Background: The logopenic variant of primary progressive aphasia (lvPPA) is a language-led neurodegenerative syndrome commonly associated with Alzheimer's disease pathology and temporo-parietal degeneration. Although acalculia has been reported in lvPPA, numerical cognition has not been systematically investigated, and the specific profile of impairment remains poorly defined. Objective: To characterize numerical cognition impairment in lvPPA using a comprehensive, theory-driven assessment battery and to examine its clinical relevance for diagnosis and cognitive characterization. Methods: Fourteen individuals with lvPPA and twenty-eight demographically matched healthy controls completed the dCALQ, a standardized battery assessing number recognition and comprehension, number production (transcoding), and calculation. Participants also underwent global cognitive screening (Montreal Cognitive Assessment), language assessment (Detection Test for Language Impairments in Adults and the Aged), and measures of working memory and executive functioning. Group comparisons, intra-group domain analyses, correlation analyses, and receiver operating characteristic (ROC) analyses were performed. Results: Individuals with lvPPA showed significant impairments across all numerical domains compared with controls, with the most severe deficits in calculation, followed by transcoding, and milder impairment in number recognition and comprehension. Within the lvPPA group, performance differed significantly across domains, revealing a graded pattern of impairment. ROC analyses demonstrated excellent diagnostic accuracy for the dCALQ total score and strong discrimination for the calculation and transcoding domains. Conclusions: Numerical impairment is a robust and systematic feature of lvPPA rather than an incidental finding. The distinct numerical profile identified highlights the contribution of parietal-based cognitive dysfunction and supports the clinical utility of structured numerical assessment for cognitive characterization and diagnosis in dementia syndromes.

Over the past few years, several fluid biomarker candidates have been proposed for frontotemporal dementia (FTD). We have previously identified CSF proteins that could separate individuals with genetic FTD from controls. However, it is unknown whether alterations in these CSF protein levels are associated with neurodegenerative processes. The aim of this study was to explore how these CSF biomarker candidates correlate with symptom severity as well as cortical and subcortical atrophy. The levels of fourteen proteins were measured in CSF from 202 individuals, 131 mutation carriers with mutations in C9orf72, GRN, or MAPT, and 71 controls, in a cross-sectional subset from the GENFI cohort. The association between the levels of these proteins and CDR plus NACC FTLD-NM sum-of-boxes, cortical thickness, and subcortical volumes were estimated in the mutation carriers. Elevated CSF levels of five out of fourteen proteins were associated with an increased CDR score in the mutation carriers. Additionally, elevated levels of three of these proteins, NEFM, PTPRN2 and SERPINA3, were associated with reduced cortical thickness and/or subcortical volume among all mutation carriers. Some mutation-specific associations were also observed, with SPP1 and CTSS being associated with CDR and atrophy only in MAPT mutation carriers, while NPTX2 was specific for GRN mutation carriers. As indicated by the association to brain atrophy, the proposed fluid biomarker candidates continue to show promise and additional studies will further elucidate their relationship to cortical atrophy in genetic FTD, and their potential as biomarkers for diagnosis, prognosis, and disease staging.

Temporal measures, such as time from diagnosis or symptom onset are often used to track disease severity in neurodegenerative diseases. Due to variations in symptom awareness, clinical presentation timing, diagnostic delays, and disease progression rates, these temporal proxies introduce substantial variance and bias, making it very difficult to map progression clearly and accurately, and to severity-match across contrastive patient groups. To address this challenge, we explored a data-driven approach to derive a transdiagnostic severity metric that is independent of time and, instead, treats temporal metrics as observed, dependent data. We analysed data from the Genetic Frontotemporal Dementia Initiative (GENFI 1 and 2). We entered neuropsychological scores for symptomatic individuals including any visits prior to conversion from at-risk to symptomatic (n = 265, 522 visits) in an unrotated principal component analysis to derive a transdiagnostic phenotype-severity model. A single component emerged (Kaiser-Meyer-Olkin = 0.92), explaining 65% of the variance, with all neuropsychological assessments loading highly. This global severity component fitted the data equally well across genetically or clinically defined groups, as well as severity levels. The severity measure’s validity was supported by a clear relationship with the Clinical Dementia Rating scale, and its stability was confirmed when a much broader range of neuropsychological and behavioural measures were included. Additionally, the severity score accounted for a high portion of the total variance in neuropsychological test scores, substantially more than the low proportion accounted for by standard temporal measures. To derive a time-efficient sub-battery, we demonstrated that three neuropsychological assessments (Digit Symbol, Verbal fluency (letters) and Trail Making Test- Part B were able to explain the majority of unique variance in cognitive severity. Finally, by treating time as an observed dependent variable, we showed that the baseline velocity (change in severity measure over time) varied by genetic group, with progranulin mutation carriers being the fastest. This data-driven approach provides an objective, precise measure of disease severity and progression, and it may shed new light on when clinical heterogeneity reflects distinct subtypes rather than differences in disease stage.

Background Brain structural changes in frontotemporal dementia (FTD) can occur decades before symptom onset. Precise characterisation of grey matter changes is necessary for developing models of biomarker progression, while better understanding the trajectory of the pathology is invaluable for prognosis and detecting treatment effects as we enter the era of clinical trials.Methods Cortical and subcortical grey matter volume and thickness from structural MRI were assessed in a large cohort of 892 participants including presymptomatic and symptomatic carriers of mutations within the three main genetic causes of FTD (C9 open reading-frame 72 (C9orf72), progranulin (GRN) and microtubule-associated protein tau (MAPT)) compared with mutation-negative relatives (controls). We compared the distribution of grey matter changes of each metric at different stages of the disease cross sectionally. We aimed to identify grey matter composites for each genetic group which would show the earliest changes and which separated presymptomatic carriers from controls.Results While C9orf72 mutation carriers showed widespread presymptomatic grey matter changes, MAPT and particularly GRN mutation carriers showed changes more proximally to symptom onset. Our composite grey matter signatures, which discriminate asymptomatic/prodromal carriers from controls with high to very high areas under the curve, involved bilateral thalami volumes, precuneus and postcentral thickness in C9orf72; left caudal middle frontal thickness, frontal pole and pars orbitalis volumes in GRN; right temporal pole volume and left insula thickness in MAPT mutation carriers.Conclusion We propose the use of cortical thickness and volume measurements combined from multiple regions into a composite region of interest for each FTD genetic group to identify the earliest changes and track disease progression. Our quasi-longitudinal design illustrates that these regions continue to evolve throughout the symptomatic stages. Investigating how our selected composites progress and validating these in longitudinal samples will be invaluable for future clinical trials.

Frontotemporal dementia (FTD) shows autosomal dominant transmission in up to a third of families, enabling the study of presymptomatic and prodromal phases. Despite self-reported well-being and normal daily cognitive functioning, brain structural changes are evident a decade or more before the expected onset of disease. This divergence between cognitive function and brain structure contrasts with the coupling of structural and functional decline after symptom onset. In healthy ageing, it has been shown that functional connectivity is a better predictor of cognitive function than volumetric structural imaging. We previously proposed that in the presymptomatic phase of genetic FTD, the maintenance of brain functional network integrity enables carriers of pathogenic variants to sustain cognitive performance. However, prior work has focused on a small number of, often predefined, networks. This provides a limited and potentially biased characterisation of the substrates and moderators of brain network integration. Here, we test the hypothesis that brain-wide functional integration in FTD determines resilience to progressive pathology before symptom onset. We assess functional connectome integration in 289 presymptomatic carriers of pathogenic variants associated with FTD using functional magnetic resonance imaging in relation to cognition and contrast with 271 family members without pathogenic variants. Because structural atrophy, functional integration and cognitive profiles are multivariate, we used canonical correlation models, supplemented by multiple linear regression models for each imaging modality. We confirmed progressive atrophy and normal cognitive function in presymptomatic carriers compared to non-carriers. Notably, functional integration was preserved in presymptomatic carriers across age, while it declined in familial non-carriers. The strongest effects were observed in cognitive control networks. The changes in functional integration in presymptomatic carriers were behaviourally relevant and independent of the severity of atrophy, suggesting a resilience mechanism in those at risk of dementia. To generate hypotheses about the genetic and neurometabolic basis of resilience, we assessed the spatial overlap between behaviourally-relevant functional integration maps and gene transcription profiles. These spatial correlations suggested resilience signatures to glial cell composition (astrocytes, microglia, oligodendrocytes), revealing cellular mechanisms inaccessible to standard neuroimaging. Our findings suggest that resilience to atrophy is associated with enhanced functional integration, protecting against clinical conversion for many years in individuals at risk of dementia. This result has implications for the design of presymptomatic disease-modifying therapy trials and gives hope for therapeutic strategies aimed at enhancing resilience and ability to maintain function despite the presence of genetically determined neuropathology.

Abstract BACKGROUND Genetic frontotemporal dementia (FTD) shows large differences in symptom profiles, brain atrophy patterns, and progression rate, making clinical trials difficult to design and power. There is a need for biomarkers that can model disease progression, identify biologically distinct groups, and support efficient trial enrichment. METHODS We applied contrastive trajectory inference (cTI), a machine-learning method, to structural MRI, white matter hyperintensity, and demographic data from 736 participants in the GENFI cohort, including non-carriers and carriers of C9orf72 , GRN , or MAPT mutations. cTI produced an individual “genetic FTD progression score” (0–1) and grouped mutation carriers into data-driven subtypes. We tested construct validity using correlations between progression score and cognitive/functional measures, examined subtype differences in brain–behavior coupling, plasma neurofilament light (NfL), and longitudinal decline, and compared cTI-based trial enrichment against age, cortical thickness and NfL using analytic and simulation-based power analyses. RESULTS Genetic FTD progression scores correlated strongly with global dementia severity and multiple cognitive domains (all p < 0.001), confirming robust clinical scoring. Two mutation-carrier subtypes emerged: a Progressive Track (Subtype 2) with strong associations between progression score and cognitive/functional impairment, rising NfL, and faster longitudinal decline; and a Dissociated Track (Subtype 3) with comparable levels of structural variation but weak or absent clinical and NfL changes, suggesting relative biological stability. Baseline subtype membership added prognostic value for future decline in processing speed and language beyond baseline severity. Notably, for C9orf72 and GRN , cTI-informed enrichment reduced required recruited sample size per arm by about 61–75% compared with unenriched designs, and outperformed enrichment using age, cortical thickness or NfL in both analytic and simulation-based power analyses. CONCLUSIONS Machine-learning stratification of genetic FTD reveals a progressive and a dissociated disease track and provides individualized progression scores that closely track clinical status. cTI progression scores offer a powerful tool for trial enrichment, enabling smaller, more efficient prevention and early-intervention trials than conventional MRI or NfL markers alone.

Plusieurs tests cliniques (p. ex., MMSE) permettent un screening et suivi rapide des atteintes cognitives dans le cadre des maladies neurodégénératives. En revanche, de tels tests rapides manquent dans le domaine des troubles du langage qui font partie d’un grand nombre de maladies neurodégénératives et plus particulièrement des aphasies primaires progressives (APP).Le ‘Mini Linguistic State Examination’ (MLSE), test rapide des capacités langagières, est en cours de développement dans une vingtaine de pays. Nous avons élaboré sa version franco-canadienne (MLSE-fc) qui a été appliquée à 219 sujets sains (validation/valeurs normatives), 57 patients APP (variante nonfluente/agrammatique [APPvnfa, n=8], variante logopénique [APPvl, n=26], variante sémantique [APPvs, n=10]), et 13 patients atteints de la maladie d’Alzheimer typique/amnésique (MA).Le MLSE-fc évalue, comme les autres versions du MLSE, 5 domaines langagiers (élocution, phonologie, sémantique, syntaxe, mémoire de travail verbale (MTV), à l’aide de 11 sous-tests. Les stimuli étaient appariés avec ceux de la version initiale/anglaise. Résultats La durée de passation était d’environ 12minutes pour les patients. La consistance inter-examinateur était élevée (kappa=92 %). Il n’y avait pas d’effet plafond chez les sujets sains et les scores variaient selon l’âge et le niveau socio-culturel (stratification). Les patients APP avaient des scores totaux inférieurs aux patients MA. Des scores aux sous-tests distinguaient les 3 variantes d’APP avec des scores les plus faibles dans les domaines ‘élocution’, ‘MTV’ et ‘sémantique’ respectivement dans l’APPvnfa, APPvl, et APPvs.En somme, le MLSE-fc est un outil rapide et examinateur-indépendant, validé/normé avec une large population de sujets sains. Il permet de distinguer les patients APP des patients MA, et de contribuer à classer les 3 variantes d’APP. Les versions MLSE en différentes langues représenteront un outil international de l’évaluation et du suivi des troubles du langage dans les APP et dans d’autres maladies neurologiques. Elles permettront également de fournir des ‘endpoints’ dans des essais cliniques/thérapeutiques.

Fluid biomarkers to diagnose frontotemporal lobar degeneration (FTLD) are currently lacking. In this study, we aimed to identify proteomic changes in cerebrospinal fluid (CSF) associated with FTLD pathogenesis, focusing on signatures unique to different genetic groups. Additionally, we sought proteins distinguishing FTLD-spectrum disorders from controls. To this end, we measured a comprehensive library of over 2900 proteins in CSF using proximity extension assay technology in two well-characterized FTLD cohorts. The discovery cohort, selected from the GENFI cohort, included 47 symptomatic pathogenic variant carriers (22 C9orf72, 14 GRN, 10 MAPT and 1 TARDBP), 124 presymptomatic pathogenic variant carriers (55 C9orf72, 44 GRN, 24 MAPT and 1 TARDBP) and 57 healthy non-carriers. The validation cohort comprised individuals clinically diagnosed with an FTLD-spectrum disorder (n = 132) and cognitively intact controls (n = 32). We assessed differentially abundant proteins using linear regression, adjusting for age and sex. Overrepresentation analysis was conducted for the three genetic groups using Gene Ontology Biological Processes as ontology source. To develop diagnostic tools, we applied a LASSO regression, establishing two types of panels: one to distinguish individuals with an FTLD-spectrum disorder from controls (FTLD panel) and another to differentiate individuals with underlying TDP pathology from controls (TDP panel). We observed 23 dysregulated proteins in symptomatic carriers. Of these, four were also significantly dysregulated (NEFL, TPM3, MSLN and DNM3) in the validation cohort. When focusing on genetic subgroups, 63 upregulated proteins were observed in symptomatic MAPT carriers, with enriched biological pathways linked to immune function. In symptomatic C9orf72 carriers, four proteins – related to energy metabolism – were upregulated. When limiting symptomatic carriers to GRN, six proteins were dysregulated, with enriched pathways involved in neuronal development and projection. Notably, NEFL and TPM3 were consistently significant in all comparisons across both cohorts. We developed two diagnostic panels: one for FTLD and one for FTLD-TDP. The FTLD panel consisted of six proteins (NEFL, RBFOX3, NPTX1, TFF1, ENTPD5, and CNP). The TDP panel was made up of seven proteins (NEFL, RBFOX3, CBLN4, ENTPD5, CCL25, CNP, and MMP1). Both panels were successfully replicated in the validation cohort (AUC of 0.94 and 0.96 respectively). This study highlights distinct proteomic signatures across FTLD genetic subgroups and their associated pathologies using a targeted proteomic approach. Additionally, we present two diagnostic panels—comprising both established and novel proteins—that effectively differentiate individuals with FTLD-spectrum disorders from healthy controls, offering promising avenues for improved clinical diagnosis.

BACKGROUND:What drives the heterogeneity of survival estimates in genetic frontotemporal dementia is unknown. We sought to understand the natural history and predictors of disease trajectory, which are crucial not only for effective care but also for the design of therapeutic clinical trials and efficacy evaluation. METHODS:In this international, cohort study, we used the Kaplan-Meier method to retrospectively assess survival estimates in patients enrolled in the GENFI cohort, which included 32 research sites located in Belgium, Canada, Finland, France, Germany, Italy, the Netherlands, Portugal, Spain, Sweden, and the UK, and comprised participants carrying a causal C9orf72 expansion or a causal mutation in GRN or MAPT genes. Survival was calculated as the time from symptom onset to time of death or censoring date; median survival estimate for all patients was the primary endpoint. Cox proportional hazards models were used to identify predictors of survival, which were subsequently externally validated in an independent cohort. We further designed a structural equation model to assess the relationships between predictors, applying a least absolute shrinkage and selection operator method. FINDINGS:Of 278 participants of the GENFI cohort included in this study, 160 (58%) were men and 118 (42%) were women. 162 died during follow-up (58%) and 116 were still alive (42%) on June 1, 2024, the chosen censoring date. 138 participants carried a C9orf72 expansion, 94 carried a GRN mutation, and 46 a MAPT mutation. 179 participants were diagnosed with behavioural variant frontotemporal dementia, 46 with primary progressive aphasia, and 31 with frontotemporal dementia-amyotrophic lateral sclerosis. 22 participants had other diagnoses. The median survival estimate for all patients with genetic frontotemporal dementia was 6·94 years (95% CI 6·59-7·80) from symptom onset. The median survival estimate for patients with GRN mutations was 6·63 years (6·08-7·98), for patients with a C9orf72 expansion was 7·04 years (6·45-8·77), and for patients with MAPT mutations was 8·56 years (7·06-13·50). Older age at onset, shorter disease duration from onset to enrolment in the GENFI study, clinical presentation (ie, frontotemporal dementia-amyotrophic lateral sclerosis), domain of first symptom (ie, motor or language onset), and geographical area of residency (ie, central and southern Europe) were associated with poorer prognosis. Genetic group did not directly affect survival estimates; rather its effect was mediated by age at onset and clinical phenotype. We computed a genetic frontotemporal dementia survival risk index, which can be used at an individual patient level. INTERPRETATION:Our results highlight that motor impairment in addition to cognitive and behavioural symptoms should be considered when estimating prognosis in genetic frontotemporal dementia. Individual risk scores might be of help for patient stratification in future therapeutic trials, although refinement and prospective validation are now needed. FUNDING:Italian Ministry of Health (Ricerca Corrente), Fondation Philippe Chatrier, and Fondation Vaincre Alzheimer.

ABSTRACT Frontotemporal lobar degeneration (FTLD) is a common cause of early-onset dementias marked by progressive declines in behavior, cognition, and/or movement. FTLD neuropathologies, including TDP-43 proteinopathies and primary tauopathies, do not have reliable fluid biomarkers for in-vivo diagnosis nor biomarkers that directly correspond to FTLD clinical features. Fluid biomarkers that forecast and track FTLD clinical progression, irrespective of pathology or clinical syndrome, are urgently needed to improve clinical trial designs. We previously identified the ratio between two cerebrospinal fluid (CSF) synaptic proteins, YWHAG and NPTX2, as a prognostic biomarker of cognitive decline in Alzheimer’s disease (AD), independent of core AD pathologies, amyloid and tau. Here, we evaluate its utility in sporadic and familial FTLD compared to other neurodegenerative diseases. Using CSF assays from four independent cohorts (UCSF-MAC, ALLFTD, GENFI, PDBP), we find CSF YWHAG:NPTX2 is substantially elevated across all sporadic and familial FTLD syndromes, AD, and dementia with Lewy bodies. CSF YWHAG:NPTX2 robustly correlates with clinical severity across sporadic and familial FTLD ( C9orf72 , GRN , or MAPT mutations), independent of current gold-standard neurodegeneration biomarker neurofilament light (NfL). In presymptomatic familial FTLD, CSF YWHAG:NPTX2 is estimated to rise roughly a decade before symptom onset and improves prediction of imminent symptomatic conversion by 1.7-fold compared to plasma NfL alone, more than halving the estimated sample size required for an FTLD prevention clinical trial. These findings underscore CSF YWHAG:NPTX2 as a cross-dementia synaptic biomarker of cognitive decline and a promising biomarker for disease staging and prognosis across the clinico-pathological continuum of FTLD.

Frontotemporal dementia (FTD) is a neurodegenerative disease characterized by significant clinical and genetic heterogeneity, with approximately 40

IntroductionArtificial Intelligence (AI) is increasingly being integrated into clinical practice to optimize diagnosis in neurocognition. By capturing distinct cognitive signatures, this approach may offer a more precise alternative to the traditional interpretation of the Montreal Cognitive Assessment (MoCA) which often relies on a fixed cutoff score (26/30). We aimed to evaluate whether machine learning models, by integrating detailed MoCA subtest scores, demographic variables, and cognitive chart-derived metrics, can improve the detection of cognitive impairment and classification of dementia subtypes.MethodsWe analyzed 38,746 clinical observations (17,188 unique individuals) from the National Alzheimer’s Coordinating Center database. Five supervised learning algorithms, Extreme Gradient Boosting (XGBoost), Random Forest, Support Vector Machine (SVM), Logistic Regression, and k-Nearest Neighbors (KNN), were trained using detailed MoCA subtest scores, demographic variables, and cognitive chart-derived metrics as predictors. To ensure generalizability of results and prevent data leakage, we applied a rigorous nested Repeated Grouped Cross-Validation strategy. Decision thresholds were optimized via the Youden Index on independent calibration sets, and model interpretability was ensured through SHAP value analysis.ResultsMachine learning models consistently outperformed conventional approach. For the global detection of cognitive impairment, XGBoost achieved the best performance (Youden Index 0.61 vs. 0.54 for the standard cutoff). Regarding subtype classification, models demonstrated variable discriminative capacity depending on clinical homogeneity: primary progressive aphasia was best classified (Youden ≈ 0.77), followed by Lewy body dementia and Alzheimer’s disease, while vascular dementia remained more challenging to isolate. Feature importance analysis highlighted the Cognitive Quotient as a robust universal predictor, while pinpointing disease-specific drivers such as delayed recall for Alzheimer’s disease and verbal fluency for primary progressive aphasia.ConclusionOur findings suggest interpretable machine learning enhances diagnostic utility of the MoCA, yielding superior accuracy compared to a fixed cutoff. By synthesizing individualized subtest profiles within a transparent framework, this approach offers a clinically actionable solution. It transforms the MoCA from a simple screening tool to a precision diagnostic aid, optimizing patient triage in the era of disease-modifying therapies.

Background and Objectives:Converging evidence hints at neurodevelopmental effects in genetic frontotemporal degeneration (FTD). In cross-sectional studies, for some genes, young adult FTD variant carriers show differences in brain volumes and cognition compared to familial non-carriers. However, longitudinal trajectories may more sensitively capture FTD-related neurodevelopmental vs. neurodegenerative changes than cross-sectional approaches. This study examined longitudinal trajectories of brain volumes, executive function, and plasma biomarkers in young adult carriers compared to familial non-carriers, as measures of neurodevelopmental and neurodegenerative outcomes of FTD-causing variants. Methods:This longitudinal cohort study comprised participants, aged 18-30 years, from the FTD Prevention Initiative across Europe, Canada, and the USA. Genetic groups included C9orf72 (47%), MAPT (30%), and GRN (23%). Linear mixed-effects models were computed to assess longitudinal outcomes across age between groups, controlling for sex, scanner (for brain volumes), and education (for executive function); random effects accounted for between-subject variability nested within family membership. Results:Variant carriers ( n =147) and familial non-carriers ( n =113) did not differ in age (mean±SD, 25.9±3.2 years), sex (53% female), or number of visits (2.1±1.7). Young adult C9orf72 repeat expansion carriers exhibited smaller thalamic volumes than non-carriers at the reference age of 26 years ( b =-982.8mm 3 , SE=317.0, p= 0.0046, f 2 =0.32), with relatively stable trajectories across ages 18-30 (i.e., no change over time). Trajectories of rostral anterior cingulate volumes differed in C9orf72 carriers and non-carriers across age, where carriers showed relatively stable trajectories and non-carriers showed age-appropriate declines ( b =64.4mm 3 , SE=29.9, p= 0.035, f 2 =0.07). For MAPT and GRN , there were little to no differences in total brain, cortical, or subcortical volumes between groups and over time. No longitudinal differences were observed between carriers and non-carriers in executive function, or plasma NfL or GFAP for any genetic group. Discussion:C9orf72 repeat expansions were linked to smaller average thalamic volumes and stable trajectories between ages 18 to 30, supporting potential neurodevelopmental origins. The modest evidence supporting an absence of difference in neurodegenerative biomarkers and executive function suggests minimal early neurodegeneration and functional preservation in young adulthood.

Individuals with autosomal dominant frontotemporal dementia (FTD) exhibit considerable variability in disease onset and progression. Both modifiable and non-modifiable factors-such as sex, educational attainment or geographic region of residence-may contribute to this heterogeneity, potentially through their influence on cognitive reserve. The aim of the present study was to investigate the role of cognitive reserve modulators within the Genetic Frontotemporal dementia Initiative (GENFI) cohort. To this end, we used functional MRI (i.e. spatial chronnectome measures) and neurodegenerative markers (i.e. plasma neurofilament light chains levels) to determine disease stage using a Discriminative Event-Based Model (DEBM). We then examined how potential modulators influence the relationship between disease stage and cognitive performance. We analysed a total of 711 participants, including 106 patients with genetic FTD, 325 presymptomatic mutation carriers and 280 non-carriers healthy controls. Female participants showed a weaker association between disease stage and cognitive performance compared to males (P < 0.001), with difference becoming progressively more pronounced across symptomatic stages. Educational attainment exhibited a similar effect: individuals with higher education demonstrated an attenuated association compared to those with secondary or primary schooling (P < 0.001), with differences already detectable at prodromal disease stages. The effect of geographical region of residence was associated with education levels, but appeared to have an indirect and less strong influence. In summary, sex and educational attainment significantly affect the development and maintenance of cognitive reserve in individuals with genetic FTD. These findings underscore the importance of identifying disease-modifying interventions since the presymptomatic stages of the disease.

Recent proteomic studies have identified both established and novel proteins in genetic frontotemporal lobar degeneration (FTLD). However, it remains unclear at what point in the disease these proteins deviate from normal levels and how their trajectories relate to one another. Defining the temporal sequence of protein abnormalities could not only improve disease staging but also help identify biomarkers most sensitive to early disease activity in pathogenic variant carriers. We aimed to apply discriminative event-based modelling (DEBM) to characterize the progression profiles of proteins identified in a previous cross-sectional proteomic analysis. Building on our prior cross-sectional CSF proteomic analysis of genetic FTLD using a proximity extension assay, we selected the top ten significant proteins for each genetic group (C9orf72, GRN, MAPT). We then applied DEBM to characterize temporal dynamics of these proteins separately in each genetic group and evaluated their potential as early disease markers. To validate model performance, each individual was assigned a disease stage according to their position along the estimated disease timeline, based on protein levels and independent of clinical labels. Next, we assessed how well these stages discriminated symptomatic from presymptomatic carriers and non-carriers. Across all genetic groups, NfL consistently became abnormal before TPM3, although the earliest abnormal proteins differed between groups. In C9orf72, ELAVL4 is the first protein to become abnormal; in GRN SEMA3G and GRN, and in MAPT MMP-10. Estimated individual-level disease stage effectively distinguished symptomatic carriers from presymptomatic carriers and non-carriers, demonstrating high diagnostic accuracy (range AUC 0.74–0.98). Our data-driven findings provide a temporal ordering of multiple CSF proteins, highlighting potential early biomarkers and disease dynamics in different forms of genetic FTLD. In addition, the model’s accurate estimation of disease stages underscores the value of DEBM for patient stratification, offering a promising tool to support clinical trial design.

IntroductionWritten language impairment is a frequent but still incompletely understood feature of the logopenic variant of primary progressive aphasia (lvPPA), and although spoken language deficits have been extensively studied, less is known about the mechanisms underlying impaired written word production and the conditions under which orthographic representations become particularly vulnerable. In this study, we investigated written language processing in individuals with lvPPA using a battery of tasks designed to dissociate phonological and orthographic contributions.MethodsTwelve participants with lvPPA and thirteen healthy controls completed written picture naming, written word dictation, written non-word dictation, grapheme dictation, and a picture-based letter-in-word judgment task that probed orthographic knowledge without requiring overt written output.ResultsCompared with controls, participants with lvPPA showed significant impairments in written picture naming, written word dictation, grapheme dictation, and letter-in-word judgment, whereas written non-word dictation was relatively preserved at the group level. Within grapheme dictation, vowel graphemes were disproportionately affected relative to consonants. In the letter-in-word judgment task, the largest group differences were observed in conditions requiring access to orthographic knowledge when phonological cues were non-informative.DiscussionTogether, these findings indicate that written language impairment in lvPPA cannot be explained by a primary phonological deficit alone but instead reflects vulnerability of abstract orthographic representations, particularly under conditions of increased orthographic ambiguity. Identifying tasks that require access to orthographic knowledge independently of phonological support may therefore provide sensitive markers for characterizing written language impairment in lvPPA and contribute to a more precise understanding of language network dysfunction in neurodegenerative disease.

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.

Patients with Primary Progressive Aphasias (PPAs) almost systematically inquire about the longitudinal evolution of their disease in clinics but very little research exists on the issue. We studied 82 PPA patients from the Research Chair on PPA – Fondation de la Famille Lemaire Cohort over a 10-year span (42 logopenic, 21 non-fluent/agrammatic and 19 semantic PPAs) and collected data from 5 domains (language, cognition, motor, psychiatric, functional) at 5 time points from onset to death. Logistical regression analyses and repeated measures ANOVAs were conducted to delineate the longitudinal profile of each variant PPA. All patients presented anomia and executive impairments over time. Language deficits tended to be more significant for lvPPA, particularly after 3 years of evolution and this group showed broader cognitive impairments. Psychiatric symptoms were more frequent in svPPA and nfvPPA, particularly after 5 years of evolution. Motor features predominantly affected patients with nfvPPA after 2 years of evolution. Overall functional abilities remained preserved the longest in svPPA (up to 5 years). To our knowledge, this naturalistic study on all major PPA symptoms over a 10-year span from onset to death is the largest to date. Data from this study can help clinicians better inform and prepare their patients for future challenges as well as design more focused interventions.

BACKGROUND:Optical coherence tomography (OCT) and OCT angiography (OCT-A) have been studied as biomarkers for Alzheimer's disease (AD), with promising results. Nevertheless, their potential in the logopenic variant of primary progressive aphasia (lvPPA), which shares the same amyloid pathology, has not yet been explored. This work aimed to characterize retinal changes in lvPPA compared to healthy controls. METHODS:Ten participants with lvPPA and eleven controls underwent OCT and OCT-A imaging. Amyloid pathology in lvPPA was confirmed by lumbar puncture. Retinal parameters included retinal nerve fiber layer (RNFL) thickness and foveal avascular zone (FAZ). RESULTS:Compared to controls, lvPPA participants exhibited reduced RNFL thickness in the temporal sector (p = 0.013) and significantly decreased FAZ circularity (p = 0.002). DISCUSSION:RNFL thinning may reflect trans-synaptic degeneration from cortical atrophy, while reduced FAZ circularity suggests early microvascular changes related to amyloid burden. Our findings support OCT and OCT-A as potential biomarkers for lvPPA. Highlights:For the first time, OCT and OCT-A are studied as potential biomarkers for lvPPA.Compared to healthy controls, retinal nerve thickness is decreased in lvPPA patients.Retinal vasculature exhibits structural alterations in lvPPA patients.

BACKGROUND:Due to their shared embryological origin, retinal and brain tissues are affected by neurodegenerative diseases in similar ways. Optical coherence tomography (OCT) and OCT-angiography, two non-invasive retinal imaging modalities, have been increasingly studied as potential biomarkers for Alzheimer's disease (AD) in recent years. However, correlations between OCT/OCT-A and neuroimaging remain understudied. We thus performed a systematic review of OCT/OCT-A - MRI correlations in different neurodegenerative disorders associated with cognitive decline. METHOD:Medline, Embase, and other databases were searched from January to June 2023, using keywords related to neurodegenerative conditions and OCT/OCT-A parameters. RESULT:We screened 2962 citations and 93 full-text articles. We included 28 studies in the final review. For non-vascular neurodegenerative diseases, layer-specific retinal metrics, especially retinal nerve fiber layer (RNFL) thinning, and region-specific retinal parameters (e.g. decreased foveal thickness) best correlated with changes on brain MRI. Vascular retinal biomarkers, especially reduced vessel and perfusion densities, have the unique capacity to reflect cerebrovascular lesions in vascular cognitive conditions. Both layer- or region-specific retinal biomarkers and vascular retinal metrics can reflect global brain atrophy patterns. Microstructural alterations of the brain parenchyma best correlated with layer-specific thinning of retina. CONCLUSION:Layer- or region-specific retinal markers are better suited for non-vascular dementias, while vascular markers more closely reflect vascular neurodegeneration. Future research must overcome several challenges including methodological heterogeneity and the complex interactions between different degenerative mechanisms. A better understanding of the associations between retinal and brain lesions could ultimately lead to the clinical use of retinal biomarkers for the early diagnosis of neurodegenerative diseases.