{"paper_id":"63cfa0cf-f35f-4fe6-9dad-e15febf87ff6","body_text":"Disruption of Macroscale Functional Network Organisation in Patients with Frontotemporal Dementia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Disruption of Macroscale Functional Network Organisation in Patients with Frontotemporal Dementia Raffaella Migliaccio, Arabella Bouzigues, Valérie Godefroy, Vincent Le Du, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3894211/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Nov, 2024 Read the published version in Molecular Psychiatry → Version 1 posted 18 You are reading this latest preprint version Abstract Neurodegenerative dementias have a profound impact on higher-order cognitive and behavioural functions. Investigating macroscale functional networks through cortical gradients provides valuable insights into the neurodegenerative dementia process and overall brain function. This approach allows for the exploration of unimodal-multimodal differentiation and the intricate interplay between functional brain networks. We applied cortical gradients mapping in frontotemporal dementia (FTD) patients (behavioural-bvFTD, non-fluent and semantic) and healthy controls. In healthy controls, two principal gradients maximally distinguished sensorimotor from default-mode network (DMN) and visual from salience network (SN). However, in bvFTD, this unimodal-multimodal differentiation was disrupted, impacting the interaction among all networks. Importantly, these disruptions extended beyond the observed atrophy distribution. Semantic and non-fluent variants exhibited more focal alterations in limbic and sensorimotor networks, respectively. The DMN and visual networks demonstrated contrasting correlations with social cognition performances, suggesting either early damage (DMN) or compensatory processes (visual). In conclusion, optimal brain function requires networks to operate in a segregated yet collaborative manner. In FTD, our findings indicate a collapse and loss of differentiation between networks that goes beyond the observed atrophy distribution. These specific cortical gradients’ fingerprints could serve as a novel biomarker for identifying early changes in neurodegenerative diseases or potential compensatory processes. Health sciences/Biomarkers/Prognostic markers Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Complex behaviours and higher-order cognition rely on distributed brain systems working synergistically for both serial and parallel processing 1,2. Extensively studied over the past 30 years, these functional networks are investigated by measuring temporal correlations between distributed and adjacent brain areas at rest 3. Some networks, like visual or sensorimotor networks, are implicated in sensory processing, while others, such as the salience network (SN) and the default-mode network (DMN), are crucial for higher-order cognitive tasks like detecting salient stimuli or engaging in mental wandering. Conditions like neurodegenerative disorders selectively impair networks like the DMN and SN 4. Examining functional networks through resting-state functional magnetic resonance imaging (rs-fMRI) to measure functional connectivity (FC) between regions3,5 is thus valuable to understand how neurodegeneration affects the brain and consequently, cognitive and behavioural functions. Pioneering studies in resting-state FC have suggested that brain networks are anticorrelated with one another, meaning that cognitive and behavioural functions are due to the simultaneous decrease and increase of activity within different networks 6. Anticorrelations play a crucial role in the brain's functional architecture, developing during brain development 6,7. Similarly, changes in FC during aging and neurodegeneration involve not only abnormalities within networks but also a reorganisation of the interactions between large-scale networks 8,9. With this in mind, a cortical gradient mapping approach offers a lens through which to characterise the relationship between connectivity patterns of functional networks. Applied to a large group of healthy individuals, this method describes a principal gradient of connectivity differentiation along the cortical surface, with sensory cortices showing maximal FC pattern differences from regions involved in transmodal association processing 10 (Fig. 1 ). Put to the test in clinical populations, gradient mapping identifies that the distance between sensory and transmodal networks is modified in neurological diseases such as ischaemic stroke 11 , autism spectrum disorders 12 , generalised epilepsy 13 and depression 14 , 15 . The FC space in which the networks operate in contracts and networks become less differentiated ( de differentiated), suggesting an alteration of the interplay between networks. Gradient mapping presents advantages over other methods by minimising prior, not assuming sharp boundaries between functional networks, and therefore enabling the investigation of the interrelationships between functional networks within a continuous functional connectivity space. It has thus been suggested this framework provides a realistic model of brain functioning and distant potential effects of pathology. Frontotemporal dementia (FTD) is a heterogeneous group of neurodegenerative conditions presenting with distinct deterioration of behaviour 16 language 17 and/or motor functions involving frontotemporal brain regions. There is a growing body of evidence on FC changes in FTD 18 – 20 . However, reported findings remain variable as well as their contribution to better understanding brain dynamics, effects of neurodegeneration and clinical consequences. The aim of the present study was to apply a cortical gradient mapping approach to investigate FC changes in people affected by behavioural (bvFTD), and language variants; semantic (svPPA) and non-fluent (nfvPPA). We hypothesised that we would find a principal gradient of functional network organisation, spanning from the DMN to primary sensory networks in controls and that this would be broadly maintained in the FTD patient groups. However, our first hypothesis was that all patients would show evidence of an overall constriction of the cortical hierarchy compared with controls (i.e. reduced connectome gradient range), particularly involving changes at the transmodal extremes (i.e. the DMN and SN). Secondly, in view of the clinical symptoms and associated patterns of atrophy, we expected that people with bvFTD would show the most widespread alterations. Finally, we expected such macroscale network changes to be functionally relevant and relate to these patients’ clinical symptoms. Methods Subjects Participants were recruited through two independent research studies within two research centres. Data collection from site 1 was part of the ECOCAPTURE study, sponsored by the French national institute for biomedical research (INSERM, C16-87), based at the Paris Brain Institute (more details here: https://www.clinicaltrials.gov/ct2/show/NCT03272230 ). Data collection from site 2 was part of the Longitudinal Investigation of FTD (LIFTD) study which took place at the Dementia Research Centre within University College London (more details here: https://www.ucl.ac.uk/drc/longitudinal-investigation-ftd ). All participants provided written informed consent prior to participating in the studies in accordance with the declaration of Helsinki. Each study was granted approval by the respective local ethics committee. Anonymity was preserved for all participants. A total of 129 participants were included in this study; 52 healthy control subjects and 42 bvFTD patients across both sites, as well as 17 patients with svPPA and 18 with nfvPPA from site 2 (Table 1 ). Diagnoses were established based on current diagnostic criteria 16 , 17 . There were no significant differences in age, education level, gender and MMSE scores between the two sites for each participant group (p > 0.05), thus these independently acquired datasets were merged for subsequent analyses. Table 1 Demographic details for controls and each FTD patient group. Group N Site (1:2) Age Sex (F:M) Education MMSE Disease Duration miniSEA Letter Fluency Category Fluency Controls 52 18:34 63.6 ± 6.4 27:25 14.9 ± 3.0 29.4 ± 0.8 - 26.0 ± 1.48 (N = 28) 15.5 ± 5.7 (N = 29) 24.0 ± 6.2 (N = 29) bvFTD 42 22:20 65.9 ± 7.7 12:30 14.2 ± 3.9 23.2 ± 4.0 4.2 ± 2.1 19.2 ± 4.99 (N = 23) - - svPPA 17 0:17 64.0 ± 6.7 5:12 14.9 ± 3.0 22.8 ± 7.9 4.5 ± 1.7 - 9.1 ± 4.66 (N = 10) 7.4 ± 4.17 (N= 10) nfvPPA 18 0:17 70.6 ± 8.5 9:9 13.5 ± 2.6 20.9 ± 9.3 3.6 ± 1.5 - 5.7 ± 4.24 (N = 10) 9.2 ± 6.03 (N = 10) Age and Disease Duration are presented as mean number of years ± standard deviation, MMSE is presented as mean score ± standard deviation. Site as number of participants recruited from site 1 to site 2 ratio and sex as number of females to males ratio. Abbreviations: behavioural variant FTD = bvFTD, semantic variant FTD = svPPA, non-fluent variant FTD = nfvPPA, Mini Mental State Examination = MMSE. Cognitive Assessments Participants carried out the Mini Mental State Examination (MMSE), the mini-Social cognition & Emotional Assessment battery 21 to evaluate deficits in social cognition (miniSEA), phonemic fluency (letter F) and category fluency (animals). Mean scores, standard deviations and sample sizes are presented in Table 1 . Patient groups’ and controls’ performance on these cognitive tests were compared using t-tests. Imaging Data Acquisition and Preprocessing Volumetric T1 scans and resting-state fMRI scans were acquired at the neuroimaging core facility (CENIR) of the Paris Brain Institute and at University College London Hospital (UCLH). Sites were respectively equipped with a 3T Siemens Prisma and Trio whole-body scanner and a 12-channel head coil. T1-weighted images were acquired using a magnetisation prepared rapid acquisition gradient echo pulse sequence (MPRAGE). Site 1 anatomical protocol involved TR = 2.4s TE = 2.17ms; TE = 2.17ms; flip angle = 8°; voxel size = 1mm isotropic; slice thickness = 0.7mm. Site 2 anatomical protocol involved TR = 2.4s TE = 2s; TE = 2.93ms; flip angle = 8°; voxel size = 1mm isotropic; slice thickness = 1.1mm. Functional data based on the blood oxygenation level-dependent (BOLD) signal were acquired using a T2*-weighted echo-planar image (EPI) pulse sequence. Site 1 functional protocol involved TR = 2050ms, TE = 25ms, flip angle = 80°, oblique axial slices of the brain were acquired at 290 or 436 time points with a voxel resolution of 2 mm. Site 2 functional protocol involved TR = 2500ms, TE = 30ms, flip angle = 80°, oblique axial slices of the brain were acquired at 200 time points with a voxel resolution of 2 mm. Participants were asked to lie with their eyes closed, without falling asleep during the resting-state acquisition run. T1 scans and fMRI resting-state time series for all participants were preprocessed using fMRIprep 21.0.1 22 , an automated Nipype-based preprocessing pipeline for fMRI data implemented in Python, which uses tools from software packages including FSL, ANTs, FreeSurfer and AFNI. Briefly, the pipeline included bias field correction, skull stripping, brain tissue segmentation, slice time correction, correction for head motion parameters, co-registration to corresponding structural image, and non-linear spatial normalisation to MNI space. Further details on anatomical and functional data preprocessing can be found in Supplementary Methods. Imaging Data Analysis Anatomical scans Measures of cortical thickness were obtained using FreeSurfer’s automated anatomical statistics extraction pipeline for each participant and for each parcel of the Schaefer atlas (400 parcels). To transfer the Schaefer parcellation volume to subject space, we used the Multi Atlas Transfer Tool 23 . To show structural grey matter differences in patient groups compared to controls, we averaged cortical thickness for each parcel within each patient group and presented these as percentage cortical thickness of control mean. Resting-state scans We used mean framewise-displacement 24 as a quality assurance parameter. Thus, subjects were included in subsequent analyses if their mean framewise head displacement in the MRI was below the threshold of 0.55 mm, as used in previous work with similar patient populations 25 . Six bvFTD (three from each site) and two svPPA patients did not meet these criteria and were therefore excluded from subsequent analyses. To remove physiological and other sources of noise from the fMRI time series, nuisance covariates were regressed out according to the 36-parameter model 26 . The fMRI confounds generated with fMRIprep were loaded using the load_confound (v. 0.6.4.) Python package. Six motion parameters, signals estimated from cerebrospinal fluid (CSF) and white matter (WM), the whole-brain global signal, their derivatives, quadratic terms, and squares of derivatives were regressed out from functional data separately for each run. The rs-fMRI data from each subject was smoothed with a full width at half maximum 6 mm Gaussian kernel, temporally bandpass filtered in the 0.01–0.1 Hz frequency range and spatially parcellated (400 parcels) according to the Schaefer atlas 26 . To estimate connectivity gradients, we applied generalised Canonical Correlation Analysis (gCCA) to subject’s mean functional connectivity matrix. This decomposes the functional connectome into primary components, referred to as gradients, with each gradient explaining varying levels of variance in connectivity. These gradients discriminate across levels of the cortical hierarchy (i.e., sensory processing vs. higher-order cognition), whereas region specific values along the gradient, referred to as embedding values, reflect the similarity in connectivity along the sensory-transmodal axis. Further details on the connectome gradient mapping pipeline can be found in Supplementary Methods. Gradient mapping We investigated gradient differences between FTD groups and controls. Each of the 400 brain parcels for which we extracted embedding values along the principal and secondary gradients belongs to a canonical functional network within the partition scheme described by Yeo and colleagues 27 (Fig. 1 ). The present work focused on these two first functional gradients which show discernible patterns of regional variation and explain the most variance. The tertiary gradient is presented in Supplementary Fig. 1. We performed a mixed effects model to compare principal and secondary gradient values for each parcel allocated to a given functional network between controls and each FTD group. Thus, gradient scores for each parcel were included in the model as the dependent variable, while network label as well as group were entered as fixed effects. Subject, parcel label and site of data acquisition were entered as random effects. Finally, age and sex were also included in the model as fixed effects. We then investigated the Group x Network interaction and performed post-hoc pairwise tests, comparing each network between controls and each FTD group. Resulting p-values were corrected for multiple comparisons including the three group comparisons and the 400 parcels using the Benjamini-Hochberg FDR correction. Correlation of gradient changes with cognitive measures As only a small subgroup of PPA patients completed the cognitive tests, we only computed correlations with mean network gradient embedding values in bvFTD patients. To investigate the clinical relevance of altered connectome gradients in bvFTD patients, we correlated the miniSEA with average network principal and secondary gradient values using Pearson’s correlations. Only extreme end networks which showed significant differences compared to controls were included in these correlations. Correlations between verbal fluency test scores and principal and secondary gradients’ end networks which showed significant differences compared to controls were not statistically performed in the PPA subgroups because of limited cognitive data availability. We investigated whether a broad trend towards linear relationships existed, and these are presented in Supplementary materials. All statistical tests were conducted in RStudio (v 4.2.0). Results on the cortical surface are presented using the opensource python package ‘visbrain’. Results Demographics and Cognition As demographic details did not significantly differ between the two sites for each participant group (p > 0.05), these independently acquired datasets were merged for subsequent analyses. There were no significant demographic differences between patient groups and controls (p > 0.05), except for nfvPPA patients being older than the other patient groups (p < 0.02) (Table 1 ). As expected, patient groups all had a significantly lower MMSE score than controls (p < 0.001) (Table 1 ). Moreover, compared to controls, bvFTD patients showed significantly reduced scores on the miniSEA (p < 0.0001) and both nfvPPA and svPPA patients showed significantly lower scores on the verbal fluency test (p < 0.0001) (Table 1 ). Violin plots of bvFTD patients’ and controls performance on the miniSEA can be found in Supplementary Fig. 2. Cortical thickness Each clinical FTD group showed reduced cortical thickness compared to controls in expected regions. Thus, bvFTD patients showed an average of around 10% reduction of cortical thickness compared to controls in bilateral medial prefrontal cortex, anterior cingulate cortex and middle temporal gyrus as well as around a 20% reduction in the bilateral anterior temporal lobes and the frontoinsula region. svPPA patients showed reduced cortical thickness by up to 30% in left anterior temporal lobe, particularly the temporal pole, as well as up to around 25% in right temporal pole compared to controls. In nfvPPA, the pattern of reduced cortical thickness compared to controls was a lot more widespread within the frontal and parietal lobes involving up to 20% reductions within the supplementary motor area, particularly on the left, as well as up to 15% reduction in medial and inferior frontal cortex and left superior temporal gyrus (Fig. 2 ). Cortical gradients We applied a gCCA gradient approach on rs-fMRI-based connectivity data from different FTD patient groups and a healthy control group to derive cortical connectivity gradients reflecting processing hierarchies spanning sensory and transmodal areas. In controls, the first two gradients explained a total of 48% of the variance; 29% and 19% respectively (Supplementary Fig. 3). There was no significant difference in the variance explained by each gradient between controls and the patient groups (Mann-Whitney-U, p < 0.05). The principal gradient anchored sensorimotor areas at its positive extreme and DMN at its negative extreme (Fig. 3 A), with a gradual transition from sensory to transmodal association networks similar to what has been reported in previous work (Margulies et al., 2016). This axis separates immediate sensorimotor processing from higher-order mind-wandering processes and could be considered an axis dissociating and thus enabling external vs internal processing. Along our secondary gradient, the visual network occupied the negative extreme, while areas from the SN populated the positive end of this gradient (Fig. 3 B). This more complex pattern possibly reflects a higher-order gradient separating regions attending to externally presented visual cues from regions devoted to interpreting their social relevance 15 which could be considered an axis facilitating observed vs predicted states. Figure 4 reports results in patient populations. Local alterations were notable, particularly widespread in bvFTD patients and more focal in the language variants, suggestive of changes in functional network segregation which are discussed and further interpreted below. Principal and secondary gradients group comparisons The mixed model comparing groups on the principal and secondary gradients scores for each of the 400 parcels identified main effects of Network (p < 0.0001) as well as significant Group x Network interactions (p < 0.0001). All p-values reported are corrected for multiple comparisons. Pairwise comparisons at each network level found that bvFTD patients showed significantly different principal gradient scores within the DMN, the SN and visual network compared to controls (p < 0.0001), their embedding values shifting towards the centre of the spectrum. Similarly, nfvPPA patients showed significantly different principal gradient scores within the DMN and sensorimotor network compared with controls (p < 0.0001), with both extreme-end networks’ embedding values shifting towards the centre of the spectrum. Finally, svPPA patients also showed significantly different principal gradient values within the limbic network (p < 0.0001) and the DMN compared to controls (p = 0.04), again these networks’ embedding values shifted towards the center of the spectrum. Figure 5 (left) shows the adjusted embedding value means along the principal gradient for each functional network, grouped for each FTD variant. Controls’ means are presented on the left for comparison. More details from the statistical model are presented in Supplementary Table 1. All networks along the secondary gradient in bvFTD patients showed significant differences compared to controls (p < 0.01), with the largest changes occurring within the networks on either end of the spectrum, the SN and visual network (p < 0.0001). Though the SN showed convergence towards the centre of the spectrum, the visual network expanded the axis by shifting away from the SN. Moreover, the middle networks’ embedding values mostly shifted towards the SN end of the spectrum, apart from the DMN which shifted towards the visual end. Similarly, nfvPPA patients showed significant changes along the secondary gradient with the same direction of changes of the SN and visual network as in bvFTD patients compared to controls (p < 0.0001). Moreover, nfvPPA patients also showed a significant shift of the limbic (p = 0.03) and sensorimotor network (p < 0.0001) towards the SN and thus away from the visual network. Finally, svPPA patients showed an expansion of the spectrum with visual network shifting away from the SN (p < 0.0001) whilst the limbic (p = 0.010 and sensorimotor (p < 0.0001) networks’ embedding values significantly shifted along the secondary gradient, towards the SN. Figure 5 (right) shows the adjusted embedding value means along the principal gradient for each functional network, grouped for each FTD variant. Controls’ means are presented on the left for comparison. More details from the statistical model are presented in Supplementary Table 1. Figure 6 presents an overview of the distribution of the embedding values along the principal (left) and secondary (right) gradient, grouped according to functional network with a distribution plot for each FTD variant and controls. Clinical relevance of principal and secondary gradients To assess the relevance of principal connectome gradient changes to behavioural symptoms, we correlated miniSEA with average DMN principal gradient values in bvFTD patients as this extreme end network showed a significant difference compared to controls. The DMN average principal gradient score was significantly correlated with the miniSEA score in bvFTD patients (r = -0.44, p = 0.05, uncorrected). Similarly, average secondary gradient values within the visual network and SN were correlated with the miniSEA and we found that visual network secondary gradient values negatively correlated with the miniSEA (r= -0.53, p = 0.04, corrected for multiple comparisons). In both cases, the more the DMN or visual network embedding values were towards the center of the gradient, the worse the score on the miniSEA and the more they were towards the extreme end of the gradient the better the score on the miniSEA (Fig. 7 ). Some linear relationships can be observed in both svPPA and nfvPPA patients between verbal fluency scores and extreme-end networks of the principal and secondary gradients (Supplementary Fig. 4). These will not be further discussed as not investigated using statistical analyses. Discussion Summary of findings In the present study, we used a connectome gradient mapping approach to investigate functional network organisation in a large sample of patients affected by FTD. Healthy control subjects presented a prominent expected hierarchy and differentiation of functional networks’ connectivity patterns. The principal gradient captured a progressive hierarchy between sensorimotor (external processes) and transmodal association regions (internal processes), whilst the secondary gradient underpinned a sharp distinction between the SN (predicted processes) and visual cortex (observed processes). Together, these principles of network organisation explained 48% of the variance within the data. Such macroscale functional network organisation captures a healthy topography enabling the transition from concrete perception to abstract cognitive functions, the basis of the evolutionary transition from apes to humans and of higher-order cognitive and behavioural functions 28 . Though such gradients of network organisation were broadly maintained in patients with FTD, we found evidence of extreme-end networks shifting towards the centre of these axes, leading to an overall FC space constriction in all patient groups, which was more severe in bvFTD, and confirming our first hypothesis of a breakdown of this organisation in FTD. Importantly, connectome gradient mapping enabled us to identify changes at the macroscale level. We found that vital hierarchies of network organisation were constricted in all three FTD patient groups, with bvFTD patients’ pathology particularly impacting the axis dissociating predicted vs observed processes captured by the secondary gradient. Moreover, nfvPPA and svPPA patients showed specific sensorimotor and limbic network behavioural changes. Finally, the visual network showed an opposite pattern of modification in relation to other networks in all FTD patient groups. The DMN and visual networks demonstrated contrasting correlations with social cognition performances measured by the miniSEA test. DMN and SN dedifferentiation in bvFTD Previous work in healthy individuals has described a principal gradient spanning from the DMN to the sensorimotor network, with intermediate networks positioned in between 10 . This axis differentiates patterns of connectivity of regions involved in internal processes versus those responsible for external processes. In bvFTD patients, we found a noteworthy shift in DMN embedding values towards the center, contracting the principal gradient axis. Altered DMN FC in bvFTD patients have been found by several studies using classical methods. Some studies find increased FC in patients with bvFTD compared to controls (i.e. hyperconnectivity) 18 , 29 – 33 and others have found decreased (i.e. hypoconnectivity) or a mix of directional changes 19 , 34 – 37 . In response to the accumulation of brain pathology, the recruitment of neural resources not usually involved may be compensatory to sustain normal cognition 38 . However, it remains unclear whether hyperconnectivity represents compensatory processes or aberrant excitation due to early damage. In bvFTD patients, we found that the DMN, originally anchored at the internal processing end of the principal gradient spectrum, shifts towards regions associated with sensory/external processing. This alteration suggests a decrease in the specificity of DMN activation patterns, leading to neural de differentiation whereby the DMN may activate in situations in which it would not normally be activated in or vice versa . This phenomenon may explain the mixed findings of hypo- and hyper- connectivity observed in previous studies. Additionally, we found a correlation between the DMN principal gradient embedding values and scores of social cognition test (miniSEA) in bvFTD patients. A shift of the DMN towards the center, constraining the axis, correlated with poorer miniSEA scores. This highlights the significance of maintaining an axis that distinguishes FC patterns of regions involved in external (sensorimotor network) versus internal (DMN) processes for better social cognition. Taken together, these results suggest that the DMN is losing its network differentiation, and this loss of functional specificity relates to the behavioural and cognitive deficits seen in bvFTD patients. Thus, by examining the global network organisation, our work sidesteps questions about the direction of change and highlights core features at play in bvFTD patients. There is a growing body of evidence indicating significant decreases in FC of the SN in bvFTD patients 9 , 18 – 20 , 29 – 32 , 36 , 37 , 39 – 46 . The frontoinsula and anterior cingulate cortex, hubs within the SN, are home to a concentrated number of von Economo neurons and fork cells. Such cells are believed to be particularly vulnerable to misfolded FTD pathological protein accumulation (Seeley et al. 2007, 2012). This may explain why the SN is found to be particularly disrupted in bvFTD patients. SN disruption has been related to symptoms in bvFTD patients, with previous research highlighting the interplay of FC changes involving both the DMN and SN 18 , 29 , 47 , 48 . These findings support a model in which the SN and DMN are anticorrelated and exert an inhibitory influence on each other which seems crucial for responding to prevailing goals and conditions 49 . In the current study, the SN, located at the external processing extreme of the principal gradient, exhibited a significant shift towards the DMN/internal processing extreme compared to controls. Moreover, the SN’s change along the secondary gradient, which placed this network at the opposite end from the visual network, was much the same. This reduced differentiation of FC activity patterns between the SN and DMN, observed along both the principal and secondary gradients, is novel and may contribute to other networks’ FC patterns and, consequently, symptomatology in bvFTD patients. These findings emphasise the intricate relationship between the DMN, SN, and broader network dynamics in neurodegeneration. Visual network functional compensation? In our study, the secondary gradient maximally differentiated the SN from the visual network in healthy subjects, underlying a functional dissociation between predicted and observed processes. In patients, both bvFTD and nfvPPA patients exhibited a significant shift of the SN towards the center. Moreover, the visual network displayed an opposite change with a shift outwardly, expanding the gradient's range in all three FTD variants. Alterations in FC within the visual network have been reported in previous FTD studies 9 , 50 . Some work has suggested changes in the relationship between visual network and others such as the SN 20 , 29 and dorsal attentional network 51 . The collapse of the SN in bvFTD and nfvPPA, whether pathological or functional, may prompt changes in the visual network’s behaviour. The shift of visual network outwardly along the secondary gradient could be interpreted as a compensatory mechanism, preserving the neural differentiation between regions involved in predicted and observed processes. The dissociation of such mechanisms is vital for sustaining cognitive abilities. This hypothesis gains support from the observed correlation between visual network embedding values along the secondary gradient and scores on the miniSEA in bvFTD patients. Enhanced differentiation of the visual network from other networks correlated with better miniSEA scores, reinforcing the behavioural relevance of this possible compensatory response. The relationship between social cognition and the visual system is supported by parallels with studies in autism spectrum disorder patients, where visual gaze patterns are linked to social cognition 52 . Moreover, a recent behavioural study found that patients with bvFTD showed increased fixations to the eyes of emotional faces compared to controls, which may enable the allocation of attention to emotionally relevant cues to compensate for a core deficit in contextualising emotional information 53 . However, further investigation is needed to establish a direct link between visual behavioural changes and social cognition in bvFTD patients. Another hypothesis is that visual network may show overexcitation due to lack of inhibitory control processes because of neurodegeneration. Functional rearrangements during neurodegeneration can manifest as a prevalent and progressive increase of FC in earlier stages of the disease and a subsequent decrease as neurodegeneration progresses towards a more severe stage. Similar FC phenomenon in visual networks have also been observed in other focal neurodegenerative diseases 54 . In the framework of a loss of inhibitory control, previous work has suggested that the SN, for example, plays a role in modulating the activity of large-scale networks, relating to aberrant judgement and interpersonal behaviour in patients with bvFTD 47 . The current findings of significant shifts in all networks along the secondary gradient in bvFTD patients imply a large-scale disinhibited system lacking neural specificity which supports the modulation role of SN. Nevertheless, if this hypothesis were to be confirmed, the reasons behind changes in visual network activity in the present svPPA patients remain unclear due to not finding SN modifications in these patients 55 . While the study could not explore this functional relevance in the PPA subgroups due to sample size constraints, previous work has reported svPPA patients to frequently exhibit frank changes in their visual attention allocation 56 , 57 , aberrant gaze patterns 58 and disproportionate impairments with naming visual objects 59 and visual attribute reporting relative to other modalities 60 , 61 . Despite these functions not exclusively relying on the visual network, evidence suggests visual behavioural changes across all FTD variants. Sensorimotor and limbic network alterations in PPA variants Though PPA variants exhibited certain network changes akin to those observed in bvFTD patients, these patients manifested distinctive alterations within sensorimotor and limbic networks. While nfvPPA patients showed a constriction of the principal gradient at the DMN/internal processing end of the axis, they also showed large constrictions at the sensorimotor network/external processing end which was less differentiated from other networks. Other work highlights the involvement of sensorimotor regions by showing reduced grey matter volume 62 , 63 hypometabolism 64 , 65 or disrupted white-matter tracts 63 , 66 , 67 within motor cortices in nfvPPA patients. Work investigating FC changes in these patients is sparse and has only suggested local FC reductions involving frontotemporal cortex and subcortical structures 68 , 69 . Using a priori regions of interest, a previous study found that right supplementary motor area showed lower FC with speech and language regions and this correlated with their articulatory error score 70 . Using whole-brain connectome gradient mapping, without limiting our analyses to specific networks, we highlight intrinsic sensorimotor network changes at a whole network level particularly in nfvPPA patients. Moreover, the changes of sensorimotor network along the SN/predicted versus visual/observed axis enables us to conjecture further. In our controls, this network demonstrated a bimodal distribution including two distinct modes along this secondary gradient thus dissociating two groups of regions with differing patterns of connectivity. nfvPPA patients showed a striking smoothing of this distribution. Previous studies using gradient mapping may have also uncovered a bimodal distribution of sensorimotor network in controls 71 but the authors do not comment upon this limiting our interpretation of what the single mode distribution may mean in nfvPPA patients. A hypothesis could be that this bimodal distribution dissociates sets of body part representations. The 17-network parcellation described by Yeo and colleagues divided the sensorimotor strip into dorsal and ventral subnetworks and the boundary between these was roughly positioned between the hand and tongue representations 27 . Thus, the high kurtosis found in nfvPPA patients could represent a blurring of this boundary, which may relate to the specific oro-facial symptoms characteristic of this patient group 17 . Similarly, svPPA patients showed pronounced changes within the limbic network along the principal gradient. Limbic network shifted away from the DMN/internal processing end and towards the sensorimotor/external processing end in these patients. Additionally, controls also showed a bimodal distribution of the limbic network along the secondary gradient, which was smoothed in all FTD groups, but particularly in svPPA patients. Very little work has assessed limbic network FC changes in svPPA, with some studies mentioning changes to limbic structures 31 or specific disconnections of anterior and inferior temporal lobe from other brain regions 34 , 72 . A recent study found that svPPA patients as well as bvFTD patients showed lower mean FC within the limbic network compared to controls 55 , 73 . Our results are consistent with these recent suggestions. However, the resting-state functional networks described in this work 27 do not include subcortical structures or the basal ganglia, making our results limited to the network at the cortical level only. Furthermore, Yeo’s 17-network parcellation also dissociates the limbic network into two subnetworks, orbitofrontal cortex versus temporal pole, which may be the reason for the bimodal distribution we found in controls. Our FC changes may be blurred by the fact that atrophy in svPPA is particularly pronounced in temporal pole and less so in orbitofrontal cortex. Finally, limbic network interpretation is limited by orbitofrontal and temporal lobe regions being particularly affected by susceptibility gradients causing image distortions and signal loss 74 . Overall, most previous work in PPA has highlighted changes in DMN or SN 31 , 51 , 75 or disconnections of specific brain regions with others 34 , 68 , 69 , 72 rather than investigating global functional network changes. Our findings of a specific FC fingerprint involving sensorimotor and limbic network changes in nfvPPA and svPPA patients respectively using a whole-brain functional connectome approach are therefore novel and warrant further investigation to understand the specific behavioural patterns of the networks, their relationship to function and their potential future use as a biomarker in these PPA variants. Relationship between Atrophy and FC Atrophy and FC are undeniably closely related 4 . Findings of DMN and SN changes are fully consistent with grey matter hubs of atrophy in bvFTD 76 and we found up to 20–30% reductions in grey matter volumes in regions within these networks in bvFTD patients compared to controls. Similarly, findings of changes within sensorimotor and limbic network in nfvPPA and svPPA respectively are consistent with their atrophy patterns within sensorimotor cortices or orbitofrontal cortex and temporal poles 77 . Although related, our study highlights that atrophy patterns do not perfectly overlap with functional network changes 78 . Thus, functional network changes, including reorganisation, do not seem to be fully explained by atrophy alone in any FTD subtype 79 . However, recent work has suggested that longitudinal spread of atrophy can be predicted using an individual’s functional connectome 80 . In this previous work, shortest path length (in FC space) to the epicenter, combined with quantifying atrophy within a given region’s network neighbours, accurately estimated the spatial pattern of subsequent atrophy in patients with bvFTD and svPPA. Such work highlights the use of FC profiles in atrophy pattern progression prediction. The onset, evolution and relationship of pathological structural and functional changes in FTD is still not fully understood, even at the group level, as studies have lacked multimodal longitudinal data 81 . Concluding remarks and perspectives Gradient mapping offers a lens through which to characterise large-scale brain network relationships, the building blocks of brain function. Though there may be specific network changes, this method enabled us to highlight that FC changes involve a widespread disruption of the evolutionarily derived global network topography in each FTD variant. We found important FC pattern dissociations between internal/external processes, as well as between predicted/observed processes to be highly diminished in these patients. This was found to be behaviourally relevant in bvFTD patients for whom the changes were the most pronounced and widespread. It has been proposed that FTD disease is a ‘molecular nexopathy’ whereby a specific conjunction of pathogenetic protein and neural circuit characteristics lead to the specific form the disease takes on 82 , 83 . Nexopathies target particular types of network connections and therefore transcend canonical macro-network boundaries. Connectome gradient mapping is therefore an appropriate approach to identify such FC profiles. There is a need to correlate FC profiles with underlying molecular pathologies which is thought to drive much of this clinico-anatomical heterogeneity found amongst bvFTD patients. Fine-tuning these findings will enable us to identify the specific role FC can play for disease detection or staging, differential diagnosis and measurement of disease progression in FTD. Our study also highlighted alterations in the activity of the visual network as a potential outcome of the effect of pathology on other networks. It is not clear if this is compensatory or purely dysfunctional. These results contribute to the perspective that FTD symptomatology is linked to a dysfunctional interplay of networks, particularly along an axis differentiating observed from predicted processes. The FC of the visual network along this axis could also serve as a biomarker for disease staging or classifying patients with FTD. Finally, if FC changes are found to precede brain atrophy as some studies suggest 37 , these measures may be appropriate for stratifying patient enrolment and providing sensitive markers for evaluating the effects and efficacy of disease-modifying therapies, particularly in a framework of dynamic changes of biomarkers 8 . As up to 30% of cases of FTD are due to inheriting an autosomal dominant genetic mutation 84 , there is a community of individuals carrying FTD-causing mutations but who have not yet developed symptoms. These individuals offer a unique window of opportunity to study FTD at the presymptomatic stage. More work in such presymptomatic cases as well as multimodal longitudinal data are needed to clarify the role FC may have as a biomarker in FTD. Declarations Acknowledgments We would like to thank all the participants from both the Paris and London sites for their dedication to research. A.B. is funded by a PhD Fellowship from the Fondation Recherche Alzheimer and this work was started when supported by Fondation Vaincre Alzheimer. J.D.W. receives grant support from the Alzheimer’s Society, Alzheimer’s Research UK, the Royal National Institute for Deaf People and the UCL/UCLH NIHR Biomedical Research Centre. J.D.R. is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship and the NIHR Rare Disease Translational Research Collaboration. D.S.M. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme, the Wellcome Trust Core Award and the NIHR Oxford BRC. R.M. is supported by France Alzheimer, Fondation Recherche Alzheimer, Philippe Chatrier Foundation and Rosita Gomez association. 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Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files SupplementaryMaterialsBouzigues2024.docx Cite Share Download PDF Status: Published Journal Publication published 24 Nov, 2024 Read the published version in Molecular Psychiatry → Version 1 posted Editorial decision: revise 29 Apr, 2024 Review # 6 received at journal 14 Apr, 2024 Review # 2 received at journal 27 Mar, 2024 Review # 3 received at journal 27 Mar, 2024 Review # 1 received at journal 27 Mar, 2024 Review # 5 received at journal 26 Mar, 2024 Reviewer # 6 agreed at journal 18 Mar, 2024 Review # 4 received at journal 15 Mar, 2024 Reviewer # 5 agreed at journal 13 Mar, 2024 Reviewer # 4 agreed at journal 12 Mar, 2024 Reviewer # 3 agreed at journal 12 Mar, 2024 Reviewer # 2 agreed at journal 12 Mar, 2024 Reviewer # 1 agreed at journal 12 Mar, 2024 Reviewers invited by journal 12 Mar, 2024 Submission checks completed at journal 26 Jan, 2024 First submitted to journal 25 Jan, 2024 Unknown event 25 Jan, 2024 Editor assigned by journal 24 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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13:35:23\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3894211/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3894211/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1038/s41380-024-02847-4\",\"type\":\"published\",\"date\":\"2024-11-24T05:00:00+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":52711548,\"identity\":\"d5d85ba5-8606-4886-bdf2-ea559403e722\",\"added_by\":\"auto\",\"created_at\":\"2024-03-14 19:58:23\",\"extension\":\"jpeg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":271524,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSchematic representation of spatial relationships of canonical resting state networks and corresponding brain areas. \\u003c/strong\\u003eCortical connectivity gradients reflecting processing hierarchies spanning sensory and transmodal areas and the seven resting-state network parcellation on the cortical surface, colour coded according to previous work (Yeo et al., 2011, adapted from Margulies et al., 2016).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3894211/v1/23dc4f690f7efb892a0edfcc.jpeg\"},{\"id\":52711546,\"identity\":\"350f1d9b-7fa6-4e67-8624-6d7371ed71a1\",\"added_by\":\"auto\",\"created_at\":\"2024-03-14 19:58:22\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1304282,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGrey matter atrophy. \\u003c/strong\\u003eCortical thickness for each FTD group expressed as percentages of control means for each parcel. bvFTD = behavioural variant FTD, svPPA = semantic variant of Primary Progressive Aphasia, nfvPPA = non-fluent variant of Primary Progressive Aphasia\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage262.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3894211/v1/6f02172aabbafe414bb27442.png\"},{\"id\":52712067,\"identity\":\"f00cb55d-2c87-4cb5-87a9-8d4f34f514e0\",\"added_by\":\"auto\",\"created_at\":\"2024-03-14 20:06:22\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":770911,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePrincipal and secondary gradients in controls.\\u003c/strong\\u003e Distribution of values along the gradient for each canonical resting-state network in healthy controls, colour-coded according to Yeo et al., 2018 partition scheme. Below, the cortical surface is presented according to each parcel’s embedding value along the gradient. Along the principal gradient, the sensorimotor network is anchored on one end of the spectrum in dark blue/purple (precentral and postcentral gyri) and on the opposite end lies the default-mode network in bright red (posterior cingulate cortex/precuneus, medial prefrontal cortex, inferior parietal lobule). Along the secondary gradient, the salience network is anchored on one end of the spectrum in dark blue/purple (anterior insula, anterior cingulate cortex) and on the opposite end lies the visual network in bright red (occipital lobe).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage358.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3894211/v1/2b985ab620dcf688695d7303.png\"},{\"id\":52711552,\"identity\":\"28159c6a-8d4e-4577-b26b-a88d41b8816d\",\"added_by\":\"auto\",\"created_at\":\"2024-03-14 19:58:23\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1857704,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGradients in the different FTD variants.\\u003c/strong\\u003e The cortical surface is presented according to each parcel’s average embedding value along the gradient in each FTD variant group. bvFTD = behavioural variant FTD, svPPA = semantic variant of Primary Progressive Aphasia, nfvPPA = non-fluent variant of Primary Progressive Aphasia\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage446.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3894211/v1/a4ac31fd27cf0a8a1065f5b6.png\"},{\"id\":52712068,\"identity\":\"17673def-50e8-4aa2-ad05-919acab7aa98\",\"added_by\":\"auto\",\"created_at\":\"2024-03-14 20:06:23\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":71745,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eNetwork differences between groups along each gradient. \\u003c/strong\\u003eBar plot of average principal (left) and secondary (right) gradients for each group and for each network, colour-coded according to their position along each gradient axis. \\u003cstrong\\u003e*\\u003c/strong\\u003e indicate significant differences for each network, between the patient group and controls who are presented on the left in both graphs (p\\u0026lt;0.05, corrected for multiple comparisons). bvFTD = behavioural variant FTD, svPPA = semantic variant of Primary Progressive Aphasia, nfvPPA = non-fluent variant of Primary Progressive Aphasia\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage531.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3894211/v1/1b6d91841bc46a8a476a3ae7.png\"},{\"id\":52712071,\"identity\":\"e5631134-3419-4f13-8a39-48dfdbaa6fd6\",\"added_by\":\"auto\",\"created_at\":\"2024-03-14 20:06:23\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":277115,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDistribution of values along the gradients for each network and for each group.\\u003c/strong\\u003e Controls’ density plot colour-coded according to Yeo et al., 2018 partition scheme. Arrows indicate significant shift along the gradient for each group. bvFTD patients’ density plot/arrow in solid black line, nfvPPA patients’ density plot/arrow in dashed black line and svPPA patients’ density plot/arrow in dotted black line. bvFTD = behavioural variant FTD, svPPA = semantic variant of Primary Progressive Aphasia, nfvPPA = non-fluent variant of Primary Progressive Aphasia\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage619.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3894211/v1/72f394069cd635a763570521.png\"},{\"id\":52711551,\"identity\":\"453e462b-12e1-4edb-80b7-c6260901a821\",\"added_by\":\"auto\",\"created_at\":\"2024-03-14 19:58:23\",\"extension\":\"jpeg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":125790,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCorrelation between social cognition and gradient values.\\u003c/strong\\u003e Significant correlations between mini-Social cognition \\u0026amp; Emotional Assessment battery (miniSEA) scores with the average principal gradient values of the default-mode network (left; r= -0.44, p=0.05, uncorrected) and with the average secondary gradient values of the visual network (right; r= -0.53, p=0.04, corrected) in behavioural variant FTD patients. The more the default-mode network embedding values were shifted towards the center of the gradient, contracting the axis, the worse the score on the miniSEA (left). The more the visual network embedding values were shifted towards the negative end of the gradient, expanding the axis, the better the score on the miniSEA (right). DMN = default-mode network, miniSEA = mini-Social cognition \\u0026amp; Emotional Assessment.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage76.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3894211/v1/6b75ff3b35d09cf7c52beff6.jpeg\"},{\"id\":69721598,\"identity\":\"bd6dfd76-4894-46a1-a3f6-a9819ffe5bd4\",\"added_by\":\"auto\",\"created_at\":\"2024-11-24 08:06:02\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":5726542,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3894211/v1/177a7998-60fa-4788-8cef-ecd41487db99.pdf\"},{\"id\":52711550,\"identity\":\"6446cd85-e3a5-45c4-bdff-2157e65d69a6\",\"added_by\":\"auto\",\"created_at\":\"2024-03-14 19:58:23\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":880614,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementaryMaterialsBouzigues2024.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3894211/v1/8be55ec9022d0ab2fea9517e.docx\"}],\"financialInterests\":\"The authors have declared there is \\u003cb\\u003eNO\\u003c/b\\u003e conflict of interest to disclose\",\"formattedTitle\":\"Disruption of Macroscale Functional Network Organisation in Patients with Frontotemporal Dementia\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eComplex behaviours and higher-order cognition rely on distributed brain systems working synergistically for both serial and parallel processing 1,2. Extensively studied over the past 30 years, these functional networks are investigated by measuring temporal correlations between distributed and adjacent brain areas at rest 3. Some networks, like visual or sensorimotor networks, are implicated in sensory processing, while others, such as the salience network (SN) and the default-mode network (DMN), are crucial for higher-order cognitive tasks like detecting salient stimuli or engaging in mental wandering. Conditions like neurodegenerative disorders selectively impair networks like the DMN and SN 4. Examining functional networks through resting-state functional magnetic resonance imaging (rs-fMRI) to measure functional connectivity (FC) between regions3,5 is thus valuable to understand how neurodegeneration affects the brain and consequently, cognitive and behavioural functions.\\u003c/p\\u003e \\u003cp\\u003ePioneering studies in resting-state FC have suggested that brain networks are anticorrelated with one another, meaning that cognitive and behavioural functions are due to the simultaneous decrease and increase of activity within different networks 6. Anticorrelations play a crucial role in the brain's functional architecture, developing during brain development 6,7. Similarly, changes in FC during aging and neurodegeneration involve not only abnormalities within networks but also a reorganisation of the interactions between large-scale networks 8,9.\\u003c/p\\u003e \\u003cp\\u003eWith this in mind, a cortical gradient mapping approach offers a lens through which to characterise the relationship between connectivity patterns of functional networks. Applied to a large group of healthy individuals, this method describes a principal gradient of connectivity differentiation along the cortical surface, with sensory cortices showing maximal FC pattern differences from regions involved in transmodal association processing \\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Put to the test in clinical populations, gradient mapping identifies that the distance between sensory and transmodal networks is modified in neurological diseases such as ischaemic stroke \\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e, autism spectrum disorders \\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e, generalised epilepsy \\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e and depression \\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e. The FC space in which the networks operate in contracts and networks become less differentiated (\\u003cem\\u003ede\\u003c/em\\u003edifferentiated), suggesting an alteration of the interplay between networks. Gradient mapping presents advantages over other methods by minimising prior, not assuming sharp boundaries between functional networks, and therefore enabling the investigation of the interrelationships between functional networks within a continuous functional connectivity space. It has thus been suggested this framework provides a realistic model of brain functioning and distant potential effects of pathology.\\u003c/p\\u003e \\u003cp\\u003eFrontotemporal dementia (FTD) is a heterogeneous group of neurodegenerative conditions presenting with distinct deterioration of behaviour \\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e language \\u003csup\\u003e\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e and/or motor functions involving frontotemporal brain regions. There is a growing body of evidence on FC changes in FTD \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR19\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e. However, reported findings remain variable as well as their contribution to better understanding brain dynamics, effects of neurodegeneration and clinical consequences.\\u003c/p\\u003e \\u003cp\\u003eThe aim of the present study was to apply a cortical gradient mapping approach to investigate FC changes in people affected by behavioural (bvFTD), and language variants; semantic (svPPA) and non-fluent (nfvPPA). We hypothesised that we would find a principal gradient of functional network organisation, spanning from the DMN to primary sensory networks in controls and that this would be broadly maintained in the FTD patient groups. However, our first hypothesis was that all patients would show evidence of an overall constriction of the cortical hierarchy compared with controls (i.e. reduced connectome gradient range), particularly involving changes at the transmodal extremes (i.e. the DMN and SN). Secondly, in view of the clinical symptoms and associated patterns of atrophy, we expected that people with bvFTD would show the most widespread alterations. Finally, we expected such macroscale network changes to be functionally relevant and relate to these patients’ clinical symptoms.\\u003c/p\\u003e \"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eSubjects\\u003c/p\\u003e\\u003cp\\u003eParticipants were recruited through two independent research studies within two research centres. Data collection from site 1 was part of the ECOCAPTURE study, sponsored by the French national institute for biomedical research (INSERM, C16-87), based at the Paris Brain Institute (more details here: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.clinicaltrials.gov/ct2/show/NCT03272230\\u003c/span\\u003e\\u003cspan address=\\\"https://www.clinicaltrials.gov/ct2/show/NCT03272230\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). Data collection from site 2 was part of the Longitudinal Investigation of FTD (LIFTD) study which took place at the Dementia Research Centre within University College London (more details here: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ucl.ac.uk/drc/longitudinal-investigation-ftd\\u003c/span\\u003e\\u003cspan address=\\\"https://www.ucl.ac.uk/drc/longitudinal-investigation-ftd\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). All participants provided written informed consent prior to participating in the studies in accordance with the declaration of Helsinki. Each study was granted approval by the respective local ethics committee. Anonymity was preserved for all participants. A total of 129 participants were included in this study; 52 healthy control subjects and 42 bvFTD patients across both sites, as well as 17 patients with svPPA and 18 with nfvPPA from site 2 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Diagnoses were established based on current diagnostic criteria \\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e. There were no significant differences in age, education level, gender and MMSE scores between the two sites for each participant group (p \\u0026gt; 0.05), thus these independently acquired datasets were merged for subsequent analyses.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\"±\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\"±\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\"±\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eDemographic details for controls and each FTD patient group.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"11\\\"\\u003e\\u003c/colgroup\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGroup\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eN\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eSite (1:2)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eAge\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eSex (F:M)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eEducation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eMMSE\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eDisease Duration\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003eminiSEA\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eLetter Fluency\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003eCategory Fluency\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eControls\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e52\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e18:34\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e63.6 ± 6.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e27:25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e14.9 ± 3.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e29.4 ± 0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e26.0 ± 1.48 (N = 28)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e15.5 ± 5.7 (N = 29)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e24.0 ± 6.2 (N = 29)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ebvFTD\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e42\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e22:20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e65.9 ± 7.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e12:30\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e14.2 ± 3.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e23.2 ± 4.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4.2 ± 2.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e19.2 ± 4.99 (N = 23)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003esvPPA\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0:17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e64.0 ± 6.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5:12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e14.9 ± 3.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e22.8 ± 7.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4.5 ± 1.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e9.1 ± 4.66 (N = 10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e7.4 ± 4.17 (N=\\u0026nbsp;10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003enfvPPA\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0:17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e70.6 ± 8.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e9:9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e13.5 ± 2.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"±\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e20.9 ± 9.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e3.6 ± 1.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e5.7 ± 4.24 (N = 10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e9.2 ± 6.03 (N = 10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"11\\\"\\u003eAge and Disease Duration are presented as mean number of years ± standard deviation, MMSE is presented as mean score ± standard deviation. Site as number of participants recruited from site 1 to site 2 ratio and sex as number of females to males ratio. Abbreviations: behavioural variant FTD = bvFTD, semantic variant FTD = svPPA, non-fluent variant FTD = nfvPPA, Mini Mental State Examination = MMSE.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eCognitive Assessments\\u003c/p\\u003e\\u003cp\\u003eParticipants carried out the Mini Mental State Examination (MMSE), the mini-Social cognition \\u0026amp; Emotional Assessment battery\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e to evaluate deficits in social cognition (miniSEA), phonemic fluency (letter F) and category fluency (animals). Mean scores, standard deviations and sample sizes are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Patient groups’ and controls’ performance on these cognitive tests were compared using t-tests.\\u003c/p\\u003e\\u003cp\\u003eImaging Data Acquisition and Preprocessing\\u003c/p\\u003e\\u003cp\\u003eVolumetric T1 scans and resting-state fMRI scans were acquired at the neuroimaging core facility (CENIR) of the Paris Brain Institute and at University College London Hospital (UCLH). Sites were respectively equipped with a 3T Siemens Prisma and Trio whole-body scanner and a 12-channel head coil. T1-weighted images were acquired using a magnetisation prepared rapid acquisition gradient echo pulse sequence (MPRAGE). Site 1 anatomical protocol involved TR = 2.4s TE = 2.17ms; TE = 2.17ms; flip angle = 8°; voxel size = 1mm isotropic; slice thickness = 0.7mm. Site 2 anatomical protocol involved TR = 2.4s TE = 2s; TE = 2.93ms; flip angle = 8°; voxel size = 1mm isotropic; slice thickness = 1.1mm. Functional data based on the blood oxygenation level-dependent (BOLD) signal were acquired using a T2*-weighted echo-planar image (EPI) pulse sequence. Site 1 functional protocol involved TR = 2050ms, TE = 25ms, flip angle = 80°, oblique axial slices of the brain were acquired at 290 or 436 time points with a voxel resolution of 2 mm. Site 2 functional protocol involved TR = 2500ms, TE = 30ms, flip angle = 80°, oblique axial slices of the brain were acquired at 200 time points with a voxel resolution of 2 mm. Participants were asked to lie with their eyes closed, without falling asleep during the resting-state acquisition run.\\u003c/p\\u003e\\u003cp\\u003eT1 scans and fMRI resting-state time series for all participants were preprocessed using fMRIprep 21.0.1 \\u003csup\\u003e22\\u003c/sup\\u003e, an automated Nipype-based preprocessing pipeline for fMRI data implemented in Python, which uses tools from software packages including FSL, ANTs, FreeSurfer and AFNI. Briefly, the pipeline included bias field correction, skull stripping, brain tissue segmentation, slice time correction, correction for head motion parameters, co-registration to corresponding structural image, and non-linear spatial normalisation to MNI space. Further details on anatomical and functional data preprocessing can be found in Supplementary Methods.\\u003c/p\\u003e\\u003cp\\u003eImaging Data Analysis\\u003c/p\\u003e\\u003cp\\u003e \\u003c/p\\u003e\\u003col style=\\\"list-style-type: lower-alpha;\\\"\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eAnatomical scans\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eMeasures of cortical thickness were obtained using FreeSurfer’s automated anatomical statistics extraction pipeline for each participant and for each parcel of the Schaefer atlas (400 parcels). To transfer the Schaefer parcellation volume to subject space, we used the Multi Atlas Transfer Tool \\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e. To show structural grey matter differences in patient groups compared to controls, we averaged cortical thickness for each parcel within each patient group and presented these as percentage cortical thickness of control mean.\\u003c/p\\u003e\\u003cp\\u003e \\u003c/p\\u003e\\u003col style=\\\"list-style-type: lower-alpha;\\\" start=2\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eResting-state scans\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eWe used mean framewise-displacement \\u003csup\\u003e\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e as a quality assurance parameter. Thus, subjects were included in subsequent analyses if their mean framewise head displacement in the MRI was below the threshold of 0.55 mm, as used in previous work with similar patient populations \\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e. Six bvFTD (three from each site) and two svPPA patients did not meet these criteria and were therefore excluded from subsequent analyses.\\u003c/p\\u003e\\u003cp\\u003eTo remove physiological and other sources of noise from the fMRI time series, nuisance covariates were regressed out according to the 36-parameter model \\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e. The fMRI confounds generated with fMRIprep were loaded using the load_confound (v. 0.6.4.) Python package. Six motion parameters, signals estimated from cerebrospinal fluid (CSF) and white matter (WM), the whole-brain global signal, their derivatives, quadratic terms, and squares of derivatives were regressed out from functional data separately for each run. The rs-fMRI data from each subject was smoothed with a full width at half maximum 6 mm Gaussian kernel, temporally bandpass filtered in the 0.01–0.1 Hz frequency range and spatially parcellated (400 parcels) according to the Schaefer atlas \\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eTo estimate connectivity gradients, we applied generalised Canonical Correlation Analysis (gCCA) to subject’s mean functional connectivity matrix. This decomposes the functional connectome into primary components, referred to as gradients, with each gradient explaining varying levels of variance in connectivity. These gradients discriminate across levels of the cortical hierarchy (i.e., sensory processing vs. higher-order cognition), whereas region specific values along the gradient, referred to as embedding values, reflect the similarity in connectivity along the sensory-transmodal axis. Further details on the connectome gradient mapping pipeline can be found in Supplementary Methods.\\u003c/p\\u003e\\u003cp\\u003eGradient mapping\\u003c/p\\u003e\\u003cp\\u003eWe investigated gradient differences between FTD groups and controls. Each of the 400 brain parcels for which we extracted embedding values along the principal and secondary gradients belongs to a canonical functional network within the partition scheme described by Yeo and colleagues \\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The present work focused on these two first functional gradients which show discernible patterns of regional variation and explain the most variance. The tertiary gradient is presented in Supplementary Fig.\\u0026nbsp;1. We performed a mixed effects model to compare principal and secondary gradient values for each parcel allocated to a given functional network between controls and each FTD group. Thus, gradient scores for each parcel were included in the model as the dependent variable, while network label as well as group were entered as fixed effects. Subject, parcel label and site of data acquisition were entered as random effects. Finally, age and sex were also included in the model as fixed effects. We then investigated the Group x Network interaction and performed post-hoc pairwise tests, comparing each network between controls and each FTD group. Resulting p-values were corrected for multiple comparisons including the three group comparisons and the 400 parcels using the Benjamini-Hochberg FDR correction.\\u003c/p\\u003e\\u003cp\\u003eCorrelation of gradient changes with cognitive measures\\u003c/p\\u003e\\u003cp\\u003eAs only a small subgroup of PPA patients completed the cognitive tests, we only computed correlations with mean network gradient embedding values in bvFTD patients. To investigate the clinical relevance of altered connectome gradients in bvFTD patients, we correlated the miniSEA with average network principal and secondary gradient values using Pearson’s correlations. Only extreme end networks which showed significant differences compared to controls were included in these correlations. Correlations between verbal fluency test scores and principal and secondary gradients’ end networks which showed significant differences compared to controls were not statistically performed in the PPA subgroups because of limited cognitive data availability. We investigated whether a broad trend towards linear relationships existed, and these are presented in Supplementary materials.\\u003c/p\\u003e\\u003cp\\u003eAll statistical tests were conducted in RStudio (v 4.2.0). Results on the cortical surface are presented using the opensource python package ‘visbrain’.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eDemographics and Cognition\\u003c/p\\u003e \\u003cp\\u003eAs demographic details did not significantly differ between the two sites for each participant group (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05), these independently acquired datasets were merged for subsequent analyses. There were no significant demographic differences between patient groups and controls (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05), except for nfvPPA patients being older than the other patient groups (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.02) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). As expected, patient groups all had a significantly lower MMSE score than controls (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Moreover, compared to controls, bvFTD patients showed significantly reduced scores on the miniSEA (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) and both nfvPPA and svPPA patients showed significantly lower scores on the verbal fluency test (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Violin plots of bvFTD patients\\u0026rsquo; and controls performance on the miniSEA can be found in Supplementary Fig.\\u0026nbsp;2.\\u003c/p\\u003e \\u003cp\\u003eCortical thickness\\u003c/p\\u003e \\u003cp\\u003eEach clinical FTD group showed reduced cortical thickness compared to controls in expected regions. Thus, bvFTD patients showed an average of around 10% reduction of cortical thickness compared to controls in bilateral medial prefrontal cortex, anterior cingulate cortex and middle temporal gyrus as well as around a 20% reduction in the bilateral anterior temporal lobes and the frontoinsula region. svPPA patients showed reduced cortical thickness by up to 30% in left anterior temporal lobe, particularly the temporal pole, as well as up to around 25% in right temporal pole compared to controls. In nfvPPA, the pattern of reduced cortical thickness compared to controls was a lot more widespread within the frontal and parietal lobes involving up to 20% reductions within the supplementary motor area, particularly on the left, as well as up to 15% reduction in medial and inferior frontal cortex and left superior temporal gyrus (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eCortical gradients\\u003c/p\\u003e \\u003cp\\u003eWe applied a gCCA gradient approach on rs-fMRI-based connectivity data from different FTD patient groups and a healthy control group to derive cortical connectivity gradients reflecting processing hierarchies spanning sensory and transmodal areas. In controls, the first two gradients explained a total of 48% of the variance; 29% and 19% respectively (Supplementary Fig.\\u0026nbsp;3). There was no significant difference in the variance explained by each gradient between controls and the patient groups (Mann-Whitney-U, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The principal gradient anchored sensorimotor areas at its positive extreme and DMN at its negative extreme (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA), with a gradual transition from sensory to transmodal association networks similar to what has been reported in previous work (Margulies et al., 2016). This axis separates immediate sensorimotor processing from higher-order mind-wandering processes and could be considered an axis dissociating and thus enabling external \\u003cem\\u003evs\\u003c/em\\u003e internal processing. Along our secondary gradient, the visual network occupied the negative extreme, while areas from the SN populated the positive end of this gradient (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB). This more complex pattern possibly reflects a higher-order gradient separating regions attending to externally presented visual cues from regions devoted to interpreting their social relevance\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e which could be considered an axis facilitating observed \\u003cem\\u003evs\\u003c/em\\u003e predicted states.\\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e reports results in patient populations. Local alterations were notable, particularly widespread in bvFTD patients and more focal in the language variants, suggestive of changes in functional network segregation which are discussed and further interpreted below.\\u003c/p\\u003e \\u003cp\\u003ePrincipal and secondary gradients group comparisons\\u003c/p\\u003e \\u003cp\\u003eThe mixed model comparing groups on the principal and secondary gradients scores for each of the 400 parcels identified main effects of Network (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) as well as significant Group x Network interactions (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). All p-values reported are corrected for multiple comparisons.\\u003c/p\\u003e \\u003cp\\u003ePairwise comparisons at each network level found that bvFTD patients showed significantly different principal gradient scores within the DMN, the SN and visual network compared to controls (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001), their embedding values shifting towards the centre of the spectrum. Similarly, nfvPPA patients showed significantly different principal gradient scores within the DMN and sensorimotor network compared with controls (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001), with both extreme-end networks\\u0026rsquo; embedding values shifting towards the centre of the spectrum. Finally, svPPA patients also showed significantly different principal gradient values within the limbic network (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) and the DMN compared to controls (p\\u0026thinsp;=\\u0026thinsp;0.04), again these networks\\u0026rsquo; embedding values shifted towards the center of the spectrum. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e (left) shows the adjusted embedding value means along the principal gradient for each functional network, grouped for each FTD variant. Controls\\u0026rsquo; means are presented on the left for comparison. More details from the statistical model are presented in Supplementary Table\\u0026nbsp;1.\\u003c/p\\u003e \\u003cp\\u003eAll networks along the secondary gradient in bvFTD patients showed significant differences compared to controls (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), with the largest changes occurring within the networks on either end of the spectrum, the SN and visual network (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). Though the SN showed convergence towards the centre of the spectrum, the visual network expanded the axis by shifting away from the SN. Moreover, the middle networks\\u0026rsquo; embedding values mostly shifted towards the SN end of the spectrum, apart from the DMN which shifted towards the visual end. Similarly, nfvPPA patients showed significant changes along the secondary gradient with the same direction of changes of the SN and visual network as in bvFTD patients compared to controls (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). Moreover, nfvPPA patients also showed a significant shift of the limbic (p\\u0026thinsp;=\\u0026thinsp;0.03) and sensorimotor network (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) towards the SN and thus away from the visual network. Finally, svPPA patients showed an expansion of the spectrum with visual network shifting away from the SN (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) whilst the limbic (p\\u0026thinsp;=\\u0026thinsp;0.010 and sensorimotor (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) networks\\u0026rsquo; embedding values significantly shifted along the secondary gradient, towards the SN. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e (right) shows the adjusted embedding value means along the principal gradient for each functional network, grouped for each FTD variant. Controls\\u0026rsquo; means are presented on the left for comparison. More details from the statistical model are presented in Supplementary Table\\u0026nbsp;1.\\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e presents an overview of the distribution of the embedding values along the principal (left) and secondary (right) gradient, grouped according to functional network with a distribution plot for each FTD variant and controls.\\u003c/p\\u003e \\u003cp\\u003eClinical relevance of principal and secondary gradients\\u003c/p\\u003e \\u003cp\\u003eTo assess the relevance of principal connectome gradient changes to behavioural symptoms, we correlated miniSEA with average DMN principal gradient values in bvFTD patients as this extreme end network showed a significant difference compared to controls. The DMN average principal gradient score was significantly correlated with the miniSEA score in bvFTD patients (r = -0.44, p\\u0026thinsp;=\\u0026thinsp;0.05, uncorrected). Similarly, average secondary gradient values within the visual network and SN were correlated with the miniSEA and we found that visual network secondary gradient values negatively correlated with the miniSEA (r= -0.53, p\\u0026thinsp;=\\u0026thinsp;0.04, corrected for multiple comparisons). In both cases, the more the DMN or visual network embedding values were towards the center of the gradient, the worse the score on the miniSEA and the more they were towards the extreme end of the gradient the better the score on the miniSEA (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig12\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). Some linear relationships can be observed in both svPPA and nfvPPA patients between verbal fluency scores and extreme-end networks of the principal and secondary gradients (Supplementary Fig.\\u0026nbsp;4). These will not be further discussed as not investigated using statistical analyses.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eSummary of findings\\u003c/p\\u003e \\u003cp\\u003eIn the present study, we used a connectome gradient mapping approach to investigate functional network organisation in a large sample of patients affected by FTD. Healthy control subjects presented a prominent expected hierarchy and differentiation of functional networks\\u0026rsquo; connectivity patterns. The principal gradient captured a progressive hierarchy between sensorimotor (external processes) and transmodal association regions (internal processes), whilst the secondary gradient underpinned a sharp distinction between the SN (predicted processes) and visual cortex (observed processes). Together, these principles of network organisation explained 48% of the variance within the data. Such macroscale functional network organisation captures a healthy topography enabling the transition from concrete perception to abstract cognitive functions, the basis of the evolutionary transition from apes to humans and of higher-order cognitive and behavioural functions\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e. Though such gradients of network organisation were broadly maintained in patients with FTD, we found evidence of extreme-end networks shifting towards the centre of these axes, leading to an overall FC space constriction in all patient groups, which was more severe in bvFTD, and confirming our first hypothesis of a breakdown of this organisation in FTD. Importantly, connectome gradient mapping enabled us to identify changes at the macroscale level. We found that vital hierarchies of network organisation were constricted in all three FTD patient groups, with bvFTD patients\\u0026rsquo; pathology particularly impacting the axis dissociating predicted \\u003cem\\u003evs\\u003c/em\\u003e observed processes captured by the secondary gradient. Moreover, nfvPPA and svPPA patients showed specific sensorimotor and limbic network behavioural changes. Finally, the visual network showed an opposite pattern of modification in relation to other networks in all FTD patient groups. The DMN and visual networks demonstrated contrasting correlations with social cognition performances measured by the miniSEA test.\\u003c/p\\u003e \\u003cp\\u003eDMN and SN dedifferentiation in bvFTD\\u003c/p\\u003e \\u003cp\\u003ePrevious work in healthy individuals has described a principal gradient spanning from the DMN to the sensorimotor network, with intermediate networks positioned in between\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e. This axis differentiates patterns of connectivity of regions involved in internal processes \\u003cem\\u003eversus\\u003c/em\\u003e those responsible for external processes. In bvFTD patients, we found a noteworthy shift in DMN embedding values towards the center, contracting the principal gradient axis. Altered DMN FC in bvFTD patients have been found by several studies using classical methods. Some studies find increased FC in patients with bvFTD compared to controls (i.e. hyperconnectivity) \\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR30 CR31 CR32\\\" citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e and others have found decreased (i.e. hypoconnectivity) or a mix of directional changes \\u003csup\\u003e\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR35 CR36\\\" citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e. In response to the accumulation of brain pathology, the recruitment of neural resources not usually involved may be compensatory to sustain normal cognition \\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e. However, it remains unclear whether hyperconnectivity represents compensatory processes or aberrant excitation due to early damage. In bvFTD patients, we found that the DMN, originally anchored at the internal processing end of the principal gradient spectrum, shifts towards regions associated with sensory/external processing. This alteration suggests a decrease in the specificity of DMN activation patterns, leading to neural \\u003cem\\u003ede\\u003c/em\\u003edifferentiation whereby the DMN may activate in situations in which it would not normally be activated in or \\u003cem\\u003evice versa\\u003c/em\\u003e. This phenomenon may explain the mixed findings of hypo- and hyper- connectivity observed in previous studies.\\u003c/p\\u003e \\u003cp\\u003eAdditionally, we found a correlation between the DMN principal gradient embedding values and scores of social cognition test (miniSEA) in bvFTD patients. A shift of the DMN towards the center, constraining the axis, correlated with poorer miniSEA scores. This highlights the significance of maintaining an axis that distinguishes FC patterns of regions involved in external (sensorimotor network) \\u003cem\\u003eversus\\u003c/em\\u003e internal (DMN) processes for better social cognition. Taken together, these results suggest that the DMN is losing its network differentiation, and this loss of functional specificity relates to the behavioural and cognitive deficits seen in bvFTD patients. Thus, by examining the global network organisation, our work sidesteps questions about the direction of change and highlights core features at play in bvFTD patients.\\u003c/p\\u003e \\u003cp\\u003eThere is a growing body of evidence indicating significant decreases in FC of the SN in bvFTD patients \\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR19\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR30 CR31\\\" citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR40 CR41 CR42 CR43 CR44 CR45\\\" citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/sup\\u003e. The frontoinsula and anterior cingulate cortex, hubs within the SN, are home to a concentrated number of von Economo neurons and fork cells. Such cells are believed to be particularly vulnerable to misfolded FTD pathological protein accumulation (Seeley et al. 2007, 2012). This may explain why the SN is found to be particularly disrupted in bvFTD patients. SN disruption has been related to symptoms in bvFTD patients, with previous research highlighting the interplay of FC changes involving both the DMN and SN \\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e. These findings support a model in which the SN and DMN are anticorrelated and exert an inhibitory influence on each other which seems crucial for responding to prevailing goals and conditions \\u003csup\\u003e\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e\\u003c/sup\\u003e. In the current study, the SN, located at the external processing extreme of the principal gradient, exhibited a significant shift towards the DMN/internal processing extreme compared to controls. Moreover, the SN\\u0026rsquo;s change along the secondary gradient, which placed this network at the opposite end from the visual network, was much the same. This reduced differentiation of FC activity patterns between the SN and DMN, observed along both the principal and secondary gradients, is novel and may contribute to other networks\\u0026rsquo; FC patterns and, consequently, symptomatology in bvFTD patients. These findings emphasise the intricate relationship between the DMN, SN, and broader network dynamics in neurodegeneration.\\u003c/p\\u003e \\u003cp\\u003eVisual network functional compensation?\\u003c/p\\u003e \\u003cp\\u003eIn our study, the secondary gradient maximally differentiated the SN from the visual network in healthy subjects, underlying a functional dissociation between predicted and observed processes. In patients, both bvFTD and nfvPPA patients exhibited a significant shift of the SN towards the center. Moreover, the visual network displayed an opposite change with a shift outwardly, expanding the gradient's range in all three FTD variants. Alterations in FC within the visual network have been reported in previous FTD studies \\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e. Some work has suggested changes in the relationship between visual network and others such as the SN \\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e and dorsal attentional network \\u003csup\\u003e\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e. The collapse of the SN in bvFTD and nfvPPA, whether pathological or functional, may prompt changes in the visual network\\u0026rsquo;s behaviour.\\u003c/p\\u003e \\u003cp\\u003eThe shift of visual network outwardly along the secondary gradient could be interpreted as a compensatory mechanism, preserving the neural differentiation between regions involved in predicted and observed processes. The dissociation of such mechanisms is vital for sustaining cognitive abilities. This hypothesis gains support from the observed correlation between visual network embedding values along the secondary gradient and scores on the miniSEA in bvFTD patients. Enhanced differentiation of the visual network from other networks correlated with better miniSEA scores, reinforcing the behavioural relevance of this possible compensatory response. The relationship between social cognition and the visual system is supported by parallels with studies in autism spectrum disorder patients, where visual gaze patterns are linked to social cognition \\u003csup\\u003e\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u003c/sup\\u003e. Moreover, a recent behavioural study found that patients with bvFTD showed increased fixations to the eyes of emotional faces compared to controls, which may enable the allocation of attention to emotionally relevant cues to compensate for a core deficit in contextualising emotional information \\u003csup\\u003e\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e\\u003c/sup\\u003e. However, further investigation is needed to establish a direct link between visual behavioural changes and social cognition in bvFTD patients.\\u003c/p\\u003e \\u003cp\\u003eAnother hypothesis is that visual network may show overexcitation due to lack of inhibitory control processes because of neurodegeneration. Functional rearrangements during neurodegeneration can manifest as a prevalent and progressive increase of FC in earlier stages of the disease and a subsequent decrease as neurodegeneration progresses towards a more severe stage. Similar FC phenomenon in visual networks have also been observed in other focal neurodegenerative diseases \\u003csup\\u003e\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u003c/sup\\u003e. In the framework of a loss of inhibitory control, previous work has suggested that the SN, for example, plays a role in modulating the activity of large-scale networks, relating to aberrant judgement and interpersonal behaviour in patients with bvFTD \\u003csup\\u003e\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u003c/sup\\u003e. The current findings of significant shifts in all networks along the secondary gradient in bvFTD patients imply a large-scale disinhibited system lacking neural specificity which supports the modulation role of SN. Nevertheless, if this hypothesis were to be confirmed, the reasons behind changes in visual network activity in the present svPPA patients remain unclear due to not finding SN modifications in these patients \\u003csup\\u003e\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eWhile the study could not explore this functional relevance in the PPA subgroups due to sample size constraints, previous work has reported svPPA patients to frequently exhibit frank changes in their visual attention allocation \\u003csup\\u003e\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u003c/sup\\u003e, aberrant gaze patterns \\u003csup\\u003e\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e\\u003c/sup\\u003e and disproportionate impairments with naming visual objects \\u003csup\\u003e\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e\\u003c/sup\\u003e and visual attribute reporting relative to other modalities \\u003csup\\u003e\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u003c/sup\\u003e. Despite these functions not exclusively relying on the visual network, evidence suggests visual behavioural changes across all FTD variants.\\u003c/p\\u003e \\u003cp\\u003eSensorimotor and limbic network alterations in PPA variants\\u003c/p\\u003e \\u003cp\\u003eThough PPA variants exhibited certain network changes akin to those observed in bvFTD patients, these patients manifested distinctive alterations within sensorimotor and limbic networks. While nfvPPA patients showed a constriction of the principal gradient at the DMN/internal processing end of the axis, they also showed large constrictions at the sensorimotor network/external processing end which was less differentiated from other networks. Other work highlights the involvement of sensorimotor regions by showing reduced grey matter volume \\u003csup\\u003e\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e\\u003c/sup\\u003e hypometabolism \\u003csup\\u003e\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e\\u003c/sup\\u003e or disrupted white-matter tracts \\u003csup\\u003e\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e\\u003c/sup\\u003e within motor cortices in nfvPPA patients. Work investigating FC changes in these patients is sparse and has only suggested local FC reductions involving frontotemporal cortex and subcortical structures \\u003csup\\u003e\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e\\u003c/sup\\u003e. Using a priori regions of interest, a previous study found that right supplementary motor area showed lower FC with speech and language regions and this correlated with their articulatory error score \\u003csup\\u003e\\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e70\\u003c/span\\u003e\\u003c/sup\\u003e. Using whole-brain connectome gradient mapping, without limiting our analyses to specific networks, we highlight intrinsic sensorimotor network changes at a whole network level particularly in nfvPPA patients.\\u003c/p\\u003e \\u003cp\\u003eMoreover, the changes of sensorimotor network along the SN/predicted versus visual/observed axis enables us to conjecture further. In our controls, this network demonstrated a bimodal distribution including two distinct modes along this secondary gradient thus dissociating two groups of regions with differing patterns of connectivity. nfvPPA patients showed a striking smoothing of this distribution. Previous studies using gradient mapping may have also uncovered a bimodal distribution of sensorimotor network in controls \\u003csup\\u003e\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e\\u003c/sup\\u003e but the authors do not comment upon this limiting our interpretation of what the single mode distribution may mean in nfvPPA patients. A hypothesis could be that this bimodal distribution dissociates sets of body part representations. The 17-network parcellation described by Yeo and colleagues divided the sensorimotor strip into dorsal and ventral subnetworks and the boundary between these was roughly positioned between the hand and tongue representations \\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e. Thus, the high kurtosis found in nfvPPA patients could represent a blurring of this boundary, which may relate to the specific oro-facial symptoms characteristic of this patient group \\u003csup\\u003e\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eSimilarly, svPPA patients showed pronounced changes within the limbic network along the principal gradient. Limbic network shifted away from the DMN/internal processing end and towards the sensorimotor/external processing end in these patients. Additionally, controls also showed a bimodal distribution of the limbic network along the secondary gradient, which was smoothed in all FTD groups, but particularly in svPPA patients. Very little work has assessed limbic network FC changes in svPPA, with some studies mentioning changes to limbic structures \\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e or specific disconnections of anterior and inferior temporal lobe from other brain regions \\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e\\u003c/sup\\u003e. A recent study found that svPPA patients as well as bvFTD patients showed lower mean FC within the limbic network compared to controls \\u003csup\\u003e\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e73\\u003c/span\\u003e\\u003c/sup\\u003e. Our results are consistent with these recent suggestions.\\u003c/p\\u003e \\u003cp\\u003eHowever, the resting-state functional networks described in this work \\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e do not include subcortical structures or the basal ganglia, making our results limited to the network at the cortical level only. Furthermore, Yeo\\u0026rsquo;s 17-network parcellation also dissociates the limbic network into two subnetworks, orbitofrontal cortex versus temporal pole, which may be the reason for the bimodal distribution we found in controls. Our FC changes may be blurred by the fact that atrophy in svPPA is particularly pronounced in temporal pole and less so in orbitofrontal cortex. Finally, limbic network interpretation is limited by orbitofrontal and temporal lobe regions being particularly affected by susceptibility gradients causing image distortions and signal loss \\u003csup\\u003e\\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e74\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eOverall, most previous work in PPA has highlighted changes in DMN or SN \\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e\\u003c/sup\\u003e or disconnections of specific brain regions with others \\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e\\u003c/sup\\u003e rather than investigating global functional network changes. Our findings of a specific FC fingerprint involving sensorimotor and limbic network changes in nfvPPA and svPPA patients respectively using a whole-brain functional connectome approach are therefore novel and warrant further investigation to understand the specific behavioural patterns of the networks, their relationship to function and their potential future use as a biomarker in these PPA variants.\\u003c/p\\u003e \\u003cp\\u003eRelationship between Atrophy and FC\\u003c/p\\u003e \\u003cp\\u003eAtrophy and FC are undeniably closely related \\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e. Findings of DMN and SN changes are fully consistent with grey matter hubs of atrophy in bvFTD \\u003csup\\u003e\\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e76\\u003c/span\\u003e\\u003c/sup\\u003e and we found up to 20\\u0026ndash;30% reductions in grey matter volumes in regions within these networks in bvFTD patients compared to controls. Similarly, findings of changes within sensorimotor and limbic network in nfvPPA and svPPA respectively are consistent with their atrophy patterns within sensorimotor cortices or orbitofrontal cortex and temporal poles \\u003csup\\u003e\\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e77\\u003c/span\\u003e\\u003c/sup\\u003e. Although related, our study highlights that atrophy patterns do not perfectly overlap with functional network changes \\u003csup\\u003e\\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e78\\u003c/span\\u003e\\u003c/sup\\u003e. Thus, functional network changes, including reorganisation, do not seem to be fully explained by atrophy alone in any FTD subtype \\u003csup\\u003e\\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e79\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eHowever, recent work has suggested that longitudinal spread of atrophy can be predicted using an individual\\u0026rsquo;s functional connectome \\u003csup\\u003e\\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e\\u003c/sup\\u003e. In this previous work, shortest path length (in FC space) to the epicenter, combined with quantifying atrophy within a given region\\u0026rsquo;s network neighbours, accurately estimated the spatial pattern of subsequent atrophy in patients with bvFTD and svPPA. Such work highlights the use of FC profiles in atrophy pattern progression prediction. The onset, evolution and relationship of pathological structural and functional changes in FTD is still not fully understood, even at the group level, as studies have lacked multimodal longitudinal data \\u003csup\\u003e\\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e81\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eConcluding remarks and perspectives\\u003c/p\\u003e \\u003cp\\u003eGradient mapping offers a lens through which to characterise large-scale brain network relationships, the building blocks of brain function. Though there may be specific network changes, this method enabled us to highlight that FC changes involve a widespread disruption of the evolutionarily derived global network topography in each FTD variant. We found important FC pattern dissociations between internal/external processes, as well as between predicted/observed processes to be highly diminished in these patients. This was found to be behaviourally relevant in bvFTD patients for whom the changes were the most pronounced and widespread.\\u003c/p\\u003e \\u003cp\\u003eIt has been proposed that FTD disease is a \\u0026lsquo;molecular nexopathy\\u0026rsquo; whereby a specific conjunction of pathogenetic protein and neural circuit characteristics lead to the specific form the disease takes on \\u003csup\\u003e\\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e82\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e83\\u003c/span\\u003e\\u003c/sup\\u003e. Nexopathies target particular types of network connections and therefore transcend canonical macro-network boundaries. Connectome gradient mapping is therefore an appropriate approach to identify such FC profiles. There is a need to correlate FC profiles with underlying molecular pathologies which is thought to drive much of this clinico-anatomical heterogeneity found amongst bvFTD patients. Fine-tuning these findings will enable us to identify the specific role FC can play for disease detection or staging, differential diagnosis and measurement of disease progression in FTD.\\u003c/p\\u003e \\u003cp\\u003eOur study also highlighted alterations in the activity of the visual network as a potential outcome of the effect of pathology on other networks. It is not clear if this is compensatory or purely dysfunctional. These results contribute to the perspective that FTD symptomatology is linked to a dysfunctional interplay of networks, particularly along an axis differentiating observed from predicted processes. The FC of the visual network along this axis could also serve as a biomarker for disease staging or classifying patients with FTD.\\u003c/p\\u003e \\u003cp\\u003eFinally, if FC changes are found to precede brain atrophy as some studies suggest \\u003csup\\u003e\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e, these measures may be appropriate for stratifying patient enrolment and providing sensitive markers for evaluating the effects and efficacy of disease-modifying therapies, particularly in a framework of dynamic changes of biomarkers \\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e. As up to 30% of cases of FTD are due to inheriting an autosomal dominant genetic mutation \\u003csup\\u003e\\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e84\\u003c/span\\u003e\\u003c/sup\\u003e, there is a community of individuals carrying FTD-causing mutations but who have not yet developed symptoms. These individuals offer a unique window of opportunity to study FTD at the presymptomatic stage. More work in such presymptomatic cases as well as multimodal longitudinal data are needed to clarify the role FC may have as a biomarker in FTD.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eAcknowledgments\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to thank all the participants from both the Paris and London sites for their dedication to research. A.B. is funded by a PhD Fellowship from the Fondation Recherche Alzheimer and this work was started when supported by Fondation Vaincre Alzheimer. J.D.W. receives grant support from the Alzheimer\\u0026rsquo;s Society, Alzheimer\\u0026rsquo;s Research UK, the Royal National Institute for Deaf People and the UCL/UCLH NIHR Biomedical Research Centre. J.D.R. is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship and the NIHR Rare Disease Translational Research Collaboration. D.S.M. received funding from the European Research Council (ERC) under the European Union\\u0026rsquo;s Horizon 2020 research and innovation programme, the Wellcome Trust Core Award and the NIHR Oxford BRC. R.M. is supported by France Alzheimer, Fondation Recherche Alzheimer, Philippe Chatrier Foundation and Rosita Gomez association.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAuthor Contributions\\u003c/p\\u003e\\n\\u003cp\\u003eConceptualisation: R.M., A.B., D.S.M..; Data collection: L.L.R., B.B., R.L., J.D.W.; J.D.R.; Methodology: A.B., V.L.D., D.S.M.; Data analysis: A.B., M.H., D.S.M., R.M.; Manuscript writing: A.B., D.S.M., R.M.; Manuscript reviewing: A.B., V.G., L.L.R., V.L.D., M.H., I.L.B., B.B., R.L., J.D.W., J.D.R., D.S.M., R.M.\\u003c/p\\u003e\\n\\u003cp\\u003eConflicts of interest\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors disclaim no conflicts of interest with the current work.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eFelleman, D. J. \\u0026amp; Van Essen, D. C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex N. Y. 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Neurol. 24, 542 (2011).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"molecular-psychiatry\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"mp\",\"sideBox\":\"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)\",\"snPcode\":\"41380\",\"submissionUrl\":\"https://mts-mp.nature.com/cgi-bin/main.plex\",\"title\":\"Molecular Psychiatry\",\"twitterHandle\":\"@molpsychiatry\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3894211/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3894211/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eNeurodegenerative dementias have a profound impact on higher-order cognitive and behavioural functions. Investigating macroscale functional networks through cortical gradients provides valuable insights into the neurodegenerative dementia process and overall brain function. This approach allows for the exploration of unimodal-multimodal differentiation and the intricate interplay between functional brain networks. We applied cortical gradients mapping in frontotemporal dementia (FTD) patients (behavioural-bvFTD, non-fluent and semantic) and healthy controls. In healthy controls, two principal gradients maximally distinguished sensorimotor from default-mode network (DMN) and visual from salience network (SN). However, in bvFTD, this unimodal-multimodal differentiation was disrupted, impacting the interaction among all networks. Importantly, these disruptions extended beyond the observed atrophy distribution. Semantic and non-fluent variants exhibited more focal alterations in limbic and sensorimotor networks, respectively. The DMN and visual networks demonstrated contrasting correlations with social cognition performances, suggesting either early damage (DMN) or compensatory processes (visual). In conclusion, optimal brain function requires networks to operate in a segregated yet collaborative manner. In FTD, our findings indicate a collapse and loss of differentiation between networks that goes beyond the observed atrophy distribution. These specific cortical gradients\\u0026rsquo; fingerprints could serve as a novel biomarker for identifying early changes in neurodegenerative diseases or potential compensatory processes.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Disruption of Macroscale Functional Network Organisation in Patients with Frontotemporal Dementia\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-03-14 19:58:18\",\"doi\":\"10.21203/rs.3.rs-3894211/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"revise\",\"date\":\"2024-04-29T13:29:31+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2024-04-15T02:59:37+00:00\",\"index\":6,\"fulltext\":\"This content is not available.\"},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2024-03-27T17:21:41+00:00\",\"index\":2,\"fulltext\":\"This content is 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