List-learning tasks (LLTs) are important for characterizing memory in Alzheimer’s disease and related dementias. However, as the Uniform Data Set Neuropsychological Battery (UDS-NB) historically did not require a specific LLT, centers have administered different LLTs. The newest UDS version (v4.0) requires one of two LLTs. To support harmonized UDS memory research, we developed a memory composite incorporating UDS memory tests and multiple LLTs. Item-banking confirmatory factor analysis was applied to develop a memory composite in a diagnostically heterogeneous sample (n = 8716) that completed the UDS-NB and one of five LLTs. Construct validity was evaluated through associations with demographics, disease severity, cognitive tasks, brain volume, amyloid/tau PET positivity, and plasma phosphorylated tau. Analyses were replicated in a racially/ethnically diverse cohort (n = 839). Psychometric properties were adequate. Expected associations with demographics and clinical measures within development and validation cohorts supported validity. This composite supports memory research across cohorts that administer the UDS and LLTs. A user-friendly UI is freely available to create the score in other datasets.

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.

OBJECTIVE:Age of symptom onset is highly variable in familial frontotemporal lobar degeneration (f-FTLD). Accurate prediction of onset would inform clinical management and trial enrollment. Prior studies indicate that individualized maps of brain atrophy can predict conversion to dementia in f-FTLD. We used a Bayesian linear mixed-effect (BLME) prediction method for identifying accelerated brain volume loss to predict conversion to dementia. METHODS:Participants included 234 asymptomatic or prodromal carriers of C9orf72, GRN, or MAPT mutations (including 21 dementia converters) with ≥3 longitudinal magnetic resonance imaging (MRI) T1-weighted scans. The BLME models established individual voxel-wise gray matter trajectories using the first 2 scans. Person-specific clusters of accelerated volume loss were estimated in subsequent scans and tested as predictors of dementia conversion compared with other approaches in time-varying Cox proportional hazard models covarying for age. Receiver-operating characteristic (ROC) curves estimated utility of cluster volume in discriminating which participants converted to dementia within 24 months. RESULTS:The BLME cluster volume predicted conversion to dementia in f-FTLD mutation carriers overall and separately in C9orf72, GRN, and MAPT, with comparable hazard ratios observed for atrophy W-maps and regional volumes. Within a 24-month timeframe, BLME cluster volume discriminated dementia converters from non-converters with larger areas under the curve (AUCs) than other approaches. INTERPRETATION:Bayesian-modeled individualized atrophy scores predict dementia progression among asymptomatic f-FTLD mutation carriers and may have increased utility compared with other structural imaging methods when studying individuals over shorter timeframes that align with clinical trial design. ANN NEUROL 20269999:n/a-n/a.

INTRODUCTION:List-learning tasks are important for characterizing memory in ADRD research, but the Uniform Data Set neuropsychological battery (UDS-NB) lacks a list-learning paradigm; thus, sites administer a range of tests. We developed a harmonized memory composite that incorporates UDS memory tests and multiple list-learning tasks. METHODS:Item-banking confirmatory factor analysis was applied to develop a memory composite in a diagnostically heterogenous sample (n=5943) who completed the UDS-NB and one of five list-learning tasks. Construct validity was evaluated through associations with demographics, disease severity, cognitive tasks, brain volume, and plasma phosphorylated tau (p-tau181 and p-tau217). Test-retest reliability was assessed. Analyses were replicated in a racially/ethnically diverse cohort (n=1058). RESULTS:Fit indices, loadings, distributions, and test-retest reliability were adequate. Expected associations with demographics and clinical measures within development and validation cohorts supported validity. DISCUSSION:This composite enables researchers to incorporate multiple list-learning tasks with other UDS measures to create a single metric.

Machine learning algorithms are a promising automated candidate that can help mitigate the growing need for dementia experts. Despite the substantial development in MRI-based machine learning analyses, case misclassification is a universal finding, yet the reasons behind misclassification are poorly understood. We implemented a multi-class classification approach that uses relevance vector machine and logistic classification to classify research participants based on their whole-brain T1-weighted MRI scans. A total of 468 participants from seven diagnostic classes were included: 144 healthy controls, 84 Alzheimer's disease, 108 behavioral variant frontotemporal dementia (bvFTD), 30 semantic variant primary progressive aphasia (svPPA), 30 non-fluent variant primary progressive aphasia (nfvPPA), 30 corticobasal syndrome (CBS), and 42 progressive supranuclear palsy syndrome (PSPS). We compared the algorithm's diagnostic accuracy against the clinical, pathological, genetic, and quantitative imaging data. The exact neurodegenerative syndrome was predicted in 71% of the cases, the neurodegenerative disease spectrum was predicted in 80% of the cases, and the algorithm distinguished controls from any dementia in 85% of the cases. The algorithm showed high performance in diagnosing healthy controls, moderate performance in diagnosing AD, bvFTD, and svPPA, and low performance in diagnosing CBS, nfvPPA, and PSPS. Based on the quantitative imaging data, most of the misclassified neurodegenerative cases had minimal atrophy and brain volumes comparable to healthy controls. In AD, early-onset AD cases with minimal brain atrophy represented most of the misclassified cases. In bvFTD, FTD genetic mutation carriers (predominantly C9orf72 repeat expansion), FTD phenocopy, patients meeting only possible bvFTD criteria represented most misclassified cases. Case misclassification in machine learning studies in neurodegenerative diseases results from neurodegenerative disease heterogeneity and the limitations of structural MRI's ability to capture the whole gamut of biological changes. Larger and more inclusive datasets that are representative of population biologic heterogeneity are needed to train better machine learning techniques, and a margin of error is expected and should be acceptable, like the uncertainty of a clinical diagnosis by a dementia expert.

BACKGROUND AND OBJECTIVES:TMEM106B has been proposed as a modifier of disease risk in FTLD-TDP, particularly in GRN pathogenic variant carriers. Furthermore, TMEM106B has been investigated as a disease modifier in the context of healthy aging and across multiple neurodegenerative diseases. The objective of this study was to evaluate and compare the effect of TMEM106B on gray matter volume and cognition in each of the common genetic FTD groups and in patients with sporadic FTD. METHODS:Participants were enrolled through the ARTFL/LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) study, which includes symptomatic and presymptomatic individuals with a pathogenic variant in C9orf72, GRN, MAPT, VCP, TBK1, TARDBP, symptomatic nonpathogenic variant carriers, and noncarrier family controls. All participants were genotyped for the TMEM106B rs1990622 SNP. Cross-sectionally, linear mixed-effects models were fitted to assess an association between TMEM106B and genetic group interaction with each outcome measure (gray matter volume and UDS3-EF for cognition), adjusting for education, age, sex, and CDR+NACC-FTLD sum of boxes. Subsequently, associations between TMEM106B and each outcome measure were investigated within the genetic group. For longitudinal modeling, linear mixed-effects models with time by TMEM106B predictor interactions were fitted. RESULTS:The minor allele of TMEM106B rs1990622, linked to a decreased risk of FTD, associated with greater gray matter volume in GRN pathogenic variant carriers under the recessive dosage model (N = 82, beta = 3.25, 95% CI [0.37-6.19], p = 0.034). This was most pronounced in the thalamus in the left hemisphere (beta = 0.03, 95% CI [0.01-0.06], p = 0.006), with a retained association when considering presymptomatic GRN pathogenic variant carriers only (N = 42, beta = 0.03, 95% CI [0.01-0.05], p = 0.003). The minor allele of TMEM106B rs1990622 also associated with greater cognitive scores among all C9orf72 pathogenic variant carriers (N = 229, beta = 0.36, 95% CI [0.05-0.066], p = 0.021) and in presymptomatic C9orf72 pathogenic variant carriers (N = 106, beta = 0.33, 95% CI [0.03-0.63], p = 0.036), under the recessive dosage model. DISCUSSION:We identified associations of TMEM106B with gray matter volume and cognition in the presence of GRN and C9orf72 pathogenic variants. The association of TMEM106B with outcomes of interest in presymptomatic GRN and C9orf72 pathogenic variant carriers could additionally reflect TMEM106B's effect on divergent pathophysiologic changes before the appearance of clinical symptoms.

Importance Frontotemporal lobar degeneration (FTLD) is relatively rare, behavioral and motor symptoms increase travel burden, and standard neuropsychological tests are not sensitive to early-stage disease. Remote smartphone-based cognitive assessments could mitigate these barriers to trial recruitment and success, but no such tools are validated for FTLD. Objective To evaluate the reliability and validity of smartphone-based cognitive measures for remote FTLD evaluations. Design, Setting, and Participants In this cohort study conducted from January 10, 2019, to July 31, 2023, controls and participants with FTLD performed smartphone application (app)-based executive functioning tasks and an associative memory task 3 times over 2 weeks. Observational research participants were enrolled through 18 centers of a North American FTLD research consortium (ALLFTD) and were asked to complete the tests remotely using their own smartphones. Of 1163 eligible individuals (enrolled in parent studies), 360 were enrolled in the present study; 364 refused and 439 were excluded. Participants were divided into discovery (n = 258) and validation (n = 102) cohorts. Among 329 participants with data available on disease stage, 195 were asymptomatic or had preclinical FTLD (59.3%), 66 had prodromal FTLD (20.1%), and 68 had symptomatic FTLD (20.7%) with a range of clinical syndromes. ExposureParticipants completed standard in-clinic measures and remotely administered ALLFTD mobile app (app) smartphone tests. Main Outcomes and Measures Internal consistency, test-retest reliability, association of smartphone tests with criterion standard clinical measures, and diagnostic accuracy. Results In the 360 participants (mean [SD] age, 54.0 [15.4] years; 209 [58.1%] women), smartphone tests showed moderate-to-excellent reliability (intraclass correlation coefficients, 0.77-0.95). Validity was supported by association of smartphones tests with disease severity (r range, 0.38-0.59), criterion-standard neuropsychological tests (r range, 0.40-0.66), and brain volume (standardized beta range, 0.34-0.50). Smartphone tests accurately differentiated individuals with dementia from controls (area under the curve [AUC], 0.93 [95% CI, 0.90-0.96]) and were more sensitive to early symptoms (AUC, 0.82 [95% CI, 0.76-0.88]) than the Montreal Cognitive Assessment (AUC, 0.68 [95% CI, 0.59-0.78]) (z of comparison, -2.49 [95% CI, -0.19 to -0.02]; P = .01). Reliability and validity findings were highly similar in the discovery and validation cohorts. Preclinical participants who carried pathogenic variants performed significantly worse than noncarrier family controls on 3 app tasks (eg, 2-back beta = -0.49 [95% CI, -0.72 to -0.25]; P < .001) but not a composite of traditional neuropsychological measures (beta = -0.14 [95% CI, -0.42 to 0.14]; P = .32). Conclusions and Relevance The findings of this cohort study suggest that smartphones could offer a feasible, reliable, valid, and scalable solution for remote evaluations of FTLD and may improve early detection. Smartphone assessments should be considered as a complementary approach to traditional in-person trial designs. Future research should validate these results in diverse populations and evaluate the utility of these tests for longitudinal monitoring.

Pathogenic mutations in the MAPT (tau) gene cause familial frontotemporal lobar degeneration (f-FTLD). No disease-modifying treatments exist. Tau therapeutics being tested in other tauopathies may be suitable candidates, but the small number of identified mutation carriers prohibits traditional clinical trial designs from achieving adequate power. Platform trial designs permit multiple therapies to be tested in parallel and improve power by sharing placebo groups across treatment arms. We evaluate whether a platform design could improve the likelihood of identifying an effective treatment for MAPT mutation carriers. ALLFTD and GENFI observational data were used to simulate MAPT prevention and early symptomatic clinical trials (CDR®+NACC-FTLD≤1). Only presymptomatic participants (CDR®+NACC-FTLD = 0) within 2.5 years of expected symptom onset based on disease progression models (DPM) were included. First, we evaluated the time and sample size required to test five treatments using a platform design compared to sequential randomized control trials. Platform designs assumed a max of three treatment arms at any time, with new arms enrolling upon completion of prior arms. Each arm randomized 60 mutations carriers 3:1 treatment to placebo, with a placebo group shared across arms. Minimum follow up was 1.5 years with common close. Sequential trials assumed 90 participants allocated 1:1 treatment to placebo, with three-month intervals between trials. Using a platform design, we compared a novel method for testing therapeutic effects using DPMs to account for differential rates of expected progression compared to traditional mixed models for repeated measures (MMRM) tests controlling for baseline CDR®+NACC-FTLD. Five treatments could be tested in a platform design using 300 participants (75% treated) over five years, compared to 450 participants (50% treated) over 13 years using traditional, sequential trials. Platform trial simulations indicated that with 80% power, DPM-based analytic approach could detect a 25% treatment effect, whereas traditional MMRM could only detect a 40% effect. Platform designs combined with DPM-based analyses provide a path to testing more treatments with fewer participants. Adequately powered MAPT platform trials are feasible but require a global clinical trial infrastructure.

White matter hyperintensities (WMHs) are imaging abnormalities of white matter noted by hyperintense signals on fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI). When present among patients with CVRF (hypertension, diabetes, hypercholesteremia and smoking), WMHs are interpreted as cerebral vascular changes. However, WMHs in older individuals can be a feature of late onset leukodystrophies or varied neurodegenerative diseases such as Alzheimer disease (AD), Parkinson’s disease, and Frontotemporal Lobar Degeneration (FTLD). Additionally, WMHs can be present before symptom onset in neurodegenerative disorders and have been linked to the presence of some mutations, including progranulin (GRN). A clearer understanding of the underling etiology of WMHs in bvFTD and svPPA may improve management and treatment considerations. In this cross-sectional study, we included all participants meeting research diagnostic criteria for bvFTD (n = 152) and svPPA (n = 49) from ongoing studies at the UCSF Memory and Aging Center between the dates of September 2008 and December 2021 in comparison to a group of healthy controls enrolled at the same center with similar imaging parameters (n = 152, 1:1 matching for age and sex). All participants underwent a 3T FLAIR MRI and automated quantification of WMHs was performed. Linear regression analyses were performed to determine if WMHs were more frequent in participants with bvFTD or svPPA in comparison to controls and to explore associations with CVRF. After adjusting for age, sex, apolipoprotein E4 (APO-E 4) status, and intracranial volume (ICV), both groups demonstrated a higher burden compared to controls (bvFTD (p = 0.012, R 2 = 0.146) and svPPA (p = 0.008, R 2 = 0.329). Modeling cardiovascular risk factors (CVRFs) as none or at least one, we did not find an association between CVRFs and WMH volume in models adjusting for ICV, ApoE4, and age among those with svPPA and bvFTD combined (p = 0.450). Individuals with bvFTD and svPPA appear to face a greater burden of WMHs compared to age- and sex-matched healthy controls. WMHs in bvFTD and svPPA do not appear to be related CVRF.

STRUCTURED ABSTRACT INTRODUCTION Enlarged perivascular spaces (EPVS) are considered a conduit for the brain’s waste clearance system. With aging, the brain’s ability to clear molecules is thought to decline, contributing to the retention of Alzheimer’s disease (AD) neuropathology. However, the role of EPVS in late-onset AD (LOAD) is complicated by co-morbidities. Early-onset AD (EOAD) offers a unique opportunity to understand the role of EPVS in AD. METHODS Automatically-segmented EPVS volumes in biomarker-confirmed EOAD ( n =58), LOAD ( n =43), and age-matched controls ( n =60) were correlated with amyloid and tau PET and cognition. Linear regression models were used. RESULTS In LOAD, higher EPVS volumes were associated with better memory and functional performance. However, this association was not observed in EOAD. Additionally, higher tau was linked to increased EPVS in LOAD, but not in EOAD. DISCUSSION EOAD and LOAD demonstrate distinct associations between EPVS, AD hallmarks, and cognition, suggesting differences in EPVS’s role in these AD subtypes, necessitating further investigation.

INTRODUCTION:Accumulating evidence indicates disproportionate tau burden and tau-related clinical progression in females. However, sex differences in plasma phosphorylated tau (p-tau)217 prediction of subclinical cognitive and brain changes are unknown. METHODS:We measured baseline plasma p-tau217, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) in 163 participants (85 cognitively unimpaired [CU], 78 mild cognitive impairment [MCI]). In CU, linear mixed effects models examined sex differences in plasma biomarker prediction of longitudinal domain-specific cognitive decline and brain atrophy. Cognitive models were repeated in MCI. RESULTS:In CU females, baseline plasma p-tau217 predicted verbal memory and medial temporal lobe trajectories such that trajectories significantly declined once p-tau217 concentrations surpassed 0.053 pg/ml, a threshold that corresponded to early levels of cortical amyloid aggregation in secondary amyloid positron emission tomography analyses. CU males exhibited similar rates of cognitive decline and brain atrophy, but these trajectories were not dependent on plasma p-tau217. Plasma GFAP and NfL exhibited similar female-specific prediction of medial temporal lobe atrophy in CU. Plasma p-tau217 exhibited comparable prediction of cognitive decline across sex in MCI. DISCUSSION:Plasma p-tau217 may capture earlier Alzheimer's disease (AD)-related cognitive and brain atrophy hallmarks in females compared to males, possibly reflective of increased susceptibility to AD pathophysiology.

Early detection of neurodegeneration, and prediction of when neurodegenerative diseases will lead to symptoms, are critical for developing and initiating disease modifying treatments for these disorders. While each neurodegenerative disease has a typical pattern of early changes in the brain, these disorders are heterogeneous, and early manifestations can vary greatly across people. Methods for detecting emerging neurodegeneration in any part of the brain are therefore needed. Prior publications have described the use of Bayesian linear mixed-effects (BLME) modeling for characterizing the trajectory of change across the brain in healthy controls and patients with neurodegenerative disease. Here, we use an extension of such a model to detect emerging neurodegeneration in cognitively healthy individuals at risk for dementia. We use BLME to quantify individualized rates of volume loss across the cerebral cortex from the first two MRIs in each person and then extend the BLME model to predict future values for each voxel. We then compare observed values at subsequent time points with the values that were expected from the initial rates of change and identify voxels that are lower than the expected values, indicating accelerated volume loss and neurodegeneration. We apply the model to longitudinal imaging data from cognitively normal participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), some of whom subsequently developed dementia, and two cognitively normal cases who developed pathology-proven frontotemporal lobar degeneration (FTLD). These analyses identified regions of accelerated volume loss prior to or accompanying the earliest symptoms, and expanding across the brain over time, in all cases. The changes were detected in regions that are typical for the likely diseases affecting each patient, including medial temporal regions in patients at risk for Alzheimer's disease, and insular, frontal, and/or anterior/inferior temporal regions in patients with likely or proven FTLD. In the cases where detailed histories were available, the first regions identified were consistent with early symptoms. Furthermore, survival analysis in the ADNI cases demonstrated that the rate of spread of accelerated volume loss across the brain was a statistically significant predictor of time to conversion to dementia. This method for detection of neurodegeneration is a potentially promising approach for identifying early changes due to a variety of diseases, without prior assumptions about what regions are most likely to be affected first in an individual.

Unlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72 , GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects.

We examined the associations of white matter hyperintensities (WMH), amyloid‐PET, and tau‐PET with multi‐domain cognitive performance in symptomatic patients on the Alzheimer’s disease (AD) continuum.

Performance on neuropsychological measures of verbal memory requires cognitive abilities beyond memory. We examined the contribution of semantic knowledge in verbal episodic memory for semantic variant primary progressive aphasia (svPPA) or Alzheimer's disease (AD). 415 AD and 68 svPPA participants completed measures of episodic memory (visual and verbal) and semantic knowledge. A double dissociation existed visual recall predicted verbal recognition in AD, whereas semantic knowledge contributed to verbal recognition in svPPA.

Abstract Introduction Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD‐MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD‐MC and could help predict risk for dementia during trial enrollment. Methods We created a dementia risk score by entering standardized gray‐matter volumes from 231 DIAD‐MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD‐MC followed longitudinally. Results Our risk score separated asymptomatic versus demented DIAD‐MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%–98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD‐MC participants for prevention trials.

INTRODUCTION:Biological sex is an increasingly recognized factor driving clinical and structural heterogeneity in Alzheimer's disease, but its role in the behavioral variant of frontotemporal dementia (bvFTD) is unknown. METHODS:We included 216 patients with bvFTD and 235 controls with magnetic resonance imaging (MRI) from a large multicenter cohort. We compared the clinical characteristics and cortical thickness between men and women with bvFTD and controls. We followed the residuals approach to study behavioral and cognitive reserve. RESULTS:At diagnosis, women with bvFTD showed greater atrophy burden in the frontotemporal regions compared to men despite similar clinical characteristics. For a similar amount of atrophy, women demonstrated better-than-expected scores on executive function and fewer changes in apathy, sleep, and appetite than men. DISCUSSION:Our findings suggest that women might have greater behavioral and executive reserve than men, and neurodegeneration must be more severe in women to produce symptoms similar in severity to those in men.

Importance:The presence of atrophy on magnetic resonance imaging can support the diagnosis of the behavioral variant of frontotemporal dementia (bvFTD), but reproducible measurements are lacking. Objective:To assess the diagnostic and prognostic utility of 6 visual atrophy scales (VAS) and the Magnetic Resonance Parkinsonism Index (MRPI). Design, Setting, and Participants:In this diagnostic/prognostic study, data from 235 patients with bvFTD and 225 age- and magnetic resonance imaging-matched control individuals from 3 centers were collected from December 1, 1998, to September 30, 2019. One hundred twenty-one participants with bvFTD had high confidence of frontotemporal lobar degeneration (FTLD) (bvFTD-HC), and 19 had low confidence of FTLD (bvFTD-LC). Blinded clinicians applied 6 previously validated VAS, and the MRPI was calculated with a fully automated approach. Cortical thickness and subcortical volumes were also measured for comparison. Data were analyzed from February 1 to June 30, 2020. Main Outcomes and Measures:The main outcomes of this study were bvFTD-HC or a neuropathological diagnosis of 4-repeat (4R) tauopathy and the clinical deterioration rate (assessed by longitudinal measurements of Clinical Dementia Rating Sum of Boxes). Measures of cerebral atrophy included VAS scores, the bvFTD atrophy score (sum of VAS scores in orbitofrontal, anterior cingulate, anterior temporal, medial temporal lobe, and frontal insula regions), the MRPI, and other computerized quantifications of cortical and subcortical volumes. The areas under the receiver operating characteristic curve (AUROC) were calculated for the differentiation of participants with bvFTD-HC and bvFTD-LC and controls. Linear mixed models were used to evaluate the ability of atrophy measures to estimate longitudinal clinical deterioration. Results:Of the 460 included participants, 296 (64.3%) were men, and the mean (SD) age was 62.6 (11.4) years. The accuracy of the bvFTD atrophy score for the differentiation of bvFTD-HC from controls (AUROC, 0.930; 95% CI, 0.903-0.957) and bvFTD-HC from bvFTD-LC (AUROC, 0.880; 95% CI, 0.787-0.972) was comparable to computerized measures (AUROC, 0.973 [95% CI, 0.954-0.993] and 0.898 [95% CI, 0.834-0.962], respectively). The MRPI was increased in patients with bvFTD and underlying 4R tauopathies compared with other FTLD subtypes (14.1 [2.0] vs 11.2 [2.6] points; P < .001). Higher bvFTD atrophy scores were associated with faster clinical deterioration in bvFTD (1.86-point change in Clinical Dementia Rating Sum of Boxes score per bvFTD atrophy score increase per year; 95% CI, 0.99-2.73; P < .001). Conclusions and Relevance:Based on these study findings, in bvFTD, VAS increased the diagnostic certainty of underlying FTLD, and the MRPI showed potential for the detection of participants with underlying 4R tauopathies. These widely available measures of atrophy can also be useful to estimate longitudinal clinical deterioration.

Objective We tested the hypothesis that plasma neurofilament light chain (NfL) identifies asymptomatic carriers of familial frontotemporal lobar degeneration (FTLD)-causing mutations at risk of disease progression. Methods Baseline plasma NfL concentrations were measured with single-molecule array in original (n = 277) and validation (n = 297) cohorts. C9orf72, GRN, and MAPT mutation carriers and noncarriers from the same families were classified by disease severity (asymptomatic, prodromal, and full phenotype) using the CDR Dementia Staging Instrument plus behavior and language domains from the National Alzheimer's Disease Coordinating Center FTLD module (CDR+NACC-FTLD). Linear mixed-effect models related NfL to clinical variables. Results In both cohorts, baseline NfL was higher in asymptomatic mutation carriers who showed phenoconversion or disease progression compared to nonprogressors (original: 11.4 +/- 7 pg/mL vs 6.7 +/- 5 pg/mL, p = 0.002; validation: 14.1 +/- 12 pg/mL vs 8.7 +/- 6 pg/mL, p = 0.035). Plasma NfL discriminated symptomatic from asymptomatic mutation carriers or those with prodromal disease (original cutoff: 13.6 pg/mL, 87.5% sensitivity, 82.7% specificity; validation cutoff: 19.8 pg/mL, 87.4% sensitivity, 84.3% specificity). Higher baseline NfL correlated with worse longitudinal CDR+NACC-FTLD sum of boxes scores, neuropsychological function, and atrophy, regardless of genotype or disease severity, including asymptomatic mutation carriers. Conclusions Plasma NfL identifies asymptomatic carriers of FTLD-causing mutations at short-term risk of disease progression and is a potential tool to select participants for prevention clinical trials. Classification of Evidence This study provides Class I evidence that in carriers of FTLD-causing mutations, elevation of plasma NfL predicts short-term risk of clinical progression.

AbstractIntroductionApolipoprotein E (APOE) ε4, the strongest non‐Mendelian genetic risk factor for Alzheimer's disease (AD), has been shown to affect brain capillaries in mice, with potential implications for AD‐related neurodegenerative disease. However, human brain capillaries cannot be directly visualized in vivo. We therefore used retinal imaging to test APOE ε4 effects on human central nervous system capillaries.MethodsWe collected retinal optical coherence tomography angiography, cognitive testing, and brain imaging in research participants and built statistical models to test genotype–phenotype associations.ResultsOur analyses demonstrate lower retinal capillary densities in early disease, in cognitively normal APOE ε4 gene carriers. Furthermore, through regression modeling with a measure of brain perfusion (arterial spin labeling), we provide support for the relevance of these findings to cerebral vasculature.DiscussionThese results suggest that APOE ε4 affects capillary health in humans and that retinal capillary measures could serve as surrogates for brain capillaries, providing an opportunity to study microangiopathic contributions to neurodegenerative disorders directly in humans.