ArticlePDF Available

Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia

Authors:

Abstract

Introduction: The presymptomatic phase of neurodegenerative disease can last many years, with sustained cognitive function despite progressive atrophy. We investigate this phenomenon in familial frontotemporal dementia (FTD). Methods: We studied 121 presymptomatic FTD mutation carriers and 134 family members without mutations, using multivariate data-driven approach to link cognitive performance with both structural and functional magnetic resonance imaging. Atrophy and brain network connectivity were compared between groups, in relation to the time from expected symptom onset. Results: There were group differences in brain structure and function, in the absence of differences in cognitive performance. Specifically, we identified behaviorally relevant structural and functional network differences. Structure-function relationships were similar in both groups, but coupling between functional connectivity and cognition was stronger for carriers than for non-carriers, and increased with proximity to the expected onset of disease. Discussion: Our findings suggest that the maintenance of functional network connectivity enables carriers to maintain cognitive performance.
Received: 25 September 2019 Revised: 7 September 2020 Accepted: 12 September 2020
DOI: 10.1002/alz.12209
RESEARCH ARTICLE
Brain functional network integrity sustains cognitive function
despite atrophy in presymptomatic genetic frontotemporal
dementia
Kamen A. Tsvetanov1,2,Stefano Gazzina1,3,P. Simon Jones1John van Swieten3
Barbara Borroni4Raquel Sanchez-Valle5Fermin Moreno6,7Robert Laforce Jr8
Caroline Graff9,10 Matthis Synofzik11,12 Daniela Galimberti13,14 Mario Masellis15
Maria Carmela Tartaglia16 Elizabeth Finger17 Rik Vandenberghe18,19 Alexandre de
Mendonça20 Fabrizio Tagliavini21 Isabel Santana22,23,24 Simon Ducharme25,26
Chris Butler27 Alexander Gerhard28,29 Adrian Danek30 Johannes Levin31
Markus Otto32 Giovanni Frisoni33,34 Roberta Ghidoni35 Sandro Sorbi36,37
Jonathan D. Rohrer38 James B. Rowe1,2the Genetic FTD Initiative, GENFI1
1Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
2Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, UK
3Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
4Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
5Alzheimer’s disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d’Investigacións Biomèdiques August Pi I Sunyer, University of
Barcelona, Barcelona, Spain
6Cognitive Disorders Unit, Department of Neurology, Hospital Universitario Donostia, San Sebastian, Gipuzkoa, Spain
7Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain
8Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, Québec, Canada
9Department NVS, Center for Alzheimer Research, Division of Neurogenetics, Karolinska Institutet, Stockholm, Sweden
10 Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital-Solna, Stockholm, Sweden
11 Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research & Center of Neurology, University of Tübingen, Germany
12 German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
13 Centro Dino Ferrari, University of Milan, Milan, Italy
14 Fondazione IRCSS Ca’ Granda, Ospedale Maggiore Policlinico,Neurodegenerative Diseases Unit, Milan, Italy
15 LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, Department of Medicine (Neurology), University of Toronto, Toronto, Ontario,
Canada
16 Tanz Centre for Research in Neurodegenerative Disease, Toronto Western Hospital, Toronto, Ontario, Canada
17 Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada
18 Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven,Belgium
19 Neurology Service, University Hospitals Leuven, Leuven, Belgium
20 Laboratory of Neurosciences, Faculty of Medicine, Institute of Molecular MedicineUniversity of Lisbon, Lisbon, Portugal
21 Fondazione Istituto di Ricovero e Cura a CarattereScientifico Istituto Neurologico Carlo Besta, Milan, Italy
22 Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2020 The Authors. Alzheimer’s & Dementia published by Wiley Periodicals, Inc. on behalf of Alzheimer’s Association
Alzheimer’s Dement. 2020;1–15. wileyonlinelibrary.com/journal/alz 1
2TSVETANOV ETAL.
23 Faculty of Medicine, University of Coimbra, Coimbra, Portugal
24 Centre of Neurosciences and Cell biology, Universidade de Coimbra, Coimbra, Portugal
25 Department of Psychiatry, McGill University Health CentreMcGill University, Montreal, Canada
26 McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
27 Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK
28 Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
29 Departments of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Germany
30 Neurologische Klinik und Poliklinik, German Center for Neurodegenerative Diseases (DZNE), Ludwig-Maximilians-Universität,Munich, Munich, Germany
31 German Center for Neurodegenerative Diseases, Munich Cluster for Systems Neurology (Synergy), Munich, Germany
32 Department of Neurology, University Hospital Ulm, Ulm, Germany
33 Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Centro San Giovannidi Dio Fatebenefratelli, Brescia, Italy
34 Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
35 Molecular Markers Laboratory, IRCCS Istituto Centro San Giovannidi Dio Fatebenefratelli, Brescia, Italy
36 Department of Neuroscience, Psychology, Drug Research and Child Health, Universityof Florence, Florence, Italy
37 Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) "Fondazione Don Carlo Gnocchi”, Florence, Italy
38 Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
Correspondence
Kamen A. Tsvetanov,Department of Clinical
Neurosciences, University of Cambridge,
Cambridge, UK.
Email: kat35@cam.ac.uk
Joint first authorship.
See Appendix for a list of GENFI consortium
members.
Abstract
Introduction: The presymptomatic phase of neurodegenerative disease can last many
years, with sustained cognitive function despite progressive atrophy. We investigate
this phenomenon in familial frontotemporal dementia (FTD).
Methods: We studied 121 presymptomatic FTD mutation carriers and 134 family
members without mutations, using multivariate data-driven approach to link cognitive
performance with both structural and functional magnetic resonance imaging. Atrophy
and brain network connectivity were compared between groups, in relation to the time
from expected symptom onset.
Results: There were group differences in brain structure and function, in the absence
of differences in cognitive performance. Specifically, we identified behaviorally rele-
vant structural and functional network differences. Structure-function relationships
were similar in both groups, but coupling between functional connectivity and cogni-
tion was stronger for carriers than for non-carriers, and increased with proximity to
the expected onset of disease.
Discussion: Our findings suggest that the maintenance of functional network connec-
tivity enables carriers to maintain cognitive performance.
KEYWORDS
frontotemporal dementia (FTD), functional magnetic resonance imaging (fMRI), network connec-
tivity, presymptomatic
1INTRODUCTION
Across the adult healthy lifespan, the structural and functional prop-
erties of brain networks are coupled, and both are predictive of cog-
nitive ability.1,2 The connections between structure, function, and
performance have been influential in developing current models of
aging and neurodegeneration.3–5 However, this work contrasts with
the emerging evidence of neuropathological and structural changes
many years before the onset of symptoms of Alzheimer’s disease
(AD) and frontotemporal dementia (FTD).6–8 Genetic FTD with highly
penetrant gene mutations provides the opportunity to examine the
precursors of symptomatic disease. Three main genes account for
10% to 20% of FTD cases: chromosome 9 open reading frame 72
(C9orf72), granulin (GRN), and microtubule-associated protein tau
TSVETANOV ETAL.3
(MAPT). These genes vary in their phenotypic expression and in
the age at onset.9Despite pleiotropy10 and environmental and sec-
ondary genetic moderation11,12 all three mutations cause signifi-
cant structural brain changes in key regions over a decade before
the expected age at disease onset,7,13 confirmed by longitudinal
studies.14,15
The divergence between early structural change and late cognitive
decline provokes the question: how do presymptomatic mutation car-
riers stay so well in the face of progressive atrophy? We propose that
the answer lies in the maintenance of network dynamics and functional
organisation.16 Across the lifespan, functional brain network connec-
tivity predicts cognitive status,17 and this connectivity-cognition rela-
tionship becomes stronger with age.18–20
Our overarching hypothesis is that for those at genetic risk of
dementia, the maintenance of network connectivity prevents the
manifestation of symptoms despite progressive structural changes. A
challenge is that neither the anatomical and functional substrates of
cognition nor the targets of neurodegenerative disease are mediated
by single brain regions: They are distributed across multi-level and
interactive networks. We therefore used a multivariate data-driven
approach to identify differences in the multidimensional brain-
behavior relationship between presymptomatic carriers and non-
carriers of mutations in FTD genes. We identified key brain networks21
from a large independent population-based age-matched data
set.22
We tested three key hypotheses: (1) presymptomatic carriers dif-
fer from non-carriers in brain structure and brain function, but not in
cognitive function, (2) brain structure and function correlate with per-
formance in both groups, but functional network indices are stronger
predictors of cognition in carriers, and (3) the dependence on net-
work integrity for maintaining cognitive functioning increases as car-
riers approach the onset of symptoms.
2METHODS
2.1 Participants
Thirteen research sites across Europe and Canada recruited partici-
pants as part of an international multicenter partnership, the Genetic
Frontotemporal Initiative (GENFI). A total of 313 participants had
usable structural and resting state functional magnetic resonance
imaging (fMRI) data.7,13 The study was approved by the institu-
tional review boards for each site, and participants providing written
informed consent. Inclusion criteria included anyone over the age of
18 who is symptomatic or an asymptomatic first-degree relative. Five
participants were excluded due to excessive head motion (see below),
resulting in 308 data sets for further analysis.
Participants were genotyped based on whether they carried a
pathogenic mutation in MAPT, GRN,andC9orf72. Mutation carriers
were classified as either symptomatic or presymptomatic based on
clinician evaluation. Participants were only classified as symptomatic
if the clinician judged that symptoms were present, consistent with
RESEARCH IN CONTEXT
1. Systematic review: The authors reviewed systematically
the literature using Web of Science, preprint reposito-
ries (eg, BioRxiv) and research citing key articles. The con-
nections between structure, function, and performance
have been influential in developing current models of
aging and neurodegeneration. We discuss these citations
in light of emerging evidence of contrasting views that
neuropathological changes occur many years before the
onset of symptoms of Alzheimer’s disease and frontotem-
poral dementia.
2. Interpretation: Our results suggest that the mainte-
nance of brain functional network integrity enables pre-
symptomatic carriers of frontotemporal dementia muta-
tions to remain cognitively well despite progressive brain
atrophy.
3. Future directions: The novel methods and results will
inform the design of pre-symptomatic disease-modifying
therapy trials and guide strategies to maintain cognitive
function with age and age-related neurodegenerative dis-
eases.
a diagnosis of a degenerative disorder, and progressive in nature. An
additional group of controls, termed non-carriers, comprised mutation-
negative family members. In this study, we focus on non-carriers
(NC, N =134) and presymptomatic carriers (PSC, N =121).
Participants and site investigators were blinded to the research geno-
typing, although a minority of participants had undergone predictive
testing outwith the GENFI study. See Table 1for demographic infor-
mation and Table 2for behavioral, cognitive, and neuropsychologi-
cal information of both groups. In keeping with other GENFI reports,
the years to expected onset (EYO) were calculated as the differ-
ence between age at assessment and mean age at onset within the
family.7
2.2 Neurocognitive assessment
Each participant completed a standard clinical assessment consist-
ing of medical history, family history, functional status, and physical
examination, in complement with collateral history from a family mem-
ber or a close friend. In the current study, 13 behavioral measures
of cognitive function were correlated with neuroimaging measures.
These included the Uniform Data Set23: the Logical Memory subtest
of the Wechsler Memory Scale-Revised with Immediate and Delayed
Recall scores, Digit Span forwards and backwards from the Wech-
sler Memory Scale-Revised, a Digit Symbol Task, Parts A and B of the
Trail Making Test, the short version of the Boston Naming Test, and
Category Fluency (animals). Additional tests included Letter Fluency,
4TSVETANOV ETAL.
TAB LE 1 Demographics of participants included in the analysis,
grouped by genetic status as non-carriers (NCs) and presymptomatic
carriers (PSCs)
Gene status group Statistical testsa
NC PSC X2 or F-test P-value
N 134 121
Mutated gene, n (%) 0.86 0.649
MAPT 17 (12.7) 19 (15.7)
GRN 77 (57.5) 63 (52.1)
C9Orf72 40 (29.9) 39 (32.2)
Gender, n (%) 0.01 0.908
Male 53 (39.6) 47 (38.8)
Handedness, n (%) 0.06 0.806
Right-handed 122 (91) 107 (88.4)
Age (y) 2.68 0.103
Mean/SD 49/14 46/11
Range [Min/Max] 19/86 20/70
Expected years to
onset
0.23 0.631
Mean/SD –10/12 –11/11
Range [Min/Max] –25/10 –25/10
Education (y) 0.05 0.826
Mean/SD 14/3 14/3
Range [Min/Max] 5/24 5/22
aStatistical test to indicate whether demographics vary between NC and
PSC groups.
Wechsler Abbreviated Scale of Intelligence Block Design task, and the
Mini-Mental State Examination. Latency measures for the Trail Making
Test were inverted so that higher values across all tests reflect better
performance.
2.3 Neuroimaging assessment
Figure 1provides a schematic representation of imaging data process-
ing pipeline and the analysis strategy for linking brain-behavior data.
MRI data were acquired using 3T scanners and 1.5T where no 3T
scanning was available from various vendors, with optimized scanning
protocols to maximize synchronization across scanners andsites.7,13
A three-dimensional (3D) structural MRI was acquired for each par-
ticipant using T1-weighted magnetic prepared rapid gradient echo
(MPRAGE) sequence over at least 283 s (283 to 462 s) and had a
median isotropic resolution of 1.1 mm (1 to 1.3 mm), repetition time
of 2000 ms (6.6 to 2400), echo time of 2.9 ms (2.6 to 3.5 ms), inversion
time of 8 ms (8 to 9 ms), and field of view 256 ×256 ×208 mm (192
to 256 ×192 to 256 ×192 to 208 mm). The co-registered T1 images
were segmented to extract probabilistic maps of six tissue classes: grey
matter (GM), white matter (WM), cerebrospinal fluid (CSF), bone, soft
tissue, and residual noise. The native-space GM and WM images were
submitted to diffeomorphic registration to create equally represented
TAB LE 2 Behavioral, cognitive, and neuropsychological estimates
in presymptomatic carriers and non-carriers
Gene status group Statistical testsa
NC PSC X2P-value
Behavioral
Cambridge Behavioral
Inventory—Revised
(/180)
3.5 ( 5.4) 4.7 (10) 0.03 .864
Cognitive
Mini-Mental State
Examination
29.3 (1.1) 29.2 (1.3) <0.01 .963
Neuropsychological
Logical
Memory—Immediate
Recall
15.2 (5.6) 15.7 (5.6) 0.47 .495
Logical Memory—Delayed
Recall
14.1 (4.7) 14 (5) 0.97 .356
Digit Span—Forwards 6.4 (1.2) 6.3 (1.3) 0.52 .470
Digit Span—Backwards 4.9 (1.2) 4.8 (1.2) 1.62 .203
Digit Symbol Task 32 (14.1) 35(14) 0.35 .556
TrailMaking Test Part A 28.9 (17.2) 28.9 (11.5) 0.97 .325
TrailMaking Test Part B 72.5 (43.7) 72.3 (45.5) 0.02 .895
Verbal Fluency—Letter 42 (12.2) 40.7 (15.1) 0.95 .330
Verbal Fluency—Animal 23.3 (6) 23.7 (5.8) 0.58 .445
Boston Naming Test 28.1 (2.1) 27.6 (2.7) 0.58 .446
Block Design 41.8 (16.1) 42.5 (17.1) 0.17 .683
aStatistical test to indicate whether scores vary between NC and PSC
groups.
gene-group template images (DARTEL24). The templates for all tissue
types were normalized to the Montreal Neurological Institute (MNI)
template using a 12-parameter affine transformation. The normalized
images were smoothed using an 8-mm Gaussian kernel.
For resting state fMRI measurements, echo-planar imaging (EPI)
data were acquired with at least 6 minutes of scanning. Analogous
imaging sequences were developed by the GENFI Imaging Core team,
and used at each GENFI study site to accommodate different scanner
models and field strengths. EPI data were acquired over at least 300 s
(interquartile range [IQR] 309 to 440) and had a median repetition time
of 2200 ms (2200 to 3000 ms), echo time of 30 ms, in-plane resolution
of 2.75 ×2.75 mm (2.75 to 3.31 ×2.75 to 3.31), and slice thickness of
3.3 mm (3.0 to 3.3).
The imaging data were analyzed using Automatic Analysis [AA
4.025] pipelines and modules, which called relevant functions from Sta-
tistical Parametric Mapping (SPM12).26 To quantify the total motion
for each participant, the root mean square volume-to-volume dis-
placement was computed using the approach of Jenkinson et al.27
Participants with 3.5 or more standard deviations (SD) above the
group mean motion displacement were excluded from further analy-
sis (N =5). To further ensure that potential group bias in head motion
did not affect later analysis of connectivity,we took three further steps:
TSVETANOV ETAL.5
FIGURE 1 Schematic representation of data processing and analysis pipeline to test for brain-behavior differences between presymptomatic
carriers (PSCs) and non-carriers (NCs) as a function of expected years to onset (EYO) of symptoms, while controlling for covariates of no interest
(Covs). Brain structural measures were based on the mean gray matter volume (GMV) in 246 nodes, as defined in the Brainnetome atlas.35 Brain
functional measures were based on the functional connectivity between 15 nodes as part of four large-scale networks, which were defined in an
independent cohort of 298 age-matched individuals part of the Cam-CAN data set
(1) fMRI data were further postprocessed using whole-brain indepen-
dent component analysis (ICA) of single subject time-series denoising,
with noise components selected and removed automatically using a
priori heuristics and the ICA-based algorithm,28 (2) postprocessing of
network node time-series (see below), and (3) a subject-specific esti-
mate of head movement for each participant27 included as a covariate
in group-level analysis.29
2.4 Network definition
The location of the key cortical regions in each network was identified
by spatial-ICA in an independent data set of 298 age-matched healthy
individuals from a large population-based cohort.22 Full details about
preprocessing and node definition have been described previously.30
Four networks commonly affected by neurodegenerative diseases
including FTD21 were identified by spatially matching to pre-existing
templates.31 The default mode network (DMN) contained five nodes:
the ventral anterior cingulate cortex (vACC), dorsal and ventral pos-
terior cingulate cortex (vPCC and dPCC), and right and left inferior
parietal lobes (rIPL and lIPL). The salience network (SN) was defined
using right and left anterior insular (rAI and lAI) and dorsal anterior cin-
gulate cortex (dACC). The frontoparietal network (FPN) was defined
using right and left anterior superior frontal gyrus (raSFG and laSFG)
and right and left angular gyrus (rAG and lAG). The dorsal attention net-
work (DAN) was defined using right and left intraparietal sulcus (rIPS
and lIPS). The node time-series were defined as the first principal com-
ponent resulting from the singular value decomposition of voxels in an
8-mm radius sphere, which was centered on the peak voxel for each
node.18 Visual representation of the spatial distribution of the nodes
is shown in Figure 2.
We aimed to further reduce the effects of noise confounds on func-
tional connectivity effects of node time-series using the general lin-
ear model (GLM).29 This model included linear trends, expansions of
realignment parameters, as well as average signal in WM and CSF,
including their derivative and quadratic regressors from the time-
courses of each node. The WM and CSF signals were created by using
the average signal across all voxels with corresponding tissue proba-
bility >0.7 in associated tissue probability maps available in SPM12. A
band-pass filter (0.0078 to 0.1 Hz) was implemented by including a dis-
crete cosine transform set in the GLM. Finally,the functional connectiv-
ity (FC) between each pair of nodes was computed using Pearson cor-
relation on postprocessed time-series.
2.5 Statistical analysis
2.5.1 Group differences in brain structure,
function, and cognition
To assess the group differences in neuroimaging and behavioral data
set we used multiple linear regression with a well-conditioned shrink-
6TSVETANOV ETAL.
FIGURE 2 Visualization of spatial localization of the nodes part of the four large-scale networks and their mean functional connectivity
(circular plot) across all participants in this study. Nodes and networks were defined in an independent cohort of 298 age-matchedindividuals part
of the Cam-CAN data set.30The default mode network (DMN) contained five nodes: the ventral anterior cingulate cortex (vACC), dorsal and
ventral posterior cingulate cortex (vPCC and dPCC), and right and left inferior parietal lobes (rIPL and lIPL). The salience network (SN) was defined
using right and left anterior insular (rAI and lAI) and dorsal anterior cingulate cortex (dACC). The frontoparietal network (FPN) was defined using
right and left anterior superior frontal gyrus (raSFG and laSFG) and right and left angular gyrus (rAG and lAG). The dorsal attention network (DAN)
was defined using right and left intraparietal sulcus (rIPS and lIPS)
age regularization32,33 and 10-fold cross-validation.34 In the analysis of
brain structure we used as independent variables the mean GM volume
(GMV) of the 246 brain nodes in the Brainnetome atlas.35 The Brain-
netome atlas was developed to link functional and structural char-
acteristics of the human brain35 and provides a fine-grained whole-
brain parcellation with a superior representation of age-related dif-
ferences in brain structure compared to other cortical parcellation
schemes.36,37 In the analysis of brain function, we used the functional
connectivity between 15 nodes, which were part of the four large-scale
functional networks described earlier. In the analysis of cognitive func-
tion, the independent variables comprised the performance measures
on the 13 neuropsychological tests performed outside of the scanner.
In all three analyses the dependent variable was the genetic status
(PSC vs NC) including age as a covariate of no interest. GENFI’s large-
sampled cohort was created using harmonized multi-site neuroimag-
ing data. Although, scanning protocols were optimized to maximize
comparability across scanners and sites,7,13 different scanning plat-
forms can introduce systematic differences that might confound true
effects of interest.38 Therefore, in the analysis of neuroimaging data
we included scanner site and head motion as additional covariates of
no interest.
2.5.2 Brain-behavior relationships
For the brain-behavior analysis, we adopted a two-level procedure.
In the first-level analysis, we assessed the multidimensional brain-
behavior relationships using partial least squares.39 This analysis
described the linear relationships between the two multivariate data
sets, namely neuroimaging (either GMV or FC) and behavioral perfor-
mance, by providing pairs of latent variables (Brain-LVs and Cognition-
LVs) as linear combinations of the original variables that are optimized
to maximize their covariance. Namely, data set 1 consisted of a brain
feature set, which could be either GMV (GMV data set) or functional
connectivity strength between pairs of regions for each individual (FC
data set). Data set 2 included the performance measures on the 13
tests (ie, Cognition data set), as considered in the multiple linear regres-
sion analysis of group differences in cognition. Covariates of no interest
included head motion, scanner site, gender, and handedness. In addi-
tion, we also included average GMV across all 15 nodes as a covariate
of no interest in the FC-behavior analysis to ensure that the observed
effects are over and above differences in the level of atrophy.
Next, we tested whether the identified behaviorally relevant LVs of
brain structure and function were differentially expressed by NC and
TSVETANOV ETAL.7
FIGURE 3 Group differences between PSC and NC in gray matter volume (left panel) and functional connectivity between nodes within four
large scale networks (right panel). Hot color scheme indicates the strength of effect size of PSC showing higher GMV and FC than NC, while cold
color scheme indicates the opposite effect (ie, NC >PSC)
PSC as a function of expected years to onset. To this end, we performed
a second-level analysis using multiple linear regression with robust fit-
ting algorithm as implemented in MATLAB’s function “fitlm.m.” Inde-
pendent variables included subjects’ brain scores from first level PLS
(either Structure-LV or Function-LV subject scores), group information,
expected years to onset and their interaction terms (eg, brain scores x
group, brain scores x years to expected onset, and so on). The depen-
dent variable was subjects’ cognitive scores from the first level anal-
ysis in the corresponding PLS (Cognition-LV). Given that the interac-
tion effects were derived from continuous variables, we tested and
interpreted interactions based on simple slope analysis and slope dif-
ference tests.40–42 Covariates of no interest included gender, hand-
edness, head movement, and education (Figure 1). In addition, we
included average GMV across all 15 nodes as a covariate in the FC-
behavior analysis to ensure that the observed effects are over and
above differences in the level of atrophy.
3RESULTS
3.1 Group differences in neuroimaging and
cognitive data
3.1.1 Brain structure
The multiple linear regression model testing for overall group differ-
ences in GMV between PSC and NC was significant (r =.14, P=.025),
reflecting expected presymptomatic differences in brain-wide atrophy.
The frontal, parietal, and subcortical regions had most atrophy in PSC
(Figure 3). As expected, the group difference in GMV of these regions
increased as EYO decreased (see Supplementary Materials).
3.1.2 Brain function
The multiple linear regression model testing for overall group differ-
ences in functional connectivity between PSC and NC was marginally
significant (r =.12, P=.049). The pattern of connectivity indicated
mainly increased connectivity between SN-DMN and SN-FPN in
presymptomatic carriers, coupled with decreased connectivity within
the networks and DMN-FPN connectivity (Figure 3).
3.1.3 Cognitive function
We did not identify group differences in cognition and behavior
(r =.002, P=.807), confirming the impression of “healthy” status
among presymptomatic carriers. However, in the next section, we con-
sider the relationships between structure, function, and cognition that
underlie this maintenance of cognitive function.
3.2 Brain-behavior relationships
3.2.1 Structure-cognition
Partial least squares analysis of GMV and cognition identified one
significant pair of latent variables (r =.40, P=.019). This volumetric
latent variable expressed negative loadings in frontal (superior frontal
8TSVETANOV ETAL.
FIGURE 4 PLS analysis of gray matter volume (GMV) and cognition indicating the spatial distribution of GMV loading values (A), where hot
and cold color schemes are used for the strength of positive and negative correlations with the profile of Cognitive-LV (B). (C) The scatter plot on
the left represents the relationship between subjects scores of GMV-LV and Cognition-LV for presymptomatic carriers (PSCs) and non-carriers
(NCs). The scatter plots in the middle and right hand-side represent GMV-Cognition LV relationship as a function of expected years to onset (EYO,
split in two groups, near and far, see text) in each genetic status group separately
gyrus, precentral gyrus, paracentral lobule),parietal (postcentral gyrus,
precuneus, superior and inferior parietal lobule), and occipital (lateral
and medial occipital cortex) regions and positive loadings in parahip-
pocampal and hippocampal regions in addition to inferior temporal and
insular cortex (Figure 4). The Cognition-LV profile expressed positively
a large array of cognitive tests, with strongest values on delayed mem-
ory, Trail Making, Digit Symbol, Boston Naming, and Fluency tests. The
positive correlation between volumetric and cognitive LV’s confirms
the expected relationship across the cohort as a whole, between corti-
cal GMV and executive, language, and mnemonic function (Figure 4).
To understand the structure-cognition relationship in each group
and in relation to the expected years of onset, we performed a
second-level interaction analysis using a regression model: We entered
Cognition-LV subject scores as dependent variable, and GMV LV
subject scores, genetic status (ie, mutation carrier or non-carrier),
expected years to onset, and their interactions as independent vari-
ables in addition to covariates of no interest. The results indicated that
the relationship between GMV and cognition could not be explained
by genetic status, expected years to onset, or their interactions with
GMV LV subject scores. There was no evidence for genetic status- and
onset-dependent differences (over and above aging and other covari-
ates) in the associations between GMV and cognition in this analysis
(Figure 4).
3.2.2 Connectivity-cognition
PLS analysis of functional connectivity and cognition also iden-
tified one significant pair of LVs (Function-LV and Cognition-LV,
r=.32, P=.020; see Figure 5). This Function-LV reflected weak
between-network connectivity, coupled with strong within-network
connectivity. This pattern indicates the segregation or modularity
of large-scale brain networks. The Cognition-LV expressed all tests,
with positive loading values indicating that higher performance on a
wide range of cognitive tests is associated with stronger functional
network segregation. Cognitive deficits were associated with loss
of segregation, with increased between-network connectivity and
decreased within-network connectivity.
To further test whether the observed behaviorally relevant pat-
tern of connectivity is differentially expressed between genetic sta-
tus groups and expected years of onset, we constructed a second-level
regression model with robust error estimates by including Function-LV
subject scores, genetic status, expected years of onset, and their inter-
action terms as independent variables and Cognition-LV as dependent
variable in addition to covariates of no interest (Figure 5).
We found evidence for significant interaction between expected
years of onset and Function-LV (r =.21, P<.001) and between
group and Function-LV (r =.16, P=.002) explaining unique vari-
TSVETANOV ETAL.9
FIGURE 5 PLS analysis of functional connectivity and cognition indicating the connectivity pattern of loading values (A), where hot and cold
color schemes are used for the strength of positive and negative correlations with the profile of cognitive-LV (B). (C) The scatter plot on the left
represents the relationship between subjects scores of function LV and cognition LV for presymptomatic carriers (PSCs) and non-carriers (NCs).
The scatter plots in the middle and right hand-side represents function-cognition LV relationship as a function of expected years to onset (EYO
split in two groups, near and far, see text) in each genetic status group separately. This is also represented using a bar chart in (D), where
continuous and dashed lines indicate significance of effect differences and difference in differences, respectively. and * denote significant tests at
P-value <.05 (one- and two-sided, respectively)
ance in Cognition-LV. We used simple slope analysis and slope dif-
ference tests40–42 to test formally for differences in the relationship
between Function-LV and Cognition-LV for PSC and NC. The relation-
ship between Function-LV and Cognition-LV was stronger for PSC rel-
ative to NC (r =.16, P=.002), indicating the increasing importance of
functional connectivity between the large-scale networks for PSC par-
ticipants to maintain performance (Figure 5).
For ease of interpretation and illustration, we also computed the
correlation between Cognition-LV and Function-LV for high and low
levels of expected yearsto onset (or EYO) within each group separately,
where the levels were taken to be 1 SD above and belowthe mean val-
ues of EYO following the simple slopes approach.40–42 The two EYO
subgroups were labeled “near” and “far,” with “near” for EYO values
close to zero (ie, participant’s age is “near” the age at which disease
symptoms were demonstrated in the family), and “far” for EYO being
a largely negative value (ie, participant’s age is “far” from the age at
which disease symptoms were demonstrated in the family). The analy-
sis indicated that as the EYO decreases (ie, participant’s age is reach-
ing the years of onset of symptoms) the relationship between func-
tional connectivity and performance becomes stronger. This effect was
highly significant in presymptomatic carriers (r =.31, P<.001) and
tended towards significance in non-carriers (r =.12, P=.038, one-
sided). The differences in effects between presymptomatic carriers and
non-carriers was qualified by a significant interaction term (t =2.27,
P=.024, ie, the effect in presymptomatic mutation carriers was statis-
tically stronger than the effect detected in non-carriers). These findings
indicate that the relationship between FC and cognition is stronger in
PSC relative to NC, and that this relationship increases as a function of
EYO.
4DISCUSSION
In the present study, we confirmed previous findings of group differ-
ences in brain structure and function, in the absence of differences in
cognitive performance between non-carriers and presymptomatic car-
riers of FTD-related genetic mutations. But, although the relationship
between structure and cognition was similar in both groups, the cou-
pling between function and cognition was stronger for presymptomatic
carriers, and increased as they approached the expected onset of dis-
ease.
These results suggest that people can maintain good cognitive abili-
ties and successful day-to-day functioning despite significant neuronal
loss and atrophy. This disjunction between structure and function is a
10 TSVETANOV ETAL.
feature of healthy aging, but we have shown that it also characterizes
presymptomatic FTD, over and abovethe age effects in their other fam-
ily members, despite widespread progressive atrophy. The multivari-
ate approach reveals two key findings: (1) stronger within-network and
weaker between-network functional connectivity is associated with
better cognition, more strongly in presymptomatic carriers than in age-
matched non-carriers, and (2) as carriers approach their estimated age
of symptom onset, and atrophy becomes evident, the maintenance of
good cognition is increasingly associated with sustaining balance of
within- and between-network integration.
This balance of within- and between-network connectivity is
characteristic of segregated and specialized network organization of
brain systems. Such functional segregation varies with physiological
aging,17,18,43 with cognitive function,18 and in individuals at risk
for AD.44 Graph-theoretic quantification of network organization
confirms the relevance of modularity and efficiency to function in
FTD.16 Conversely, the loss of neural systems’ modularity mirrors the
loss of functional specialization with age45 and dementia.44 Here, we
show the significance of the maintenance of this functional network
organization, with a progressively stronger correlation with cognitive
performance as seemingly healthy adults approach the age of expected
onset of FTD.
The uncoupling of brain function from brain structure indicates that
there may be independent and synergistic effects of multiple factors
leading to cognitive preservation. This is consistent with a previous
work in healthy aging where brain activity and connectivity provide
independent and synergistic predictions of performance across the
lifespan.19 Therefore, future studies need to consider the independent
and synergistic effects of many possible biomarkers, based on MRI,
computed tomography, positron emission tomography, CSF, blood, and
brain histopathology.For example, functional network impairment may
be related to tau expression and tau pathology, amyloid load, or neu-
rotransmitter deficits in neurodegenerative diseases, independent of
atrophy.30,46–48 It is important to note that studies need to recog-
nize the rich multivariate nature of cognition and of neuroimaging
in order to improve stratification procedures, for example, based on
integrative approaches that explain individual differences in cognitive
impairment.30,49 On a clinical level, this may facilitate future studies to
establish whether presymptomatic carriers who maintain such connec-
tivity profiles and thereby neuropsychological function in the presence
of atrophy may have a lower risk of progression and better prognosis—
information that will be important for future triallists, patients, and
carers.
We also recognize the difficulty in determining a unique contri-
bution of each factor (eg, brain structure and brain function), given
the increasing interaction between factors in advanced stages of
disease.50 This is further complicated by these alterations becoming
irreversible with progression of neurodegeneration.51 This suggests
that the critical interplay between multiple factors (including brain
structure and function) may be better studied in the asymptomatic
and preclinical stages as well as across the healthy lifespan, which
could still be modifiable and their influences are likely to be more
separable.
Our findings agree with the model of compensation in the presymp-
tomatic and early phases of Huntington disease, where network
coupling predicted better cognitive performance.52 In a recent longi-
tudinal study, a non-linear concave-down pattern of both brain activity
and behavior was present, despite a linear decline in brain volume
over time.53 Similar effects have been observed also in healthy aging
and amnestic mild cognitive impairment, where greater connectivity
with the default-mode network and weaker connectivity between
default-mode network and dorsal-attention network was associated
with higher cognitive status in both groups.54 Network integrity
may also play a role in compensatory mechanisms in non-cognitive
symptoms, such as motor impairment in Parkinson disease.55 Accord-
ingly, increased network efficiency and connectivity have been shown
in prodromal phases, followed by decreased local connectivity in
symptomatic phases, suggesting the emergence and dissipation of
neural compensation.56
The current study has several limitations. First, despite the large size
of the overall GENFI cohort, we did not analyze each genetic group sep-
arately. The subdivision of each clinical group (PSC, NC) by three genes
would have led to small and unbalanced subgroups, lowering statisti-
cal power and robustness. Moreover, genetic FTD is also characterized
by multiple mutations within MAPT and GRN, and pleiotropy of clini-
cal phenotypes from the same mutation.10 Pleiotropy of clinical phe-
notype is avoided by the study of presymptomatic carriers, but we can-
not rule out pleiotropy of intermediate phenotypes expressed as say
neural network diversity. In FTD as in other dementias, clinical hetero-
geneity is modified by environmental factors such as education (which
may be a surrogate of cognitive reserve12,57). In addition, our analysis
included the estimated age at onset in some models, but we recognize
that the precision of the estimated years at onset (based on family his-
tory of onset) varies across mutations and families,7,58 being highest
for MAPT and low for C9orF72 expansion. Genetic modifiers such as
TMEM106B59 and APOE60 have also been identified. Further work with
larger cohorts is required to test for gene-specific effects, and the role
of environmental and genetic moderators on the relationships between
brain structure, functional networks, and cognition. The harmoniza-
tion of sequences and data acquisition protocols in this multi-site neu-
roimaging study aimed to reduce the susceptibility to systematic differ-
ences across scanning platforms, but residual site variance cannot be
ruled out.38,61 The inclusion of study site as a covariate of no interest61
and the nature of our multivariate approach to identify shared signals
between brain and behavioral data reduce residual effects of scanner
variance.38,62 Future studies may use alternative brain measures that
reflect differences in cortical surface and thickness estimates,63,64 or
which infer neural connectivity directly from neurophysiology or from
the separation of neurovascular from neuronal contributors to blood
oxygen level–dependent (BOLD) fMRI variance,18,65 given the con-
founding effects of age, drug, or disease on neurovascular signals.66,67
The current study is cross-sectional. Therefore, we cannot infer lon-
gitudinal progression within subjects as the unambiguous cause of the
effects we observe in relation to expected years of onset. Accumulat-
ing evidence suggests that network integrity serves to maintain per-
formance with either physiological ageing or pathological conditions.
TSVETANOV ETAL.11
However, longitudinal mediation studies and pharmacological or elec-
troceutical interventions would be needed to prove its causal role in
cognitive preservation. Finally, our findings are limited to autosomal
dominant FTD, which represents a minority of FTD: Generalization to
sporadic forms of disease would be speculative.
In conclusion, we used a multivariate data-driven approach to
demonstrate that brain functional integrity may facilitate presymp-
tomatic carriers to maintain cognitive performance in the presence of
progressive brain atrophy for years before the onset of symptoms. The
multivariate approach to cognition and brain function is well-suited to
address the effects of multiple interacting risk factors on biomarkers
of the progression of neurodegeneration, ahead of clinical conversion
to dementia. The approach and our findings have implications for the
design of presymptomatic disease-modifying therapy trials, which are
likely to rely initially on surrogate markers of brain health rather than
clinical end points.
ACKNOWLEDGMENTS
K.A.T. is supported by the British Academy Postdoctoral Fellowship
(PF160048) and the Guarantors of Brain (101149). J.B.R. is sup-
ported by the Wellcome Trust (103838), the Medical Research Council
(SUAG/051 G101400), and the Cambridge NIHR Biomedical Research
Centre. R. S.-V. is supported by the Instituto de Salud Carlos III and
the JPND network PreFrontAls (01ED1512/AC14/0013) and the Fun-
dació Marató de TV3 (20143810). M.M and E.F are supported by the
UK Medical Research Council, the Italian Ministry of Health, and the
Canadian Institutes of Health Research as part of a Centres of Excel-
lence in Neurodegeneration grant, and also a Canadian Institutes of
Health Research operating grant (MOP 327387) and funding from the
Weston Brain Institute. J.D.R., D.C., and K.M.M. are supported by the
NIHR Queen Square Dementia Biomedical Research Unit, the NIHR
UCL/H Biomedical Research Centre, and the Leonard Wolfson Experi-
mental Neurology Centre (LWENC) Clinical Research Facility. J.D.R. 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), the MRC UK GENFI grant
(MR/ M023664/1), and The Bluefield Project. F.T. is supported by
the Italian Ministry of Health (Grant NET-2011-02346784). L.C.J. and
J.V.S. are supported by the Association for Frontotemporal Dementias
Research Grant 2009, ZonMw Memorabel project number 733050103
and 733050813, and the Bluefield project. R.G. is supported by Ital-
ian Ministry of Health, Ricerca Corrente. J.L. was funded by the
Deutsche Forschungsgemeinschaft (DFG, German Research Founda-
tion) under Germany’s Excellence Strategy within the framework of
the Munich Cluster for Systems Neurology (EXC 2145; SyNergy - ID
390857198). The Swedish contributors C.G., L.O., and C.A. were sup-
ported by grants from JPND Prefrontals Swedish Research Council
(VR) 529-2014-7504, JPND GENFI-PROX Swedish Research Coun-
cil (VR) 2019-02248, Swedish Research Council (VR) 2015- 02926,
Swedish Research Council (VR) 2018-02754, Swedish FTD Initiative-
Schorling Foundation, Swedish Brain Foundation, Swedish Alzheimer
Foundation, Stockholm County Council ALF, Karolinska Institutet Doc-
toral Funding, and StratNeuro, Swedish Demensfonden, during the
conduct of the study.
CONFLICT OF INTEREST
There were no financial or other conflicts of interest requiring declara-
tion.
REFERENCES
1. Persson J, Nyberg L, Lind J, et al. Structure-function correlates of cog-
nitive decline in aging. CerebCortex. 2006;16:907-915.
2. Geerligs L, Cam-CAN, Henson RN. Functional connectivity and struc-
tural covariance between regions of interest can be measured
more accurately using multivariate distance correlation. Neuroimage.
2016;135:16-31.
3. Cope TE, Rittman T, Borchert RJ, et al. Tau burden and the func-
tional connectome in Alzheimer’s disease and progressive supranu-
clear palsy. Brain. 2018;141:550-567.
4. Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurode-
generative diseases target large-scale human brain networks. Neuron.
2009;62:42-52.
5. Raj A, Kuceyeski A, Weiner M. A network diffusion model of disease
progression in dementia. Neuron. 2012;73:1204-1215.
6. Kinnunen KM, Cash DM, Poole T, et al. Presymptomatic atrophy in
autosomal dominant Alzheimer’s disease: a serial magnetic resonance
imaging study. Alzheimer’s Dement. 2018;14:43-53.
7. Rohrer JD, Nicholas JM, Cash DM, et al. Presymptomatic cognitiveand
neuroanatomical changes in genetic frontotemporal dementia in the
Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-
sectional analysis. Lancet Neurol. 2015;14:253-262.
8. Vatsavayai SC, Yoon SJ, Gardner RC, et al. Timing and significance of
pathological features in C9orf72 expansion-associated frontotemporal
dementia. Brain. 2016;139:3202-3216.
9. Deleon J, Miller BL, Frontotemporal dementia, 2018, p. 409-430.
https://doi.org/10.1016/B978-0-444- 64076-5.00027-2.
10. Snowden JS, Adams J, Harris J, et al. Distinct clinical and patholog-
ical phenotypes in frontotemporal dementia associated with MAPT.
PGRN and C9orf72 mutations Amyotroph Lateral Scler Front Degener.
2015;16:497-505.
11. Murphy NA, Arthur KC, Tienari PJ, Houlden H, Chiò A, Traynor BJ.
Age-related penetrance of the C9orf72 repeat expansion. Sci Rep.
2017;7:2116. .
12. Premi E, Grassi M, Van Swieten J, et al. Cognitive reserve and
TMEM106B genotype modulate brain damage in presymptomatic
frontotemporal dementia: a GENFI study. Brain. 2017;140:1784-
1791.
13. Cash DM, Bocchetta M, Thomas DL, et al. Patternsof gray matter atro-
phy in genetic frontotemporal dementia: results from the GENFI study.
Neurobiol Aging. 2018;62:191-196.
14. Olm CA, McMillan CT, Irwin DJ, et al. Longitudinal structural gray
matter and white matter MRI changes in presymptomatic progranulin
mutation carriers. NeuroImage Clin. 2018;19:497-506.
15. Floeter MK, Danielian LE, BraunLE, Wu T. Longitudinal diffusion imag-
ing across the C9orf72 clinical spectrum. JNeurolNeurosurgPsychiatry.
2018;89:53-60.
16. Rittman DT, Borchert MR, Jones MS, et al. Functional network
resilience to pathology in presymptomatic genetic frontotemporal
dementia. Neurobiol Aging. 2019;77:169-177.
17. Chan MY, Park DC, Savalia NK, Petersen SE, Wig GS. Decreased seg-
regation of brain systems across the healthy adult lifespan. Proc Natl
Acad Sci. 2014;111:4997-5006.
18. Tsvetanov KA, Henson RNA, Tyler LK, et al. Extrinsic and intrin-
sic brain network connectivity maintains cognition across the lifes-
12 TSVETANOV ETAL.
pan despite accelerated decay of regional brain activation. J Neurosci.
2016;36:3115-3126.
19. Tsvetanov KA, Ye Z, Hughes L, et al. Activity and connectivity differ-
ences underlying inhibitory control across the adult lifespan. JNeu-
rosci. 2018;38:7887-7900.
20. Bethlehem RAI, Paquola C, Seidlitz J, et al. Dispersion of functional
gradients across the adult lifespan. Neuroimage;2020:117299. https:
//doi.org/10.1016/j.neuroimage.2020.117299.
21. Chhatwal JP, Schultz AP, Johnson KA, et al. Preferential degradation
of cognitive networks differentiates Alzheimer’s disease from ageing.
Brain. 2018;141:1486-1500.
22. Shafto MA, Tyler LK, Dixon M, et al. The Cambridge Centre for Ageing
and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifes-
pan, multidisciplinary examination of healthy cognitive ageing. BMC
Neurol. 2014;14:204. .
23. Morris JC, WeintraubS, Chui HC, et al. The uniform data set (uds): clin-
ical and cognitive variables and descriptive data from Alzheimer dis-
ease centers. Alzheimer Dis Assoc Disord. 2006;20:210-216.
24. Ashburner J. A fast diffeomorphic image registration algorithm. Neu-
roimage. 2007;38:95-113.
25. Cusack R, Vicente-Grabovetsky A, Mitchell DJ, et al. Automatic anal-
ysis (aa): efficient neuroimaging workflows and parallel processing
using Matlab and XML. Front Neuroinform. 2014;8:90. .
26. Friston KJ, Ashburner J, Kiebel S, Nichols T, Penny WD. Statistical
Parametric Mapping : The Analysis of Funtional Brain Images. Elsevier;
2007.
27. Jenkinson M, Bannister P, BradyM, Smith S. Improved optimization for
the robust and accurate linear registration and motion correction of
brain images. Neuroimage. 2002;17:825-841.
28. Pruim RHR, Mennes M, Buitelaar JK, Beckmann CF. Evaluation of ICA-
AROMA and alternative strategies for motion artifact removal in rest-
ing state fMRI. Neuroimage. 2015;112:278-287.
29. Geerligs L, Tsvetanov KA, Cam-CAN, Henson RN. Challenges in mea-
suring individual differences in functional connectivity using fMRI: the
case of healthy aging. Hum Brain Mapp. 2017;38:4125-4156.
30. Passamonti L, Tsvetanov KA, Jones PS, et al. Neuroinflammation and
functional connectivity in Alzheimer’s disease: interactive influences
on cognitive performance. J Neurosci. 2019;39:2574-2518.
31. Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD. Decod-
ing subject-driven cognitive states with whole-brain connectivity pat-
terns. Cereb Cortex. 2012;22:158-165.
32. Blankertz B, Lemm S, Treder M, Haufe S, Müller K-R. Single-trial
analysis and classification of ERP components–a tutorial. Neuroimage.
2011;56:814-825.
33. Ledoit O, Wolf M. A well-conditioned estimator for large-dimensional
covariance matrices. JMultivarAnal. 2004;88:365-411.
34. Lemm S, Blankertz B, Dickhaus T, Müller K-R. Introduction to machine
learning for brain imaging. Neuroimage. 2011;56:387-399.
35. Fan L, Li H, Zhuo J, et al. The human brainnetome atlas: a new brain
atlas based on connectional architecture. Cereb Cortex. 2016;26:3508-
3526.
36. Madan CR, KensingerEA. Predicting age from cortical structure across
the lifespan. Eur J Neurosci. 2018;47:399-416.
37. Long Z, Huang J, Li B, et al. A comparative atlas-based recognition of
mild cognitive impairment with voxel-based morphometry. Front Neu-
rosci. 2018;12:916.
38. Chen J, Liu J, Calhoun VD, et al. Exploration of scanning effects in
multi-site structural MRI studies. J Neurosci Methods. 2014;230:37-
50.
39. Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial Least Squares
(PLS) methods for neuroimaging: a tutorial and review. Neuroimage.
2011;56:455-475.
40. Aiken LS, West SG. Multiple Regression: Testing and Interpreting Interac-
tions. Thousand Oaks, CA: Sage Publications, Inc; 1991.
41. Dawson JF, Richter AW. Probing three-way interactions in moderated
multiple regression: development and application of a slope difference
test. J Appl Psychol. 2006;91:917-926.
42. Dawson JF. Moderation in management research: what, why, when,
and how. J Bus Psychol. 2014;29:1-19.
43. Samu D, Campbell KL, Tsvetanov KA, et al. Preserved cognitive func-
tions with age are determined by domain-dependent shifts in network
responsivity. Nat Commun. 2017;8:ncomms14743.
44. Contreras JA, Goñi J, Risacher SL, et al. Cognitive complaints in older
adults at risk for Alzheimer’s disease are associated with altered
resting-state networks. Alzheimer’s Dement. 2017;6:40-49.
45. Cabeza R, Albert M, Belleville S, et al. Maintenance, reserve and com-
pensation: the cognitive neuroscience of healthy ageing. Nat Rev Neu-
rosci. 2018;19:701-710.
46. Hedden T, Van Dijk KRA, Becker JA, et al. Disruption of functional con-
nectivity in clinically normal older adults harboring amyloid burden. J
Neurosci. 2009;29:12686-12694.
47. Murley AG, Rowe JB. Neurotransmitter deficits from frontotemporal
lobar degeneration. Brain. 2018;141:1263-1285.
48. Rittman T, Rubinov M, Vértes PE, et al. Regional expression of the
MAPT gene is associated with loss of hubs in brain networks and cog-
nitive impairment in Parkinson disease and progressive supranuclear
palsy. Neurobiol Aging. 2016;48:153-160.
49. Geerligs L, Tsvetanov KA. The use of resting state data in an integrative
approach to studying neurocognitive ageing – Commentary on Camp-
bell and Schacter (2016). Lang Cogn Neurosci. 2016;32. https://doi.org/
10.1080/23273798.2016.1251600.
50. Vemuri P, Lesnick TG, Przybelski SA, et al. Vascular and amyloid
pathologies are independent predictors of cognitive decline in normal
elderly. Brain. 2015;138:761-171.
51. Rodrigue KM, Kennedy KM, Devous MD, et al. β-Amyloid burden in
healthy aging: regional distribution and cognitive consequences. Neu-
rology. 2012;78:387-395.
52. Klöppel S, Gregory S, Scheller E, et al. Compensation in preclin-
ical Huntington’s disease: evidence from the track-on hd study.
EBioMedicine. 2015;2:1420-1429.
53. Gregory S, Long JD, Klöppel S, et al. Operationalizing compensa-
tion over time in neurodegenerative disease. Brain. 2017;140:1158-
1165.
54. Sullivan MD, Anderson JAE,Turner GR, Spreng RN. Intrinsic neurocog-
nitive network connectivity differences between normal aging and
mild cognitive impairment are associated with cognitive status and
age. Neurobiol Aging. 2019;73:219-228.
55. Blesa J, Trigo-Damas I, Dileone M, del Rey NL-G, Hernandez LF, Obeso
JA. Compensatory mechanisms in Parkinson’s disease: circuits adap-
tations and role in disease modification. Exp Neurol. 2017;298:148-
161.
56. Wen M-C, Heng HSE, Hsu J-L, et al. Structural connectome alterations
in prodromal and de novo Parkinson’s disease patients. Parkinsonism
Relat Disord. 2017;45:21-27.
57. Stern Y. Cognitive reserve in ageing and Alzheimer’s disease. Lancet
Neurol. 2012;11:1006-1012.
58. Moore KM, Nicholas J, Grossman M, et al. Age at symptom onset and
death and disease duration in genetic frontotemporal dementia: an
international retrospective cohort study. Lancet Neurol. 2020;19:145-
156.
59. Lattante S, Le Ber I, Galimberti D, et al. Defining the association
of TMEM106B variants among frontotemporal lobar degeneration
patients with GRN mutations and C9orf72 repeat expansions. Neuro-
biol Aging. 2014;35:2658.e1-2658.e5.
60. Koriath C, Kenny J, Adamson G, et al. Predictors for a dementia
gene mutation based on gene-panel next-generation sequencing of a
large dementia referral series. Mol Psychiatry. 2018. https://doi.org/10.
1038/s41380-018- 0224-0.
TSVETANOV ETAL.13
61. Alfaro-Almagro F, McCarthy P, Afyouni S, et al. Confound modelling in
UK Biobank brain imaging. Neuroimage;2020:117002. https://doi.org/
10.1016/j.neuroimage.2020.117002.
62. Li H, Smith SM, Gruber S, et al. Denoising scanner effects from mul-
timodal MRI data using linked independent component analysis. Neu-
roimage. 2020;208:116388. .
63. Brodoehl S, Gaser C, Dahnke R, Witte OW, Klingner CM. Surface-
based analysis increases the specificity of cortical activation patterns
and connectivity results. Sci Rep. 2020;10:1-13.
64. Coalson TS, Van Essen DC, Glasser MF. The impact of traditional neu-
roimaging methods on the spatial localization of cortical areas. Proc
Natl Acad Sci U S A. 2018;115:E6356-E6365.
65. Sami S, Williams N, Hughes LE, et al. Neurophysiological signatures of
Alzheimer’s disease and frontotemporal lobar degeneration: pathol-
ogy versus phenotype. Brain. 2018;141:2500-2510.
66. Campbell KL, Shafto MA, Wright P, et al. Idiosyncratic responding dur-
ing movie-watching predicted by age differences in attentional con-
trol. Neurobiol Aging. 2015;36:3045-3055.
67. Tsvetanov KA, Henson RNA, Rowe JB. Separating vascular and neu-
ronal effects of age on fMRI BOLD signals. Philos Trans R Soc B Biol Sci.
2020. https://doi.org/10.1098/rstb.2019.0631.
How to cite this article: Tsvetanov KA, Gazzina S, Jones PS,
et al. Brain functional network integrity sustains cognitive
function despite atrophy in presymptomatic genetic
frontotemporal dementia. Alzheimer’s Dement. 2020;1–15.
https://doi.org/10.1002/alz.12209
APPENDIX
List of other GENFI consortium members
Sónia Afonso - Instituto Ciencias Nucleares Aplicadas a Saude, Univer-
sidade de Coimbra, Coimbra, Portugal
Maria Rosario Almeida - Centre of Neurosciences and Cell Biology,
Universidade de Coimbra, Coimbra, Portugal
Sarah Anderl-Straub - Department of Neurology, Ulm University,
Ulm, Germany
Christin Andersson - Medical Psychology, Karolinska University
hospital-Solna, Stockholm Sweden
Anna Antonell - Alzheimer’s disease and other cognitive disorders
unit, Neurology Department, Hospital Clinic, Institut d’Investigacions
Biomèdiques, Barcelona, Spain
Silvana Archetti - Biotechnology Laboratory, Department of Diag-
nostics, Spedali Civili Hospital, Brescia, Italy
Andrea Arighi - Fondazione IRCSS Ca’ Granda, Ospedale Maggiore
Policlinico, Neurodegenerative Diseases Unit, Milan, Italy
Mircea Balasa - Alzheimer’s disease and other cognitive disorders
unit, Neurology Department, Hospital Clinic, Institut d’Investigacions
Biomèdiques, Barcelona, Spain
Myriam Barandiaran - Neuroscience Area, Biodonostia Health
Research Institute, Paseo Dr Begiristain sn, CP 20014, San Sebastian,
Gipuzkoa, Spain
Nuria Bargalló - Radiology Department, Image Diagnosis Center,
Hospital Clínic and Magnetic Resonance Image core facility, IDIBAPS,
Barcelona, Spain
Robart Bartha - Department of Medical Biophysics, Robarts
Research Institute, University of Western Ontario, London, Ontario,
Canada
Benjamin Bender - Department of Diagnostic and Interventional
Neuroradiology, University of Tuebingen, Tuebingen, Germany
Luisa Benussi - Istituto di Ricovero e Cura a Carattere Scien-
tifico Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia,
Italy
Valentina Bessi - Department of Neuroscience, Psychology, Drug
Research, and Child Health, University of Florence, Florence, Italy
Giuliano Binetti - Istituto di Ricovero e Cura a Carattere Scientifico
Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
Sandra Black - LC Campbell Cognitive Neurology Research Unit,
Sunnybrook Research Institute, 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 Department, Hospital Clinic, Institut
d’Investigacions Biomèdiques, Barcelona, Spain
Jose Bras - Dementia Research Institute, Department of Neurode-
generative Disease, UCL Institute of Neurology, Queen Square, Lon-
don, UK
Rose Bruffaerts - Laboratory for Cognitive Neurology, Department
of Neurosciences, KU Leuven, Leuven, Belgium
Paola Caroppo - Fondazione Istituto di Ricovero e Cura a Carattere
Scientifico Istituto Neurologico Carlo Besta, Milan, Italy
David Cash - Dementia Research Centre, Department of Neurode-
generative Disease, UCL Institute of Neurology, Queen Square, Lon-
don, UK
Miguel Castelo-Branco - Neurology Department, Centro Hospitalar
e Universitário de Coimbra, Instituto de Ciências Nucleares Aplicadas
à Saúde (ICNAS), Coimbra, Portugal
Rhian Convery - Dementia Research Centre, Department of Neu-
rodegenerative Disease, UCL Institute of Neurology, Queen Square,
London, UK
Thomas Cope - Department of Clinical Neuroscience, University of
Cambridge, Cambridge, UK
Maura Cosseddu - Centre for Neurodegenerative Disorders, Neu-
rology Unit, Spedali Civili Hospital, Brescia, Italy
María de Arriba - Neuroscience Area, Biodonostia Health Research
Institute, Paseo Dr Begiristain sn, CP 20014, San Sebastian, Gipuzkoa,
Spain
Giuseppe Di Fede - Fondazione Istituto di Ricovero e Cura a Carat-
tere Scientifico Istituto Neurologico Carlo Besta, Milan, Italy
Zigor Díaz - CITA Alzheimer, San Sebastian, Spain
Katrina M Moore - Dementia Research Centre, Department of Neu-
rodegenerative Disease, UCL Institute of Neurology, Queen Square,
London, UK
Diana Duro - Faculty of Medicine, Universidade de Coimbra, Coim-
bra, Portugal
Chiara Fenoglio - University of Milan, Centro Dino Ferrari, Milan,
Italy
14 TSVETANOV ETAL.
Camilla Ferrari - Department of Neuroscience, Psychology,
Drug Research, and Child Health, University of Florence, Florence,
Italy
Carlos Ferreira - Instituto Ciências Nucleares Aplicadas à Saúde,
Universidade de Coimbra, Coimbra, Portugal
Catarina B. Ferreira - Faculty of Medicine, University of Lisbon, Lis-
bon, Portugal
Toby Flanagan - Faculty of Biology, Medicine and Health, Division of
Neuroscience and Experimental Psychology,University of Manchester,
Manchester, UK
Nick Fox - Dementia Research Centre, Department of Neurodegen-
erative Disease, UCL Institute of Neurology, Queen Square, London,
UK
Morris Freedman - Division of Neurology, Baycrest Centre for Geri-
atric Care, University of Toronto, Toronto, Canada
Giorgio Fumagalli - Fondazione IRCSS Ca’ Granda, Ospedale
Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan,
Italy; Department of Neuroscience, Psychology, Drug Research and
Child Health, University of Florence, Florence, Italy
Alazne Gabilondo - Neuroscience Area, Biodonostia Health
Research Institute, Paseo Dr Begiristain sn, CP 20014, San Sebastian,
Gipuzkoa, Spain
Roberto Gasparotti - Neuroradiology Unit, University of Brescia,
Brescia, Italy
Serge Gauthier - Department of Neurology and Neurosurgery,
McGill University, Montreal, Québec, Canada
Stefano Gazzina - Centre for Neurodegenerative Disorders, Neurol-
ogy Unit, Department of Clinical and Experimental Sciences, University
of Brescia, Brescia, Italy
Giorgio Giaccone - Fondazione Istituto di Ricovero e Cura a Carat-
tere Scientifico Istituto Neurologico Carlo Besta, Milan, Italy
Ana Gorostidi - Neuroscience Area, Biodonostia Health Research
Institute, Paseo Dr Begiristain sn, CP 20014, San Sebastian, Gipuzkoa,
Spain
Caroline Greaves - Dementia Research Centre, Department of Neu-
rodegenerative Disease, UCL Institute of Neurology, Queen Square
London, UK
Rita Guerreiro - Dementia Research Institute, Department of
Neurodegenerative Disease, UCL Institute of Neurology, London,
UK
Carolin Heller - Dementia Research Centre, Department of Neu-
rodegenerative Disease, UCL Institute of Neurology, Queen Square,
London, UK
Tobias Hoegen - Department of Neurology, Ludwig-Maximilians-
University of Munich, Munich, Germany
Begoña Indakoetxea - Cognitive Disorders Unit, Department of
Neurology, Donostia University Hospital, Paseo Dr Begiristain sn, CP
20014, San Sebastian, Gipuzkoa, Spain
Vesna Jelic - Division of Clinical Geriatrics, Karolinska Institutet,
Stockholm, Sweden, Theme Aging, Karolinska University Hospital-
Huddinge, Sweden
Lize Jiskoot - Department of Neurology, Erasmus Medical Center,
Rotterdam, The Netherlands
Hans-Otto Karnath - Section of Neuropsychology, Department of
Cognitive Neurology, Center for Neurology & Hertie-Institute for Clin-
ical Brain Research, Tübingen, Germany
Karolinska Institutet - Department NVS, Center for Alzheimer
Research, Division of Neurogenetics, Stockholm, Sweden and Unit for
Hereditary Dementias, Theme Aging, Karolinska University Hospital-
Solna, Stockholm, Sweden
Ron Keren - University Health Network Memory Clinic, Toronto
Western Hospital, Toronto, Canada
Tobias Langheinrich - Division of Neuroscience and Experimental
Psychology, Wolfson Molecular Imaging Centre, University of Manch-
ester, Manchester, UK
Maria João Leitão - Centre of Neurosciences and Cell Biology, Uni-
versidade de Coimbra, Coimbra, Portugal
Albert Lladó - Alzheimer’s disease and other cognitive disorders
unit, Neurology Department, Hospital Clinic, Institut d’Investigacions
Biomèdiques, Barcelona, Spain
Gemma Lombardi - Department of Neuroscience, Psychology, Drug
Research and Child Health, University of Florence, Florence, Italy
Sandra Loosli - Department of Neurology, Ludwig-Maximilians-
University of Munich, Munich, Germany
Carolina Maruta - Lisbon Faculty of Medicine, Language Research
Laboratory, Lisbon, Portugal
Simon Mead - MRC Prion Unit, Department of Neurodegener-
ative 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, Lis-
bon, Portugal
Rick van Minkelen - Department of Clinical Genetics, Erasmus Med-
ical Center, Rotterdam, The Netherlands
Sara Mitchell - LC Campbell Cognitive Neurology Research Unit,
Sunnybrook Research Institute, Toronto, Canada
Benedetta Nacmias - Department of Neuroscience, Psychology,
Drug Research and Child Health, University of Florence, Florence, Italy
Mollie Neason - Dementia Research Centre, Department of Neu-
rodegenerative Disease, UCL Institute of Neurology, Queen Square,
London, UK
Jennifer Nicholas - Department of Medical Statistics, London School
of Hygiene and Tropical Medicine, London, UK
Jaume Olives - Alzheimer’s disease and other cognitive disorders
unit, Neurology Department, Hospital Clinic, Institut d’Investigacions
Biomèdiques, Barcelona, Spain
Alessandro Padovani - Centre for Neurodegenerative Disorders,
Neurology Unit, Department of Clinical and Experimental Sciences,
University of Brescia, Brescia, Italy
Jessica Panman - Department of Neurology, Erasmus Medical Cen-
ter, Rotterdam, The Netherlands
Janne Papma - Department of Neurology, Erasmus Medical Center,
Rotterdam, The Netherlands
Irene Piaceri - Department of Neuroscience, Psychology, Drug
Research and Child Health, University of Florence, Florence
TSVETANOV ETAL.15
Michela Pievani - Istituto di Ricovero e Cura a Carattere Scientifico
Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
Yolande Pijnenburg - VUMC, Amsterdam, The Netherlands
Cristina Polito - Department of Biomedical, Experimental and Clin-
ical Sciences “Mario Serio”, Nuclear Medicine Unit, University of Flo-
rence, Florence, Italy
Enrico Premi - Stroke Unit, Neurology Unit, Spedali Civili Hospital,
Brescia, Italy
Sara Prioni - Fondazione Istituto di Ricovero e Cura a Carattere Sci-
entifico Istituto Neurologico Carlo Besta, Milan, Italy
Catharina Prix - Department of Neurology, Ludwig-Maximilians-
University Munich, Germany
Rosa Rademakers - Department of Neurosciences, Mayo Clinic,
Jacksonville, Florida, USA
Veronica Redaelli - Fondazione Istituto di Ricovero e Cura
a Carattere Scientifico Istituto Neurologico Carlo Besta, Milan,
Italy
Tim Rittman - Department of Clinical Neurosciences, University of
Cambridge, Cambridge, UK
Ekaterina Rogaeva - Tanz Centre for Research in Neurodegenera-
tive Diseases, University of Toronto, Toronto, Canada
Pedro Rosa-Neto - Translational Neuroimaging Laboratory, McGill
University Montreal, Québec, Canada
Giacomina Rossi - Fondazione Istituto di Ricovero e Cura a
Carattere Scientifico Istituto Neurologico Carlo Besta, Milan,
Italy
Martin Rossor - Dementia Research Centre, Department of Neu-
rodegenerative Disease, UCL Institute of Neurology, Queen Square,
London, UK
Beatriz Santiago - Neurology Department, Centro Hospitalar e Uni-
versitário de Coimbra, Coimbra, Portugal
Elio Scarpini - University of Milan, Centro Dino Ferrari, Milan, Italy;
Fondazione IRCSS Ca’ Granda, Ospedale Maggiore Policlinico, Neu-
rodegenerative Diseases Unit, Milan, Italy
Sonja Schönecker - Neurologische Klinik, Ludwig-Maximilians-
Universität München, Munich, Germany
Elisa Semler - Department of Neurology, Ulm University, Ulm, Ger-
many
Rachelle Shafei - Dementia Research Centre, Department of Neu-
rodegenerative Disease, UCL Institute of Neurology, Queen Square,
London, UK
Christen Shoesmith - Department of Clinical Neurological Sciences,
University of Western Ontario, London, Ontario, Canada
Miguel Tábuas-Pereira - Centre of Neurosciences and Cell Biology,
Universidade de Coimbra, Coimbra, Portugal
Mikel Tainta - Neuroscience Area, Biodonostia Health Research
Institute, Paseo Dr Begiristain sn, CP 20014, San Sebastian, Gipuzkoa,
Spain
Ricardo Taipa - Neuropathology Unit and Department of Neurology,
Centro Hospitalar do Porto - Hospital de Santo António, Oporto, Por-
tugal
David Tang-Wai - University Health Network Memory Clinic,
Toronto Western Hospital, Toronto, Canada
David L Thomas - Neuroradiological Academic Unit, UCL Institute of
Neurology, London, UK
Paul Thompson - Division of Neuroscience and Experimental Psy-
chology, WolfsonMolecular Imaging Centre, University of Manchester,
Manchester, UK
Hakan Thonberg - Center for Alzheimer Research, Division of Neu-
rogeriatrics, Karolinska Institutet, Stockholm, Sweden
Carolyn Timberlake - University of Cambridge, Cambridge,
UK
Pietro Tiraboschi - Fondazione Istituto di Ricovero e Cura a Carat-
tere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy
Philip Vandamme - Neurology Service, University Hospitals Leu-
ven, 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 Neuro-
sciences, KU Leuven, Leuven, Belgium
Michele Veldsman - University of Oxford, UK
Ana Verdelho- Department of Neurosciences, Santa Maria Hospital,
University of Lisbon, Portugal
Jorge Villanua - OSATEK Unidad de Donostia, San Sebastian,
Gipuzkoa, Spain
Jason Warren – Dementia Research Centre, Department of Neu-
rodegenerative Disease, UCL Institute of Neurology, Queen Square,
London, UK.
Carlo Wilke - Hertie Institute for Clinical Brain Research, University
of Tuebingen, Tuebingen, Germany.
Ione Woollacott – Dementia Research Centre, Department of Neu-
rodegenerative Disease, UCL Institute of Neurology, Queen Square,
London, UK.
Elisabeth Wlasich - Neurologische Klinik, Ludwig-Maximilians-
Universität München, Munich, Germany.
Henrik Zetterberg - Department of Neurodegenerative Disease,
UCL Institute of Neurology, London, UK.
Miren Zulaica - Neuroscience Area, Biodonostia Health Research
Institute, Paseo Dr Begiristain sn, CP 20014, San Sebastian, Gipuzkoa,
Spain.
... In contrast, reduced functional connectivity has been described in thalamic, frontotemporal and motor networks in a less extensive but similar anatomical distribution to symptomatic cohorts (Shoukry et al., 2020). It is hypothesised that the maintenance of functional network topography facilitates cognitive resilience in face of relentless structural changes (Rittman et al., 2019;Tsvetanov et al., 2021). The integrity of these functional networks then rapidly declines as patients become symptomatic (Rittman et al., 2019). ...
... It remains unclear whether increased connectivity represents a compensatory mechanism (Lee et al., 2019) reduced inhibition or stems from methodological factors (Proudfoot et al., 2018). Some studies suggest that the maintenance of functional network organisation contributes to cognitive resilience in face of evolving structural degeneration (Rittman et al., 2019;Tsvetanov et al., 2021;Bede et al., 2021a). The subsequent loss of functional network organisation is associated with emergent cognitive symptoms (Rittman et al., 2019;Tsvetanov et al., 2021). ...
... Some studies suggest that the maintenance of functional network organisation contributes to cognitive resilience in face of evolving structural degeneration (Rittman et al., 2019;Tsvetanov et al., 2021;Bede et al., 2021a). The subsequent loss of functional network organisation is associated with emergent cognitive symptoms (Rittman et al., 2019;Tsvetanov et al., 2021). While some studies detect complex functional reorganisation, others do not detect functional connectivity alterations Pievani et al., 2014;Premi et al., 2021). ...
Article
Full-text available
Computational imaging and quantitative biomarkers offer invaluable insights in the pre-symptomatic phase of neurodegenerative conditions several years before clinical manifestation. In recent years, there has been a focused effort to characterize pre-symptomatic cerebral changes in familial frontotemporal dementias using computational imaging. Accordingly, a systematic literature review was conducted of original articles investigating pre-symptomatic imaging changes in frontotemporal dementia focusing on study design, imaging modalities, data interpretation, control cohorts and key findings. The review is limited to the most common genotypes: chromosome 9 open reading frame 72 ( C9orf72 ), progranulin ( GRN ), or microtubule-associated protein tau ( MAPT ) genotypes. Sixty-eight studies were identified with a median sample size of 15 (3–141) per genotype. Only a minority of studies were longitudinal (28%; 19/68) with a median follow-up of 2 (1–8) years. MRI (97%; 66/68) was the most common imaging modality, and primarily grey matter analyses were conducted (75%; 19/68). Some studies used multimodal analyses 44% (30/68). Genotype-associated imaging signatures are presented, innovative study designs are highlighted, common methodological shortcomings are discussed and lessons for future studies are outlined. Emerging academic observations have potential clinical implications for expediting the diagnosis, tracking disease progression and optimising the timing of pharmaceutical trials.
... 1,2 Understanding the timing and consequence of such changes for clinical syndromes is key to accounting for heterogeneity in progression and risk-stratifying asymptomatic individuals for preventative clinical trials. We have previously shown that functional network integrity is important in maintaining cognitive performance in individuals at risk of dementia, 3,4 with the corollary that loss of network integrity may herald symptom onset and predict cognitive decline. Genetic frontotemporal dementia (FTD) provides an opportunity to characterize functional networks throughout the course of the disease. ...
... Image acquisition for the two cohorts and fMRI preprocessing have been published previously 3,4,24 ...
Article
Full-text available
Introduction: We tested whether changes in functional networks predict cognitive decline and conversion from the presymptomatic prodrome to symptomatic disease in familial frontotemporal dementia (FTD). Methods: For hypothesis generation, 36 participants with behavioral variant FTD (bvFTD) and 34 controls were recruited from one site. For hypothesis testing, we studied 198 symptomatic FTD mutation carriers, 341 presymptomatic mutation carriers, and 329 family members without mutations. We compared functional network dynamics between groups, with clinical severity and with longitudinal clinical progression. Results: We identified a characteristic pattern of dynamic network changes in FTD, which correlated with neuropsychological impairment. Among presymptomatic mutation carriers, this pattern of network dynamics was found to a greater extent in those who subsequently converted to the symptomatic phase. Baseline network dynamic changes predicted future cognitive decline in symptomatic participants and older presymptomatic participants. Discussion: Dynamic network abnormalities in FTD predict cognitive decline and symptomatic conversion. Highlights: We investigated brain network predictors of dementia symptom onset Frontotemporal dementia results in characteristic dynamic network patterns Alterations in network dynamics are associated with neuropsychological impairment Network dynamic changes predict symptomatic conversion in presymptomatic carriers Network dynamic changes are associated with longitudinal cognitive decline.
... These findings are consistent with a growing body of literature suggesting that pulsatile, rather than steady, blood pressure is an important factor for brain health and higher cognitive functions 15,16,62,[108][109][110] . Future work needs to evaluate whether maintaining normal pulse pressure across the lifespan is the mediating factor of cognitive function and plays a role in the increasing relationship between brain function and cognition in old age [111][112][113][114][115] . ...
Article
Full-text available
Cardiovascular ageing contributes to cognitive impairment. However, the unique and synergistic contributions of multiple cardiovascular factors to cognitive function remain unclear because they are often condensed into a single composite score or examined in isolation. We hypothesized that vascular risk factors, electrocardiographic features and blood pressure indices reveal multiple latent vascular factors, with independent contributions to cognition. In a population-based deep-phenotyping study (n = 708, age 18–88), path analysis revealed three latent vascular factors dissociating the autonomic nervous system response from two components of blood pressure. These three factors made unique and additive contributions to the variability in crystallized and fluid intelligence. The discrepancy in fluid relative to crystallized intelligence, indicative of cognitive decline, was associated with a latent vascular factor predominantly expressing pulse pressure. This suggests that higher pulse pressure is associated with cognitive decline from expected performance. The effect was stronger in older adults. Controlling pulse pressure may help to preserve cognition, particularly in older adults. Our findings highlight the need to better understand the multifactorial nature of vascular aging.
... A number of studies have found associations between FC and specific FTD-related mutations (Lee et al., 2017;Shoukry et al., 2020;Tsvetanov et al., 2021;Whitwell et al., 2011), but there are also studies that failed to detect gene-related changes in FC (Bouts et al., 2018;Pievani, Paternicò, et al., 2014). The influence of specific FTDrelated genes in FC is complex enough to warrant a separate review. ...
Article
Full-text available
Introduction Functional connectivity (FC)—which reflects relationships between neural activity in different brain regions—has been used to explore the functional architecture of the brain in neurodegenerative disorders. Although an increasing number of studies have explored FC changes in behavioral variant frontotemporal dementia (bvFTD), there is no focused, in‐depth review about FC in bvFTD. Methods Comprehensive literature search and narrative review to summarize the current field of FC in bvFTD. Results (1) Decreased FC within the salience network (SN) is the most consistent finding in bvFTD; (2) FC changes extend beyond the SN and affect the interplay between networks; (3) results within the Default Mode Network are mixed; (4) the brain as a network is less interconnected and less efficient in bvFTD; (5) symptoms, functional impairment, and cognition are associated with FC; and (6) the functional architecture resembles patterns of neuropathological spread. Conclusions FC has potential as a biomarker, and future studies are expected to advance the field with multicentric initiatives, longitudinal designs, and methodological advances.
... Still, it is similar to other task-based fMRI studies in neurodegenerative populations [24,33,38]. Future studies may focus on other imaging modalities, like connectivity [28,34] and on the time-course of compensatory mechanisms in relation to disease progression. ...
Article
Background It has been argued that symptom onset in neurodegeneration reflects the overload of compensatory mechanisms. The present study aimed to investigate whether neural functional compensation can be observed in the manifest neurodegenerative disease stage, by focusing on a core deficit in frontotemporal dementia, i.e. social cognition, and by combining psychophysical assessment, structural MRI and functional MRI with multidimensional neural markers that allow quantification of neural computations.Methods Nineteen patients with clinically manifest behavioral variant frontotemporal dementia (bvFTD) and 20 controls performed facial expression recognition tasks in the MRI-scanner and offline. Group differences in grey matter volume, neural response amplitude and neural patterns were assessed via a combination of voxel-wise whole-brain, searchlight, and ROI-analyses and these measures were correlated with psychophysical measures of emotion, valence and arousal ratings.ResultsSignificant group effects were observed only outside task-relevant regions, converging in the caudate nucleus. This area showed a diagnostic neural pattern as well as hyperactivation and stronger neural representation of facial expressions in the bvFTD sample. Furthermore, response amplitude was associated with behavioral arousal ratings.Conclusions The combined findings reveal converging support for compensatory processes in clinically manifest neurodegeneration, complementing accounts that clinical onset synchronizes with the breakdown of compensatory processes. Furthermore, active compensation may proceed along nodes in intrinsically connected networks, rather than along the more task-specific networks. The findings underscore the potential of distributed multidimensional functional neural characteristics that may provide a novel class of biomarkers with both diagnostic and therapeutic implications, including biomarkers for clinical trials.
... Neurophysiological changes can occur prior to atrophy or in the absence of atrophy. This is in part because of the loss of synapses [80,81] and reductions in critical neurotransmitters [6] in bvFTD/PSP [32,33,82,83] and other neurodegenerative disorders [84][85][86]. Magnetoencephalography, or electroencephalography, may therefore provide sensitive markers of disease progression and drug response. ...
Article
Full-text available
There is a pressing need to accelerate therapeutic strategies against the syndromes caused by frontotemporal lobar degeneration, including symptomatic treatments. One approach is for experimental medicine, coupling neurophysiological studies of the mechanisms of disease with pharmacological interventions aimed at restoring neurochemical deficits. Here we consider the role of glutamatergic deficits and their potential as targets for treatment. We performed a double-blind placebo-controlled crossover pharmaco-magnetoencephalography study in 20 people with symptomatic frontotemporal lobar degeneration (10 behavioural variant frontotemporal dementia, 10 progressive supranuclear palsy) and 19 healthy age- and gender-matched controls. Both magnetoencephalography sessions recorded a roving auditory oddball paradigm: on placebo or following 10 mg memantine, an uncompetitive NMDA-receptor antagonist. Ultra-high-field magnetic resonance spectroscopy confirmed lower concentrations of GABA in the right inferior frontal gyrus of people with frontotemporal lobar degeneration. While memantine showed a subtle effect on early-auditory processing in patients, there was no significant main effect of memantine on the magnitude of the mismatch negativity (MMN) response in the right frontotemporal cortex in patients or controls. However, the change in the right auditory cortex MMN response to memantine (vs. placebo) in patients correlated with individuals’ prefrontal GABA concentration. There was no moderating effect of glutamate concentration or cortical atrophy. This proof-of-concept study demonstrates the potential for baseline dependency in the pharmacological restoration of neurotransmitter deficits to influence cognitive neurophysiology in neurodegenerative disease. With changes to multiple neurotransmitters in frontotemporal lobar degeneration, we suggest that individuals’ balance of excitation and inhibition may determine drug efficacy, with implications for drug selection and patient stratification in future clinical trials.
Preprint
Full-text available
Maintaining good cognitive function is crucial for well-being across the lifespan. We proposed that the degree of cognitive maintenance is determined by the functional interactions within and between large-scale brain networks. Such connectivity can be represented by the white matter architecture of structural brain networks that shape intrinsic neuronal activity into integrated and distributed functional networks. We explored how the function-structure connectivity convergence, and the divergence of functional connectivity from structural connectivity, contribute to the maintenance of cognitive function across the adult lifespan. Multivariate analyses were used to investigate the relationship between function-structure connectivity convergence and divergence with multivariate cognitive profiles, respectively. Cognitive function was increasingly dependent on function-structure connectivity convergence as age increased. The dependency of cognitive function on connectivity was particularly strong for high-order cortical networks and subcortical networks. The results suggest that brain functional network integrity sustains cognitive functions in old age, as a function of the integrity of the brain's structural connectivity.
Article
Full-text available
Human coronavirus disease 2019 (COVID-19) due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has multiple neurological consequences, but its long-term effect on brain health is still uncertain. The cerebrovascular consequences of COVID-19 may also affect brain health. We studied the chronic effect of COVID-19 on cerebrovascular health, in relation to acute severity, adverse clinical outcomes and in contrast to control group data. Here we assess cerebrovascular health in 45 patients six months after hospitalisation for acute COVID-19 using the resting state fluctuation amplitudes (RSFA) from functional magnetic resonance imaging, in relation to disease severity and in contrast with 42 controls. Acute COVID-19 severity was indexed by COVID-19 WHO Progression Scale, inflammatory and coagulatory biomarkers. Chronic widespread changes in frontoparietal RSFA were related to the severity of the acute COVID-19 episode. This relationship was not explained by chronic cardiorespiratory dysfunction, age, or sex. The level of cerebrovascular dysfunction was associated with cognitive, mental, and physical health at follow-up. The principal findings were consistent across univariate and multivariate approaches. The results indicate chronic cerebrovascular impairment following severe acute COVID-19, with the potential for long-term consequences on cognitive function and mental wellbeing.
Article
Full-text available
The preservation of cognitive function in old age is a public health priority. Cerebral hypoperfusion is a hallmark of dementia but its impact on maintaining cognitive ability across the lifespan is less clear. We investigated the relationship between baseline cerebral blood flow (CBF) and blood oxygenation level-dependent (BOLD) response during a fluid reasoning task in a population-based adult lifespan cohort. As age differences in CBF could lead to non-neuronal contributions to the BOLD signal, we introduced commonality analysis to neuroimaging to dissociate performance-related CBF effects from the physiological confounding effects of CBF on the BOLD response. Accounting for CBF, we confirmed that performance- and age-related differences in BOLD responses in the multiple-demand network were implicated in fluid reasoning. Age differences in CBF explained not only performance-related BOLD responses but also performance-independent BOLD responses. Our results suggest that CBF is important for maintaining cognitive function, while its non-neuronal contributions to BOLD signals reflect an age-related confound. Maintaining perfusion into old age may serve to support brain function and preserve cognitive performance.
Article
Introduction: The pathophysiological processes of neurodegenerative diseases begin years before diagnosis. However, pre-diagnostic changes in cognition and physical function are poorly understood, especially in sporadic neurodegenerative disease. Methods: UK Biobank data were extracted. Cognitive and functional measures in individuals who subsequently developed Alzheimer's disease (AD), Parkinson disease, frontotemporal dementia, progressive supranuclear palsy, dementia with Lewy bodies, or multiple system atrophy were compared against individuals without neurodegenerative diagnoses. The same measures were regressed against time to diagnosis, after adjusting for the effects of age. Results: There was evidence for pre-diagnostic cognitive impairment and decline with time, particularly in AD. Pre-diagnostic functional impairment and decline were observed in multiple diseases. Discussion: The scale and longitudinal follow-up of UK Biobank participants provides evidence for cognitive and functional decline years before symptoms become obvious in multiple neurodegenerative diseases. Identifying pre-diagnostic functional and cognitive changes could improve selection for preventive and early disease-modifying treatment trials.
Article
Full-text available
Accurate identification of brain function is necessary to understand the neurobiology of cognitive ageing, and thereby promote well-being across the lifespan. A common tool used to investigate neurocognitive ageing is functional magnetic resonance imaging (fMRI). However, although fMRI data are often interpreted in terms of neuronal activity, the blood oxygenation level-dependent (BOLD) signal measured by fMRI includes contributions of both vascular and neuronal factors, which change differentially with age. While some studies investigate vascular ageing factors, the results of these studies are not well known within the field of neurocognitive ageing and therefore vascular confounds in neurocognitive fMRI studies are common. Despite over 10 000 BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct for vascular effects. However, neurovascular ageing is not only a confound in fMRI, but an important feature in its own right, to be assessed alongside measures of neuronal ageing. We review current approaches to dissociate neuronal and vascular components of BOLD-fMRI of regional activity and functional connectivity. We highlight emerging evidence that vascular mechanisms in the brain do not simply control blood flow to support the metabolic needs of neurons, but form complex neurovascular interactions that influence neuronal function in health and disease. This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.
Article
Full-text available
Ageing is commonly associated with changes to segregation and integration of functional brain networks, but, in isolation, current network-based approaches struggle to elucidate changes across the many axes of functional organisation. However, the advent of gradient mapping techniques in neuroimaging provides a new means of studying functional organisation in a multi-dimensional connectivity space. Here, we studied ageing and behaviourally-relevant differences in a three-dimensional connectivity space using the Cambridge Centre for Ageing Neuroscience cohort (n=643). Building on gradient mapping techniques, we developed a set of measures to quantify the dispersion within and between functional communities. We detected a strong shift of the visual network across the adult lifespan from an extreme to a more central position in the 3D gradient space. In contrast, the dispersion distance between transmodal communities (dorsal attention, ventral attention, frontoparietal and default mode) did not change. However, these communities themselves were increasingly dispersed with increasing age, reflecting more dissimilar functional connectivity profiles within each community. Increasing dispersion of frontoparietal, attention and default mode networks, in particular, were associated negatively with cognition, measured by fluid intelligence. By using a technique that explicitly captures the ordering of functional systems in a multi-dimensional hierarchical framework, we identified behaviorally-relevant age-related differences of within and between network organisation. We propose that the study of functional gradients across the adult lifespan could provide insights that may facilitate the development of new strategies to maintain cognitive ability across the lifespan in health and disease.
Article
Full-text available
Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.
Article
Full-text available
Spatial smoothing of functional magnetic resonance imaging (fMRI) data can be performed on volumetric images and on the extracted surface of the brain. Smoothing on the unfolded cortex should theoretically improve the ability to separate signals between brain areas that are near together in the folded cortex but are more distant in the unfolded cortex. However, surface-based method approaches (SBA) are currently not utilized as standard procedure in the preprocessing of neuroimaging data. Recent improvements in the quality of cortical surface modeling and improvements in its usability nevertheless advocate this method. In the current study, we evaluated the benefits of an up-to-date surface-based smoothing in comparison to volume-based smoothing. We focused on the effect of signal contamination between different functional systems using the primary motor and primary somatosensory cortex as an example. We were particularly interested in how this signal contamination influences the results of activity and connectivity analyses for these brain regions. We addressed this question by performing fMRI on 19 subjects during a tactile stimulation paradigm and by using simulated BOLD responses. We demonstrated that volume-based smoothing causes contamination of the primary motor cortex by somatosensory cortical responses, leading to false positive motor activation. These false positive motor activations were not found by using surface-based smoothing for reasonable kernel sizes. Accordingly, volume-based smoothing caused an exaggeration of connectivity estimates between these regions. In conclusion, this study showed that surface-based smoothing decreases signal contamination considerably between neighboring functional brain regions and improves the validity of activity and connectivity results.
Article
Full-text available
A large body of literature is available on wound healing in humans. Nonetheless, a standardized ex vivo wound model without disruption of the dermal compartment has not been put forward with compelling justification. Here, we present a novel wound model based on application of negative pressure and its effects for epidermal regeneration and immune cell behaviour. Importantly, the basement membrane remained intact after blister roof removal and keratinocytes were absent in the wounded area. Upon six days of culture, the wound was covered with one to three-cell thick K14+Ki67+ keratinocyte layers, indicating that proliferation and migration were involved in wound closure. After eight to twelve days, a multi-layered epidermis was formed expressing epidermal differentiation markers (K10, filaggrin, DSG-1, CDSN). Investigations about immune cell-specific manners revealed more T cells in the blister roof epidermis compared to normal epidermis. We identified several cell populations in blister roof epidermis and suction blister fluid that are absent in normal epidermis which correlated with their decrease in the dermis, indicating a dermal efflux upon negative pressure. Together, our model recapitulates the main features of epithelial wound regeneration, and can be applied for testing wound healing therapies and investigating underlying mechanisms.
Article
Full-text available
Pooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared data from imaging repositories, presents exceptional opportunities to advance and enhance reproducibility of neuroscience research. However, scanner confounds hinder pooling data collected on different scanners or across software and hardware upgrades on the same scanner, even when all acquisition protocols are harmonized. These confounds reduce power and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach that implements a data-driven linked independent component analysis (LICA) to identify scanner-related effects for removal from multimodal MRI to denoise scanner effects. We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters. Our proposed denoising method shows a greater reduction of scanner-related variance compared with standard GLM confound regression or ICA-based single-modality denoising. Although we did not test it here, for combining data across different scanners, LICA should prove even better at identifying scanner effects as between-scanner variability is generally much larger than within-scanner variability. Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.
Article
Full-text available
Neuroinflammation is a key part of the etio-pathogenesis of Alzheimer's disease. We test the relationship between neuroinflammation and the disruption of functional connectivity in large-scale networks, and their joint influence on cognitive impairment.We combined [11C]PK11195 positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI) in 28 humans (12 females/16 males) with clinical diagnosis of probable Alzheimer's disease or mild cognitive impairment with positive PET biomarker for amyloid, and 14 age-, sex-, and education-matched healthy humans (8 females/6 males). Source-based 'inflammetry' was used to extract principal components of [11C]PK11195 PET signal variance across all participants. rs-fMRI data were pre-processed via independent component analyses to classify neuronal and non-neuronal signals. Multiple linear regression models identified sources of signal co-variance between neuroinflammation and brain connectivity profiles, in relation to group and cognitive status.Patients showed significantly higher [11C]PK11195 binding relative to controls, in a distributed spatial pattern including the hippocampus, medial, and inferior temporal cortex. Patients with enhanced loading on this [11C]PK11195 binding distribution displayed diffuse abnormal functional connectivity. The expression of a stronger association between such abnormal connectivity and higher levels of neuroinflammation correlated with worse cognitive deficits.Our study suggests that neuroinflammation relates to the pathophysiological changes in network function that underlie cognitive deficits in Alzheimer's disease. Neuroinflammation, and its association with functionally-relevant reorganisation of brain networks, is proposed as a target for emerging immuno-therapeutic strategies aimed at preventing or slowing the emergence of dementia.SIGNIFICANCE STATEMENTNeuroinflammation is an important aspect of Alzheimer's disease (AD), but it was not known whether the influence of neuroinflammation on brain network function in humans was important for cognitive deficit.Our study provides clear evidence that in vivo neuroinflammation in AD impairs large-scale network connectivity; and that the link between inflammation and functional network connectivity is relevant to cognitive impairment.We suggest that future studies should address how neuroinflammation relates to network function as AD progresses; and whether the neuroinflammation in AD is reversible, as the basis of immunotherapeutic strategies to slow the progression of AD.
Article
Background Frontotemporal dementia is a heterogenous neurodegenerative disorder, with about a third of cases being genetic. Most of this genetic component is accounted for by mutations in GRN, MAPT, and C9orf72. In this study, we aimed to complement previous phenotypic studies by doing an international study of age at symptom onset, age at death, and disease duration in individuals with mutations in GRN, MAPT, and C9orf72. Methods In this international, retrospective cohort study, we collected data on age at symptom onset, age at death, and disease duration for patients with pathogenic mutations in the GRN and MAPT genes and pathological expansions in the C9orf72 gene through the Frontotemporal Dementia Prevention Initiative and from published papers. We used mixed effects models to explore differences in age at onset, age at death, and disease duration between genetic groups and individual mutations. We also assessed correlations between the age at onset and at death of each individual and the age at onset and at death of their parents and the mean age at onset and at death of their family members. Lastly, we used mixed effects models to investigate the extent to which variability in age at onset and at death could be accounted for by family membership and the specific mutation carried. Findings Data were available from 3403 individuals from 1492 families: 1433 with C9orf72 expansions (755 families), 1179 with GRN mutations (483 families, 130 different mutations), and 791 with MAPT mutations (254 families, 67 different mutations). Mean age at symptom onset and at death was 49·5 years (SD 10·0; onset) and 58·5 years (11·3; death) in the MAPT group, 58·2 years (9·8; onset) and 65·3 years (10·9; death) in the C9orf72 group, and 61·3 years (8·8; onset) and 68·8 years (9·7; death) in the GRN group. Mean disease duration was 6·4 years (SD 4·9) in the C9orf72 group, 7·1 years (3·9) in the GRN group, and 9·3 years (6·4) in the MAPT group. Individual age at onset and at death was significantly correlated with both parental age at onset and at death and with mean family age at onset and at death in all three groups, with a stronger correlation observed in the MAPT group (r=0·45 between individual and parental age at onset, r=0·63 between individual and mean family age at onset, r=0·58 between individual and parental age at death, and r=0·69 between individual and mean family age at death) than in either the C9orf72 group (r=0·32 individual and parental age at onset, r=0·36 individual and mean family age at onset, r=0·38 individual and parental age at death, and r=0·40 individual and mean family age at death) or the GRN group (r=0·22 individual and parental age at onset, r=0·18 individual and mean family age at onset, r=0·22 individual and parental age at death, and r=0·32 individual and mean family age at death). Modelling showed that the variability in age at onset and at death in the MAPT group was explained partly by the specific mutation (48%, 95% CI 35–62, for age at onset; 61%, 47–73, for age at death), and even more by family membership (66%, 56–75, for age at onset; 74%, 65–82, for age at death). In the GRN group, only 2% (0–10) of the variability of age at onset and 9% (3–21) of that of age of death was explained by the specific mutation, whereas 14% (9–22) of the variability of age at onset and 20% (12–30) of that of age at death was explained by family membership. In the C9orf72 group, family membership explained 17% (11–26) of the variability of age at onset and 19% (12–29) of that of age at death. Interpretation Our study showed that age at symptom onset and at death of people with genetic frontotemporal dementia is influenced by genetic group and, particularly for MAPT mutations, by the specific mutation carried and by family membership. Although estimation of age at onset will be an important factor in future pre-symptomatic therapeutic trials for all three genetic groups, our study suggests that data from other members of the family will be particularly helpful only for individuals with MAPT mutations. Further work in identifying both genetic and environmental factors that modify phenotype in all groups will be important to improve such estimates. Funding UK Medical Research Council, National Institute for Health Research, and Alzheimer's Society.
Article
Background: Frontotemporal dementia is a heterogenous neurodegenerative disorder, with about a third of cases being genetic. Most of this genetic component is accounted for by mutations in GRN, MAPT, and C9orf72. In this study, we aimed to complement previous phenotypic studies by doing an international study of age at symptom onset, age at death, and disease duration in individuals with mutations in GRN, MAPT, and C9orf72. Methods: In this international, retrospective cohort study, we collected data on age at symptom onset, age at death, and disease duration for patients with pathogenic mutations in the GRN and MAPT genes and pathological expansions in the C9orf72 gene through the Frontotemporal Dementia Prevention Initiative and from published papers. We used mixed effects models to explore differences in age at onset, age at death, and disease duration between genetic groups and individual mutations. We also assessed correlations between the age at onset and at death of each individual and the age at onset and at death of their parents and the mean age at onset and at death of their family members. Lastly, we used mixed effects models to investigate the extent to which variability in age at onset and at death could be accounted for by family membership and the specific mutation carried. Findings: Data were available from 3403 individuals from 1492 families: 1433 with C9orf72 expansions (755 families), 1179 with GRN mutations (483 families, 130 different mutations), and 791 with MAPT mutations (254 families, 67 different mutations). Mean age at symptom onset and at death was 49·5 years (SD 10·0; onset) and 58·5 years (11·3; death) in the MAPT group, 58·2 years (9·8; onset) and 65·3 years (10·9; death) in the C9orf72 group, and 61·3 years (8·8; onset) and 68·8 years (9·7; death) in the GRN group. Mean disease duration was 6·4 years (SD 4·9) in the C9orf72 group, 7·1 years (3·9) in the GRN group, and 9·3 years (6·4) in the MAPT group. Individual age at onset and at death was significantly correlated with both parental age at onset and at death and with mean family age at onset and at death in all three groups, with a stronger correlation observed in the MAPT group (r=0·45 between individual and parental age at onset, r=0·63 between individual and mean family age at onset, r=0·58 between individual and parental age at death, and r=0·69 between individual and mean family age at death) than in either the C9orf72 group (r=0·32 individual and parental age at onset, r=0·36 individual and mean family age at onset, r=0·38 individual and parental age at death, and r=0·40 individual and mean family age at death) or the GRN group (r=0·22 individual and parental age at onset, r=0·18 individual and mean family age at onset, r=0·22 individual and parental age at death, and r=0·32 individual and mean family age at death). Modelling showed that the variability in age at onset and at death in the MAPT group was explained partly by the specific mutation (48%, 95% CI 35-62, for age at onset; 61%, 47-73, for age at death), and even more by family membership (66%, 56-75, for age at onset; 74%, 65-82, for age at death). In the GRN group, only 2% (0-10) of the variability of age at onset and 9% (3-21) of that of age of death was explained by the specific mutation, whereas 14% (9-22) of the variability of age at onset and 20% (12-30) of that of age at death was explained by family membership. In the C9orf72 group, family membership explained 17% (11-26) of the variability of age at onset and 19% (12-29) of that of age at death. Interpretation: Our study showed that age at symptom onset and at death of people with genetic frontotemporal dementia is influenced by genetic group and, particularly for MAPT mutations, by the specific mutation carried and by family membership. Although estimation of age at onset will be an important factor in future pre-symptomatic therapeutic trials for all three genetic groups, our study suggests that data from other members of the family will be particularly helpful only for individuals with MAPT mutations. Further work in identifying both genetic and environmental factors that modify phenotype in all groups will be important to improve such estimates. Funding: UK Medical Research Council, National Institute for Health Research, and Alzheimer's Society.