# Anatomical progression of genetic frontotemporal lobar degeneration across the lifespan

Vincent Planche, Boris Mansencal, Vladimir Fonov, José Manjon, Thomas Tourdias, Arabella Bouzigues, Lucy Russell, Phoebe Foster, Eve Ferry-Bolder, John van Swieten, et al.

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Vincent Planche, Boris Mansencal, Vladimir Fonov, José Manjon, Thomas Tourdias, et al. Anatomical progression of genetic frontotemporal lobar degeneration across the lifespan. Brain - A Journal of Neurology, 2025, 10.1093/brain/awaf195. hal-05090549

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Anatomical progression of genetic frontotemporal lobar degeneration across the lifespan

<table><tr><td>Journal:</td><td>Brain</td></tr><tr><td>Manuscript ID:</td><td>BRAIN-2024-02906.R2</td></tr><tr><td>Manuscript Type:</td><td>Original Article</td></tr><tr><td>Date Submitted by the Author:</td><td>n/a</td></tr><tr><td>Complete List of Authors:</td><td>Planche, Vincent; Institut des Maladies Neurodegenerative, Centre Broca Mansencal, Boris; Bordeaux INP, LaBRI Fonov, Vladimir; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Manjon, Jose; Universitat Politècnica de València, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas Tourdias, Thomas; Université Victor Segalen Bordeaux 2, EA 2966 Neurobiologie des affections de la myeline Bouzigues, Arabella; University College London Dementia Research Centre Russell, Lucy; UCL, Dementia Research Centre Foster, Phoebe; Dementia Research Centre, UCL Queen Square Institute of Neurology Ferry-Bolder, Eve; University College London Dementia Research Centre van Swieten, John; Erasmus Medical Center, Neurology Jiskoot, Lize; Erasmus Medical Center, Neurology; Erasmus MC, Seelaar, Harro; Erasmus Medical Centre, Neurology Sanchez-Valle, Raquel; Hospital Clinic de Barcelona, Alzheimer's Disease and Other Cognitive Disorders Unit; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Laforce, Robert; Laval University, Clinique Interdisciplinaire de Mémoire du CHU de Québec Graff, Caroline; Karolinska Institute, Department of Neurobiology, Care Sciences and Society, KI-ADRC, KASPAC, Novum Galimberti, Daniela; University of Milan, Ospedale Maggiore Policlinico, Neurological Sciences Vandenberghe, Rik; University Hospitals Leuven, Neurology de Mendonça, Alexandre; University of Lisbon, Tiraboschi, Pietro; 1Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico "Carlo Besta", Milan, Italy, Santana, Isabel; University of Coimbra Gerhard, Alexander; The University of Manchester, WMIC; University of Manchester, Levin, Johannes; University of Munich, Department of Neurology Sorbi, Sandro; University of Florence, Department of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA) Otto, Markus; Martin-Luther-Universitat Halle-Wittenberg, Neurology; Bertoux, Maxime; Inserm U1171 - Degenerative and vascular cognitive</td></tr></table>
disorders. ,
Lebouvier, Thibaud; UMR 1172, Alzheimer & Tauopathies
Butler, Christopher; Imperial College London
Le Ber, Isabelle; CR-ICM UMRS975, Hopital de la Salpêtrière; Centre de référence des démences rares, Hopital de la Salpêtrière
Finger, Elizabeth; University of Western Ontario, Clinical Neurological Sciences
Tartaglia, Maria Carmela; University of Toronto,
Masellis, Mario; University of Toronto, Sunnybrook Health Sciences
Centre, Sunnybrook Research Institute
Rowe, James; University of Cambridge, Clinical Neurosciences ;
UNiveristy of Cambridge
Synofzik, Matthis; Hertie Institute for Clinical Brain Research, University
Clinic Tübingen, Neurodegeneration
Moreno, Fermin; Donostia University Hospital, San Sebastian
Borroni, Barbara; Universita degli Studi di Brescia Dipartimento di Scienze Cliniche e Sperimentali, clinical and Experimental sciences
Rohrer, Jonathan; Dementia Research Centre, UCL Institute of Neurology
Collins, D. Louis; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery; McConnell Brain Imaging, Montreal Neurological Institute
Ducharme, Simon; Montreal Neurological Institute and Hospital,
Psychiatry; Psychiatry
Coupé, Pierrick; Laboratoire Bordelais de Recherche en Informatique

Methodology: IMAGING

Subject area: DEMENTIA, Neuropsychiatry
# Anatomical progression of genetic frontotemporal lobar degeneration across the lifespan

Vincent Planche¹,², Boris Mansencal³, Vladimir Fonov⁴, José V. Manjon⁵, Thomas Tourdias⁶,⁷, Arabella Bouzigues⁸, Lucy L. Russell⁸, Phoebe H. Foster⁸, Eve Ferry-Bolder⁸, John C. van Swieten⁹, Lize C. Jiskoot⁹, Harro Seelaar⁹, Raquel Sanchez-Valle¹⁰, Robert Laforce¹¹, Caroline Graff¹², Daniela Galimberti¹³,¹⁴, Rik Vandenberghe¹⁵, Alexandre de Mendonça¹⁶, Pietro Tiraboschi¹⁷, Isabel Santana¹⁸, Alexander Gerhard¹⁹, Johannes Levin²⁰,²¹,²², Sandro Sorbi²³, Markus Otto²⁴, Maxime Bertoux²⁵, Thibaud Lebouvier²⁵, Chris R. Butler²⁶, Isabelle Le Ber²⁷, Elizabeth Finger²⁸, Maria Carmela Tartaglia²⁹, Mario Masellis³⁰, James B. Rowe³¹, Matthis Synofzik³², Fermin Moreno³³, Barbara Borroni³⁴,³⁵, Jonathan D. Rohrer⁸, D. Louis Collins⁴, Simon Ducharme⁴,³⁶,†, and Pierrick Coupé³,†,
on behalf of the ALLFTD Consortium and the Genetic FTD Initiative (GENFI)

† These authors contributed equally to this work.

## Abstract

The recent development of brain charts for the human lifespan offers an ideal modeling framework for pathologies such as genetic frontotemporal lobar degeneration (FTLD) which likely involve both neurodevelopmental and neurodegenerative processes over a lifetime. We have therefore combined this new methodological approach with MRI data from asymptomatic and symptomatic subjects, carrying *C9orf72*, *MAPT* or *GRN* mutations from the GENFI and ALLFTD cohorts.

We analyzed 37,532 MRIs from control subjects covering the entire lifespan and a total of 1,341 MRIs from subjects with a pathogenic FTLD mutation, aged from 18 to 86 years old. We detected the first significant regional brain volume differences on average at 27 years old in *C9orf72* and *MAPT* mutation carriers, and at 42 years old in *GRN* mutation carriers. The delay between the onset of anatomical changes and the average age of symptom onset (i.e. the presymptomatic phase) was 13 years for *MAPT*, 17 years for *GRN*, and 34 years for *C9orf72* mutation carriers. In terms of effect size, cumulative atrophy over the lifespan was twice as severe in affected brain regions in *MAPT* than in *GRN* or *C9orf72* mutation carriers. However, the neurodegenerative process was spatially more extensive in *C9orf72* (35 brain regions affected out of the 61 tested) compared to *GRN* or *MAPT* mutations carriers (25 and 18 regions,
respectively). Schematically, the chronological staging of atrophy progression showed an initial involvement of the thalamus in *C9orf72* expansion carriers, followed by the fronto-temporo-insular regions, the striatum and the amygdala. In *GRN* mutation carriers, atrophy began in fronto-insular areas, before progressing toward subcortical structures. In *MAPT* mutation carriers, atrophy affected the anterior temporal pole with the amygdala and hippocampus, before progressing to fronto-insular regions and the striatum.

Our results using brain charts for the human lifespan show that *C9orf72* is the most diffuse but also the slowest to emerge among genetic FTLD. *MAPT* FTLD is more aggressive and focal, while *GRN* FTLD is also rapidly progressive but with a later onset of the presymptomatic phase. Beyond quantification of the anatomical progression of genetic FTLD over the lifespan, these results may help determine the best timing to model and test disease-modifying strategies in FTLD, and monitor their effect in future clinical trials.

## Author affiliations:

1. Univ. Bordeaux, CNRS, UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France

2. Centre Mémoire Ressources Recherches, Service de Neurologie des Maladies Neurodégénératives, Pôle de Neurosciences Cliniques, CHU de Bordeaux, France

3. CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, Talence, France

4. McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada.

5. Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Spain

6. Inserm U1215 - Neurocentre Magendie, Bordeaux, France

7. Service de Neuroimagerie diagnostique et thérapeutique, CHU de Bordeaux, France

8. Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK.

9. Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands

10. Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d'Investigacións Biomèdiques August Pi I Sunyer, University of Barcelona, Spain

11. Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, QC, Canada

12. Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Neurogeriatrics, Bioclinicum, Karolinska Institutet, Solna, Sweden

13. Fondazione Ca' Granda, IRCCS Ospedale Policlinico, Milan, Italy

14. Dept. of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy

15. Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium

16. Faculty of Medicine, University of Lisbon, Portugal.

17. Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy
18. University Hospital of Coimbra (HUC), Neurology Service, Faculty of Medicine, University of Coimbra, Portugal

19. Division of Psychology Communication and Human Neuroscience, Wolfson Molecular Imaging Centre, University of Manchester, UK

20. Department of Neurology, Ludwig-Maximilians University Hospital, Munich, Germany

21. German Center for Neurodegenerative Diseases, Munich, Germany

22. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

23. IRCCS Don Carlo Gnocchi Foundation, Department of Neurofarba, University of Florence, Italy

24. Department of Neurology, University of Ulm, Germany

25. Lille Neuroscience & Cognition U1172, University of Lille, Inserm, CHU Lille, France

26. Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK

27. Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France

28. Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada

29. Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, ON, Canada

30. Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Department of Medicine, Division of Neurology, University of Toronto, Canada

31. Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, UK

32. Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Germany

33. Cognitive Disorders Unit, Department of Neurology, Hospital Universitario Donostia, San Sebastian, Spain.

34. Department of Clinical and Experimental Sciences, University of Brescia, Italy

35. IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

36. Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada.

**Correspondance to :** Prof. Vincent Planche, MD., PhD.

Institut des Maladies Neurodégénératives, UMR CNRS 5293,
Centre Broca Nouvelle-Aquitaine, 1

46 rue Léo Saignat

33076 Bordeaux cedex, France;

vincent.planche@u-bordeaux.fr

**Running title:** Anatomical progression of genetic FTLD

**Key words:** frontotemporal lobar degeneration; *C9orf72*; *GRN*; *MAPT*; brain charts; lifespan
# Introduction

Frontotemporal lobar degeneration (FTLD) encompasses a class of neurodegenerative diseases that affects behavior, cognition and movement, most commonly in the form of frontotemporal dementia (FTD). FTLD is a common cause of early-onset dementia (before 65 years old) that can be sporadic or genetic. Genetic cases of FTLD are autosomal dominant and caused most often by mutations in one of three major genes: chromosome 9 open reading frame 72 (*C9orf72*), progranulin (*GRN*) and microtubule-associated protein tau (*MAPT*). Genetic FTLD can manifest through various syndromic presentations, which vary in frequency depending on mutations. Furthermore, they lead to different neuropathologies (neuronal inclusions of tau proteins for *MAPT* mutations and inclusions of TDP-43 proteins for *GRN* mutations and *C9orf72* repeat expansions), and may exhibit different patterns of brain atrophy¹. For instance, thalamic atrophy is frequently described in *C9orf72* expansion carriers, while anterior temporal atrophy is known to be particularly severe in *MAPT* mutation carriers, and more posterior temporal and parietal brain regions may be affected in *GRN* mutation carriers²,³,⁴,⁵.

In recent years, significant progress has been made in describing the early natural history of genetic FTLD, thanks to the establishment of large consortia bringing together data from both asymptomatic and symptomatic mutation carriers⁵,⁶. Early neurodegenerative hallmarks and cognitive changes have been identified up to 10 years before the expected onset of symptoms in a given family⁶ and cortical and subcortical atrophy are known to progress with age⁷. Moreover, some studies in very young subjects, especially *C9orf72* expansion carriers, suggest the possibility of neurodevelopmental effects of mutations involved in genetic FTLD⁷,⁸,⁹. Despite the establishment of these large family cohorts encompassing both asymptomatic and symptomatic subjects, the description of the overall progression of genetic FTLD through neurodevelopmental processes to death is currently lacking. The neuroanatomical staging of these diseases appears however indispensable to refine individual prognosis in the context of precision medicine. Furthermore, with the actual emergence of promising gene therapies for genetic FTLD¹⁰, an anatomical staging of disease progression could help determine the most relevant therapeutic windows in the late presymptomatic stages of these diseases¹¹.

Leveraging big data sharing in the neuroimaging community, the human lifespan brain charts recently developed independently by Coupé and colleagues¹² and by Bethlehem and colleagues¹³ represent major tools for better understanding neurodevelopmental and neurodegenerative processes. Brain charts robustly define lifespan volumetric trajectories and
age-related neuroanatomical changes in multiple brain regions to identify previously unreported milestones in brain morphological changes related to various pathological conditions. They also provide standardized effect-sizes to quantify brain atrophy and timeframes of disease progression. Moreover, we have previously shown that these extrapolated dynamic volumetric models based on cross-sectional MRI were highly correlated with models using ‘truly’ longitudinal data to describe brain atrophy progression in Alzheimer disease¹⁴. The combination of brain charts methodology with brain MRIs from FTLD mutation carriers at all ages and stages of the disease thus offers the unique possibility of defining the archetype course of anatomical alterations of genetic FTLD through pseudo-longitudinal modeling over a lifetime. In this study, we therefore used brain MRIs from control subjects at different ages (n=37,532) collected from multiple studies and brain MRIs from asymptomatic and symptomatic subjects from ALLFTD and GENFI cohorts (n=584 *C9orf72* mutation carriers, n=471 *GRN* mutation carriers and n=286 *MAPT* mutation carriers) to describe and compare the anatomical staging of the three main causes of genetic FTLD.
# Materials and methods

## Standard protocol approvals, registrations and patient consents

All data were obtained in deidentified format upon request from external study centers, who ensured compliance with ethical guidelines. All subjects included in the MRI databases used in this study provided informed consent. The protocol for each study/cohort was approved by the institution review board at all sites (see the Acknowledgments section).

## Datasets

Control and genetic FTLD brain charts were estimated thanks to the aggregation of datasets of T1-weighted MRIs scanned on 1.5T or 3T magnets. After quality control (see below), 37,532 MRIs from control subjects, covering the entire lifespan (from 8 months to 94 years of age) were included in the study. The 21 cohorts with control subjects used in this study are listed in Table 1. References and websites are listed in the Acknowledgements section.

For genetic FTLD, we used MRIs from the ARTFL/LEFFTDS Longitudinal Frontotemporal Lobar Degeneration study (ALLFTD; NCT04363684 which involves 18 centers across the US and Canada) and the Genetic Frontotemporal Initiative (GENFI, which involves 25 research centers across Europe and Canada). All participants carrying a pathogenic mutation in the *GRN* or *MAPT* genes or with a pathogenic expansion in the *C9orf72* gene were included in this study, whether they were asymptomatic or symptomatic. The asymptomatic or symptomatic status of each participant was defined at the time of the MRI by a specialist physician based on standard multidisciplinary assessment that includes neurological history and examination, collateral source interview, and neuropsychological testing, as reported in previous ALLFTD and GENFI publications⁵,⁶. Symptomatic patients were included in the present study regardless of their syndromic presentation, with the exception of *C9orf72* patients with isolated Amyotrophic Lateral Sclerosis (ALS) who were excluded, since we designed this study for the anatomical staging of FTD specifically.

## Image processing

All the T1-weighted MRI were processed with AssemblyNet (freely available at https://github.com/volBrain/AssemblyNet/), which is robust to scanner field strength, as shown in scan-rescan experiments¹⁵. This software produces whole brain segmentation of fine-grained structures using a large ensemble of deep neural networks. AssemblyNet is robust to acquisition protocols, age of subjects and presence of brain pathology¹⁵. All images were preprocessed to
locate them into a common geometrical and intensity space. The preprocessing steps started with denoising<sup>16</sup>, then the images were corrected for inhomogeneity<sup>17</sup> and affine-registered into the Montreal Neurological Institute (MNI) space using ANTS<sup>18</sup>. Finally, a tissue-based intensity normalization was used<sup>19</sup>. For the segmentation process, the intracranial cavity was segmented using the DeepICE method<sup>20</sup>. Afterwards, structure segmentation was achieved using 250 U-Nets through a multi-scale framework<sup>15</sup>.

All images were automatically quality controlled using the artificial intelligence-based method RegQCNET<sup>21</sup>. After this first check, a human-based multi-stage quality control procedure was performed blinded to the subject's group, as previously described<sup>12,22</sup>. A visual assessment was done for all input images by checking screen shots of one sagittal, coronal, and axial slices in the middle of the 3D volume. Images were rejected if partial head coverage, severe motion artefact, high distortion or abnormal noise level was detected. Then, a visual assessment of processing quality was carried out using the segmentation report, which provides screenshots for each pipeline step. Images were rejected after this step for cases of inaccurate registration in the MNI space, inaccurate intracranial cavity extraction, missing brain structures or over/under-segmentation of brain structures.

On the 132 structures produced by AssemblyNet following the Neuromorphometrics labeling scheme<sup>23</sup>, we considered 121 regions of interest (60 left, 60 right and the brainstem as a central structure): 9 subcortical structures, 17 frontal gyri/lobules, 8 temporal gyri/lobules, 6 parietal gyri/lobules, 8 occipital gyri/lobules, 6 gyri in the limbic cortex, 5 sub-regions of the insular cortex, and cerebellar gray matter. Although some patients exhibited asymmetric atrophy, it did not affect on average one hemisphere more than the other in our preliminary analyses, regardless of the mutation. Thus, as we were interested here in the progression of atrophy based on the genetic cause rather than on the syndromic presentation as in our previous work on FTD variants<sup>24</sup>, we added left and right volumes of symmetric brain structures to obtain a global volume, and the analyses therefore focused on 61 structures.

## Modeling brain charts and Statistical Analyses

To limit for the variability introduced by head size difference, models were estimated on normalized volumes (% of total intracranial volume). Moreover, we used z-scores of normalized volumes to compare structures of different sizes. The normal distribution of each normalized volume was tested using Kolmogorov-Smirnov test at 95%. Statistics were performed with Matlab using default parameters.
To build brain charts and study volumetric trajectories of genetic FTLD across the entire lifespan, we followed the strategy we have previously proposed for Alzheimer Disease¹⁴, sporadic FTD²⁴, progressive supranuclear palsy²⁵ and multiple sclerosis²⁶. Our framework assumed that anatomical changes in genetic FTLD are a continuous and progressive process spanning from neurodevelopment to neurodegeneration and ultimately leading to death. Therefore, we built brain charts using both asymptomatic and symptomatic cases at different stages of the disease. Because the youngest mutation carriers were 18 years old in the dataset, we used MRI scans of control subjects to model the early years of life in genetic FTLD brain charts, assuming a very small effect size on anatomical changes at this very young age. Herein, we combined the MRIs of *C9orf72* expansion carriers or *GRN* or *MAPT* mutations carriers with MRIs of 2202 control subjects younger than 18 years to build our lifespan genetic FTLD models.

Different strategies were then considered to model the control and pathological trajectories of each brain structure over time, as previously described²². Briefly, the candidate models were tested from the simplest to the most complex: (1) a linear model, (2) a quadratic model, and (3) a cubic model. A model was kept as a potential candidate only when simultaneously F-statistic based on ANOVA (i.e., model vs. constant model) was significant (p<0.05) and when all its coefficients were significant using t-statistic (p<0.05). We finally used the Bayesian Information Criterion (BIC) to compare the candidate models and we selected the model providing the lowest BIC. This model selection procedure was applied to all the considered structures.

Afterwards, distances between control and genetic FTLD trajectories were computed on the estimated models (Glass delta effect size estimator). The prediction bounds were estimated with an adjusted 95% confidence interval (i.e. 99.92% after a conservative correction for multiple comparisons using Bonferroni correction). A brain structure was considered to be significantly smaller in one pathological condition compared to healthy aging when the two structural trajectories diverged and when their adjusted 95% confidence intervals no longer overlapped (Fig. 1 and Suppl. Fig. 1). Then, all divergent structures were mapped across time and space on standardized sagittal, coronal and axial MRI planes (Fig. 2, 3 & 4). Finally, the sequence of significant divergence of the affected brain structures was listed in chronological order to obtain the MRI staging scheme of *C9orf72*, *GRN*, and *MAPT* FTLD (Fig. 2, 3 & 4).
# Results

## Dataset description

To build the control brain charts and the genetic FTLD brain charts across the entire lifespan, we compiled several open-access databases to construct 4 datasets (Table 1). Their composition and characteristics are described in Table 2. After quality control, 37,532 MRIs from control subjects remained for the analyses and 1,341 MRIs from participants with a pathogenic FTLD mutation.

We included 584 participants with a pathogenic *C9orf72* expansion (405 asymptomatic and 179 symptomatic), 471 participants with a *GRN* mutation (364 asymptomatic and 107 symptomatic), and 286 participants with a *MAPT* mutation (203 asymptomatic and 83 symptomatic). The mean age at symptom onset was 55.5 for people with *C9orf72* expansion, 59.7 for *GRN* mutation and 48.1 for people with *MAPT* mutation. The symptomatic mutation carriers represented a wide range of disease severity at the time of their MRI, as shown by the Clinical Dementia Rating (CDR) scale adapted to FTLD syndromes: the CDR plus NACC FTLD-sum of boxes scores ranged from 0.5 (mild cognitive/behavioral impairment) to 24 (the maximum score) (Table 2).

## Identification of brain structures diverging between control and genetic FTLD brain charts.

We built brain charts for all the 61 anatomical structures proposed by our segmentation atlas and we identified 40 brain structures that significantly diverged during lifespan between control aging models and at least one genetic FTLD group (see examples of diverging brain charts in Figure 1 and the complete list with their corresponding brain charts in Supplementary Fig.1).

The 3 most affected structures over the entire course of these diseases were the amygdala, the caudate nucleus and the thalamus in *C9orf72* expansion carriers (for example, distance between control aging model and *C9orf72* trajectory at 70 years old = 1.8, 1.8 and 1.5 delta effect-size, respectively); the caudate nucleus, the putamen, the nucleus accumbens and the anterior insula in *GRN* mutation carriers (distance = 1.8, 1.3, 1.2 and 1.2) and the amygdala, the hippocampus and the anterior insula in *MAPT* mutation carriers (distance = 4.8, 3.0 and 2.9). Atrophy was therefore more severe in *MAPT* mutations than in other genetic FTLD, adjusted for age and sex.

Atrophy was more diffuse in *C9orf72* expansion carriers: a total of 35 structures were significantly smaller than controls in *C9orf72* expansion carriers, 25 in *GRN* mutation carriers and 18 in *MAPT* mutation carriers. Nine brain structures were specifically affected in *C9orf72*
expansion carriers, which were more “posterior” regions such as the precentral and postcentral gyrus, the superior parietal lobule, the posterior cingulate gyrus and the occipital pole. Three structures were specifically affected in *MAPT* mutation carriers: the entorhinal cortex, the parahippocampal gyrus and the inferior temporal gyrus. Two structures were significantly atrophied only in *GRN* mutation carriers: the angular gyrus and the brainstem (Figure 1 and Supplementary Fig1).

## The anatomical progression of genetic FTLD across the lifespan

We mapped in time and space on standardized brains the divergence of the three genetic FTLD volumetric trajectories from normal aging trajectories (upper panel in Fig. 2, 3 & 4). For each genetic FTLD, we also built timelines highlighting the sequential divergence of the top 50% atrophic structures in *C9orf72* expansion carriers, *GRN* mutation carriers and *MAPT* mutation carriers (lower panel in Fig. 2, 3 & 4). Schematically, by regrouping anatomical structures, we observed that atrophy begins in the thalamus in *C9orf72* expansion carriers, followed by the fronto-temporo-insular regions, the striatum (caudate, putamen, nucleus accumbens) and eventually the amygdala. In *GRN* mutation carriers, atrophy began in neocortical regions, particularly fronto-insular areas, before progressing toward subcortical or archeocortical structures such as the striatum, the hippocampus, and then to the thalamus. In *MAPT* mutation carriers, atrophy affected the anterior temporal pole including the amygdala and hippocampus, before progressing to fronto-insular regions and the striatum.

Figures 2, 3, and 4 showed that atrophy is significantly measurable on average at 27 years old in *C9orf72* and *MAPT* mutation carriers, whereas it occurred later in patients with *GRN* mutations (42 years old). Interestingly, the presymptomatic phase of the disease, between the onset of anatomical changes and the beginning of symptoms, varied significantly depending on the mutation. In our brain chart based timelines, this neuroanatomical presymptomatic phase lasts on average for 34 years for *C9orf72* expansion carriers (atrophy onset at 27, symptoms onset at 61), 17 years for *GRN* mutation carriers (atrophy onset at 42, symptom onset at 59), and 13 years for *MAPT* mutation carriers (atrophy onset at 27, symptom onset at 50).
Discussion

In this study, we combined the use of brain charts for the human lifespan with two large datasets of asymptomatic and symptomatic pathogenic FTLD mutation carriers to better understand the anatomical progression of *C9orf72*, *GRN* and *MAPT* FTLD. *C9orf72* expansions resulted in a slower disease process, with 34 years from the first significant anatomical changes to the average onset of symptoms. In contrast, *MAPT* mutations were more ‘aggressive’ with 13 years between the onset of atrophy and the appearance of symptoms, although first anatomical changes were detectable at an average age of 27 years in both cases. Atrophy began later in *GRN* mutations (at age 42 on average). The initial site of identifiable volume loss, and the staging of atrophy progression, were different among genetic FTLD, with a subcortico-cortical evolution for *C9orf72* expansion carriers and a more cortico-subcortical progression for *GRN* and *MAPT* mutation carriers. The extent of atrophy was more important in *C9orf72* expansion carriers than in *GRN* and *MAPT* mutation carriers (with 35, 25, and 18 affected structures in our atlas, respectively). Conversely, the effect-size of atrophy was twice as pronounced in *MAPT* mutation carriers compared to *C9orf72* or *GRN* mutations. The temporopolar region, including the amygdala and hippocampus, was the most severely affected over the entire course of the disease in *MAPT* mutations, whereas the peak of atrophy involved the striatum and insula for *GRN* mutations and the thalamus, the striatum, and the amygdala for *C9orf72* expansion carriers.

Our MRI staging schemes of FTLD anatomical progression are concordant and extend previous cross-sectional MRI studies of pathogenic mutation carriers at different stages of the disease. For example, we confirm in our pseudo-longitudinal analysis that thalamic atrophy is the earliest anatomical abnormality in *C9orf72* expansion carriers<sup>4,27</sup>. While atrophy clearly begins in the fronto-insular regions in *GRN* patients, we also find later subcortical and parietal involvement (in the angular gyrus and precuneus), as previously shown in symptomatic subjects<sup>3,4</sup>. Patients with a *C9orf72* expansion have the most diffuse involvement (including in posterior brain regions reputed to be preserved in FTLD), with moderate but significant atrophy even in the occipital cortex. We confirm that temporal atrophy, including the amygdala and hippocampus, is both very severe and early in *MAPT* mutation carriers, as shown in cross-sectional studies<sup>3</sup> as well as in a small group of 8 subjects with longitudinal data at the presymptomatic stage<sup>28</sup>.
Although the dataset has been expanded with data from ALLFTD and the analysis method is very different, our results are very close to a recent article suggesting the possibility of neurodevelopmental anomalies in very young asymptomatic subjects carrying genetic FTLD mutations⁹. This previous cross-sectional study specifically investigated the youngest adults of the GENFI cohort and found smaller total brain and thalamic volumes in *C9orf72* compared to control at a mean age of 26, which is very close to what we found using brain charts, with significant thalamic atrophy starting at 27. It reinforces the validity of our modeling strategy which we have also already validated in other conditions¹⁴,²⁴,²⁵,²⁶ and tested with ‘truly’ longitudinal data¹⁴. In turn, we were able to extend these early findings to *MAPT* mutation carriers. The fast rate of progression and the effect-size of brain volume loss following symptoms onset in *MAPT* mutation carriers suggest an early and aggressive neurodegenerative disease, quite different from slow pace indolent neurodegenerative damage seen in the case of *C9orf72* expansion. This hypothesis of fundamentally distinct pathophysiological mechanisms for *C9orf72* expansions compared to other pathogenic variants has been previously proposed in a study examining rates of atrophy across diseases stages in genetic FTLD²⁹. The early onset, rate of progression, and magnitude of atrophy in *MAPT* mutation carriers also indicate that the neurodegenerative process caused by tau aggregates is more severe than that caused by TDP-43 inclusions induced by *C9orf72* or *GRN* mutations³⁰. It also suggests that the spreading of these different proteinopathies follows distinct pathways through the brain connectome and that local transcriptomic vulnerabilities induced by the three different mutations may explain anatomical heterogeneity³¹.

Another explanation for the heterogeneity in the initiation and progression of atrophy patterns
depending on the mutations stems from the diversity of the syndromic presentation of
symptomatic subjects. For example, the brainstem involvement found only in *GRN* mutation
carriers can be explained by the presence of patients with corticobasal syndrome (CBS) in this
group³². Additionally, the small but significant pre- and post-central atrophy specifically
described in our *C9orf72* brain charts is consistent with the association of FTD with
amyotrophic lateral sclerosis occurring in the context of this mutation, whether symptomatic
or subclinical³³. Another source of heterogeneity in the anatomical progression of genetic FTLD
may be related to the mutation itself. For example, it has been shown using the Subtype and
Stage Inference (SuStaIn) technique that different MAPT mutations give rise to distinct atrophy
patterns³⁴. It is therefore important to keep in mind that our analyses only define an "average"
model of initiation and progression of atrophy in *C9orf72*, *GRN* and *MAPT* mutations, but there
can be significant inter-individual variability linked to clinical presentation and genetic background.

Beyond the anatomical description of the lifespan progression of genetic FTLD, our study using brain charts for the human lifespan provides new perspectives regarding the duration of the presymptomatic phase of these diseases. Indeed, a modeling of neuroimaging changes before symptom onset has been proposed in genetic FTLD by using cross-sectional data from asymptomatic mutation carriers and using the age at symptom onset within the family<sup>6</sup>, similar to what has been successfully done in the past with dominantly inherited Alzheimer disease<sup>35</sup>. However, this strategy has been questioned since the onset of the symptomatic phase of the disease varies in the same family for C9orf72 and GRN mutations<sup>36</sup>. The studies based on the 'estimated' time to symptom onset found a delay of about 10 years between the onset of temporal or insular atrophy and symptoms<sup>6</sup>. This duration of the presymptomatic phase is confirmed in our model for the MAPT mutation (13 years) but appears to be underestimated for GRN mutations (17 years in our model) and especially for C9orf72 expansions (34 years in our model), for which the heterogeneity in age of onset is now well recognized<sup>37</sup>.

Beyond describing the natural history of atrophy progression in genetic FTLD over the lifespan, our results could help predict the onset of the symptomatic phase within a precision medicine framework to determine the optimal timing for testing future disease-modifying therapies. The severity of atrophy in a given brain structure appears challenging to link to the age of symptom onset within our modeling strategy. However, monitoring the staging of atrophy progression could be an important parameter. For example, in asymptomatic MAPT mutation carriers, the transition to the symptomatic phase appears imminent when atrophy spreads from the amygdala, hippocampus, and temporal pole to inferior temporal structures (fusiform gyrus and inferior temporal gyrus) (Fig. 4). Longitudinally monitoring the significant involvement of the inferior temporal lobe in a given asymptomatic MAPT mutation carrier could thus serve as a marker of imminent transition to the symptomatic phase and an optimal time window for testing neuroprotective strategies. However, the predictive value of our anatomical models in clinical practice will need to be assessed using appropriate methodologies in future studies.

A limitation had emerged in our previous works using brain charts for the anatomical staging of Alzheimer disease, the different syndromic variants of sporadic FTD, and progressive supranuclear palsy (PSP)<sup>14,24,25</sup>. For these sporadic neurodegenerative diseases, the modelling of atrophy progression in relation to patients' age using brain charts required to extrapolate the
presymptomatic anatomical phase along a continuum between control and diseased subjects. Although this approach was validated with longitudinal data¹⁴, this methodological option could be questioned. However, the extensive MRI data available from mutation carriers, of all ages, and at all presymptomatic and symptomatic stages of the disease, allow us to use brain chart modeling with even more confidence, to depict the natural history of genetic FTLD. We also acknowledge that the use of brain charts and other normative models using outlier analysis³⁸ may overlook the spatial heterogeneity of diseases, contrary to the SuStaIn algorithm which specifically looks for subtypes. We also did not distinguish between the different syndromic presentations of FTLD in our analyses. Our results rather reflect an average progression of genetic FTLD, which is also an important reference point for clinicians and researchers, and may have better translational potential.

While studying lifelong brain volumetric trajectories in genetic mutation carriers can shed light on the neurobiological processes leading to FTD symptom onset, we acknowledge that uncovering potential alterations early in life is a sensitive topic for the affected population. As a research group, GENFI has established a patient representative body to discuss these issues, and we strongly encourage future research groups to involve patient partners in similar projects.

To conclude, our results show that *C9orf72* is the most diffuse but also the slowest neuroanatomically progressing genetic FTLD. *MAPT* FTLD is most aggressive and focal, while *GRN* FTLD is also rapidly progressive but with a later onset of the presymptomatic phase. *C9orf72* expansions lead to a pathology that progresses predominantly from subcortical to cortical regions, while the other two genetic forms have a progression that is more cortical to subcortical. Although atrophy was already extensive at the onset of symptoms in our models (Figs. 2, 3, 4), our study identifies the order of appearance of atrophy and thus proposes a new way to anticipate the onset of the symptomatic phase. This approach may help determine the best timing for testing disease-modifying strategies in FTLD and monitoring their effect in future clinical trials.
**Data availability statement**

MRI raw data from the different cohorts are available online (ALLFTD and control datasets: see Acknowledgements). De-identified data from the GENFI cohort can be accessed on reasonable request to [genfi@ucl.ac.uk](mailto:genfi@ucl.ac.uk). The other information is available from the corresponding author.

**Acknowledgements**

The C-MIND data used in the preparation of this article were obtained from the C-MIND Data
Repository created by the C-MIND study of Normal Brain Development. A listing of the
participating sites and a complete listing of the study investigators can be found at
https://research.cchmc.org/c-mind. The NDAR data used in the preparation of this manuscript
were obtained from the NIH-supported National Database for Autism Research (NDAR). This
is supported by the National Institute of Child Health and Human Development, the National
Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of
Neurological Disorders and Stroke. A listing of the participating sites and a complete listing of
the study investigators can be found at
[http://pediatricmri.nih.gov/nihpd/info/participatingCenters.html](http://pediatricmri.nih.gov/nihpd/info/participatingCenters.html). The ABIDE data used in the
preparation of this manuscript were supported by ABIDE funding resources listed at
[http://fcon_1000/projects.nitrc.org/indi/abide/](http://fcon_1000/projects.nitrc.org/indi/abide/). The ICBM data used in the preparation of this
manuscript were supported by Human Brain Project grant PO1MHO52176-11 and Canadian
Institutes of Health Research grant MOP- 34996. The IXI data used in the preparation of this
manuscript were supported by the U.K. Engineering and Physical Sciences Research Council
(EPSRC) GR/S21533/02 - [http://www.brain-development.org/](http://www.brain-development.org/). The ADNI is funded by the
National Institute on Aging and the National Institute of Biomedical Imaging and
Bioengineering and through generous contributions from private partners as well as nonprofit
partners listed at: https://ida.loni.usc.edu/collaboration/access/appLicense.jsp. Private sector
contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health
(www.fnih.org). The grantee organization is the Northern California Institute for Research and
Education, and the study was coordinated by the Alzheimer Disease Cooperative Study at the
University of California, San Diego. ADNI data are disseminated by the Laboratory for
NeuroImaging at the University of California, Los Angeles. This research was also supported by NIH grants P30AG010129, K01 AG030514 and the Dana Foundation. The Open Access Series of Imaging Studies (OASIS) data used in the preparation of this manuscript were obtained from the OASIS project funded by grants P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584. See http://www.oasis-brains.org/ for more details. The ADHAD, DLBS and SALD data used in the preparation of this article were obtained from http://fcon_1000 projects.nitrc.org. The ISYB study was supported by the Chinese Academy of Sciences and MRI were downloaded from https://www.scidb.cn. The MIRIAD dataset is made available through the support of the UK Alzheimer's Society (Grant RF116). The original data collection was funded through an unrestricted educational grant from GlaxoSmithKline (Grant 6GKC). The Parkinson's Progression Markers Initiative (PPMI) is a public-private partnership funded by The Michael J. Fox Foundation for Parkinson's Research and funding partners that can be found at https://www.ppmi-info.org. The Amsterdam open MRI collection AOMIC ID-1000/PIOP1/PIOP2 data used in the preparation of this article were obtained from https://nilab.uva.github.io/AOMIC.github.io/. The Calgary preschool MRI dataset was available at https://osf.io/axz5r/ and supported by University of Calgary and CIHR (IHD-134090 & MOP-136797). CamCAN (https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/) funding was provided by the UK Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1), together with support from the UK Medical Research Council and University of Cambridge, UK. We used the UK Biobank Resource under application number 80509. See https://www.ukbiobank.ac.uk for further details. The Pixar database and related fundings were available at https://openneuro.org/datasets/ds000228/versions/1.1.0. Data used in the preparation of this work were also obtained from the DecNef Project Brain Data Repository (https://bicr-resource.atr.jp/srpbsopen/) gathered by a consortium as part of the Japanese Strategic Research Program for the Promotion of Brain Science (SRPBS) supported by the Japanese Advanced Research and Development Programs for Medical Innovation. Data collection and dissemination of the data presented in this manuscript was supported by the ALLFTD Consortium (U19: AG063911, funded by the National Institute on Aging and the National Institute of Neurological Diseases and Stroke) and the former ARTFL & LEFFTDS Consortia (ARTFL: U54 NS092089, funded by the National Institute of Neurological Diseases and Stroke and National Center for Advancing Translational Sciences; LEFFTDS: U01 AG045390, funded by the National Institute on Aging and the National Institute of
Neurological Diseases and Stroke). The Genetic Frontotemporal dementia Initiative (GENFI) is a group of research centres across Europe and Canada with expertise in familial FTD, and is co-ordinated by Prof. Jonathan Rohrer at University College London.

The authors would like to thank Dr. Vincent Bouteloup for his statistical advice during the revision of the manuscript. We also recognize the contribution of Mahdie Soltaninejad (PhD candidate, McGill University) for her role in GENFI data management and processing. We acknowledge the invaluable contributions of the study participants and families as well as the assistance of the support staffs at each of the participating sites. This manuscript reflects the views of the authors and may not reflect the opinions or views of the database providers.

## Funding

This work benefited from the support of the project HoliBrain of the French National Research Agency (ANR-23-CE45-0020-01) and from the French government in the framework of the University of Bordeaux's France 2030 program / RRI IMPACT and the PEPR StratifyAging. This work also benefited from the support of the project PID2020-118608RB-I00 of the Ministerio de Ciencia e Innovacion de España. We also thank the PSP-France Foundation. JBR is funded by the Medical Research Council (MC_UU_00030/14; MR/T033371/1) and NIHR Cambridge Biomedical Research Centre (NIHR203312; the views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care). The sponsors did not participate in any aspect of the design or performance of the study, including data collection, management, analysis, and the interpretation or preparation, review, and approval of the manuscript.

## Competing interests

During the past 3 years, V. Planche was a local unpaid investigator or sub-investigator for clinical trials granted by NovoNordisk, Biogen, Janssen and Alector. He received consultant fees for MRI studies in animals from Motac Neuroscience Ltd, outside the submitted work. R. Sanchez-Valle has served in Advisory board meetings for Wave Life Sciences, Ionis, and Novo Nordisk and received personal fees for participating in educational activities from Janssen,
Roche Diagnostics, and Neuroxpharma and funding to her institution for research projects from Biogen and Sage Pharmaceuticals. B. Borroni has served at scientific boards for Denali, Wave, Alector, and Aviadobio. M. Synofzik has received consultancy honoraria from Janssen Pharmaceuticals, Ionis Pharmaceuticals, and Orphazyme Pharmaceuticals, all unrelated to the present manuscript. J. Levin reports speaker fees from Bayer Vital, Biogen, and Roche, consulting fees from Axon Neuroscience and Biogen, author fees from Thieme medical publishers and W. Kohlhammer GmbH medical publishers. In addition, J. Levin reports compensation for serving as a chief medical officer for MODAG GmbH, is beneficiary of the phantom share program of MODAG GmbH, and is inventor in a patent "Pharmaceutical Composition and Methods of Use" (EP 22 159 408.8) filed by MODAG GmbH, all activities outside the submitted work. Simon Ducharme received salary funding from the *Fonds de Recherche du Québec*. He has conducted sponsored clinical trials research for Biogen, NovoNordisk, Alnylam, Janssen, Innodem Neurosciences and Passage Bio. He has received advisory or speaker fees from Eisai, Eli Lilly and Quralis. He is the co-founder of AFX Medical Inc. James Rowe has undertaken consultancy for Astronautx, Astex, Asceneuron, Alector, CumulusNeuro, Curasen, Eisai, Prevail, and SVHealth unrelated to the current work. The other authors declare no competing financial interests.

**Appendix 1: GENFI consortium members**

Full details are available in the Supplementary material.

Rhian Convery, Martina Bocchetta, David Cash, Sophie Goldsmith, Kiran Samra, David L. Thomas, Maura Malpetti, Antonella Alberici, Enrico Premi, Roberto Gasparotti, Valentina Cantoni, Andrea Arighi, Chiara Fenoglio, Vittoria Borracci, Maria Serpente, Tiziana Carandini, Emanuela Rotondo, Giacomina Rossi, Giorgio Giaccone, Giuseppe Di Fede, Paola Caroppo, Sara Prioni, Veronica Redaelli, David Tang-Wai, Ekaterina Rogaeva, Johanna Krüger, Miguel Castelo-Branco, Morris Freedman, Ron Keren, Sandra Black, Sara Mitchell, Christen Shoesmith, Robart Bartha, Rosa Rademakers, Jackie Poos, Janne M. Papma, Lucia Giannini, Liset de Boer, Julie de Houwer, Rick van Minkelen, Yolande Pijnenburg, Benedetta Nacmias, Camilla Ferrari, Cristina Polito, Gemma Lombardi, Valentina Bessi, Enrico Fainardi, Stefano Chiti, Mattias Nilsson, Henrik Viklund, Melissa Taheri Rydell, Vesna Jelic, Linn Öijerstedt, Tobias Langheinrich, Albert Lladó, Anna Antonell, Jaume Olives, Mircea Balasa, Sergi
Borrego-Ecija, Ana Verdelho, Carolina Maruta, Tiago Costa-Coelho, Gabriel Miltenberger, Frederico Simões do Couto, Alazne Gabilondo, Ioana Croitoru, Mikel Tainta, Myriam Barandiaran, Patricia Alves, Benjamin Bender, David Mengel, Lisa Graf, Annick Vogels, Mathieu Vandenbulcke, Philip Van Damme, Rose Bruffaerts, Koen Poesen, Pedro Rosa-Neto, Maxime Montembeault, Raphaella Lara Migliaccio, Ninon Burgos, Daisy Rinaldi, Catharina Prix, Elisabeth Wlasich, Olivia Wagemann, Sonja Schönecker, Alexander Maximilian Bernhardt, Anna Stockbauer, Jolina Lombardi, Sarah Anderl-Straub, Adeline Rollin, Gregory Kuchcinski, Vincent Deramecourt, João Durães, Marisa Lima, Maria João Leitão, Maria Rosario Almeida, Miguel Tábuas-Pereira, Sónia Afonso, João Lemos.
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## Figure legends

**Figure 1. Lifespan brain charts based on z-scores of normalized brain volumes for cognitively normal control subjects (CN) and carriers of mutation for genetic FTLD.** Only the most relevant subcortical and cortical structures are represented here among the 41 brain structures that significantly diverged during lifespan between normal aging brain charts in at least one of the three genetic FTLD brain charts (all brain charts are available in Suppl. Fig. 1). The solid colored lines represent the normalized volumes trajectories for CN (blue), *C9orf72* (cyan), *GRN* (yellow) and *MAPT* (red) and correspond to the vertical axes on the left. The colored areas under the curves correspond the axes on the right (distance to normal, Glass delta effect size) and indicate the time period where adjusted confidence intervals at 95% of both control and disease models no longer overlap.

**Figure 2. The anatomical progression of *C9orf72* FTLD across the lifespan.** The upper panel maps the progression of atrophy in the three axis (neurological convention, all brain structures identified in brain charts reported in Suppl. Fig. 1). The lower panel is a timeline representing the sequential divergence of the most atrophic structures (top 50%) between control subjects and *C9orf72* expansion carriers brain charts. The effect-size of structural divergence is color-coded according to the bar at the bottom right of the figure.

**Figure 3. The anatomical progression of *GRN* FTLD across the lifespan.** The upper panel maps the progression of atrophy in the three axis (neurological convention, all brain structures identified in brain charts reported in Suppl. Fig. 1). The lower panel is a timeline representing the sequential divergence of the most atrophic structures (top 50%) between control subjects and *GRN* mutation carriers brain charts. The effect-size of structural divergence is color-coded according to the bar at the bottom right of the figure.

**Figure 4. The anatomical progression of *MAPT* FTLD across the lifespan.** The upper panel maps the progression of atrophy in the three axis (neurological convention, all brain structures identified in brain charts reported in Suppl. Fig. 1). The lower panel is a timeline representing the sequential divergence of the most atrophic structures (top 50%) between control subjects and *MAPT* mutation carriers brain charts. The effect-size of structural divergence is color-coded according to the bar at the bottom right of the figure.
<figure><img src="image_2.png" /><figcaption>Figure 1. Lifespan brain charts based on z-scores of normalized brain volumes for cognitively normal control subjects (CN) and carriers of mutation for genetic FTLD. Only the most relevant subcortical and cortical structures are represented here among the 41 brain structures that significantly diverged during lifespan between normal aging brain charts in at least one of the three genetic FTLD brain charts (all brain charts are available in Suppl. Fig. 1). The solid colored lines represent the normalized volumes trajectories for CN (blue), C9orf72 (cyan), GRN (yellow) and MAPT (red) and correspond to the vertical axes on the left. The colored areas under the curves correspond the axes on the right (distance to normal, Glass delta effect size) and indicate the time period where adjusted confidence intervals at 95% of both control and disease models no longer overlap.</figcaption></figure>

480x488mm (150 x 150 DPI)
<figure><img src="image_3.png" /><figcaption>Figure 2. The anatomical progression of C9orf72 FTLD across the lifespan. The upper panel maps the progression of atrophy in the three axis (neurological convention, all brain structures identified in brain charts reported in Suppl. Fig. 1). The lower panel is a timeline representing the sequential divergence of the most atrophic structures (top 50%) between control subjects and C9orf72 expansion carriers brain charts. The effect-size of structural divergence is color-coded according to the bar at the bottom right of the figure.</figcaption></figure>

216x183mm (150 x 150 DPI)
<figure><img src="image_4.png" /><figcaption>Figure 3. The anatomical progression of GRN FTLD across the lifespan. The upper panel maps the progression of atrophy in the three axis (neurological convention, all brain structures identified in brain charts reported in Suppl. Fig. 1). The lower panel is a timeline representing the sequential divergence of the most atrophic structures (top 50%) between control subjects and GRN mutation carriers brain charts. The effect-size of structural divergence is color-coded according to the bar at the bottom right of the figure.</figcaption></figure>

217x180mm (150 x 150 DPI)
<figure><img src="image_5.png" /><figcaption>Figure 4. The anatomical progression of MAPT FTLD across the lifespan. The upper panel maps the progression of atrophy in the three axis (neurological convention, all brain structures identified in brain charts reported in Suppl. Fig. 1). The lower panel is a timeline representing the sequential divergence of the most atrophic structures (top 50%) between control subjects and MAPT mutation carriers brain charts. The effect-size of structural divergence is color-coded according to the bar at the bottom right of the figure.</figcaption></figure>

216x185mm (150 x 150 DPI)
# Anatomical progression of genetic frontotemporal lobar degeneration across the lifespan

Vincent Planche et al.,

on behalf of the ALLFTD Consortium and the Genetic FTD Initiative (GENFI)

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## GENFI Consortium Authors:

Rhian Convery, Department of Neurodegenerative Disease, Dementia Research Centre, UCL
Queen Square Institute of Neurology, London, UK

Martina Bocchetta, Department of Neurodegenerative Disease, Dementia Research Centre,
UCL Queen Square Institute of Neurology, London, UK

David Cash, Department of Neurodegenerative Disease, Dementia Research Centre, UCL
Queen Square Institute of Neurology, London, UK

Sophie Goldsmith, Department of Neurodegenerative Disease, Dementia Research Centre,
UCL Queen Square Institute of Neurology, London, UK

Kiran Samra, 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

Thomas Cope, Cambridge University Hospitals NHS Trust, Cambridge UK

Maura Malpetti, Department of Clinical Neurosciences, University of Cambridge, Cambridge,
UK

Antonella Alberici, 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

Emanuele Buratti, ICGEB Trieste, 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

Chiara Fenoglio, University of Milan, Centro Dino Ferrari, Milan, Italy

Vittoria Borracci, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico,
Neurodegenerative Diseases Unit, Milan, Italy

Maria Serpente, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico,
Neurodegenerative Diseases Unit, Milan, Italy

Tiziana Carandini, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico,
Neurodegenerative Diseases Unit, Milan, Italy

Emanuela Rotondo, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico,
Neurodegenerative Diseases Unit, 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

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

Johanna Krüger, Research Unit of Clinical Medicine, Neurology, University of Oulu, Oulu, Finland

Miguel Castelo-Branco, Faculty of Medicine, ICNAS, CIBIT, 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

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

Liset de Boer, Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands

Julie de Houwer, 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

Enrico Fainardi, Neuroradiology Unit, Department of Experimental and Clinical Biomedical
Sciences, University of Florence, Florence, Italy

Stefano Chiti, Neuroradiology Unit, Department of Experimental and Clinical Biomedical
Sciences, University of Florence, Florence, Italy.

Mattias Nilsson, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm,
Sweden

Henrik Viklund, Karolinska University Hospital Huddinge

Melissa Taheri Rydell, Department of Neurobiology, Care Sciences and Society; Center for
Alzheimer Research, Division of Neurogeriatrics, Bioclinicum, Karolinska Institutet, Solna,
Sweden

Vesna Jelic, Department of Neurobiology, Care Sciences and Society; Division of Clinical
Geriatrics, Karolinska Institutet, Stockholm, Sweden

Linn Öijerstedt, Department of Neurobiology, Care Sciences and Society; Center for Alzheimer
Research, Division of Neurogeriatrics, Bioclinicum, Karolinska Institutet, Solna, Sweden

Tobias Langheinrich, Division of Neuroscience and Experimental Psychology, Wolfson
Molecular Imaging Centre, University of Manchester, 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

Tiago Costa-Coelho, 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

Ioana Croitoru, Instituto de Investigación Sanitaria Biogipuzkoa, Neurosciences Area, Group
of Neurodegenerative Diseases, San Sebastian, Spain.
Mikel Tainta, Instituto de Investigación Sanitaria Biogipuzkoa, Neurosciences Area, Group of Neurodegenerative Diseases, San Sebastian, Spain.

Myriam Barandiaran, Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain

Patricia Alves, Instituto de Investigación Sanitaria Biogipuzkoa, Neurosciences Area, Group of Neurodegenerative Diseases, San Sebastian, Spain.

Benjamin Bender, Department of Diagnostic and Interventional Neuroradiology, University of Tübingen, Tübingen, Germany

David Mengel, Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, 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

Maxime Montembeault, Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, Québec, Canada

Raphaella Lara Migliaccio, Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France

Ninon Burgos Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France

Daisy Rinaldi 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

Sonja Schönecker, Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany

Alexander Maximilian Bernhardt, Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany
Anna Stockbauer, 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

Vincent Deramecourt, Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France

João Durães, Neurology Department, Centro Hospitalar e Universitario de Coimbra, Coimbra,
Portugal

Marisa Lima, Neurology Department, Centro Hospitalar e Universitario de 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; Faculty of Medicine, University of Coimbra, Coimbra, Portugal.

Sónia Afonso, Instituto Ciencias Nucleares Aplicadas a Saude, Universidade de Coimbra,
Coimbra, Portugal

João Lemos, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
STROBE statement: Reporting guidelines checklist for cohort, case-control and cross-sectional studies

<table><thead><tr><th>SECTION</th><th>ITEM NUMBER</th><th>CHECKLIST ITEM</th><th>REPORTED ON PAGE NUMBER:</th></tr></thead><tbody><tr><td colspan="2">TITLE AND ABSTRACT</td><td></td><td></td></tr><tr><td></td><td>1a</td><td>Indicate the study's design with a commonly used term in the title or the abstract</td><td>3</td></tr><tr><td></td><td>1b</td><td>Provide in the abstract an informative and balanced summary of what was done and what was found</td><td>3</td></tr><tr><td colspan="2">INTRODUCTION</td><td></td><td></td></tr><tr><td></td><td>Background and objectives</td><td>Explain the scientific background and rationale for the investigation being reported</td><td>4</td></tr><tr><td></td><td></td><td>State specific objectives, including any pre-specified hypotheses</td><td>5</td></tr><tr><td colspan="2">METHODS</td><td></td><td></td></tr><tr><td></td><td>Study design</td><td>Present key elements of study design early in the paper</td><td>6-8 + table 1</td></tr><tr><td></td><td>Setting</td><td>Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection</td><td>6 + table 1</td></tr><tr><td></td><td>Participants</td><td>Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up<br>Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls<br>Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants</td><td>6 + Table 1</td></tr><tr><td></td><td></td><td>Cohort study—For matched studies, give matching criteria and number of exposed and unexposed<br>Case-control study—For matched studies, give matching criteria and the number of controls per case<br>Variables</td><td></td></tr><tr><td></td><td>Variables</td><td>Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable</td><td>6 - 8</td></tr></tbody></table>
<table><thead><tr><th>SECTION</th><th>ITEM NUMBER</th><th>CHECKLIST ITEM</th><th>REPORTED ON PAGE NUMBER:</th></tr></thead><tbody><tr><td>Data sources/measurements</td><td>8*</td><td>For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group.</td><td>6 + table 1</td></tr><tr><td>Bias</td><td>9</td><td>Describe any efforts to address potential sources of bias.</td><td>8</td></tr><tr><td>Study size</td><td>10</td><td>Explain how the study size was arrived at</td><td>6 – 8 + table 1</td></tr><tr><td>Quantitative variables</td><td>11</td><td>Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why.</td><td>8</td></tr><tr><td rowspan="3">Statistical methods</td><td>12a</td><td>Describe all statistical methods, including those used to control for confounding</td><td>8</td></tr><tr><td>12b</td><td>Describe any methods used to examine subgroups and interactions</td><td>NA</td></tr><tr><td>12c</td><td>Explain how missing data were addressed</td><td>NA</td></tr><tr><td></td><td>12d</td><td>Cohort study—If applicable, explain how loss to follow-up was addressed<br>Case-control study—If applicable, explain how matching of cases and controls was addressed<br>Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy</td><td>NA</td></tr><tr><td>12e</td><td></td><td>Describe any sensitivity analyses</td><td>8</td></tr><tr><td colspan="3"><strong>RESULTS</strong></td><td></td></tr><tr><td rowspan="3">Participants</td><td>13a</td><td>Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed</td><td>9 + table 2</td></tr><tr><td>13b</td><td>Give reasons for non-participation at each stage</td><td>NA</td></tr><tr><td>13c</td><td>Consider use of a flow diagram</td><td>NA</td></tr><tr><td rowspan="3">Descriptive Data</td><td>14a</td><td>Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders</td><td>Table 2</td></tr><tr><td>14b</td><td>Indicate number of participants with missing data for each variable of interest</td><td>NA</td></tr><tr><td>14c</td><td>Cohort study—Summarise follow-up time (eg, average and total amount)</td><td>NA</td></tr><tr><td>Outcome Data</td><td>15*</td><td>Cohort study—Report numbers of outcome events or summary measures over time</td><td>NA</td></tr></tbody></table>
<table><thead><tr><th>SECTION</th><th>ITEM NUMBER</th><th>CHECKLIST ITEM</th><th>REPORTED ON PAGE NUMBER:</th></tr></thead><tbody><tr><td></td><td></td><td>Case-control study—Report numbers in each exposure category, or summary measures of exposure</td><td></td></tr><tr><td></td><td></td><td>Cross-sectional study—Report numbers of outcome events or summary measures</td><td></td></tr><tr><td rowspan="3">Main Results</td><td>16a</td><td>Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g. 95% confidence interval). Make clear which confounders were adjusted for and why they were included</td><td>Fig 2, 3, 4</td></tr><tr><td>16b</td><td>Report category boundaries when continuous variables were categorized</td><td>Fig 2, 3, 4</td></tr><tr><td>16c</td><td>If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period</td><td>NA</td></tr><tr><td rowspan="3">Other Analyses</td><td>16d</td><td>Report results of any adjustments for multiple comparisons</td><td>NA</td></tr><tr><td>17a</td><td>Report other analyses done—e.g. analyses of subgroups and interactions, and sensitivity analyses</td><td>NA</td></tr><tr><td>17b</td><td>If numerous genetic exposures (genetic variants) were examined, summarize results from all analyses undertaken</td><td>NA</td></tr><tr><td>17c</td><td>If detailed results are available elsewhere, state how they can be accessed</td><td>8</td></tr><tr><td colspan="2"><strong>DISCUSSION</strong></td><td></td><td></td></tr><tr><td>Key Results</td><td>18</td><td>Summarise key results with reference to study objectives</td><td>11</td></tr><tr><td>Limitations</td><td>19</td><td>Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias</td><td>13</td></tr><tr><td>Interpretation</td><td>20</td><td>Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence</td><td>11 - 13</td></tr><tr><td>Generalisability</td><td>21</td><td>Discuss the generalisability (external validity) of the study results</td><td>11 - 13</td></tr><tr><td colspan="2"><strong>FUNDING</strong></td><td></td><td></td></tr></tbody></table>
<table><caption>Table 1. Complete list of the cohorts used in this study.</caption><thead><tr><td>DATASET</td><td>Group</td><td>n</td><td>Gender</td><td>Age range (years)</td></tr></thead><tbody><tr><td>C-MIND (Cincinnati MR Imaging of Neurodevelopment)</td><td>Controls</td><td>236</td><td>F = 129 / M = 107</td><td>1 - 19</td></tr><tr><td>NDAR (National Database for Autism Research)</td><td>Controls</td><td>382</td><td>F = 174 / M = 208</td><td>1 - 50</td></tr><tr><td>ABIDE (Autism Brain Imaging Data Exchange)</td><td>Controls</td><td>492</td><td>F = 84 / M = 408</td><td>6 - 52</td></tr><tr><td>ICBM (International Consortium for Brain Mapping)</td><td>Controls</td><td>294</td><td>F = 142 / M = 152</td><td>18 - 80</td></tr><tr><td>IXI (Information extraction from Images)</td><td>Controls</td><td>549</td><td>F = 307 / M = 242</td><td>20 - 86</td></tr><tr><td>ADNI 1&amp;2 (Alzheimer's Disease Neuroimaging Initiative)</td><td>Controls</td><td>404</td><td>F = 203 / M = 201</td><td>56 - 90</td></tr><tr><td>OASIS (Open Access Series of Imaging Studies)</td><td>Controls</td><td>298</td><td>F = 187 / M = 111</td><td>18 - 94</td></tr><tr><td>ADHD-200 (Attention-Deficit Hyperactivity Disorder-200 Consortium)</td><td>Controls</td><td>544</td><td>F = 263 / M = 281</td><td>7 - 26</td></tr><tr><td>DLBS (Dallas Lifespan Brain Study)</td><td>Controls</td><td>315</td><td>F = 198 / M = 117</td><td>21 - 89</td></tr><tr><td>ISYB (Imaging Chinese Young Brains)</td><td>Controls</td><td>213</td><td>F = 155 / M = 58</td><td>18 - 30</td></tr><tr><td>MIRIAD (Minimal Interval Resonance Imaging in Alzheimer's Disease)</td><td>Controls</td><td>23</td><td>F = 11 / M = 12</td><td>58 - 86</td></tr><tr><td>PPMI (Parkinson's Progression Markers Initiative)</td><td>Controls</td><td>162</td><td>F = 59 / M = 103</td><td>31 - 83</td></tr><tr><td>AOMIC (Amsterdam open MRI collection)</td><td>Controls</td><td>1361</td><td>F = 731 / M = 630</td><td>18 - 26</td></tr><tr><td>Calgary preschool MRI dataset</td><td>Controls</td><td>267</td><td>F = 117 / M = 150</td><td>3 - 7</td></tr><tr><td>CamCAN (Cambridge Centre for Ageing and Neuroscience)</td><td>Controls</td><td>653</td><td>F = 330 / M = 323</td><td>18 - 89</td></tr><tr><td>PIXAR</td><td>Controls</td><td>155</td><td>F = 84 / M = 71</td><td>4 - 39</td></tr><tr><td>SALD (Southwest University Adult Lifespan Dataset)</td><td>Controls</td><td>494</td><td>F = 307 / M = 185</td><td>19 - 80</td></tr><tr><td>SRPBS (Japanese Strategic Research Program for the Promotion of Brain Science)</td><td>Controls</td><td>791</td><td>F = 365 / M = 426</td><td>18 - 80</td></tr><tr><td>UKbiobank</td><td>Controls</td><td>29251</td><td>F=14334 / M=14917</td><td>44 - 82</td></tr><tr><td>SLIM</td><td>Controls</td><td>574</td><td>F = 320 / M = 254</td><td>17 - 27</td></tr><tr><td>COBRE</td><td>Controls</td><td>74</td><td>F = 23 / M = 51</td><td>18 - 65</td></tr><tr><td>ARTFL/LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD)</td><td>Genetic FTLD</td><td>371</td><td>F = 200 / M = 171</td><td>18 - 80</td></tr><tr><td>Genetic Frontotemporal Dementia Initiative (GENFI)</td><td>Genetic FTLD</td><td>970</td><td>F = 539 / M = 431</td><td>19 - 86</td></tr></tbody></table>
We combined the control groups of 23 cohorts covering the entire lifespan to model control brain charts and patients from the ALLFTD and GENFI cohort to extrapolate lifespan volumetric models of genetic FTLD.

For Peer Review
<table><caption>Table 2: Final datasets description.</caption><thead><tr><th></th><th>Controls</th><th>C9orf72 expansion carriers</th><th>GRN mutation carriers</th><th>MAPT Mutation carriers</th></tr></thead><tbody><tr><td>Total number of subjects</td><td>37532</td><td>584</td><td>471</td><td>286</td></tr><tr><td>Mean age, years [range]</td><td>57 [1 – 94]</td><td>49 [19 – 82]</td><td>51 [19 – 86]</td><td>44 [18 – 74]</td></tr><tr><td>Sex</td><td>F = 18524; M = 19008</td><td>F = 315; M = 269</td><td>F = 272; M = 199</td><td>F = 152; M = 134</td></tr><tr><td>Number of asymptomatic carriers</td><td>-</td><td>405</td><td>364</td><td>203</td></tr><tr><td>Number of symptomatic carriers</td><td>-</td><td>179</td><td>107</td><td>83</td></tr><tr><td>Mean age at symptom onset, years (SD)</td><td>-</td><td>55.5 (11.5)</td><td>59.7 (10.1)</td><td>48.1 (9.6)</td></tr><tr><td>Clinical phenotype of symptomatic subjects*</td><td>-</td><td>bvFTD = 120<br>FTD-ALS = 17<br>PPA = 13<br>Undetermined=29</td><td>bvFTD = 45<br>PPA = 38<br>CBS = 5<br>Undetermined=19</td><td>bvFTD = 60<br>PPA= 3<br>CBS=2<br>PSP=2<br>Undetermined=16</td></tr><tr><td>CDR plus NACC FTLD-SB of symptomatic subjects<sup>§</sup>, Median [range]</td><td>-</td><td>7.0 [0.5 – 24.0]</td><td>6.5 [0.5 – 24.0]</td><td>4.2 [0.5 – 23.0]</td></tr></tbody></table>

This table provides the total number of considered brain MRI (after quality control) to build the control and pathological brain charts. It also provides demographic and clinical characteristics of genetic FTLD mutation carriers.

*All syndromic presentations were considered in the present study except pure amyotrophic lateral sclerosis (ALS) for C9orf72 mutation carriers.

§data were available for 152 C9orf72, 88 GRN and 68 MAPT mutation carriers.

bvFTD=behavioral variant of frontotemporal dementia; CDR= clinical dementia rating scale; CBS=corticobasal syndrome; PPA=primary progressive aphasia; PSP=progressive supranuclear palsy
C9orf72

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GRN

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MAPT

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## Non-scientific summary

Thanks to brain charts that track changes over a person's lifetime, Planche et al. identified when the first signs of brain abnormalities appear, how long the disease remains symptom-free, how brain atrophy progresses over time, and how severe atrophy is for three genetic types of frontotemporal lobar degeneration

For Peer Review