Dickerson, Bradford Clark

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.

Importance Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used. Objective To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads. Data Sources PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025). Study Selection Studies were included if they provided Centiloids, radiotracer, age, and sex. Data Extraction and Synthesis Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis. Main Outcomes and Measures Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available. Results This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I 2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I 2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I 2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I 2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I 2 = 80%). Conclusions and Relevance In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.

Abstract The standard approach to quantify amyloid (Aβ) PET averages uptake within a single cortical mask that assumes no clinically relevant spatial heterogeneity in uptake patterns. Here, in a sample of 12,379 clinically impaired participants taken from four phenotypically diverse cohorts we use data-driven approaches to discover heterogeneous patterns of Aβ-PET binding, uncovering a reproducible and clinically relevant pattern of posterior predominant Aβ-PET binding. In particular, Aβ-PET positive participants who have posterior predominant binding are less likely to be APOE -ε4 carriers, more severely impaired, have thinner cortex in posterior regions, and greater posterior tau PET burden. Furthermore, in a subsample of participants with neuropathological assessment, participants with posterior predominant Aβ binding have a higher likelihood of having cerebral amyloid angiopathy at autopsy. These findings suggest that Aβ-PET accumulates along two orthogonal axes with biological and clinical relevance, indicating that the standard approach to assess Aβ-PET is insufficient to capture meaningful signal from Aβ-PET imaging. This work has implications for the diagnosis and treatment of Alzheimer’s disease and extends our understanding of the mechanisms governing variable Aβ-PET distribution and its downstream effects.

Abstract Patients with atypical variants of Alzheimer's disease (AD) often present at a younger age with predominantly non‐amnestic impairments and a more aggressive disease course. Historically, individuals with atypical presentations have not been included in large‐scale clinical trials, which typically focus on late‐onset, sporadic amnestic‐predominant AD. Consequently, treatment options and research efforts specific to atypical AD remain limited. The emergence of amyloid‐targeting therapies that slow disease progression underscores these challenges, as evidence supporting their efficacy in early‐onset amnestic and non‐amnestic AD variants is scarce. This perspective article argues that atypical AD represents an excellent disease model for clinical trials and proposes strategies to address critical gaps in clinical trial design for this population. Key considerations include optimizing participant selection approaches, establishing syndrome‐specific or surrogate biological and clinical endpoints, and fostering advocacy to enhance early and accurate diagnosis, equitable representation, and outcomes for these populations.

Importance TMEM106B is a frontotemporal lobar degeneration (FTLD) genetic susceptibility factor, and TMEM106B protein aggregates are a feature of aging and neurodegeneration. Whether TMEM106B protein levels are associated with clinical features is unknown. Objective To investigate the clinical associations of cerebrospinal fluid (CSF) TMEM106B in FTLD. Design, Setting, and Participants This cross-sectional study was conducted in 2 independent frontotemporal dementia (FTD) cohorts (recruitment from April 2009 through July 2023, with analyses from January 2025 through April 2026), with a 2-year follow up. This multicenter clinical study integrated clinical, genetic, biomarker, and neuroimaging data. Individuals were recruited through the University of California, San Francisco (n = 3733), or ALLFTD (n = 2343). Participants with available CSF were included. A discovery cohort (n = 271) included participants with sporadic neuropathology-confirmed FTLD; presymptomatic or symptomatic carriers of pathogenic variants in C9orf72 , GRN , or MAPT ; or controls. An independent validation cohort (n = 383) included participants with clinically diagnosed sporadic FTD, Alzheimer disease (AD), and controls. Exposures CSF samples for TMEM106B quantification with aptamer proteomics (SomaScan version 3.0 [discovery cohort] and SomaScan version 4.1 [validation cohort]). Main Outcomes and Measures Parametric tests compared the primary outcome, CSF TMEM106B, by disease severity, TMEM106B rs1990622 genotype, sex, clinical syndrome, pathological diagnosis, and pathogenic variant and determined associations with brain volume. Results In the discovery (n = 271; 136 women [51%]; median [IQR] age, 59 [38-80] years) and validation (n = 383; 183 women [48%]; median [IQR] age, 64 [50-78] years) cohorts, lower CSF TMEM106B was associated with more severe disease (β, −0.15; 95% CI, −0.24 to −0.04; P = .003), lower frontotemporal brain volumes (β, 0.42; 95% CI, 0.24-0.61; P < .001), and faster clinical progression (β, −2.21; 95% CI, −3.70 to −0.72; P = .001). Associations of TMEM106B with clinical disease severity were independent of those with neurofilament light chain. TMEM106B levels were influenced by TMEM106B rs1990622 genotype, where individuals with the protective G/G genotype had lower levels than the risk A/A genotype. CSF TMEM106B levels did not differentiate between FTLD subtypes or between FTLD and AD. Conclusions and Relevance Per the results of this cross-sectional study, TMEM106B is detectable in CSF and levels reflect disease severity in sporadic and genetic FTLD and AD, but levels are also influenced by the TMEM106B rs1990622 genotype. CSF TMEM106B could support further studies to understand the mechanisms of disease and develop clinical tools in FTLD and other neurodegenerative diseases.

Abstract INTRODUCTION Research into cognitive dispersion – a cognitive process score measuring the intra‐individual variability (IIV) across a single testing session – suggests utility in neurodegenerative populations. Given widespread deficits observed in sporadic early‐onset Alzheimer's disease (EOAD), however, it is unclear if examining cognitive dispersion shows benefit in this condition. METHODS A total of 309 participants (188 amyloid‐positive EOAD, 43 amyloid‐negative early‐onset dementia [EOnonAD], 78 cognitively normal [CN]) completed neuropsychological testing twice over 12 months. Dispersion‐related differences among groups were assessed, as was cognitive dispersion's capacity to predict domain‐specific cognitive trajectories, and its convergence with imaging biomarkers. RESULTS EOAD participants displayed higher cognitive dispersion than EOnonAD participants, and associations with EOAD‐specific biomarkers. Additionally, cognitive dispersion was associated with 12‐month reliable change across several cognitive domains. DISCUSSION These preliminary results examine cognitive dispersion in sporadic EOAD. When used with baseline cognitive performance, cognitive dispersion measures may enrich future clinical trials in EOAD by enhancing treatment monitoring over time.

Abstract The growing availability of large-scale biomarker datasets has allowed data-driven methods to characterize Alzheimer’s disease biological heterogeneity. However, most prior studies have focused on cohorts of late-onset amnestic cases, leaving early-onset Alzheimer’s disease underexplored. We aimed to characterize tau-PET-based subtypes through a robust data-driven approach in the Longitudinal Early-Onset Alzheimer’s Disease Study. Baseline [18F]Flortaucipir PET scans from 365 amyloid-PET-positive participants with sporadic early-onset Alzheimer’s disease were quantified in the left and right medial temporal, lateral temporal, occipital, parietal, and frontal cortices. Tau PET values were z-scored against 85 amyloid-PET-negative cognitively normal age-matched participants and fitted into Subtype and Stage Inference (SuStaIn)—an unsupervised clustering algorithm that simultaneously models subtypes and progression from cross-sectional data. The derived subtypes were subsequently characterized by baseline and longitudinal clinical, cognitive, MRI, tau and amyloid PET features. We identified three tau-PET-based subtypes: on average, Subtype 1/Typical (n = 144, 40%) showed a predominant bilateral temporoparietal pattern typical of Alzheimer’s disease. Subtype 2/Left temporal (n = 111, 31%) showed predominant left temporal binding. Subtype 3/Posterior (n = 104, 29%) showed early and permeating occipitoparietal involvement. Subtypes did not differ in demographics or global amyloid burden, but were relatively more enriched for specific clinical presentations: S1/Typical for amnestic presentations, S2/Left Temporal for primary progressive aphasia, and S3/Posterior for posterior cortical atrophy. Baseline tau PET subtypes aligned with cortical atrophy patterns and domain-specific cognitive impairment. When follow-up tau PET scans were fitted to SuStaIn trained on baseline data, 85.6% (n = 172/201) of participants retained the same subtype classification, indicating subtype temporal stability, and progressed within subtypes by 0.56 ± 0.70 SuStaIn stage/year. Longitudinal voxel-wise linear mixed-effects modelling revealed tau accumulation patterns for each subtype in regions relatively spared at baseline: occipital lobe accumulation predominated in S1/Typical, bilateral frontal and right temporal in S2/Left Temporal, and bilateral frontotemporal lobes in S3/Posterior. All subtypes showed longitudinal increases in Clinical Dementia Rating-Sum of Boxes, but with slower worsening in S3/Posterior compared with the other subtypes. Our findings reveal robust subtypes in sporadic early-onset Alzheimer’s disease characterized by distinct spatiotemporal tau patterns that parallel differences in clinical presentations and trajectories of neurodegeneration. These subtypes extend beyond traditional clinical syndromes and support a more nuanced framework for individualized prognosis and care. Incorporating tau PET subtyping into clinical trial design could enable more targeted therapeutic approaches for this younger population.

Abstract INTRODUCTION : Despite expanding use of plasma biomarkers for Alzheimer's disease (AD), minimal examination has been undertaken in early‐onset AD (EOAD). Prior analyses assessed criterion and convergent validity of common plasma biomarkers in a well‐characterized sample with sporadic EOAD. METHODS : Plasma amyloid beta (Aβ) 42/40, tau phosphorylated at threonine 231 (p‐tau231), glial fibrillary acidic protein (GFAP), and neurofilament light change (NfL) levels were obtained for 189 EOAD, 52 early‐onset non‐AD (EOnonAD), and 83 cognitively normal (CN) participants. Diagnostic group differences, convergence with imaging biomarkers, and the capacity to predict specific cognitive domains were investigated. RESULTS : After controlling for cognitive status, EOAD participants exhibited more pathologic levels of Aβ42/40, p‐tau231, and GFAP relative to EOnonAD participants. However, NfL displayed the greatest sensitivity to non‐amnestic cognitive impairment across the sample. DISCUSSION : These results establish criterion and convergent validity of plasma biomarkers in sporadic EOAD and highlight complementary roles that AD‐specific and non‐specific plasma markers may play across the course of AD care.

INTRODUCTION Recent work has identified unique cognitive profiles for early‐onset Alzheimer's disease (EOAD) relative to late‐onset Alzheimer's disease (LOAD), however, examination has been limited in determining whether the association between age and cognitive severity at presentation also differs across conditions. METHODS A series of linear spline regression models was conducted across baseline cognitive data from 325 EOAD and 314 LOAD participants, after accounting for education, sex, and apolipoprotein ε4 status. RESULTS Significant differences existed in the relationship between baseline age and cognitive performance between EOAD and LOAD samples for Processing Speed/Attention, Executive Functioning, and Episodic Immediate Memory. Younger participants from both EOAD and LOAD groups performed disproportionately worse on non‐amnestic cognitive domains, with this occurring to a greater extent in EOAD than LOAD. DISCUSSION In the age of disease‐modifying treatments, results highlight the importance of assessing for cognitive declines in individuals starting much earlier than age 65. Highlights Early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD) participants each displayed cognitive impairments relative to same‐aged peers across most domains. Both groups displayed positive relationships between impairment among non‐amnestic cognitive domains and baseline age. This relationship displayed a significantly greater effect in EOAD than LOAD, with domains of Processing Speed/Attention and Executive Functioning skills being the most pronounced. Of those participants developing AD, age displayed a disproportionate impact on their symptom onset.