Comparison of clinical rating scales in genetic frontotemporal dementia within the GENFI cohort

BACKGROUND: Therapeutic trials are now underway in genetic forms of frontotemporal dementia (FTD) but clinical outcome measures are limited. The two most commonly used measures, the Clinical Dementia Rating (CDR)+National Alzheimer’s Disease Coordinating Center (NACC) Frontotemporal Lobar Degeneration (FTLD) and the FTD Rating Scale (FRS), have yet to be compared in detail in the genetic forms of FTD. METHODS: The CDR+NACC FTLD and FRS were assessed cross-sectionally in 725 consecutively recruited participants from the Genetic FTD Initiative: 457 mutation carriers (77 microtubule-associated protein tau (MAPT), 187 GRN, 193 C9orf72) and 268 family members without mutations (non-carrier control group). 231 mutation carriers (51 MAPT, 92 GRN, 88 C9orf72) and 145 non-carriers had available longitudinal data at a follow-up time point. RESULTS: Cross-sectionally, the mean FRS score was lower in all genetic groups compared with controls: GRN mutation carriers mean 83.4 (SD 27.0), MAPT mutation carriers 78.2 (28.8), C9orf72 mutation carriers 71.0 (34.0), controls 96.2 (7.7), p<0.001 for all comparisons, while the mean CDR+NACC FTLD Sum of Boxes was significantly higher in all genetic groups: GRN mutation carriers mean 2.6 (5.2), MAPT mutation carriers 3.2 (5.6), C9orf72 mutation carriers 4.2 (6.2), controls 0.2 (0.6), p<0.001 for all comparisons. Mean FRS score decreased and CDR+NACC FTLD Sum of Boxes increased with increasing disease severity within each individual genetic group. FRS and CDR+NACC FTLD Sum of Boxes scores were strongly negatively correlated across all mutation carriers (r_{s} =−0.77, p<0.001) and within each genetic group (r_{s} =−0.67 to −0.81, p<0.001 in each group). Nonetheless, discrepancies in disease staging were seen between the scales, and with each scale and clinician-judged symptomatic status. Longitudinally, annualised change in both FRS and CDR+NACC FTLD Sum of Boxes scores initially increased with disease severity level before decreasing in those with the most severe disease: controls −0.1 (6.0) for FRS, −0.1 (0.4) for CDR+NACC FTLD Sum of Boxes, asymptomatic mutation carriers −0.5 (8.2), 0.2 (0.9), prodromal disease −2.3 (9.9), 0.6 (2.7), mild disease −10.2 (18.6), 3.0 (4.1), moderate disease −9.6 (16.6), 4.4 (4.0), severe disease −2.7 (8.3), 1.7 (3.3). Sample sizes were calculated for a trial of prodromal mutation carriers: over 180 participants per arm would be needed to detect a moderate sized effect (30%) for both outcome measures, with sample sizes lower for the FRS. CONCLUSIONS: Both the FRS and CDR+NACC FTLD measure disease severity in genetic FTD mutation carriers throughout the timeline of their disease, although the FRS may be preferable as an outcome measure. However, neither address a number of key symptoms in the FTD spectrum, for example, motor and neuropsychiatric deficits, which future scales will need to incorporate

MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes

Objective: Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes. Methods: We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask–WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA). Results: Radiomic features were predictive of WMH burden (R2 = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-valuesCV1–6 < 0.001, p-valueCV7 = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes. Conclusion: Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients’ brain health

Clinical associations and prognostic value of MRI-visible perivascular spaces in patients with ischemic stroke or TIA: a pooled analysis

BACKGROUND AND OBJECTIVES: Visible perivascular spaces are an MRI marker of cerebral small vessel disease and might predict future stroke. However, results from existing studies vary. We aimed to clarify this through a large collaborative multicenter analysis. METHODS: We pooled individual patient data from a consortium of prospective cohort studies. Participants had recent ischemic stroke or transient ischemic attack (TIA), underwent baseline MRI, and were followed up for ischemic stroke and symptomatic intracranial hemorrhage (ICH). Perivascular spaces in the basal ganglia (BGPVS) and perivascular spaces in the centrum semiovale (CSOPVS) were rated locally using a validated visual scale. We investigated clinical and radiologic associations cross-sectionally using multinomial logistic regression and prospective associations with ischemic stroke and ICH using Cox regression. RESULTS: We included 7,778 participants (mean age 70.6 years; 42.7% female) from 16 studies, followed up for a median of 1.44 years. Eighty ICH and 424 ischemic strokes occurred. BGPVS were associated with increasing age, hypertension, previous ischemic stroke, previous ICH, lacunes, cerebral microbleeds, and white matter hyperintensities. CSOPVS showed consistently weaker associations. Prospectively, after adjusting for potential confounders including cerebral microbleeds, increasing BGPVS burden was independently associated with future ischemic stroke (versus 0-10 BGPVS, 11-20 BGPVS: HR 1.19, 95% CI 0.93-1.53; 21+ BGPVS: HR 1.50, 95% CI 1.10-2.06; = 0.040). Higher BGPVS burden was associated with increased ICH risk in univariable analysis, but not in adjusted analyses. CSOPVS were not significantly associated with either outcome. DISCUSSION: In patients with ischemic stroke or TIA, increasing BGPVS burden is associated with more severe cerebral small vessel disease and higher ischemic stroke risk. Neither BGPVS nor CSOPVS were independently associated with future ICH

Network impact score is an independent predictor of post-stroke cognitive impairment: A multicenter cohort study in 2341 patients with acute ischemic stroke

BACKGROUND: Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction. AIMS: To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline. METHODS: We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI 24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site. RESULTS: We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) 24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34-1.68) and multivariable (OR 1.27, 95%CI 1.10-1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline. CONCLUSIONS: The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/

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

Structural MRI predicts clinical progression in presymptomatic genetic frontotemporal dementia: findings from the GENetic Frontotemporal dementia Initiative (GENFI) cohort

Abstract Biomarkers that can predict disease progression in individuals with genetic frontotemporal dementia are urgently needed. We aimed to identify whether baseline MRI-based grey and white matter abnormalities are associated with different clinical progression profiles in presymptomatic mutation carriers in the GENetic Frontotemporal dementia Initiative. 387 mutation carriers were included (160 GRN, 160 C9orf72, 67 MAPT), together with 240 non-carrier cognitively normal controls. Cortical and subcortical grey matter volumes were generated using automated parcellation methods on volumetric 3 T T1-weighted MRI scans, while white matter characteristics were estimated using diffusion tensor imaging. Mutation carriers were divided into two disease stages based on their global CDR®+NACC-FTLD score: presymptomatic (0 or 0.5) and fully symptomatic (1 or greater). W-scores in each grey matter volumes and white matter diffusion measures were computed to quantify the degree of abnormality compared to controls for each presymptomatic carrier, adjusting for their age, sex, total intracranial volume, and scanner type. Presymptomatic carriers were classified as “normal” or “abnormal” based on whether their grey matter volume and white matter diffusion measure w-scores were above or below the cut point corresponding to the 10th percentile of the controls. We then compared the change in disease severity between baseline and one year later in both the “normal” and “abnormal” groups within each genetic subtype, as measured by the CDR®+NACC-FTLD sum-of-boxes score and revised Cambridge Behavioural Inventory total score. Overall, presymptomatic carriers with normal regional w-scores at baseline did not progress clinically as much as those with abnormal regional w-scores. Having abnormal grey or white matter measures at baseline was associated with a statistically significant increase in the CDR®+NACC-FTLD of up to 4 points in C9orf72 expansion carriers, and 5 points in the GRN group as well as a statistically significant increase in the revised Cambridge Behavioural Inventory of up to 11 points in MAPT, 10 points in GRN, and 8 points in C9orf72 mutation carriers. Baseline regional brain abnormalities on MRI in presymptomatic mutation carriers are associated with different profiles of clinical progression over time. These results may be helpful to inform stratification of participants in future trials

Progression of Behavioral Disturbances and Neuropsychiatric Symptoms in Patients with Genetic Frontotemporal Dementia

Group Information: The Genetic FTD Initiative Group Investigators and Coordinators are listed in Supplement 2 available online at https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2774641#note-ZOI200951-1 .Correction: This article was corrected on March 31, 2021, to include the nonauthor collaborator names in a supplement.Corresponding Author: Barbara Borroni, MD, Clinica Neurologica, Università degli Studi di Brescia, P.le Spedali Civili 1, 25123 Brescia, Italy ([email protected]).Author Contributions: Dr Borroni had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.Copyright © 2021 Benussi A. et al. Importance: Behavioral disturbances are core features of frontotemporal dementia (FTD); however, symptom progression across the course of disease is not well characterized in genetic FTD. Objective: To investigate behavioral symptom frequency and severity and their evolution and progression in different forms of genetic FTD. Design, Setting, and Participants: This longitudinal cohort study, the international Genetic FTD Initiative (GENFI), was conducted from January 30, 2012, to May 31, 2019, at 23 multicenter specialist tertiary FTD research clinics in the United Kingdom, the Netherlands, Belgium, France, Spain, Portugal, Italy, Germany, Sweden, Finland, and Canada. Participants included a consecutive sample of 232 symptomatic FTD gene variation carriers comprising 115 with variations in C9orf72, 78 in GRN, and 39 in MAPT. A total of 101 carriers had at least 1 follow-up evaluation (for a total of 400 assessments). Gene variations were included only if considered pathogenetic. Main Outcomes and Measures: Behavioral and neuropsychiatric symptoms were assessed across disease duration and evaluated from symptom onset. Hierarchical generalized linear mixed models were used to model behavioral and neuropsychiatric measures as a function of disease duration and variation. Results: Of 232 patients with FTD, 115 (49.6%) had a C9orf72 expansion (median [interquartile range (IQR)] age at evaluation, 64.3 [57.5-69.7] years; 72 men [62.6%]; 115 White patients [100%]), 78 (33.6%) had a GRN variant (median [IQR] age, 63.4 [58.3-68.8] years; 40 women [51.3%]; 77 White patients [98.7%]), and 39 (16.8%) had a MAPT variant (median [IQR] age, 56.3 [49.9-62.4] years; 25 men [64.1%]; 37 White patients [94.9%]). All core behavioral symptoms, including disinhibition, apathy, loss of empathy, perseverative behavior, and hyperorality, were highly expressed in all gene variant carriers (>50% patients), with apathy being one of the most common and severe symptoms throughout the disease course (51.7%-100% of patients). Patients with MAPT variants showed the highest frequency and severity of most behavioral symptoms, particularly disinhibition (79.3%-100% of patients) and compulsive behavior (64.3%-100% of patients), compared with C9orf72 carriers (51.7%-95.8% of patients with disinhibition and 34.5%-75.0% with compulsive behavior) and GRN carriers (38.2%-100% with disinhibition and 20.6%-100% with compulsive behavior). Alongside behavioral symptoms, neuropsychiatric symptoms were very frequently reported in patients with genetic FTD: anxiety and depression were most common in GRN carriers (23.8%-100% of patients) and MAPT carriers (26.1%-77.8% of patients); hallucinations, particularly auditory and visual, were most common in C9orf72 carriers (10.3%-54.5% of patients). Most behavioral and neuropsychiatric symptoms increased in the early-intermediate phases and plateaued in the late stages of disease, except for depression, which steadily declined in C9orf72 carriers, and depression and anxiety, which surged only in the late stages in GRN carriers. Conclusions and Relevance: This cohort study suggests that behavioral and neuropsychiatric disturbances differ between the common FTD gene variants and have different trajectories throughout the course of disease. These findings have crucial implications for counseling patients and caregivers and for the design of disease-modifying treatment trials in genetic FTD.This work is supported by the Joint Programme–Neurodegenerative Disease Research grant no. JPND2019-466-090 “GENFI-prox” (Drs Synofzik, van Swieten, Otto, Graff, Rohrer, and Borroni), the Centre d’Investigation Clinique grant no. ANR/DGOS PRTS 2015-2019 PREV-DEMALS (Dr Le Ber), the Centre pour l’Acquisition et le Traitement des Images platform grant no. ANR-10-IAIHU-06 (Dr Le Ber), the UK Medical Research Council grant no. MR/M023664/1 (Dr Rohrer), the Italian Ministry of Health grant no. 733051042 (Dr Galimberti), and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant no. MOP 327387 (Dr Masellis), a Canadian Institutes of Health Research operating grant

The CBI-R detects early behavioural impairment in genetic frontotemporal dementia

Supporting Information available at: https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Facn3.51544&file=acn351544-sup-0001-supinfo.docx (Word 2007 document, 330.8 KB).Copyright © 2022 The Authors. Introduction: Behavioural dysfunction is a key feature of genetic frontotemporal dementia (FTD) but validated clinical scales measuring behaviour are lacking at present. Methods: We assessed behaviour using the revised version of the Cambridge Behavioural Inventory (CBI-R) in 733 participants from the Genetic FTD Initiative study: 466 mutation carriers (195 C9orf72, 76 MAPT, 195 GRN) and 267 non-mutation carriers (controls). All mutation carriers were stratified according to their global CDR plus NACC FTLD score into three groups: asymptomatic (CDR = 0), prodromal (CDR = 0.5) and symptomatic (CDR = 1+). Mixed-effects models adjusted for age, education, sex and family clustering were used to compare between the groups. Neuroanatomical correlates of the individual domains were assessed within each genetic group. Results: CBI-R total scores were significantly higher in all CDR 1+ mutation carrier groups compared with controls [C9orf72 mean 70.5 (standard deviation 27.8), GRN 56.2 (33.5), MAPT 62.1 (36.9)] as well as their respective CDR 0.5 groups [C9orf72 13.5 (14.4), GRN 13.3 (13.5), MAPT 9.4 (10.4)] and CDR 0 groups [C9orf72 6.0 (7.9), GRN 3.6 (6.0), MAPT 8.5 (13.3)]. The C9orf72 and GRN 0.5 groups scored significantly higher than the controls. The greatest impairment was seen in the Motivation domain for the C9orf72 and GRN symptomatic groups, whilst in the symptomatic MAPTgroup, the highest-scoring domains were Stereotypic and Motor Behaviours and Memory and Orientation. Neural correlates of each CBI-R domain largely overlapped across the different mutation carrier groups. Conclusions: The CBI-R detects early behavioural change in genetic FTD, suggesting that it could be a useful measure within future clinical trials.Research Funding: Alzheimer's Society. Grant Number: AS-JF-19a-004-517 EU Joint Programme - Neurodegenerative Disease Research. Grant Numbers: 2019-02248, 739510 Medical Research Council. Grant Numbers: MR/M008525/1, MR/M023664/1 The National Brain Appeal. Grant Number: RCN 290173 JPND GENFI-PROX. Grant Number: 2019-02248 NIHR Rare Disease Translational Research Collaboration. Grant Number: BRC149/NS/MH UK Dementia Research Institute NIHR UCL/H Biomedical Research Centre The Wolfson Foundation Brain Research UK Alzheimer’s Research UK Article Funding: Open Access funding enabled and organized by Projekt DEAL

Differential early subcortical involvement in genetic FTD within the GENFI cohort

Supplementary data: available online at: https://www.sciencedirect.com/science/article/pii/S2213158221000905?via%3Dihub#s0070 .Background: Studies have previously shown evidence for presymptomatic cortical atrophy in genetic FTD. Whilst initial investigations have also identified early deep grey matter volume loss, little is known about the extent of subcortical involvement, particularly within subregions, and how this differs between genetic groups. Methods: 480 mutation carriers from the Genetic FTD Initiative (GENFI) were included (198 GRN, 202 C9orf72, 80 MAPT), together with 298 non-carrier cognitively normal controls. Cortical and subcortical volumes of interest were generated using automated parcellation methods on volumetric 3 T T1-weighted MRI scans. Mutation carriers were divided into three disease stages based on their global CDR® plus NACC FTLD score: asymptomatic (0), possibly or mildly symptomatic (0.5) and fully symptomatic (1 or more). Results: In all three groups, subcortical involvement was seen at the CDR 0.5 stage prior to phenoconversion, whereas in the C9orf72 and MAPT mutation carriers there was also involvement at the CDR 0 stage. In the C9orf72 expansion carriers the earliest volume changes were in thalamic subnuclei (particularly pulvinar and lateral geniculate, 9–10%) cerebellum (lobules VIIa-Crus II and VIIIb, 2–3%), hippocampus (particularly presubiculum and CA1, 2–3%), amygdala (all subregions, 2–6%) and hypothalamus (superior tuberal region, 1%). In MAPT mutation carriers changes were seen at CDR 0 in the hippocampus (subiculum, presubiculum and tail, 3–4%) and amygdala (accessory basal and superficial nuclei, 2–4%). GRN mutation carriers showed subcortical differences at CDR 0.5 in the presubiculum of the hippocampus (8%). Conclusions: C9orf72 expansion carriers show the earliest and most widespread changes including the thalamus, basal ganglia and medial temporal lobe. By investigating individual subregions, changes can also be seen at CDR 0 in MAPT mutation carriers within the limbic system. Our results suggest that subcortical brain volumes may be used as markers of neurodegeneration even prior to the onset of prodromal symptoms.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 National Institute for Health Research (NIHR) Queen Square Dementia Biomedical Research Unit and the University College London Hospitals 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 MRC UK GENFI grant (MR/M023664/1), the Italian Ministry of Health (CoEN015 and Ricerca Corrente), the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, a Canadian Institutes of Health Research operating grant, the Alzheimer's Society grant (AS-PG-16-007), the Bluefield Project and the JPND GENFI-PROX grant (2019-02248). MB is supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517). MB’s work was 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. MB acknowledges the support of NVIDIA Corporation with the donation of the Titan V GPU used for part of the analyses in this research. JDR is an MRC Clinician Scientist (MR/M008525/1) and has received funding from the NIHR Rare Diseases Translational Research Collaboration (BRC149/NS/MH), the Bluefield Project and the Association for Frontotemporal Degeneration. JEI is supported by the European Research Council (Starting Grant 677697, project BUNGEE-TOOLS), Alzheimer’s Research UK (ARUK-IRG2019A003) and NIH 1RF1MH123195-01. JBR is funded by the Wellcome Trust (103838) and the National Institute for Health Research Cambridge Biomedical Research Centre. This work was funded 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). Several authors of this publication (JCvS, MS, RSV, AD, MO, JDR) are members of the European Reference Network for Rare Neurological Diseases (ERN-RND) - Project ID No 739510