# Early neurotransmitters changes in prodromal frontotemporal dementia: A GENFI study

Enrico Premi, Marta Pengo, Irene Mattioli, Valentina Cantoni, Juergen Dukart, Roberto Gasparotti, Emanuele Buratti, Alessandro Padovani, Martina Bocchetta, Emily G Todd, et al.

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## To cite this version:

Enrico Premi, Marta Pengo, Irene Mattioli, Valentina Cantoni, Juergen Dukart, et al. Early neu-
rotransmitters changes in prodromal frontotemporal dementia: A GENFI study. Neurobiology of
Disease, 2023, 179, pp.106068. 10.1016/j.nbd.2023.106068. hal-04523318

HAL Id: hal-04523318

https://hal.sorbonne-universite.fr/hal-04523318

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Early neurotransmitters changes in prodromal frontotemporal dementia:
A GENFI study

Enrico Premi<sup>a, 1</sup>, Marta Pengo<sup>b, c, 1</sup>, Irene Mattioli<sup>c</sup>, Valentina Cantoni<sup>c</sup>, Juergen Dukart<sup>d, e</sup>, Roberto Gasparotti<sup>f</sup>, Emanuele Buratti<sup>g</sup>, Alessandro Padovani<sup>a, c</sup>, Martina Bocchetta<sup>h, i</sup>, Emily G. Todd<sup>h</sup>, Arabella Bouzigues<sup>h</sup>, David M. Cash<sup>h, j</sup>, Rhian S. Convery<sup>h</sup>, Lucy L. Russell<sup>h</sup>, Phoebe Foster<sup>h</sup>, David L. Thomas<sup>k</sup>, John C. van Swieten<sup>l</sup>, Lize C. Jiskoot<sup>l</sup>, Harro Seelaar<sup>l</sup>, Daniela Galimberti<sup>m, n</sup>, Raquel Sanchez-Valle<sup>o</sup>, Robert Laforce Jr<sup>p</sup>, Fermin Moreno<sup>q</sup>, Matthis Synofzik<sup>r, s</sup>, Caroline Graff<sup>t, u</sup>, Mario Masellis<sup>v</sup>, Maria Carmela Tartaglia<sup>w</sup>, James B. Rowe<sup>x</sup>, Kamen A. Tsvetanov<sup>x</sup>, Rik Vandenberghe<sup>y</sup>, Elizabeth Finger<sup>z</sup>, Pietro Tiraboschi<sup>aa</sup>, Alexandre de Mendonça<sup>ab</sup>, Isabel Santana<sup>ac</sup>, Chris R. Butler<sup>ad</sup>, Simon Ducharme<sup>ae</sup>, Alexander Gerhard<sup>af, ag</sup>, Johannes Levin<sup>ah, ai, aj</sup>, Markus Otto<sup>ak</sup>, Sandro Sorbi<sup>al, am</sup>, Isabelle Le Ber<sup>an, ao, ap, aq</sup>, Florence Pasquier<sup>ar, as, at</sup>, Jonathan D. Rohrer<sup>h</sup>, Barbara Borroni<sup>a, c, *</sup>, on behalf of the Genetic Frontotemporal dementia Initiative (GENFI)

<footnote><sup>a</sup> Neurology, Department of Neurological and Vision Sciences, ASST Spedali Civili, Brescia, Italy</footnote>

<footnote><sup>b</sup> Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy</footnote>

<footnote><sup>c</sup> Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy</footnote>

<footnote><sup>d</sup> Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany</footnote>

<footnote><sup>e</sup> Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany</footnote>

<footnote><sup>f</sup> Neuroradiology Unit, Department of Medical and Surgical Specialties, University of Brescia, Brescia, Italy</footnote>

<footnote><sup>g</sup> ICGB, Trieste, Italy</footnote>

<footnote><sup>h</sup> Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom</footnote>

<footnote><sup>i</sup> Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London, United Kingdom</footnote>

<footnote><sup>j</sup> Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom</footnote>

<footnote><sup>k</sup> Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom</footnote>

<footnote><sup>l</sup> Department of Neurology and Alzheimer center, Erasmus Medical Center Rotterdam, the Netherlands</footnote>

<footnote><sup>m</sup> Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy</footnote>

<footnote><sup>n</sup> Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy</footnote>

<footnote><sup>o</sup> Neurology Department, Hospital Clinic, Institut d'Investigacions Biomèdiques, Barcelona, Spain</footnote>

<footnote><sup>p</sup> Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, Faculté de Médecine, Université Laval, Québec, Canada</footnote>

<footnote><sup>q</sup> Hospital Universitario Donostia, San Sebastian, Spain</footnote>

<footnote><sup>r</sup> Division Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany</footnote>

<footnote><sup>s</sup> German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany</footnote>

<footnote><sup>t</sup> Karolinska Institutet, Department NVS, Division of Neurogeriatrics, Stockholm, Sweden</footnote>

<footnote><sup>u</sup> Unit for Hereditray Dementia, Theme Aging, Karolinska University Hospital, Solna, Stockholm, Sweden</footnote>

<footnote><sup>v</sup> Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, Toronto, ON, Canada</footnote>

<footnote><sup>w</sup> Toronto Western Hospital, Tanz Centre for Research in Neurodegenerative Disease, Toronto, ON, Canada</footnote>

<footnote><sup>x</sup> Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust and Medical Research Council Cognition and brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom</footnote>

<footnote><sup>y</sup> Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium</footnote>

<footnote><sup>z</sup> Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada</footnote>

<footnote><sup>aa</sup> Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Istituto Neurologico Carlo Besta, Milan, Italy</footnote>

<footnote><sup>ab</sup> Faculty of Medicine, University of Lisbon, Lisbon, Portugal</footnote>

<footnote><sup>ac</sup> Neurology Department, Centro Hospitalar e Universitário de Coimbra, Portugal</footnote>

<footnote><sup>ad</sup> Department of Clinical Neurology, University of Oxford, Oxford, United Kingdom</footnote>

<footnote><sup>ae</sup> Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada</footnote>

<footnote>* Corresponding author at: Clinica Neurologica, Dipartimento Scienze Cliniche e Sperimentali, Università degli Studi di Brescia, P.le Spedali Civili 1, 25123 Brescia, Italy.</footnote>

<footnote>E-mail address: bborroni7@gmail.com (B. Borroni).</footnote>
<figure><img src="image_3.png" /></figure>

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Early neurotransmitters changes in prodromal frontotemporal dementia:
A GENFI study

Enrico Premi<sup>a, 1</sup>, Marta Pengo<sup>b, c, 1</sup>, Irene Mattioli<sup>c</sup>, Valentina Cantoni<sup>c</sup>, Juergen Dukart<sup>d, e</sup>, Roberto Gasparotti<sup>f</sup>, Emanuele Buratti<sup>g</sup>, Alessandro Padovani<sup>a, c</sup>, Martina Bocchetta<sup>h, i</sup>, Emily G. Todd<sup>h</sup>, Arabella Bouzigues<sup>h</sup>, David M. Cash<sup>h, j</sup>, Rhian S. Convery<sup>h</sup>, Lucy L. Russell<sup>h</sup>, Phoebe Foster<sup>h</sup>, David L. Thomas<sup>k</sup>, John C. van Swieten<sup>l</sup>, Lize C. Jiskoot<sup>l</sup>, Harro Seelaar<sup>l</sup>, Daniela Galimberti<sup>m, n</sup>, Raquel Sanchez-Valle<sup>o</sup>, Robert Laforce Jr<sup>p</sup>, Fermin Moreno<sup>q</sup>, Matthis Synofzik<sup>r, s</sup>, Caroline Graff<sup>t, u</sup>, Mario Masellis<sup>v</sup>, Maria Carmela Tartaglia<sup>w</sup>, James B. Rowe<sup>x</sup>, Kamen A. Tsvetanov<sup>x</sup>, Rik Vandenberghe<sup>y</sup>, Elizabeth Finger<sup>z</sup>, Pietro Tiraboschi<sup>aa</sup>, Alexandre de Mendonça<sup>ab</sup>, Isabel Santana<sup>ac</sup>, Chris R. Butler<sup>ad</sup>, Simon Ducharme<sup>ae</sup>, Alexander Gerhard<sup>af, ag</sup>, Johannes Levin<sup>ah, ai, aj</sup>, Markus Otto<sup>ak</sup>, Sandro Sorbi<sup>al, am</sup>, Isabelle Le Ber<sup>an, ao, ap, aq</sup>, Florence Pasquier<sup>ar, as, at</sup>, Jonathan D. Rohrer<sup>h</sup>, Barbara Borroni<sup>a, c, *</sup>, on behalf of the Genetic Frontotemporal dementia Initiative (GENFI)

<footnote><sup>a</sup> Neurology, Department of Neurological and Vision Sciences, ASST Spedali Civili, Brescia, Italy</footnote>

<footnote><sup>b</sup> Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy</footnote>

<footnote><sup>c</sup> Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy</footnote>

<footnote><sup>d</sup> Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany</footnote>

<footnote><sup>e</sup> Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany</footnote>

<footnote><sup>f</sup> Neuroradiology Unit, Department of Medical and Surgical Specialties, University of Brescia, Brescia, Italy</footnote>

<footnote><sup>g</sup> ICGB, Trieste, Italy</footnote>

<footnote><sup>h</sup> Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom</footnote>

<footnote><sup>i</sup> Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London, United Kingdom</footnote>

<footnote><sup>j</sup> Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom</footnote>

<footnote><sup>k</sup> Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom</footnote>

<footnote><sup>l</sup> Department of Neurology and Alzheimer center, Erasmus Medical Center Rotterdam, the Netherlands</footnote>

<footnote><sup>m</sup> Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy</footnote>

<footnote><sup>n</sup> Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy</footnote>

<footnote><sup>o</sup> Neurology Department, Hospital Clinic, Institut d'Investigacions Biomèdiques, Barcelona, Spain</footnote>

<footnote><sup>p</sup> Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, Faculté de Médecine, Université Laval, Québec, Canada</footnote>

<footnote><sup>q</sup> Hospital Universitario Donostia, San Sebastian, Spain</footnote>

<footnote><sup>r</sup> Division Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany</footnote>

<footnote><sup>s</sup> German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany</footnote>

<footnote><sup>t</sup> Karolinska Institutet, Department NVS, Division of Neurogeriatrics, Stockholm, Sweden</footnote>

<footnote><sup>u</sup> Unit for Hereditray Dementia, Theme Aging, Karolinska University Hospital, Solna, Stockholm, Sweden</footnote>

<footnote><sup>v</sup> Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, Toronto, ON, Canada</footnote>

<footnote><sup>w</sup> Toronto Western Hospital, Tanz Centre for Research in Neurodegenerative Disease, Toronto, ON, Canada</footnote>

<footnote><sup>x</sup> Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust and Medical Research Council Cognition and brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom</footnote>

<footnote><sup>y</sup> Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium</footnote>

<footnote><sup>z</sup> Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada</footnote>

<footnote><sup>aa</sup> Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Istituto Neurologico Carlo Besta, Milan, Italy</footnote>

<footnote><sup>ab</sup> Faculty of Medicine, University of Lisbon, Lisbon, Portugal</footnote>

<footnote><sup>ac</sup> Neurology Department, Centro Hospitalar e Universitário de Coimbra, Portugal</footnote>

<footnote><sup>ad</sup> Department of Clinical Neurology, University of Oxford, Oxford, United Kingdom</footnote>

<footnote><sup>ae</sup> Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada</footnote>

<footnote>* Corresponding author at: Clinica Neurologica, Dipartimento Scienze Cliniche e Sperimentali, Università degli Studi di Brescia, P.le Spedali Civili 1, 25123 Brescia, Italy.</footnote>

<footnote>E-mail address: bborroni7@gmail.com (B. Borroni).</footnote>
ARTICLE INFO

Keywords:

Frontotemporal dementia
Frontotemporal lobar degeneration
Genes
Magnetic resonance imaging
Positron emission tomography
Neurotransmitters
Mutation

ABSTRACT

**Background:** Neurotransmitters deficits in Frontotemporal Dementia (FTD) are still poorly understood. Better knowledge of neurotransmitters impairment, especially in prodromal disease stages, might tailor symptomatic treatment approaches.

**Methods:** In the present study, we applied JuSpace toolbox, which allowed for cross-modal correlation of Magnetic Resonance Imaging (MRI)-based measures with nuclear imaging derived estimates covering various neurotransmitter systems including dopaminergic, serotonergic, noradrenergic, GABAergic and glutamatergic neurotransmission.

We included 392 mutation carriers (157 GRN, 164 C9orf72, 71 MAPT), together with 276 non-carrier cognitively healthy controls (HC). We tested if the spatial patterns of grey matter volume (GMV) alterations in mutation carriers (relative to HC) are correlated with specific neurotransmitter systems in prodromal (CDR® plus NACC FTLD ≥ 0.5) and in symptomatic (CDR® plus NACC FTLD ≥ 1) FTD.

**Results:** In prodromal stages of C9orf72 disease, voxel-based brain changes were significantly associated with spatial distribution of dopamine and acetylcholine pathways; in prodromal MAPT disease with dopamine and serotonin pathways, while in prodromal GRN disease no significant findings were reported (*p* < 0.05, Family Wise Error corrected). In symptomatic FTD, a widespread involvement of dopamine, serotonin, glutamate and acetylcholine pathways across all genetic subtypes was found. Social cognition scores, loss of empathy and poor response to emotional cues were found to correlate with the strength of GMV colocalization of dopamine and serotonin pathways (all *p* < 0.01).

**Conclusions:** This study, indirectly assessing neurotransmitter deficits in monogenic FTD, provides novel insight into disease mechanisms and might suggest potential therapeutic targets to counteract disease-related symptoms.

1. Introduction

Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by progressive behavioral, linguistic, dysexecutive and motor disturbances (Rascovsky et al., 2011; Gorno-Tempini et al., 2011). Its causes are genetic in about a third of cases, with mutations in microtubule-associated protein tau (MAPT), progranulin (GRN) and chromosome 9 open reading frame 72 (C9orf72) being the commonest causes (Borroni and Padovani, 2013; Greaves and Rohrer, 2019). Behavioral variant FTD (bvFTD) is the most common presentation, followed by Primary Progressive Aphasias (PPAs) (Greaves and Rohrer, 2019). Symptomatic MAPT mutation carriers show a symmetrical brain atrophy involving mainly the anteromedial temporal lobes, symptomatic GRN mutation carriers exhibit a striking asymmetrical pattern of cortical atrophy, whereas symptomatic C9orf72 mutation carriers display diffuse and symmetric cortical atrophy, involving also posterior regions, thalamus and cerebellum (Cash et al., 2018; Boeve et al., 2012; Whitwell et al., 2012). Early neuroimaging alterations are described around 5–10 years before phenoc conversion with a specific distribution in each group (Rohrer et al., 2015).

Despite the continuous advancement of knowledge on disease-related mechanisms, little is known about neurotransmitter processes that occur in FTD. Exploring neurotransmitter pathways involved might shed more light on disease pathogenesis; moreover, since each mutation group is characterized by different clinical and imaging features, we might hypothesise that different neurotransmitter pathways are involved. As a consequence, research in this field might aid in identifying tailored therapeutic targets for symptomatic interventions.

Although impairment of dopaminergic, serotoninergic, GABAergic and glutamatergic pathways in autopsy studies has been demonstrated (Murley and Rowe, 2018), clinical trials have failed to report substantial benefits from neurotransmitter modulation on clinical symptoms in FTD (Panza et al., 2020). This discrepancy may be due to weaknesses in research methodology and small studies in unstratified populations.

Recent advancements in positron emission tomography (PET) and single photon computed emission tomography (SPECT) tracer development resulted in novel tracers that can reliably measure the availability of specific receptors. However, the need of large samples and of comparing multiple tracers in the same subjects have prevented reliable results on in vivo neurotransmitter pathways in neurodegenerative disorders, and especially in FTD. Indeed, only a few small series studies or case reports are available in FTD and in FTD-related mutations (Sperfeld et al., 1999; Miyoshi et al., 2010; Meloni et al., 2017; Carecchio et al., 2014; Leuzy et al., 2016; Takeshige et al., 2018; Murley et al., 2020).

To fill this gap, JuSpace toolbox has been recently developed with the aim to gather neurotransmitter pathways abnormalities combining MRI-based measures and a list of included PET and SPECT maps covering various neurotransmitter pathways (Dukart et al., 2018). JuSpace considers spatial pattern of brain alterations based on MRI measures derived by comparison between different groups (e.g. patients versus healthy controls), and it performs a correlation between these alterations and each receptor/transporter map included in the toolbox (Dukart et al., 2021). JuSpace therefore is able to explore if the spatial patterns of observed brain changes in the disease of interest are related to the distribution of specific neurotransmitters pathways, as derived

<footnote>a† Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, United Kingdom</footnote>

<footnote>a§ Departments of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Germany</footnote>

<footnote>ab Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich, Germany</footnote>

<footnote>a) German Center for Neurodegenerative Diseases (DZNE), Munich, Germany</footnote>

<footnote>a) Munich Cluster of System Neurology, Munich, Germany</footnote>

<footnote>ak Department of Neurology, University Hospital Halle, Halle, Germany</footnote>

<footnote>a) Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy</footnote>

<footnote>am IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy</footnote>

<footnote>a") Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote>ao Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP - Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote>aP Département de Neurologie, AP-HP - Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote>ad Reference Network for Rare Neurological Diseases (ERN-RND)</footnote>

<footnote>a) University of Lille, France</footnote>

<footnote>a) Inserm 1172, Lille, France</footnote>

<footnote>at CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</footnote>
## ARTICLE INFO

### Keywords:

- Frontotemporal dementia
- Frontotemporal lobar degeneration
- Genes
- Magnetic resonance imaging
- Positron emission tomography
- Neurotransmitters
- Mutation

## ABSTRACT

**Background:** Neurotransmitters deficits in Frontotemporal Dementia (FTD) are still poorly understood. Better knowledge of neurotransmitters impairment, especially in prodromal disease stages, might tailor symptomatic treatment approaches.

**Methods:** In the present study, we applied JuSpace toolbox, which allowed for cross-modal correlation of Magnetic Resonance Imaging (MRI)-based measures with nuclear imaging derived estimates covering various neurotransmitter systems including dopaminergic, serotonergic, noradrenergic, GABAergic and glutamatergic neurotransmission.

We included 392 mutation carriers (157 GRN, 164 C9orf72, 71 MAPT), together with 276 non-carrier cognitively healthy controls (HC). We tested if the spatial patterns of grey matter volume (GMV) alterations in mutation carriers (relative to HC) are correlated with specific neurotransmitter systems in prodromal (CDR® plus NACC FTLD ≥ 0.5) and in symptomatic (CDR® plus NACC FTLD ≥ 1) FTD.

**Results:** In prodromal stages of C9orf72 disease, voxel-based brain changes were significantly associated with spatial distribution of dopamine and acetylcholine pathways; in prodromal MAPT disease with dopamine and serotonin pathways, while in prodromal GRN disease no significant findings were reported (*p* < 0.05, Family Wise Error corrected). In symptomatic FTD, a widespread involvement of dopamine, serotonin, glutamate and acetylcholine pathways across all genetic subtypes was found. Social cognition scores, loss of empathy and poor response to emotional cues were found to correlate with the strength of GMV colocalization of dopamine and serotonin pathways (all *p* < 0.01).

**Conclusions:** This study, indirectly assessing neurotransmitter deficits in monogenic FTD, provides novel insight into disease mechanisms and might suggest potential therapeutic targets to counteract disease-related symptoms.

# 1. Introduction

Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by progressive behavioral, linguistic, dysexecutive and motor disturbances (Rascovsky et al., 2011; Gorno-Tempini et al., 2011). Its causes are genetic in about a third of cases, with mutations in microtubule-associated protein tau (MAPT), progranulin (GRN) and chromosome 9 open reading frame 72 (C9orf72) being the commonest causes (Borroni and Padovani, 2013; Greaves and Rohrer, 2019). Behavioral variant FTD (bvFTD) is the most common presentation, followed by Primary Progressive Aphasias (PPAs) (Greaves and Rohrer, 2019). Symptomatic MAPT mutation carriers show a symmetrical brain atrophy involving mainly the anteromedial temporal lobes, symptomatic GRN mutation carriers exhibit a striking asymmetrical pattern of cortical atrophy, whereas symptomatic C9orf72 mutation carriers display diffuse and symmetric cortical atrophy, involving also posterior regions, thalamus and cerebellum (Cash et al., 2018; Boeve et al., 2012; Whitwell et al., 2012). Early neuroimaging alterations are described around 5–10 years before phenoc conversion with a specific distribution in each group (Rohrer et al., 2015).

Despite the continuous advancement of knowledge on disease-related mechanisms, little is known about neurotransmitter processes that occur in FTD. Exploring neurotransmitter pathways involved might shed more light on disease pathogenesis; moreover, since each mutation group is characterized by different clinical and imaging features, we might hypothesise that different neurotransmitter pathways are involved. As a consequence, research in this field might aid in identifying tailored therapeutic targets for symptomatic interventions.

Although impairment of dopaminergic, serotoninergic, GABAergic and glutamatergic pathways in autopsy studies has been demonstrated (Murley and Rowe, 2018), clinical trials have failed to report substantial benefits from neurotransmitter modulation on clinical symptoms in FTD (Panza et al., 2020). This discrepancy may be due to weaknesses in research methodology and small studies in unstratified populations.

Recent advancements in positron emission tomography (PET) and single photon computed emission tomography (SPECT) tracer development resulted in novel tracers that can reliably measure the availability of specific receptors. However, the need of large samples and of comparing multiple tracers in the same subjects have prevented reliable results on in vivo neurotransmitter pathways in neurodegenerative disorders, and especially in FTD. Indeed, only a few small series studies or case reports are available in FTD and in FTD-related mutations (Sperfeld et al., 1999; Miyoshi et al., 2010; Meloni et al., 2017; Carecchio et al., 2014; Leuzy et al., 2016; Takeshige et al., 2018; Murley et al., 2020).

To fill this gap, JuSpace toolbox has been recently developed with the aim to gather neurotransmitter pathways abnormalities combining MRI-based measures and a list of included PET and SPECT maps covering various neurotransmitter pathways (Dukart et al., 2018). JuSpace considers spatial pattern of brain alterations based on MRI measures derived by comparison between different groups (e.g. patients versus healthy controls), and it performs a correlation between these alterations and each receptor/transporter map included in the toolbox (Dukart et al., 2021). JuSpace therefore is able to explore if the spatial patterns of observed brain changes in the disease of interest are related to the distribution of specific neurotransmitters pathways, as derived

<footnote>a¹ Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, United Kingdom</footnote>

<footnote>a² Departments of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Germany</footnote>

<footnote>a³ Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich, Germany</footnote>

<footnote>a⁴ German Center for Neurodegenerative Diseases (DZNE), Munich, Germany</footnote>

<footnote>a⁵ Munich Cluster of System Neurology, Munich, Germany</footnote>

<footnote>a⁶ Department of Neurology, University Hospital Halle, Halle, Germany</footnote>

<footnote>a⁷ Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy</footnote>

<footnote>a⁸ IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy</footnote>

<footnote>a⁹ Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote>a¹⁰ Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP - Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote>a¹¹ Département de Neurologie, AP-HP - Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote>a¹² Reference Network for Rare Neurological Diseases (ERN-RND)</footnote>

<footnote>a¹³ University of Lille, France</footnote>

<footnote>a¹⁴ Inserm 1172, Lille, France</footnote>

<footnote>a¹⁵ CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</footnote>
from independent healthy volunteer populations.

In the present study, we aimed to indirectly unravel neurotransmitter pathways changes, in particular in the earliest disease phases, namely in prodromal FTD, and to assess correlation with clinical symptoms. To achieve this, we applied JuSpace tool on a large sample of subjects from the international Genetic FTD Initiative (GENFI), considering individuals at different disease stages and with different pathogenetic mutations, and we evaluated impairment of dopamine, serotonin, glutamate, GABA, noradrenaline and acetylcholine systems.

## 2. Methods

### 2.1. Subjects

Data for this study were drawn from the GENFI multicenter cohort study, which consists of 26 research centers in Europe and Canada. Inclusion and exclusion criteria have been previously described (Rohrer et al., 2015). Local ethics committees approved the study at each site and all participants provided written informed consent according to the Declaration of Helsinki.

We considered both symptomatic patients fulfilling current clinical criteria for FTD (Rascovsky et al., 2011; Gorno-Tempini et al., 2011), and asymptomatic participants at risk to carry GRN, C9orf72 or MAPT mutations. Between January 2012 and March 2020, we considered 668 participants, of which 392 were mutation carriers (157 with C9orf72, 164 with GRN, and 71 with MAPT mutations) and 276 were mutation non-carriers. Mutation carriers were grouped according to disease severity, as measured by Clinical Dementia Rating Dementia Staging Instrument plus behaviour and language domains from the National Alzheimer's Coordinating Center and Frontotemporal lobar degeneration modules (CDR® plus NACC FTLD, from here on referred as CDR) (Miyagawa et al., 2020) into asymptomatic subjects (CDR = 0), prodromal FTD (CDR = 0.5) or symptomatic FTD patients (CDR ≥ 1). Mutation non-carriers were considered as healthy control group (HC).

Included subjects underwent a careful recording of demographic data and a standardized clinical and neuropsychological assessment, as previously published (Premi et al., 2019).

### 2.2. MRI acquisition

MRI protocol was common to all the GENFI sites, and adapted for different scanners. Each subject underwent a 3 T MRI at each local site from three different manufacturers (Philips Healthcare- 215 subjects, GE Healthcare Life Sciences- 19 subjects, Siemens Healthcare Diagnostic- 434 subjects). The protocol included a volumetric T1-weighted MRI scan (magnetization-prepared rapid gradient echo, MPRAGE), as previously reported (Rohrer et al., 2015; Premi et al., 2017; Cash et al., 2018; Gazzina et al., 2019; Borrego-Écija et al., 2021). During scanning, subjects were asked to keep their eyes closed, not to think of anything in particular, and not to fall asleep.

### 2.3. MRI preprocessing and analyses

T1-weighted images were processed and analysed with the voxel-based morphometry (VBM) pipeline implemented in the Computational Anatomy Toolbox (CAT12 v.1742) (www.neuro.uni-jena.de/cat) for SPM12 (SPM12 v.7219) (www.fil.ion.ucl.ac.uk/spm/softw/are/spm12) running on MATLAB R2019b (the MathWorks, Inc., Natick, Massachusetts, United States). The VBM pipeline consists of several stages (tissue segmentation, spatial normalization to a standard Montreal National Institute [MNI] template, modulation and smoothing), as previously described (Kurth et al., 2015). CAT12 potentially provides more robust and accurate performances compared to other VBM pipelines (Farokhian et al., 2017). The normalized and modulated grey matter images were then smoothed with 8 mm full width at half-maximum Gaussian kernel to reduce the probability of misalignment

errors, increasing the chance to detect differences over small regions of the brain.

To test for group differences in grey matter volume (GMV) a General Linear Model using SPM12 was implemented, considering age, gender and site as nuisance variables. The statistical threshold was set to *p* < 0.05 corrected for multiple comparisons (whole-brain family-wise error, FWE).

### 2.4. Spatial correlation with neurotransmitter density maps

We used the JuSpace toolbox to test if the spatial patterns of GMV alterations in asymptomatic, prodromal and symptomatic FTD subjects (relative to HC) are correlated with specific neurotransmitter systems (Dukart et al., 2021). We considered a list of included PET and SPECT maps in JuSpace toolbox, covering various neurotransmitter systems (Dukart et al., 2021).

JuSpace creates a spatial pattern of GMV, comparing two different groups (e.g. patients versus healthy controls), and therefore aims to assess if the spatial patterns of brain changes observed in patients (as compared to healthy controls) are related to the distribution of specific neurotransmitters systems, these latter derived from independent healthy volunteer populations (Dukart et al., 2021). Thus, it performs a correlation between these alterations and each receptor/transporter map included in the toolbox.

Confounding effects of age, gender and site were regressed out from all images prior to these analyses (Dukart et al., 2021).

We considered serotonin transmission, i.e. the 5-hydroxytryptamine 1a (5-HT1a) receptor, the 5-HT1b receptor, the 5-HT2a receptor, and the serotonin transporter SERT; dopamine transmission, i.e. the D1 receptor, the D2 receptor, the dopamine transporter (DAT), and the FluoroDOPA, the GABAA receptors, the vesicular acetylcholine transporter (VACHT), the metabotropic glutamate receptor type 5 (mGLUR5), and the noradrenaline transporter (NAT). Each map included in JuSpace toolbox was derived by PET data with the exception of DAT, which was derived from SPECT data, and each map was built up with specific numbers of healthy volunteers (Supplementary Table 1 for details). Using JuSpace toolbox, native normalized, modulated and smoothed grey matter images were parceled in regions of interest using the Neuromorphometrics Atlas (MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling, www.masi.vuse.vanderbilt.edu/workshop2012/index.php/Challenge_Details). Mean regional values of GMV were extracted for all patients and HC. Spearman correlation coefficients (Fisher's Z transformed) were calculated between these z-transformed GMV maps of the patients and the spatial distribution of the respective neurotransmitter maps included in JuSpace toolbox. Exact permutation-based *p*-values as included in JuSpace (10,000 permutations randomly assigning group labels using orthogonal permutations) were computed to check if the distribution of the observed Fisher's z-transformed individual correlation coefficients were significantly different from zero. All analyses were Family Wise Error (FWE) corrected for the number of tests. Spearman correlation coefficients (Fisher's Z transformed) were calculated between these z-transformed GMV maps and the spatial distribution of the respective neurotransmitter maps. Exact permutation-based *p*-values as implemented in JuSpace (10,000 permutations randomly assigning group labels using orthogonal permutations) were computed to test if the observed correlation coefficients across patients deviate from a null distribution.

### 2.5. Statistical analysis

Comparisons of demographic and clinical characteristics were performed by the Student's *t*-test for continuous variables and the $\chi^2$ test for categorical variables.

Spearman correlation was used to assess the relationship between each neurotransmitter output obtained with Juspace (i.e., the GMV-neurotransmitters correlation, Fisher's Z transformed) and clinical or
from independent healthy volunteer populations.

In the present study, we aimed to indirectly unravel neurotransmitter pathways changes, in particular in the earliest disease phases, namely in prodromal FTD, and to assess correlation with clinical symptoms. To achieve this, we applied JuSpace tool on a large sample of subjects from the international Genetic FTD Initiative (GENFI), considering individuals at different disease stages and with different pathogenetic mutations, and we evaluated impairment of dopamine, serotonin, glutamate, GABA, noradrenaline and acetylcholine systems.

## 2. Methods

### 2.1. Subjects

Data for this study were drawn from the GENFI multicenter cohort study, which consists of 26 research centers in Europe and Canada. Inclusion and exclusion criteria have been previously described (Rohrer et al., 2015). Local ethics committees approved the study at each site and all participants provided written informed consent according to the Declaration of Helsinki.

We considered both symptomatic patients fulfilling current clinical criteria for FTD (Rascovsky et al., 2011; Gorno-Tempini et al., 2011), and asymptomatic participants at risk to carry GRN, C9orf72 or MAPT mutations. Between January 2012 and March 2020, we considered 668 participants, of which 392 were mutation carriers (157 with C9orf72, 164 with GRN, and 71 with MAPT mutations) and 276 were mutation non-carriers. Mutation carriers were grouped according to disease severity, as measured by Clinical Dementia Rating Dementia Staging Instrument plus behaviour and language domains from the National Alzheimer's Coordinating Center and Frontotemporal lobar degeneration modules (CDR® plus NACC FTLD, from here on referred as CDR) (Miyagawa et al., 2020) into asymptomatic subjects (CDR = 0), prodromal FTD (CDR = 0.5) or symptomatic FTD patients (CDR ≥ 1). Mutation non-carriers were considered as healthy control group (HC).

Included subjects underwent a careful recording of demographic data and a standardized clinical and neuropsychological assessment, as previously published (Premi et al., 2019).

### 2.2. MRI acquisition

MRI protocol was common to all the GENFI sites, and adapted for different scanners. Each subject underwent a 3 T MRI at each local site from three different manufacturers (Philips Healthcare- 215 subjects, GE Healthcare Life Sciences- 19 subjects, Siemens Healthcare Diagnostic- 434 subjects). The protocol included a volumetric T1-weighted MRI scan (magnetization-prepared rapid gradient echo, MPRAGE), as previously reported (Rohrer et al., 2015; Premi et al., 2017; Cash et al., 2018; Gazzina et al., 2019; Borrego-Écija et al., 2021). During scanning, subjects were asked to keep their eyes closed, not to think of anything in particular, and not to fall asleep.

### 2.3. MRI preprocessing and analyses

T1-weighted images were processed and analysed with the voxel-based morphometry (VBM) pipeline implemented in the Computational Anatomy Toolbox (CAT12 v.1742) (www.neuro.uni-jena.de/cat) for SPM12 (SPM12 v.7219) (www.fil.ion.ucl.ac.uk/spm/softw/are/spm12) running on MATLAB R2019b (the MathWorks, Inc., Natick, Massachusetts, United States). The VBM pipeline consists of several stages (tissue segmentation, spatial normalization to a standard Montreal National Institute [MNI] template, modulation and smoothing), as previously described (Kurth et al., 2015). CAT12 potentially provides more robust and accurate performances compared to other VBM pipelines (Farokhian et al., 2017). The normalized and modulated grey matter images were then smoothed with 8 mm full width at half-maximum Gaussian kernel to reduce the probability of misalignment

errors, increasing the chance to detect differences over small regions of the brain.

To test for group differences in grey matter volume (GMV) a General Linear Model using SPM12 was implemented, considering age, gender and site as nuisance variables. The statistical threshold was set to *p* < 0.05 corrected for multiple comparisons (whole-brain family-wise error, FWE).

### 2.4. Spatial correlation with neurotransmitter density maps

We used the JuSpace toolbox to test if the spatial patterns of GMV alterations in asymptomatic, prodromal and symptomatic FTD subjects (relative to HC) are correlated with specific neurotransmitter systems (Dukart et al., 2021). We considered a list of included PET and SPECT maps in JuSpace toolbox, covering various neurotransmitter systems (Dukart et al., 2021).

JuSpace creates a spatial pattern of GMV, comparing two different groups (e.g. patients versus healthy controls), and therefore aims to assess if the spatial patterns of brain changes observed in patients (as compared to healthy controls) are related to the distribution of specific neurotransmitters systems, these latter derived from independent healthy volunteer populations (Dukart et al., 2021). Thus, it performs a correlation between these alterations and each receptor/transporter map included in the toolbox.

Confounding effects of age, gender and site were regressed out from all images prior to these analyses (Dukart et al., 2021).

We considered serotonin transmission, i.e. the 5-hydroxytryptamine 1a (5-HT1a) receptor, the 5-HT1b receptor, the 5-HT2a receptor, and the serotonin transporter SERT; dopamine transmission, i.e. the D1 receptor, the D2 receptor, the dopamine transporter (DAT), and the FluoroDOPA, the GABAA receptors, the vesicular acetylcholine transporter (VACHT), the metabotropic glutamate receptor type 5 (mGLUR5), and the noradrenaline transporter (NAT). Each map included in JuSpace toolbox was derived by PET data with the exception of DAT, which was derived from SPECT data, and each map was built up with specific numbers of healthy volunteers (Supplementary Table 1 for details). Using JuSpace toolbox, native normalized, modulated and smoothed grey matter images were parceled in regions of interest using the Neuromorphometrics Atlas (MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling, www.masi.vuse.vanderbilt.edu/workshop2012/index.php/Challenge_Details). Mean regional values of GMV were extracted for all patients and HC. Spearman correlation coefficients (Fisher's Z transformed) were calculated between these z-transformed GMV maps of the patients and the spatial distribution of the respective neurotransmitter maps included in JuSpace toolbox. Exact permutation-based *p*-values as included in JuSpace (10,000 permutations randomly assigning group labels using orthogonal permutations) were computed to check if the distribution of the observed Fisher's z-transformed individual correlation coefficients were significantly different from zero. All analyses were Family Wise Error (FWE) corrected for the number of tests. Spearman correlation coefficients (Fisher's Z transformed) were calculated between these z-transformed GMV maps and the spatial distribution of the respective neurotransmitter maps. Exact permutation-based *p*-values as implemented in JuSpace (10,000 permutations randomly assigning group labels using orthogonal permutations) were computed to test if the observed correlation coefficients across patients deviate from a null distribution.

### 2.5. Statistical analysis

Comparisons of demographic and clinical characteristics were performed by the Student's *t*-test for continuous variables and the $\chi^2$ test for categorical variables.

Spearman correlation was used to assess the relationship between each neurotransmitter output obtained with Juspace (i.e., the GMV-neurotransmitters correlation, Fisher's Z transformed) and clinical or
behavioral data. Statistical significance was set at *p* < 0.05, corrected for multiple comparisons (Family Wise Error-FWE) (SPSS Statistics 22.0, Chicago, USA).

# 3. Results

## 3.1. Participants

Demographic characteristics of mutation carriers and non-carriers are reported in Table 1. In the present study, we considered 157 *C9orf72* expansion carriers, namely 85 asymptomatic, 33 prodromal and 39 symptomatic subjects; 164 with *GRN* mutation carriers, namely 107 asymptomatic, 33 prodromal and 24 symptomatic subjects, and 71 *MAPT* mutation carriers, namely 39 asymptomatic, 18 prodromal and 14 symptomatic subjects.

Standard voxel-wise analyses of GMV demonstrated the typical pattern of brain atrophy in mutations subgroups, according to disease stage, as previously published (Rohrer et al., 2015; Cash et al., 2018; Beck et al., 2008; Boeve et al., 2012; Josephs et al., 2009; Mahoney et al., 2012; Sha et al., 2012; Whitwell et al., 2012) (see Supplementary Fig. 1).

## 3.2. Neurotransmitters deficits in *C9orf72* expansion carriers

In prodromal stage of *C9orf72* disease (CDR = 0.5), as compared to HC, voxel-based brain changes were significantly associated with spatial distribution of dopamine transporter DAT (*r* = −0.13, *p* = 0.02) and acetylcholine transporter (*r* = −0.12, *p* = 0.02). In fully symptomatic stage (CDR ≥ 1), additional voxel-based brain changes were significantly associated with spatial distribution of.

5-HT1a receptors (*r* = −0.30, *p* = 0.01), D1 receptors (*r* = −0.28, *p* = 0.01), FDOPA (*r* = −0.13, *p* = 0.02), and mGluR5 (*r* = −0.20, *p* = 0.01) (see Fig. 1 and Table 2). The negative correlation coefficients indicate GMV reduction in patients as compared to HC in areas with high neurotransmitters density.

There was no significant difference in spatial distribution in asymptomatic expansion carriers (CDR = 0) as compared to HC.

## 3.3. Neurotransmitters deficits in *GRN* mutation carriers

No voxel-based brain changes were significantly associated with neurotransmitter spatial distribution in prodromal *GRN* disease (CDR = 0.5). In fully symptomatic stage (CDR ≥ 1), as compared to HC, voxel-based brain changes were significantly associated with spatial distribution of 5-HT1a receptors (*r* = −0.25, *p* = 0.01), D1 receptors (*r* = −0.024, *p* = 0.01), dopamine transporter DAT (*r* = −0.14, *p* = 0.01), FDOPA (*r* = −0.11, *p* = 0.02), acetylcholine transporter (*r* = −0.16, *p* = 0.02), and mGluR5 (*r* = −0.23, *p* = 0.01) (see Fig. 1 and Table 2).

There was no significant difference in spatial distribution in asymptomatic mutation carriers (CDR = 0) as compared to HC.

<table><caption>**Table 1**
Demographic and clinical characteristics of the studied group.</caption><thead><tr><th>Variable</th><th>HC</th><th>C9orf72</th><th>GRN</th><th>MAPT</th><th>p-value*</th></tr></thead><tbody><tr><td>Number</td><td>276</td><td>157</td><td>164</td><td>71</td><td></td></tr><tr><td></td><td>46.5 ±</td><td>49.6 ±</td><td>48.5 ±</td><td>44.6 ±</td><td></td></tr><tr><td>Age, years</td><td>13.2</td><td>13.2</td><td>12.9</td><td>12.8</td><td>0.02</td></tr><tr><td>Sex, female %</td><td>57.6</td><td>54.1</td><td>59.8</td><td>52.1</td><td>0.14^</td></tr><tr><td>Education, years</td><td>14.3 ±</td><td>14.0 ± 3.2</td><td>14.2 ±</td><td>14.6 ±</td><td></td></tr><tr><td></td><td>3.3</td><td></td><td>3.8</td><td>3.0</td><td>0.56</td></tr></tbody></table>

Demographic characteristics are expressed as mean ± standard deviation, unless otherwise specified. HC = Healthy controls, *C9orf72* = chromosome 9 open reading frame 72 mutation carriers; GRN = progranulin mutation carriers; MAPT = microtubule-associated protein tau mutation carriers; \*Student-t-test, unless otherwise specified; ^Chi-Square test.

## 3.4. Neurotransmitters deficits in *MAPT* mutation carriers

In prodromal stage of *MAPT* disease (CDR = 0.5), as compared to HC, voxel-based brain changes were significantly associated with spatial distribution of 5-HT1a receptors (*r* = −0.34, *p* = 0.01), D1 receptors (*r* = −0.20, *p* = 0.01), dopamine transporter DAT (*r* = −0.30, *p* = 0.01), FDOPA (*r* = −0.16, *p* = 0.01), and SERT (*r* = −0.16, *p* = 0.01). In fully symptomatic stage (CDR ≥ 1), additional voxel-based brain changes were significantly associated with spatial distribution of 5-HT1b receptors (*r* = 0.14, *p* = 0.02) and acetylcholine transporter (*r* = −0.18, *p* = 0.02) (see Fig. 1 and Table 2).

There was no significant difference in spatial distribution in asymptomatic mutation carriers (CDR = 0) as compared to HC.

## 3.5. Neurotransmitter impairment and social cognition in monogenic FTD

We assessed the relationship between GMV-neurotransmitters correlation coefficients and social cognition/loss of empathy data in monogenic FTD patients (CDR > 0). We considered only GMV-neurotransmitters correlation coefficients significantly impaired in FTD and we excluded those highly correlated to each other (Spearman correlations coefficients > 0.80), namely FDOPA. Thus, we included in the present analyses 5-HT1a receptors, D1 receptors, DAT, VACH, and mGLUR5.

We considered a) Ekman facial emotion recognition task and Faux-pas recognition test (mini-SEA) scores (the lower the scores the worse the performances) (Funkiewicz et al., 2012), and b) loss of empathy and c) poor response to social/emotional cues, as reported by caregiver (which were rated on a 5-point scale: 0 = absent, 0.5 = questionable/ very mild, 1 = mild, 2 = moderate, and 3 = severe). Significant threshold was set at *p* ≤ 0.002, after correction for multiple comparisons.

In *C9orf72* expansion carriers, mini-SEA scores (n = 58) were positively correlated with the strength of GMV colocalization of 5-HT1a receptors (*r* = 0.449, *p* < 0.001) and D1 receptors (*r* = 0.402, *p* = 0.002); loss of empathy (n = 71) was negatively correlated with D1 receptors (*r* = −0.423, *p* < 0.001) and poor response to emotional cues (n = 71) with 5-HT1a receptors (*r* = −0.406, *p* < 0.001) and D1 receptors (*r* = −0.454, *p* < 0.001). No other significant correlations between cognitive data and GMV neurotransmitters co-localization at pre-established statistical threshold were reported.

In *GRN* mutation carriers, loss of empathy (n = 57) was negatively correlated with D1 receptors (*r* = −0.439, *p* = 0.001) and poor response to emotional cues (n = 57) with D1 receptors (*r* = −0.542, *p* < 0.001) and DAT (*r* = −0.497, *p* < 0.001).

The relatively low number of prodromal or symptomatic *MAPT* mutation carriers prevented us to run correlation analyses in this group.

# 4. Discussion

In the last years, a giant step forward has been made in the knowledge of genetic basis of FTD and gene-related pathogenetic mechanisms, and more recently experimental therapeutic trials targeting *C9orf72*, *GRN*, or *MAPT* have been proposed.

Despite this, neurotransmitter impairment in monogenic FTD and differences according to causative gene have not been assessed yet. Restoring these deficits, individually or in combination, has the potential advantage to improve clinical and behavioral symptoms and may help in further understanding of the disease.

In the present work, we investigated if the spatial distribution of grey matter atrophy observed in different subtypes of monogenic prodromal and symptomatic FTD are related to the localization of specific neurotransmitters pathways as derived from independent healthy volunteer populations (Dukart et al., 2021). These data have been obtained by JuSpace toolbox, which compares PET and SPECT derived neurotransmitter maps with other imaging modalities such as MRI data (Dukart
behavioral data. Statistical significance was set at *p* < 0.05, corrected for multiple comparisons (Family Wise Error-FWE) (SPSS Statistics 22.0, Chicago, USA).

# 3. Results

## 3.1. Participants

Demographic characteristics of mutation carriers and non-carriers are reported in Table 1. In the present study, we considered 157 *C9orf72* expansion carriers, namely 85 asymptomatic, 33 prodromal and 39 symptomatic subjects; 164 with *GRN* mutation carriers, namely 107 asymptomatic, 33 prodromal and 24 symptomatic subjects, and 71 *MAPT* mutation carriers, namely 39 asymptomatic, 18 prodromal and 14 symptomatic subjects.

Standard voxel-wise analyses of GMV demonstrated the typical pattern of brain atrophy in mutations subgroups, according to disease stage, as previously published (Rohrer et al., 2015; Cash et al., 2018; Beck et al., 2008; Boeve et al., 2012; Josephs et al., 2009; Mahoney et al., 2012; Sha et al., 2012; Whitwell et al., 2012) (see Supplementary Fig. 1).

## 3.2. Neurotransmitters deficits in *C9orf72* expansion carriers

In prodromal stage of *C9orf72* disease (CDR = 0.5), as compared to HC, voxel-based brain changes were significantly associated with spatial distribution of dopamine transporter DAT (*r* = −0.13, *p* = 0.02) and acetylcholine transporter (*r* = −0.12, *p* = 0.02). In fully symptomatic stage (CDR ≥ 1), additional voxel-based brain changes were significantly associated with spatial distribution of.

5-HT1a receptors (*r* = −0.30, *p* = 0.01), D1 receptors (*r* = −0.28, *p* = 0.01), FDOPA (*r* = −0.13, *p* = 0.02), and mGluR5 (*r* = −0.20, *p* = 0.01) (see Fig. 1 and Table 2). The negative correlation coefficients indicate GMV reduction in patients as compared to HC in areas with high neurotransmitters density.

There was no significant difference in spatial distribution in asymptomatic expansion carriers (CDR = 0) as compared to HC.

## 3.3. Neurotransmitters deficits in *GRN* mutation carriers

No voxel-based brain changes were significantly associated with neurotransmitter spatial distribution in prodromal *GRN* disease (CDR = 0.5). In fully symptomatic stage (CDR ≥ 1), as compared to HC, voxel-based brain changes were significantly associated with spatial distribution of 5-HT1a receptors (*r* = −0.25, *p* = 0.01), D1 receptors (*r* = −0.024, *p* = 0.01), dopamine transporter DAT (*r* = −0.14, *p* = 0.01), FDOPA (*r* = −0.11, *p* = 0.02), acetylcholine transporter (*r* = −0.16, *p* = 0.02), and mGluR5 (*r* = −0.23, *p* = 0.01) (see Fig. 1 and Table 2).

There was no significant difference in spatial distribution in asymptomatic mutation carriers (CDR = 0) as compared to HC.

<table><caption>**Table 1**
Demographic and clinical characteristics of the studied group.</caption><thead><tr><th>Variable</th><th>HC</th><th><i>C9orf72</i></th><th><i>GRN</i></th><th><i>MAPT</i></th><th>p-value*</th></tr></thead><tbody><tr><td>Number</td><td>276</td><td>157</td><td>164</td><td>71</td><td></td></tr><tr><td></td><td>46.5 ±</td><td>49.6 ±</td><td>48.5 ±</td><td>44.6 ±</td><td></td></tr><tr><td>Age, years</td><td>13.2</td><td>13.2</td><td>12.9</td><td>12.8</td><td>0.02</td></tr><tr><td>Sex, female %</td><td>57.6</td><td>54.1</td><td>59.8</td><td>52.1</td><td>0.14^</td></tr><tr><td>Education, years</td><td>14.3 ±</td><td>14.0 ± 3.2</td><td>14.2 ±</td><td>14.6 ±</td><td></td></tr><tr><td></td><td>3.3</td><td></td><td>3.8</td><td>3.0</td><td>0.56</td></tr></tbody></table>

Demographic characteristics are expressed as mean ± standard deviation, unless otherwise specified. HC = Healthy controls, *C9orf72* = chromosome 9 open reading frame 72 mutation carriers; *GRN* = progranulin mutation carriers; *MAPT* = microtubule-associated protein tau mutation carriers; \*Student-t-test, unless otherwise specified; ^Chi-Square test.

## 3.4. Neurotransmitters deficits in *MAPT* mutation carriers

In prodromal stage of *MAPT* disease (CDR = 0.5), as compared to HC, voxel-based brain changes were significantly associated with spatial distribution of 5-HT1a receptors (*r* = −0.34, *p* = 0.01), D1 receptors (*r* = −0.20, *p* = 0.01), dopamine transporter DAT (*r* = −0.30, *p* = 0.01), FDOPA (*r* = −0.16, *p* = 0.01), and SERT (*r* = −0.16, *p* = 0.01). In fully symptomatic stage (CDR ≥ 1), additional voxel-based brain changes were significantly associated with spatial distribution of 5-HT1b receptors (*r* = 0.14, *p* = 0.02) and acetylcholine transporter (*r* = −0.18, *p* = 0.02) (see Fig. 1 and Table 2).

There was no significant difference in spatial distribution in asymptomatic mutation carriers (CDR = 0) as compared to HC.

## 3.5. Neurotransmitter impairment and social cognition in monogenic FTD

We assessed the relationship between GMV-neurotransmitters correlation coefficients and social cognition/loss of empathy data in monogenic FTD patients (CDR > 0). We considered only GMV-neurotransmitters correlation coefficients significantly impaired in FTD and we excluded those highly correlated to each other (Spearman correlations coefficients > 0.80), namely FDOPA. Thus, we included in the present analyses 5-HT1a receptors, D1 receptors, DAT, VACH, and mGLUR5.

We considered a) Ekman facial emotion recognition task and Faux-pas recognition test (mini-SEA) scores (the lower the scores the worse the performances) (Funkiewicz et al., 2012), and b) loss of empathy and c) poor response to social/emotional cues, as reported by caregiver (which were rated on a 5-point scale: 0 = absent, 0.5 = questionable/very mild, 1 = mild, 2 = moderate, and 3 = severe). Significant threshold was set at *p* ≤ 0.002, after correction for multiple comparisons.

In *C9orf72* expansion carriers, mini-SEA scores (n = 58) were positively correlated with the strength of GMV colocalization of 5HT1a receptors (*r* = 0.449, *p* < 0.001) and D1 receptors (*r* = 0.402, *p* = 0.002); loss of empathy (n = 71) was negatively correlated with D1 receptors (*r* = −0.423, *p* < 0.001) and poor response to emotional cues (n = 71) with 5HT1a receptors (*r* = −0.406, *p* < 0.001) and D1 receptors (*r* = −0.454, *p* < 0.001). No other significant correlations between cognitive data and GMV neurotransmitters co-localization at pre-established statistical threshold were reported.

In *GRN* mutation carriers, loss of empathy (n = 57) was negatively correlated with D1 receptors (*r* = −0.439, *p* = 0.001) and poor response to emotional cues (n = 57) with D1 receptors (*r* = −0.542, *p* < 0.001) and DAT (*r* = −0.497, *p* < 0.001).

The relatively low number of prodromal or symptomatic *MAPT* mutation carriers prevented us to run correlation analyses in this group.

# 4. Discussion

In the last years, a giant step forward has been made in the knowledge of genetic basis of FTD and gene-related pathogenetic mechanisms, and more recently experimental therapeutic trials targeting *C9orf72*, *GRN*, or *MAPT* have been proposed.

Despite this, neurotransmitter impairment in monogenic FTD and differences according to causative gene have not been assessed yet. Restoring these deficits, individually or in combination, has the potential advantage to improve clinical and behavioral symptoms and may help in further understanding of the disease.

In the present work, we investigated if the spatial distribution of grey matter atrophy observed in different subtypes of monogenic prodromal and symptomatic FTD are related to the localization of specific neurotransmitters pathways as derived from independent healthy volunteer populations (Dukart et al., 2021). These data have been obtained by JuSpace toolbox, which compares PET and SPECT derived neurotransmitter maps with other imaging modalities such as MRI data (Dukart
<table><caption>**Fig. 1.** Results of spatial correlation analyses according to mutation and disease stage.</caption><thead><tr><th rowspan="2">Mutation</th><th colspan="2">C9orf72</th><th colspan="2">GRN</th><th colspan="2">MAPT</th></tr><tr><th>0.5</th><th>≥1</th><th>0.5</th><th>≥1</th><th>0.5</th><th>≥1</th></tr></thead><tbody><tr><td>CDR plus NACC FTLD</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT1a</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT1b</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT2a</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SERT</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D1</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D2</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DAT</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FDOPA</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GABA-A</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NAT</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>VACHT</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>mGLUR5</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Significant correlations for each neurotransmitter map in the different genetic groups are represented, considering each mutation group (C9orf72, GRN, MAPT) and disease stage according to Clinical Dementia Rating Dementia Staging Instrument plus behaviour and language domains from the National Alzheimer's Coordinating Center and Frontotemporal lobar degeneration modules (CDR plus NACC FTLD, here referred as CDR).

5HT = 5-hydroxytryptamine; SERT = serotonin transporter; D = dopamine; DAT = dopamine transporter; FDOPA = Fluorodopa; GABAa = γ-Aminobutyric acid type A; NAT = noradrenaline; VACHT = Vesicular acetylcholine transporter; mGLUR5 = metabotropic glutamate receptor type 5.

p-values<0.01 in blue and p-values<0.05 in red, corrected for multiple comparisons (Bonferroni's correction). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

et al., 2021). In our study, we considered grey matter atrophy as an imaging marker of neurodegeneration; however, other biomarkers might be even more sensitive, in particular in the prodromal phase (e.g. measures derived from functional imaging data).

We reported that grey matter alterations in the prodromal disease stages specifically co-localised with different neurotransmitters pathways, involving dopamine and cholinergic systems in C9orf72 expansion carriers, dopamine and serotonin in MAPT mutation carriers, and with no significant detectable changes in GRN mutation carriers. Indeed, it has been reported that TDP-43 proteinopathy, the pathological hallmark of C9orf72 expansions, may cause dopamine alterations (Funkiewiez et al., 2012) and that C9orf72 expansion carriers exhibit more pronounced memory deficits as compared to MAPT and GRN mutation carriers (Funkiewiez et al., 2012), tasks for which cholinergic system is key. On the other hand, in regard to MAPT mutations, it has been proposed a link between dopamine and serotonin neurotransmission and phosphorylation state of tau protein (Koppel et al., 2019; Ramos-Rodriguez et al., 2013), with tau being able to disrupt the survival of dopaminergic and serotoninergic neurons in *Drosophila* and in animal models (Wu et al., 2013; Khan et al., 2022). Finally, the lack of significant findings in GRN mutation carriers is in line with previous imaging studies reporting less functional and structural brain abnormalities in the prodromal stages than other genetic subtypes (Cash et al., 2018; Borroni et al., 2012; Premi et al., 2016; Premi et al., 2021).

Conversely, symptomatic disease was associated with a broad involvement of different circuits and significant changes of dopaminergic, serotoninergic and cholinergic pathways in all monogenic FTD subtypes. We also found additional glutamatergic pathway involvement in C9orf72 and GRN symptomatic mutations carriers. Of note, GABAergic and noradrenergic pathways resulted spared in monogenic FTD. These findings confirm and extend previous literature data on autopsy studies as well as a recent study on a large group of PPA patients (Premi et al., 2022), but also suggest an additional involvement of cholinergic system in monogenic FTD which is absent in sporadic disease (Murley and Rowe, 2018; Benussi et al., 2019). As compared to previous studies (Murley and Rowe, 2018), we indeed failed to confirm a co-localization of grey matter alteration and the GABAergic system.

Interestingly, we also suggest that dopamine and serotonin pathways may be associated with social cognition deficits and loss of empathy, which represents an early clinical feature in FTD (Toller et al., 2023). Dopamine, in addition to be linked to movement disorders, has long been known for its role in reward processing and emotional recognition (Fernandez et al., 2017; Schuster et al., 2022), and most recently a central role of serotonin circuits has been recognized in emotion

regulation and social behaviour (Canli and Lesch, 2007; Kanen et al., 2021; Duerler et al., 2022).

Most studies evaluating pharmacological approaches in FTD have not reported clear-cut results (Panza et al., 2020). Findings herein reported argue for further considering pharmacological manipulation of specific neurotransmitters, specifically considering FTD subtypes and disease stage to counteract related symptoms. In this view, investigating neurotransmitter pathways involved might aid in identifying biochemical alterations, which together with clinical, biological and neuroimaging biomarkers might be helpful to characterize more in detail the different FTD subtypes. In comparison to other biomarkers, exploring neurotransmitter impairment might hold the advantage to identify tailored therapeutic targets to improve symptomatic treatment.

Nonetheless, we acknowledge that this study entails some limitations. First, future implemented neurotransmitters maps in JuSpace may further refine the present findings. Moreover, the maps available have been recently obtained, and present some limitations that might to be addressed, e.g. the variability in the number of controls cases in each map and receptor density assessment is not necessarily related to neurotransmitter density. Moreover, JuSpace toolbox has not yet been validated in aging populations with substantial atrophy. Second, we considered prodromal monogenic FTD, and these results cannot be extended to prodromal sporadic disease. Third, other toolbox mapping neurotransmitter systems and their implementation, such as NeuroMaps, may be also considered (Markello et al., 2022). Finally, JuSpace toolbox indirectly assess neurotransmitter impairment, and post mortem studies are warranted to confirm the results herein observed.

In conclusions, this study suggests that JuSpace is a helpful tool to indirectly assess neurotransmitter deficits in neurodegenerative demeitias and may provide novel insight into disease mechanisms and intervention pharmacological targets.

## Fundings

This work is supported by JPND grant "GENFI-prox" (2019-02248), the Centre d'Investigation Clinique (ICM, France) and the Centre pour l'Acquisition et le Traitement des Images platform (CATI, France), the UK Medical Research Council, the Italian Ministry of Health (CoEN015 and Ricerca Corrente), and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, a Canadian Institutes of Health Research operating grant, and the Italian Ministry of Health (GR-2018-12365105). This work was also supported by the MRC UK GENFI grant (MR/M023664/1), the Alzheimer's Society grant (AS- PG-16-007), and the Bluefield Project. This work was funded by the
<table><caption>**Fig. 1.** Results of spatial correlation analyses according to mutation and disease stage.</caption><thead><tr><th rowspan="2">Mutation</th><th colspan="2">C9orf72</th><th colspan="2">GRN</th><th colspan="2">MAPT</th></tr><tr><th>0.5</th><th>≥1</th><th>0.5</th><th>≥1</th><th>0.5</th><th>≥1</th></tr></thead><tbody><tr><td>CDR plus NACC FTLD</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT1a</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT1b</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT2a</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SERT</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D1</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D2</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DAT</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FDOPA</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GABA-A</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NAT</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>VACHT</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>mGLUR5</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Significant correlations for each neurotransmitter map in the different genetic groups are represented, considering each mutation group (C9orf72, GRN, MAPT) and disease stage according to Clinical Dementia Rating Dementia Staging Instrument plus behaviour and language domains from the National Alzheimer's Coordinating Center and Frontotemporal lobar degeneration modules (CDR plus NACC FTLD, here referred as CDR).

5HT = 5-hydroxytryptamine; SERT = serotonin transporter; D = dopamine; DAT = dopamine transporter; FDOPA = Fluorodopa; GABAa = γ-Aminobutyric acid type A; NAT = noradrenaline; VACHT = Vesicular acetylcholine transporter; mGLUR5 = metabotropic glutamate receptor type 5.

p-values<0.01 in blue and p-values<0.05 in red, corrected for multiple comparisons (Bonferroni's correction). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

et al., 2021). In our study, we considered grey matter atrophy as an imaging marker of neurodegeneration; however, other biomarkers might be even more sensitive, in particular in the prodromal phase (e.g. measures derived from functional imaging data).

We reported that grey matter alterations in the prodromal disease stages specifically co-localised with different neurotransmitters pathways, involving dopamine and cholinergic systems in C9orf72 expansion carriers, dopamine and serotonin in MAPT mutation carriers, and with no significant detectable changes in GRN mutation carriers. Indeed, it has been reported that TDP-43 proteinopathy, the pathological hallmark of C9orf72 expansions, may cause dopamine alterations (Funkiewiez et al., 2012) and that C9orf72 expansion carriers exhibit more pronounced memory deficits as compared to MAPT and GRN mutation carriers (Funkiewiez et al., 2012), tasks for which cholinergic system is key. On the other hand, in regard to MAPT mutations, it has been proposed a link between dopamine and serotonin neurotransmission and phosphorylation state of tau protein (Koppel et al., 2019; Ramos-Rodriguez et al., 2013), with tau being able to disrupt the survival of dopaminergic and serotoninergic neurons in *Drosophila* and in animal models (Wu et al., 2013; Khan et al., 2022). Finally, the lack of significant findings in GRN mutation carriers is in line with previous imaging studies reporting less functional and structural brain abnormalities in the prodromal stages than other genetic subtypes (Cash et al., 2018; Borroni et al., 2012; Premi et al., 2016; Premi et al., 2021).

Conversely, symptomatic disease was associated with a broad involvement of different circuits and significant changes of dopaminergic, serotoninergic and cholinergic pathways in all monogenic FTD subtypes. We also found additional glutamatergic pathway involvement in C9orf72 and GRN symptomatic mutations carriers. Of note, GABAergic and noradrenergic pathways resulted spared in monogenic FTD. These findings confirm and extend previous literature data on autopsy studies as well as a recent study on a large group of PPA patients (Premi et al., 2022), but also suggest an additional involvement of cholinergic system in monogenic FTD which is absent in sporadic disease (Murley and Rowe, 2018; Benussi et al., 2019). As compared to previous studies (Murley and Rowe, 2018), we indeed failed to confirm a co-localization of grey matter alteration and the GABAergic system.

Interestingly, we also suggest that dopamine and serotonin pathways may be associated with social cognition deficits and loss of empathy, which represents an early clinical feature in FTD (Toller et al., 2023). Dopamine, in addition to be linked to movement disorders, has long been known for its role in reward processing and emotional recognition (Fernandez et al., 2017; Schuster et al., 2022), and most recently a central role of serotonin circuits has been recognized in emotion

regulation and social behaviour (Canli and Lesch, 2007; Kanen et al., 2021; Duerler et al., 2022).

Most studies evaluating pharmacological approaches in FTD have not reported clear-cut results (Panza et al., 2020). Findings herein reported argue for further considering pharmacological manipulation of specific neurotransmitters, specifically considering FTD subtypes and disease stage to counteract related symptoms. In this view, investigating neurotransmitter pathways involved might aid in identifying biochemical alterations, which together with clinical, biological and neuroimaging biomarkers might be helpful to characterize more in detail the different FTD subtypes. In comparison to other biomarkers, exploring neurotransmitter impairment might hold the advantage to identify tailored therapeutic targets to improve symptomatic treatment.

Nonetheless, we acknowledge that this study entails some limitations. First, future implemented neurotransmitters maps in JuSpace may further refine the present findings. Moreover, the maps available have been recently obtained, and present some limitations that might to be addressed, e.g. the variability in the number of controls cases in each map and receptor density assessment is not necessarily related to neurotransmitter density. Moreover, JuSpace toolbox has not yet been validated in aging populations with substantial atrophy. Second, we considered prodromal monogenic FTD, and these results cannot be extended to prodromal sporadic disease. Third, other toolbox mapping neurotransmitter systems and their implementation, such as NeuroMaps, may be also considered (Markello et al., 2022). Finally, JuSpace toolbox indirectly assess neurotransmitter impairment, and post mortem studies are warranted to confirm the results herein observed.

In conclusions, this study suggests that JuSpace is a helpful tool to indirectly assess neurotransmitter deficits in neurodegenerative demeitias and may provide novel insight into disease mechanisms and intervention pharmacological targets.

## Fundings

This work is supported by JPND grant "GENFI-prox" (2019-02248), the Centre d'Investigation Clinique (ICM, France) and the Centre pour l'Acquisition et le Traitement des Images platform (CATI, France), the UK Medical Research Council, the Italian Ministry of Health (CoEN015 and Ricerca Corrente), and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, a Canadian Institutes of Health Research operating grant, and the Italian Ministry of Health (GR-2018-12365105). This work was also supported by the MRC UK GENFI grant (MR/M023664/1), the Alzheimer's Society grant (AS- PG-16-007), and the Bluefield Project. This work was funded by the
<caption>**Table 2**</caption>

Results of spatial correlation analyses for included participants according to mutation subtype and disease stage.

<table><thead><tr><th rowspan="2">Mutation</th><th colspan="2">CDR = 0.5</th><th colspan="2">CDR ≥ 1</th></tr><tr><th>r</th><th>p-value</th><th>r</th><th>p-value</th></tr></thead><tbody><tr><td><strong>C9orf72</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT1a</td><td>-0.08</td><td>0.38</td><td><strong>-0.30</strong></td><td><strong>0.01</strong></td></tr><tr><td>5HT1b</td><td>0.01</td><td>0.98</td><td>-0.01</td><td>0.91</td></tr><tr><td>5HT2a</td><td>-0.01</td><td>0.98</td><td>-0.09</td><td>0.26</td></tr><tr><td>SERT</td><td>-0.09</td><td>0.08</td><td>-0.08</td><td>0.91</td></tr><tr><td>D1</td><td>-0.01</td><td>0.09</td><td><strong>-0.28</strong></td><td><strong>0.01</strong></td></tr><tr><td>D2</td><td>-0.08</td><td>0.26</td><td>0.02</td><td>0.91</td></tr><tr><td>DAT</td><td><strong>-0.13</strong></td><td><strong>0.02</strong></td><td><strong>-0.22</strong></td><td><strong>0.01</strong></td></tr><tr><td>FDOPA</td><td>-0.05</td><td>0.64</td><td><strong>-0.13</strong></td><td><strong>0.02</strong></td></tr><tr><td>GABAa</td><td>0.07</td><td>0.98</td><td>-0.07</td><td>0.91</td></tr><tr><td>NAT</td><td>-0.08</td><td>0.16</td><td>0.04</td><td>0.55</td></tr><tr><td>VACHT</td><td><strong>-0.12</strong></td><td><strong>0.02</strong></td><td><strong>-0.20</strong></td><td><strong>0.01</strong></td></tr><tr><td>mGluR5</td><td>-0.07</td><td>0.45</td><td><strong>-0.20</strong></td><td><strong>0.01</strong></td></tr><tr><td><strong>GRN</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT1a</td><td>-0.05</td><td>0.94</td><td><strong>-0.25</strong></td><td><strong>0.01</strong></td></tr><tr><td>5HT1b</td><td>-0.03</td><td>0.94</td><td>-0.02</td><td>0.73</td></tr><tr><td>5HT2a</td><td>-0.04</td><td>0.94</td><td>-0.08</td><td>0.14</td></tr><tr><td>SERT</td><td>0.02</td><td>0.94</td><td>-0.02</td><td>0.73</td></tr><tr><td>D1</td><td>-0.06</td><td>0.94</td><td><strong>-0.24</strong></td><td><strong>0.01</strong></td></tr><tr><td>D2</td><td>0.02</td><td>0.94</td><td>0.01</td><td>0.73</td></tr><tr><td>DAT</td><td>-0.03</td><td>0.94</td><td><strong>-0.14</strong></td><td><strong>0.01</strong></td></tr><tr><td>FDOPA</td><td>-0.02</td><td>0.94</td><td><strong>-0.11</strong></td><td><strong>0.02</strong></td></tr><tr><td>GABAa</td><td>-0.01</td><td>0.94</td><td>-0.01</td><td>0.73</td></tr><tr><td>NAT</td><td>0.01</td><td>0.94</td><td>0.06</td><td>0.22</td></tr><tr><td>VACHT</td><td>-0.08</td><td>0.44</td><td><strong>-0.16</strong></td><td><strong>0.01</strong></td></tr><tr><td>mGluR5</td><td>-0.09</td><td>0.36</td><td><strong>-0.23</strong></td><td><strong>0.01</strong></td></tr><tr><td><strong>MAPT</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT1a</td><td><strong>-0.34</strong></td><td><strong>0.01</strong></td><td><strong>-0.50</strong></td><td><strong>0.01</strong></td></tr><tr><td>5HT1b</td><td>0.09</td><td>0.30</td><td><strong>0.14</strong></td><td><strong>0.02</strong></td></tr><tr><td>5HT2a</td><td>-0.01</td><td>0.97</td><td>0.02</td><td>0.73</td></tr><tr><td>SERT</td><td><strong>-0.16</strong></td><td><strong>0.01</strong></td><td><strong>-0.26</strong></td><td><strong>0.01</strong></td></tr><tr><td>D1</td><td><strong>-0.20</strong></td><td><strong>0.01</strong></td><td><strong>-0.37</strong></td><td><strong>0.01</strong></td></tr><tr><td>D2</td><td>0.04</td><td>0.97</td><td>0.13</td><td>0.07</td></tr><tr><td>DAT</td><td><strong>-0.30</strong></td><td><strong>0.01</strong></td><td><strong>-0.45</strong></td><td><strong>0.01</strong></td></tr><tr><td>FDOPA</td><td><strong>-0.16</strong></td><td><strong>0.01</strong></td><td><strong>-0.36</strong></td><td><strong>0.01</strong></td></tr><tr><td>GABAa</td><td>-0.01</td><td>0.97</td><td>0.06</td><td>0.73</td></tr><tr><td>NAT</td><td>0.01</td><td>0.97</td><td>0.11</td><td>0.08</td></tr><tr><td>VACHT</td><td>-0.12</td><td>0.14</td><td><strong>-0.18</strong></td><td><strong>0.02</strong></td></tr><tr><td>mGluR5</td><td>-0.05</td><td>0.97</td><td>-0.05</td><td>0.73</td></tr></tbody></table>

Fisher's z-transformed correlation coefficients (r) for each neurotransmitter map are reported, with corresponding *p*-values. The negative correlation coefficients indicate GMV reduction in patients as compared to HC in areas with high neurotransmitters density.

Significant results in boldface; *p*-values corrected for multiple comparison (FWE correction).

CDR = Clinical Dementia Rating Dementia Staging Instrument plus behaviour and language domains from the National Alzheimer's Coordinating Center and Frontotemporal lobar degeneration; 5HT = 5-hydroxytryptamine; SERT = serotonin transporter; D = dopamine; DAT = dopamine transporter; FDOPA = Fluorodopa; GABAa = γ-Aminobutyric acid type A; NAT = noradrenaline; VACHT = Vesicular acetylcholine transporter; mGLUR5 = metabotropic glutamate receptor type 5.

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).

## Disclosures

MB is supported by a Fellowship award from the Alzheimer's Society, UK (AS-JF-19a-004-517). RS-V was funded at the Hospital Clinic de Barcelona by Instituto de Salud Carlos III, Spain (grant code PI20/00448 to RSV) and Fundació Marató TV3, Spain (grant code 20143810 to RSV). RL is supported by the Canadian Institutes of Health Research and the

Chaire de Recherche sur les Aphasies Primaires Progressives Fondation Famille Lemaire. MS has been supported by the German Federal Ministry of Education and Research (BMBF) by a grant given to the German FTLD Consortium (FKZ O1GI1007A), and by the German Research Foundation DFG (SCHR 774/5–1). CG is supported by the Swedish Frontotemporal Dementia Initiative Schörling Foundation, Swedish Research Council, JPND Prefrontals, 2015–02926,2018–02754, Swedish Alzheimer Foundation, Swedish Brain Foundation, Karolinska Institutet Doctoral Funding, KI Strat-Neuro, Swedish Dementia Foundation, and Stockholm County Council ALF/Region Stockholm. MM was, in part, funded by the UK Medical Research Council, the Italian Ministry of Health and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, and also Canadian Institutes of Health Research operating grants (Grant #s: MOP- 371851 and PJT-175242) and funding from the Weston Brain Institute to Mario Masellis. JBR is supported by the Wellcome Trust (103,838;220,258), Medical Research Council (SUAG/092 G116768) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20,014; the views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care). RV's work is supported by the Mady Browaeys Fonds voor Onderzoek naar Frontotemporale Degeneratie. SD receives salary funding from the Fonds de Recherche du Québec - Santé. JL is 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). JDR is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). BB is supported by JPND grant "GENFI-prox" (2019–02248). Several authors of this publication (JCVS, MS, RSV, MO, RV, JDR) are members of the European Reference Network for Rare Neurological Diseases (ERN-RND) - Project ID No 739510. All other authors report no biomedical financial interests or potential conflicts of interest.

## Contributors

The corresponding author is responsible for ensuring that the descriptions are accurate and agreed by all authors. EP and MP planned the study, carried out statistical analysis, contributed to interpretation of the results and drafted the initial version of the manuscript; IM carried out statistical analysis and contributed to interpretation of the results; JD contributed to interpretation of the results. VC, RG, EB, AP, MB, EGT, AB, DMC, RSC, LLR, PF, DLT, JVS, LCJ, HS, DG, RS-V, RL, FM, MS, CG, MM, MCT, JBR, KAT, RV, EF, PT, AdeM, IS, CRB, SD, AG, JL, MO, SS, ILB, FP and JDR contributed to the conception of GENFI and acquisition of data and revised the manuscript for content. BB planned the study, carried out statistical analysis, contributed to interpretation of the results and drafted the initial version of the manuscript.

## Data availability

All study data, including raw and analysed data, and materials will be available from the GENFI Coordinator upon reasonable request. The software applied is publicly available at https://github.com/juryxy/JuSpace.

## Acknowledgements

We thank our participant volunteers and their families for their contribution to the study; the radiographers/technologists and research nurses from all centers involved in this study for their invaluable support in data acquisition.
<caption>**Table 2**</caption>

Results of spatial correlation analyses for included participants according to mutation subtype and disease stage.

<table><thead><tr><th rowspan="2">Mutation</th><th colspan="2">CDR = 0.5</th><th colspan="2">CDR ≥ 1</th></tr><tr><th>r</th><th>p-value</th><th>r</th><th>p-value</th></tr></thead><tbody><tr><td><strong>C9orf72</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT1a</td><td>-0.08</td><td>0.38</td><td><strong>-0.30</strong></td><td><strong>0.01</strong></td></tr><tr><td>5HT1b</td><td>0.01</td><td>0.98</td><td>-0.01</td><td>0.91</td></tr><tr><td>5HT2a</td><td>-0.01</td><td>0.98</td><td>-0.09</td><td>0.26</td></tr><tr><td>SERT</td><td>-0.09</td><td>0.08</td><td>-0.08</td><td>0.91</td></tr><tr><td>D1</td><td>-0.01</td><td>0.09</td><td><strong>-0.28</strong></td><td><strong>0.01</strong></td></tr><tr><td>D2</td><td>-0.08</td><td>0.26</td><td>0.02</td><td>0.91</td></tr><tr><td>DAT</td><td><strong>-0.13</strong></td><td><strong>0.02</strong></td><td><strong>-0.22</strong></td><td><strong>0.01</strong></td></tr><tr><td>FDOPA</td><td>-0.05</td><td>0.64</td><td><strong>-0.13</strong></td><td><strong>0.02</strong></td></tr><tr><td>GABAa</td><td>0.07</td><td>0.98</td><td>-0.07</td><td>0.91</td></tr><tr><td>NAT</td><td>-0.08</td><td>0.16</td><td>0.04</td><td>0.55</td></tr><tr><td>VACHT</td><td><strong>-0.12</strong></td><td><strong>0.02</strong></td><td><strong>-0.20</strong></td><td><strong>0.01</strong></td></tr><tr><td>mGluR5</td><td>-0.07</td><td>0.45</td><td><strong>-0.20</strong></td><td><strong>0.01</strong></td></tr><tr><td><strong>GRN</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT1a</td><td>-0.05</td><td>0.94</td><td><strong>-0.25</strong></td><td><strong>0.01</strong></td></tr><tr><td>5HT1b</td><td>-0.03</td><td>0.94</td><td>-0.02</td><td>0.73</td></tr><tr><td>5HT2a</td><td>-0.04</td><td>0.94</td><td>-0.08</td><td>0.14</td></tr><tr><td>SERT</td><td>0.02</td><td>0.94</td><td>-0.02</td><td>0.73</td></tr><tr><td>D1</td><td>-0.06</td><td>0.94</td><td><strong>-0.24</strong></td><td><strong>0.01</strong></td></tr><tr><td>D2</td><td>0.02</td><td>0.94</td><td>0.01</td><td>0.73</td></tr><tr><td>DAT</td><td>-0.03</td><td>0.94</td><td><strong>-0.14</strong></td><td><strong>0.01</strong></td></tr><tr><td>FDOPA</td><td>-0.02</td><td>0.94</td><td><strong>-0.11</strong></td><td><strong>0.02</strong></td></tr><tr><td>GABAa</td><td>-0.01</td><td>0.94</td><td>-0.01</td><td>0.73</td></tr><tr><td>NAT</td><td>0.01</td><td>0.94</td><td>0.06</td><td>0.22</td></tr><tr><td>VACHT</td><td>-0.08</td><td>0.44</td><td><strong>-0.16</strong></td><td><strong>0.01</strong></td></tr><tr><td>mGluR5</td><td>-0.09</td><td>0.36</td><td><strong>-0.23</strong></td><td><strong>0.01</strong></td></tr><tr><td><strong>MAPT</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>5HT1a</td><td><strong>-0.34</strong></td><td><strong>0.01</strong></td><td><strong>-0.50</strong></td><td><strong>0.01</strong></td></tr><tr><td>5HT1b</td><td>0.09</td><td>0.30</td><td><strong>0.14</strong></td><td><strong>0.02</strong></td></tr><tr><td>5HT2a</td><td>-0.01</td><td>0.97</td><td>0.02</td><td>0.73</td></tr><tr><td>SERT</td><td><strong>-0.16</strong></td><td><strong>0.01</strong></td><td><strong>-0.26</strong></td><td><strong>0.01</strong></td></tr><tr><td>D1</td><td><strong>-0.20</strong></td><td><strong>0.01</strong></td><td><strong>-0.37</strong></td><td><strong>0.01</strong></td></tr><tr><td>D2</td><td>0.04</td><td>0.97</td><td>0.13</td><td>0.07</td></tr><tr><td>DAT</td><td><strong>-0.30</strong></td><td><strong>0.01</strong></td><td><strong>-0.45</strong></td><td><strong>0.01</strong></td></tr><tr><td>FDOPA</td><td><strong>-0.16</strong></td><td><strong>0.01</strong></td><td><strong>-0.36</strong></td><td><strong>0.01</strong></td></tr><tr><td>GABAa</td><td>-0.01</td><td>0.97</td><td>0.06</td><td>0.73</td></tr><tr><td>NAT</td><td>0.01</td><td>0.97</td><td>0.11</td><td>0.08</td></tr><tr><td>VACHT</td><td>-0.12</td><td>0.14</td><td><strong>-0.18</strong></td><td><strong>0.02</strong></td></tr><tr><td>mGluR5</td><td>-0.05</td><td>0.97</td><td>-0.05</td><td>0.73</td></tr></tbody></table>

Fisher's z-transformed correlation coefficients (r) for each neurotransmitter map are reported, with corresponding *p*-values. The negative correlation coefficients indicate GMV reduction in patients as compared to HC in areas with high neurotransmitters density.

Significant results in boldface; *p*-values corrected for multiple comparison (FWE correction).

CDR = Clinical Dementia Rating Dementia Staging Instrument plus behaviour and language domains from the National Alzheimer's Coordinating Center and Frontotemporal lobar degeneration; 5HT = 5-hydroxytryptamine; SERT = serotonin transporter; D = dopamine; DAT = dopamine transporter; FDOPA = Fluorodopa; GABAa = γ-Aminobutyric acid type A; NAT = noradrenaline; VACHT = Vesicular acetylcholine transporter; mGLUR5 = metabotropic glutamate receptor type 5.

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).

## Disclosures

MB is supported by a Fellowship award from the Alzheimer's Society, UK (AS-JF-19a-004-517). RS-V was funded at the Hospital Clinic de Barcelona by Instituto de Salud Carlos III, Spain (grant code PI20/00448 to RSV) and Fundació Marató TV3, Spain (grant code 20143810 to RSV). RL is supported by the Canadian Institutes of Health Research and the

Chaire de Recherche sur les Aphasies Primaires Progressives Fondation Famille Lemaire. MS has been supported by the German Federal Ministry of Education and Research (BMBF) by a grant given to the German FTLD Consortium (FKZ O1GI1007A), and by the German Research Foundation DFG (SCHR 774/5-1). CG is supported by the Swedish Frontotemporal Dementia Initiative Schörling Foundation, Swedish Research Council, JPND Prefrontals, 2015-02926,2018-02754, Swedish Alzheimer Foundation, Swedish Brain Foundation, Karolinska Institutet Doctoral Funding, KI Strat-Neuro, Swedish Dementia Foundation, and Stockholm County Council ALF/Region Stockholm. MM was, in part, funded by the UK Medical Research Council, the Italian Ministry of Health and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, and also Canadian Institutes of Health Research operating grants (Grant #s: MOP- 371851 and PJT-175242) and funding from the Weston Brain Institute to Mario Masellis. JBR is supported by the Wellcome Trust (103,838;220,258), Medical Research Council (SUAG/092 G116768) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20,014; the views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care). RV's work is supported by the Mady Browaeys Fonds voor Onderzoek naar Frontotemporale Degeneratie. SD receives salary funding from the Fonds de Recherche du Québec - Santé. JL is 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). JDR is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). BB is supported by JPND grant "GENFI-prox" (2019-02248). Several authors of this publication (JCVS, MS, RSV, MO, RV, JDR) are members of the European Reference Network for Rare Neurological Diseases (ERN-RND) - Project ID No 739510. All other authors report no biomedical financial interests or potential conflicts of interest.

## Contributors

The corresponding author is responsible for ensuring that the descriptions are accurate and agreed by all authors. EP and MP planned the study, carried out statistical analysis, contributed to interpretation of the results and drafted the initial version of the manuscript; IM carried out statistical analysis and contributed to interpretation of the results; JD contributed to interpretation of the results. VC, RG, EB, AP, MB, EGT, AB, DMC, RSC, LLR, PF, DLT, JVS, LCJ, HS, DG, RS-V, RL, FM, MS, CG, MM, MCT, JBR, KAT, RV, EF, PT, AdeM, IS, CRB, SD, AG, JL, MO, SS, ILB, FP and JDR contributed to the conception of GENFI and acquisition of data and revised the manuscript for content. BB planned the study, carried out statistical analysis, contributed to interpretation of the results and drafted the initial version of the manuscript.

## Data availability

All study data, including raw and analysed data, and materials will be available from the GENFI Coordinator upon reasonable request. The software applied is publicly available at https://github.com/juryxy/JuSpace.

## Acknowledgements

We thank our participant volunteers and their families for their contribution to the study; the radiographers/technologists and research nurses from all centers involved in this study for their invaluable support in data acquisition.
Appendix A. Appendix

List of GENFI consortium authors:

<table><thead><tr><th>Author</th><th>Affiliation</th></tr></thead><tbody><tr><td>Aitana Sogorb Esteve</td><td>Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK;</td></tr><tr><td>Carolin Heller</td><td>UK Dementia Research Institute at University College London, UCL Queen Square Institute of Neurology, London, UK</td></tr><tr><td>Caroline V Greaves</td><td>Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK</td></tr><tr><td>Henrik Zetterberg</td><td>UK Dementia Research Institute at University College London, UCL Queen Square Institute of Neurology, London, UK; Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden</td></tr><tr><td>Imogen J Swift</td><td>Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK; UK Dementia Research Institute at University College London, UCL Queen Square Institute of Neurology, London, UK</td></tr><tr><td>Kiran Samra</td><td>Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK</td></tr><tr><td>Rachelle Shafei</td><td>Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK</td></tr><tr><td>Carolyn Timberlake</td><td>Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK</td></tr><tr><td>Thomas Cope</td><td>Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK</td></tr><tr><td>Timothy Rittman</td><td>Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK</td></tr><tr><td>Andrea Arighi</td><td>Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy</td></tr><tr><td>Chiara Fenoglio</td><td>Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy</td></tr><tr><td>Elio Scarpini</td><td>Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy</td></tr><tr><td>Giorgio Fumagalli</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Vittoria Boracci</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Giacomina Rossi</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Giorgio Giaccone</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Giuseppe Di Fede</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Paola Caroppo</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Pietro Tiraboschi</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Sara Prioni</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Veronica Redaelli</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>David Tang-Wai</td><td>The University Health Network, Krembil Research Institute, Toronto, Canada</td></tr><tr><td>Ekaterina Rogaeva</td><td>Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada</td></tr><tr><td>Miguel Castelo-Branco</td><td>Faculty of Medicine, University of Coimbra, Coimbra, Portugal</td></tr><tr><td>Morris Freedman</td><td>Baycrest Health Sciences, Rotman Research Institute, University of Toronto, Toronto, Canada</td></tr><tr><td>Ron Keren</td><td>The University Health Network, Toronto Rehabilitation Institute, Toronto, Canada</td></tr><tr><td>Sandra Black</td><td>Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada</td></tr><tr><td>Sara Mitchell</td><td>Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada</td></tr><tr><td>Christen Shoesmith</td><td>Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada</td></tr><tr><td>Robart Bartha</td><td>Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Roberts Research Institute, The University of Western Ontario, London, Ontario, Canada</td></tr><tr><td>Rosa Rademakers</td><td>Center for Molecular Neurology, University of Antwerp</td></tr><tr><td>Jackie Poos</td><td>Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands</td></tr><tr><td>Janne M. Papma</td><td>Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands</td></tr><tr><td>Lucia Giannini</td><td>Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands</td></tr><tr><td>Rick van Minkelen</td><td>Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands</td></tr><tr><td>Yolande Pijnenburg</td><td>Amsterdam University Medical Centre, Amsterdam VUMc, Amsterdam, Netherlands</td></tr><tr><td>Benedetta Nacmias</td><td>Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy</td></tr><tr><td>Camilla Ferrari</td><td>Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy</td></tr><tr><td>Cristina Polito</td><td>Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", Nuclear Medicine Unit, University of Florence, Florence, Italy</td></tr><tr><td>Gemma Lombardi</td><td>Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy</td></tr><tr><td>Valentina Bessi</td><td>Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy</td></tr><tr><td>Michele Veldsman</td><td>Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK</td></tr><tr><td>Christin Andersson</td><td>Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden</td></tr><tr><td>Hakan Thonberg</td><td>Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden</td></tr><tr><td>Linn Öijerstedt</td><td>Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden; Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden</td></tr><tr><td>Vesna Jelic</td><td>Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden</td></tr><tr><td>Paul Thompson</td><td>Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK</td></tr><tr><td>Tobias Langheinrich</td><td>Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK; Manchester Centre for Clinical Neurosciences, Department of Neurology, Salford Royal NHS Foundation Trust, Manchester, UK</td></tr><tr><td>Albert Lladó</td><td>Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Anna Antonell</td><td>Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Jaume Olives</td><td>Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Mircea Balasa</td><td>Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Nuria Bargalló</td><td>Imaging Diagnostic Center, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Sergi Borrego-Ecija</td><td>Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Ana Verdelho</td><td>Department of Neurosciences and Mental Health, Centro Hospitalar Lisboa Norte - Hospital de Santa Maria & Faculty of Medicine, University of Lisbon, Lisbon, Portugal</td></tr><tr><td>Carolina Maruta</td><td>Laboratory of Language Research, Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal</td></tr><tr><td>Catarina B. Ferreira</td><td>Laboratory of Neurosciences, Faculty of Medicine, University of Lisbon, Lisbon, Portugal</td></tr><tr><td>Gabriel Miltenberger</td><td>Faculty of Medicine, University of Lisbon, Lisbon, Portugal</td></tr></tbody></table>

(continued on next page)
Appendix A. Appendix

List of GENFI consortium authors:

<table><thead><tr><th>Author</th><th>Affiliation</th></tr></thead><tbody><tr><td>Aitana Sogorb Esteve</td><td>Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK;</td></tr><tr><td>Carolin Heller</td><td>UK Dementia Research Institute at University College London, UCL Queen Square Institute of Neurology, London, UK</td></tr><tr><td>Caroline V Greaves</td><td>Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK</td></tr><tr><td>Henrik Zetterberg</td><td>UK Dementia Research Institute at University College London, UCL Queen Square Institute of Neurology, London, UK; Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden</td></tr><tr><td>Imogen J Swift</td><td>Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK; UK Dementia Research Institute at University College London, UCL Queen Square Institute of Neurology, London, UK</td></tr><tr><td>Kiran Samra</td><td>Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK</td></tr><tr><td>Rachelle Shafei</td><td>Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK</td></tr><tr><td>Carolyn Timberlake</td><td>Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK</td></tr><tr><td>Thomas Cope</td><td>Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK</td></tr><tr><td>Timothy Rittman</td><td>Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK</td></tr><tr><td>Andrea Arighi</td><td>Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy</td></tr><tr><td>Chiara Fenoglio</td><td>Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy</td></tr><tr><td>Elio Scarpini</td><td>Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy</td></tr><tr><td>Giorgio Fumagalli</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Vittoria Boracci</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Giacomina Rossi</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Giorgio Giaccone</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Giuseppe Di Fede</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Paola Caroppo</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Pietro Tiraboschi</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Sara Prioni</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>Veronica Redaelli</td><td>Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy</td></tr><tr><td>David Tang-Wai</td><td>The University Health Network, Krembil Research Institute, Toronto, Canada</td></tr><tr><td>Ekaterina Rogaeva</td><td>Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada</td></tr><tr><td>Miguel Castelo-Branco</td><td>Faculty of Medicine, University of Coimbra, Coimbra, Portugal</td></tr><tr><td>Morris Freedman</td><td>Baycrest Health Sciences, Rotman Research Institute, University of Toronto, Toronto, Canada</td></tr><tr><td>Ron Keren</td><td>The University Health Network, Toronto Rehabilitation Institute, Toronto, Canada</td></tr><tr><td>Sandra Black</td><td>Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada</td></tr><tr><td>Sara Mitchell</td><td>Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada</td></tr><tr><td>Christen Shoesmith</td><td>Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada</td></tr><tr><td>Robart Bartha</td><td>Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Roberts Research Institute, The University of Western Ontario, London, Ontario, Canada</td></tr><tr><td>Rosa Rademakers</td><td>Center for Molecular Neurology, University of Antwerp</td></tr><tr><td>Jackie Poos</td><td>Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands</td></tr><tr><td>Janne M. Papma</td><td>Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands</td></tr><tr><td>Lucia Giannini</td><td>Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands</td></tr><tr><td>Rick van Minkelen</td><td>Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands</td></tr><tr><td>Yolande Pijnenburg</td><td>Amsterdam University Medical Centre, Amsterdam VUMc, Amsterdam, Netherlands</td></tr><tr><td>Benedetta Nacmias</td><td>Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy</td></tr><tr><td>Camilla Ferrari</td><td>Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy</td></tr><tr><td>Cristina Polito</td><td>Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", Nuclear Medicine Unit, University of Florence, Florence, Italy</td></tr><tr><td>Gemma Lombardi</td><td>Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy</td></tr><tr><td>Valentina Bessi</td><td>Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy</td></tr><tr><td>Michele Veldsman</td><td>Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK</td></tr><tr><td>Christin Andersson</td><td>Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden</td></tr><tr><td>Hakan Thonberg</td><td>Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden</td></tr><tr><td>Linn Öijerstedt</td><td>Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden; Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden</td></tr><tr><td>Vesna Jelic</td><td>Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden</td></tr><tr><td>Paul Thompson</td><td>Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK</td></tr><tr><td>Tobias Langheinrich</td><td>Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK; Manchester Centre for Clinical Neurosciences, Department of Neurology, Salford Royal NHS Foundation Trust, Manchester, UK</td></tr><tr><td>Albert Lladó</td><td>Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Anna Antonell</td><td>Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Jaume Olives</td><td>Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Mircea Balasa</td><td>Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Nuria Bargalló</td><td>Imaging Diagnostic Center, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Sergi Borrego-Ecija</td><td>Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain</td></tr><tr><td>Ana Verdelho</td><td>Department of Neurosciences and Mental Health, Centro Hospitalar Lisboa Norte - Hospital de Santa Maria & Faculty of Medicine, University of Lisbon, Lisbon, Portugal</td></tr><tr><td>Carolina Maruta</td><td>Laboratory of Language Research, Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal</td></tr><tr><td>Catarina B. Ferreira</td><td>Laboratory of Neurosciences, Faculty of Medicine, University of Lisbon, Lisbon, Portugal</td></tr><tr><td>Gabriel Miltenberger</td><td>Faculty of Medicine, University of Lisbon, Lisbon, Portugal</td></tr></tbody></table>

(continued on next page)
(continued)

<table><thead><tr><th>Author</th><th>Affiliation</th></tr></thead><tbody><tr><td>Frederico Simões do Couto</td><td>Faculdade de Medicina, Universidade Católica Portuguesa</td></tr><tr><td>Alazne Gabilondo</td><td>Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain; Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Ana Gorostidi</td><td>Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Jorge Villanua</td><td>OSATEK, University of Donostia, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Marta Cañada</td><td>CITA Alzheimer, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Mikel Tainta</td><td>Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Miren Zulaica</td><td>Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Myriam Barandiaran</td><td>Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain; Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Patricia Alves</td><td>Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain; Department of Educational Psychology and Psychobiology, Faculty of Education, International University of La Rioja, Logroño, Spain</td></tr><tr><td>Benjamin Bender</td><td>Department of Diagnostic and Interventional Neuroradiology, University of Tübingen, Tübingen, Germany</td></tr><tr><td>Carlo Wilke</td><td>Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany; Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany</td></tr><tr><td>Lisa Graf</td><td>Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany</td></tr><tr><td>Annick Vogels</td><td>Department of Human Genetics, KU Leuven, Leuven, Belgium</td></tr><tr><td>Mathieu Vandenbulcke</td><td>Geriatric Psychiatry Service, University Hospitals Leuven, Belgium; Neuropsychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium</td></tr><tr><td>Philip Van Damme</td><td>Neurology Service, University Hospitals Leuven, Belgium; Laboratory for Neurobiology, VIB-KU Leuven Centre for Brain Research, Leuven, Belgium</td></tr><tr><td>Rose Bruffaerts</td><td>Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium; Biomedical Research Institute, Hasselt University, 3500 Hasselt, Belgium</td></tr><tr><td>Koen Poesen</td><td>Laboratory for Molecular Neurobiomarker Research, KU Leuven, Leuven, Belgium</td></tr><tr><td>Pedro Rosa-Neto</td><td>Translational Neuroimaging Laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, Québec, Canada</td></tr><tr><td>Serge Gauthier</td><td>Alzheimer's disease Research Unit, McGill Centre for Studies in Aging, Department of Neurology & Neurosurgery, McGill University, Montreal, Québec, Canada</td></tr><tr><td>Agnès Camuzat</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Alexis Brice</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Reference Network for Rare Neurological Diseases (ERN-RND)</td></tr><tr><td>Anne Bertrand</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Institut du Cerveau et la Moelle, Paris, France</td></tr><tr><td>Aurélie Funkiewicz</td><td>Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Département de Neurologie, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Daisy Rinaldi</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Dario Saracino</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Institut du Cerveau et la Moelle, Paris, France</td></tr><tr><td>Olivier Colliot</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Sabrina Sayah</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Catharina Prix</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Elisabeth Wlasich</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Olivia Wagemann</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Sandra Loosli</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Sonja Schönecker</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Tobias Hoegen</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Jolina Lombardi</td><td>Department of Neurology, University of Ulm, Ulm</td></tr><tr><td>Sarah Anderl-Straub</td><td>Department of Neurology, University of Ulm, Ulm, Germany</td></tr><tr><td>Adeline Rollin</td><td>CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</td></tr><tr><td>Gregory Kuchcinski</td><td>Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</td></tr><tr><td>Maxime Bertoux</td><td>Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</td></tr><tr><td>Thibaud Lebouvier</td><td>Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</td></tr><tr><td>Vincent Deramecourt</td><td>Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</td></tr><tr><td>Beatriz Santiago</td><td>Neurology Department, Centro Hospitalar e Universitario de Coimbra, Coimbra, Portugal</td></tr><tr><td>Diana Duro</td><td>Faculty of Medicine, University of Coimbra, Coimbra, Portugal</td></tr><tr><td>Maria João Leitão</td><td>Centre of Neurosciences and Cell Biology, Universidade de Coimbra, Coimbra, Portugal</td></tr><tr><td>Maria Rosario Almeida</td><td>Faculty of Medicine, University of Coimbra, Coimbra, Portugal</td></tr><tr><td>Miguel Tábuas-Pereira</td><td>Neurology Department, Centro Hospitalar e Universitario de Coimbra, Coimbra, Portugal</td></tr><tr><td>Sónia Afonso</td><td>Instituto Ciencias Nucleares Aplicadas a Saude, Universidade de Coimbra, Coimbra, Portugal</td></tr></tbody></table>

## Appendix B. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.nbd.2023.106068.

## References

Beck, J., Rohrer, J.D., Campbell, T., Isaacs, A., Morrison, K.E., Goodall, E.F., et al., 2008. A distinct clinical, neuropsychological and radiological phenotype is associated with progranulin gene mutations in a large UK series. Brain 131, 706-720.

Benussi, A., Alberici, A., Buratti, E., Ghidoni, R., Gardoni, F., Di, Luca M., et al., 2019. Toward a glutamate hypothesis of frontotemporal dementia. Front. Neurosci. 13 https://doi.org/10.3389/FNINS.2019.00304.

Boeve, B.F., Boylan, K.B., Graff-Radford, N.R., Dejesus-Hernandez, M., Knopman, D.S., Pedraza, O., et al., 2012. Characterization of frontotemporal dementia and/or amyotrophic lateral sclerosis associated with the GGGGCC repeat expansion in C9ORF72. Brain 135, 765-783.
(continued)

<table><thead><tr><th>Author</th><th>Affiliation</th></tr></thead><tbody><tr><td>Frederico Simões do Couto</td><td>Faculdade de Medicina, Universidade Católica Portuguesa</td></tr><tr><td>Alazne Gabilondo</td><td>Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain; Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Ana Gorostidi</td><td>Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Jorge Villanua</td><td>OSATEK, University of Donostia, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Marta Cañada</td><td>CITA Alzheimer, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Mikel Tainta</td><td>Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Miren Zulaica</td><td>Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Myriam Barandiaran</td><td>Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain; Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain</td></tr><tr><td>Patricia Alves</td><td>Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain; Department of Educational Psychology and Psychobiology, Faculty of Education, International University of La Rioja, Logroño, Spain</td></tr><tr><td>Benjamin Bender</td><td>Department of Diagnostic and Interventional Neuroradiology, University of Tübingen, Tübingen, Germany</td></tr><tr><td>Carlo Wilke</td><td>Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany; Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany</td></tr><tr><td>Lisa Graf</td><td>Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany</td></tr><tr><td>Annick Vogels</td><td>Department of Human Genetics, KU Leuven, Leuven, Belgium</td></tr><tr><td>Mathieu Vandenbulcke</td><td>Geriatric Psychiatry Service, University Hospitals Leuven, Belgium; Neuropsychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium</td></tr><tr><td>Philip Van Damme</td><td>Neurology Service, University Hospitals Leuven, Belgium; Laboratory for Neurobiology, VIB-KU Leuven Centre for Brain Research, Leuven, Belgium</td></tr><tr><td>Rose Bruffaerts</td><td>Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium; Biomedical Research Institute, Hasselt University, 3500 Hasselt, Belgium</td></tr><tr><td>Koen Poesen</td><td>Laboratory for Molecular Neurobiomarker Research, KU Leuven, Leuven, Belgium</td></tr><tr><td>Pedro Rosa-Neto</td><td>Translational Neuroimaging Laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, Québec, Canada</td></tr><tr><td>Serge Gauthier</td><td>Alzheimer's disease Research Unit, McGill Centre for Studies in Aging, Department of Neurology & Neurosurgery, McGill University, Montreal, Québec, Canada</td></tr><tr><td>Agnès Camuzat</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Alexis Brice</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Reference Network for Rare Neurological Diseases (ERN-RND)</td></tr><tr><td>Anne Bertrand</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Institut du Cerveau et la Moelle, Paris, France</td></tr><tr><td>Aurélie Funkiewicz</td><td>Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Département de Neurologie, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Daisy Rinaldi</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Dario Saracino</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Institut du Cerveau et la Moelle, Paris, France</td></tr><tr><td>Olivier Colliot</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Sabrina Sayah</td><td>Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hópital Pitié-Salpêtrière, Paris, France</td></tr><tr><td>Catharina Prix</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Elisabeth Wlasich</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Olivia Wagemann</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Sandra Loosli</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Sonja Schönecker</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Tobias Hoegen</td><td>Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany</td></tr><tr><td>Jolina Lombardi</td><td>Department of Neurology, University of Ulm, Ulm</td></tr><tr><td>Sarah Anderl-Straub</td><td>Department of Neurology, University of Ulm, Ulm, Germany</td></tr><tr><td>Adeline Rollin</td><td>CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</td></tr><tr><td>Gregory Kuchcinski</td><td>Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</td></tr><tr><td>Maxime Bertoux</td><td>Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</td></tr><tr><td>Thibaud Lebouvier</td><td>Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</td></tr><tr><td>Vincent Deramecourt</td><td>Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France</td></tr><tr><td>Beatriz Santiago</td><td>Neurology Department, Centro Hospitalar e Universitario de Coimbra, Coimbra, Portugal</td></tr><tr><td>Diana Duro</td><td>Faculty of Medicine, University of Coimbra, Coimbra, Portugal</td></tr><tr><td>Maria João Leitão</td><td>Centre of Neurosciences and Cell Biology, Universidade de Coimbra, Coimbra, Portugal</td></tr><tr><td>Maria Rosario Almeida</td><td>Faculty of Medicine, University of Coimbra, Coimbra, Portugal</td></tr><tr><td>Miguel Tábuas-Pereira</td><td>Neurology Department, Centro Hospitalar e Universitario de Coimbra, Coimbra, Portugal</td></tr><tr><td>Sónia Afonso</td><td>Instituto Ciencias Nucleares Aplicadas a Saude, Universidade de Coimbra, Coimbra, Portugal</td></tr></tbody></table>

## Appendix B. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.nbd.2023.106068.

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Meloni, M., Farris, R., Solla, P., Mascia, M.M., Marrosu, F., Cannas, A., 2017. C9ORF72 intermediate repeat expansion in a patient with psychiatric disorders and progressive cerebellar ataxia. Neurologist 22, 245-246.

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Murley, A.G., Rowe, J.B., 2018. Neurotransmitter deficits from frontotemporal lobar degeneration. Brain 141, 1263-1285.

Murley, A.G., Rouse, M.A., Simon Jones, P., Ye, R., Hezemans, F.H., O'Callaghan, C., et al., 2020. GABA and glutamate deficits from frontotemporal lobar degeneration are associated with disinhibition. Brain 143, 3449.

Panza, F., Lozupone, M., Seripa, D., Daniele, A., Watling, M., Giannelli, G., Imbimbo, B. P., 2020. Development of disease-modifying drugs for frontotemporal dementia spectrum disorders. Nat. Rev. Neurol. 16, 213-228.

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Premi, E., Grassi, M., Van Swieten, J., Galimberti, D., Graff, C., Masellis, M., et al., 2017. Cognitive reserve and TMEM106B genotype modulate brain damage in presymptomatic frontotemporal dementia: a GENFI study. Brain 140, 1784-1791.

Premi, E., Calhoun, V.D., Diano, M., Gazzina, S., Cosseddu, M., Alberici, A., et al., 2019. The inner fluctuations of the brain in presymptomatic frontotemporal dementia: the chronnectome fingerprint. Neuroimage 189, 645-654.

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Sperfeld, A.D., Collatz, M.B., Baier, H., Palmbach, M., Storch, A., Schwarz, J., et al., 1999. FTDP-17: an early-onset phenotype with parkinsonism and epileptic seizures caused by a novel mutation. Ann. Neurol. 46, 708-715.

Takeshige, H., Nakayama, S., Nishioka, K., Li, Y., Motoi, Y., Hattori, N., 2018. Marked reduction in the striatal dopamine transporter uptake during the early stage of motor symptoms in patients with the MAPT N279K mutation. Intern. Med. 57, 3015-3019.

Toller, G., Cobigo, Y., Callahan, P., Appleby, B.S., Brushaber, D., Domoto-Reilly, K., et al., 2023. Multisite ALLFTD study modeling progressive empathy loss from the earliest stages of behavioral variant frontotemporal dementia. Alzheimers Dement. https://doi.org/10.1002/ALZ.12898.

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Borroni, B., Padovani, A., 2013. Dementia: a new algorithm for molecular diagnostics in FTLD. Nat. Rev. Neurol. 9, 241-242.

Borroni, B., Alberici, A., Cercignani, M., Premi, E., Serra, L., Cerini, C., et al., 2012. Granulin mutation drives brain damage and reorganization from preclinical to symptomatic FTLD. *Neurobiol. Aging* 33, 2506-2520.

Canli, T., Lesch, K.P., 2007. Long story short: the serotonin transporter in emotion regulation and social cognition. Nat. Neurosci. 10, 1103-1109.

Carecchio, M., Galimberti, D., Fenoglio, C., Serpente, M., Scarpini, E., Comi, C., et al., 2014. Evidence of pre-synaptic dopaminergic deficit in a patient with a novel progranulin mutation presenting with atypical parkinsonism. J. Alzheimers Dis. 38, 747-752.

Cash, D.M., Bocchetta, M., Thomas, D.L., Dick, K.M., van Swieten, J.C., Borroni, B., et al., 2018. Patterns of gray matter atrophy in genetic frontotemporal dementia: results from the GENFI study. *Neurobiol. Aging* 62, 191-196.

Duerler, P., Vollenweider, F.X., Preller, K.H., 2022. A neurobiological perspective on social influence: serotonin and social adaptation. J. Neurochem. 162, 60-79.

Dukart, J., Holiga, S., Chatham, C., Hawkins, P., Forsyth, A., McMillan, R., et al., 2018. Cerebral blood flow predicts differential neurotransmitter activity. Sci. Rep. 8 https://doi.org/10.1038/s41598-018-22444-0.

Dukart, J., Holiga, S., Rullmann, M., Lanzenberger, R., Hawkins, P.C.T., Mehta, M.A., et al., 2021. JuSpace: a tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Hum. Brain Mapp. 42, 555-566.

Farokhian, F., Beheshti, I., Sone, D., Matsuda, H., 2017. Comparing CAT12 and VBM8 for detecting brain morphological abnormalities in temporal lobe epilepsy. Front. Neurol. 8 https://doi.org/10.3389/FNEUR.2017.00428.

Fernandez, RW, et al., 2017 Aug. Modulation of social space by dopamine in *Drosophila melanogaster*, but no effect on the avoidance of the *Drosophila* stress odorant. *Biol Lett* 13 (8), 20170369.

Funkiewicz, A., Bertoux, M., de Souza, L.C., Lévy, R., Dubois, B., 2012. The SEA (Social cognition and Emotional Assessment): a clinical neuropsychological tool for early diagnosis of frontal variant of frontotemporal lobar degeneration. Neuropsychology 26, 81-90.

Gazzina, S., Grassi, M., Premi, E., Cosseddu, M., Alberici, A., Archetti, S., et al., 2019. Education modulates brain maintenance in presymptomatic frontotemporal dementia. J. Neurol. Neurosurg. Psychiatry 90, 1124-1130.

Gorno-Tempini, M.L., Hillis, A.E., Weintraub, S., Kertesz, A., Mendez, M., Cappa, S.F., et al., 2011. Classification of primary progressive aphasia and its variants. Neurology 76, 1006-1014.

Greaves, C.V., Rohrer, J.D., 2019. An update on genetic frontotemporal dementia. J. Neurol. 266, 2075-2086.

Josephs, K.A., Whitwell, J.L., Jack, C.R., Boeve, B.F., Senjem, M.L., Baker, M., et al., 2009. Voxel-based morphometry patterns of atrophy in FTLD with mutations in MAPT or PGRN. Neurology 72, 813-820.

Kanen, J.W., Arntz, F.E., Yellowlees, R., Cardinal, R.N., Price, A., Christmas, D.M., et al., 2021. Serotonin depletion amplifies distinct human social emotions as a function of individual differences in personality. Transl. Psychiatry 11. https://doi.org/10.1038/s41398-020-00880-9.

Khan K, Betters R, Lee G, Hefti MM, Marcinkiewcz CA (2022): 1,5 1.

Koppel, J., Jimenez, H., Adrien, L.H., Chang, E., Malhotra, A.K., Davies, P., 2019. Increased tau phosphorylation follows impeded dopamine clearance in a P301L and novel P301L/COMT-deleted (DM) tau mouse model. J. Neurochem. 148, 127-135.

Kurth, F., Gaser, C., Luders, E., 2015. A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM). Nat. Protoc. 10, 293-304.

Leuzy, A., Zimmer, E.R., Dubois, J., Pruessner, J., Cooperman, C., Soucy, J.P., et al., 2016. In vivo characterization of metabotropic glutamate receptor type 5 abnormalities in behavioral variant FTD. Brain Struct. Funct. 221, 1387-1402.

Mahoney, C.J., Beck, J., Rohrer, J.D., Lashley, T., Mok, K., Shakespeare, T., et al., 2012. Frontotemporal dementia with the C9ORF72 hexanucleotide repeat expansion: clinical, neuroanatomical and neuropathological features. Brain 135, 736-750.

Markello, R.D., Hansen, J.Y., Liu, Z.Q., Bazinet, V., Shafiei, G., Suárez, L.E., et al., 2022. Neuromaps: structural and functional interpretation of brain maps. Nat. Methods 19, 1472-1479.

Meloni, M., Farris, R., Solla, P., Mascia, M.M., Marrosu, F., Cannas, A., 2017. C9ORF72 intermediate repeat expansion in a patient with psychiatric disorders and progressive cerebellar ataxia. Neurologist 22, 245-246.

Miyagawa, T., Brushaber, D., Syrjanen, J., Kremers, W., Fields, J., Forsberg, L.K., et al., 2020. Utility of the global CDR ® plus NACC FTLD rating and development of scoring rules: data from the ARTFL/LEFFTDS consortium. Alzheimers Dement. 16, 106-117.

Miyoshi, M., Shinotoh, H., Wszolek, Z.K., Strongosky, A.J., Shimada, H., Arakawa, R., et al., 2010. In vivo detection of neuropathologic changes in presymptomatic MAPT mutation carriers: a PET and MRI study. Parkinsonism Relat. Disord. 16, 404-408.

Murley, A.G., Rowe, J.B., 2018. Neurotransmitter deficits from frontotemporal lobar degeneration. Brain 141, 1263-1285.

Murley, A.G., Rouse, M.A., Simon Jones, P., Ye, R., Hezemans, F.H., O'Callaghan, C., et al., 2020. GABA and glutamate deficits from frontotemporal lobar degeneration are associated with disinhibition. Brain 143, 3449.

Panza, F., Lozupone, M., Seripa, D., Daniele, A., Watling, M., Giannelli, G., Imbimbo, B. P., 2020. Development of disease-modifying drugs for frontotemporal dementia spectrum disorders. Nat. Rev. Neurol. 16, 213-228.

Premi, E., Cauda, F., Costa, T., Diano, M., Gazzina, S., Gualeni, V., et al., 2016. Looking for neuroimaging markers in frontotemporal lobar degeneration clinical trials: a multi-voxel pattern analysis study in granulin disease. J. Alzheimers Dis. 51, 249-262.

Premi, E., Grassi, M., Van Swieten, J., Galimberti, D., Graff, C., Masellis, M., et al., 2017. Cognitive reserve and TMEM106B genotype modulate brain damage in presymptomatic frontotemporal dementia: a GENFI study. Brain 140, 1784-1791.

Premi, E., Calhoun, V.D., Diano, M., Gazzina, S., Cosseddu, M., Alberici, A., et al., 2019. The inner fluctuations of the brain in presymptomatic frontotemporal dementia: the chronnectome fingerprint. Neuroimage 189, 645-654.

Premi, E., Giunta, M., Iraji, A., Rachakonda, S., Calhoun, V.D., Gazzina, S., et al., 2021. Dissemination in time and space in presymptomatic granulin mutation carriers: a GENFI spatial chronnectome study. Neurobiol. Aging 108, 155-167.

Premi, E., Dukart, J., Mattioli, I., Libri, I., Gadola, Y., Cotelli, M., et al., 2022. Unravelling neurotransmitters impairment in primary progressive aphasias. SSRN Electron. J. 1-9.

Ramos-Rodriguez, J.J., Molina-Gil, S., Rey-Brea, R., Berrocoso, E., Garcia-Alloza, M., 2013. Specific serotonergic denervation affects tau pathology and cognition without altering senile plaques deposition in APP/PS1 mice. PLoS One 8. https://doi.org/10.1371/journal.pone.0079947.

Rascovsky, K., Hodges, J.R., Knopman, D., Mendez, M.F., Kramer, J.H., Neuhaus, J., et al., 2011. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134, 2456-2477.

Rohrer, J.D., Nicholas, J.M., Cash, D.M., van Swieten, J., Dopper, E., Jiskoot, L., et al., 2015. Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the genetic frontotemporal dementia initiative (GENFI) study: a cross-sectional analysis. Lancet Neurol. 14, 253-262.

Schuster, B.A., Sowden, S., Rybicki, A.J., Fraser, D.S., Press, C., Holland, P., Cook, J.L., 2022. Dopaminergic modulation of dynamic emotion perception. J. Neurosci. 42, 4394-4400.

Sha, S.J., Takada, L.T., Rankin, K.P., Yokoyama, J.S., Rutherford, N.J., Fong, J.C., et al., 2012. Frontotemporal dementia due to C9ORF72 mutations: clinical and imaging features. Neurology 79, 1002-1011.

Sperfeld, A.D., Collatz, M.B., Baier, H., Palmbach, M., Storch, A., Schwarz, J., et al., 1999. FTDP-17: an early-onset phenotype with parkinsonism and epileptic seizures caused by a novel mutation. Ann. Neurol. 46, 708-715.

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