Cognitive composites for genetic frontotemporal dementia: GENFI-Cog

Background Clinical endpoints for upcoming therapeutic trials in frontotemporal dementia (FTD) are increasingly urgent. Cognitive composite scores are often used as endpoints but are lacking in genetic FTD. We aimed to create cognitive composite scores for genetic frontotemporal dementia (FTD) as well as recommendations for recruitment and duration in clinical trial design. Methods A standardized neuropsychological test battery covering six cognitive domains was completed by 69 C9orf72, 41 GRN, and 28 MAPT mutation carriers with CDR® plus NACC-FTLD ≥ 0.5 and 275 controls. Logistic regression was used to identify the combination of tests that distinguished best between each mutation carrier group and controls. The composite scores were calculated from the weighted averages of test scores in the models based on the regression coefficients. Sample size estimates were calculated for individual cognitive tests and composites in a theoretical trial aimed at preventing progression from a prodromal stage (CDR® plus NACC-FTLD 0.5) to a fully symptomatic stage (CDR® plus NACC-FTLD ≥ 1). Time-to-event analysis was performed to determine how quickly mutation carriers progressed from CDR® plus NACC-FTLD = 0.5 to ≥ 1 (and therefore how long a trial would need to be). Results The results from the logistic regression analyses resulted in different composite scores for each mutation carrier group (i.e. C9orf72, GRN, and MAPT). The estimated sample size to detect a treatment effect was lower for composite scores than for most individual tests. A Kaplan-Meier curve showed that after 3 years, ~ 50% of individuals had converted from CDR® plus NACC-FTLD 0.5 to ≥ 1, which means that the estimated effect size needs to be halved in sample size calculations as only half of the mutation carriers would be expected to progress from CDR® plus NACC FTLD 0.5 to ≥ 1 without treatment over that time period. Discussion We created gene-specific cognitive composite scores for C9orf72, GRN, and MAPT mutation carriers, which resulted in substantially lower estimated sample sizes to detect a treatment effect than the individual cognitive tests. The GENFI-Cog composites have potential as cognitive endpoints for upcoming clinical trials. The results from this study provide recommendations for estimating sample size and trial duration

A modified Camel and Cactus Test detects presymptomatic semantic impairment in genetic frontotemporal dementia within the GENFI cohort

Impaired semantic knowledge is a characteristic feature of some forms of frontotemporal dementia (FTD), particularly the sporadic disorder semantic dementia. Less is known about semantic cognition in the genetic forms of FTD caused by mutations in the genes MAPT, C9orf72, and GRN. We developed a modified version of the Camel and Cactus Test (mCCT) to investigate the presence of semantic difficulties in a large genetic FTD cohort from the Genetic FTD Initiative (GENFI) study. Six-hundred-forty-four participants were tested with the mCCT including 67 MAPT mutation carriers (15 symptomatic, and 52 in the presymptomatic period), 165 GRN mutation carriers (33 symptomatic, 132 presymptomatic), and 164 C9orf72 mutation carriers (56 symptomatic, 108 presymptomatic) and 248 mutation-negative members of FTD families who acted as a control group. The presymptomatic mutation carriers were further split into those early and late in the presymptomatic period (more than vs. within 10 years of expected symptom onset). Groups were compared using a linear regression model, adjusting for age and education, with bootstrapping. Performance on the mCCT had a weak negative correlation with age (rho = −0.20) and a weak positive correlation with education (rho = 0.13), with an overall abnormal score (below the 5th percentile of the control population) being below 27 out of a total of 32. All three of the symptomatic mutation groups scored significantly lower than controls: MAPT mean 22.3 (standard deviation 8.0), GRN 24.4 (7.2), C9orf72 23.6 (6.5) and controls 30.2 (1.6). However, in the presymptomatic groups, only the late MAPT and late C9orf72 mutation groups scored lower than controls (28.8 (2.2) and 28.9 (2.5) respectively). Performance on the mCCT correlated strongly with temporal lobe volume in the symptomatic MAPT mutation group (rho > 0.80). In the C9orf72 group, mCCT score correlated with both bilateral temporal lobe volume (rho > 0.31) and bilateral frontal lobe volume (rho > 0.29), whilst in the GRN group mCCT score correlated only with left frontal lobe volume (rho = 0.48). This study provides evidence for presymptomatic impaired semantic knowledge in genetic FTD. The different neuroanatomical associations of the mCCT score may represent distinct cognitive processes causing deficits in different groups: loss of core semantic knowledge associated with temporal lobe atrophy (particularly in the MAPT group), and impaired executive control of semantic information associated with frontal lobe atrophy. Further studies will be helpful to address the longitudinal change in mCCT performance and the exact time at which presymptomatic impairment occurs

Plasma glial fibrillary acidic protein is raised in progranulin-associated frontotemporal dementia

Background There are few validated fluid biomarkers in frontotemporal dementia (FTD). Glial fibrillary acidic protein (GFAP) is a measure of astrogliosis, a known pathological process of FTD, but has yet to be explored as potential biomarker. Methods Plasma GFAP and neurofilament light chain (NfL) concentration were measured in 469 individuals enrolled in the Genetic FTD Initiative: 114 C9orf72 expansion carriers (74 presymptomatic, 40 symptomatic), 119 GRN mutation carriers (88 presymptomatic, 31 symptomatic), 53 MAPT mutation carriers (34 presymptomatic, 19 symptomatic) and 183 non-carrier controls. Biomarker measures were compared between groups using linear regression models adjusted for age and sex with family membership included as random effect. Participants underwent standardised clinical assessments including the Mini-Mental State Examination (MMSE), Frontotemporal Lobar Degeneration-C linical Dementia Rating scale and MRI. Spearman's correlation coefficient was used to investigate the relationship of plasma GFAP to clinical and imaging measures. Results Plasma GFAP concentration was significantly increased in symptomatic GRN mutation carriers (adjusted mean difference from controls 192.3 pg/mL, 95% CI 126.5 to 445.6), but not in those with C9orf72 expansions (9.0, -61.3 to 54.6), MAPT mutations (12.7, -33.3 to 90.4) or the presymptomatic groups. GFAP concentration was significantly positively correlated with age in both controls and the majority of the disease groups, as well as with NfL concentration. In the presymptomatic period, higher GFAP concentrations were correlated with a lower cognitive score (MMSE) and lower brain volume, while in the symptomatic period, higher concentrations were associated with faster rates of atrophy in the temporal lobe. Conclusions Raised GFAP concentrations appear to be unique to GRN-related FTD, with levels potentially increasing just prior to symptom onset, suggesting that GFAP may be an important marker of proximity to onset, and helpful for forthcoming therapeutic prevention trials

A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia

© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/ by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] CSF and blood biomarkers for genetic frontotemporal dementia have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain and phosphorylated neurofilament heavy chain), synapse dysfunction [neuronal pentraxin 2 (NPTX2)], astrogliosis (glial fibrillary acidic protein) and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage frontotemporal dementia, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic frontotemporal dementia using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. Two-hundred and seventy-five presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on previous diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF neurofilament light chain, blood phosphorylated neurofilament heavy chain, blood glial fibrillary acidic protein and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The areas under the curve to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic frontotemporal dementia revealed that NPTX2 and neurofilament light chain are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions.This study was supported in the Netherlands by two Memorabel grants from Deltaplan Dementie (The Netherlands Organisation for Health Research and Development and Alzheimer Nederland; grant numbers 733050813,733050103 and 733050513), the Bluefield Project to Cure Frontotemporal Dementia, the Dioraphte foundation (grant number 1402 1300), the European Joint Programme—Neurodegenerative Disease Research and the Netherlands Organisation for Health Research and Development (PreFrontALS: 733051042, RiMod-FTD: 733051024); V.V. and S.K. have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 666992 (EuroPOND). E.B. was supported by the Hartstichting (PPP Allowance, 2018B011); in Belgium by the Mady Browaeys Fonds voor Onderzoek naar Frontotemporale Degeneratie; in the UK by the MRC UK GENFI grant (MR/M023664/1); J.D.R. is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH); I.J.S. is supported by the Alzheimer’s Association; J.B.R. is supported by the Wellcome Trust (103838); in Spain by the Fundació Marató de TV3 (20143810 to R.S.V.); in Germany by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy—ID 390857198) and by grant 779357 ‘Solve-RD’ from the Horizon 2020 Research and Innovation Programme (to MS); in Sweden by grants from the Swedish FTD Initiative funded by the Schörling Foundation, grants from JPND PreFrontALS Swedish Research Council (VR) 529–2014-7504, Swedish Research Council (VR) 2015–02926, Swedish Research Council (VR) 2018–02754, Swedish Brain Foundation, Swedish Alzheimer Foundation, Stockholm County Council ALF, Swedish Demensfonden, Stohnes foundation, Gamla Tjänarinnor, Karolinska Institutet Doctoral Funding and StratNeuro. H.Z. is a Wallenberg Scholar.info:eu-repo/semantics/publishedVersio

Language impairment in the genetic forms of behavioural variant frontotemporal dementia

Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.Copyright © The Author(s) 2022. Background: Behavioural variant fronto-temporal dementia (bvFTD) is characterised by a progressive change in personality in association with atrophy of the frontal and temporal lobes. Whilst language impairment has been described in people with bvFTD, little is currently known about the extent or type of linguistic difficulties that occur, particularly in the genetic forms. Methods: Participants with genetic bvFTD along with healthy controls were recruited from the international multicentre Genetic FTD Initiative (GENFI). Linguistic symptoms were assessed using items from the Progressive Aphasia Severity Scale (PASS). Additionally, participants undertook the Boston Naming Test (BNT), modified Camel and Cactus Test (mCCT) and a category fluency test. Participants underwent a 3T volumetric T1-weighted MRI, with language network regional brain volumes measured and compared between the genetic groups and controls. Results: 76% of the genetic bvFTD cohort had impairment in at least one language symptom: 83% C9orf72, 80% MAPT and 56% GRN mutation carriers. All three genetic groups had significantly impaired functional communication, decreased fluency, and impaired sentence comprehension. C9orf72 mutation carriers also had significantly impaired articulation and word retrieval as well as dysgraphia whilst the MAPT mutation group also had impaired word retrieval and single word comprehension. All three groups had difficulties with naming, semantic knowledge and verbal fluency. Atrophy in key left perisylvian language regions differed between the groups, with generalised involvement in the C9orf72 group and more focal temporal and insula involvement in the other groups. Correlates of language symptoms and test scores also differed between the groups. Conclusions: Language deficits exist in a substantial proportion of people with familial bvFTD across all three genetic groups. Significant atrophy is seen in the dominant perisylvian language areas and correlates with language impairments within each of the genetic groups. Improved understanding of the language phenotype in the main genetic bvFTD subtypes will be helpful in future studies, particularly in clinical trials where accurate stratification and monitoring of disease progression is required.We thank the research participants and their families for their contribution to the study. Several authors of this publication are members of the European Reference Network for Rare Neurological Diseases—Project ID No 739510. The Dementia Research Centre is supported by Alzheimer’s Research UK, Alzheimer’s Society, Brain Research UK, and The Wolfson Foundation. This work was supported by the NIHR UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility, and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer’s Research UK. This work was also supported by the JPND GENFI-PROX grant (2019-02248; to JDR, MO, BB, CG, JvS and MS. [latter via DLR/DFG 01ED2008B]). JDR is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). This work was also supported by the MRC UK GENFI grant (MR/M023664/1), the Bluefield Project and the JPND GENFI-PROX grant (2019-02248). Several authors of this publication are members of the European Reference Network for Rare Neurological Diseases—Project ID No 739510. RC/CG are supported by a Frontotemporal Dementia Research Studentships in Memory of David Blechner funded through The National Brain Appeal (RCN 290173). MB is supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517). MB’s work is also supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. JCVS was supported by the Dioraphte Foundation grant 09-02-03-00, the Association for Frontotemporal Dementias Research Grant 2009, The Netherlands Organization for Scientific Research (NWO) grant HCMI 056-13-018, ZonMw Memorabel (Deltaplan Dementie, project number 733 051 042), Alzheimer Nederland and the Bluefield project. FM received funding from the Tau Consortium and the Center for Networked Biomedical Research on Neurodegenerative Disease (CIBERNED). RS-V is supported by an Alzheimer’s Research UK Clinical Research Training Fellowship (ARUK-CRF2017B-2), and has received funding from Fundació Marató de TV3, Spain (grant no. 20143810). CG received funding from JPND-Prefrontals VR Dnr 529-2014-7504, VR 2015-02926 and 2018-02754, the Swedish FTD Inititative-Schörling Foundation, Alzheimer Foundation, Brain Foundation and Stockholm County Council ALF. MM has received funding from a Canadian Institute of Health Research operating grant and the Weston Brain Institute and Ontario Brain Institute. JBR has received funding from the Welcome Trust (103838) and is supported by the Cambridge University Centre for Frontotemporal Dementia, the Medical Research Council (SUAG/051 G101400) and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). EF has received funding from a CIHR grant #327387. DG received support from the EU Joint Programme—Neurodegenerative Disease Research (JPND) and the Italian Ministry of Health (PreFrontALS) grant 733051042. RV has received funding from the Mady Browaeys Fund for Research into Frontotemporal Dementia. MO has received funding from BMBF (FTLDc). JL received funding for this work by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy—ID 390857198). Group authorship for the Genetic FTD Initiative (GENFI): Annabel Nelson: Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK. David L Thomas: Neuroimaging Analysis Centre, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London, UK. Emily Todd: Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK. Hanya Benotmane: UK Dementia Research Institute at University College London, UCL Queen Square Institute of Neurology, London, UK. Jennifer Nicholas: Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK. Rachelle Shafei: Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK. Carolyn Timberlake: Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK. Thomas Cope: Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK. Timothy Rittman: Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK. Alberto Benussi: Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy. Enrico Premi: Stroke Unit, ASST Brescia Hospital, Brescia, Italy. Roberto Gasparotti: Neuroradiology Unit, University of Brescia, Brescia, Italy. Silvana Archetti: Biotechnology Laboratory, Department of Diagnostics, ASST Brescia Hospital, Brescia, Italy. Stefano Gazzina: Neurology, ASST Brescia Hospital, Brescia, Italy. Valentina Cantoni: Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy. Andrea Arighi: Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy. Chiara Fenoglio: Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy. Elio Scarpini: Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy. Giorgio Fumagalli: Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy. Vittoria Borracci: Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy. Giacomina Rossi: Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy. Giorgio Giaccone: Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy. Giuseppe Di Fede: Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy. Paola Caroppo: Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy. Pietro Tiraboschi: Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy. Sara Prioni: Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy. Veronica Redaelli: Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy. David Tang-Wai: The University Health Network, Krembil Research Institute, Toronto, Canada. Ekaterina Rogaeva: Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada. Miguel Castelo-Branco: Faculty of Medicine, University of Coimbra, Coimbra, Portugal. Morris Freedman: Baycrest Health Sciences, Rotman Research Institute, University of Toronto, Toronto, Canada. Ron Keren: The University Health Network, Toronto Rehabilitation Institute, Toronto, Canada. Sandra Black: Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada. Sara Mitchell: Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada. Christen Shoesmith: Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada. Robart Bartha: Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada. Rosa Rademakers: Center for Molecular Neurology, University of Antwerp. Jackie Poos: Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands. Janne M. Papma: Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands. Lucia Giannini: Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands. Rick van Minkelen: Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands. Yolande Pijnenburg: Amsterdam University Medical Centre, Amsterdam VUmc, Amsterdam, Netherlands. Benedetta Nacmias: Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy. Camilla Ferrari: Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy. Cristina Polito: Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, Nuclear Medicine Unit, University of Florence, Florence, Italy. Gemma Lombardi: Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy. Valentina Bessi: Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy. Michele Veldsman: Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK. Christin Andersson: Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. Hakan Thonberg: Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden. Linn Öijerstedt: Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden; Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden. Vesna Jelic: Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden. Paul Thompson: Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK. Tobias Langheinrich: Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK; Manchester Centre for Clinical Neurosciences, Department of Neurology, Salford Royal NHS Foundation Trust, Manchester, UK. Albert Lladó: Alzheimer’s disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain. Anna Antonell: Alzheimer’s disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain. Jaume Olives: Alzheimer’s disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain. Mircea Balasa: Alzheimer’s disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain. Nuria Bargalló: Imaging Diagnostic Center, Hospital Clínic, Barcelona, Spain. Sergi Borrego-Ecija: Alzheimer’s disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain. Ana Verdelho: Department of Neurosciences and Mental Health, Centro Hospitalar Lisboa Norte—Hospital de Santa Maria & Faculty of Medicine, University of Lisbon, Lisbon, Portugal. Carolina Maruta: Laboratory of Language Research, Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal. Catarina B. Ferreira: Laboratory of Neurosciences, Faculty of Medicine, University of Lisbon, Lisbon, Portugal. Gabriel Miltenberger: Faculty of Medicine, University of Lisbon, Lisbon, Portugal. Frederico Simões do Couto: Faculdade de Medicina, Universidade Católica Portuguesa. Alazne Gabilondo: Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain; Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain. Ana Gorostidi: Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain. Jorge Villanua: OSATEK, University of Donostia, San Sebastian, Gipuzkoa, Spain. Marta Cañada: CITA Alzheimer, San Sebastian, Gipuzkoa, Spain. Mikel Tainta: Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain. Miren Zulaica: Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain. Myriam Barandiaran: Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain; Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain. Patricia Alves: Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain; Department of Educational Psychology and Psychobiology, Faculty of Education, International University of La Rioja, Logroño, Spain. Benjamin Bender: Department of Diagnostic and Interventional Neuroradiology, University of Tübingen, Tübingen, Germany. Carlo Wilke: Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany; Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany. Lisa Graf: Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany. Annick Vogels: Department of Human Genetics, KU Leuven, Leuven, Belgium. Mathieu Vandenbulcke: Geriatric Psychiatry Service, University Hospitals Leuven, Belgium; Neuropsychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium. Philip Van Damme: Neurology Service, University Hospitals Leuven, Belgium; Laboratory for Neurobiology, VIB-KU Leuven Centre for Brain Research, Leuven, Belgium. Rose Bruffaerts: Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium; Biomedical Research Institute, Hasselt University, 3500 Hasselt, Belgium. Koen Poesen: Laboratory for Molecular Neurobiomarker Research, KU Leuven, Leuven, Belgium. Pedro Rosa-Neto: Translational Neuroimaging Laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, Québec, Canada. Serge Gauthier: Alzheimer Disease Research Unit, McGill Centre for Studies in Aging, Department of Neurology & Neurosurgery, McGill University, Montreal, Québec, Canada. Agnès Camuzat: Sorbonne Université, Paris Brain Institute—Institut du Cerveau—ICM, Inserm U1127, CNRS UMR 7225, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France. Alexis Brice: Sorbonne Université, Paris Brain Institute—Institut du Cerveau—ICM, Inserm U1127, CNRS UMR 7225, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France; Reference Network for Rare Neurological Diseases (ERN-RND). Anne Bertrand: Sorbonne Université, Paris Brain Institute—Institut du Cerveau—ICM, Inserm U1127, CNRS UMR 7225, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Institut du Cerveau et la Moelle, Paris, France. Aurélie Funkiewiez: Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France; Sorbonne Université, Paris Brain Institute – Institut du Cerveau—ICM, Inserm U1127, CNRS UMR 7225, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France. Daisy Rinaldi: Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France; Sorbonne Université, Paris Brain Institute—Institut du Cerveau—ICM, Inserm U1127, CNRS UMR 7225, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France; Département de Neurologie, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France. Dario Saracino: Sorbonne Université, Paris Brain Institute – Institut du Cerveau—ICM, Inserm U1127, CNRS UMR 7225, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France. Olivier Colliot: Sorbonne Université, Paris Brain Institute—Institut du Cerveau—ICM, Inserm U1127, CNRS UMR 7225, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Institut du Cerveau et la Moelle, Paris, France. Sabrina Sayah: Sorbonne Université, Paris Brain Institute—Institut du Cerveau—ICM, Inserm U1127, CNRS UMR 7225, AP-HP—Hôpital Pitié-Salpêtrière, Paris, France. Catharina Prix: Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany. Elisabeth Wlasich: Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany. Olivia Wagemann: Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany. Sandra Loosli: Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany. Sonja Schönecker: Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany. Tobias Hoegen: Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany. Jolina Lombardi: Department of Neurology, University of Ulm, Ulm. Sarah Anderl-Straub: Department of Neurology, University of Ulm, Ulm, Germany. Adeline Rollin: CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France. Gregory Kuchcinski: Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France. Maxime Bertoux: Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France. Thibaud Lebouvier: Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France. Vincent Deramecourt: Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France. Beatriz Santiago: Neurology Department, Centro Hospitalar e Universitario de Coimbra, Coimbra, Portugal. Diana Duro: Faculty of Medicine, University of Coimbra, Coimbra, Portugal. Maria João Leitão: Centre of Neurosciences and Cell Biology, Universidade de Coimbra, Coimbra, Portugal. Maria Rosario Almeida: Faculty of Medicine, University of Coimbra, Coimbra, Portugal. Miguel Tábuas-Pereira: Neurology Department, Centro Hospitalar e Universitario de Coimbra, Coimbra, Portugal. Sónia Afonso: Instituto Ciencias Nucleares Aplicadas a Saude, Universidade de Coimbra, Coimbra, Portugal

Differential impairment of cerebrospinal fluid synaptic biomarkers in the genetic forms of frontotemporal dementia

Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.Copyright © The Author(s) 2022. Background: Approximately a third of frontotemporal dementia (FTD) is genetic with mutations in three genes accounting for most of the inheritance: C9orf72, GRN, and MAPT. Impaired synaptic health is a common mechanism in all three genetic variants, so developing fluid biomarkers of this process could be useful as a readout of cellular dysfunction within therapeutic trials. Methods: A total of 193 cerebrospinal fluid (CSF) samples from the GENetic FTD Initiative including 77 presymptomatic (31 C9orf72, 23 GRN, 23 MAPT) and 55 symptomatic (26 C9orf72, 17 GRN, 12 MAPT) mutation carriers as well as 61 mutation-negative controls were measured using a microflow LC PRM-MS set-up targeting 15 synaptic proteins: AP-2 complex subunit beta, complexin-2, beta-synuclein, gamma-synuclein, 14–3-3 proteins (eta, epsilon, zeta/delta), neurogranin, Rab GDP dissociation inhibitor alpha (Rab GDI alpha), syntaxin-1B, syntaxin-7, phosphatidylethanolamine-binding protein 1 (PEBP-1), neuronal pentraxin receptor (NPTXR), neuronal pentraxin 1 (NPTX1), and neuronal pentraxin 2 (NPTX2). Mutation carrier groups were compared to each other and to controls using a bootstrapped linear regression model, adjusting for age and sex. Results: CSF levels of eight proteins were increased only in symptomatic MAPT mutation carriers (compared with controls) and not in symptomatic C9orf72 or GRN mutation carriers: beta-synuclein, gamma-synuclein, 14–3-3-eta, neurogranin, Rab GDI alpha, syntaxin-1B, syntaxin-7, and PEBP-1, with three other proteins increased in MAPT mutation carriers compared with the other genetic groups (AP-2 complex subunit beta, complexin-2, and 14–3-3 zeta/delta). In contrast, CSF NPTX1 and NPTX2 levels were affected in all three genetic groups (decreased compared with controls), with NPTXR concentrations being affected in C9orf72 and GRN mutation carriers only (decreased compared with controls). No changes were seen in the CSF levels of these proteins in presymptomatic mutation carriers. Concentrations of the neuronal pentraxins were correlated with brain volumes in the presymptomatic period for the C9orf72 and GRN groups, suggesting that they become abnormal in proximity to symptom onset. Conclusions: Differential synaptic impairment is seen in the genetic forms of FTD, with abnormalities in multiple measures in those with MAPT mutations, but only changes in neuronal pentraxins within the GRN and C9orf72 mutation groups. Such markers may be useful in future trials as measures of synaptic dysfunction, but further work is needed to understand how these markers change throughout the course of the disease.NIHR UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility, and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd., funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK

Temporal dynamics predict symptom onset and cognitive decline in familial frontotemporal dementia

Copyright © 2022 The Authors. Introduction We tested whether changes in functional networks predict cognitive decline and conversion from the presymptomatic prodrome to symptomatic disease in familial frontotemporal dementia (FTD). Methods For hypothesis generation, 36 participants with behavioral variant FTD (bvFTD) and 34 controls were recruited from one site. For hypothesis testing, we studied 198 symptomatic FTD mutation carriers, 341 presymptomatic mutation carriers, and 329 family members without mutations. We compared functional network dynamics between groups, with clinical severity and with longitudinal clinical progression. Results: We identified a characteristic pattern of dynamic network changes in FTD, which correlated with neuropsychological impairment. Among presymptomatic mutation carriers, this pattern of network dynamics was found to a greater extent in those who subsequently converted to the symptomatic phase. Baseline network dynamic changes predicted future cognitive decline in symptomatic participants and older presymptomatic participants. Discussion: Dynamic network abnormalities in FTD predict cognitive decline and symptomatic conversion. Highlights: 1. We investigated brain network predictors of dementia symptom onset. 2. Frontotemporal dementia results in characteristic dynamic network patterns. 3. Alterations in network dynamics are associated with neuropsychological impairment. 4. Network dynamic changes predict symptomatic conversion in presymptomatic carriers. 5. Network dynamic changes are associated with longitudinal cognitive decline.Medical Research Council UK. Grant Numbers: MR/M023664/1, SUAG/092116768 JPND GENFI-PROX. Grant Number: DLR/BMBF 2019-02248 Munich Cluster for Systems Neurology. Grant Number: 390857198 National Institute for Health Research (NIHR) Biomedical Research Centre at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge. Grant Number: BRC-1215-20014 Cambridge Centre for Parkinson-plus. Grant Number: RG95450 Wellcome Trust. Grant Number: 22025

Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia

Copyright © The Author(s) 2022. Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative. B.M. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN #017-04265) and from the Canada Research Chairs Program. S.D. receives salary support from the Fonds de Recherche du Québec—Santé (FRQS). G.S. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Fonds de recherche du Québec—Nature et Technologies (FRQNT). V.B. acknowledges support from the Fonds de recherche du Québec—Nature et Technologies (FRQNT). FTLDNI data collection and sharing was funded by the Frontotemporal Lobar Degeneration Neuroimaging Initiative (National Institutes of Health Grant R01 AG032306) and is coordinated through the University of California, San Francisco, Memory and Aging Center. FTLDNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

Diagnostic accuracy of research criteria for prodromal frontotemporal dementia

Background The Genetic Frontotemporal Initiative Staging Group has proposed clinical criteria for the diagno sis of prodromal frontotemporal dementia (FTD), termed mild cognitive and/or behavioral and/or motor impair ment (MCBMI). The objective of the study was to validate the proposed research criteria for MCBMI‑FTD in a cohort of genetically confirmed FTD cases against healthy controls. Methods A total of 398 participants were enrolled, 117 of whom were carriers of an FTD pathogenic variant with mild clinical symptoms, while 281 were non‑carrier family members (healthy controls (HC)). A subgroup of patients underwent blood neurofilament light (NfL) levels and anterior cingulate atrophy assessment. Results The core clinical criteria correctly classified MCBMI vs HC with an AUC of 0.79 (p < 0.001), while the addition of either blood NfL or anterior cingulate atrophy significantly increased the AUC to 0.84 and 0.82, respectively (p < 0.001). The addition of both markers further increased the AUC to 0.90 (p < 0.001). Conclusions The proposed MCBMI criteria showed very good classification accuracy for identifying the prodromal stage of FTD.MRC UK GENFI grant (MR/M023664/1), the Bluefield Project, and the JPND GENFI-PROX grant (2019-02248). Several authors of this publication are members of the European Reference Network for Rare Neurological Diseases – Project ID No 739510. AB was supported by the Airalzh-AGYR2020, by Fondazione Cariplo (grant n° 2021-1516), and by the Fondation pour la Recherche sur Alzheimer. JCVS was supported by the Dioraphte Foundation grant 09-02-03-00, the Association for Frontotemporal Dementias Research Grant 2009, the Netherlands Organisation for Scientific Research (NWO) grant HCMI 056-13-018, ZonMw Memorabel (Deltaplan Dementie, project number 733 051 042), Alzheimer Nederland, and the Bluefield project. FM received funding from the Tau Consortium and the Center for Networked Biomedical Research on Neurodegenerative Disease (CIBERNED). RS-V has received funding from Fundació Marató de TV3, Spain (grant no. 20143810). CG received funding from JPND-Prefrontals VR Dnr 529-2014-7504, VR 2015-02926, and 2018-02754; the Swedish FTD Inititative-Schörling Foundation; Alzheimer Foundation; Brain Foundation; and Stockholm County Council ALF. MM has received funding from a Canadian Institute of Health Research operating grant and the Weston Brain Institute and Ontario Brain Institute. JBR has received funding from the Welcome Trust (220258), the Cambridge University Centre for Frontotemporal Dementia, the Medical Research Council (SUAG/051 G101400), and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). EF has received funding from a CIHR grant #327387. DG received support from the EU Joint Programme – Neurodegenerative Disease Research (JPND) and the Italian Ministry of Health (PreFrontALS) grant 733051042. RV has received funding from the Mady Browaeys Fund for Research into Frontotemporal Dementia. MO has received funding from BMBF (FTLDc). HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018-02532); the European Research Council (#681712), Swedish State Support for Clinical Research (#ALFGBG-720931); the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862); the AD Strategic Fund and the Alzheimer’s Association (#ADSF-21-831376-C, #ADSF-21-831381-C, and #ADSF-21-831377-C); the Olav Thon Foundation; the Erling-Persson Family Foundation, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2019-0228); the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 860197 (MIRIADE); the European Union Joint Program for Neurodegenerative Disorders (JPND2021-00694); and the UK Dementia Research Institute at UCL. JL received funding for this work from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy – ID 390857198). JDR is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH)