Introduction Le langage est essentiel à la communication. Sa complexité et son organisation hiérarchique peuvent fournir des informations diagnostiques. Les études systématiques pour la dégénérescence frontotemporale comportementale (DFTc) restent rares. Objectifs L’objectif est de caractériser de manière exhaustive les fonctions langagières chez les patients avec une DFTc afin d’affiner et de renforcer le diagnostic différentiel clinique. Méthodes Dans un cadre neurolinguistique global, couvrant les niveaux lexical, syntaxique et discursif ainsi que le niveau intermodal de transposition/transcodage, une évaluation approfondie avec la batterie GREMOTs a été réalisée chez 85 patients (DFTc=34, maladie d’Alzheimer ou MA=30, troubles psychiatriques ou TP=21) et 40 témoins. Des indices composites, quantitatifs et qualitatifs, ont été calculés pour chaque niveau. Des données IRM structurelles ont été analysées par morphométrie basée sur les voxels. Résultats Des altérations lexicales sont observées dans l’ensemble des groupes cliniques, nettement plus accrues dans la DFTc. Ce groupe présente également des troubles syntaxiques et discursifs, témoignant de dysrégulations pragmatiques, et des déficits mineurs de transcodage. Une régression logistique indique que 12 tâches sur 23 permettent de classifier correctement 85,9 % des cas. Les performances linguistiques s’avèrent cohérentes avec les patterns d’atrophie frontotemporale, plus étendue dans la DFTc et plus restreinte dans la MA et les TP. Discussion Nos résultats révèlent un continuum linguistique dans les troubles neurodégénératifs et psychiatriques. La DFTc montre des atteintes lexicales, syntaxiques et discursives majeures tandis que la MA et les TP présentent plutôt des déficits lexicaux. Les corrélats neuronaux observés appuient un modèle distribué du langage impliquant contrôle frontal-insulaire et systèmes sémantiques temporaux dans plusieurs réseaux cérébraux impliqués. Conclusion L’intégration de ces évaluations à différents niveaux dans la pratique clinique pourrait améliorer la précision diagnostique et fournir des critères d’évaluation cognitifs valides pour les essais futurs.

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.

Neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit substantial biological and clinical heterogeneity, complicating diagnosis, subtype characterization, and prediction of disease progression. We introduce PatientSpace, a multimodal graph-based latent representation framework designed to model neurodegenerative disease heterogeneity using T1-weighted MRI and FDG-PET. PatientSpace is built upon a structured variational autoencoder that integrates multimodal neuroimaging features while organizing patients within a latent space constrained by age, diagnosis, and a consistency regularization term encouraging similarity between neuroimaging phenotypes. This design enables the construction of an interpretable patient graph in which neighborhood relationships reflect biological similarity. Applied to cohorts of cognitively normal individuals, AD, and FTD patients, PatientSpace revealed multiple disease clusters associated with distinct neuroimaging patterns and clinical severity. Diagnostic classification achieved performance comparable to state-of-the-art deep learning models, while graph-based neighborhood inference enabled prediction of structural volumes, metabolic activity, and cognitive severity. Projection of mild cognitive impairment (MCI) subjects from an independent cohort further showed that cluster membership was associated with differential risks of dementia conversion and distinct longitudinal trajectories. Together, these results demonstrate that PatientSpace provides an interpretable framework linking multimodal neuroimaging representations to disease subtypes, patient-level characterization, and progression modeling in neurodegenerative disorders.

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.

Language is essential to social communication. Its complexity and hierarchical organization, from low-level operations to high-order integrative processes, may provide valuable diagnostic insights in neurodegeneration beyond classical aphasia syndromes. However, systematic investigations in these conditions, particularly in behavioural variant frontotemporal degeneration (bvFTD), remain scarce. To refine differential diagnosis, an exhaustive characterization of language functions is required. We systematically compared multi-level language functioning across bvFTD, Alzheimer's disease (AD) and primary psychiatric disorders (PPD), within an integrative neurolinguistic framework distinguishing lexical, syntactic and discursive levels, together with a cross-modal transposition/transcoding dimension. A total of 85 patients (including 34 with bvFTD, 30 with AD, 21 with PPD) and 40 matched healthy controls underwent an extensive language assessment using the GREMOTs battery. Composite, quantitative as well as qualitative indices were computed for each linguistic level. Structural MRI data were analysed using voxel-based morphometry (P < 0.05 corrected for multiple comparisons). All clinical groups exhibited lexical impairments relative to controls, with bvFTD presenting the most severe and widespread deficits across fluency, naming and comprehension (partial eta-squared, ηp 2, ranging from 0.18 to 0.49). AD and PPD showed milder lexical inefficiencies (ηp 2 = 0.11-0.39 and ηp 2 = 0.07-0.29, respectively). Syntactic processing was also more impaired in bvFTD (ηp 2 = 0.03-0.27) than in AD and PPD (ηp 2 = 0.02-0.07 and ηp 2 = 0.00-0.15, respectively). At the discourse-level, bvFTD displayed key deficits (ηp 2 = 0.05-0.29), with pervasive pragmatic breakdowns whereas AD and PPD showed milder integrative deficits with preserved global coherence (ηp 2 = 0.00-0.17 and ηp 2 = 0.02-0.13). Transcoding and transposing tasks revealed minor deficits, mainly in bvFTD (ηp 2 = 0.01-0.25). A logistic regression identified that a subset of 12/23 tasks accurately classified 85.9% of bvFTD cases (sensitivity: 57.6%; specificity: 95.6%). The analysis of the types of responses (including errors) allowed to provide a more comprehensive group profiling. In bvFTD, the decrease of language performance related to widespread frontotemporal and posterior (including cerebellar) atrophy, whereas AD showed more restricted frontal and temporal involvement. PPD displayed smaller fronto-temporal, insular and precuneal associations. In conclusion, these findings delineate a graded, multi-level linguistic profile across neurodegenerative and psychiatric conditions. bvFTD is mainly characterized by pervasive lexical and discursive-pragmatic impairments, alongside syntactic difficulties, while AD and PPD primarily show lexical inefficiencies with preserved syntax. Convergent neural evidence supports a distributed network model of language integrating frontal-insular control and temporal semantic systems. Embedding such multi-level assessments into clinical practice could enhance diagnostic precision and provide valid cognitive endpoints for future trials.

Introduction Les troubles affectifs sont fréquents dans la dégénérescence frontotemporale comportementale (DFTc), les troubles psychiatriques (TP) et la maladie d’Alzheimer (MA), requérant d’être objectivés pour une caractérisation différentielle/transdiagnostique. Objectifs L’objectif est de déterminer si des analyses du langage naturel basées sur l’intelligence artificielle (IA) peuvent fournir des marqueurs fiables pour le diagnostic différentiel. Méthodes Le discours narratif (GREMOTs) de 74 patients suivis au CMRR de Lille (DFTc=31, MA=28, TP=15) et 39 témoins a été analysé à l’aide d’un modèle ajusté d’IA de reconnaissance des émotions vocales estimant la valence, l’arousal, la dominance et l’entropie. Les différences entre les groupes ont été évaluées par ANCOVA, l’utilité diagnostique par régression logistique et les corrélats neuroanatomiques par morphométrie basée sur les voxels. Résultats Des profils affectifs spécifiques à chaque groupe ont émergé. Dans l’ensemble, les patients avec un TP présentaient une valence plus faible. Les patients avec une DFTc et une MA démontraient un arousal réduit et une dominance élevée. Les dimensions affectives prédisaient la DFTc avec une précision de 80,4 % tout en étant liés aux réseaux fronto-insulaires et temporaux. L’entropie a été identifiée comme un marqueur transdiagnostique. Discussion L’analyse du langage naturel par l’IA met en évidence des altérations de la prosodie expressive, propre à certains troubles mais aussi transdiagnostique. L’entropie s’impose comme un marqueur particulièrement fiable de la dérégulation affective, en accord avec les modèles dimensionnels et transdiagnostiques actuels. Conclusion Une tâche linguistique brève et écologique, avec l’IA, fournit des biomarqueurs objectifs et évolutifs pour le diagnostic différentiel de la DFTc, le suivi de la maladie et la santé numérique.

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.

Background Primary progressive aphasias (PPA) are syndromes characterised by a progressive loss of language functions. To date, four main variants have been identified: semantic (svPPA), non-fluent/agrammatic (nfvPPA), logopenic (lvPPA) and Primary Progressive Apraxia of Speech (PPAoS). Currently, little is known about their clinical course, especially in French-speaking populations, though such data could aid prognosis and guide therapy. Objective To characterise longitudinal changes in the language profiles in PPA using the GRÉMOTS, a French language assessment tool specifically developed for neurodegenerative disorders. Methods We retrospectively included PPA patients, as well as patients with typical amnesic Alzheimer's disease (AD) and behavioural variant frontotemporal degeneration (bvFTD), from the Lille and Toulouse Memory Centres, who underwent longitudinal assessment ≥6 months with the GRÉMOTS. We performed between-groups comparisons and within-group paired analyses of GRÉMOTS subscores. Results Eighty-two patients were included: 20 svPPA, 9 lvPPA, 12 nfvPPA, 9 PPAoS, 20 AD and 12 bvFTD. svPPA patients showed a predominant decline in lexico-semantic domains. nfvPPA patients deteriorated mainly in phonetics and syntactic production. Apraxia remained the prominent symptom in PPAoS patients, although mild agrammatical aphasia emerged at follow-up. lvPPA patients exhibited a significant decline in lexical oral comprehension alongside their core impairments. Language impairments remained modest in AD and bvFTD. Conclusion Our results highlighted a progression of core language impairments across PPA variants, with lvPPA additionally showing significant decline beyond its defining criteria. These results support the development of tailored speech-language interventions that anticipate the evolving needs of PPA patients in French-speaking settings.

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 INTRODUCTION Affective disturbances are common across behavioral variant frontotemporal degeneration (bvFTD), primary psychiatric disorders (PPD), and Alzheimer's disease (AD). Objective markers are needed for differential and transdiagnostic characterization. We tested whether artificial intelligence (AI)‐based analysis of natural speech could provide such markers. METHODS Speech from 112 participants (bvFTD = 31, AD = 28, PPD = 15, controls = 39) was analyzed using a fine‐tuned speech emotion recognition model estimating valence, arousal, dominance, and entropy. Group differences were assessed with analyses of covariance, diagnostic utility with logistic regression, and neuroanatomical correlates with voxel‐based morphometry. RESULTS Group‐specific affective profiles emerged. Overall, PPD exhibited a lower valence. bvFTD and AD showed a reduced arousal and a higher dominance. Affective dimensions predicted bvFTD with 80.4% accuracy (area under the curve = 0.732) and mapped onto fronto‐insular and temporal networks. Entropy was identified as a transdiagnostic marker. DISCUSSION AI‐based speech analysis provides objective, scalable biomarkers for differential diagnosis, transdiagnostic characterization, and disease monitoring.

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.

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.

Introduction Dans le cadre des aphasies primaires progressives (APP), de récents travaux data-driven démontrent l’intérêt de s’affranchir des catégories diagnostiques. De tels travaux n’ont jamais été menés sur l’imagerie métabolique. Objectifs L’objectif est de montrer la pertinence d’une approche multidimensionnelle et transdiagnostique des APP basée sur le métabolisme cérébral, en faisant l’hypothèse d’un continuum métabolique entre variantes. Méthodes Les scans 18F-FGD-PET de patients avec une APP ont été rétrospectivement collectés aux CMRR de Toulouse et de Lille. Sur la base des standard uptake values, nous avons mené une analyse de profils (profile analysis via multidimensional scaling) afin de révéler les principaux profils métaboliques sous-jacents au sein de l’échantillon (profils latents). Un k-means clustering a ensuite groupé les patients sur la base de leur correspondance avec chaque profil. Résultats De 139 patients (Toulouse : 92, Lille : 47) (12 APPP, 48 vlAPP, 42 vnfAPP, 34 vs. APP, 3 non-classés) ont émergé 5 profils métaboliques. Les correspondances individuelles avec ces profils ont groupé les patients en 3 clusters, fortement associés aux variantes d’APP (p<0,001 ; taille d’effet=0,77). Toutefois, 15 % des individus étaient classés dans un cluster où leur variante n’était pas prédominante, et une analyse de sensibilité a montré un important chevauchement (jusqu’à 46 %) entre les variantes. Discussion Ces résultats confirment la validité des critères diagnostiques de référence des variantes d’APP, tout en démontrant que leurs signatures métaboliques ne sont pas exclusives. Ainsi, les APP peuvent être décrites le long d’un continuum métabolique. Par conséquent, considérant certaines signatures métaboliques, des individus avec des variantes différentes peuvent être plus semblables que des individus d’une même variante. Conclusion Cette étude démontre l’intérêt d’une approche dimensionnelle, plutôt que catégorielle des APP pour rendre compte de leur variabilité phénotypique. Ces approches offrent des perspectives pour une médecine personnalisée. Informations complémentaires – remerciements, financements, etc Nous remercions Mme. Aurore Mahut-Dubos, Mme. Emilie Sauret, Mme. Brigitte Debachy, M. Alexandre Da Costa, et les équipes de médecine nucléaire de Lille et de Toulouse pour leur contribution à ces travaux.

BACKGROUND:Socio-cognitive assessment in neurocognitive disorders (NCDs) is rare in clinical practice and no consensus exists as to a uniform operationalization of socio-cognitive measures for NCDs in memory clinics. The SIGNATURE initiative aims to optimize the use of socio-cognitive measures in memory clinics, defining expert recommendations. We report consortium guidelines for the use of socio-cognitive measures in NCDs based on available evidence from the literature and the current state of practices in memory clinics. METHODS:Using a Delphi consensus method supported by a literature review and the results of an international survey, 22 specialists defined recommendations for the context of use, relevance in NCD diagnosis, priorities for future research and facilitators/obstacles of socio-cognitive assessment in major and mild NCDs. RESULTS:Overall, panelists recommended social cognition testing in routine diagnostic assessment to evaluate both socio-cognitive and socio-behavioral alterations. A set of clinical, methodological, implementation and external factors facilitating or hampering the use of socio-cognitive tasks was identified. CONCLUSIONS:This is the first focused endeavor to favor the implementation of socio-cognitive assessment, which is required by DSM-5 but seldom performed despite clear evidence of its clinical relevance for diagnosis and care. Our results provide an initial set of recommendations, refinable through the future actions of the SIGNATURE initiative. Future collaborative clinical research projects should overcome current limitations and foster the use of ecological and cross-culturally validated measures in clinics.

BACKGROUND:The cerebrospinal fluid (CSF) Aβ42/40 ratio has proven to be a more reliable biomarker for amyloid pathology than CSF Aβ42 in Alzheimer's disease (AD), helping to correctly classify patients with positive tau biomarkers (T+) that would otherwise have remained outside of the AD continuum. It was shown that the Aβ42/40 ratio better captures a relative decrease of Aβ42 in patients with high CSF Aβ. However, whether patients with high-amyloid (HiA) AD, in whom A+ is defined by the Aβ42/40 ratio, exactly compare with their low-amyloid (LoA) counterparts, in whom A+ is defined by Aβ42 solely, deserves further analysis. METHODS:We retrospectively included patients with A+T+ AD and evidence of cognitive and neurodegenerative changes (N+). LoA patients were operationally defined as patients with T+N+ and low CSF Aβ42, while HiA patients were defined as patients with T+N+ and normal CSF Aβ42 but abnormal Aβ42/40 ratio. Tau CSF biomarkers, neuropsychological profile, rates of cognitive decline, structural and metabolic imaging, ApoE genotype and brain neuropathology were compared between the HiA and LoA groups. RESULTS:At the time of the lumbar puncture, LoA patients were significantly younger than the HiA patients (68.9±8.7years vs. 71.8±9.4; P=0.0015) and had a lower Mini-Mental Status Examination (MMSE) (18.7±6.4 vs. 20.7±6.2; P=0.0005). There was no difference in the neuropsychological profile nor in the annual rates of cognitive decline between the two groups with early AD. No differences were retrieved between groups on CSF Tau and P-Tau biomarkers, atrophy and brain metabolism, distribution of the APOE4 allele and APOE4/E4 genotype, and neuropathology. CONCLUSIONS:Overall, our study supports the surrogate use of the Aβ42/40 ratio as an equivalent to Aβ42 to define AD. We showed that HiA CSF profiles were not associated with differences in cognition, brain structures and metabolism, APOE genotype tau CSF biomarkers or the rates of cognitive decline, but may be the associated with later-onset and early-stage AD.

Over the past years, social cognition has been envisaged as a promising domain to distinguish behavioral variant frontotemporal degeneration (bvFTD) from its main differential diagnoses that is primary psychiatric disorders (PPD). The core-processes approach, which has emphasized the importance of emotion recognition and mentalizing, has been particularly useful to better characterize each condition and enhance the earliness of FTD’s diagnosis. However, new findings evidencing conflicting results regarding the ability of social cognition to distinguish bvFTD from PPD have underlined the importance of moving beyond the core processes approach. We reviewed all cases with a suspission of bvFTD in the last 8 years in the Lille memory clinic, at least followed-up for 24 months with a neuropsychological assessment and an MRI and/or PET-scan. We then applied a quantitative comparison approach based on total scores, then a qualitative approach, based on responses and errors types. Then, neuroimaging analyses were run, and biomarkers were analyzed. Data of 56 patients with a probable to certain bvFTD and 47 patients with a primary psychiatric disorders (late major depression, bipolar disorders, schizophrenia…) were analyzed at classical and social cognitive (mini-SEA) tests. Overall, clinical groups were not different on executive functionning, attention, motor & perceptual functions. Minor differences were retrieved in memory and langage processing. Important differences were retrieved in social cognition. Qualitative differences were retrieved in facial emotion recognition (inter & intra valence errors) and mentalizing (type of mental inference, emotional labelling), as well as memory functioning (primacy/recency ratio, intrusions). Anatomical and functional brain networks involved showed a combination of overlaping and distincts areas. Regarding biomarkers, NFLs showed promising results with AUC = 0.88). While the usual approach (considering general or subscores scores) may not be the more efficient way, a more qualitative neuropsychological approach has the potential to provide relevant cognitive markers for the clinical distinction between bvFTD and PPD, particularly regarding social cognition.