{"paper_id":"3eeff301-2eb0-4138-9346-9b86142269d1","body_text":"1 \n \nSocial-semantic knowledge in frontotemporal dementia and \nafter anterior temporal lobe resection \nMatthew A. Rouse,1 Ajay D. Halai,1 Siddharth Ramanan,1 Timothy T. Rogers,2 Peter Garrard,3 \nKaralyn Patterson,1,4 James B. Rowe,1,4,5 Matthew A. Lambon Ralph1   \n \n \n \nAuthor affiliations: \n1 MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, \nUK \n2 Department of Psychology, University of Wisconsin-Madison, Madison, WI 53706, USA \n3 Molecular and Clinical Sciences Research Institute, St George’s, University of London, \nLondon, SW17 0RE, UK  \n4 Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0SZ, UK  \n5 Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0SZ, UK \n \n \nCorrespondence to: Dr Matthew A. Rouse \nMRC Cognition and Brain Sciences Unit \nUniversity of Cambridge \nCB2 7EF  \nCambridge, UK \nE-mail: matthew.rouse@mrc-cbu.cam.ac.uk\n \n \n \nRunning title: Social knowledge in frontotemporal dementia \n \n \nOpen Access: For the purpose of open access, the UKRI -funded authors have applied a CC \nBY public copyright licence to any Author Accepted Manuscript version aris ing from this \nsubmission.  \n \nKeywords: behavioural-variant frontotemporal dementia ; principal component analysis; \nsemantic dementia; social concept, temporal lobe epilepsy; transdiagnostic \n \n  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n 2 \nAbstract  \nDegraded semantic memory is a prominent feature of frontotemporal dementia (FTD) . It is \nclassically associated with semantic dementia and anterior temporal lobe (ATL)  atrophy, but \nsemantic knowledge can also be compromised in behavioural -variant FTD (bvFTD). \nMotivated by understanding behavioural change in FTD, r ecent research has focused \nselectively on social-semantic knowledge, with proposals that the right ATL is specialised for \nsocial concepts. Previous studies have assessed very different types of social concepts and have \nnot compared performance to that on matched non-social concepts. Consequently, it remains \nunclear to what extent various social concepts are (i) concurrently impaired in FTD, (ii) distinct \nfrom general semantic memory and (iii) differentially supported by the left and right ATL. This \nstudy assessed multiple aspects of social- semantic knowledge and general conceptual \nknowledge across cohorts with  ATL-damage arising from either neurodegeneration or \nresection. W e assembled  a test battery measuring knowledge of multiple types of social \nconcept. Performance was compared to  non-social general conceptual knowledge, measured \nusing the Cambridge Semantic Memory Test Battery and other  matched non-social-semantic \ntests. Our transdiagnostic approach include d bvFTD, semantic dementia and “mixed” \nintermediate cases to capture the FTD clinical spectrum, as well as age- matched healthy \ncontrols. People with unilateral left or right ATL  resection for temporal lobe epilepsy (TLE) \nwere also recruited to assess how selective damage to the left or right ATL impacts social- and \nnon-social-semantic knowledge. S ocial- and non- social-semantic deficits were severe and  \nhighly correlated in FTD. Much milder impairments were found after unilateral ATL resection, \nwith no left vs. right differences in social-semantic knowledge or general semantic processing, \nand with only naming showing a greater deficit following left vs. right damage. A principal \ncomponent analysis of all behavioural measures in the FTD cohort extracted three components, \ninterpreted as capturing : (1) FTD severity, (2) semantic memory and (3) executive function. \nSocial and non-social measures both loaded heavily on the same semantic memory component, \nand scores on this factor were uniquely associated with bilateral ATL grey matter volume but \nnot with the degree of ATL asymmetry. Together, these findings demonstrate that both social- \nand non- social-semantic knowledge degrade in FTD (semantic dementia and bvFTD) \nfollowing bilateral ATL atrophy. We propose tha t social-semantic knowledge is  part of a \nbroader conceptual system underpinned by a bilaterally -implemented, functionally-unitary \nsemantic hub in the ATLs . Our results also highlight the value of a transdiagnostic approach \nfor investigating the neuroanatomical underpinnings of cognitive deficits in FTD.  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 3 \nIntroduction  \nDegraded semantic memory is a prominent feature of frontotemporal dementia (FTD) . It is \nclassically associated with  semantic dementia (SD ; also called semantic- variant primary \nprogressive aphasia (svPPA)) and atrophy in the anterior temporal lobes (ATLs),1-6 but is also \noften a feature in behavioural-variant FTD (bvFTD).7-9 Motivated by the behavioural changes \nthat are commonly observed in FTD, a line of recent research has focussed on a specific aspect \nof the conceptual system , social-semantic knowledge, and the potential ly pivotal importance \nof the right ATL.10-16 The degree to which this type of knowledge is ( i) impaired in FTD, (ii) \nconcurrently impaired across different types of social concept , (iii) distinct from general \nsemantic memory and (iv) supported by the left and right ATLs is unclear. To address this gap \nin clinical knowledge, and to better understa nd whether social - and non- social-semantic \nknowledge are distinct domains with different neuroanatomical underpinnings , we assembled \na novel “broadband” battery spanning the many different types of social concept that have often \nbeen assessed only singly in past studies. An FTD cohort was recruited, including bvFTD, SD \n(including svPPA (commonly L>R ATL atrophy ) and R>L ATL “right” SD ) and “mixed” \nintermediate cases to ensure full coverage of the FTD clinical space and the underlying \nvariations in atrophy across the associated frontotemporal neuroanatomy. To provide important \nconvergent data on the function of the left and right ATL, people with left or right unilateral \nATL resection for temporal lobe epilepsy (TLE) also took part. Social-semantic performance \nwas compared with general conceptual knowledge, assessed using the Cambridge Semantic \nMemory Test Battery\n17,18 and other matched non-social-semantic tasks. Thus, for the first time, \nwe were able to test multiple aspects of soc ial-semantic knowledge in parallel and  compare \nthis to general conceptual knowledge in FTD and after ATL resection. \n \nSeparate investigations in clinical and cognitive neuroscience have highlighted roles for the \nATLs in semantic memory 19-21 and/or social cognition. 11,22-24 People with SD  experience a \ndegradation of semantic memory following bilateral ATL atrophy 2-4,6,18 and also display \nbehavioural changes.25-27 In their severest form, these semantic and behavioural changes are \nreminiscent of the  classic Klüver and Bucy studies which found concurrent multimodal \nassociative agnosia and chronic behaviour change following bilateral (but not unilateral) ATL \nablation in macaques.28 Provided appropriate techniques which maximise ventral ATL signal \nare used,29 contemporary fMRI studies have detected bilateral ventrolateral ATL activation \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 4 \nwhen healthy participants engage in semantic processing for all types of concepts19,30 including \nsocial concepts31,32 and for other aspects of social cognition such as theory of mi nd.22,23 One \nexplanation for a shared contribution to semantic and social processing is that the ATLs store \nsocial knowledge as part of semantic memory more generally .10,13 A challenge of the current \nliterature in providing clear answers to these questions is that there is no general consensus on \nwhat makes a concept ‘social’13,33,34 and thus it is unclear what types of concept are critical to \nsocial behaviours. P ast investigations have each tended to focus on one type of “socia l” \nconcept, which collectively span a very diverse range of concrete- to-abstract concepts, \nincluding people,14 social behaviours12,16,35 and emotions.36-38 \n \nThe neuroanatomical basis of social -semantic knowledge is also a subject of current \ndebate.13,34,39 According to one hypothesis , the right ATL is specialised for social -semantic \nknowledge, whereas the left ATL supports verbal semantic knowledge.14,16 This dichotomy is \nlargely based on the clinical observations of R>L SD patients (also sometimes known as right-\ntemporal variant FTD) , who often have prosopagnosia in the very earliest stages (typically \nbefore most patients present to clinic )1,40 followed by the emergence of behavioural changes \nand a generalised semantic impairment.14,41-44 This clinical evidence accords with more formal \nresearch showing that  rightward-biased ATL atrophy/hypometabolism is  associated with \ndeficits in person knowledge ,14 social concept ual knowledge ,16 emotion recognition 45 and \ntheory of mind. 46 Direct comparisons between left versus right ATL atrophy in SD  are not \nstraightforward, however: even if asymmetrical, the pathology is always bilateral, making it \nhard to unpick the relative contributions of each side. 1,47,48 Indeed, L>R SD patients can also \ndevelop behavioural impairment. Furthermore, R>L patients typically present to clinic later \nthan L>R patients and , consequently, often have more severe temporal lobe atrophy 1,41 and \nincreased atrophy in prefrontal regions important for social behaviour.49,50 \n \nRecently, social-semantic knowledge has been integrated within the hub-and-spokes model of \nsemantic m emory.\n10,12,13 According to this framework , the bilateral ATLs underpin a \ntransmodal, transtemporal hub for all concepts, which supports semantic representation \nthrough interaction with modality-specific cortical “spokes”.20,21 Accordingly, social-semantic \nknowledge is not a ‘special’ type of knowledge  with a distinct neural architecture , but is part \nof a broader conceptual system supported by the same bilateral ATL hub as non- social \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 5 \nconcepts.10,12,13 A key advantage of this framework is that it not only explains the FTD data but \nassimilates findings from other patient groups and healthy participants. First, a recent study has \nidentified that social-semantic impairments in FTD are  associated with bilateral ATL \natrophy.12 Second, there are indications that selective right ATL damage may not cause a \nselective impairment for social concepts or behaviour change. 47,51 Rather, unila teral ATL \ndamage yields a mild general semantic impairment,52,53 with similarly subtle deficits in person \nknowledge and emotion recognition after left or right resection .47,51 Third, there is no strong \nevidence for a left/right difference in social conceptual processing from studies in healthy \nparticipants; (i) distortion- corrected fMRI studies have detected overlapping bilateral \nventrolateral ATL activation for social and matched non-social concepts (although with some \nselective activation for social concepts in the bilateral superior ATL)31,32,54 and (ii) transcranial \nmagnetic stimulation (TMS) to left or  right superior ATL causes a cognitively and \nanatomically-selective disruption to social conceptual decision making.55 \n \nIn this study, we investigated social-semantic knowledge in two clinical groups associated with \nATL damage – neurodegenerative FTD and surgical ATL resection. Our study was designed \nto overcome two methodological issues from previous studies which would help to determine \nthe neural basis of social-semantic knowledge. First, we took an ‘inclusive’ approach with  a \nbroad range of social concepts, as well as carefully matched general (i.e., non-social) semantic \ntasks. This is critical to clarify whether social- semantic deficits are (a) selective to a specific \ntype of social concept, (b) reflective of a domain- specific social-semantic impairment, or (c) \npart of a broader domain-general conceptual degradation. Second, comparisons of diagnostic \ngroups defined categorically were supplemented by multivariate analytics that accommodate \nfor the cognitive and neuroanatomical systematic variation in FTD. By positioning individuals \nalong graded dimensions, it is possible simultaneously to model the contribution of total ATL \nvolume, ATL laterality, and volume loss in other brain regions to social-semantic knowledge. \nIn contrast to FTD, people with unilateral ATL resection provide a more selective lesion model \nof the left versus right ATL. Inclusion of these participants thus provided important and novel \ncross-aetiological data on the impact of (i) unilateral v s. bilateral and (ii) left vs. right ATL \ndamage on social-semantic knowledge. In summary, this study had broad coverage and high \nsystematicity with respect to the materials, participants and analysis.  \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 6 \nMaterials and methods  \nParticipants \nForty-eight people with FTD were recruited from dementia clinics in Addenbrooke’s Hospital, \nCambridge ( N = 40), St George’s Hospital, London ( N = 4) and John Radcliffe Hospital, \nOxford (N = 4). Twenty-six patients had a primary diagnosis of bvFTD56 and 22 met diagnostic \ncriteria for SD5. Eighteen people who had undergone unilateral ATL resection for TLE (left = \n11, right = 7) were recruited from Salford Royal Hospital, Manchester and the Walton Centre, \nLiverpool. All the TLE cases were left language dominant based on Wada test ing and at least \n12 months post -surgery. Nineteen healthy controls (age- matched to the FTD cohort) were \nrecruited from the MRC Cognition and Brain Sciences Unit, University of Cambridge. All \nparticipants provided written informed consent  obtained according to the Declaration of \nHelsinki. If participants lacked capacity to consent, their next of kin was consulted using the \n‘personal consultee’ process as established by UK law. Demographic and clinical information \nis reported in Table 1.  \n \nTable 1 Demographic and clinical information for each group \n    \n \n \nControl \n \nbvFTD \n  \nSD  \n \nLeft  \nTLE  \nRight  \nTLE  \nGroup \n difference \nPost-hoc \nN 19 26 22 11 7   \nSex (M:F) \n \n9:10 18: 8 8:14 6:5 3:4 χ2 = 5.67a, ns - \nAge (years) 64.4 (6.7) 64.3 (9.1) 66.1 (6.8) 46.8 (11.4) 53.1 (9.7) H(4) = 28.4b p < 0.0001 L < C, bvFTD, SD \n R < SD \nYears of Education 15.6 (3.4) 11.5 (1.9) 13.7 (2.9) 13.4 (2.8) 13.7 (2.1) H(4) = 20.3b, p < 0.001 bvFTD < C \nYears since symptom onset - 6.1 (3.5) 5.8 (3.3) -  - WS = 286c, ns - \nYears since diagnosis - 1.7 (1.6) 2.3 (1.8) - - WS = 223c, ns - \nYears since resection - - - 11.0 (3.7) 15.9 (2.4) t = 3.35d, p < 0.01 - \nNumber of anti-epileptic \ndrugs \n-   - - 2.2 (1.3) 1.6 (1.3) t = 0.99d, ns - \n \naChi-square test., bKruskal-Wallis test; cWilcoxon rank-sum test; dIndependent t-test \nMean and standard deviations are reported for each group. Significant p-values are highlighted in bold.  C = control, L=left TLE, ns = not \nsignificant, R=right TLE, TLE = temporal lobe epilepsy \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 7 \nNeuropsychology \nGeneral semantic memory and background neuropsychology \nGeneral semantic memory was assessed using a battery of tasks across a range of verbal and \nnon-verbal modalities. Tests included the modified picture version of the Camel and Cactus \nTest (CCT) and naming task from the Cambridge Semantic Memory Test Battery, 17,18,57,58 a \nsynonym judgement task,57,59 and the 30-item Boston Naming Test.60,61 \n \nGlobal cognition was assessed using the Addenbrooke’s Cognitive Examination- Revised \n(ACE-R), which provides a total score as we ll as five subscales: Attention and Orientation, \nMemory, Language, Fluency and Visuospatial Function. 62 The Brixton Spatial Anticipation \nTest63 and Raven’s Coloured Progressive Matrices Set B 64 were used to assess executive \nfunction. Full details of each task are reported in the Supplementary material. \n \nSocial-semantic battery  \nPerson knowledge \nPerson knowledge was assessed using face -to-name and face -to-profession matching tasks.47 \nParticipants also completed a landmark-to-name matching task,47 which was included to assess \nnon-social yet specific -level, or ‘unique entity’ concepts .65,66 Perceptual face matching was \nassessed using a 22-item task that required matching photographs of faces with different photos \nof the same person. 47,67 Half of the trials used famous faces as items, whereas the other half \nused unfamiliar faces.  \n \nAbstract social concepts \nComprehension of abstract social concepts was assessed using a verbal abstract social synonym \njudgement task that has been utilis ed previously not only in patient assessment but also in \nstudies of healthy participants.15,16,31,32,35,55 Participants also completed an abstract non -social \nsynonym judgement task with items matched to the social concepts for lexical frequency, \nimageability and semantic diversity.31,68 \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 8 \nSocial word-picture matching \nParticipants completed a four-alternative forced-choice (4AFC) word-picture matching task. In \neach of the 35 trials, the presented word denoted a type of person with a characteristic age \nand/or gender (e.g. ‘infant’, ‘woman,’ ‘ uncle’, etc.) and options were colo ur photographs of \nindividuals. The participants also completed a non- social 4AFC word-picture matching task  \nwith words denoting manmade objects that were individually matched to the social items for \nlexical frequency. \n \nEmotion knowledge \nTwo tests of emotion knowledge were employed: a ‘basic emotion’ recognition task using  19 \nstimuli from the Fac e and Gesture Recognition Network Database 69 and a 23-item ‘complex \nemotion’ recognition task using more nuanced words such as embarrassment and jealousy, \ndrawn from the Cambridge Mind Reading Face Battery (children’s version) .70 In each task, \nparticipants were shown dynamic video clips of a person displaying an emotion and were \ninstructed to point to the word best matching the emotion, from four response options.  \n \nSocial norms knowledge \nThe Social Norms Questionnaire (SNQ) includes 22 items describing a behaviour. Participants \nanswer whether it would be socially appropriate to perform each behaviour in the presence of \na stranger or acquaintance ( i.e. not a close friend  or family member). The  wording of some \nitems was modified to UK-English, with permission from Dr. Katherine Rankin, developer of \nthe questionnaire (Supplementary material).   \n \nSarcasm detection \nParticipants completed the Social Inference- Minimal Test from the Awareness of Social \nInference Test (TASIT -SM) which assesses the ability to detect sarcasm from paralinguistic \ncues.71 \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 9 \nStatistical analysis \nGroup comparisons were assessed  using the ‘rstatix’ package 72 in R studio version 4.0.3. 73 \nNormality of data and equality of variance were assessed  using Shapiro- Wilk tests and \nLevene’s test s. Where data were normally distributed, one -way ANOVAs and post-hoc \nTukey’s range tests were conducted if  there was equality of variance across groups , whereas \nWelch ANOVAs and post-hoc Games Howell tests were conducted if variances were unequal. \nWhere data were not normally distributed, Krukal-Wallis tests and post-hoc Dunn’s tests were \nconducted. A level of P < 0.05 was used to determine statistical significance.   \n \nStructural MRI \nMRI acquisition and preprocessing \nSixty-nine participants had a T1-weighted 3T structural MRI scan on a Siemens PRISMA at \nthe University of Cambridge, Wolfson Brain Imaging Centre (bvFTD = 14, SD = 6, control = \n19) or the University of Cambridge MRC Cognition and Brain Sciences Unit (bvFTD = 1, SD \n= 13, control = 16).  Sixteen ATL-resected participants (left TLE = 9, right  TLE = 7) and a \nseparate cohort of 20 age- matched controls had a T1- weighed 3T structural MRI scan on a \nPhilips Achieva scanner at the Manchester Clinical Research Facility, University of \nManchester. Raw MRI data were converted to the Brain Imaging Dataset format 74 and pre-\nprocessed using the Computational Anatomy Toolbox version 12 in SPM 12. 75 Images were \nsegmented into grey matter, white matter and CSF, and modulated and normalised to MNI \nspace using geodesic shooting.76 Normalised grey matter images were spatially smoothed using \na Gaussian kernel with 10mm FWHM.  \n \nGrey matter differences between groups \nVoxel-based morphometry (VBM) was conducted to explore grey matter differences between \ngroups. Separate general linear models were built with age, intracranial volume  (ICV) and \nscanner site as covariates, and groups compared using independent t-tests. An explicit objective \naverage-based mask was used, which is recommended for VBM of severely atrophic brains.77 \nSignificant clusters were extracted using a cluster -level threshold of Q < 0.05, based on an \ninitial voxel-level threshold of P < 0.001. Results were visualised using the xjView toolbox \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 10 \n(https://www.alivelearn.net/xjview) and brain regions were labelled using the automated \nlabelling atlas 3.78  \n \nGrey matter indices in frontotemporal regions of interest \nFor each participant, grey matter volume indices were calculated in two regions atrophied in \nFTD – the ATL and orbitofrontal cortex (OFC). The ATL masks were derived from a previous \nmeta-analysis54 and the OFC masks were derived from the Harvard -Oxford Cortical Atlas \n(Supplementary Fig. 1). Grey matter intensity values for each region of interest ( ROI) were \nextracted, and linear regression models fitted  using the control data with each ROI as the \ndependent variable and age, ICV and scanner site as regressors. Each patient’s data were \nplugged into the model, and the residuals used to calculate two indices per brain region: \nmagnitude (left + right residual) and asymmetry (left - right residual). \n \nExtracting neuropsychological components \nA standard principal component analysis (PCA) with varimax -rotation was conducted on all \nneuropsychological tasks in the FTD cohort to extract the underlying dimensions of variation \nin the data. R aw scores were converted to percentages and missing data were imputed using \nprobabilistic principal component analysis (PPCA) .\n79,80 As PPCA requires the number of \nextracted principal components to be pre -specified, k- fold cross validation was used  to \ndetermine the optimum number of components  for missing data imputation .81 A three-\ncomponent solution had the lowest root means squared error, and thus PPCA was conducted \nwith three components. P articipants were scored at chance level on tasks they were too \nimpaired to complete. The PCA was then conducted on the full FTD sample ( N = 48) with \nmissing data imputed. The number of principal components was determined using the elbow \nmethod on the scree plot of eigenvalues82 and factor scores were calculated using the regression \nmethod. Sampling adequacy and suitability of the data for PCA were assessed using the Keiser-\nMeyer-Olkin (KMO) test and Bartlett’s test of sphericity.   \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 11 \nAssociations between grey matter volume and PCA-derived factor \nscores \nThe neuroanatomical correlates of neuropsychological performance in FTD were explored \nusing voxel-based correlational methodology (VBCM).83 A linear regression model was fitted \nto explore the association between grey matter intensity and factor scores on each \nneuropsychological component, with age, ICV and scanner site included as covariates. \nSignificant clusters were extracted using a cluster -level threshold of Q < 0.05, based on  an \ninitial voxel- level threshold of P  < 0.001. To explore the contributions of not only ATL \nmagnitude, but also ATL asymmetry and OFC magnitude/asymmetry, forced-entry multiple \nlinear regression models were fitted to predict scores  on each neuropsychologi cal task, with \nthe four ROIs as predictors.  \n \nResults \nNeuroimaging comparisons \nGrey matter volume differences between groups \nThe VBM results align closely with the expected patterns for each clinical group (Fig. 1 and \nSupplementary Table 1). Direct comparisons between FTD subgroups revealed reduced grey \nmatter in the bilateral ATLs in SD  (Supplementary Fig. 2) and no significant clusters for the \nreverse contrast. As expected, the resected TLE patients provide a neuroanatomical model of \n(i) purely unilateral and complete resection and (ii) no detected frontal changes – which is a \npowerful comparison to the concurrent frontal, temporal and insular atrophy of the FTD \npatients. These patterns were underlined by the ROI analyses (see next). \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 12 \n \nFigure 1. Voxel -based morphometry results. Each row displays clusters of reduced grey \nmatter volume relative to age -matched controls for (A) FTD and (B) TLE. Images are \nthresholded using a cluster-level threshold of Q < 0.05 (after an initial voxel-level threshold of \nP < 0.001). Significant clusters are overlaid on the MNI avg152 T1 template. Co-ordinates are \nreported in Montreal Neurological Institute space.   \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 13 \n \nMagnitude and asymmetry in core frontotemporal regions  \nGrey matter indices for each participant are displayed in Fig. 2 and reported in Table 2. There \nwas a significant group difference in ATL magnitude ( F(3, 46) = 14.60, P < 0.0001),  with \npost-hoc tests revealing that the SD group had lower magnitude than bvFTD (P < 0.0001) and \nleft TLE (P < 0.0001), but not right TLE ( P = 0.37), whereas the right TLE group had lower \nmagnitude compared to bvFTD (P = 0.03). Groups also differed in ATL asymmetry (F(3, 46) \n= 64.78, P < 0.0001). Both TLE groups had significantly greater absolute asymmetry value s \nthan both bvFTD and SD (P < 0.0001). There was a significant group effect on OFC magnitude \n(F(3, 46) = 12.19, P  < 0.0001). As expected, both FTD subgroups had significantly lower \nmagnitude than TLE (all P < 0.01), with no significant differences between bvFTD and SD (P \n= 0.83) or between left and right TLE ( P = 0.96). There was no main effect of group on OFC \nasymmetry (F(3, 46) = 0.46, P = 0.72). ATL and OFC magnitude were positively correlated in \nboth FTD subgroups (bvFTD; r  = 0.54, P = 0.04, SD; r = 0.78, P < 0.0001), but not in TLE \n(left TLE; r  = 0.58, P = 0.10, right TLE; r = 0.48, P  = 0.27). Asymmetry indices also were \nstrongly positively correlated in bvFTD ( r = 0.74, P = 0.002), SD (r = 0.83, P < 0.0001) and \nleft TLE (r = 0.76, P = 0.03), although not in right TLE (r = 0.35, P = 0.44). \n \n \n \n \n \n \n \n \n \n \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 14 \nTable 2 Magnitude and asymmetry indices for each group  \n \n bvFTD  SD Left \n TLE  \nRight  \nTLE  \nGroup difference Post-hoc  \nN 15 19 9 7 - - \nATL Magnitude -0.17 (0.16) -0.38 (0.08) -0.18 (0.07) -0.30 (0.05) F(3, 46)=14.60, P < 0.0001 SD, R < bvFTD \n SD < L \nATL Asymmetry (absolute value) 0.04 (0.05) 0.07 (0.05) 0.24 (0.05) 0.26 (0.04) F(3, 46)=64.78, P < 0.0001 bvFTD, SD < L, R \nOFC Magnitude -0.19 (0.13) -0.16 (0.08) 0.007 (0.06) -0.02 (0.08) F(3, 46) = 12.19, P < 0.0001 bvFTD, SD < L, R \nOFC Asymmetry (absolute value) 0.04 (0.03) 0.04 (0.02) 0.03 (0.03) 0.03 (0.006) F(3, 46)=0.46, P = 0.72 - \nATL Magnitude vs. ATL Asymmetry  r = 0.21 r = -0.19 r = 0.85** r = -0.57 - - \nATL Magnitude vs. OFC Magnitude r = 0.54* r = 0.78**** r = 0.58 r = 0.48 - - \nATL Magnitude vs. OFC Asymmetry  r = -0.03 r = -0.09 r = 0.68* r = 0.21 - - \nATL Asymmetry vs. OFC Magnitude  r = 0.10 r = -0.12 r = 0.57 r = -0.25 - - \nATL Asymmetry vs. OFC Asymmetry r = 0.74** r = 0.83**** r = 0.76* r = 0.35 - - \nO\nFC Asymmetry vs. OFC Magnitude  r = -0.10 r = 0.009 r = 0.67* r = -0.34 - - \n \nTop four rows display mean and standard deviations for each group. Bottom four rows display Pearson’s correlation coefficients between \neach index. Significant p-values are highlighted in bold.  \n*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 \nbvFTD = behavioural-variant frontotemporal dementia, C = control, L = left TLE, R = right TLE, SD = semantic dementia  \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 15 \n \nFigure 2. Scatter plots displaying ATL and OFC indices for each patient. Lower magnitude \nvalues indicate greater volume loss and negative asymmetry values indicate left > right volume \nloss. In each scatter plot, the grey points represent the extremity boundaries. (A) ATL \nmagnitude vs. ATL asymmetry. (B) OFC magnitude vs. OFC asymmetry. (C) OFC asymmetry \nvs. ATL asymmetry. (D) ATL magnitude vs. OFC magnitude. \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 16 \nNeuropsychological comparisons \nGeneral semantic memory and background neuropsychology \nTable 3 displays scores for each group on each test . Both FTD subgroups were impaired on \nevery semantic task and each ACE -R subscale (P < 0.05). Relative to bvFTD, the SD group \nperformed more poorly on the Cambridge (P = 0.0006) and Boston (P = 0.001) Naming tests, \nand the ACE-R Language subscale ( P = 0.008) and the bvFTD had lower scores than SD on \nthe Raven’s (P = 0.006). The left TLE group were impaired on the  Boston Naming test ( P = \n0.02), synonym judgement task (P = 0.002), as well as the Memory ( P = 0.03), Fluency (P = \n0.04) and Language ( P = 0.04) ACE -R subscales. There were no significant differences \nbetween left and right TLE on any tasks. \n \nTable 3 Mean scores on each task \n \n Control bvFTD SD Left  \nTLE \nRight \n TLE  \nGroup  \nDifference \nPost-hoc \nN 19 26 22 11 7 - - \nACE-R Total (100) 96.8 (2.3) 60.2 (22.0) 45.4 (23.5) 80.5 (9.8) 87.7 (6.1) H(4) = 58.9** L, bvFTD, SD < C \nSD < L \nbvFTD, SD < R \nMMSE (30) 29.8 (0.4) 21.3 (6.9) 19.1 (8.9) 27.2 (1.5) 28.9 (1.1) H(4) = 50.8** L, bvFTD, SD < C \nbvFTD, SD < R \nACE-R Attention \n(18) \n17.9 (0.2) 13.4 (4.7) 12.5 (5.8) 17.4 (0.9) 17.9 (0.4) H(4) = 41.0** bvFTD, SD < C, L, R \nACE-R Memory \n(26) \n24.5 (2.0) 13.2 (7.9) 8.1 (6.5) 16.6 (5.5) 19.9 (4.4) H(4) = 45.2** L, bvFTD, SD < C \nSD < R \nACE-R Fluency (14) 13.2 (1.2) 4.0 (3.3) 4.5 (3.4) 9.1 (1.9) 11.0 (1.9) H(4) = 59.0** L, bvFTD, SD < C \n bvFTD, SD < L, R \nACE-R Language \n(26) \n25.7 (0.5) 18.2 (7.1) 8.8 (5.4) 21.9 (3.8) 23.4 (1.7) H(4) = 57.5** L, bvFTD, SD < C \n SD < L, R, bvFTD \nACE-R Visuospatial \n(16) \n15.6 (0.8) 11.3 (3.8) 11.5 (4.9) 15.5 (0.8) 15.6 (0.5) H(4) = 31.8** bvFTD, SD < C, L, R  \nCambridge Naming \n(32) \n31.9 (0.2) 27.3 (7.8) 13.0 (9.7) 31.2 (1.4) 31.9 (0.4) H(4) = 57.5** bvFTD, SD < C \nSD < L, R, bvFTD \nBoston Naming (30) 29.7 (0.5) 21.5 (8.5) 6.8 (5.5) 26.1 (2.4) 27.9 (2.3) H(4) = 60.8** L, bvFTD, SD < C \n SD < L, R, bvFTD \nCamel and cactus \ntest (32) \n30.7 (1.1) 21.8 (7.4) 15.7 (5.1) 28.8 (1.8) 29.0 (1.6) H(4) = 47.2** bvFTD, SD < C \nSD < L, R \nSynonym judgement \n(48) \n47.8 (0.4) 39.0 (7.9) 35.9 (7.5) 42.9 (1.8) 44.9 (2.9) H(4) = 47.5** L, bvFTD, SD < C \nSD < R \nRaven’s (12) 10.5 (1.5) 5.0 (2.7) 8.3 (3.4) 10.2 (1.4) 10.3 (1.9) H(4) = 35.8** bvFTD < C, L, R, SD \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 17 \nBrixton (10)  6.4 (2.0) 2.9 (2.0) 4.8 (2.8) 6.6 (2.2) 5.7 (2.0) H(4) = 25.5** bvFTD < C, L \nFace-name matching \n(44) \n38.9 (3.4) 28.6 (11.1) 16.4 (7.9) 36.8 (4.2) 34.3 (10.1) H(4) = 34.8** bvFTD, SD < C \nSD < L, R, bvFTD  \nFace-profession \nmatching (44) \n40.3 (3.7) 27.7 (11.4) 20.0 (10.0) 39.6 (3.3) 37.3 (6.9) H(4) = 35.2** bvFTD, SD < C, L \nSD < R  \nLandmark-name \nmatching (42) \n38.5 (1.8) 24.3 (9.0) 16.1 (6.3) 27.2 (3.6) 30.9 (5.9) W(4, 24) = 77.2** bvFTD, SD, L < C \n SD < L, R, bvFTD  \nFamous face \nmatching (22) \n21.2 (0.8) 18.6 (2.8) 18.0 (2.1) 20.6 (2.0) 18.9 (2.4) H(4) = 26.7** bvFTD, SD < C \n SD < L  \nUnfamiliar face \nmatching (22) \n20.3 (1.4) 17.1 (3.1) 18.1 (2.8) 19.1 (1.2) 16.7 (2.3) H(4) = 19.1* bvFTD, SD, R < C \nSocial abstract \nsynonym judgement \n(36) \n33.9 (1.3) 26.4 (5.3) 25.1 (5.8) 31.1 (1.3) 32.3 (2.1) H(4) = 42.8** bvFTD, SD < C \n SD < R \nNon-social abstract \nsynonym judgement \n(36) \n35.6 (0.6) 28.1 (6.1) 25.5 (6.7) 33.1 (3.0) 34.9 (1.2) H(4) = 42.2** bvFTD, SD < C \n SD < R \nSocial word-picture \nmatching (35) \n  \n34.4 (0.8) 29.8 (5.8) 27.1 (6.0) 32.5 (1.1) 32.9 (1.3) H(4) = 40.2** bvFTD, SD < C \nNon-social word-\npicture matching \n(36)  \n35.9 (0.2) 33.5 (5.7) 29.3 (6.9) 35.8 (0.4) 36.0 (0.0) H(4) = 35.6** SD < C, L, R, bvFTD \nBasic emotion \nmatching (19) \n16.3 (1.5) 11.8 (3.2) 11.0 (3.5) 15.3 (1.7) 14.7 (1.8) H(4) = 36.0** bvFTD, SD < C \nSD < L  \nComplex emotion \nmatching (23) \n18.5 (1.9) 12.9 (4.9) 12.2 (5.0) 16.9 (2.1) 17.4 (3.9) W(4, 24.4) = 9.5** bvFTD, SD < C, L \nSocial Norms \nQuestionnaire (22) \n20.0 (1.2) 15.4 (3.9) 15.5 (2.7) 19.4 (1.1) 19.9 (0.7) H(4) = 32.5** bvFTD, SD < C, L, R \nTASIT-Sarcasm (20) 18.7 (1.9) 9.9 (5.5) 8.1 (5.1) 15.3 (2.7) 15.0 (2.7) H(4) = 38.0** bvFTD, SD < C \nMeans and standard deviations for each group reported. Maximum scores for each task are reported in parentheses in the first column. \nGroup differences were assessed using Kruskal -Wallis tests with post -hoc Dunn’s tests (corrected for multiple comparisons using Holm \nmethod), or Welch one-way ANOVA tests (Games Howell post-hoc tests). \n*P < 0.001; **P < 0.0001 \nbvFTD = behavioural-variant frontotemporal dementia, C = control, L = left TLE, R = right TLE, SD = semantic dementia \n \nSocial-semantic battery \nFTD groups were impaired across all tasks in the social -semantic battery (P < 0.05), the only \nexception being bvFTD on the non- social word -picture matching task ( P = 0.22). Direct \ncomparisons between FTD subtypes found that the SD  group performed more poorly on non-\nsocial word-picture matching (P = 0.004), face-name matching (P = 0.03) and landmark-name \nmatching (P = 0.01). The left TLE group were impaired on the landmark-name matching (P < \n0.0001), whereas the right TLE group were impaired on unfamiliar perc eptual face matching \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 18 \n(P = 0.005). As with the general semantic tasks, there were no significant differences between \nleft and right TLE.  \n \nExtracting neuropsychological components \nThe percentage of participants too impaired to complete each task and hence scored at chance-\nlevel is reported in Supplementary Table 2. The KMO statistic was 0.87, indicating meritorious \nsampling adequacy,84 and Bartlett’s test for sphericity was significant (P < 0.0001), indicating \npresence of at least some common factors in the covariance matrix . Visual inspection of the \nscree plot indicated three principal components (Supplementary Fig. 3) which explained 78.5% \nof the total variance.  \n \nTask and factor loadings are displayed in Fig . 3. The first principal component (PC) had an \neigenvalue of 14.5 and explained 60.2% of the total variance. The tasks loading positively onto \nthis component were ACE-R Attention, ACE-R Visuospatial, ACE-R Fluency, CCT, synonym \njudgement, Raven’s, social word-picture matching, non-social word-picture matching, famous \nand unfamiliar perceptual face matching, emotion matching, and the abstract social and non-\nsocial synonym judgement tasks. There is no specific cognitive process shared by all tasks, but \nrather this component reflects  FTD severity – in keeping with sampling FTD specifically  \n(rather than many different kinds of dementia or aetiologies)  and testing them on a collection \nof tasks known to be affected in this group. In keeping with this interpretation, scores on this \nfactor were strongly correlated with total atrophy across the patients while the other factors \nwere not (Supplementary Fig. 4). There were no statistically reliable differences in mean factor \nscores between bvFTD and SD on this component (t = 0.44, P = 0.66). \n \nThe second PC had an eigenvalue of 2.99 and explained 12.5% of the remaining variance. \nTasks loading positively were the ACE -R Memory, ACE -R Language, Cambridge Naming, \nBoston Naming, CCT, synonym judgement, face -name matching, landmark -name matching, \nSNQ, and abstract social and non- social synonym judgement tasks. This component was \nlabelled semantic memory  as it primarily included semantic tasks. The SD group had \nsignificantly lower factor scores (i.e. poorer performance) on this component compared to \nbvFTD (t = 5.38, P < 0.0001). Crucially, both social and non-social semantic tasks co-loaded \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 19 \nonto this component, and thus we use ‘semantic memory’ to refer to both ‘social- and non-\nsocial-semantic memory’. Indeed, when we extracted separate ‘social’ and ‘non-social’ factors \n(by entering the two sets of assessment results into two separate one -factor PCAs), scores on \nthese two factors were highly correlated ( r = 0.85), which strongly suggests the generalised \ndegradation of a unitary conceptual system affecting both social and non- social concepts in \nFTD.  \n \nThe third PC had an eigenvalue of 1.41 and explained 5.9% of the remaining variance. Tasks \nloading positively were the two executive function tasks and the TASIT -sarcasm. \nConsequently, this PC was labelled executive functio n. The bvFTD group had significantly \nlower factor scores on this component than SD (t = 3.97, P = 0.0002).  \n \nProjection of TLE participants into the FTD-defined PCA space \nThe TLE patients’ neuropsychological scores were projected into the FTD-defined PCA space \nusing the regression method (Fig. 3). We then used ANOVAs to assess whether the TLE groups \ndiffered from bvFTD and SD in their average scores on each factor. There was no significant \neffect of group on FTD severity factor scores (F(3, 62) = 1.05, P = 0.38), but there was a large \ngroup effect on semantic memory factor scores (F(3, 62) = 24.14, P < 0.0001) with SD having \nlower scores than both TLE groups (P < 0.0001), as well as a large effect on executive function \nfactor scores ( F(3, 62) = 16.74, P  < 0.0001) where the bvFTD scores were lower  than both \nTLE groups (P < 0.0001). Most of the  left (90.9%) and right (57.1%) TLE participants had a \nsemantic memory factor score below the control-derived cut-off (defined as the factor score of \na hypothetical participant scoring 1.96 SDs below the control average on all tasks), but no TLE \nparticipant had a factor  score below the cut -off for executive function . There were no \ndifferences between left and right TLE on FTD severity ( P = 0.99), semantic memory ( P = \n0.93) or executive function ( P = 0.99). Taken together, these findings suggest that unilateral \nATL resection yields a mild generalised semantic impairment in the context of preserved \nexecutive function, with no clear left vs. right differences. \n \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 20 \n \nFigure 3. PCA factor loadings and factor scores. (A)  Factor loadings.  Red dashed lines \nindicate factor loading cut-offs (>|0.5|). (B) PC1 (FTD severity) plotted against PC2 (semantic \nmemory). (C) PC2 (semantic memory) plotted again st PC3 (executive function). (D) PC3 \n(executive function) plotted against PC1 (FTD severity). The dashed lines indicate the factor \nscore of a control scoring 1.96 standard deviations below the control average on each task.  \n \n \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 21 \nAssociation between grey matter volume and neuropsychological \nperformance \nFTD severity factor scores were associated with grey matter volume in the precentral gyrus, \nfrontal/orbital gyri, cingulate cortex, insula and supplementary motor area  (Fig. 4A). \nReinforcing the interpretation of PC1 as representing FTD severity, changes of grey matter in \na very similar set of regions were found to correlate with the global atrophy measure (Fig 4B). \nIndeed, (i) total grey matter volume and FTD severity scores were found to be strongly \ncorrelated ( r = 0.46; P = 0.006), and (ii) when total grey matter volume was entered as a \ncovariate into the FTD severity VBCM analysis then no regions remained. Semantic memory \nfactor scores were associated  with grey matter volume in the bilateral ATLs, maximal at the \ntemporal poles and ventral ATL regions (Fig.4C). These semantic-to-atrophy correlations were \nunchanged when total atrophy was entered as a covariate (and the semantic PCA scores were \nnot significantly correla ted with global atrophy: r = 0.31, P = 0.07). No significant clusters \nemerged for executive function.  \n \n \n \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 22 \n \nFigure 4. Regions of grey matter volume associated with factor scores Regions of grey \nmatter positively correlated with (A) FTD severity factor scores, (B) Total grey matter volume \nand (C) Semantic memory factor scores. Images are thresholded using a cluster-level threshold \nof Q < 0.05 (above an initial voxel -level threshold of P  < 0.001). Significant clusters are \noverlaid on the MNI avg152 T1 template. Co-ordinates are reported in Montreal Neurological \nInstitute space. \n \nTo explore the importance of the bilateral and/or asymmetr ic nature of the atrophy, l inear \nmultiple regression models were fitted with the ATL and OFC indices as predictors. The model \nwas significant for semantic memory factor scores (F(4, 29) = 18.30, P  < 0.0001) with the \nmagnitude of ATL atrophy the only significant individual predictor ( t = 7.82, P  < 0.0001).  \nHowever, ATL asymmetry was not significant ( t = 0.29, P  = 0.78). The linear multiple \nregression model was significant for ACE -R Memory (F(4, 29) = 7.05, P  = 0.0004), ACE-R \nLanguage (F(4, 29) = 12.97, P  < 0.0001), Cambridge Naming ( F(4, 29) = 8.56, P  = 0.0001), \nBoston Naming ( F(4, 29) = 15.06, P < 0.0001), face -name matching ( F(4, 25) = 8.22, P  = \n0.0002), face-profession matching (F(4, 24) = 10.69, P < 0.0001) and landmark-name matching \n(F(4, 25) = 5.09, P = 0.004). ATL magnitude was the only significant predictor in every case, \nexcept for the landmark-name matching (also predicted by OFC magnitude; t = -2.65, P = 0.01) \nand the two naming tasks which were also predicted by ATL asymmetry (Cambridge Naming; \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 23 \nt = 2.16, P = 0.04, Boston Naming; t  = 2.17, P = 0.04). Full details of each regression model \nare reported in Table 4.  \n \nTable 4 Model summaries and standardised beta-values for each regression model \n \nDependent variable ANOVA R2 ATL \n Magnitude \nATL \n Asymmetry \nOFC  \nMagnitude \nOFC \n Asymmetry \nPC2 - Semantic \nmemory \nF(4, 29) = 18.30, P < 0.0001 0.72 0.88**** 0.05 -0.13 -0.12 \nACE-R Memory F(4, 29) = 7.05, P = 0.0004 0.49 0.57*** 0.09 0.21 0.02 \nACE-R Language F(4, 29) = 12.97, P < 0.0001 0.64 0.73**** 0.31 -0.04 -0.20 \nCambridge Naming F(4, 29) = 8.56, P = 0.0001 0.54 0.55*** 0.48* 0.06 -0.27 \nBoston Naming F(4, 29) = 15.06, P < 0.0001 0.68 0.71*** 0.41* -0.05 -0.23 \nFace-name matching F(4, 25) = 8.22, P = 0.0002 0.57 0.83**** -0.24 -0.21 0.05 \nFace-profession \nmatching \nF(4, 24) = 10.69, P < 0.0001 0.64 0.87**** -0.12 -0.22 -0.12 \nLandmark-name \nmatching \nF(4, 25) = 5.09, P = 0.004 0.45 0.70*** -0.23 -0.43* -0.11 \n \nSignificant p-values are highlighted in bold. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. \nACE-R = Addenbrookes Cognitive Examination -Revised, ATL = anterior temporal lobe, OFC = orbitofrontal cortex , PC = principal \ncomponent \n \nDiscussion  \nThis study considered  how social-semantic knowledge  is (a) impaired in FTD relat ive to \ngeneral semantic memory  and (b) differentially supported by the left vs. right ATL s. We \nconducted a comprehensive and systematic investigation of social concepts using a battery \ncomprising diverse types of social concept and non-social-semantic tasks. The results suggest \nthat semantic knowledge in both social and non -social domains are equally affected by ATL \ndamage, with little difference between left - vs. right-predominant abnormality in either \ndomain. P eople who had undergone unilateral ATL  resection provided convergent data \nsupporting this conclusion. In the following sections, we discuss the key findings and clinical \nimplications. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 24 \nSocial and non-social concepts are underpinned by the bilateral \nanterior temporal lobes \nA selective degradation of conceptual knowledge is the defining feature of SD .2,3,5,6 Research \nover recent decades has revealed that this degradation occurs for all types of concepts, in their \nverbal and non- verbal modalities, following bilateral ATL atrophy .4,18,20,21 In this study, we \nhave demonstrated that the conceptual degradation extends to a very wide range of social \nconcepts. Although milder than in SD, a parallel decline in social - and non-social-semantic \nknowledge was also found in bvFTD, highlighting the phenotypic overlap between the \nsyndromes and mirroring the neuroanatomical overlap including ATL atrophy.8,9,85,86 Indeed, \na very clear picture emerges by adopting a transdiagnostic approach: the PCA conducted across \nSD and bvFTD patients indicated that both social- and non-social-semantic deficits were highly \ncorrelated and heavily co -loaded onto the same semantic memory component. Factor scores \nwere associated with grey matter volume only in the bilateral ATLs when the entire FTD group \nwas analysed together. This is true not only in the SD subset of cases (i.e., the classical ATL-\nsemantically impaired patient population) but also in the remaining FTD patients (i.e., when a \npatient with more frontally-centred atrophy presents with a semantic impairment, this is due to \nconcurrent ATL atrophy , rather than representing a distinct new subtype of bvFTD ). In \naddition, the ROI regression analyses showed that semantic scores were associated with total \nbilateral ATL volume, but not ATL asymmetry. Indeed, ATL asymmetry was not associated \nwith performance on any individual social -semantic comprehension task. These findings \ndemonstrate that social - and non- social-semantic knowledge is supported by the ATLs \nbilaterally. There was no evidence (i) that social-semantic knowledge is neuroanatomically \ndistinct from general conceptual knowledge or (ii) that R>L ATL atrophy causes increased \nsocial-semantic impairments relative to L>R atrophy. It is important to note that the analyses \nof this large dataset were able to detect asymmetrically supported funct ions where they did \noccur: as found in previous studies of SD and many other patient groups,1,51,87,88 plus in healthy \nparticipants after rTMS, naming and speech production are substantially more affected by \ndamage/stimulation to the left than right ATL .89 Past neuroanatomically -constrained \ncomputational models have shown that this follows as a corollary of a bilaterally -supported \nATL semantic system driving left-lateralised speech production.88,90  \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 25 \nUnilateral ATL resection yielded mild impairments across s ocial- and non- social-semantic \ntasks, and on the PCA semantic memory factor score. These findings replicate previous studies, \nwhere unilateral ATL damage is associated with a mild semantic impairment when sensitive \nassessments are used.47,51-53 The chasm between the subtle unilateral and severe bilateral effects \non semantic processing cannot be explained solely by the degree of total ATL damage, as many \nof the TLE participants had a magnitude of ATL grey matter loss similar to that in some cases \nof SD (see Fig. 2). In other words, although the level of semantic impairment in these patients \nis clearly governed by the overall amount of ATL damage, the distribution of damage across \nthe left and right ATLs is also crucial. A bilateral-implementation may configure the semantic \nsystem to be resilient to unilateral damage, a hypothesis that has been formally captured and \nexplored computationally.\n90  \n \nSecondary to the mild generalised semantic impairment, graded neuropsychological \ndifferences can emerge from left vs. right ATL unilateral damage. Consistent with the results \nfrom SD and associated computational models (see above), increased anomia is found after left \nATL resection .51,87 Despite left versus right differences for naming and perceptual face \nmatching, we found no evidence of any differences in social (or non-social) semantics in the \nsurgical cases – again mirroring the findings from FTD. Moreover, in contrast to the right ATL \nhypothesis for social processing, the TLE participants (right and left) show no behavioural \nchanges, even when formally assessed using the same neuropsychiatric tools as those used in \nFTD.\n51  \n \nThe chronic epilepsy in TLE raises the possibility of pre -surgical functional reorganisation \naway from seizure centres .91 However, there is evidence that any such reorganisation  is \nminimal, at least for semantic representation. First, as described above,  very mild generalised \nsemantic impairments are found in unilateral ATL -resected cases 47,51-53 and the degree of \nsemantic impairment is associated with the amount of resected tissue.53 Second, the increased \nanomia caused by left ATL resection mimics the relatively more severe anomia in L>R SD ,88 \nwhich implies that semantic memory is organised similarly in pre-surgical TLE as in SD. Third, \ndirect cortical grid electrode studies of pre-surgical TLE patients detect semantic-related neural \nactivity in the left and right ventrolateral ATLs and cortical stimulation generates a transient \nsemantic impairment in exactly the same semantic “hot -spot” as that observed in healthy \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 26 \nparticipant fMRI studies.19,92,93 Finally, task-based fMRI in resected TLE patients shows that, \nrather than shifts of semantic function to new locations, the patients’ semantic system \nupregulates activation in the same (remaining) regions as those observed in healthy \nparticipants94; this pattern is closely mirrored in healthy participants after rTMS to the ATL.95,96 \n \nClinical implications \nSocial-semantic knowledge and ‘right’ semantic dementia  \nFTD patients with R>L ATL atrophy often present to clinic with behavioural changes.41-43 This \nclinical observation has driven the hypothesis that the right ATL has a specialised role in social \nprocessing14,16 and proposals that R>L SD  is a distinct clinical syndrome .14,41,44 Our results \nchallenge this  view. From both FTD and resected TLE , we found no evidence of right -\nlateralised specialisation for social concepts, but rather equal contributions from left and right \nATL to all types of semantic knowledge. As noted above, this finding aligns with parallel fMRI \nand rTMS ATL explorations in healthy participants. 31,32,54,55 What, then, is the cause of the \ncommonly observed social problems in patients with R>L SD? R>L cases typically present to \nclinic later than L>R, and even though they must exist, there is a paucity of early R>L SD  \npatients in the literature, either as single cases or as part of group studies, including the current \ninvestigation (for a review, see 1). Group studies have found that R>L SD  patients typically \nhave more overall temporal lobe atrophy than L>R1,41 and increased prefrontal atrophy.49 There \nare at least three (non -mutually exclusive) alternative explanations for the increased \nbehavioural change in R>L SD . First, R>L SD cases have greater overall ATL volume loss, \nbilaterally, which would cause a relatively greater degradation of semantic  memory (for both \nsocial and non- social concepts) which is important for supporting appropriate social \nbehaviour.13 Second, the increased behavioural changes result from increased  prefrontal \ndamage in areas important for controlled social behaviour, such as the OFC.50 Third, we \ndemonstrated that ATL and OFC asymmetry are correlated in FTD, raising the possibility that \nR>L OFC asymmetry may also contribute to the increased behaviour change. Indeed, theories \nof behavioural change in FTD have highlighted the importance of right prefrontal regions, in \nparticular, in social functioning.\n97  \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 27 \nA transdiagnostic approach to frontotemporal dementia \nAlthough there were broad group level differences in keeping with the paradigmatic \nphenotypes of each FTD syndrome (i.e., poorer semantic memory in SD and poorer executive \nfunction in bvFTD), these differences were not absolute. Rather, there was graded variation \nwith considerable overlap along these dimensions  (Fig. 2). The phenotypic overlap occurred \nalongside radiological overlap; bvFTD and SD patients did not divide absolutely along a frontal \nvs. temporal division. These findings are in keeping with the  increasing evidence for many \noverlapping clinical features across FTD syndromes,\n8,9,86,98-100 meaning that although the \nclassical syndromes clearly exist, the re is considerable variation within each of them and the  \nboundaries between them are fuzzy.  \nThe cognitive and neuroanatomical variation in FTD can be captured by a transdiagnostic \napproach, whereby FTD is conceptualised as a multidimensional space in which patients \nrepresent different phenotypical points along various dimensions .9,86,98,101. There are two key \nadvantages of this conceptualisation of  FTD. First, a transdiagnostic approach can not only \naccommodate but also explain “mixed” cases who may not fall neatly into a category ,102 and \nas such may be excluded from research studies/clinical trials, despite being relative common.  \nSecond, recent large-scale studies have utilised a transdiagnostic approach and applied data -\ndriven analyses to reveal the shared clinical, cognitive and behavioural dimensions in FTD and \ntheir neurobiological mechanisms.1,9,86,98,103 This has key implications for the development of \nsymptomatic treatments , which could target  specific cognitive/behaviour al dimensions that \nspan across FTD syndromes (and potentially other neurological disorders) and stratify patients \nfor symptomatic trials based on the presence/absence of a dimension regardless  of the \ndiagnostic label or neuropathology. Furthermore, it may be possible to titrate interventions \nbased on an individual patient’s position across these dimensions.  \n \nLimitations and future directions \nNeuronal loss occur s relatively late in the cascade of pathology in neurodegenerative \ndisorders.104 Consequently , structural MRI can be insensitive to other markers of \nneuropathology such as hypometabolism,48 synaptic loss105 and neurotransmitter alterations.106 \nCombining structural MRI with additional neuroimaging measures may thus provide important \nfurther insight into the neural architecture of social-semantic knowledge.  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 28 \nSemantic memory relies on a network of brain regions, including the bilateral ATL hub and \nmodality-specific spokes which dynamically interact with the hub to support coherent \nconceptual representations.20,21,107 Illuminating the specific cortical “spokes” that are important \nfor the formation of social concepts is an important topic for future research. There is ongoing \ninterest in the role of the OFC in socially -relevant concepts, with suggestions that this region \n‘tags’ social concepts with hedonic value .11-13,108 Evidence from neuropsychology, rTMS and \ncomputational models has shown that selective lesions/perturbations to cortical ‘spoke’ regions \ncan generate category-specific semantic impairments ,109-111 raising the intriguing possibility \nthat OFC damage could selectively impair comprehension of social concepts. The widespread \ncorrelated atrophy  in FTD means that  disentangling category-selective deficits from a \ngeneralised semantic impairment is difficult, howe ver future studies could explore selective \nsocial-semantic deficits in people with OFC lesions. \n \nWe and others have proposed that at least some of the changed behaviours associated with FTD \nmight result from a degradation of social-semantic knowledge, in keeping with other theories \nof behavioural change in FTD.13,14,16,112 It is currently not known which specific concepts are \ncritical to supporting social behaviours in FTD, and whether distinct behavioural profiles result \nfrom degraded conceptual knowledge from ATL atrophy vs. atrophy in other areas including \nthe OFC, anterior cingulate cortex and insula. Future studies should formally investigate how \ndegraded social-semantic knowledge is related to the behavioural changes in FTD and distinct \nfrom disinhibition as the cause of ‘impulsive’ challenging behaviours.  \n \nData availability  \nDue to the limits of the ethics approval for these patient studies, the data cannot be openly \nshared. Requests for suitably anonymised data can be addressed to the senior author and may \nrequire a data transfer agreement. \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 29 \nAcknowledgements  \nWe thank the patients and their families for giving up the time to take part in the study. We \nalso thank Dr Thomas Cope, Prof Masud Husain, Dr Sian Thompson and Dr Sofia Toniolo for \ntheir help with FTD recruitment. \nFunding  \nM.A.R is supported by the Medical Research Council (SUAG/096 G116768). A.D.H is \nsupported by the Medical Research Council (Career Development Award: MR/V031481/1).  \nJ.B.R is supported by the Medical Research Council (MC_UU_00030/14; MR/T033371,1), \nWellcome Trust (220258), and the NIHR Cambridge Biomedical Research Centre \n(NIHR203312). M.A.L.R is supported by a Medical Research Council programme grant \n(MR/R023883/1) and intramural funding (MC_UU_00005/18). The views expressed are those \nof the authors and not  necessarily those of the NIHR or the Department of Health and Social \nCare. \nCompeting interests  \nThe authors report no competing interests. \n \n \n \n \n \n \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 30 \nReferences  \n1. Ding J, Chen K, Liu H, et al. A unified neurocognitive model of semantics l anguage \nsocial behaviour and face recognition in semantic dementia. Nat Commun. 2020;11(1):2595. \n2. Gorno- Tempini ML, Hillis AE, Weintraub S, et al. Classification of primary \nprogressive aphasia and its variants. Neurology. 2011;76(11):1006-14. \n3. Hodges JR, Patterson K, Oxbury S, Funnell E. Semantic dementia. Progressive fluent \naphasia with temporal lobe atrophy. Brain. 1992;115 ( Pt 6)(6):1783-806. \n4. Mummery CJ, Patterson K, Price CJ, Ashburner J, Frackowiak RS, Hodges JR. A \nvoxel‐based morphometry study  of semantic dementia: relationship between temporal lobe \natrophy and semantic memory. Ann Neurol. 2000;47(1):36-45. \n5. Neary D, Snowden JS, Gustafson L, et al. Frontotemporal lobar degeneration: a \nconsensus on clinical diagnostic criteria. Neurology. 1998;51(6):1546-1554. \n6. Snowden JS, Goulding PJ, Neary D. Semantic dementia: A form of circumscribed \ncerebral atrophy. Behav Neurol. 1989;2:167-182. \n7. Hardy CJ, Buckley AH, Downey LE, et al. The Language Profile of Behavioral Variant \nFrontotemporal Dementia. J Alzheimers Dis. 2016;50(2):359-71. \n8. Snowden JS, Harris JM, Saxon JA, et al. Naming and conceptual understanding in \nfrontotemporal dementia. Cortex. 2019;120:22-35. \n9. Ramanan S, El-Omar H, Roquet D, et al. Mapping behavioural, cognitive and affective \ntransdiagnostic dimensions in frontotemporal dementia. Brain Commun. 2023;5(1):fcac344. \n10. Binney RJ, Ramsey R. Social Semantics: The role of conceptual knowledge and \ncognitive control in a neurobiological model of the social brain. Neurosci Biobehav Rev. \n2020;112:28-38. \n11. Olson IR, McCoy D, Klobusicky E, Ross LA. Social cognition and the anterior \ntemporal lobes: a review and theoretical framework. Soc Cogn Affect Neurosci. 2013;8(2):123-\n33. \n12. Rijpma MG, Montembeault M, Shdo S, Kramer JH, Miller BL, Rankin KP. Semantic \nknowledge of social interactions is mediated by the hedonic evaluation system in the brain. \nCortex. 2023;161:26-37. \n13. Rouse MA, Binney RJ, Patterson K, Rowe JB, Lambon Ralph MA. A neuroanatomical \nand cognitive model of impaired social behaviour in frontotemporal dementia. Brain. 2024; \n14. Younes K, Borghesani V, Montembeault M, et al. Right temporal degeneration and \nsocioemotional semantics: semantic behavioural variant frontotemporal dementia. Brain . \n2022;145(11):4080-4096. \n15. Zahn R, Moll J, Krueger F, Huey ED, Garrido G, Grafman J. Social concepts are \nrepresented in the superior anterior temporal cortex. Proc Natl Acad Sci U S A . \n2007;104(15):6430-5. \n16. Zahn R, Moll J, Iyengar V, et al. Social conceptual impairments in frontotemporal lobar \ndegeneration with right anterior temporal hypometabolism. Brain. 2009;132(Pt 3):604-16. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 31 \n17. Adlam AL, Patterson K, Bozeat S, Hodges JR. The Cambridge Semantic Memory Test \nBattery: detection of semantic deficits in semantic dementia and Alzheimer's disease. \nNeurocase. 2010;16(3):193-207. \n18. Bozeat S, Lambon Ralph MA, Patterson K, Garrard P, Hodges JR. Non-verbal semantic \nimpairment in semantic dementia. Neuropsychologia. 2000;38(9):1207-15. \n19. Binney RJ, Embleton KV, Jefferies E, Parker GJ, Lambon Ralph MA. The ventral and \ninferolateral aspects of the anterior temporal lobe are crucial in semantic memory: evidence \nfrom a novel direct comparison of distortion- corrected fMRI, rTMS, and semantic dementia. \nCereb Cortex. 2010;20(11):2728-38. \n20. Lambon Ralph MA, Jefferies E, Patterson K, Rogers TT. The neural and computational \nbases of semantic cognition. Nat Rev Neurosci. 2017;18(1):42-55. \n21. Patterson K, Nestor PJ, Rogers TT. Where do you know what you know? The \nrepresentation of semantic knowledge in the human brain. Nat Rev Neurosci. 2007;8(12):976-\n87. \n22. Balgova E, Diveica V, Walbrin J, Binney RJ. The role of the ventrolateral anterior \ntemporal lobes in social cognition. Hum Brain Mapp. 2022;43(15):4589-4608. \n23. Balgova E, Diveica V, Jackson RL, Binney RJ. Overlapping Neural Correlates \nUnderpin Theory of Mind and Semantic Cognition: Evidence from a Meta -Analysis of 344 \nFunctional Neuroimaging Studies. bioRxiv. 2023:2023.08. 16.553506. \n24. Frith U, Frith CD. Development and neurophysiology of mentalizing. Philos Trans R \nSoc Lond B Biol Sci. 2003;358(1431):459-73. \n25. Snowden JS, Bathgate D, Varma A, Blackshaw A, Gibbons ZC, Neary D. Dis tinct \nbehavioural profiles in frontotemporal dementia and semantic dementia. J Neurol Neurosurg \nPsychiatry. 2001;70(3):323-32. \n26. Bozeat S, Gregory CA, Lambon Ralph MA, Hodges JR. Which neuropsychiatric and \nbehavioural features distinguish frontal and temporal variants of frontotemporal dementia from \nAlzheimer's disease? J Neurol Neurosurg Psychiatry. 2000;69(2):178-86. \n27. Liu W, Miller BL, Kramer JH, et al. Behavioral disorders in the frontal and temporal \nvariants of frontotemporal dementia. Neurology. 2004;62(5):742-8. \n28. Klüver H, Bucy PC. Preliminary analysis of functions of the temporal lobes in \nmonkeys. Arch Neurol Psychiatry. 1939;42(6):979-1000. \n29. Halai AD, Welbourne SR, Embleton K, Parkes LM. A comparison of dual gradient -\necho and spin-echo fMRI of the inferior temporal lobe. Hum Brain Mapp. 2014;35(8):4118-\n28. \n30. Visser M, Jefferies E, Lambon Ralph MA. Semantic processing in the anterior temporal \nlobes: a meta -analysis of the functional neuroimaging literature. J Cogn Neurosci . \n2010;22(6):1083-94. \n31. Binney RJ, Hoffman P, Lambon Ralph MA. Mapping the Multiple Graded \nContributions of the Anterior Temporal Lobe Representational Hub to Abstract and Social \nConcepts: Evidence from Distortion-corrected fMRI. Cereb Cortex. 2016;26(11):4227-4241. \n32. Rice GE, Hoffman P, Binney RJ, Lambon Ralph MA. Concrete versus abstract forms \nof social concept: an fMRI comparison of knowledge about people versus social terms. Philos \nTrans R Soc Lond B Biol Sci. 2018;373(1752) \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 32 \n33. Diveica V, Pexman PM, Binney RJ. Quantifying social semantics: An inclusive \ndefinition of socialness and ratings for 8388 English words. Behav Res Methods . \n2023;55(2):461-473. \n34. Pexman PM, Diveica V, Binney RJ. Social semantics: the organization and grounding \nof abstract concepts. Philos Trans R Soc Lond B Biol Sci. 2023;378(1870):20210363. \n35. Zahn R, Green S, Beaumont H, et al. Frontotemporal lobar degeneration and social \nbehaviour: Dissociation between the knowledge of its consequences and its conceptual \nmeaning. Cortex. 2017;93:107-118. \n36. Bertoux M, Duclos H, Caillaud M, et al. When affect overlaps with concept: emotion \nrecognition in semantic variant of primary progressive aphasia. Brain . 2020;143(12):3850-\n3864. \n37. Kumfor F, Ibañez A, Hutchings R, Hazelton JL, Hodges JR, Piguet O. Beyond the face: \nhow context modulates emotion processing in frontotemporal dementia subtypes. Brain . \n2018;141(4):1172-1185. \n38. Yang WFZ, Toller G, Shdo S, et al. Resting functional connectivity in the semantic \nappraisal network predicts accuracy of emotion identification. Neuroimage Clin. \n2021;31:102755. \n39. Gainotti G. Is the difference between right and left ATLs due to the distinction between \ngeneral and social cognition or between verbal and non- verbal representations ? Neurosci \nBiobehav Rev. 2015;51:296-312. \n40. Joubert S, Felician O, Barbeau E, et al. Impaired configurational processing in a case \nof progressive prosopagnosia associated with predominant right temporal lobe atrophy. Brain. \n2003;126(Pt 11):2537-50. \n41. C han D, Anderson V, Pijnenburg Y, et al. The clinical profile of right temporal lobe \natrophy. Brain. 2009;132(Pt 5):1287-98. \n42. Edwards- Lee T, Miller BL, Benson DF, et al. The temporal variant of frontotemporal \ndementia. Brain. 1997;120 ( Pt 6)(6):1027-40. \n43. Miller BL, Chang L, Mena I, Boone K, Lesser IM. Progressive right frontotemporal \ndegeneration: clinical, neuropsychological and SPECT characteristics. Dementia . 1993;4(3-\n4):204-13. \n44. Ulugut Erkoyun H, Groot C, Heilbron R, et al. A clinical-radiological framework of the \nright temporal variant of frontotemporal dementia. Brain. 2020;143(9):2831-2843. \n45. Irish M, Kumfor F, Hodges JR, Piguet O. A tale of two hemispheres: contrasting \nsocioemotional dysfunction in right - versus left -lateralised semantic d ementia. Dement \nNeuropsychol. 2013;7(1):88-95. \n46. Irish M, Hodges JR, Piguet O. Right anterior temporal lobe dysfunction underlies \ntheory of mind impairments in semantic dementia. Brain. 2014;137(Pt 4):1241-53. \n47. Rouse MA, Ramanan S, Halai AD, et al. The impact of bilateral versus unilateral \nanterior temporal lobe damage on face recognition, person knowledge and semantic memory. \nmedRxiv. 2024:2024.02. 10.24302526. \n48. Nestor PJ, Fryer TD, Hodges JR. Declarative memory impairments in Alzheimer's \ndisease and semantic dementia. Neuroimage. 2006;30(3):1010-20. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 33 \n49. Seeley WW, Bauer AM, Miller BL, et al. The natural history of temporal variant \nfrontotemporal dementia. Neurology. 2005;64(8):1384-90. \n50. Viskontas IV, Possin KL, Miller BL. Symptoms of frontotemporal dementia provide \ninsights into orbitofrontal cortex function and social behavior. Ann N Y Acad Sci . \n2007;1121:528-45. \n51. Rice GE, Caswell H, Moore P, Hoffman P, Lambon Ralph MA. The Roles of Left \nVersus Right Anterior Temporal Lobes in Semantic Memory : A Neuropsychological \nComparison of Postsurgical Temporal Lobe Epilepsy Patients. Cereb Cortex. \n2018;28(4):1487-1501. \n52. Wilkins A, Moscovitch M. Selective impairment of semantic memory after temporal \nlobectomy. Neuropsychologia. 1978;16(1):73-9. \n53. Lambon Ralph MA, Ehsan S, Baker GA, Rogers TT. Semantic memory is impaired in \npatients with unilateral anterior temporal lobe resection for temporal lobe epilepsy. Brain . \n2012;135(Pt 1):242-58. \n54. Rice GE, Lambon Ralph MA, Hoffman P. The Roles of Left Versus  Right Anterior \nTemporal Lobes in Conceptual Knowledge: An ALE Meta -analysis of 97 Functional \nNeuroimaging Studies. Cereb Cortex. 2015;25(11):4374-91. \n55. Pobric G, Lambon Ralph MA, Zahn R. Hemispheric Specialization within the Superior \nAnterior Temporal Cortex for Social and Nonsocial Concepts. J Cogn Neurosci . \n2016;28(3):351-60. \n56. Rascovsky K, Hodges JR, Knopman D, et al. Sensitivity of revised diagnosti c criteria \nfor the behavioural variant of frontotemporal dementia. Brain. 2011;134(Pt 9):2456-77. \n57. Halai AD, De Dios Perez B, Stefaniak JD, Lambon Ralph MA. Efficient and effective \nassessment of deficits and their neural bases in stroke aphasia. Cortex. 2022;155:333-346. \n58. Moore K, Convery R, Bocchetta M, et al. A modified Camel and Cactus Test detects \npresymptomatic semantic impairment in genetic frontotemporal dementia within the GENFI \ncohort. Appl Neuropsychol Adult. 2022;29(1):112-119. \n59. Jefferies E, Patterson K, Jones RW, Lambon Ralph MA. Comprehension of concrete \nand abstract words in semantic dementia. Neuropsychology. 2009;23(4):492-9. \n60. Goodglass H, Kaplan E, Weintraub S. Boston naming test. Lea & Febiger Philadelphia, \nPA; 1983. \n61. Mack WJ, Freed DM, Williams BW, Henderson VW. Boston Naming Test: shortened \nversions for use in Alzheimer's disease. J Gerontol. 1992;47(3):P154-8. \n62. Mioshi E, Dawson K, Mitchell J, Arnold R, Hodges JR. The Addenbrooke's Cognitive \nExamination Revised (ACE -R): a  brief cognitive test battery for dementia screening. Int J \nGeriatr Psychiatry. 2006;21(11):1078-85. \n63. Burgess PW, Shallice T. The hayling and brixton tests. 1997; \n64. Raven JC. Coloured Progressive Matrices, Sets A, A_B, B. HK Lewis . 1962; \n65. Grabowski TJ, Damasio H, Tranel D, Ponto LL, Hichwa RD, Damasio AR. A role for \nleft temporal pole in the retrieval of words for unique entities. Hum Brain Mapp. \n2001;13(4):199-212. \n66. Ross LA, Olson IR. What's unique about unique entities? An fMRI investigation of the \nsemantics of famous faces and landmarks. Cereb Cortex. 2012;22(9):2005-15. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 34 \n67. Volfart A, Yan X, Maillard L, et al. Intracerebral electrical stimulation of the right \nanterior fusiform gyrus impairs human face identity recognition. Neuroimage . \n2022;250:118932. \n68. Hoffman P, Lambon Ralph MA, Rogers TT. Semantic diversity: a measure of semantic \nambiguity based on variability in the contextual usage of words. Behav Res Methods . \n2013;45(3):718-30. \n69. Wallhoff F, Schuller B, Hawellek M, Rigoll G. Efficient  recognition of authentic \ndynamic facial expressions on the feedtum database. IEEE; 2006:493-496. \n70. Golan O, Sinai-Gavrilov Y, Baron-Cohen S. The Cambridge Mindreading Face-Voice \nBattery for Children (CAM -C): complex emotion recognition in children with and without \nautism spectrum conditions. Mol Autism. 2015;6:22. \n71. McDonald S, Flanagan S, Rollins J, Kinch J. TASIT: A new clinical tool for assessing \nsocial perception after traumatic brain injury. J Head Trauma Rehabil. 2003;18(3):219-38. \n72. Kassambara A. Rstatix: pipe-friendly framework for basic statistical tests. 2021. 2022. \n73. R Core Team R. R: A language and environment for statistical computing. 2013; \n74. Gorgolewski KJ, Auer T, Calhoun VD, et al. The brain imaging data structure, a format \nfor or ganizing and describing outputs of neuroimaging experiments. Sci Data. \n2016;3(1):160044. \n75. Gaser C, Dahnke R, Thompson PM, Kurth F, Luders E. CAT – A Computational \nAnatomy Toolbox for the Analysis of Structural MRI Data. Cold Spring Harbor Laboratory; \n2022. \n76. Ashburner J, Friston KJ. Diffeomorphic registration using geodesic shooting and \nGauss-Newton optimisation. Neuroimage. 2011;55(3):954-67. \n77. Ridgway GR, Omar R, Ourselin S, Hill DL, Warren JD, Fox NC. Issues with threshold \nmasking in voxel-based morphometry of atrophied brains. Neuroimage. 2009;44(1):99-111. \n78. Rolls ET, Huang CC, Lin CP, Feng J, Joliot M. Automated anatomical labelling atlas \n3. Neuroimage. 2020;206:116189. \n79. Ilin A, Raiko T. Practical Approaches to Principal Component Analysis in the Presence \nof Missing Values. Journal of Machine Learning Research. 2010;11:1957-2000. \n80. Tipping ME, Bishop CM. Probabilistic Principal Component Analysis. Journal of the \nRoyal Statistical Society Series B: Statistical Methodology. 1999;61(3):611-622. \n81. Ballabio D. A MATLAB toolbox for Principal Component Analysis and unsupervised \nexploration of data structure. Chemometrics and Intelligent Laboratory Systems . 2015;149:1-\n9. \n82. Cattell RB. The Scree Test For The Number Of Factors. M ultivariate Behav Res . \n1966;1(2):245-76. \n83. Tyler LK, Marslen -Wilson W, Stamatakis EA. Dissociating neuro -cognitive \ncomponent processes: voxel -based correlational methodology. Neuropsychologia. \n2005;43(5):771-8. \n84. Kaiser HF, Rice J. Little jiffy, mark I V. Educational and psychological measurement. \n1974;34(1):111-117. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 35 \n85. Cousins KAQ, Grossman M. Evidence of semantic processing impairments in \nbehavioural variant frontotemporal dementia and Parkinson's disease. Curr Opin Neurol . \n2017;30(6):617-622. \n86. Mur ley AG, Coyle -Gilchrist I, Rouse MA, et al. Redefining the multidimensional \nclinical phenotypes of frontotemporal lobar degeneration syndromes. Brain . \n2020;143(5):1555-1571. \n87. Drane DL, Ojemann JG, Phatak V, et al. Famous face identification in temporal lobe \nepilepsy: support for a multimodal integration model of semantic memory. Cortex . \n2013;49(6):1648-67. \n88. Lambon Ralph MA, McClelland JL, Patterson K, Galton CJ, Hodges JR. No right to \nspeak? The relationship between object naming and semantic impairment: neuropsychological \nevidence and a computational model. J Cogn Neurosci. 2001;13(3):341-356. \n89. Woollams AM, Lindley LJ, Pobric G, Hoffman P. Laterality of anterior temporal lobe \nrepetitive transcranial magnetic stimulation determines the degree of dis ruption in picture \nnaming. Brain Struct Funct. 2017;222(8):3749-3759. \n90. Schapiro AC, McClelland JL, Welbourne SR, Rogers TT, Lambon Ralph MA. Why \nbilateral damage is worse than unilateral damage to the brain. J Cogn Neurosci . \n2013;25(12):2107-23. \n91. Goldmann RE, Golby AJ. Atypical language representation in epilepsy: implications \nfor injury-induced reorganization of brain function. Epilepsy Behav. 2005;6(4):473-87. \n92. Rogers TT, Cox CR, Lu Q, et al. Evidence for a deep, distributed and dynamic code for \nanimacy in human ventral anterior temporal cortex. Elife. 2021;10:e66276. \n93. Shimotake A, Matsumoto R, Ueno T, et al. Direct Exploration of the Role of the Ventral \nAnterior Temporal Lobe in Semantic Memory: Cortical Stimulation and Local Field Potential \nEvidence From Subdural Grid Electrodes. Cereb Cortex. 2015;25(10):3802-17. \n94. Rice GE, Caswell H, Moore P, Lambon Ralph MA, Hoffman P. Revealing the Dynamic \nModulations That Underpin a Resilient Neural Network for Semantic Cognition: An fMRI \nInvestigation in Patients With Anterior Temporal Lobe Resection. Cereb Cortex. \n2018;28(8):3004-3016. \n95. Binney RJ, Lambon Ralph MA. Using a combination of fMRI and anterior temporal \nlobe rTMS to measure intrinsic and induced activation changes across the semantic cogni tion \nnetwork. Neuropsychologia. 2015;76:170-81. \n96. Jung J, Lambon Ralph MA. Mapping the Dynamic Network Interactions Underpinning \nCognition: A cTBS-fMRI Study of the Flexible Adaptive Neural System for Semantics. Cereb \nCortex. 2016;26(8):3580-3590. \n97. Seeley WW, Zhou J, Kim E -J. Frontotemporal dementia: what can the behavioral \nvariant teach us about human brain organization? The Neuroscientist. 2012;18(4):373-385. \n98. Lansdall CJ, Coyle -Gilchrist ITS, Jones PS, et al. Apathy and impulsivity in \nfrontotemporal lobar degeneration syndromes. Brain. 2017;140(6):1792-1807. \n99. Kertesz A, Davidson W, Munoz DG. Clinical and pathological overlap between \nfrontotemporal dementia, primary progressive aphasia and corticobasal degeneration: the Pick \ncomplex. Dementia and geriatric cognitive disorders. 1999;10(Suppl. 1):46-49. \n100. Roy AR, Datta S, Hardy E, et al. Behavioural subphenotypes and their anatomic \ncorrelates in neurodegenerative disease. Brain communications. 2023;5(2):fcad038. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint \n\n 36 \n101. Davenport F, Gallacher J, Kourtzi Z, et al. Neurodegenerative disease of the brain: a \nsurvey of interdisciplinary approaches. J R Soc Interface. 2023;20(198):20220406. \n102. Ramanan S, Irish M, Patterson K, Rowe JB, Gorno-Tempini ML, Lambon Ralph MA. \nUnderstanding the multidimensional cognitive deficits of logopenic variant primary \nprogressive aphasia. Brain. 2022;145(9):2955-2966. \n103. Ramanan S, Akarca D, Henderson SK, et al. Mapping the multidimensional geometric \nlandscape of graded phenotypic variation and progression in neurodegenerative syndromes. \nmedRxiv. 2023:2023.10. 11.23296861. \n104. Jack CR, Knopman DS, Jagust WJ, et al. Hypothetical model of dynamic biomarkers \nof the Alzheimer's pathological cascade. The Lancet Neurology. 2010;9(1):119-128. \n105. Malpetti M, Jones PS, Cope TE, et al. Synaptic Loss in Frontotemporal Dementia \nRevealed by [(11) C]UCB-J Positron Emission Tomography. Ann Neurol. 2023;93(1):142-154. \n106. Murley AG, Rouse MA, Jones PS, et al. GABA and glutamate deficits from \nfrontotemporal lobar degeneration are associated with disinhibition. Brain . \n2020;143(11):3449-3462. \n107. Chiou R, Lambon Ralph MA. Unveiling the dynamic interplay between the hub- and \nspoke-components of the brain's semantic system and its impact on human behaviour. \nNeuroimage. 2019;199:114-126. \n108. Riberto M, Pobric G, Talmi D. The Emotional Facet of Subjective and Neural Indices \nof Similarity. Brain Topogr. 2019;32(6):956-964. \n109. Humphreys GW, Riddoch MJ. A case series analysis of \"category-specific\" deficits of \nliving things:the hit account. Cogn Neuropsychol. 2003;20(3):263-306. \n110. Pobric G, Jefferies E, Lambon Ralph MA. Category -specific versus category-general \nsemantic impairment  induced by transcranial magnetic stimulation. Curr Biol . \n2010;20(10):964-8. \n111. Chen L, Lambon Ralph MA, Rogers TT. A unified model of human semantic \nknowledge and its disorders. Nat Hum Behav. 2017;1(3) \n112. Magrath Guimet N, Miller BL, Allegri RF, Rankin KP. What do we mean by behavioral \ndisinhibition in frontotemporal dementia? Front Neurol. 2021;12:1154. \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted April 12, 2024. ; https://doi.org/10.1101/2024.04.11.24305610doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}