# Executive Function Deficits in Genetic Frontotemporal Dementia: Results From the GENFI Study

Lucie-Louise Russell, A. Bouzigues, R. S. Convery, P. H. Foster, E.

Ferry-Bolder, D. M. Cash, J. C. van Swieten, L. C. Jiskoot, H. Seelaar, F.

Moreno, et al.

► To cite this version:

Lucie-Louise Russell, A. Bouzigues, R. S. Convery, P. H. Foster, E. Ferry-Bolder, et al. Executive Function Deficits in Genetic Frontotemporal Dementia: Results From the GENFI Study. Neurology Genetics, 2025, Neurol.-Genet., 11, pp.e200248. 10.1212/NXG.0000000000200248. [https://lilloa.hal.science/hal-05372195v1](https://lilloa.hal.science/hal-05372195v1)

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# Executive Function Deficits in Genetic Frontotemporal Dementia

## Results From the GENFI Study

Lucy Louise Russell,<fnref n="1" /> Arabella Bouzigues,<fnref n="1" /> Rhian S. Convery,<fnref n="1" /> Phoebe H. Foster,<fnref n="1" /> Eve Ferry-Bolder,<fnref n="1" />

David M. Cash,<fnref n="1" />,² John C. Van Swieten,³ Lize Corrine Jiskoot,³ Harro Seelaar,³ Fermin Moreno,⁴,⁵

Raquel Sánchez-Valle,⁶ Robert Laforce, Jr.,⁷ Caroline Graff,⁸,⁹ Mario Masellis,¹⁰ Maria Carmela Tartaglia,¹¹

James B. Rowe,¹² Barbara Borroni,¹³ Elizabeth Finger,¹⁴ Matthis Synofzik,¹⁵,¹⁶ Daniela Galimberti,¹⁷,¹⁸

Rik Vandenberghe,¹⁹,²⁰,²¹ Alexandre de Mendonça,²² Christopher Butler,²³ Alexander Gerhard,²⁴,²⁵,²⁶

Simon Ducharme,²⁷,²⁸ Isabelle Le Ber,²⁹,³⁰,³¹ Isabel Santana,³²,³³,³⁴ Florence Pasquier,³⁵,³⁶,³⁷

Johannes Levin,³⁸,³⁹,⁴⁰ Sandro Sorbi,⁴¹,⁴² Markus Otto,⁴³ and Jonathan Daniel Rohrer,<fnref n="1" /> for the Genetic FTD Initiative (GENFI)

Neurol Genet 2025;11:e200248. doi:10.1212/NXG.0000000000200248

**Correspondence**

Dr. Russell
l.russell@ucl.ac.uk

## Abstract

MORE ONLINE

## Background and Objectives

Supplementary Material

Executive dysfunction is a core feature of frontotemporal dementia (FTD). While there has been extensive research into such impairments in sporadic FTD, there has been little research in the familial forms.

## Methods

Seven hundred fifty-two individuals were recruited in total: 214 C9orf72; 205 progranolin (GRN) and 86 microtubule associated protein tau (MAPT) mutation carriers, stratified into asymptomatic, prodromal, and fully symptomatic; and 247 mutation-negative controls. Attention and executive function were measured using the Weschler Memory Scale-Revised (WMS-R) Digit Span Backwards (DSB), Wechsler Adult Intelligence Scale-Revised Digit Symbol task, Trail Making Test Parts A and B, and the Delis-Kaplan Executive Function System Color Word Interference Test. Linear regression models with bootstrapping were used to assess differences between groups. Correlation of task score with disease severity was also performed, as well as an analysis of the neuroanatomical correlates of each task.

The article processing charge was funded by University College London.

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

<footnote n="1">¹Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, United Kingdom; ²Centre for Medical Image Computing, University College London, United Kingdom; ³Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands; ⁴Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain; ⁵Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain; ⁶Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigació Biomèdiques August Pi I Sunyer, University of Barcelona, Spain; ⁷Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Clinique Interdisciplinaire de Mémoire, Université Laval, Canada; ⁸Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden; ⁹Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden; ¹⁰Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Canada; ¹¹Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Canada; ¹²Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust and Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom; ¹³Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Italy; ¹⁴Department of Clinical Neurological Sciences, University of Western Ontario, London, Canada; ¹⁵Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Germany; ¹⁶Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany; ¹⁷Department of Biomedical, Surgical and Dental Sciences, University of Milan, Italy; ¹⁸Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy; ¹⁹Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium; ²⁰Neurology Service, University Hospitals Leuven, Belgium; ²¹Leuven Brain Institute, KU Leuven, Belgium; ²²Faculty of Medicine, University of Lisbon, Portugal; ²³Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, United Kingdom; ²⁴Division of Psychology Communication and Human Neuroscience Wolfson Molecular Imaging Centre, University of Manchester, United Kingdom; ²⁵Department of Nuclear Medicine, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Germany; ²⁶Department of Geriatric Medicine, Klinikum Hochsauerland, Arnsberg, Germany; ²⁷Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, Canada; ²⁸Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; ²⁹Paris Brain Institute—Institut du Cerveau—ICM, Inserm U1127, CNRS UMR 7225, AP-HP-Hôpital Pitié-Salpêtrière (DMU Neurosciences Paris 6), Sorbonne Université, France; ³⁰Département de Neurologie, Centre de référence des démences rares ou précoces, IM2A, AP-HP-Hôpital Pitié-Salpêtrière (DMU Neurosciences Paris 6), Paris, France; ³¹Département de Neurologie, AP-HP-Hôpital Pitié-Salpêtrière (DMU Neurosciences Paris 6), France; ³²Neurology Department, Centro Hospitalar e Universitário de Coimbra, Portugal; ³³Faculty of Medicine, University of Coimbra, Portugal; ³⁴Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Portugal; ³⁵Univ Lille, France; ³⁶Inserm 1172, Lille, France; ³⁷CHU Lille, CNR-MAJ, Labex Distal, LiCEND, France; ³⁸Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich, Germany; ³⁹German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; ⁴⁰Munich Cluster of Systems Neurology, Germany; ⁴¹Department of Neurofarba, University of Florence, Italy; ⁴²IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy; and ⁴³Department of Neurology, University of Ulm, German.</footnote>
# Glossary

**AMD** = adjusted mean difference; **CDR** = Clinical Dementia Rating; **DLPFC** = dorsolateral prefrontal cortex; **DSB** = Digit Span Backwards; **DSST** = Digit Symbol substitution task; **FTD** = frontotemporal dementia; **GENFI** = Genetic FTD Initiative; **GIF** = Geodesic Information Flow; **GRN** = progranulin; **MAPT** = microtubule-associated protein tau; **OFC** = orbitofrontal cortex; **SB** = sum of boxes; **TMT** = Trail Making Test; **WMS-R** = Weschler Memory Scale-Revised.

# Results

Fully symptomatic C9orf72, GRN, and MAPT mutation carriers were significantly impaired on all tasks compared with controls (all *p* < 0.001), except on the WMS-R DSB in the MAPT mutation carriers (*p* = 0.147). While asymptomatic and prodromal C9orf72 individuals also demonstrated differences compared with controls, neither the GRN or MAPT asymptomatic or prodromal mutation carriers showed significant deficits. All tasks were significantly correlated with disease severity in each of the genetic groups (all *p* < 0.001).

# Discussion

Some individuals with C9orf72 mutations show difficulties with executive function from very early on in the disease and this continues to deteriorate with disease severity. By contrast, similar difficulties occur only in the later stages of the disease in GRN and MAPT mutation carriers. This differential performance across the genetic groups will be important in neuropsychological task selection in upcoming clinical trials.

# Introduction

Frontotemporal dementia (FTD) is a neurodegenerative disease that causes impairments in behavior and cognition. While a number of different changes in personality can occur, such as apathy, loss of empathy, and obsessive-compulsive behaviors,¹,² the core cognitive deficit is a change in executive function, a set of processes that includes inhibitory control, working memory, and cognitive flexibility.³

Executive function has been extensively studied in sporadic FTD, where it has been demonstrated that such abilities are commonly compromised. However, there have been fewer studies examining changes in the familial forms of FTD due to pathogenic variants in the progranulin (GRN) and microtubule-associated protein tau (MAPT) genes and a pathogenic expansion in chromosome 9 open reading frame 72 (C9orf72), which account for about one-third of all FTDs.⁴⁻⁹

The aim of this study was therefore to investigate executive function in a large cohort of presymptomatic and symptomatic individuals with familial FTD using participants from the Genetic FTD Initiative (GENFI). In particular, we explore whether there are differences across the 3 main genetic causes and whether deficits occur in the presymptomatic stages of the disease. Such information will be important in guiding task selection for inclusion and outcome measures in upcoming therapeutic trials.

# Methods

## Cohort

The fifth GENFI data freeze included 831 individuals from 25 sites in Europe and Canada. 752 of these individuals had

completed at least 1 of the executive function tasks in the GENFI neuropsychological battery: 214 carried the C9orf72 expansion, 205 had a GRN pathogenic variant, and 86 had a MAPT pathogenic variant ("mutation carriers" from here onward); 247 individuals were mutation-negative family members who acted as controls. The specific pathogenic gene variants are listed in eTable 1.

## Standard Protocol Approvals, Registrations, and Patient Consents

Ethical approval was gained at each of the individual sites, and all participants provided fully informed consent.

## Protocol

All participants underwent the standard GENFI protocol including the GENFI neuropsychological battery,¹⁰,¹¹ as well as the Mini-Mental State Examination and the Clinical Dementia Rating (CDR) Dementia Staging Instrument with the National Alzheimer's Coordinating Center Frontotemporal Lobar De-generation component (NACC FTLD). The latter generates 2 types of scores: a sum-of-boxes (SB) score and a global score that allows staging of the disease into 0—asymptomatic, 0.5—prodromal, and 1 or more (1+)—symptomatic (1 mild, 2 moderate, and 3 severe). Symptomatic individuals were diagnosed according to current criteria¹²⁻¹⁴: 91 had bvFTD (C9orf72 = 49, GRN = 24, MAPT = 18), 20 had primary progressive aphasia (C9orf72 = 3, GRN = 16, MAPT = 1), and 9 had FTD with amyotrophic lateral sclerosis (C9orf72 = 9), while the other symptomatic participants consisted of smaller diagnostic groups including those with atypical parkinsonism. Demographic information is provided in Table 1.

Compared with controls, all 3 symptomatic groups were older (all *p* > 0.001) and so too was the GRN prodromal group (*p* =
0.004). The MAPT asymptomatic group was significantly younger than the controls (p = 0.001). All 3 symptomatic groups were also significantly older than their asymptomatic (all p < 0.001) and prodromal (all p < 0.003) counterparts. The C9orf72 and GRN prodromal groups were also significantly older than their asymptomatic counterparts (p = 0.025 and p = 0.012, respectively). The C9orf72 asymptomatic group was significantly older than the MAPT asymptomatic group (p = 0.016), and the GRN symptomatic group was significantly older than the MAPT symptomatic group (p = 0.040).

Differences in sex were found within the groups. The C9orf72 and MAPT symptomatic groups consisted of significantly more men than the control group ($X^2(1) = 9.8, p = 0.002$; $X^2(1) = 4.8, p = 0.028$, respectively), and there were more men in the C9orf72 symptomatic group than the other C9orf72 groups (asymptomatic: $X^2(1) = 8.8, p = 0.003$; prodromal: $X^2(1) = 6.4, p = 0.012$). This was also the case for the MAPT symptomatic carriers (asymptomatic: $X^2(1) = 4.7, p = 0.030$; prodromal: $X^2(1) = 5.1, p = 0.023$).

When investigating education across the groups, the GRN symptomatic individuals had significantly lower levels of education than the control group (p < 0.001), GRN asymptomatic group (p < 0.01), and C9orf72 (p = 0.035) and MAPT (p = 0.030) symptomatic groups. The C9orf72 symptomatic group had a lower level of education than the control group (p = 0.010) and C9orf72 asymptomatic group (p = 0.027).

## Executive Function Tasks

The following executive function tasks were included within the GENFI neuropsychology battery: the Wechsler Adult Intelligence Scale-Revised Digit Symbol substitution task (DSST),¹⁵ Weschler Memory Scale-Revised (WMS-R) Digit Span Backwards (DSB),¹⁶ Delis-Kaplan Executive Function System Color-Word Interference Test,¹⁷ and Trail Making Test (TMT) Parts A and B (TMT A and TMT B).

## Magnetic Resonance Image Acquisition

T1-weighted MRI volumetric brain scans were performed on 703 participants as per the GENFI protocol.¹⁰ 55 images were removed because they either failed the quality control check for motion and scanner artifacts or an abnormal finding was found in the form of significant vascular disease or other structural brain lesions. Subsequently, 648 scans were included in the analysis: 217 controls, 184 C9orf72 expansion carriers, 172 GRN mutation carriers, and 75 MAPT mutation carriers.

## Statistical Analysis

### Healthy Controls

To explore the normative performance on the tasks in the control group, percentile scores and cumulative frequencies were calculated for each, and a lower 5th percentile was generated to indicate an abnormal score. t Tests were performed to assess any differences on each of the tasks that were normally distributed and Mann-Whitney U tests for those that

were not. Correlations between task performance and both age and education were calculated using Pearson correlation for normally distributed data and Spearman rank correlation for those that were not. Linear regressions were performed to assess the impact of language (i.e., the language spoken by the participant) on each of the tasks within the control group. Pairwise post hoc comparisons were performed to assess the difference between the groups if the overall model was significant. For data that were not normally distributed, bootstrapping with 2000 replications was used. All analyses were performed using Stata/IC (version 14.2).

### Mutation Carriers

Multiple linear regressions were performed to assess performance on the tasks in each of the groups. Age, sex, education, and language were included in the models as covariates. Post hoc pairwise comparisons were calculated to assess the differences between the groups. For data that were not normal, bootstrapping with 2000 replications was used and the 95% bootstrapped CIs reported.

A Pearson correlational analysis was conducted on each of the tasks to measure the association with disease severity and task performance using the CDR plus NACC FTLD SB score. For data that were not normally distributed, Spearman rank correlation (rho) was used instead.

To assess the impact of phenotype in the symptomatic groups, participants were grouped into bvFTD,¹⁸ PPA,¹³ and FTD-ALS.¹⁴ Other phenotypes were not included in the analysis because of their low numbers. A linear regression was performed and included age, sex, and education as covariates in the model. For data that were not normally distributed, the model was bootstrapped (2000 replications).

## Structural Brain Imaging Analysis

An automated atlas segmentation propagation and label fusion strategy—Geodesic Information Flow (GIF)¹⁹—was used on the T1-weighted volumetric MRI scans to generate brain volumes of regions of interest known to be involved in executive function³,²⁰: the orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DLPFC), ventromedial prefrontal cortex, parietal lobe, and striatum. All the individual regional volumes were expressed as a percentage of total intracranial volume, as computed with SPM12 (Statistical Parametric Mapping, Welcome Trust Centre for Neuroimaging, London, United Kingdom) running under Matlab R2014b (Mathworks, USA:²¹). Using RStudio (version 1.2.1335, 2009–2019), partial correlations were performed to investigate the association between the brain regions and score on each of the executive function tasks, while taking into consideration disease severity as measured using the CDR plus NACC FTLD SB score, as well as the age of the participant.

## Data Availability

The data sets generated and/or analyzed during this study are not publicly available because the conditions of our ethical
<table><caption>**Table 1** Demographic Information and Task Performance for Participants Split by Genetic Group and CDR Plus NACC FTLD Global Score</caption><thead><tr><th>Genetic Group</th><th>Controls</th><th>C9orf72</th><th>GRN</th><th>MAPT</th></tr></thead><tbody><tr><td>CDR with NACC FTLD global</td><td>0</td><td>0</td><td>0.5</td><td>1+</td></tr><tr><td>N</td><td>247</td><td>110</td><td>36</td><td>68</td></tr><tr><td>% Male</td><td>43</td><td>42</td><td>39</td><td>65</td></tr><tr><td>Age at visit</td><td>45.3 (12.9)</td><td>44.2 (11.7)</td><td>49.3 (11.4)</td><td>62.2 (8.8)</td></tr><tr><td>Education</td><td>14.4 (3.3)</td><td>14.3 (3.0)</td><td>14.1 (2.5)</td><td>13.2 (3.7)</td></tr><tr><td>MMSE</td><td>29.3 (1.1)</td><td>29.2 (1.2)</td><td>28.6 (2.0)</td><td>23.7 (6.1)</td></tr><tr><td>CDR with NACC FTLD-SB</td><td>0.0 (0.0)</td><td>0.0 (0.0)</td><td>1.2 (0.8)</td><td>10.7 (5.5)</td></tr><tr><td>DSST (s)</td><td>58.5 (13.9)</td><td><strong>53.9</strong><br>(<strong>12.6</strong>)</td><td>52.5 (15.8)</td><td><strong>25.8</strong> (13.2)</td></tr><tr><td>DSB (n)</td><td>6.7 (2.2)</td><td>6.7 (2.2)</td><td>6.9 (2.6)</td><td><strong>3.6</strong> (2.2)</td></tr><tr><td>D-KEFS: Color (s)</td><td>28.6 (5.9)</td><td><strong>31.5</strong> (8.3)</td><td><strong>33.3</strong><br>(<strong>10.3</strong>)</td><td><strong>60.9</strong> (23.8)</td></tr><tr><td>D-KEFS: Word (s)</td><td>22.7 (5.6)</td><td>24.3 (7.9)</td><td>25.2 (7.2)</td><td><strong>38.8</strong> (19.4)</td></tr><tr><td>D-KEFS: Ink (s)</td><td>49.5 (12.1)</td><td><strong>59.4</strong><br>(<strong>25.1</strong>)</td><td><strong>61.6</strong><br>(<strong>17.7</strong>)</td><td><strong>129.0</strong><br>(<strong>60.8</strong>)</td></tr><tr><td>TMT A (s)</td><td>27.0 (12.3)</td><td><strong>30.2</strong><br>(<strong>12.6</strong>)</td><td>33.0 (18.2)</td><td><strong>67.2</strong> (37.8)</td></tr><tr><td>TMT B (s)</td><td>62.5 (31.4)</td><td><strong>75.4</strong><br>(<strong>48.1</strong>)</td><td><strong>91.9</strong><br>(<strong>68.2</strong>)</td><td><strong>196.2</strong><br>(<strong>88.8</strong>)</td></tr></tbody></table>

Means and SDs (in parentheses) are given. Results in bold show significant differences between the mutation carrier groups and controls.

CDR with NACC FTLD-SB: CDR. Dementia Staging Instrument with the National Alzheimer's Coordinating Center Frontotemporal Lobar Degeneration component Sum-of-Boxes score; DSST: Wechsler Adult Intelligence Scale-Revised Digit Symbol substitution task; DSB: Weschler Memory Scale-Revised Digit Span Backwards; D-KEFS: Delis Kaplan Executive Function System (Color-Word Interference Test—Color = Color naming, Word = Word naming, Ink = Ink color naming); TMT: Trail Making Test (A = Part A; B = Part B).

approval do not permit public archiving of individual anonymized data, but are available from the corresponding author on reasonable request.

# Results

## Healthy Controls

### Age

Increasing age correlated with worse performance on the DSST ($r = -0.4, p < 0.001$); D-KEFS: Color ($Rho = 0.3, p < 0.001$); D-KEFS: Word ($Rho = 0.1, p = 0.049$); D-KEFS: Ink ($Rho = 0.3, p < 0.001$); TMT A ($Rho = 0.4, p < 0.001$); and TMT B ($Rho = 0.3, p < 0.001$) but not the DSB. Performance across each decade is summarized in eTable 2.

### Sex

There was a significant effect of sex on the DSST ($T = 3.9, p < 0.001$, with women scoring higher) and TMT B ($U = -2.1, p = 0.035$, with women performing quicker). No other differences were found between men and women on any of the other tasks (eTable 3).

### Education

Across all the tasks, there was a significant influence of education on task performance in the control group, with higher levels of education associated with better task performance (DSST: $r = 0.4, p < 0.001$; DSB: $r = 0.3, p < 0.001$; D-KEFS: Color: $Rho = -0.2, p = 0.004$, D-KEFS: Word: $Rho = -0.2, p = 0.004$; D-KEFS: Ink: $Rho = -0.2, p < 0.001$; TMT A: $Rho = -0.1, p = 0.028$; TMT B: $Rho = -0.3, p < 0.001$).

### Language

Only the D-KEFS: Word and Ink tasks saw an overall influence of language on performance (D-KEFS: Word: $\chi^2(7) = 20.2, p = 0.005$; D-KEFS: Ink: $\chi^2(7) = 15.4, p = 0.030, r^2 = 0.056$) (eTable 4).

### Percentile Scores

Normative percentile scores were calculated on each of the tasks using the control data (eTable 5). A score less than 38 on the DSST and less than 3 on the DSB task would be considered abnormal (<5th percentile). If it took more than 40 seconds, 31 seconds, and 71 seconds to complete the D-KEFS: Color, Word, and Ink tasks, respectively, and more
than 48 seconds and 125 seconds for the TMT A and B tasks, respectively, the participant's performance would also be considered abnormal (<5th percentile).

# Mutation Carriers

## Group Comparisons

The means and SDs for the scores on the executive function tasks in each of the mutation carrier groups are listed in Table 1 and Figure. The differences between the groups are presented in eTables 6–12 and eFigure 1.

All 3 symptomatic mutation carrier groups were significantly impaired on all executive function tasks compared with controls, as well as when compared with their asymptomatic and prodromal genetic groups, except for on the DSB task where no differences were seen between the different MAPT groups.

The C9orf72 prodromal group was significantly impaired in comparison with the controls on the D-KEFS: Color and Ink tasks and TMT B, with a trend toward a poorer performance on the DSST (p = 0.066) and TMT A (p = 0.099). No differences were seen between the prodromal GRN or MAPT groups and controls on any of the tasks.

The C9orf72 asymptomatic group was significantly impaired compared with controls on all tasks, except for the DSB and D-KEFS: Word task. No differences were seen between the asymptomatic GRN or MAPT groups and controls on any of the tasks.

When comparing between the genetic groups at each disease severity stage, for symptomatic mutation carriers, the C9orf72 group performed significantly worse than the MAPT group on the DSB, DSST, and D-KEFS: Color and Ink tasks while the GRN symptomatic group performed worse than the MAPT symptomatic carriers on the DSB, DSST, and TMT A tasks; for prodromal mutation carriers, the C9orf72 group performed significantly worse than the MAPT group on the D-KEFS: Ink task; for asymptomatic mutation carriers, the C9orf72 group performed significantly worse than the other 2 groups on the DSST, D-KEFS: Ink, and TMT B tasks as well as worse than the GRN group on the D-KEFS: Color and TMT A tasks.

## Correlation With Disease Severity

All tasks significantly correlated with disease severity as measured using the CDR plus NACC FTLD-SB score in each of the genetic groups (Table 2).

## Phenotypic Analysis

When compared with controls, all phenotypic groups were significantly impaired on all tasks of executive function (all *p* < 0.001, except for the FTD-ALS group on the TMT A where *p* = 0.016). The PPA group scored significantly worse on the DSB task than the bvFTD group (adjusted mean difference [AMD] = -1.5, *p* = 0.011) and FTD-ALS group (AMD = -1.5, *p* = 0.012).

and significantly worse than the bvFTD group on the TMT B (bvFTD: AMD = 56.2, *p* = 0.011).

## Imaging Analysis

The partial correlations between task score and regional brain volume for each group are presented in eTable 13. For C9orf72 expansion carriers, correlations between task performance and regional brain volumes were seen with the DLPFC (DSST, DSB, and D-KEFS: Ink) and parietal cortex (DSST and TMT B) and with the striatum on the DSST. For GRN mutation carriers, fewer significant correlations were seen: right OFC with Trail Making Test Part B and DLPFC with D-KEFS: Color and Ink tasks. For MAPT mutation carriers, significant correlations were seen mainly with the DLPFC (D-KEFS: Color and Ink and TMT B) and striatum (DSST and D-KEFS: Color), as well as the left orbitfrontal cortex with D-KEFS: Ink.

# Discussion

In this study, the executive function abilities of a large cohort of individuals with genetic FTD were comprehensively assessed. It demonstrates that executive dysfunction can be present in both individuals who are symptomatic of familial FTD across all 3 genetic groups, as well as in those with a C9orf72 expansion who are asymptomatic and prodromal. Neither the GRN nor the MAPT asymptomatic or prodromal mutation carriers showed significant differences on the executive function tasks compared with the control group suggesting that executive function changes occur later in the disorder than in C9orf72-associated FTD.

The control group data indicate that of all the demographic covariates, age and education were most associated with executive function score. This was to be expected because many studies have found that older age and lower education leads to greater impairment on many of the executive function tasks.²²⁻²⁶ Only 2 tasks showed an effect of sex on score, which is supported by previous work, with women performing better on the DSST, while men achieved better scores on the TMT B task.²⁷⁻²⁹ Finally, language did also have an influence on task performance, affecting those tests that had an element of language to them: the D-KEFS: Word and Ink tasks. This may well be due to the slight variation in the length of words in the different languages used in the study, which may take longer to pronounce. Further to note is the differences in sample sizes across the control groups, which may also have influenced the findings in this study.

As expected, and in line with the previous literature, the symptomatic individuals displayed executive dysfunction irrespective of genetic group.⁴⁻⁹ This was the case when they were compared with both the control group and their asymptomatic and prodromal counterparts. The only exception to this was on the DSB task where the symptomatic MAPT mutation carriers were not significantly impaired.
<figure><img src="image_2.png" /><figcaption>**Figure** Performance of Mutation Carrier Groups on Each Executive Function Task Expressed as a Z-Score to Allow Comparison Across Tasks</figcaption></figure>

DSST = Wechsler Adult Intelligence Scale-Revised Digit Symbol substitution task; DSB: Wechsler Memory Scale-Revised Digit Span Backwards; D-KEFS: Delis Kaplan Executive Function System (Color-Word Interference Test - Color = Color naming, Word = Word naming, Ink = Ink color naming); TMT: Trail Making Test (A = Part A; B = Part B).

The asymptomatic and prodromal C9orf72 groups displayed early executive dysfunction in tasks assessing inhibition, set switching/cognitive flexibility, processing speed and general cognitive function, while working memory abilities were less affected early on. This is in line with a recent study that produced a cognitive composite for each of the genetic mutations that displayed widespread cognitive dysfunction in C9orf72 mutation carriers, including deficits in executive function.³⁰ Although 2 of the tasks in the prodromal group did not show a significant difference compared with controls despite this being seen in the asymptomatic group, it is likely that this was due to the smaller sample size in the prodromal

group and thus was not sufficiently powered to detect a deficit. Differences at these early stages are small and are likely to represent changes in some individuals but not others; future work will focus on investigating this further.

Neither the GRN nor MAPT asymptomatic or prodromal carriers displayed any early executive dysfunction when compared with the controls. For the GRN mutation carriers, this is consistent with previous work that has demonstrated a rapid decline in symptoms in GRN mutation carriers during the first year of diagnosis compared with C9orf72 and MAPT mutation carriers,⁸ with a rapid increase in atrophy rates after

<table><caption>**Table 2** Correlations of Each Task With Disease Severity as Measured Using the CDR Plus NACC FTLD-SB (for the 3 Mutation Carrier Groups)</caption><thead><tr><th>Task</th><th>Correlation coefficient</th><th>C9orf72</th><th>GRN</th><th>MAPT</th></tr></thead><tbody><tr><td>DSST</td><td>r</td><td>-0.69</td><td>&lt;0.001</td><td>-0.68</td><td>&lt;0.001</td><td>-0.62</td><td>&lt;0.001</td></tr><tr><td>DSB</td><td>r</td><td>-0.60</td><td>&lt;0.001</td><td>-0.50</td><td>&lt;0.001</td><td>-0.30</td><td>&lt;0.001</td></tr><tr><td>D-KEFS: Color</td><td>Rho</td><td>0.60</td><td>&lt;0.001</td><td>0.50</td><td>&lt;0.001</td><td>0.50</td><td>&lt;0.001</td></tr><tr><td>D-KEFS: Word</td><td>Rho</td><td>0.50</td><td>&lt;0.001</td><td>0.30</td><td>&lt;0.001</td><td>0.40</td><td>0.001</td></tr><tr><td>D-KEFS: Ink</td><td>Rho</td><td>0.60</td><td>&lt;0.001</td><td>0.50</td><td>&lt;0.001</td><td>0.50</td><td>0.005</td></tr><tr><td>TMT A</td><td>Rho</td><td>0.60</td><td>&lt;0.001</td><td>0.50</td><td>&lt;0.001</td><td>0.60</td><td>&lt;0.001</td></tr><tr><td>TMT B</td><td>Rho</td><td>0.60</td><td>&lt;0.001</td><td>0.50</td><td>&lt;0.001</td><td>0.60</td><td>&lt;0.001</td></tr></tbody></table>

CDR with NACC FTLD-SB: CDR. Dementia Staging Instrument with the National Alzheimer's Coordinating Center Frontotemporal Lobar Degeneration component Sum-of-Boxes score; DSST: Wechsler Adult Intelligence Scale-Revised Digit Symbol substitution task; DSB: Wechsler Memory Scale-Revised Digit Span Backwards; D-KEFS: Delis Kaplan Executive Function System (Color-Word Interference Test—Color = Color naming, Word = Word naming, Ink = Ink color naming); TMT: Trail Making Test (A = Part A; B = Part B). The DSST and DSB were normally distributed and so Pearson correlations were performed. For the remainder, Spearman rank correlations were performed.
## Author Contributions

L.L. Russell: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data. A. Bouzigues: major role in the acquisition of data. R.S. Convery: major role in the acquisition of data. P.H. Foster: major role in the acquisition of data. E. Ferry-Bolder: major role in the acquisition of data. D.M. Cash: drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data. J.C. Van Swieten: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. L.C. Jiskoot: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. H. Seelaar: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. F. Moreno: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. R. Sánchez-Valle: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. R. Laforce, Jr: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. C. Graff: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. M. Masellis: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. M.C. Tartaglia: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. J.B. Rowe: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. B. Borroni: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. E. Finger: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. M. Synofzik: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. D. Galimberti: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. R. Vandenberghe: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. A. de Mendonça: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. C. Butler: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data;

data; study concept or design. A. Gerhard: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. S. Ducharme: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. I. Le Ber: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. I. Santana: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. F. Pasquier: major role in the acquisition of data. J. Levin: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. S. Sorbi: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. M. Otto: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. J.D. Rohrer: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; analysis or interpretation of data.

## Study Funding

The Dementia Research Centre is supported by Alzheimer's Research UK, Alzheimer's Society, Brain Research UK, and The Wolfson Foundation. This work was supported by the National Institute for Health and Care Research University College London/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre Clinical Research Facility, and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the United Kingdom Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. J.D. Rohrer is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). This work was also supported by the MRC UK GENFI grant (MR/M023664/1), the Bluefield Project and the JPND GENFI-PROX grant (2019-02248). Several authors of this publication (J.C.V.S., M.S., R.S.V., A.D., M.O., R.V., J.D.R.) are members of the European Reference Network for Rare Neurologic Diseases-Project ID No 739510. R.S. Convery/ C. Graff are supported by a FTD Research Studentships in memory of David Blechner funded through The National Brain Appeal (RCN 290173). J.B. Rowe is supported by the Wellcome Trust (103838), the Medical Research Council (MC_UU_00030/14; MR/T033371/1) and the NIHR Cambridge Biomedical Research Centre (NIHR203312). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The GIF template database includes volumetric MRI scans from the University College London GENFI study
(genfi.org.uk) which is funded by the Medical Research Council UK GENFI grant (MR/M023664/1). The GIF template database includes volumetric MRI scans from the University College London GENFI study (genfi.org.uk) which is funded by the Medical Research Council UK GENFI grant (MR/M023664/1).

## Disclosure

The authors report no relevant disclosures. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/NG.

## Publication History

Previously published in medRxiv (doi: 10.1101/2024.05.16.24307390). Received by Neurology® Genetics October 15, 2024. Accepted in final form March 7, 2025. Submitted and externally peer reviewed. The handling editor was Editor Stefan M. Pulst, MD, Dr med, FAAN.

## References

1. Warren JD, Rohrer JD, Rossor MN. Clinical review. frontotemporal dementia. BMJ. 2013;347:f4827. doi:10.1136/bmj.f4827

2. Woollacott IO, Rohrer JD. The clinical spectrum of sporadic and familial forms of frontotemporal dementia. J Neurochem. 2016;138(suppl 1):6-31. doi:10.1111/jnc.13654

3. LaCaille L, Patino-Fernandez AM, Monaco J, et al. Executive function. Encyclopedia Behav Med. 2013:725-727. doi:10.1007/978-1-4419-1005-9_1126

4. Lulé DE, Müller HP, Finsel J, et al. Deficits in verbal fluency in presymptomatic C9orf72 mutation gene carriers-a developmental disorder. J Neurol Neurosurg Psychiatry. 2020;91(11):1195-1200. doi:10.1136/jnnp-2020-323671

5. Staffaroni AM, Bajorek L, Casalletto KB, et al. Assessment of executive function declines in presymptomatic and mildly symptomatic familial frontotemporal dementia: NIH-EXAMINER as a potential clinical trial endpoint. Alzheimers Dement. 2020;16(1):11-21. doi:10.1016/j.jalz.2019.01.012

6. Cheran G, Wu L, Lee S, et al. Cognitive indicators of preclinical behavioral variant frontotemporal dementia in MAPT carriers. J Int Neuropsychol Soc. 2019;25(2): 184-194. doi:10.1017/S1355617718001005

7. 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. doi:10.1007/s00415-018-8850-7

8. Poos JM, Jiskoot LC, Leijdesdorff SMJ, et al. Cognitive profiles discriminate between genetic variants of behavioral frontotemporal dementia. J Neurol. 2020;267(6): 1603-1612. doi:10.1007/s00415-020-09738-y

9. Hallam BJ, Jacova C, Hsiung GYR, et al. Early neuropsychological characteristics of progranulin mutation carriers. J Int Neuropsychol Soc. 2014;20(7):694-703. doi: 10.1017/S1355617714000551

10. 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. doi:10.1016/S1474-4422(14)70324-2

11. Poos JM, MacDougall A, van den Berg E, et al. Longitudinal cognitive changes in genetic frontotemporal dementia within the GENFI cohort. Neurology. 2022;99(3): e281-e295. doi:10.1212/WNL.0000000000200384

12. Rascovsky K, Hodges JR, Knopman D, et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain. 2011;134(Pt 9): 2456-2477. doi:10.1093/brain/awr179

13. Gorno-Tempini M, Hillis AE, Weintraub S, et al. Classification of primary progressive aphasia and its variants. Neurology. 2011;76(11):1006-1014. doi:10.1212/WNL.0b013e318211036e

14. Strong MJ, Abrahams S, Goldstein LH, et al. Amyotrophic lateral sclerosis-frontotemporal spectrum disorder (ALS-FTSD): revised diagnostic criteria. Amyotroph Lateral Scler Frontotemporal Degener. 2017;18(3-4):153-174. doi:10.1080/21678421.2016.1267768

15. Wechsler D. Wechsler Adult Intelligence Scale-Revised. Psychological Corporation; 1981.

16. Wechsler D. Wechsler Adult Intelligence Scale, 3rd ed San Antonio. The Psychological Corporation; 1997.

17. Delis DC, Kaplan E, Kramer JH, Delis-Kaplan executive function system. 2001.

18. Rascovsky K, Grossman M. Clinical diagnostic criteria and classification controversies in frontotemporal lobar degeneration. Int Rev Psychiatry. 2013;25(2):145-158. doi:10.3109/09540261.2013.7633441

19. 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. doi:10.1109/TMI.2015.2418298

20. Cash DM, Bocchetta M, Thomas DL, et al. Patterns of gray matter atrophy in genetic frontotemporal dementia: results from the GENFI study. Neurobiol Aging. 2018;62: 191-196. doi:10.1016/j.neurobiolaging.2017.10.008

21. 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. doi:10.1016/j.neuroimage.2014.09.034

22. Choi HJ, Lee DY, Seo EH, et al. A normative study of the digit span in an educationally diverse elderly population. Psychiatry Invest. 2014;11(1):39-43. doi:10.4306/pi.2014.11.139

23. Gaertner B, Wagner M, Luck T, Buttery AK, Fuchs J, Busch MA. Normative data for the digit symbol substitution test in a population-based sample aged 65-79 years: results from the German health interview and examination survey for adults (DEGS1). Clin Neuropsychol. 2018;32(sup1):114-132. doi:10.1080/13854046.2018.1484168

24. Van der Elst W, Van Boxtel MPJ, Van Breukelen GJP, Jolles J. The Stroop color-word test: influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment. 2006;13(1):62-79. doi:10.1177/1073191105283427

25. Tombaugh TN. Trail Making Test A and B: normative data stratified by age and education. Arch Clin Neuropsychol. 2004;19(2):203-214. doi:10.1016/S0887-6177(03)00039-8

26. Mathuranath P, George A, Cherian PJ, Alexander A, Sarma SG, Sarma PS. Effects of age, education and gender on verbal fluency. J Clin Exp Neuropsychol. 2003;25(8): 1057-1064. doi:10.1076/jcen.25.8.1057.16736

27. Majeres RL. Sex differences in symbol-digit substitution and speeded matching. Intelligence. 1983;7(4):313-327. doi:10.1016/0160-2896(83)90007-7

28. Kertzman S, Ben-Nahum Z, Gotzlav I, Grinspan H, Birger M, Kotler M. Digit symbol substitution test performance: sex differences in a Hebrew-readers' health population. Percept Mot Skills. 2006;103(1):121-130. doi:10.2466/pms.103.1.121-130

29. Scheuringer A, Wittig R, Pletzer B. Sex differences in verbal fluency: the role of strategies and instructions. Cogn Process. 2017;18(4):407-417. doi:10.1007/s10339-017-0801-1

30. Poos JM, Moore KM, Nicholas J, et al. Cognitive composites for genetic frontotemporal dementia: GENFI-Cog. Alzheimers Res Ther. 2022;14(1):10. doi:10.1186/s13195-022-00958-0

31. Staffaroni AM, Goh SYM, Cubigo Y, et al. Rates of brain atrophy across disease stages in familial frontotemporal dementia associated with MAPT, GRN, and C9orf72 pathogenic variants. JAMA Netw Open. 2020;3(10):e2022847. doi: 10.1001/jamanetworkopen.2020.22847

32. Rojas JC, Wang P, Staffaroni AM, et al. Plasma neurofilament light for prediction of disease progression in familial frontotemporal lobar degeneration. Neurology. 2021; 96(18):e2296-e2312. doi:10.1212/WNL.0000000000011848

33. Bocchetta M, Todd EG, Peakman G, et al. Differential early subcortical involvement in genetic FTD within the GENFI cohort. Neuroimage: Clin. 2021;30:102646. doi: 10.1016/j.nicl.2021.102646

34. Moore K, Convery R, Bocchetta M, et al. A modified Camel and Cactus Test detects presymptomatic semantic impairment in genetic frontotemporal dementia within the GENFI cohort. Appl Neuropsychol Adult. 2020;29:112-119. doi: 10.1080/23279095.2020.1716357

35. Pickering-Brown SM, Rollinson S, Du Plessis D, et al. Frequency and clinical characteristics of progranulin mutation carriers in the Manchester frontotemporal lobar degeneration cohort: comparison with patients with MAPT and no known mutations. Brain. 2008;131(Pt 3):721-731. doi:10.1093/brain/awm331

36. Huey ED, Goveia EN, Paviol S, et al. Executive dysfunction in frontotemporal dementia and corticobasal syndrome. Neurology. 2009;72(5):453-459. doi:10.1212/01.wnl.0000341781.3916426

37. Tartaglia MC, Zhang Y, Racine C, et al. Executive dysfunction in frontotemporal dementia is related to abnormalities in frontal white matter tracts. J Neurol. 2012; 259(6):1071-1080. doi:10.1007/s00415-011-6300-x

38. Harciarek M, Cosentino S. Language, executive function and social cognition in the diagnosis of frontotemporal dementia syndromes. Int Rev Psychiatry. 2013;25(2): 178-196. doi:10.3109/09540261.2013.763340

39. Possin KL, Feigenbaum D, Rankin KP, et al. Dissociable executive functions in behavioral variant frontotemporal and Alzheimer dementias. Neurology. 2013;80(24): 2180-2185. doi:10.1212/WNL.0b013e318296e940

40. Leslie FV, Foxe D, Daveson N, Flannagan E, Hodges JR, Piguet O. FRONTIER Executive Screen: a brief executive battery to differentiate frontotemporal dementia and Alzheimer's disease. J Neurol Neurosurg Psychiatry. 2016;87(8):831-835. doi: 10.1136/jnnp-2015-311917

41. Ramanan S, Bertoux M, Flanagan E, et al. Longitudinal executive function and episodic memory profiles in behavioral-variant frontotemporal dementia and Alzheimer's disease. J Int Neuropsychol Soc. 2017;23(1):34-43. doi:10.1017/S1355617716000837

42. Moheb N, Mendez MF, Kremen SA, Teng E. Executive dysfunction and behavioral symptoms are associated with deficits in instrumental activities of daily living in frontotemporal dementia. Dement Geriatr Cogn Disord. 2017;43(1-2):89-99. doi: 10.1159/000455119

43. van den Berg E, Jiskoot LC, Grosveld JMH, van Swieten JC, Papma JM. Qualitative assessment of verbal fluency performance in frontotemporal dementia. Dement Geriatr Cogn Disord. 2017;44(1-2):35-44. doi:10.1159/000477538

44. Staffaroni AM, Asken BM, Casalletto KB, et al. Development and validation of the Uniform Data Set (v3.0) executive function composite score (UDS3-EF). Alzheimers Dement. 2021;17(4):574-583. doi:10.1002/alz.12214

45. Dehaene S. Inside the letterbox: how literacy transforms the human brain. Cerebrum: the Dana forum on brain science. Cerebrum; 2013.

46. Ruksenaite J, Volkmer A, Jiang J, et al. Primary progressive aphasia: toward a pathophysiological synthesis. Curr Neurol Neurosci Rep. 2021;21(3):7-12. doi:10.1007/s11910-021-01097-z
47. Taylor LJ, Brown RG, Tsermentseli S, et al. Is language impairment more common than executive dysfunction in amyotrophic lateral sclerosis? *J Neurol Neurosurg Psychiatry*. 2013;84(5):494-498. doi:10.1136/jnnp-2012-303526

48. Bettcher BM, Sturm VE. Neuropsychological assessment of primary progressive aphasia (PPA). *Perspect Neurophysiol Neurogenic Speech Lang Disord*. 2014;24(4): 128-136. doi:10.1044/nnsld24.4.128

49. Peakman G, Russell LL, Convery RS, et al. Comparison of clinical rating scales in genetic frontotemporal dementia within the GENFI cohort. *J Neurol Neurosurg Psychiatry*. 2022;93(2):158-168. doi:10.1136/jnnp-2021-326868

50. Nelson A, Russell LL, Peakman G, Greaves CV, Convery RS, Rohrer JD. The Cambridge Behavioural Inventory (revised) detects early behavioural and functional impairment in genetic frontotemporal dementia within the GENFI cohort. *Alzheimers Dement*. 2021;17(S6):e051302. doi:10.1002/alz.051302

51. Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. *Annu Rev Neurosci*. 2001;24(1):167-202. doi:10.1146/annurev.neuro.24.1.167

52. Dubreuil-Vall L, Chau P, Ruffini G, Widge AS, Camprodon JA. tDCS to the left DLPFC modulates cognitive and physiological correlates of executive function in a state-dependent manner. *Brain Stimulation*. 2019;12(6):1456-1463. doi:10.1016/j.brs.2019.06.006

53. Ptak R, Schnider A. Disorganised memory after right dorsolateral prefrontal damage. *Neurocase*. 2004;10(1):S2-59. doi:10.1080/13554790490960495

54. Hartung SL, Mandonnet E, de Witt Hamer P, et al. Impaired set-shifting from dorsal stream disconnection: insights from a european series of right parietal lower-grade glioma resection. *Cancers*. 2021;13(13):3337. doi:10.3390/cancers13133337

55. Allen MD, Owens TE, Fong AK, Richards DR. A functional neuroimaging analysis of the Trail Making Test-B: implications for clinical application. *Behav Neurol*. 2011; 24(2):159-171. doi:10.3233/BEN-2011-0278

56. Talwar N, Churchill NW, Hird MA, Tam F, Graham SJ, Schweizer TA. Functional magnetic resonance imaging of the trail-making test in older adults. *PLoS One*. 2020; 15(S):e0232469. doi:10.1371/journal.pone.0232469

57. Hausman HK, Hardcastle C, Albizu A, et al. Cingulo-opercular and frontoparietal control network connectivity and executive functioning in older adults. *GeroScience*. 2022;44(2):847-866. doi:10.1007/s11357-021-00503-1

58. Rypma B, Berger JS, Prabhakaran V, et al. Neural correlates of cognitive efficiency. *Neuroimage*. 2006;33(3):969-979. doi:10.1016/j.neuroimage.2006.05.065

59. Faber J, Wilde EA, Hanten G, et al. Ten-year outcome of early childhood traumatic brain injury: diffusion tensor imaging of the ventral striatum in relation to executive functioning. *Brain Inj*. 2016;30(13-14):1635-1641. doi:10.1080/02699052.2016.1199910

60. Tian F, Diao W, Yang X, et al. Failure of activation of striatum during the performance of executive function tasks in adult patients with bipolar disorder. *Psychol Med*. 2020; 50(4):653-665. doi:10.1017/S0033291719000473