealed the frequent presence of tau pathology heterogeneity. Clustering or event-based methods were proposed previously for the subtyping to tau pathology in AD, but they often lack robustness to varying distributions of disease severity across cohorts. To robustly discover and model tau pathology subtypes in AD, we propose in this work a novel graphical modeling framework that can disentangle the phenotypical differences of tau PET imaging due to disease heterogeneity from the spatiotemporal variations of disease stages. First, we propose a novel Reeb graph representation at the individual level to characterize the topographic patterns of salient tau pathology on cortical surfaces. Next, we use only cross-sectional tau PET data to develop a graphical model at the population level to encode the inter-subject spatiotemporal relationships, which enables us to robustly derive subtypes based on the topographic patterns of tau pathology and hence achieve increased generalization power to new samples with distinct tau pathology severity from the training data. Using synthetic and large-scale tau PET imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) studies, we compare with the state-of-the-art SuStaIn method and demonstrate the improved generalization performance of the proposed method. In addition, we validate both methods on a cohort of autosomal dominant Alzheimer's disease (ADAD) patients with known tau pathology patterns to show that our method has more robust performance in testing data with large deviations from training data. Furthermore, for preclinical patients of the A4 cohort, we demonstrate more significant differences in clinical cognitive measures can be observed across subtypes discovered by our method.

Non-linear statistical analyses on resting-state fMRI (rs-fMRI) using complexity measures have demonstrated progressive decline in complexity from cognitively normal subjects (CN) to patients with Mild Cognitive Impairment (MCI) and patients with Alzheimer’s disease (AD). While complexity has been shown to be negatively associated with tau-PET, the association with amyloid or effects of genetic characteristics (APOE4) remains unknown. From the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) we identified participants with tabulated SUVR values for amyloid and tau as well as one resting state fMRI scan for the same visit. The rs-fMRI complexity was calculated as Multiscale Sample Entropy (MSE) (r=0.5, m=2, scale=6). SUVR values from precuneus, parahippocampal, inferior temporal and entorhinal regions were used in a multivariate generalized linear model to investigate the adjusted independent effects to corresponding rs-fMRI complexity. Whether such effects were modified by either diagnosis (CN, MCI, AD) or APOE4 status (# alleles) were tested using the interaction terms in the multivariate model. Age and gender were controlled for all models. The final cohort consisted of 127 subjects (Table 1). We observed statistically significant negative associations between complexity and tau in parahippocampus, inferior temporal gyrus and precuneus (Table 2A). Diagnostic status does not modify these associations, however, APOE4 status showed a statistically significant interaction effect for tau and complexity in precuneus (Table 2C). For amyloid no associations nor interaction effects were found (Table 2B). Our study confirmed previously reported statistically significant inverse relationship between rs-fMRI complexity and tau-PET, which is indicative of disfunction of neuronal signaling in the presence of tauopathy. While diagnostic classification showed no effects, the APOE4 genetic risk had a strong modifying effect leading to a stronger negative relationship between tau-PET and fMRI-complexity. The null-finding for amyloid was expected, since, while the presence of amyloid increases risk for cognitive decline, it is the occurrence of tau that leads to cognitive decline and neurodegeneration. Overall, genetic risk potentially increases the prevalence of amyloid in this cohort and consequentially leads to a cumulative and more pronounced decrease in rs-fMRI complexity in the presence of tau.

The ways in which diverse genetic variants interact to affect the phenotype of AD is poorly understood. The relatively consistent phenotype associated with specific mutations causing autosomal dominant AD (ADAD) provides the opportunity to study how other genetic variants contribute to disease manifestations. We performed an in-depth case study of a patient with the A431E PSEN1 mutation who had onset of progressive spastic paraplegia at age 20. The proband was a Mexican woman known to be at 50% risk for inheriting the A431E PSEN1 mutation that commonly presents with cognitive changes and/or spastic paraplegia at age 40. At age 20 she began developing stiffness in her legs and difficulty walking. When seen at age 26 she had diffuse spasticity worse in the legs, slurred speech, and was cognitively intact. A flortaucipir PET scan at the time was negative as was a florbetaben scan 21 months later despite marked differences from controls in DTI measures in the corticospinal tract. Genetic screening demonstrated the presence of the A431E PSEN1 mutation and an A713T APP variant which was confirmed by WGS. The A713T APP variant was not present in her mother who had more typical disease nor in 62 other persons of Mexican origin. The A713T variant in APP has been shown to affect APP processing and Abeta nucleation and is considered to be a risk factor for AD. We hypothesize that the presence of this variant led to the early onset of spastic paraparesis in our proband despite Abeta deposition in brain being undetectable on PET. This finding suggests spastic paraplegia may be related to aberrant APP processing in a manner distinct from plaque formation. This study was funded by R01AG062007 and R01AG069013

PURPOSE:Arterial input function (AIF) extraction is a crucial step in quantitative pharmacokinetic modeling of DCE-MRI. This work proposes a robust deep learning model that can precisely extract an AIF from DCE-MRI images. METHODS:A diverse dataset of human brain DCE-MRI images from 289 participants, totaling 384 scans, from five different institutions with extracted gadolinium-based contrast agent curves from large penetrating arteries, and with most data collected for blood-brain barrier (BBB) permeability measurement, was retrospectively analyzed. A 3D UNet model was implemented and trained on manually drawn AIF regions. The testing cohort was compared using proposed AIF quality metric AIFitness and Ktrans values from a standard DCE pipeline. This UNet was then applied to a separate dataset of 326 participants with a total of 421 DCE-MRI images with analyzed AIF quality and Ktrans values. RESULTS:The resulting 3D UNet model achieved an average AIFitness score of 93.9 compared to 99.7 for manually selected AIFs, and white matter Ktrans values were 0.45/min × 10-3 and 0.45/min × 10-3, respectively. The intraclass correlation between automated and manual Ktrans values was 0.89. The separate replication dataset yielded an AIFitness score of 97.0 and white matter Ktrans of 0.44/min × 10-3. CONCLUSION:Findings suggest a 3D UNet model with additional convolutional neural network kernels and a modified Huber loss function achieves superior performance for identifying AIF curves from DCE-MRI in a diverse multi-center cohort. AIFitness scores and DCE-MRI-derived metrics, such as Ktrans maps, showed no significant differences in gray and white matter between manually drawn and automated AIFs.

OBJECTIVE:To establish a framework for validating candidate biomarkers of cerebral small vessel diseases (SVD) associated with cognitive impairment and characterize individuals enrolled by the MarkVCID2 consortium under this framework. METHODS:Participants age 60 to 90 years were enrolled across 17 MarkVCID2 sites. Recruitment was targeted to enrich in cognitive symptoms (mild dementia, mild cognitive impairment, subjective cognitive decline), defined risk factors (diabetes mellitus, advanced hypertension), and Black/African American, White, and Hispanic/Latino subgroups. Enrolled participants underwent baseline visits that included cognitive testing, multimodal magnetic resonance imaging (MRI), and biofluid collection. Provisional risk for SVD-related cognitive decline was estimated primarily by baseline cognitive symptoms plus SVD risk factors. Adjudicated risk status was estimated by cognitive symptoms plus presence of moderate-to-severe white matter hyperintensities, microbleeds, or lacunes on baseline MRI. RESULTS:MarkVCID2 enrolled 1883 individuals age 73.4 ± 7.5 years, 65.0% female, 24.2% Hispanic, and 27.1% non-Hispanic Black. Among enrollees, 44.8% were provisionally designated high-risk. After baseline MRI, 48.5% were categorized as adjudicated high-risk status, with substantial recategorization both from low- to high-risk (primarily because of MRI lesions without SVD risk factors) and high- to low-risk (primarily suspected cognitive impairment at screening not confirmed by baseline testing). INTERPRETATION:MarkVCID2 baseline data indicate successful enrollment of diverse individuals enriched in factors associated with SVD-related cognitive decline. Changes over 3 years of longitudinal follow-up will be analyzed to validate the candidate biomarkers for 2 projected contexts of use: subject selection (identifying likelihood of future SVD progression) and study outcome (efficiently measuring SVD progression). ANN NEUROL 2026;99:449-458.

BACKGROUND:The neuropathologies of Alzheimer's disease (AD) and Lewy body disease (LBD) commonly co-occur. Parkinsonism is the hallmark feature in LBD but it can be difficult to predict the presence of these co-pathologies early in the course of clinical disease. Timely diagnosis has crucial implications, especially with the advent of disease-modifying therapies. OBJECTIVES:We sought to define early motor features that predict the ultimate neuropathological diagnoses of normal, AD, AD with concurrent LB pathology, and pure LB. METHODS:We examined the associations between individuals' early motor features from their initial visit using the Unified Parkinson's Disease Rating Scale (UPDRS) Part III and their neuropathological diagnoses using the U.S. National Alzheimer's Coordinating Center (NACC) Database. RESULTS:We included data from participants with neuropathologically normal brains (n = 49), AD (n = 502), AD w/LB (n = 167), and pure LB (n = 51). Total UPDRS Part III scores were increasingly higher with purer LB pathology. Decreased facial expression at baseline differentiated those with AD w/LB pathology from those with AD. Participants having pure LB pathology more often had deficits in speech, facial expression, posture, gait, bradykinesia, and upper extremity rigidity relative to those with AD w/LB. CONCLUSION:Diminished facial expression significantly predicted the presence of LBs among those with concurrent AD pathology. Worse early speech, facial expression, posture, gait, bradykinesia, and upper extremity rigidity were suggestive of more pure LB pathology. These findings emphasize the utility of the neurological exam in the clinical assessment of persons with cognitive complaints as it can guide management.

Mutations in the presenilin 1 (PSEN1) gene cause early onset autosomal dominant Alzheimer’s Disease (AD), and the Jalisco mutation (A431E) is a subset found in people of Mexican descent (Yescas P, 2006). The Jalisco A431E mutation has been shown to produce distinct AD neuropathology such as cotton-wool amyloid plaques as well as motor dysfunction like spastic paraparesis (Orozco-Barajas, 2022). High levels of tau neuropathology have been observed with other PSEN1 mutations, but tau neuropathology in Jalisco patients has not been examined. Therefore, we sought to quantify the distribution of tau across a variety of brain regions in patients with A431E mutation and compare to patients with other PSEN1 mutations (A260V and T245P) or high stage sporadic AD. Ten coronal brain sections from 9 PSEN1 (n = 5 A431E; n = 2 A260V, n = 2 T245P) and 9 high stage AD subjects immunohistochemically labelled with AT8 were obtained from the USC Alzheimer’s Disease Research Center (ADRC). Regions analyzed included cerebral cortex (prefrontal, parietal, temporal, and occipital areas), hippocampus, striatum, midbrain, cerebellum, pons, and medulla. Ilastik, a machine-learning based software, was trained on positive AT8 staining versus background pixels and applied this training to the whole slide image. This binary output was then overlaid with a manually drawn ROI atlas in the Quantitative Imaging Toolkit (QIT), and positive pixels per pixels of the region was used to calculate density of AT8 pathology. AT8 density was significantly higher across the brain in patients with PSEN1 mutations compared to sporadic AD. Although small sample size is limited for these patient groups, we found that A431E patients had lower AT8 density in the hippocampus compared to other PSEN1 patients, most notably in dentate gyrus, as well as Brodmann Areas 35 and 36. These data show a high burden of tau fibrils as measured by AT8 across brain regions in people with PSEN1 mutations as compared to other high AD patients. Notably, A431E patients had different distributions than other PSEN1 patients. These findings help further characterize early onset autosomal dominant AD within patients of Mexican heritage and how tau neuropathology might contribute to its unique symptoms.

INTRODUCTION:The Down syndrome-associated Alzheimer's disease (DSAD) autosomal dominant Alzheimer's disease (ADAD) 2024 Conference in Barcelona, convened under an Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART) grant through the Down syndrome and Alzheimer's disease (AD) Professional Interest Area (PIA), brought together global researchers to foster collaboration and knowledge exchange between the fields of DSAD and ADAD. METHODS:This article provides a synthesis review of the conference proceedings, summarizing key discussions on biomarkers, natural history models, clinical trials, and ethical considerations in anti-amyloid therapies. RESULTS:A total of 211 attendees from 16 countries joined the meeting. Global researchers presented on disease mechanisms, therapeutic developments, and patient care strategies. Discussions focused on challenges and opportunities unique to DSAD and ADAD. Experts emphasized the urgent need for tailored clinical trials for ADAD and DSAD and debated the safety and efficacy of anti-amyloid treatments. Ethical considerations highlighted equitable access to therapies and the crucial role of patient and caregiver involvement. DISCUSSION:The conference highlighted the importance of inclusive research and collaboration across the genetic forms of AD. HIGHLIGHTS:Biomarker research and natural history models developed in Down syndrome-associated Alzheimer's disease (DSAD) and autosomal dominant Alzheimer's disease (ADAD) enable the prediction of disease progression not only for DSAD and ADAD, but also for sporadic Alzheimer's disease (AD). -Collaboration and knowledge exchange among researchers across these genetic forms of AD will accelerate our understanding of the pathophysiology and advance preventive trials in DSAD and ADAD. -Tailored clinical trials for DSAD are urgently needed to address specific safety and efficacy concerns. -Inclusive research practices are crucial for advancing treatments and understanding of DSAD and ADAD.

To validate the index of diffusivity along the perivascular space (ALPS index) as a biomarker for vascular cognitive impairment and dementia (VCID). The participants and MRI data used in this study were acquired as part of the MarkVCID consortium, which consisted of seven sites. A total of 578 participants (72.5±7.2 years old, 232 Male) who received baseline and follow-up cognitive evaluations (Montreal Cognitive Assessment (MoCA), Principal Component Analysis derived General Cognitive Function (GCF_PCA), and composite score of Executive Function (EFC)) and MRI examinations were included in this study. The diffusion tensor imaging (DTI) data were processed by using an in-house automatic processing pipeline with FMRIB Software Library 6.0.6. The mean free water (mFW) and peak width of skeletonized mean diffusivity (PSMD) were computed in the white matter (WM). The ALPS index was defined as the average of bilateral ALPS indices which were calculated by the ratio of mean of x-axis diffusivity in the projection fibers (Dxxproj) and x-axis diffusivity in the association fibers (Dxxassoc) to the mean of y-axis diffusivity in the projection fibers (Dyyproj) and z-axis diffusivity in the association fibers (Dzzassoc). The WM hyperintensity volumes (WMHV) were calculated on FLAIR images and normalized by intracranial volume (ICV). Univariate correlation (Pearson or Spearman) was used to examine the associations between imaging markers (ALPS index, mFW, PSMD, and WMHV). Linear regression models were used to evaluate the associations of baseline ALPS index with baseline and longitudinal changes of cognitive outcomes, regressing out three types of covariates: 1) age, sex, and education, 2) added vascular risk factors (VRFs), including diabetes, hypertension and smoking, 3) further added mFW, PSMD and WMHV. SAS 9.4 software was used for all statistical analyses, and P<0.05 was regarded as statistical significance. The baseline ALPS index was significantly correlated with existing biomarkers of cerebral small vessel disease (cSVD)-related VCID, i.e., mFW and WMHV (P<0.01) (Figure 1), and baseline cognitive performances, i.e., MoCA total score, GCF_PCA score, and EFC score (P<0.05) after adjusting for the demographics, VRFs, and existing biomarkers (Figure 2). the ALPS index is an independent contributor to the cognitive decline in cSVD.

AbstractINTRODUCTIONCross‐sectional resting‐state functional magnetic resonance imaging (rsfMRI) studies have revealed altered complexity with advanced Alzheimer's disease (AD) stages. The current study conducted longitudinal rsfMRI complexity analyses in AD.METHODSLinear mixed‐effects (LME) models were implemented to evaluate altered rates of disease progression in complexity across disease groups.RESULTSThe LME models revealed complexity of the higher frequency in the CNtoMCI group (those converted from cognitively normal [CN] to mild cognitive impairment [MCI]) decayed faster over time versus CN in the prefrontal and lateral occipital cortex; complexity of the lower frequency decayed faster in AD versus CN in various frontal and temporal regions (p < 0.05 & Benjamini–Hochberg corrected with q < 0.05).DISCUSSIONLocal functional brain activities decayed in the early stage of the disease, and long‐range communications were impacted in the later stage. Our study demonstrated longitudinal changes in AD‐related rsfMRI complexity, indicating its potential as an imaging biomarker of AD.Highlights We conducted longitudinal resting state functional magnetic resonance imaging (rsfMRI) complexity analyses using the Alzheimer's Disease Neuroimaging Initiative dataset. Higher‐frequency complexity in the CNtoMCI group (those transitioning from cognitively normal [CN] to mild cognitive impairment [MCI]) was found to decay faster over time compared to CN, specifically in the prefrontal and lateral occipital cortex. Lower‐frequency complexity was found to decay faster in AD versus CN in various frontal and temporal regions. This study demonstrated that longitudinal changes in rsfMRI complexity could serve as a potential imaging biomarker for Alzheimer's disease.

Background Declining motor abilities might be a noninvasive biomarker for Alzheimer's disease (AD). Studying motor ability and AD progression in younger Latinos with autosomal dominant Alzheimer's disease (ADAD) can provide insights into the interplay between motor ability and cognition in individuals with minimal confounding from age-normative changes and comorbid medical conditions. Objectives This study aimed to (1) examine motor abilities as a function of years to dementia diagnosis and (2) examine associations between motor ability and cognitive performance. Design This was a cross-sectional observational study. Setting The study took place at the University of Southern California. Participants 39 predominately Latino individuals (mean age 38.6 ± 10 years old) known to carry (carriers; n=25) or be at 50% risk for inheriting ADAD but not carrying the mutation (noncarriers; n=14). Measurements Individuals completed the motor and cognitive batteries from the National Institute of Health Toolbox (NIHTB) and the Cognitive Abilities Screening Instrument (CASI). All models included effects for age, education, primary language, and sex. Results Compared to noncarriers, ADAD mutation carriers had significantly weaker grip strength at 12 years, worse manual dexterity at 10 years, and slower gait speed seven years before the expected age of dementia diagnosis. Worse motor ability was associated with a more severe cognitive disease stage and worse CASI performance, adjusting for demographic and clinical variables. Conclusions The findings support the utility of motor performance, precisely grip strength, manual dexterity, and gait speed as potential biomarkers of preclinical AD.

INTRODUCTION:Perivascular space (PVS) alterations are traditionally linked to cardiovascular risk factors and aging, but may also play a direct role in Alzheimer's disease (AD). To reduce confounding from age-related comorbidities, we examined PVSs in autosomal dominant AD (ADAD). METHODS:In this cross-sectional study of 96 non-demented individuals (62 mutation carriers), we quantified PVS count fraction and mean diameter in white matter and basal ganglia using automated magnetic resonance imaging analysis. Linear mixed models assessed group differences along the disease course, adjusting for cardiovascular risk factors. RESULTS:Compared to non-carriers, mutation carriers showed lower PVS count fraction in white matter and basal ganglia, and larger PVS diameter in basal ganglia and the temporal lobe. Changes were evident up to 18 years before expected dementia onset and followed trajectories similar to amyloid beta 42 and tau biomarkers. DISCUSSION:ADAD is associated with early PVS alterations, suggesting perivascular changes may be integral to primary AD pathology. HIGHLIGHTS:Autosomal dominant Alzheimer's disease (ADAD) mutation carriers have reduced magnetic resonance imaging-visible perivascular space (PVS) count fraction in the white matter and basal ganglia. ADAD mutation carriers show enlarged PVS in the basal ganglia and temporal white matter. PVS alterations start 18 years before the estimated time of dementia diagnosis. The spatial localization of PVS changes overlaps with regions of amyloid beta (Aβ) accumulation. The temporal evolution of PVS alterations aligns with Aβ and tau changes in the cerebrospinal fluid.

The Uniform Data Set (UDS) neuropsychological battery, administered across Alzheimer’s Disease Centers (ADC), includes memory tests but lacks a list-learning paradigm. ADCs often supplement the UDS with their own preferred list-learning task. Given the importance of list-learning for characterizing memory, we aimed to develop a harmonized memory score that incorporates UDS memory tests while allowing centers to contribute differing list-learning tasks. We applied item-banking confirmatory factor analysis to develop a composite memory score in 5,287 participants (mean age 67.1; SD = 12.2) recruited through 18 ADCs and four consortia (DiverseVCID, MarkVCID, ALLFTD, LEADS) who completed UDS memory tasks (used as linking-items) and one of five list-learning tasks. All analyses used linear regression. We tested whether memory scores were affected by which list-learning task was administered. To assess construct validity, we tested associations of memory scores with demographics, disease severity (CDR Box Score), an independent memory task (TabCAT Favorites, n = 675), and hippocampal volume (n = 811). We compared performances between cognitively unimpaired (n = 279), AD-biomarker+ MCI (n = 26), and AD-biomarker+ dementia (n = 98). In a subsample with amyloid- and tau-PET (n = 49), we compared memory scores from participants with positive vs negative scans determined using established quantitative cutoffs. Model fit indices were excellent (e.g., CFI = 0.998) and factor loadings were strong (0.43-0.93). Differences in list-learning task had a negligible effect on scores (average Cohen’s d = 0.11). Higher memory scores were significantly ( p ’s<.001) correlated with younger age (β = -0.18), lower CDR Box Scores (β = -0.63), female sex (β = 0.12), higher education (β = 0.19), larger hippocampal volume (β = 0.42), and an independent memory task (β = 0.71, p<0.001). The memory composite declined in a stepwise fashion by diagnosis (cognitively unimpaired>MCI>AD dementia, p<0.001). On average, amyloid-PET positivity was associated with lower composite scores, but was not statistically significant (β = -0.34; p = 0.25; d = 0.40). Tau-PET positivity was associated with worse performance, demonstrating a large effect size (β = -0.75; p<0.002; d = 0.91). The harmonized memory score developed in a large national sample was stable regardless of contributing list-learning task and its validity for cross-cohort ADRD research is supported by expected associations with demographics, clinical measures, and Alzheimer’s biomarkers. A processing script will be made available to enhance cross-cohort ADRD research.

Autosomal Dominant Alzheimer's Disease (ADAD), caused by mutations in Presenilins (PSEN1/2) and Amyloid Precursor Protein (APP) genes, typically manifests with early onset (< 65 years). Age at symptom onset (AAO) is relatively consistent among carriers of the same PSEN1 mutation, but more variable for PSEN2 and APP variants, with these mutations associated with later AAOs than PSEN1. Understanding this clinical variability is crucial for understanding disease mechanisms, developing predictive models and tailored interventions in ADAD, with potential implications for sporadic AD. We performed biochemical assessment of γ-secretase dysfunction on 28 PSEN2 and 19 APP mutations, including disease-associated, unclear and benign variants. This analysis has been valuable in the assessment of PSEN1 variant pathogenicity, disease onset and progression. Our analysis reveals linear correlations between the molecular composition of Aβ profiles and AAO for both PSEN2 (R2 = 0.52) and APP (R2 = 0.69) mutations. The integration of PSEN1, PSEN2 and APP correlation data shows parallel but shifted lines, suggesting a common pathogenic mechanism with gene-specific shifts in onset. We found overall “delays” in AAOs of 27 years for PSEN2 and 8 years for APP variants, compared to PSEN1. Notably, extremely inactivating PSEN1 variants delayed onset, suggesting that reduced contribution to brain APP processing underlies the later onset of PSEN2 variants. This study supports a unified model of ADAD pathogenesis wherein γ-secretase dysfunction and the resulting shifts in Aβ profiles are central to disease onset across all causal genes. While similar shifts in Aβ occur across causal genes, their impact on AAO varies in the function of their contribution to APP processing in the brain. This biochemical analysis establishes quantitative relationships that enable predictive AAO modelling with implications for clinical practice and genetic research. Our findings also support the development of therapeutic strategies modulating γ-secretase across different genetic ADAD forms and potentially more broadly in AD.

INTRODUCTION:Older adults from minoritized and socioeconomically disadvantaged backgrounds commonly receive health care in safety net health settings and may be at high risk of dementia. We assessed the prevalence of diagnosed dementia in a large safety net health system. METHODS:International Classification of Disease 10th Revision codes were used to classify presence of dementia for 147,689 older adults with at least one ambulatory encounter in 2019 using electronic health record data. Prevalence was calculated for the sample overall and by age cohort, sex, and race/ethnicity. RESULTS:Diagnosed dementia prevalence was 0.3% for adults 50 to 64 and 3.0% for adults aged ≥ 65. Adults with diagnosed dementia were older, less likely to speak English, and had more medical comorbidities and higher health-care use than those without. DISCUSSION:This study's estimates of dementia prevalence were considerably lower than other samples, which may be due to incomplete coding or to underdiagnosis of dementia in a safety net setting. HIGHLIGHTS:Six percent to 10% of older adults do not have Medicare and often receive health care in safety net health systems; however, little information exists about dementia care in this practice setting. The Los Angeles County Department of Health Services is the nation's second largest municipal health system and provides ambulatory care to > 30,000 older adults annually, of whom > 85% are from minoritized populations and 60% of whom do not have Medicare. We used International Classification of Disease 10th Revision codes and demographic and clinical information derived from the electronic health record to estimate age-adjusted prevalence of dementia in this safety net health setting. Prevalence of diagnosed dementia was significantly less than expected based on national samples, likely reflecting significant underdiagnosis and/or undercoding of dementia in this practice setting.

Clinical trials of anti-amyloid-β (Aβ) monoclonal antibodies in Alzheimer disease (AD) infer target engagement from Aβ positron emission tomography (PET) and/or fluid biomarkers such as cerebrospinal fluid (CSF) Aβ42/40. However, these biomarkers measure brain Aβ deposits indirectly and/or incompletely. In contrast, neuropathologic assessments allow direct investigation of treatment effects on brain Aβ deposits—and on potentially myriad ‘downstream’ pathologic features. From a clinical trial of anti-Aβ monoclonal antibodies in dominantly inherited AD (DIAD), in the largest study of its kind, we measured immunohistochemistry area fractions (AFs) for Aβ deposits (10D5), tauopathy (PHF1), microgliosis (IBA1), and astrocytosis (GFAP) in 10 brain regions from 10 trial cases—gantenerumab (n = 4), solanezumab (n = 4), placebo/no treatment (n = 2)—and 10 DIAD observational study cases. Strikingly, in proportion to total drug received, Aβ deposit AFs were significantly lower in the gantenerumab arm versus controls in almost all areas examined, including frontal, temporal, parietal, and occipital cortices, anterior cingulate, hippocampus, caudate, putamen, thalamus, and cerebellar gray matter; only posterior cingulate and cerebellar white matter comparisons were non-significant. In contrast, AFs of tauopathy, microgliosis, and astrocytosis showed no differences across groups. Our results demonstrate with direct histologic evidence that gantenerumab treatment in DIAD can reduce parenchymal Aβ deposits throughout the brain in a dose-dependent manner, suggesting that more complete removal may be possible with earlier and more aggressive treatment regimens. Although AFs of tauopathy, microgliosis, and astrocytosis showed no clear response to partial Aβ removal in this limited autopsy cohort, future examination of these cases with more sensitive techniques (e.g., mass spectrometry) may reveal more subtle ‘downstream’ effects.

Background:Over 300 mutations in PSEN1 have been identified as causes of early-onset Alzheimer's disease (EOAD). While these include missense mutations and a few insertions, deletions, or duplications, none result in open reading frame shifts, and all alter γ-secretase function to increase the long/short Aβ ratio. Methods:We identified a novel heterozygous PSEN1 nonsense variant, c.325A > T, in a patient and his father, both presenting with EOAD, resulting in the substitution of lysine 109 with a premature stop codon at position (p.K109*). This produces a truncated 109 amino acid (aa) N-terminal PSEN1 fragment. Functional characterization was performed using overexpression models and a heterozygous mouse model (Psen1K109*/+). Results:In overexpression models, downstream ATGs serve as alternative starting codons, generating a > 37kDa and a > 27 kDa PSEN1 C-terminal fragment (PSEN1-CTFA and PSEN1-CTFB, respectively) that retain the two catalytic aspartates of γ-secretase. Heterozygous Psen1K109*/+ mice exhibited subtle phenotypic defects, including reduced Pen2 expression and mild APP-CTF accumulation. Notably, aged mice demonstrated significantly increased Psen2 protein expression, potentially contributing to an elevated Aβ42/Aβ38 ratio. Conclusions:These findings indicate that PSEN1 c.325A > T (p.K109*) is not a complete loss-of-function mutation. However, to what extent and by what mechanism it contributes to EOAD pathogenesis remains unclear.

The diffusion MRI signals in the human cerebral cortex are strongly associated with neurodegenerative diseases. Although models like NODDI have been extensively used to characterize cortical microstructure degeneration, they fall short in capturing detailed, orientation-specific connectivity changes within the cortex. In this study, we introduce a method to decompose cortical tissue diffusion signal to radial and tangential components. Our approach uses data from multi-shell diffusion imaging and combines it with anatomical information from brain surfaces. By applying a GPU accelerated probabilistic optimization framework, we can accurately and efficiently estimate these diffusion components while keeping the results smooth and consistent with the cortical anatomy. We test our method on data from HCP subjects and a clinical dataset of patients with autosomal dominant Alzheimer's Disease (ADAD) subjects. Our results demonstrate that the proposed method can more effectively reveal cortical gray matter connectivity changes related to tau pathology than metrics from the NODDI model. Our codebase is publicly available at https://github.com/Haibaobob/ FOD-ctx-decomp