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

Annabel Nelson, Lucy L Russell, Georgia Peakman, Rhian S Convery, Arabella Bouzigues, Caroline V Greaves, Martina Bocchetta, David M Cash, John C van Swieten, Lize Jiskoot, et al.

<figure><img src="image_1.png" /></figure>

► To cite this version:

Annabel Nelson, Lucy L Russell, Georgia Peakman, Rhian S Convery, Arabella Bouzigues, et al. The CBI-R detects early behavioural impairment in genetic frontotemporal dementia. Annals of Clinical and Translational Neurology, 2022, 9 (5), pp.644-658. 10.1002/acn.3.51544. hal-04537252

HAL Id: hal-04537252

https://hal.sorbonne-universite.fr/hal-04537252

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

Annabel Nelson, Lucy L Russell, Georgia Peakman, Rhian S Convery, Arabella Bouzigues, Caroline V Greaves, Martina Bocchetta, David M Cash, John C van Swieten, Lize Jiskoot, et al.

► To cite this version:

Annabel Nelson, Lucy L Russell, Georgia Peakman, Rhian S Convery, Arabella Bouzigues, et al. The CBI-R detects early behavioural impairment in genetic frontotemporal dementia. Annals of Clinical and Translational Neurology, 2022, 9 (5), pp.644-658. 10.1002/acn.3.51544. hal-04537252

HAL Id: hal-04537252

https://hal.sorbonne-universite.fr/hal-04537252v1

Submitted on 8 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

RESEARCH ARTICLE

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

Annabel Nelson<fnref n="1" />,<fnref n="2" />, Lucy L. Russell<fnref n="1" /> ID, Georgia Peakman<fnref n="1" />, Rhian S. Convery<fnref n="1" />, Arabella Bouzigues<fnref n="1" />, Caroline V. Greaves<fnref n="1" />, Martina Bocchetta<fnref n="1" /> ID, David M. Cash<fnref n="1" />, John C. van Swieten<fnref n="3" />, Lize Jiskoot<fnref n="3" />, Fermin Moreno<fnref n="4" />,<fnref n="5" />, Raquel Sanchez-Valle<fnref n="6" /> ID, Robert Laforce<fnref n="7" /> ID, Caroline Graff<fnref n="8" />,<fnref n="9" />, Mario Masellis<fnref n="10" />, Maria Carmela Tartaglia<fnref n="11" />, James B. Rowe<fnref n="12" />, Barbara Borroni<fnref n="13" /> ID, Elizabeth Finger<fnref n="14" /> ID, Matthis Synofzik<fnref n="15" />,<fnref n="16" /> ID, Daniela Galimberti<fnref n="17" />,<fnref n="18" /> ID, Rik Vandenberghe<fnref n="19" />,<fnref n="20" />,<fnref n="21" />, Alexandre de Mendonça<fnref n="22" />, Chris R. Butler<fnref n="23" />,<fnref n="24" />, Alexander Gerhard<fnref n="25" />,<fnref n="26" />, Simon Ducharme<fnref n="27" />,<fnref n="28" /> ID, Isabelle Le Ber<fnref n="29" />,<fnref n="30" />,<fnref n="31" />,<fnref n="32" /> ID, Isabel Santana<fnref n="33" />,<fnref n="34" />, Florence Pasquier<fnref n="35" />,<fnref n="36" />,<fnref n="37" />, Johannes Levin<fnref n="38" />,<fnref n="39" />,<fnref n="40" />, Markus Otto<fnref n="41" />, Sandro Sorbi<fnref n="42" />,<fnref n="43" />, Jonathan D. Rohrer<fnref n="1" /> ID & Genetic FTD Initiative (GENFI)†

<footnote n="1">¹Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK</footnote>

<footnote n="2">²Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK</footnote>

<footnote n="3">³Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands</footnote>

<footnote n="4">⁴Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Spain</footnote>

<footnote n="5">⁵Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain</footnote>

<footnote n="6">⁶Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d'Investigacións Biomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain</footnote>

<footnote n="7">⁷Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, Québec, Canada</footnote>

<footnote n="8">⁸Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden</footnote>

<footnote n="9">⁹Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden</footnote>

<footnote n="10">¹⁰Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada</footnote>

<footnote n="11">¹¹Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada</footnote>

<footnote n="12">¹²Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK</footnote>

<footnote n="13">¹³Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy</footnote>

<footnote n="14">¹⁴Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada</footnote>

<footnote n="15">¹⁵Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany</footnote>

<footnote n="16">¹⁶Center for Neurodegenerative Diseases (DZNE, Tübingen, Germany</footnote>

<footnote n="17">¹⁷Fondazione Ca' Granda, IRCCS Ospedale Policlinico, Milan, Italy</footnote>

<footnote n="18">¹⁸University of Milan, Centro Dino Ferrari, Milan, Italy</footnote>

<footnote n="19">¹⁹Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium</footnote>

<footnote n="20">²⁰Neurology Service, University Hospitals Leuven, Leuven, Belgium</footnote>

<footnote n="21">²¹Leuven Brain Institute, KU Leuven, Leuven, Belgium</footnote>

<footnote n="22">²²Faculty of Medicine, University of Lisbon, Lisbon, Portugal</footnote>

<footnote n="23">²³Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK</footnote>

<footnote n="24">²⁴Department of Brain Sciences, Imperial College London, London, UK</footnote>

<footnote n="25">²⁵Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK</footnote>

<footnote n="26">²⁶Departments of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Duisburg, Germany</footnote>

<footnote n="27">²⁷Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, QC, Canada</footnote>

<footnote n="28">²⁸McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada</footnote>

<footnote n="29">²⁹Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote n="30">³⁰Département de Neurologie, Centre de référence des démences rares ou précoces, IM2A, AP-HP – Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote n="31">³¹Département de Neurologie, AP-HP – Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote n="32">³²Reference Network for Rare Neurological Diseases (ERN-RND), European Union,</footnote>

<footnote n="33">³³University Hospital of Coimbra (HUC), Neurology Service, Faculty of Medicine, University of Coimbra, Coimbra, Portugal</footnote>

<footnote n="34">³⁴Center for Neuroscience and Cell Biology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal</footnote>

<footnote n="35">³⁵Univ Lille, Lille, France</footnote>

<footnote n="36">³⁶Inserm 1172, Lille, France</footnote>

<footnote n="37">³⁷CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, Lille, France</footnote>

<footnote n="38">³⁸Department of Neurology, Ludwig-Maximilians Universität München, Munich, Germany</footnote>

<footnote n="39">³⁹German Center for Neurodegenerative Diseases (DZNE), Munich, Germany</footnote>

<footnote n="40">⁴⁰Munich Cluster of Systems Neurology (SyNergy), Munich, Germany</footnote>

<footnote n="41">⁴¹Department of Neurology, University of Ulm, Ulm, Germany</footnote>

<footnote n="42">⁴²Department of Neurofarba, University of Florence, Florence, Italy</footnote>

<footnote n="43">⁴³IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy</footnote>
RESEARCH ARTICLE

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

Annabel Nelson<fnref n="1" />,<fnref n="2" />, Lucy L. Russell<fnref n="1" /> ID, Georgia Peakman<fnref n="1" />, Rhian S. Convery<fnref n="1" />, Arabella Bouzigues<fnref n="1" />, Caroline V. Greaves<fnref n="1" />, Martina Bocchetta<fnref n="1" /> ID, David M. Cash<fnref n="1" />, John C. van Swieten<fnref n="3" />, Lize Jiskoot<fnref n="3" />, Fermin Moreno<fnref n="4" />,<fnref n="5" />, Raquel Sanchez-Valle<fnref n="6" /> ID, Robert Laforce<fnref n="7" /> ID, Caroline Graff<fnref n="8" />,<fnref n="9" />, Mario Masellis<fnref n="10" />, Maria Carmela Tartaglia<fnref n="11" />, James B. Rowe<fnref n="12" />, Barbara Borroni<fnref n="13" /> ID, Elizabeth Finger<fnref n="14" /> ID, Matthis Synofzik<fnref n="15" />,<fnref n="16" /> ID, Daniela Galimberti<fnref n="17" />,<fnref n="18" /> ID, Rik Vandenberghe<fnref n="19" />,<fnref n="20" />,<fnref n="21" />, Alexandre de Mendonça<fnref n="22" />, Chris R. Butler<fnref n="23" />,<fnref n="24" />, Alexander Gerhard<fnref n="25" />,<fnref n="26" />, Simon Ducharme<fnref n="27" />,<fnref n="28" /> ID, Isabelle Le Ber<fnref n="29" />,<fnref n="30" />,<fnref n="31" />,<fnref n="32" /> ID, Isabel Santana<fnref n="33" />,<fnref n="34" />, Florence Pasquier<fnref n="35" />,<fnref n="36" />,<fnref n="37" />, Johannes Levin<fnref n="38" />,<fnref n="39" />,<fnref n="40" />, Markus Otto<fnref n="41" />, Sandro Sorbi<fnref n="42" />,<fnref n="43" />, Jonathan D. Rohrer<fnref n="1" /> ID & Genetic FTD Initiative (GENFI)†

<footnote n="1">¹Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK</footnote>

<footnote n="2">²Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK</footnote>

<footnote n="3">³Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands</footnote>

<footnote n="4">⁴Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Spain</footnote>

<footnote n="5">⁵Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain</footnote>

<footnote n="6">⁶Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d'Investigacións Biomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain</footnote>

<footnote n="7">⁷Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, Québec, Canada</footnote>

<footnote n="8">⁸Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden</footnote>

<footnote n="9">⁹Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden</footnote>

<footnote n="10">¹⁰Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada</footnote>

<footnote n="11">¹¹Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada</footnote>

<footnote n="12">¹²Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK</footnote>

<footnote n="13">¹³Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy</footnote>

<footnote n="14">¹⁴Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada</footnote>

<footnote n="15">¹⁵Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany</footnote>

<footnote n="16">¹⁶Center for Neurodegenerative Diseases (DZNE, Tübingen, Germany</footnote>

<footnote n="17">¹⁷Fondazione Ca' Granda, IRCCS Ospedale Policlinico, Milan, Italy</footnote>

<footnote n="18">¹⁸University of Milan, Centro Dino Ferrari, Milan, Italy</footnote>

<footnote n="19">¹⁹Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium</footnote>

<footnote n="20">²⁰Neurology Service, University Hospitals Leuven, Leuven, Belgium</footnote>

<footnote n="21">²¹Leuven Brain Institute, KU Leuven, Leuven, Belgium</footnote>

<footnote n="22">²²Faculty of Medicine, University of Lisbon, Lisbon, Portugal</footnote>

<footnote n="23">²³Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK</footnote>

<footnote n="24">²⁴Department of Brain Sciences, Imperial College London, London, UK</footnote>

<footnote n="25">²⁵Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK</footnote>

<footnote n="26">²⁶Departments of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Duisburg, Germany</footnote>

<footnote n="27">²⁷Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, QC, Canada</footnote>

<footnote n="28">²⁸McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada</footnote>

<footnote n="29">²⁹Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote n="30">³⁰Département de Neurologie, Centre de référence des démences rares ou précoces, IM2A, AP-HP – Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote n="31">³¹Département de Neurologie, AP-HP – Hôpital Pitié-Salpetrière, Paris, France</footnote>

<footnote n="32">³²Reference Network for Rare Neurological Diseases (ERN-RND), European Union,</footnote>

<footnote n="33">³³University Hospital of Coimbra (HUC), Neurology Service, Faculty of Medicine, University of Coimbra, Coimbra, Portugal</footnote>

<footnote n="34">³⁴Center for Neuroscience and Cell Biology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal</footnote>

<footnote n="35">³⁵Univ Lille, Lille, France</footnote>

<footnote n="36">³⁶Inserm 1172, Lille, France</footnote>

<footnote n="37">³⁷CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, Lille, France</footnote>

<footnote n="38">³⁸Department of Neurology, Ludwig-Maximilians Universität München, Munich, Germany</footnote>

<footnote n="39">³⁹German Center for Neurodegenerative Diseases (DZNE), Munich, Germany</footnote>

<footnote n="40">⁴⁰Munich Cluster of Systems Neurology (SyNergy), Munich, Germany</footnote>

<footnote n="41">⁴¹Department of Neurology, University of Ulm, Ulm, Germany</footnote>

<footnote n="42">⁴²Department of Neurofarba, University of Florence, Florence, Italy</footnote>

<footnote n="43">⁴³IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy</footnote>
**Correspondence**

Jonathan Rohrer, Dementia Research Centre,
Department of Neurodegenerative Disease,
UCL Queen Square Institute of Neurology,
London WC1N 3BG, UK. Tel: +44 0845 155
5000; E-mail: j.rohrer@ucl.ac.uk

**Funding Information**

The Dementia Research Centre is supported by Alzheimer's Research UK, Alzheimer's Society, Brain Research UK and The Wolfson Foundation. This work was supported by the NIHR UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. JDR is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). This work was also supported by the MRC UK GENFI grant (MR/ M023664/1), the Bluefield Project and the JPND GENFI-PROX grant (2019-02248). MB is supported by a Fellowship award from the Alzheimer's Society, UK (AS-JF-19a-004-517). MB's work is also supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. RSC is supported by a Frontotemporal Dementia Research Studentships in Memory of David Blechner funded through The National Brain Appeal (RCN 290173). Several authors of this publication are members of the European Reference Network for Rare Neurological Diseases (Project ID No 739510).

Received: 17 January 2022; Revised: 23 February 2022; Accepted: 25 February 2022

**Annals of Clinical and Translational Neurology 2022; 9(5): 644–658**

doi: 10.1002/acn3.51544

†List of consortium authors in Appendix.

**Introduction**

Frontotemporal dementia (FTD) is a heterogeneous neu-
rodegenerative disease associated with changes in beha-
viour, language and cognition.¹ Around one-third of FTD
is autosomal dominant² with the main genetic causes
being mutations in microtubule-associated protein tau

**Abstract**

**Introduction:** Behavioural dysfunction is a key feature of genetic frontotemporal dementia (FTD) but validated clinical scales measuring behaviour are lacking at present. **Methods:** We assessed behaviour using the revised version of the Cambridge Behavioural Inventory (CBI-R) in 733 participants from the Genetic FTD Initiative study: 466 mutation carriers (195 *C9orf72*, 76 *MAPT*, 195 *GRN*) and 267 non-mutation carriers (controls). All mutation carriers were stratified according to their global CDR plus NACC FTLD score into three groups: asymptomatic (CDR = 0), prodromal (CDR = 0.5) and symptomatic (CDR = 1+). Mixed-effects models adjusted for age, education, sex and family clustering were used to compare between the groups. Neuroanatomical correlates of the individual domains were assessed within each genetic group.

**Results:** CBI-R total scores were significantly higher in all CDR 1+ mutation carrier groups compared with controls [*C9orf72* mean 70.5 (standard deviation 27.8), *GRN* 56.2 (33.5), *MAPT* 62.1 (36.9)] as well as their respective CDR 0.5 groups [*C9orf72* 13.5 (14.4), *GRN* 13.3 (13.5), *MAPT* 9.4 (10.4)] and CDR 0 groups [*C9orf72* 6.0 (7.9), *GRN* 3.6 (6.0), *MAPT* 8.5 (13.3)]. The *C9orf72* and *GRN* 0.5 groups scored significantly higher than the controls. The greatest impairment was seen in the Motivation domain for the *C9orf72* and *GRN* symptomatic groups, whilst in the symptomatic *MAPT* group, the highest-scoring domains were Stereotypic and Motor Behaviours and Memory and Orientation. Neural correlates of each CBI-R domain largely overlapped across the different mutation carrier groups. **Conclusions:** The CBI-R detects early behavioural change in genetic FTD, suggesting that it could be a useful measure within future clinical trials.

(MAPT),³ progranulin (GRN)⁴ and chromosome 9 open
reading frame 72 (*C9orf72*).⁵ Most commonly, familial
FTD will present with changes in personality and social
conduct, known as behavioural variant FTD (bvFTD).
However, despite the development of a number of thera-
peutic drugs for genetic FTD and trials now starting, few
validated scales have been developed to detect and
**Correspondence**

Jonathan Rohrer, Dementia Research Centre,
Department of Neurodegenerative Disease,
UCL Queen Square Institute of Neurology,
London WC1N 3BG, UK. Tel: +44 0845 155
5000; E-mail: j.rohrer@ucl.ac.uk

**Funding Information**

The Dementia Research Centre is supported by Alzheimer's Research UK, Alzheimer's Society, Brain Research UK and The Wolfson Foundation. This work was supported by the NIHR UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. JDR is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). This work was also supported by the MRC UK GENFI grant (MR/ M023664/1), the Bluefield Project and the JPND GENFI-PROX grant (2019-02248). MB is supported by a Fellowship award from the Alzheimer's Society, UK (AS-JF-19a-004-517). MB's work is also supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. RSC is supported by a Frontotemporal Dementia Research Studentships in Memory of David Blechner funded through The National Brain Appeal (RCN 290173). Several authors of this publication are members of the European Reference Network for Rare Neurological Diseases (Project ID No 739510).

Received: 17 January 2022; Revised: 23 February 2022; Accepted: 25 February 2022

**Annals of Clinical and Translational Neurology 2022; 9(5): 644–658**

doi: 10.1002/acn3.51544

†List of consortium authors in Appendix.

**Introduction**

Frontotemporal dementia (FTD) is a heterogeneous neu-
rodegenerative disease associated with changes in beha-
vior, language and cognition.¹ Around one-third of FTD
is autosomal dominant² with the main genetic causes
being mutations in microtubule-associated protein tau

**Abstract**

**Introduction:** Behavioural dysfunction is a key feature of genetic frontotemporal dementia (FTD) but validated clinical scales measuring behaviour are lacking at present. **Methods:** We assessed behaviour using the revised version of the Cambridge Behavioural Inventory (CBI-R) in 733 participants from the Genetic FTD Initiative study: 466 mutation carriers (195 *C9orf72*, 76 *MAPT*, 195 *GRN*) and 267 non-mutation carriers (controls). All mutation carriers were stratified according to their global CDR plus NACC FTLD score into three groups: asymptomatic (CDR = 0), prodromal (CDR = 0.5) and symptomatic (CDR = 1+). Mixed-effects models adjusted for age, education, sex and family clustering were used to compare between the groups. Neuroanatomical correlates of the individual domains were assessed within each genetic group.

**Results:** CBI-R total scores were significantly higher in all CDR 1+ mutation carrier groups compared with controls [*C9orf72* mean 70.5 (standard deviation 27.8), *GRN* 56.2 (33.5), *MAPT* 62.1 (36.9)] as well as their respective CDR 0.5 groups [*C9orf72* 13.5 (14.4), *GRN* 13.3 (13.5), *MAPT* 9.4 (10.4)] and CDR 0 groups [*C9orf72* 6.0 (7.9), *GRN* 3.6 (6.0), *MAPT* 8.5 (13.3)]. The *C9orf72* and *GRN* 0.5 groups scored significantly higher than the controls. The greatest impairment was seen in the Motivation domain for the *C9orf72* and *GRN* symptomatic groups, whilst in the symptomatic *MAPT* group, the highest-scoring domains were Stereotypic and Motor Behaviours and Memory and Orientation. Neural correlates of each CBI-R domain largely overlapped across the different mutation carrier groups. **Conclusions:** The CBI-R detects early behavioural change in genetic FTD, suggesting that it could be a useful measure within future clinical trials.

(MAPT),³ progranulin (GRN)⁴ and chromosome 9 open
reading frame 72 (*C9orf72*).⁵ Most commonly, familial
FTD will present with changes in personality and social
conduct, known as behavioural variant FTD (bvFTD).
However, despite the development of a number of thera-
peutic drugs for genetic FTD and trials now starting, few
validated scales have been developed to detect and
monitor the underlying behavioural changes in FTD. Such measures will be important in assessing the potential effectiveness of disease-modifying therapies.

Unlike a number of already existing scales such as the Neuropsychiatric Inventory (NPI)⁶ which do not encompass all the core diagnostic features of FTD, the Cambridge Behavioural Inventory was specifically designed to focus on the changes seen in those with bvFTD.⁷ The revised version (CBI-R) is a 45-item questionnaire measuring the severity of symptoms across 10 domains. Four of the domains encompass the core behavioural criteria in the diagnosis of bvFTD¹: Motivation (including both apathy and loss of empathy), Stereotypic and Motor Behaviours (i.e. obsessive-compulsive behaviour), Eating Habits (such as preference for sweet foods) and Abnormal Behaviour (including disinhibition and impulsivity). Three further domains cover neuropsychiatric symptoms that occur regularly in FTD, particularly in the genetic forms: Beliefs (delusions and hallucinations), Mood (depression, agitation and irritability) and Sleep (increased daytime or disturbed sleep). Two domains focus on functional deficits: Everyday Skills (such as difficulties with handling money and using items around the house) and Self Care. The last domain is Memory and Orientation which includes deficits seen in FTD such as impaired attention and concentration. However, although the CBI-R has been investigated across a number of phenotypic variants of FTD,⁸⁻¹⁰ little work has been done to understand how well it measures the behavioural and functional change in genetic forms of FTD, particularly in the presymptomatic period. Furthermore, few studies have been performed to examine the neural correlates of the CBI-R.⁹

The aim of this study was therefore to assess the CBI-R as a measure of behavioural and functional change in genetic FTD using data from the Genetic FTD Initiative (GENFI) cohort, an international study of the natural history of genetic FTD with the aim of identifying early biomarkers.

# Methods

## Participants

Data were collected from participants from the fifth data freeze of the GENFI study including sites in the UK, Canada, Sweden, Netherlands, Belgium, Spain, Portugal, Italy and Germany. Participants were recruited from families with a confirmed pathogenic genetic mutation in GRN, MAPT or C9orf72, including those who were symptomatic as well as those who were at-risk first-degree relatives of mutation carriers. This second group consists of both presymptomatic mutation carriers and mutation-negative family members who therefore act as healthy controls. Of the 849 participants recruited into the fifth

data freeze, cross-sectional data on the CBI-R was available from 733 participants: 466 mutation carriers (195 C9orf72, 195 GRN, 76 MAPT) and 267 mutation-negative healthy controls. The study procedures were approved by local ethics committees and all participants provided informed written consent.

## Procedure

All participants underwent the standardised GENFI clinical assessment battery including history and examination, the Mini-Mental State Examination (MMSE), the Frontotemporal dementia Rating Scale (FRS) and the Clinical Dementia Rating Scale plus National Alzheimer Coordinating Centre FTLD module (CDR plus NACC FTLD). A global CDR plus NACC FTLD score gives a summary of the current disease stage, where 0 is asymptomatic, 0.5 is prodromal or very mildly symptomatic and 1, 2 and 3 represent the mild, moderate and severe fully symptomatic stages. For the purposes of this study, the fully symptomatic mutation carriers were grouped together as 1+. The CDR plus NACC FTLD sum of boxes score in which the total score on each domain is added together (max score = 24) provides a measure of disease severity.¹¹,¹²

## The revised version of the Cambridge Behavioural Inventory

All participants had a CBI-R questionnaire completed by a close informant, usually either a family member or a close friend. The CBI-R assesses the frequency of the given behaviour over the past month on a scale of 0–4⁷: a score of 0 means that there is no impairment, a score of 1 an occasional occurrence (a few times a month), 2 a repeated occurrence (few times a week), 3 a daily occurrence and 4 is a constant occurrence. Higher scores, therefore, represent more severe behavioural or functional deficits. There are 45 items in total, meaning that the CBI-R total score has a maximum of 180. Each domain contains between two and eight items, and therefore, the maximum domain score can vary from 8 to 32. For the purposes of comparing the different behaviours, we converted individual domain scores into percentages of the total maximum score.

## MRI

Participants underwent volumetric T1-weighted magnetic resonance imaging according to the harmonised GENFI protocol on a 3T scanner. This usually occurred on the same day as the clinical assessment but occasionally on a different day at a maximum of 12 weeks apart. All images were checked for quality control, and scans with movement or artefacts were removed from the analysis. Only
monitor the underlying behavioural changes in FTD. Such measures will be important in assessing the potential effectiveness of disease-modifying therapies.

Unlike a number of already existing scales such as the Neuropsychiatric Inventory (NPI)⁶ which do not encompass all the core diagnostic features of FTD, the Cambridge Behavioural Inventory was specifically designed to focus on the changes seen in those with bvFTD.⁷ The revised version (CBI-R) is a 45-item questionnaire measuring the severity of symptoms across 10 domains. Four of the domains encompass the core behavioural criteria in the diagnosis of bvFTD¹: Motivation (including both apathy and loss of empathy), Stereotypic and Motor Behaviours (i.e. obsessive-compulsive behaviour), Eating Habits (such as preference for sweet foods) and Abnormal Behaviour (including disinhibition and impulsivity). Three further domains cover neuropsychiatric symptoms that occur regularly in FTD, particularly in the genetic forms: Beliefs (delusions and hallucinations), Mood (depression, agitation and irritability) and Sleep (increased daytime or disturbed sleep). Two domains focus on functional deficits: Everyday Skills (such as difficulties with handling money and using items around the house) and Self Care. The last domain is Memory and Orientation which includes deficits seen in FTD such as impaired attention and concentration. However, although the CBI-R has been investigated across a number of phenotypic variants of FTD,⁸⁻¹⁰ little work has been done to understand how well it measures the behavioural and functional change in genetic forms of FTD, particularly in the presymptomatic period. Furthermore, few studies have been performed to examine the neural correlates of the CBI-R.⁹

The aim of this study was therefore to assess the CBI-R as a measure of behavioural and functional change in genetic FTD using data from the Genetic FTD Initiative (GENFI) cohort, an international study of the natural history of genetic FTD with the aim of identifying early biomarkers.

# Methods

## Participants

Data were collected from participants from the fifth data freeze of the GENFI study including sites in the UK, Canada, Sweden, Netherlands, Belgium, Spain, Portugal, Italy and Germany. Participants were recruited from families with a confirmed pathogenic genetic mutation in GRN, MAPT or C9orf72, including those who were symptomatic as well as those who were at-risk first-degree relatives of mutation carriers. This second group consists of both presymptomatic mutation carriers and mutation-negative family members who therefore act as healthy controls. Of the 849 participants recruited into the fifth

data freeze, cross-sectional data on the CBI-R was available from 733 participants: 466 mutation carriers (195 C9orf72, 195 GRN, 76 MAPT) and 267 mutation-negative healthy controls. The study procedures were approved by local ethics committees and all participants provided informed written consent.

## Procedure

All participants underwent the standardised GENFI clinical assessment battery including history and examination, the Mini-Mental State Examination (MMSE), the Frontotemporal dementia Rating Scale (FRS) and the Clinical Dementia Rating Scale plus National Alzheimer Coordinating Centre FTLD module (CDR plus NACC FTLD). A global CDR plus NACC FTLD score gives a summary of the current disease stage, where 0 is asymptomatic, 0.5 is prodromal or very mildly symptomatic and 1, 2 and 3 represent the mild, moderate and severe fully symptomatic stages. For the purposes of this study, the fully symptomatic mutation carriers were grouped together as 1+. The CDR plus NACC FTLD sum of boxes score in which the total score on each domain is added together (max score = 24) provides a measure of disease severity.¹¹,¹²

## The revised version of the Cambridge Behavioural Inventory

All participants had a CBI-R questionnaire completed by a close informant, usually either a family member or a close friend. The CBI-R assesses the frequency of the given behaviour over the past month on a scale of 0–4⁷: a score of 0 means that there is no impairment, a score of 1 an occasional occurrence (a few times a month), 2 a repeated occurrence (few times a week), 3 a daily occurrence and 4 is a constant occurrence. Higher scores, therefore, represent more severe behavioural or functional deficits. There are 45 items in total, meaning that the CBI-R total score has a maximum of 180. Each domain contains between two and eight items, and therefore, the maximum domain score can vary from 8 to 32. For the purposes of comparing the different behaviours, we converted individual domain scores into percentages of the total maximum score.

## MRI

Participants underwent volumetric T1-weighted magnetic resonance imaging according to the harmonised GENFI protocol on a 3T scanner. This usually occurred on the same day as the clinical assessment but occasionally on a different day at a maximum of 12 weeks apart. All images were checked for quality control, and scans with movement or artefacts were removed from the analysis. Only
scans from mutation carriers were included in the correl-
ative analysis: of the 466 participants, 430 scans were
available for the analysis: 179 C9orf72, 182 GRN and 69
MAPT mutation carriers.

Neuroanatomical regions of interest were subsequently
generated as previously described using an automated atlas
segmentation propagation and label fusion strategy called
Geodesic Information Flow.¹³ Specifically, volumes of the
frontal, temporal, insular and parietal cortices as well as
hippocampus, amygdala, thalamus and striatum were cal-
culated and expressed as a percentage of total intracranial
volume, computed with SPM12 (Statistical Parametric
Mapping, Welcome Trust Centre for Neuroimaging, Lon-
don, UK) running under Matlab R2014b.¹⁴

**Statistical analysis**

All statistical analyses were performed using StataCorp.
2016. Stata Statistical Software: Version 14. College Sta-
tion, TV: StataCorp LLC.

Demographic data were compared between groups
using a linear regression model, with bootstrapping when
the data were not normally distributed.

In the healthy control group, Spearman's rank correla-
tions were performed to evaluate the relationship between
the CBI-R total score and both age and education. To
assess the relationship of CBI-R total score with sex, a
Mann-Whitney *U* test was used.

In order to compare the CBI-R total score between
groups, a mixed-effects model was used that adjusted for
age, education, sex and family clustering, along with
bootstrapped confidence intervals with 2000 repetitions as
the data were not normally distributed.

The relationship of the CBI-R total score with disease
severity (both CDR plus NACC FTLD sum of boxes and
FRS) was assessed using Spearman's rank correlations
within each genetic group.

Similar mixed-effects models as performed above were
used to assess differences in each of the individual CBI-R
domains firstly, between genetic groups, and secondly,
within groups (symptomatic mutation carriers only).

Neural correlates of each individual CBI-R domain
were investigated in each genetic group by assessing non-
parametric partial correlations (adjusting for age and dis-
ease severity) of the domain score with the neuroanatom-
ical regions of interest.

**Results**

**Demographics**

The age, sex and education of the participants are shown
in Table 1. All symptomatic mutation carriers and the

prodromal GRN mutation carriers were significantly older
than controls (all *p* < 0.050) while the asymptomatic
MAPT mutation carriers were younger than controls
(*p* < 0.001). Within each of the genetic groups, the symp-
tomatic mutation carriers were significantly older than
the prodromal and asymptomatic mutation carriers (all
*p* < 0.050). The symptomatic C9orf72 group contained
more males than controls (*p* < 0.001) and compared with
their asymptomatic and prodromal groups (*p* = 0.002
and *p* = 0.019 respectively), but there were no other dif-
ferences in sex compared with controls or between genetic
groups. Symptomatic C9orf72 and GRN mutation carriers
spent significantly fewer years in education than controls
(*p* = 0.004 and *p* < 0.001 respectively) and their respec-
tive asymptomatic groups (C9orf72, *p* = 0.020; GRN,
*p* < 0.001), and for the GRN group also less than their
prodromal mutation carriers (*p* = 0.011).

CBI-R in healthy controls

Healthy controls (i.e. mutation-negative family members)
had very low scores on the CBI-R with the highest scor-
ing domain being Memory and Orientation [mean (s-
tandard deviation) 1.3 (2.3)] and Mood [1.1 (1.6)]. The
mean CBI-R total score was only 5.2 (standard deviation
7.8) out of 180. There were no significant correlations
of CBI-R total score with either age (r = 0.06,
p = 0.310) or education (r = − 0.06, p = 0.358), and
there was no difference in score between men and
women (p = 0.435).

CBI-R total score

All symptomatic mutation carrier groups scored signifi-
cantly higher than controls (Fig. 1, Table 1, Table S1):
C9orf72 70.5 (27.8), GRN 56.2 (33.5), MAPT 62.1 (36.9).
In the prodromal groups, the C9orf72 and GRN mutation
carriers scored significantly higher than controls [C9orf72
13.5 (14.4), GRN 13.3 (13.5)] with only a trend for a
higher score in the MAPT mutation carriers: 9.4 (10.4).
In the asymptomatic mutation carriers, there was no dif-
ference between the C9orf72 [6.0 (7.9) or GRN 3.6 (6.0)]
and controls but there was a significantly higher score in
the MAPT mutation carriers: 8.9 (13.3).

Comparing *within* groups, the symptomatic mutation
carriers all scored higher than their respective prodro-
mal mutation carriers and asymptomatic mutation car-
riers in each genetic group (Fig. 1, Table 1, Table S1).
Prodromal *C9orf72* and *GRN* mutation carriers
also scored higher than their asymptomatic mutation
carriers.

Comparing *between* groups of the same global CDR
plus NACC FTLD stage, symptomatic *C9orf72* mutation
scans from mutation carriers were included in the correl-
ative analysis: of the 466 participants, 430 scans were
available for the analysis: 179 C9orf72, 182 GRN and 69
MAPT mutation carriers.

Neuroanatomical regions of interest were subsequently
generated as previously described using an automated atlas
segmentation propagation and label fusion strategy called
Geodesic Information Flow.¹³ Specifically, volumes of the
frontal, temporal, insular and parietal cortices as well as
hippocampus, amygdala, thalamus and striatum were cal-
culated and expressed as a percentage of total intracranial
volume, computed with SPM12 (Statistical Parametric
Mapping, Welcome Trust Centre for Neuroimaging, Lon-
don, UK) running under Matlab R2014b.¹⁴

Statistical analysis

All statistical analyses were performed using StataCorp.
2016. Stata Statistical Software: Version 14. College Sta-
tion, TV: StataCorp LLC.

Demographic data were compared between groups
using a linear regression model, with bootstrapping when
the data were not normally distributed.

In the healthy control group, Spearman's rank correla-
tions were performed to evaluate the relationship between
the CBI-R total score and both age and education. To
assess the relationship of CBI-R total score with sex, a
Mann-Whitney U test was used.

In order to compare the CBI-R total score between
groups, a mixed-effects model was used that adjusted for
age, education, sex and family clustering, along with
bootstrapped confidence intervals with 2000 repetitions as
the data were not normally distributed.

The relationship of the CBI-R total score with disease
severity (both CDR plus NACC FTLD sum of boxes and
FRS) was assessed using Spearman's rank correlations
within each genetic group.

Similar mixed-effects models as performed above were
used to assess differences in each of the individual CBI-R
domains firstly, between genetic groups, and secondly,
within groups (symptomatic mutation carriers only).

Neural correlates of each individual CBI-R domain
were investigated in each genetic group by assessing non-
parametric partial correlations (adjusting for age and dis-
ease severity) of the domain score with the neuroanatom-
ical regions of interest.

Results

Demographics

The age, sex and education of the participants are shown
in Table 1. All symptomatic mutation carriers and the

prodromal GRN mutation carriers were significantly older
than controls (all *p* < 0.050) while the asymptomatic
MAPT mutation carriers were younger than controls
(*p* < 0.001). Within each of the genetic groups, the symp-
tomatic mutation carriers were significantly older than
the prodromal and asymptomatic mutation carriers (all
*p* < 0.050). The symptomatic C9orf72 group contained
more males than controls (*p* < 0.001) and compared with
their asymptomatic and prodromal groups (*p* = 0.002
and *p* = 0.019 respectively), but there were no other dif-
ferences in sex compared with controls or between genetic
groups. Symptomatic C9orf72 and GRN mutation carriers
spent significantly fewer years in education than controls
(*p* = 0.004 and *p* < 0.001 respectively) and their respec-
tive asymptomatic groups (C9orf72, *p* = 0.020; GRN,
*p* < 0.001), and for the GRN group also less than their
prodromal mutation carriers (*p* = 0.011).

CBI-R in healthy controls

Healthy controls (i.e. mutation-negative family members)
had very low scores on the CBI-R with the highest scor-
ing domain being Memory and Orientation [mean (s-
tandard deviation) 1.3 (2.3)] and Mood [1.1 (1.6)]. The
mean CBI-R total score was only 5.2 (standard deviation
7.8) out of 180. There were no significant correlations
of CBI-R total score with either age (r = 0.06,
p = 0.310) or education (r = − 0.06, p = 0.358), and
there was no difference in score between men and
women (p = 0.435).

CBI-R total score

All symptomatic mutation carrier groups scored signifi-
cantly higher than controls (Fig. 1, Table 1, Table S1):
C9orf72 70.5 (27.8), GRN 56.2 (33.5), MAPT 62.1 (36.9).
In the prodromal groups, the C9orf72 and GRN mutation
carriers scored significantly higher than controls [C9orf72
13.5 (14.4), GRN 13.3 (13.5)] with only a trend for a
higher score in the MAPT mutation carriers: 9.4 (10.4).
In the asymptomatic mutation carriers, there was no dif-
ference between the C9orf72 [6.0 (7.9) or GRN 3.6 (6.0)]
and controls but there was a significantly higher score in
the MAPT mutation carriers: 8.9 (13.3).

Comparing *within* groups, the symptomatic mutation
carriers all scored higher than their respective prodro-
mal mutation carriers and asymptomatic mutation car-
riers in each genetic group (Fig. 1, Table 1, Table S1).
Prodromal *C9orf72* and *GRN* mutation carriers
also scored higher than their asymptomatic mutation
carriers.

Comparing *between* groups of the same global CDR
plus NACC FTLD stage, symptomatic *C9orf72* mutation
carriers scored significantly higher than symptomatic GRN mutation carriers but there were no other differences between the symptomatic mutation carrier groups (Table S1). There were also no differences between the prodromal mutation carriers. In the asymptomatic mutation carriers, the MAPT group scored significantly higher than the GRN group but there were no other differences.

Figure 2 shows the CBI-R total score in each of the genetic groups when the fully symptomatic group is stratified into individual CDR plus NACC FTLD stages of mild (1), moderate (2) and severe (3).

## Comparison of CBI-R total score with measures of disease severity

There was a significant positive correlation between the CBI-R total score and the CDR plus NACC FTLD sum of boxes in all mutation carrier groups (C9orf72 *r* = 0.78, GRN *r* = 0.82, MAPT *r* = 0.60, all *p* = <0.001) (Fig. S1).

A significant negative correlation was seen in each mutation carrier group between CBI-R total scores and

FRS (C9orf72 *r* = −0.92, GRN *r* = −0.88, MAPT *r* = −0.88, all *p* = <0.001) (Fig. S2).

## Individual CBI-R domain scores

Looking at each domain individually (Fig. 3, Table S2), all symptomatic mutation carrier groups scored significantly higher than controls in every domain. In the prodromal mutation carriers, the C9orf72 group scored higher than controls in Stereotypic and Motor Behaviours, Abnormal Behaviour and Memory and Orientation, whilst the GRN group scored higher than controls in Motivation, Stereotypic and Motor Behaviours and Memory and Orientation. The prodromal MAPT group did not score significantly different to controls in any of the domains but the asymptomatic MAPT group scored higher than controls in Stereotypic and Motor Behaviours, Sleep and Mood. Neither asymptomatic C9orf72 or GRN groups scored higher in any domains compared with controls.

Within group differences are shown in Figure 3 and Table S2. For C9orf72 and GRN mutation carriers, the

<table><caption>**Table 1.** Demographics and CBI-R scores (total and individual domains) for healthy controls and each genetic group split by global CDR plus NACC FTLD score (0 = asymptomatic, 0.5 = prodromal, 1+ = symptomatic). *N* represents the number of participants.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Healthy controls</th><th colspan="3">C9orf72</th><th colspan="3">GRN</th><th colspan="3">MAPT</th></tr><tr><th>0</th><th>0.5</th><th>1+</th><th>0</th><th>0.5</th><th>1+</th><th>0</th><th>0.5</th><th>1+</th></tr></thead><tbody><tr><td>N</td><td>267</td><td>94</td><td>34</td><td>67</td><td>122</td><td>26</td><td>47</td><td>42</td><td>13</td><td>21</td></tr><tr><td>Sex</td><td>41%</td><td>42%</td><td>41%</td><td>66%</td><td>33%</td><td>46%</td><td>47%</td><td>41%</td><td>31%</td><td>57%</td></tr><tr><td>Age (years)</td><td>46.4 (13.0)</td><td>43.9 (11.6)</td><td>49.7 (11.2)</td><td>62.6 (9.4)</td><td>45.8 (12.1)</td><td>52.1 (13.7)</td><td>63.0 (7.4)</td><td>38.6 (11.0)</td><td>46.4 (12.8)</td><td>58.9 (9.4)</td></tr><tr><td>Education (years)</td><td>14.4 (3.4)</td><td>14.3 (3.0)</td><td>13.9 (2.6)</td><td>13.0 (3.8)</td><td>14.7 (3.5)</td><td>13.8 (4.2)</td><td>11.7 (3.4)</td><td>14.4 (3.4)</td><td>13.6 (2.5)</td><td>13.6 (4.0)</td></tr><tr><td>MMSE (/30)</td><td>29.4 (1.2)</td><td>29.1 (1.2)</td><td>28.4 (2.2)</td><td>23.3 (6.7)</td><td>29.5 (0.8)</td><td>28.4 (2.6)</td><td>20.1 (7.7)</td><td>29.5 (0.8)</td><td>28.1 (2.3)</td><td>21.9 (8.1)</td></tr><tr><td>Motivation (/20)</td><td>0.5 (1.6)</td><td>0.8 (2.1)</td><td>1.9 (3.8)</td><td>10.5 (6.1)</td><td>0.2 (0.8)</td><td>2.2 (3.7)</td><td>10.0 (6.3)</td><td>1.4 (3.1)</td><td>1.2 (2.4)</td><td>9.2 (6.6)</td></tr><tr><td>Stereotypic and Motor Behaviours (/16)</td><td>0.5 (1.3)</td><td>0.7 (1.5)</td><td>1.6 (2.2)</td><td>6.6 (4.5)</td><td>0.4 (1.1)</td><td>1.5 (2.3)</td><td>3.8 (3.8)</td><td>1.2 (2.5)</td><td>1.0 (1.4)</td><td>7.6 (4.5)</td></tr><tr><td>Eating Habits (/16)</td><td>0.3 (0.8)</td><td>0.3 (0.9)</td><td>0.9 (2.4)</td><td>6.6 (4.9)</td><td>0.1 (0.5)</td><td>1.0 (2.4)</td><td>5.2 (4.5)</td><td>0.5 (1.6)</td><td>0.5 (1.2)</td><td>6.6 (5.5)</td></tr><tr><td>Abnormal Behaviour (/24)</td><td>0.6 (1.5)</td><td>0.9 (1.7)</td><td>1.8 (2.7)</td><td>7.9 (5.4)</td><td>0.5 (1.0)</td><td>1.4 (2.4)</td><td>5.8 (5.0)</td><td>1.0 (1.9)</td><td>1.2 (1.6)</td><td>7.2 (6.0)</td></tr><tr><td>Beliefs (/12)</td><td>0.0 (0.2)</td><td>0.0 (0.0)</td><td>0.1 (0.4)</td><td>1.5 (2.4)</td><td>0.0 (0.1)</td><td>0.0 (0.0)</td><td>1.0 (2.2)</td><td>0.0 (0.0)</td><td>0.1 (0.3)</td><td>0.5 (0.8)</td></tr><tr><td>Mood (/16)</td><td>1.1 (1.8)</td><td>1.1 (1.6)</td><td>1.2 (2.1)</td><td>4.2 (3.1)</td><td>0.8 (1.5)</td><td>1.7 (1.5)</td><td>3.9 (3.3)</td><td>2.0 (2.7)</td><td>1.5 (1.7)</td><td>3.9 (3.2)</td></tr><tr><td>Sleep (/8)</td><td>0.6 (1.2)</td><td>0.6 (1.3)</td><td>1.2 (1.8)</td><td>3.0 (2.1)</td><td>0.4 (0.9)</td><td>1.0 (1.3)</td><td>2.9 (2.4)</td><td>1.1 (1.5)</td><td>0.5 (0.8)</td><td>1.7 (1.4)</td></tr><tr><td>Everyday Skills (/20)</td><td>0.2 (0.9)</td><td>0.1 (0.6)</td><td>0.6 (1.5)</td><td>8.9 (5.8)</td><td>0.1 (0.6)</td><td>0.5 (1.2)</td><td>7.1 (6.6)</td><td>0.2 (0.7)</td><td>0.2 (0.4)</td><td>6.8 (6.7)</td></tr><tr><td>Self Care (/16)</td><td>0.0 (0.4)</td><td>0.2 (0.9)</td><td>0.9 (2.5)</td><td>5.3 (5.3)</td><td>0.0 (0.1)</td><td>0.1 (0.4)</td><td>2.7 (4.6)</td><td>0.2 (1.2)</td><td>0.1 (0.3)</td><td>2.4 (4.6)</td></tr><tr><td>Memory and Orientation (/32)</td><td>1.3 (2.3)</td><td>1.3 (2.4)</td><td>2.9 (3.8)</td><td>16.0 (6.6)</td><td>1.0 (2.2)</td><td>3.9 (4.3)</td><td>13.6 (8.4)</td><td>1.3 (2.1)</td><td>3.1 (3.7)</td><td>16.2 (8.6)</td></tr><tr><td>CBI-R Total Score (/180)</td><td>5.2 (7.8)</td><td>6.0 (7.9)</td><td>13.5 (14.4)</td><td>70.5 (27.8)</td><td>3.6 (6.0)</td><td>13.3 (13.5)</td><td>56.2 (33.5)</td><td>8.9 (13.3)</td><td>9.4 (10.4)</td><td>62.1 (36.9)</td></tr></tbody></table>

Sex is shown as the percentage of males in the group. All other scores are shown as mean (standard deviation). MMSE, Mini-Mental State Examination.
carriers scored significantly higher than symptomatic GRN mutation carriers but there were no other differences between the symptomatic mutation carrier groups (Table S1). There were also no differences between the prodromal mutation carriers. In the asymptomatic mutation carriers, the MAPT group scored significantly higher than the GRN group but there were no other differences.

Figure 2 shows the CBI-R total score in each of the genetic groups when the fully symptomatic group is stratified into individual CDR plus NACC FTLD stages of mild (1), moderate (2) and severe (3).

## Comparison of CBI-R total score with measures of disease severity

There was a significant positive correlation between the CBI-R total score and the CDR plus NACC FTLD sum of boxes in all mutation carrier groups (C9orf72 *r* = 0.78, GRN *r* = 0.82, MAPT *r* = 0.60, all *p* = <0.001) (Fig. S1).

A significant negative correlation was seen in each mutation carrier group between CBI-R total scores and

FRS (C9orf72 *r* = -0.92, GRN *r* = -0.88, MAPT *r* = -0.88, all *p* = <0.001) (Fig. S2).

## Individual CBI-R domain scores

Looking at each domain individually (Fig. 3, Table S2), all symptomatic mutation carrier groups scored significantly higher than controls in every domain. In the prodromal mutation carriers, the C9orf72 group scored higher than controls in Stereotypic and Motor Behaviours, Abnormal Behaviour and Memory and Orientation, whilst the GRN group scored higher than controls in Motivation, Stereotypic and Motor Behaviours and Memory and Orientation. The prodromal MAPT group did not score significantly different to controls in any of the domains but the asymptomatic MAPT group scored higher than controls in Stereotypic and Motor Behaviours, Sleep and Mood. Neither asymptomatic C9orf72 or GRN groups scored higher in any domains compared with controls.

Within group differences are shown in Figure 3 and Table S2. For C9orf72 and GRN mutation carriers, the

<table><caption>**Table 1.** Demographics and CBI-R scores (total and individual domains) for healthy controls and each genetic group split by global CDR plus NACC FTLD score (0 = asymptomatic, 0.5 = prodromal, 1+ = symptomatic). *N* represents the number of participants.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Healthy controls</th><th colspan="3">C9orf72</th><th colspan="3">GRN</th><th colspan="3">MAPT</th></tr><tr><th>0</th><th>0.5</th><th>1+</th><th>0</th><th>0.5</th><th>1+</th><th>0</th><th>0.5</th><th>1+</th></tr></thead><tbody><tr><td>N</td><td>267</td><td>94</td><td>34</td><td>67</td><td>122</td><td>26</td><td>47</td><td>42</td><td>13</td><td>21</td></tr><tr><td>Sex</td><td>41%</td><td>42%</td><td>41%</td><td>66%</td><td>33%</td><td>46%</td><td>47%</td><td>41%</td><td>31%</td><td>57%</td></tr><tr><td>Age (years)</td><td>46.4 (13.0)</td><td>43.9 (11.6)</td><td>49.7 (11.2)</td><td>62.6 (9.4)</td><td>45.8 (12.1)</td><td>52.1 (13.7)</td><td>63.0 (7.4)</td><td>38.6 (11.0)</td><td>46.4 (12.8)</td><td>58.9 (9.4)</td></tr><tr><td>Education (years)</td><td>14.4 (3.4)</td><td>14.3 (3.0)</td><td>13.9 (2.6)</td><td>13.0 (3.8)</td><td>14.7 (3.5)</td><td>13.8 (4.2)</td><td>11.7 (3.4)</td><td>14.4 (3.4)</td><td>13.6 (2.5)</td><td>13.6 (4.0)</td></tr><tr><td>MMSE (/30)</td><td>29.4 (1.2)</td><td>29.1 (1.2)</td><td>28.4 (2.2)</td><td>23.3 (6.7)</td><td>29.5 (0.8)</td><td>28.4 (2.6)</td><td>20.1 (7.7)</td><td>29.5 (0.8)</td><td>28.1 (2.3)</td><td>21.9 (8.1)</td></tr><tr><td>Motivation (/20)</td><td>0.5 (1.6)</td><td>0.8 (2.1)</td><td>1.9 (3.8)</td><td>10.5 (6.1)</td><td>0.2 (0.8)</td><td>2.2 (3.7)</td><td>10.0 (6.3)</td><td>1.4 (3.1)</td><td>1.2 (2.4)</td><td>9.2 (6.6)</td></tr><tr><td>Stereotypic and Motor Behaviours (/16)</td><td>0.5 (1.3)</td><td>0.7 (1.5)</td><td>1.6 (2.2)</td><td>6.6 (4.5)</td><td>0.4 (1.1)</td><td>1.5 (2.3)</td><td>3.8 (3.8)</td><td>1.2 (2.5)</td><td>1.0 (1.4)</td><td>7.6 (4.5)</td></tr><tr><td>Eating Habits (/16)</td><td>0.3 (0.8)</td><td>0.3 (0.9)</td><td>0.9 (2.4)</td><td>6.6 (4.9)</td><td>0.1 (0.5)</td><td>1.0 (2.4)</td><td>5.2 (4.5)</td><td>0.5 (1.6)</td><td>0.5 (1.2)</td><td>6.6 (5.5)</td></tr><tr><td>Abnormal Behaviour (/24)</td><td>0.6 (1.5)</td><td>0.9 (1.7)</td><td>1.8 (2.7)</td><td>7.9 (5.4)</td><td>0.5 (1.0)</td><td>1.4 (2.4)</td><td>5.8 (5.0)</td><td>1.0 (1.9)</td><td>1.2 (1.6)</td><td>7.2 (6.0)</td></tr><tr><td>Beliefs (/12)</td><td>0.0 (0.2)</td><td>0.0 (0.0)</td><td>0.1 (0.4)</td><td>1.5 (2.4)</td><td>0.0 (0.1)</td><td>0.0 (0.0)</td><td>1.0 (2.2)</td><td>0.0 (0.0)</td><td>0.1 (0.3)</td><td>0.5 (0.8)</td></tr><tr><td>Mood (/16)</td><td>1.1 (1.8)</td><td>1.1 (1.6)</td><td>1.2 (2.1)</td><td>4.2 (3.1)</td><td>0.8 (1.5)</td><td>1.7 (1.5)</td><td>3.9 (3.3)</td><td>2.0 (2.7)</td><td>1.5 (1.7)</td><td>3.9 (3.2)</td></tr><tr><td>Sleep (/8)</td><td>0.6 (1.2)</td><td>0.6 (1.3)</td><td>1.2 (1.8)</td><td>3.0 (2.1)</td><td>0.4 (0.9)</td><td>1.0 (1.3)</td><td>2.9 (2.4)</td><td>1.1 (1.5)</td><td>0.5 (0.8)</td><td>1.7 (1.4)</td></tr><tr><td>Everyday Skills (/20)</td><td>0.2 (0.9)</td><td>0.1 (0.6)</td><td>0.6 (1.5)</td><td>8.9 (5.8)</td><td>0.1 (0.6)</td><td>0.5 (1.2)</td><td>7.1 (6.6)</td><td>0.2 (0.7)</td><td>0.2 (0.4)</td><td>6.8 (6.7)</td></tr><tr><td>Self Care (/16)</td><td>0.0 (0.4)</td><td>0.2 (0.9)</td><td>0.9 (2.5)</td><td>5.3 (5.3)</td><td>0.0 (0.1)</td><td>0.1 (0.4)</td><td>2.7 (4.6)</td><td>0.2 (1.2)</td><td>0.1 (0.3)</td><td>2.4 (4.6)</td></tr><tr><td>Memory and Orientation (/32)</td><td>1.3 (2.3)</td><td>1.3 (2.4)</td><td>2.9 (3.8)</td><td>16.0 (6.6)</td><td>1.0 (2.2)</td><td>3.9 (4.3)</td><td>13.6 (8.4)</td><td>1.3 (2.1)</td><td>3.1 (3.7)</td><td>16.2 (8.6)</td></tr><tr><td>CBI-R Total Score (/180)</td><td>5.2 (7.8)</td><td>6.0 (7.9)</td><td>13.5 (14.4)</td><td>70.5 (27.8)</td><td>3.6 (6.0)</td><td>13.3 (13.5)</td><td>56.2 (33.5)</td><td>8.9 (13.3)</td><td>9.4 (10.4)</td><td>62.1 (36.9)</td></tr></tbody></table>

Sex is shown as the percentage of males in the group. All other scores are shown as mean (standard deviation). MMSE, Mini-Mental State Examination.
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Mean CBI-R total scores in healthy controls and each genetic group stratified by global CDR plus NACC FTLD scores. Significant differences between controls and within each genetic group are starred. Differences between different genetic groups are not shown. Error bars represent the standard error of the mean.</figcaption></figure>

symptomatic mutation carriers scored higher in all the domains compared with their respective prodromal and asymptomatic mutation carriers. This was similar for MAPT mutation carriers in most domains except Beliefs, Sleep and Self Care, where the symptomatic mutation carriers scored higher than the prodromal but not the asymptomatic mutation carriers. The *C9orf72* prodromal group scored higher than the asymptomatic group in Stereotypic and Motor Behaviours, whereas the *GRN* prodromal group scored higher than the asymptomatic group in Motivation, Stereotypic and Motor Behaviours, Abnormal Behaviour, Mood, Sleep and Memory and Orientation. The prodromal *MAPT* group did not score significantly different to their asymptomatic group in any of the domains.

Comparing *between* groups of the same global CDR plus NACC FTLD stage, symptomatic *C9orf72* mutation carriers scored higher than *GRN* mutation carriers for Stereotypic and Motor Behaviours and Abnormal Behaviour and Self Care and higher than *MAPT* mutation carriers for Beliefs and Sleep (Table S2). Symptomatic *MAPT* mutation carriers scored higher than *GRN* mutation carriers for Stereotypic and Motor Behaviours and vice versa for Sleep. Although there were no differences

between the prodromal groups, in the asymptomatic groups, the *MAPT* mutation carriers scored higher than the *GRN* mutation carriers for Motivation, Stereotypic and Motor Behaviours, Mood and Sleep and higher than the *C9orf72* mutation carriers for Sleep.

Comparing *between domains* in each of the symptomatic mutation carrier groups (Fig. S2, Table S3), the highest-scoring domain was Motivation in both *C9orf72* mutation carriers (significantly higher than the other domains apart from Everyday Skills and Memory and Orientation) and *GRN* mutation carriers (significantly higher than all the other domains), whilst in the *MAPT* mutation carriers, it was Memory and Orientation (significantly higher than the other domains except Motivation, Stereotypic and Motor Behaviours and Eating Habits) followed by Stereotypic and Motor Behaviours (significantly higher than other domains except Motivation, Eating Habits and Memory and Orientation).

## Neural correlates of individual CBI-R domains

Partial correlations between scores in each of the 10 domains and the neuroanatomical regions of interest
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Mean CBI-R total scores in healthy controls and each genetic group stratified by global CDR plus NACC FTLD scores. Significant differences between controls and within each genetic group are starred. Differences between different genetic groups are not shown. Error bars represent the standard error of the mean.</figcaption></figure>

symptomatic mutation carriers scored higher in all the domains compared with their respective prodromal and asymptomatic mutation carriers. This was similar for *MAPT* mutation carriers in most domains except Beliefs, Sleep and Self Care, where the symptomatic mutation carriers scored higher than the prodromal but not the asymptomatic mutation carriers. The *C9orf72* prodromal group scored higher than the asymptomatic group in Stereotypic and Motor Behaviours, whereas the *GRN* prodromal group scored higher than the asymptomatic group in Motivation, Stereotypic and Motor Behaviours, Abnormal Behaviour, Mood, Sleep and Memory and Orientation. The prodromal *MAPT* group did not score significantly different to their asymptomatic group in any of the domains.

Comparing *between* groups of the same global CDR plus NACC FTLD stage, symptomatic *C9orf72* mutation carriers scored higher than *GRN* mutation carriers for Stereotypic and Motor Behaviours and Abnormal Behaviour and Self Care and higher than *MAPT* mutation carriers for Beliefs and Sleep (Table S2). Symptomatic *MAPT* mutation carriers scored higher than *GRN* mutation carriers for Stereotypic and Motor Behaviours and vice versa for Sleep. Although there were no differences

between the prodromal groups, in the asymptomatic groups, the *MAPT* mutation carriers scored higher than the *GRN* mutation carriers for Motivation, Stereotypic and Motor Behaviours, Mood and Sleep and higher than the *C9orf72* mutation carriers for Sleep.

Comparing *between domains* in each of the symptomatic mutation carrier groups (Fig. S2, Table S3), the highest-scoring domain was Motivation in both *C9orf72* mutation carriers (significantly higher than the other domains apart from Everyday Skills and Memory and Orientation) and *GRN* mutation carriers (significantly higher than all the other domains), whilst in the *MAPT* mutation carriers, it was Memory and Orientation (significantly higher than the other domains except Motivation, Stereotypic and Motor Behaviours and Eating Habits) followed by Stereotypic and Motor Behaviours (significantly higher than other domains except Motivation, Eating Habits and Memory and Orientation).

## Neural correlates of individual CBI-R domains

Partial correlations between scores in each of the 10 domains and the neuroanatomical regions of interest
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Cross-sectional CBI-R total scores (mean with standard errors) in each genetic group by CDR plus NACC FTLD global score.</figcaption></figure>

adjusting for age and disease severity for each genetic
group are shown in Tables S4–S6. There were no signifi-
cant correlations with the Motivation domain. However,
the Stereotypic and Motor Behaviours domain score neg-
atively correlated with the hippocampal and amygdala
volume (particularly on the right) in the *C9orf72* and
*GRN* mutation carriers. Eating Habits score negatively
correlated particularly with insula volume (bilaterally) in
the *C9orf72* and *GRN* mutation carriers and with frontal
lobe volumes to a lesser extent in all three groups. The
Abnormal Behaviour domain scores negatively correlated
with hippocampal volume (right more than left) in the
*C9orf72* and *GRN* mutation carriers and with left insula
volume in the *MAPT* mutation carriers. Beliefs score neg-
atively correlated with thalamus volume in both the
*C9orf72* and *GRN* mutation carriers but also the frontal
and temporal lobes and striatum in the *C9orf72* group.
Mood and Sleep both negatively correlated with medial
temporal lobe volumes in the *GRN* group, with Mood
showing a similar correlation in the *C9orf72* mutation
carriers. The Everyday Skills domain score negatively cor-
related with frontal and temporal lobe volumes in
*C9orf72* and *MAPT* mutation carriers, with the insula in
*C9orf72* and *GRN* mutation carriers, and with the stria-
tum in the *GRN* and *MAPT* groups. Self Care score

negatively correlated with frontal, insula and parietal vol-
umes in the *C9orf72* group, but quite widespread, mainly
right-sided, cortical and subcortical volumes in the *GRN*
group. Lastly, Memory and Orientation negatively corre-
lated with hippocampal volume in all three groups and
additionally with the thalamus in the *C9orf72* mutation
carriers.

Discussion

In this study, we have shown that the CBI-R detects early
behavioural change in familial FTD, with overlapping but
distinct patterns of impairment in the three major genetic
groups. The highest-scoring domain was Motivation in
the symptomatic *C9orf72* and *GRN* groups, and Stereo-
typic and Motor Behaviours and Memory and Orienta-
tion in the *MAPT* group. CBI-R total score was positively
correlated with CDR plus NACC FTLD sum of boxes
score and negatively correlated with the FRS which sug-
gest that progression of overall behavioural change as
measured by the CBI-R tracks with disease severity.
Lastly, we found overlapping neural correlates of individ-
ual CBI-R domains across the different genetic groups.

In symptomatic mutation carriers, we found that the
CBI-R can detect behavioural and functional differences
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Cross-sectional CBI-R total scores (mean with standard errors) in each genetic group by CDR plus NACC FTLD global score.</figcaption></figure>

adjusting for age and disease severity for each genetic
group are shown in Tables S4–S6. There were no signifi-
cant correlations with the Motivation domain. However,
the Stereotypic and Motor Behaviours domain score neg-
atively correlated with the hippocampal and amygdala
volume (particularly on the right) in the *C9orf72* and
*GRN* mutation carriers. Eating Habits score negatively
correlated particularly with insula volume (bilaterally) in
the *C9orf72* and *GRN* mutation carriers and with frontal
lobe volumes to a lesser extent in all three groups. The
Abnormal Behaviour domain scores negatively correlated
with hippocampal volume (right more than left) in the
*C9orf72* and *GRN* mutation carriers and with left insula
volume in the *MAPT* mutation carriers. Beliefs score neg-
atively correlated with thalamus volume in both the
*C9orf72* and *GRN* mutation carriers but also the frontal
and temporal lobes and striatum in the *C9orf72* group.
Mood and Sleep both negatively correlated with medial
temporal lobe volumes in the *GRN* group, with Mood
showing a similar correlation in the *C9orf72* mutation
carriers. The Everyday Skills domain score negatively cor-
related with frontal and temporal lobe volumes in
*C9orf72* and *MAPT* mutation carriers, with the insula in
*C9orf72* and *GRN* mutation carriers, and with the stria-
tum in the *GRN* and *MAPT* groups. Self Care score

negatively correlated with frontal, insula and parietal vol-
umes in the *C9orf72* group, but quite widespread, mainly
right-sided, cortical and subcortical volumes in the *GRN*
group. Lastly, Memory and Orientation negatively corre-
lated with hippocampal volume in all three groups and
additionally with the thalamus in the *C9orf72* mutation
carriers.

Discussion

In this study, we have shown that the CBI-R detects early
behavioural change in familial FTD, with overlapping but
distinct patterns of impairment in the three major genetic
groups. The highest-scoring domain was Motivation in
the symptomatic *C9orf72* and *GRN* groups, and Stereo-
typic and Motor Behaviours and Memory and Orienta-
tion in the *MAPT* group. CBI-R total score was positively
correlated with CDR plus NACC FTLD sum of boxes
score and negatively correlated with the FRS which sug-
gest that progression of overall behavioural change as
measured by the CBI-R tracks with disease severity.
Lastly, we found overlapping neural correlates of individ-
ual CBI-R domains across the different genetic groups.

In symptomatic mutation carriers, we found that the
CBI-R can detect behavioural and functional differences
across the range of domains included. In this study, we found that symptomatic *C9orf72* mutation carriers scored highest in the CBI-R. This may relate in part to the particular presence of neuropsychiatric features such as delusions and hallucinations (here recorded as Beliefs) in *C9orf72* mutation carriers which occur more commonly in this group in addition to the core behavioural features common to all three genetic forms.¹⁵ However, symptomatic mutation carriers also scored higher on functional measures (e.g. Self Care) which in *C9orf72* mutation carriers can be multifactorial due to a combination of behavioural, cognitive and motor deficits.

In both symptomatic *C9orf72* and *GRN* groups, we found that Motivation was the highest scoring domain. This domain includes questions about both apathy and loss of empathy, both core behavioural features of FTD and both symptoms reported to be prominent in these two forms of genetic FTD.¹⁶,¹⁷ In contrast, in the symptomatic *MAPT* group, Stereotypic and Motor Behaviours (i.e. obsessive-compulsive behaviour) was the most common behavioural domain impaired, a feature described in prior studies.¹⁸ Additional to this, however, the *MAPT* group also scored highly in the Memory and Orientation domain, consistent with prior studies showing early episodic memory impairment in this subgroup.¹⁹

Behavioural changes have been reported presymptomatically in genetic FTD in a number of previous studies.¹⁹⁻²² Usually, these changes occur late in the presymptomatic period as seen here, where impairment can be detected in prodromal mutation carriers, significantly so in the *GRN* and *C9orf72* mutation carriers in our study. By this time, structural and functional brain changes have occurred,²³⁻²⁶ and cognitive disturbances also co-occur.²⁷⁻²⁹ Interestingly, *MAPT* mutation carriers in the prodromal stage scored slightly lower than the *C9orf72* and *GRN* groups, with only a trend to a higher score than controls. However, *MAPT* mutation carriers in the asymptomatic period scored at a similar level to the other groups, but here were significantly more impaired than controls. The domains significantly affected in this asymptomatic *MAPT* group were Stereotypic Behaviours (i.e. the most frequent behavioural symptom during the symptomatic period) as well as Mood and Sleep. Potentially, the CBI-R can therefore pick up the very early behavioural change in genetic FTD, here detecting symptoms many years before likely onset.

Neural correlates overlapped across the different genetic groups, although with some differences. The Stereotypic and Motor Behaviours domain was associated with medial temporal lobe atrophy, consistent with prior studies showing that obsessive-compulsive behaviours in FTD correlate with hippocampal and amygdala volumes³⁰ and are seen particularly in sporadic FTD in those with temporal variant FTD.³¹,³² A change in eating was associated with insula volumes in some of the groups, and frontal lobe volumes in all groups, a finding previously shown in a number of previous studies (where the association is usually with orbitofrontal atrophy).³³,³⁴ Interestingly, the Abnormal Behaviour domain correlated with medial temporal atrophy, particularly on the right in *C9orf72* and *GRN* mutation carriers. The occurrence of disinhibition has been associated mainly with (orbito)frontal lobe atrophy previously, but some studies have implicated the right medial temporal lobe, suggesting a disruption of normal reward processing.³⁵ The Beliefs domain score was associated with thalamic atrophy in both the *C9orf72* and *GRN* groups. This association of hallucinations and delusions has been shown previously in *C9orf72* mutation carriers³⁶ and has previously been thought to be particularly distinct for this genetic group. However, here we show a similar association in the *GRN* mutation carriers. For the cognitive domain of Memory and Orientation, there was an association with hippocampal volume in all three groups, consistent with the known role of the medial temporal lobe in episodic memory. However, there was an additional association with thalamus atrophy in the *C9orf72* mutation carriers, a region long associated with episodic memory function.³⁷

A limitation of our study was the relatively small sample size of the cohort once it is stratified, particularly for the *MAPT* mutation carriers, for example the prodromal *MAPT* group was relatively small, and negative results here may relate to the small sample size. We have also focused here on cross-sectional data, extrapolating to changes over time across individuals but future research examining how the CBI-R changes longitudinally over time within an individual will be important. Lastly, as the CBI-R was commonly completed by an informant from a family with genetic FTD, it may be that they were more alert to the presence of behavioural symptoms, particularly in those who were prodromal, leading to potentially higher CBI-R scores than in those with sporadic FTD.

**Figure 3.** Mean CBI-R scores (expressed as a percentage to allow comparison) in each of the individual 10 domains within healthy controls and each genetic group stratified by CDR plus NACC FTLD global scores. Significant differences between controls and within each genetic group are starred. Differences between different genetic groups are not shown. Error bars represent the standard error of the mean.
across the range of domains included. In this study, we found that symptomatic *C9orf72* mutation carriers scored highest in the CBI-R. This may relate in part to the particular presence of neuropsychiatric features such as delusions and hallucinations (here recorded as Beliefs) in *C9orf72* mutation carriers which occur more commonly in this group in addition to the core behavioural features common to all three genetic forms.¹⁵ However, symptomatic mutation carriers also scored higher on functional measures (e.g. Self Care) which in *C9orf72* mutation carriers can be multifactorial due to a combination of behavioural, cognitive and motor deficits.

In both symptomatic *C9orf72* and *GRN* groups, we found that Motivation was the highest scoring domain. This domain includes questions about both apathy and loss of empathy, both core behavioural features of FTD and both symptoms reported to be prominent in these two forms of genetic FTD.¹⁶,¹⁷ In contrast, in the symptomatic *MAPT* group, Stereotypic and Motor Behaviours (i.e. obsessive-compulsive behaviour) was the most common behavioural domain impaired, a feature described in prior studies.¹⁸ Additional to this, however, the *MAPT* group also scored highly in the Memory and Orientation domain, consistent with prior studies showing early episodic memory impairment in this subgroup.¹⁹

Behavioural changes have been reported presymptomatically in genetic FTD in a number of previous studies.¹⁹⁻²² Usually, these changes occur late in the presymptomatic period as seen here, where impairment can be detected in prodromal mutation carriers, significantly so in the *GRN* and *C9orf72* mutation carriers in our study. By this time, structural and functional brain changes have occurred,²³⁻²⁶ and cognitive disturbances also co-occur.²⁷⁻²⁹ Interestingly, *MAPT* mutation carriers in the prodromal stage scored slightly lower than the *C9orf72* and *GRN* groups, with only a trend to a higher score than controls. However, *MAPT* mutation carriers in the asymptomatic period scored at a similar level to the other groups, but here were significantly more impaired than controls. The domains significantly affected in this asymptomatic *MAPT* group were Stereotypic Behaviours (i.e. the most frequent behavioural symptom during the symptomatic period) as well as Mood and Sleep. Potentially, the CBI-R can therefore pick up the very early behavioural change in genetic FTD, here detecting symptoms many years before likely onset.

Neural correlates overlapped across the different genetic groups, although with some differences. The Stereotypic and Motor Behaviours domain was associated with medial temporal lobe atrophy, consistent with prior studies showing that obsessive-compulsive behaviours in FTD correlate with hippocampal and amygdala volumes³⁰ and are seen particularly in sporadic FTD in those with temporal variant FTD.³¹,³² A change in eating was associated with insula volumes in some of the groups, and frontal lobe volumes in all groups, a finding previously shown in a number of previous studies (where the association is usually with orbitofrontal atrophy).³³,³⁴ Interestingly, the Abnormal Behaviour domain correlated with medial temporal atrophy, particularly on the right in *C9orf72* and *GRN* mutation carriers. The occurrence of disinhibition has been associated mainly with (orbito)frontal lobe atrophy previously, but some studies have implicated the right medial temporal lobe, suggesting a disruption of normal reward processing.³⁵ The Beliefs domain score was associated with thalamic atrophy in both the *C9orf72* and *GRN* groups. This association of hallucinations and delusions has been shown previously in *C9orf72* mutation carriers³⁶ and has previously been thought to be particularly distinct for this genetic group. However, here we show a similar association in the *GRN* mutation carriers. For the cognitive domain of Memory and Orientation, there was an association with hippocampal volume in all three groups, consistent with the known role of the medial temporal lobe in episodic memory. However, there was an additional association with thalamus atrophy in the *C9orf72* mutation carriers, a region long associated with episodic memory function.³⁷

A limitation of our study was the relatively small sample size of the cohort once it is stratified, particularly for the *MAPT* mutation carriers, for example the prodromal *MAPT* group was relatively small, and negative results here may relate to the small sample size. We have also focused here on cross-sectional data, extrapolating to changes over time across individuals but future research examining how the CBI-R changes longitudinally over time within an individual will be important. Lastly, as the CBI-R was commonly completed by an informant from a family with genetic FTD, it may be that they were more alert to the presence of behavioural symptoms, particularly in those who were prodromal, leading to potentially higher CBI-R scores than in those with sporadic FTD.

**Figure 3.** Mean CBI-R scores (expressed as a percentage to allow comparison) in each of the individual 10 domains within healthy controls and each genetic group stratified by CDR plus NACC FTLD global scores. Significant differences between controls and within each genetic group are starred. Differences between different genetic groups are not shown. Error bars represent the standard error of the mean.
A

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B

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C

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D

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E

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H

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I

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C

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E

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F

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G

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I

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J

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## Conclusion

As we move into clinical trials for genetic FTD, the need for outcome measures that are both easy to assess and not time-consuming is required. There are still few validated assessment scales that focus on the behavioural and functional deficits prominent in people with FTD. The benefit of the CBI-R is that it includes core behavioural, neuropsychiatric, functional and cognitive measures within the same scale. Our study suggests the CBI-R can detect early behavioural change in genetic FTD, making it potentially a useful marker in a clinical trial setting. Measuring individual changes in behaviour over time will now be an important next step in understanding how the CBI-R might be used in such trials.

## Acknowledgements

We thank the research participants for their contribution to the study. The Dementia Research Centre is supported by Alzheimer's Research UK, Alzheimer's Society, Brain Research UK and The Wolfson Foundation. This work was supported by the NIHR UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. JDR is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). This work was also supported by the MRC UK GENFI grant (MR/M023664/1), the Bluefield Project and the JPND GENFI-PROX grant (2019-02248). MB is supported by a Fellowship award from the Alzheimer's Society, UK (AS-JF-19a-004-517). MB's work is also supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. RC is supported by a Frontotemporal Dementia Research Studentships in Memory of David Blechner funded through The National Brain Appeal (RCN 290173). Several authors of this publication are members of the European Reference Network for Rare Neurological Diseases (Project ID No 739510). Open Access funding enabled and organized by Projekt DEAL.

## Conflict of Interest

None.

## References

1. Rascovsky K, Hodges JR, Knopman D, et al. Sensitivity of revised diagnostic criteria for the behavioural variant

of frontotemporal dementia. Brain. 2011;134(9):2456-2477.

2. Rohrer J, Guerreiro R, Vandrovcova J, et al. The heritability and genetics of frontotemporal lobar degeneration. Neurology. 2009;73(18):1451-1456.

3. Hutton M, Lendon CL, Rizzu P, et al. Association of missense and 5'-splice-site mutations in tau with the inherited dementia FTDP-17. Nature. 1998;393(6686):702-705.

4. Cruts M, Gijselinck I, Van Der Zee J, et al. Null mutations in progranulin cause ubiquitin-positive frontotemporal dementia linked to chromosome 17q21. Nature. 2006;442 (7105):920-924.

5. DeJesus-Hernandez M, Mackenzie IR, Boeve BF, et al. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron. 2011;72(2):245-256.

6. Yiannopoulou KG, Papatriantafyllou JD, Ghika A, et al. Defining neuropsychiatric inventory scale differences across frontotemporal dementia syndromes. Psychogeriatrics. 2019;19(1):32-37.

7. Wear HJ, Wedderburn CJ, Mioshi E, et al. The Cambridge behavioural inventory revised. Dement Neuropsychol. 2008;2(2):102-107.

8. Lillo P, Mioshi E, Zoing MC, Kiernan MC, Hodges JR. How common are behavioural changes in amyotrophic lateral sclerosis? Amyotroph Lateral Scler. 2011;12(1):45-51.

9. Yi DS, Bertoux M, Mioshi E, Hodges JR, Hornberger M. Fronto-striatal atrophy correlates of neuropsychiatric dysfunction in frontotemporal dementia (FTD) and Alzheimer's disease (AD). Dement Neuropsychol. 2013;7 (1):75-82.

10. Wong S, Irish M, Husain M, Hodges JR, Piguet O, Kumfor F. Apathy and its impact on carer burden and psychological wellbeing in primary progressive aphasia. J Neurol Sci. 2020;416:117007.

11. Miyagawa T, Brushaber D, Syrjanen J, et al. Utility of the global CDR® plus NACC FTLD rating and development of scoring rules: data from the ARTFL/LEFFTDS consortium. Alzheimers Dement. 2020;16(1):106-117.

12. Mioshi E, Flanagan E, Knopman D. Detecting clinical change with the CDR-FTLD: differences between FTLD and AD dementia. Int J Geriatr Psychiatry. 2017;32 (9):977-982.

13. Cardoso MJ, Modat M, Wolz R, et al. Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans Med Imaging. 2015;34(9):1976-1988.

14. Malone IB, Leung KK, Clegg S, et al. Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance. NeuroImage. 2015;104:366-372.

15. Woollacott IO, Rohrer JD. The clinical spectrum of sporadic and familial forms of frontotemporal dementia. J Neurochem. 2016;138:6-31.
## Conclusion

As we move into clinical trials for genetic FTD, the need for outcome measures that are both easy to assess and not time-consuming is required. There are still few validated assessment scales that focus on the behavioural and functional deficits prominent in people with FTD. The benefit of the CBI-R is that it includes core behavioural, neuropsychiatric, functional and cognitive measures within the same scale. Our study suggests the CBI-R can detect early behavioural change in genetic FTD, making it potentially a useful marker in a clinical trial setting. Measuring individual changes in behaviour over time will now be an important next step in understanding how the CBI-R might be used in such trials.

## Acknowledgements

We thank the research participants for their contribution to the study. The Dementia Research Centre is supported by Alzheimer's Research UK, Alzheimer's Society, Brain Research UK and The Wolfson Foundation. This work was supported by the NIHR UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. JDR is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). This work was also supported by the MRC UK GENFI grant (MR/M023664/1), the Bluefield Project and the JPND GENFI-PROX grant (2019-02248). MB is supported by a Fellowship award from the Alzheimer's Society, UK (AS-JF-19a-004-517). MB's work is also supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. RC is supported by a Frontotemporal Dementia Research Studentships in Memory of David Blechner funded through The National Brain Appeal (RCN 290173). Several authors of this publication are members of the European Reference Network for Rare Neurological Diseases (Project ID No 739510). Open Access funding enabled and organized by Projekt DEAL.

## Conflict of Interest

None.

## References

1. Rascovsky K, Hodges JR, Knopman D, et al. Sensitivity of revised diagnostic criteria for the behavioural variant

of frontotemporal dementia. Brain. 2011;134(9):2456-2477.

2. Rohrer J, Guerreiro R, Vandrovcova J, et al. The heritability and genetics of frontotemporal lobar degeneration. Neurology. 2009;73(18):1451-1456.

3. Hutton M, Lendon CL, Rizzu P, et al. Association of missense and 5'-splice-site mutations in tau with the inherited dementia FTDP-17. Nature. 1998;393(6686):702-705.

4. Cruts M, Gijselinck I, Van Der Zee J, et al. Null mutations in progranulin cause ubiquitin-positive frontotemporal dementia linked to chromosome 17q21. Nature. 2006;442 (7105):920-924.

5. DeJesus-Hernandez M, Mackenzie IR, Boeve BF, et al. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron. 2011;72(2):245-256.

6. Yiannopoulou KG, Papatriantafyllou JD, Ghika A, et al. Defining neuropsychiatric inventory scale differences across frontotemporal dementia syndromes. Psychogeriatrics. 2019;19(1):32-37.

7. Wear HJ, Wedderburn CJ, Mioshi E, et al. The Cambridge behavioural inventory revised. Dement Neuropsychol. 2008;2(2):102-107.

8. Lillo P, Mioshi E, Zoing MC, Kiernan MC, Hodges JR. How common are behavioural changes in amyotrophic lateral sclerosis? Amyotroph Lateral Scler. 2011;12(1):45-51.

9. Yi DS, Bertoux M, Mioshi E, Hodges JR, Hornberger M. Fronto-striatal atrophy correlates of neuropsychiatric dysfunction in frontotemporal dementia (FTD) and Alzheimer's disease (AD). Dement Neuropsychol. 2013;7 (1):75-82.

10. Wong S, Irish M, Husain M, Hodges JR, Piguet O, Kumfor F. Apathy and its impact on carer burden and psychological wellbeing in primary progressive aphasia. J Neurol Sci. 2020;416:117007.

11. Miyagawa T, Brushaber D, Syrjanen J, et al. Utility of the global CDR® plus NACC FTLD rating and development of scoring rules: data from the ARTFL/LEFFTDS consortium. Alzheimers Dement. 2020;16(1):106-117.

12. Mioshi E, Flanagan E, Knopman D. Detecting clinical change with the CDR-FTLD: differences between FTLD and AD dementia. Int J Geriatr Psychiatry. 2017;32 (9):977-982.

13. Cardoso MJ, Modat M, Wolz R, et al. Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans Med Imaging. 2015;34(9):1976-1988.

14. Malone IB, Leung KK, Clegg S, et al. Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance. NeuroImage. 2015;104:366-372.

15. Woollacott IO, Rohrer JD. The clinical spectrum of sporadic and familial forms of frontotemporal dementia. J Neurochem. 2016;138:6-31.
16. Beck J, Rohrer JD, Campbell T, et al. A distinct clinical, neuropsychological and radiological phenotype is associated with progranulin gene mutations in a large UKseries. Brain. 2008;131(3):706-720.

17. Mahoney CJ, Beck J, Rohrer JD, et al. Frontotemporal dementia with the C9ORF72 hexanucleotide repeat expansion: clinical, neuroanatomical and neuropathological features. Brain. 2012;135(3):736-750.

18. Benussi A, Premi E, Gazzina S, Brattini C, Bonomi E, Alberici A, et al. Progression of behavioral disturbances and neuropsychiatric symptoms in patients with genetic frontotemporal dementia. JAMA Netw Open 2021;4(1):e2030194-e.

19. Poos JM, Russell LL, Peakman G, et al. Impairment of episodic memory in genetic frontotemporal dementia: a GENFI study. Alzheimer's & Dement. 2021;13(1):e12185.

20. Jiskoot LC, Dopper EGP, Heijer T, et al. Presymptomatic cognitive decline in familial frontotemporal dementia. A longitudinal study. Neurology. 2016;87(4):384-391.

21. Jiskoot LC, Panman JL, van Asseldonk L, et al. Longitudinal cognitive biomarkers predicting symptom onset in presymptomatic frontotemporal dementia. J Neurol. 2018;265(6):1381-1392.

22. Rohrer JD, Nicholas JM, Cash DM, et al. Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the genetic frontotemporal dementia initiative (GENFI) study: a cross-sectional analysis. Lancet Neurol. 2015;14(3):253-262.

23. Borroni B, Alberici A, Premi E, et al. Brain magnetic resonance imaging structural changes in a pedigree of asymptomatic progranulin mutation carriers. Rejuvenation Res. 2008;11(3):585-595.

24. Dopper EGP, Rombouts SARB, Jiskoot LC, et al. Structural and functional brain connectivity in presymptomatic familial frontotemporal dementia. Neurology. 2014;83(2):e19-e26.

25. Borroni B, Alberici A, Cercignani M, et al. Granulin mutation drives brain damage and reorganization from preclinical to symptomatic FTLD. Neurobiol Aging. 2012;33(10):2506-2520.

26. Whitwell JL, Josephs KA, Avula R, et al. Altered functional connectivity in asymptomatic MAPT subjects: a comparison to bvFTD. Neurology. 2011;77(9):866-874.

27. Dopper EGP, Rombouts SARB, Jiskoot LC, et al. Structural and functional brain connectivity in presymptomatic familial frontotemporal dementia. Neurology. 2013;80(9):814-823.

28. Rohrer JD, Warren JD, Barnes J, et al. Mapping the progression of progranulin-associated frontotemporal lobar degeneration. Nat Clin Pract Neurol. 2008;4(8):455-460.

29. Janssen JC, Schott JM, Cipolotti L, Fox NC, Scahill RI, Josephs KA, et al. Mapping the onset and progression of atrophy in familial frontotemporal lobar degeneration.

Journal of Neurology, Neurosurgery & Psychiatry. 2005;76 (2):162-8.

30. Mitchell E, Tavares TP, Palaniyappan L, Finger EC. Hoarding and obsessive-compulsive behaviours in frontotemporal dementia: clinical and neuroanatomic associations. Cortex. 2019;121:443-453.

31. Rosso SM, Roks G, Stevens M, et al. Complex compulsive behaviour in the temporal variant of frontotemporal dementia. J Neurol. 2001;248(11):965-970.

32. Seeley W, Bauer A, Miller B, et al. The natural history of temporal variant frontotemporal dementia. Neurology. 2005;64(8):1384-1390.

33. Woolley J, Gorno-Tempini M-L, Seeley W, et al. Binge eating is associated with right orbitofrontal-insular-striatal atrophy in frontotemporal dementia. Neurology. 2007;69 (14):1424-1433.

34. Whitwell JL, Warren JD, Josephs KA, et al. Voxel-based morphometry in tau-positive and tau-negative frontotemporal lobar degenerations. Neurodegener Dis. 2004;1(4-5):225-230.

35. Zamboni G, Huey E, Krueger F, Nichelli PF, Grafman J. Apathy and disinhibition in frontotemporal dementia: insights into their neural correlates. Neurology. 2008;71 (10):736-742.

36. Devenney EM, Landin-Romero R, Irish M, et al. The neural correlates and clinical characteristics of psychosis in the frontotemporal dementia continuum and the C9orf72 expansion. NeuroImage: Clin. 2017;13:439-445.

37. Fama R, Sullivan EV. Thalamic structures and associated cognitive functions: relations with age and aging. Neurosci Biobehav Rev. 2015;54:29-37.

## Supporting Information

Additional supporting information may be found online in the Supporting Information section at the end of the article.

**Table S1.** Adjusted mean differences with 95% bootstrapped bias-corrected confidence intervals in the comparison of CBI-R total scores between healthy controls and each of the genetic groups stratified by global CDR plus NACC FTLD score.

**Table S2.** Adjusted mean differences in each of the ten CBI-R domains scores between the genetic groups stratified by global CDR plus NACC FTLD scores, with 95% bootstrapped bias-corrected confidence intervals.

**Table S3.** Adjusted mean differences from within-group analysis of each of the ten CBI-R domains for symptomatic mutation carriers ((A) C9orf72, (B) GRN, (C) MAPT) with 95% bootstrapped bias-corrected confidence intervals.

**Table S4.** Partial correlations between scores in the 10 domains of the CBI-R and volumes of neuroanatomical
16. Beck J, Rohrer JD, Campbell T, et al. A distinct clinical, neuropsychological and radiological phenotype is associated with progranulin gene mutations in a large UKseries. Brain. 2008;131(3):706-720.

17. Mahoney CJ, Beck J, Rohrer JD, et al. Frontotemporal dementia with the C9ORF72 hexanucleotide repeat expansion: clinical, neuroanatomical and neuropathological features. Brain. 2012;135(3):736-750.

18. Benussi A, Premi E, Gazzina S, Brattini C, Bonomi E, Alberici A, et al. Progression of behavioral disturbances and neuropsychiatric symptoms in patients with genetic frontotemporal dementia. JAMA Netw Open 2021;4(1):e2030194-e.

19. Poos JM, Russell LL, Peakman G, et al. Impairment of episodic memory in genetic frontotemporal dementia: a GENFI study. Alzheimer's & Dement. 2021;13(1):e12185.

20. Jiskoot LC, Dopper EGP, Heijer T, et al. Presymptomatic cognitive decline in familial frontotemporal dementia. A longitudinal study. Neurology. 2016;87(4):384-391.

21. Jiskoot LC, Panman JL, van Asseldonk L, et al. Longitudinal cognitive biomarkers predicting symptom onset in presymptomatic frontotemporal dementia. J Neurol. 2018;265(6):1381-1392.

22. Rohrer JD, Nicholas JM, Cash DM, et al. Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the genetic frontotemporal dementia initiative (GENFI) study: a cross-sectional analysis. Lancet Neurol. 2015;14(3):253-262.

23. Borroni B, Alberici A, Premi E, et al. Brain magnetic resonance imaging structural changes in a pedigree of asymptomatic progranulin mutation carriers. Rejuvenation Res. 2008;11(3):585-595.

24. Dopper EGP, Rombouts SARB, Jiskoot LC, et al. Structural and functional brain connectivity in presymptomatic familial frontotemporal dementia. Neurology. 2014;83(2):e19-e26.

25. Borroni B, Alberici A, Cercignani M, et al. Granulin mutation drives brain damage and reorganization from preclinical to symptomatic FTLD. Neurobiol Aging. 2012;33(10):2506-2520.

26. Whitwell JL, Josephs KA, Avula R, et al. Altered functional connectivity in asymptomatic MAPT subjects: a comparison to bvFTD. Neurology. 2011;77(9):866-874.

27. Dopper EGP, Rombouts SARB, Jiskoot LC, et al. Structural and functional brain connectivity in presymptomatic familial frontotemporal dementia. Neurology. 2013;80(9):814-823.

28. Rohrer JD, Warren JD, Barnes J, et al. Mapping the progression of progranulin-associated frontotemporal lobar degeneration. Nat Clin Pract Neurol. 2008;4(8):455-460.

29. Janssen JC, Schott JM, Cipolotti L, Fox NC, Scahill RI, Josephs KA, et al. Mapping the onset and progression of atrophy in familial frontotemporal lobar degeneration.

Journal of Neurology, Neurosurgery & Psychiatry. 2005;76 (2):162-8.

30. Mitchell E, Tavares TP, Palaniyappan L, Finger EC. Hoarding and obsessive-compulsive behaviours in frontotemporal dementia: clinical and neuroanatomic associations. Cortex. 2019;121:443-453.

31. Rosso SM, Roks G, Stevens M, et al. Complex compulsive behaviour in the temporal variant of frontotemporal dementia. J Neurol. 2001;248(11):965-970.

32. Seeley W, Bauer A, Miller B, et al. The natural history of temporal variant frontotemporal dementia. Neurology. 2005;64(8):1384-1390.

33. Woolley J, Gorno-Tempini M-L, Seeley W, et al. Binge eating is associated with right orbitofrontal-insular-striatal atrophy in frontotemporal dementia. Neurology. 2007;69 (14):1424-1433.

34. Whitwell JL, Warren JD, Josephs KA, et al. Voxel-based morphometry in tau-positive and tau-negative frontotemporal lobar degenerations. Neurodegener Dis. 2004;1(4-5):225-230.

35. Zamboni G, Huey E, Krueger F, Nichelli PF, Grafman J. Apathy and disinhibition in frontotemporal dementia: insights into their neural correlates. Neurology. 2008;71 (10):736-742.

36. Devenney EM, Landin-Romero R, Irish M, et al. The neural correlates and clinical characteristics of psychosis in the frontotemporal dementia continuum and the C9orf72 expansion. NeuroImage: Clin. 2017;13:439-445.

37. Fama R, Sullivan EV. Thalamic structures and associated cognitive functions: relations with age and aging. Neurosci Biobehav Rev. 2015;54:29-37.

## Supporting Information

Additional supporting information may be found online in the Supporting Information section at the end of the article.

**Table S1.** Adjusted mean differences with 95% boot-strapped bias-corrected confidence intervals in the comparison of CBI-R total scores between healthy controls and each of the genetic groups stratified by global CDR plus NACC FTLD score.

**Table S2.** Adjusted mean differences in each of the ten CBI-R domains scores between the genetic groups stratified by global CDR plus NACC FTLD scores, with 95% bootstrapped bias-corrected confidence intervals.

**Table S3.** Adjusted mean differences from within-group analysis of each of the ten CBI-R domains for symptomatic mutation carriers ((A) C9orf72, (B) GRN, (C) MAPT) with 95% bootstrapped bias-corrected confidence intervals.

**Table S4.** Partial correlations between scores in the 10 domains of the CBI-R and volumes of neuroanatomical
regions of interest adjusting for disease severity and age (r values and corresponding p values are shown) for the C9orf72 mutation carriers.

**Table S5.** Partial correlations between scores in the 10 domains of the CBI-R and volumes of neuroanatomical regions of interest adjusting for disease severity and age (r values and corresponding p values are shown) for the GRN mutation carriers.

**Table S6.** Partial correlations between scores in the 10 domains of the CBI-R and volumes of neuroanatomical regions of interest adjusting for disease severity and age (r values and corresponding p values are shown) for the MAPT mutation carriers.

**Figure S1.** Correlations between CBI-R total scores and (i) on the left, CDR plus NACC FTLD sum of boxes scores [C9orf72 (r = 0.78, p < 0.001), GRN (r = 0.82, p < 0.001) and MAPT (r = 0.60, p < 0.001)], and (ii) on the right, FRS scores [C9orf72 (r = -0.92, p < 0.001), GRN (r = -0.88, p < 0.001) and MAPT (r = -0.88, p < 0.001)].

**Figure S2.** CBI-R individual domain scores (as a percentage) in each of the ten domains in all symptomatic mutation carrier groups: (A) C9orf72, (B) GRN, (C) MAPT. The error bars represent standard error of the mean.

# Appendix

## GENFI Consortium Authors

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

* Maria Rosario Almeida – Faculty of Medicine, University of Coimbra, Coimbra, Portugal;

* Sarah Anderl-Straub – Department of Neurology, University of Ulm, Ulm, Germany;

* Christin Andersson – Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden;

* Anna Antonell – Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain;

* Silvana Archetti – Biotechnology Laboratory, Department of Diagnostics, ASST Brescia Hospital, Brescia, Italy;

* Andrea Arighi – Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy;

* Mircea Balasa – Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain;

* Myriam Barandiaran – Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain; Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain;

* Nuria Bargalló – Imaging Diagnostic Center, Hospital Clínic, Barcelona, Spain;

* Robart Bartha – Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada;

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

* Alberto Benussi – Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Italy;

* Maxime Bertoux – Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distal, LiCEND Lille, France;

* Anne Bertrand – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Institut du Cerveau et la Moelle, Paris, France

* Valentina Bessi – Department of Neuroscience, Psychology, Drug Research, and Child Health, University of Florence, Florence, Italy;

* Sandra Black – Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada;

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

* Sergi Borrego-Ecija – Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain;

* Jose Bras – Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, Michigan, MI 49503, USA;

* Alexis Brice – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Reference Network for Rare Neurological Diseases (ERN-RND);

* Rose Bruffaerts – Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium;

* Agnès Camuzat – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France;

* Marta Cañada – CITA Alzheimer, San Sebastian, Gipuzkoa, Spain

* Valentina Cantoni – Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy;
regions of interest adjusting for disease severity and age (r values and corresponding p values are shown) for the C9orf72 mutation carriers.

**Table S5.** Partial correlations between scores in the 10 domains of the CBI-R and volumes of neuroanatomical regions of interest adjusting for disease severity and age (r values and corresponding p values are shown) for the GRN mutation carriers.

**Table S6.** Partial correlations between scores in the 10 domains of the CBI-R and volumes of neuroanatomical regions of interest adjusting for disease severity and age (r values and corresponding p values are shown) for the MAPT mutation carriers.

**Figure S1.** Correlations between CBI-R total scores and (i) on the left, CDR plus NACC FTLD sum of boxes scores [C9orf72 ($r = 0.78, p < 0.001$), GRN ($r = 0.82, p < 0.001$) and MAPT ($r = 0.60, p < 0.001$)], and (ii) on the right, FRS scores [C9orf72 ($r = -0.92, p < 0.001$), GRN ($r = -0.88, p < 0.001$) and MAPT ($r = -0.88, p < 0.001$)].

**Figure S2.** CBI-R individual domain scores (as a percentage) in each of the ten domains in all symptomatic mutation carrier groups: (A) C9orf72, (B) GRN, (C) MAPT. The error bars represent standard error of the mean.

# Appendix

## GENFI Consortium Authors

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

* Maria Rosario Almeida – Faculty of Medicine, University of Coimbra, Coimbra, Portugal;

* Sarah Anderl-Straub – Department of Neurology, University of Ulm, Ulm, Germany;

* Christin Andersson – Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden;

* Anna Antonell – Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain;

* Silvana Archetti – Biotechnology Laboratory, Department of Diagnostics, ASST Brescia Hospital, Brescia, Italy;

* Andrea Arighi – Fondazione IRCCS Ca' Granda Ospe-dale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy;

* Mircea Balasa – Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain;

* Myriam Barandiaran – Cognitive Disorders Unit, Department of Neurology, Donostia University Hospi-tal, San Sebastian, Gipuzkoa, Spain; Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain;

* Nuria Bargalló – Imaging Diagnostic Center, Hospital Clínic, Barcelona, Spain;

* Robart Bartha – Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada;

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

* Alberto Benussi – Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Italy;

* Maxime Bertoux – Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distal, LiCEND Lille, France;

* Anne Bertrand – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Institut du Cerveau et la Moelle, Paris, France

* Valentina Bessi – Department of Neuroscience, Psychology, Drug Research, and Child Health, University of Florence, Florence, Italy;

* Sandra Black – Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada;

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

* Sergi Borrego-Ecija – Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain;

* Jose Bras – Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, Michigan, MI 49503, USA;

* Alexis Brice – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Reference Network for Rare Neurological Diseases (ERN-RND);

* Rose Bruffaerts – Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium;

* Agnès Camuzat – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France;

* Marta Cañada – CITA Alzheimer, San Sebastian, Gipuzkoa, Spain

* Valentina Cantoni – Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy;
* Paola Caroppo – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

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

* Miguel Castelo-Branco – Faculty of Medicine, University of Coimbra, Coimbra, Portugal;

* Olivier Colliot – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Institut du Cerveau et la Moelle, Paris, France;

* Thomas Cope – Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK;

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

* María de Arriba – Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain;

* Giuseppe Di Fede – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Alina Díez – Neuroscience Area, Biodonostia Health Research Insitite, San Sebastian, Gipuzkoa, Spain

* Diana Duro – Faculty of Medicine, University of Coimbra, Coimbra, Portugal;

* Chiara Fenoglio – Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy;

* Camilla Ferrari – Department of Neuroscience, Psychology, Drug Research, and Child Health, University of Florence, Florence, Italy;

* Catarina B. Ferreira –Laboratory of Neurosciences, Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon, Portugal;

* Nick Fox – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Morris Freedman – Baycrest Health Sciences, Rotman Research Institute, University of Toronto, Toronto, Canada;

* Giorgio Fumagalli – Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy;

* Aurélie Funkiewiez – Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France;

* Alazne Gabilondo – Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain;

* Roberto Gasparotti – Neuroradiology Unit, University of Brescia, Brescia, Italy

* Serge Gauthier – Alzheimer Disease Research Unit, McGill Centre for Studies in Ageing, Department of Neurology & Neurosurgery, McGill University, Montreal, Québec, Canada;

* Stefano Gazzina – Neurology, ASST Brescia Hospital, Brescia, Italy

* Giorgio Giaccone – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Ana Gorostidi – Neuroscience Area, Biodonostia Health Research Insitite, San Sebastian, Gipuzkoa, Spain;

* Caroline Greaves – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Rita Guerreiro – Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, Michigan, MI 49503, USA;

* Carolin Heller – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Tobias Hoegen – Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany;

* Begoña Indakoetxea – Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain; Neuroscience Area, Biodonostia Health Research Insitite, San Sebastian, Gipuzkoa, Spain;

* Vesna Jelic – Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden;

* Hans-Otto Karnath – Division of Neuropsychology, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany;

* Ron Keren – The University Health Network, Toronto Rehabilitation Institute, Toronto, Canada;

* Gregory Kuchcinski – Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France;

* Tobias Langheinrich – Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK; Manchester Centre for Clinical Neurosciences, Department of Neurology, Salford Royal NHS Foundation Trust, Manchester, UK;

* Thibaud Lebouvier – Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France;

* Maria João Leitão – Centre of Neurosciences and Cell Biology, Universidade de Coimbra, Coimbra, Portugal;
* Paola Caroppo – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

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

* Miguel Castelo-Branco – Faculty of Medicine, University of Coimbra, Coimbra, Portugal;

* Olivier Colliot – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Institut du Cerveau et la Moelle, Paris, France;

* Thomas Cope – Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK;

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

* María de Arriba – Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain;

* Giuseppe Di Fede – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Alina Díez – Neuroscience Area, Biodonostia Health Research Insitite, San Sebastian, Gipuzkoa, Spain

* Diana Duro – Faculty of Medicine, University of Coimbra, Coimbra, Portugal;

* Chiara Fenoglio – Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy;

* Camilla Ferrari – Department of Neuroscience, Psychology, Drug Research, and Child Health, University of Florence, Florence, Italy;

* Catarina B. Ferreira –Laboratory of Neurosciences, Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon, Portugal;

* Nick Fox – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Morris Freedman – Baycrest Health Sciences, Rotman Research Institute, University of Toronto, Toronto, Canada;

* Giorgio Fumagalli – Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy;

* Aurélie Funkiewiez – Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France;

* Alazne Gabilondo – Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain;

* Roberto Gasparotti – Neuroradiology Unit, University of Brescia, Brescia, Italy

* Serge Gauthier – Alzheimer Disease Research Unit, McGill Centre for Studies in Ageing, Department of Neurology & Neurosurgery, McGill University, Montreal, Québec, Canada;

* Stefano Gazzina – Neurology, ASST Brescia Hospital, Brescia, Italy

* Giorgio Giaccone – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Ana Gorostidi – Neuroscience Area, Biodonostia Health Research Insitite, San Sebastian, Gipuzkoa, Spain;

* Caroline Greaves – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Rita Guerreiro – Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, Michigan, MI 49503, USA;

* Carolin Heller – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Tobias Hoegen – Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany;

* Begoña Indakoetxea – Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain; Neuroscience Area, Biodonostia Health Research Insitite, San Sebastian, Gipuzkoa, Spain;

* Vesna Jelic – Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden;

* Hans-Otto Karnath – Division of Neuropsychology, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany;

* Ron Keren – The University Health Network, Toronto Rehabilitation Institute, Toronto, Canada;

* Gregory Kuchcinski – Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France;

* Tobias Langheinrich – Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK; Manchester Centre for Clinical Neurosciences, Department of Neurology, Salford Royal NHS Foundation Trust, Manchester, UK;

* Thibaud Lebouvier – Univ Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France;

* Maria João Leitão – Centre of Neurosciences and Cell Biology, Universidade de Coimbra, Coimbra, Portugal;
* Albert Lladó – Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain;

* Gemma Lombardi – Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy;

* Sandra Loosli – Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany;

* Carolina Maruta – Laboratory of Language Research, Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal;

* Simon Mead – MRC Prion Unit, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Lieke Meeter – Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands;

* Gabriel Miltenberger – Faculty of Medicine, University of Lisbon, Lisbon, Portugal;

* Rick van Minkelen – Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands;

* Sara Mitchell – Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada;

* Katrina Moore – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London UK;

* Benedetta Nacmias – Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy;

* Linn Öijerstedt – Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden; Unit for Hereditary Dementias, Theme Ageing, Karolinska University Hospital, Solna, Sweden;

* Jaume Olives – Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain;

* Sebastien Ourselin – School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK;

* Alessandro Padovani – Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Italy

* Jessica Panman – Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands;

* Janne M. Papma – Department of Neurology, Erasmus Medical Center, Rotterdam;

* Yolande Pijnenburg – Amsterdam University Medical Centre, Amsterdam VUmc, Amsterdam, Netherlands;

* Cristina Polito – Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, Nuclear Medicine Unit, University of Florence, Florence, Italy

* Enrico Premi – Stroke Unit, ASST Brescia Hospital, Brescia, Italy

* Sara Prioni – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Catharina Prix – Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany;

* Rosa Rademakers [as London Ontario geneticist] – Department of Neurosciences, Mayo Clinic, Jacksonville, Florida, USA;

* Veronica Redaelli – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Daisy Rinaldi – Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Département de Neurologie, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Reference Network for Rare Neurological Diseases (ERN-RND);

* Tim Rittman – Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK;

* Ekaterina Rogaeva – Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada;

* Adeline Rollin – CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France;

* Pedro Rosa-Neto – Translational Neuroimaging Laboratory, McGill Centre for Studies in Ageing, McGill University, Montreal, Québec, Canada;

* Giacomina Rossi – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Martin Rossor – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Beatriz Santiago – Neurology Department, Centro Hospitalar e Universitario de Coimbra, Coimbra, Portugal;

* Dario Saracino – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France;

* Sabrina Sayah – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France;

* Elio Scarpini – Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy;

* Sonja Schönecker – Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany;
* Albert Lladó – Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain;

* Gemma Lombardi – Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy;

* Sandra Loosli – Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany;

* Carolina Maruta – Laboratory of Language Research, Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal;

* Simon Mead – MRC Prion Unit, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Lieke Meeter – Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands;

* Gabriel Miltenberger – Faculty of Medicine, University of Lisbon, Lisbon, Portugal;

* Rick van Minkelen – Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands;

* Sara Mitchell – Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Canada;

* Katrina Moore – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London UK;

* Benedetta Nacmias – Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy;

* Linn Öijerstedt – Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden; Unit for Hereditary Dementias, Theme Ageing, Karolinska University Hospital, Solna, Sweden;

* Jaume Olives – Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain;

* Sebastien Ourselin – School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK;

* Alessandro Padovani – Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Italy

* Jessica Panman – Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands;

* Janne M. Papma – Department of Neurology, Erasmus Medical Center, Rotterdam;

* Yolande Pijnenburg – Amsterdam University Medical Centre, Amsterdam VUmc, Amsterdam, Netherlands;

* Cristina Polito – Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, Nuclear Medicine Unit, University of Florence, Florence, Italy

* Enrico Premi – Stroke Unit, ASST Brescia Hospital, Brescia, Italy

* Sara Prioni – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Catharina Prix – Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany;

* Rosa Rademakers [as London Ontario geneticist] – Department of Neurosciences, Mayo Clinic, Jacksonville, Florida, USA;

* Veronica Redaelli – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Daisy Rinaldi – Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Département de Neurologie, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Reference Network for Rare Neurological Diseases (ERN-RND);

* Tim Rittman – Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK;

* Ekaterina Rogaeva – Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada;

* Adeline Rollin – CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France;

* Pedro Rosa-Neto – Translational Neuroimaging Laboratory, McGill Centre for Studies in Ageing, McGill University, Montreal, Québec, Canada;

* Giacomina Rossi – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Martin Rossor – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Beatriz Santiago – Neurology Department, Centro Hospitalar e Universitario de Coimbra, Coimbra, Portugal;

* Dario Saracino – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France; Inria, Aramis project-team, F-75013, Paris, France; Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France;

* Sabrina Sayah – Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP – Hôpital Pitié-Salpêtrière, Paris, France;

* Elio Scarpini – Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy;

* Sonja Schönecker – Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany;
* Harro Seelaar – Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands;

* Elisa Semler -Department of Neurology, University of Ulm, Ulm;

* Rachelle Shafei – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Christen Shoesmith – Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada;

* Imogen Swift – Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, UK;

* Miguel Tábuas-Pereira – Neurology Department, Centro Hospitalar e Universitario de Coimbra, Coimbra, Portugal;

* Mikel Tainta – Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain;

* Ricardo Taipa – Neuropathology Unit and Department of Neurology, Centro Hospitalar do Porto – Hospital de Santo António, Oporto, Portugal;

* David Tang-Wai -The University Health Network, Krembil Research Institute, Toronto, Canada;

* David L Thomas – Neuroimaging Analysis Centre, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London, UK;

* Paul Thompson – Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK;

* Hakan Thonberg – Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden;

* Carolyn Timberlake – Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK;

* Pietro Tiraboschi – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Emily Todd – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, UK;

* Philip Van Damme – Neurology Service, University Hospitals Leuven, Belgium; Laboratory for Neurobiology, VIB-KU Leuven Centre for Brain Research, Leuven, Belgium;

* Mathieu Vandenbulcke – Geriatric Psychiatry Service, University Hospitals Leuven, Belgium; Neuropsychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium;

* Michele Veldsman – Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK;

* Ana Verdelho – Department of Neurosciences and Mental Health, Centro Hospitalar Lisboa Norte – Hospital de Santa Maria & Faculty of Medicine, University of Lisbon, Lisbon, Portugal;

* Jorge Villanua – OSATEK, University of Donostia, San Sebastian, Gipuzkoa, Spain;

* Jason Warren – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Carlo Wilke – Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany; Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany;

* Elisabeth Wlasich – Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany;

* Henrik Zetterberg – Dementia Research Institute, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Miren Zulaica – Neuroscience Area, Biodonostia Health Research Insitite, San Sebastian, Gipuzkoa, Spain.
* Harro Seelaar – Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands;

* Elisa Semler -Department of Neurology, University of Ulm, Ulm;

* Rachelle Shafei – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Christen Shoesmith – Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada;

* Imogen Swift – Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, UK;

* Miguel Tábuas-Pereira – Neurology Department, Centro Hospitalar e Universitario de Coimbra, Coimbra, Portugal;

* Mikel Tainta – Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain;

* Ricardo Taipa – Neuropathology Unit and Department of Neurology, Centro Hospitalar do Porto – Hospital de Santo António, Oporto, Portugal;

* David Tang-Wai -The University Health Network, Krembil Research Institute, Toronto, Canada;

* David L Thomas – Neuroimaging Analysis Centre, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London, UK;

* Paul Thompson – Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK;

* Hakan Thonberg – Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden;

* Carolyn Timberlake – Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK;

* Pietro Tiraboschi – Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy;

* Emily Todd – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, UK;

* Philip Van Damme – Neurology Service, University Hospitals Leuven, Belgium; Laboratory for Neurobiology, VIB-KU Leuven Centre for Brain Research, Leuven, Belgium;

* Mathieu Vandenbulcke – Geriatric Psychiatry Service, University Hospitals Leuven, Belgium; Neuropsychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium;

* Michele Veldsman – Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK;

* Ana Verdelho – Department of Neurosciences and Mental Health, Centro Hospitalar Lisboa Norte – Hospital de Santa Maria & Faculty of Medicine, University of Lisbon, Lisbon, Portugal;

* Jorge Villanua – OSATEK, University of Donostia, San Sebastian, Gipuzkoa, Spain;

* Jason Warren – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Carlo Wilke – Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany; Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany;

* Elisabeth Wlasich – Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany;

* Henrik Zetterberg – Dementia Research Institute, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;

* Miren Zulaica – Neuroscience Area, Biodonostia Health Research Insitite, San Sebastian, Gipuzkoa, Spain.