Advertisement

Overlap of Neuroanatomical Involvement in Frontotemporal Dementia and Primary Psychiatric Disorders: A Meta-analysis

Open AccessPublished:June 07, 2022DOI:https://doi.org/10.1016/j.biopsych.2022.05.028

      Abstract

      Background

      Despite significant symptomatic overlap between behavioral variant frontotemporal dementia (bvFTD) and primary psychiatric disorders (PPDs), a potential overlap in their structural anatomical changes has not been studied systematically.

      Methods

      In this magnetic resonance imaging-based meta-analysis, we included studies on bvFTD, schizophrenia, bipolar disorder, and autism spectrum disorder that 1) used voxel-based morphometry analysis to assess regional gray matter volumes (GMVs) and 2) reported the coordinates of the regional GMV. Separate analyses were performed comparing clusters of coordinate-based changes in the GMVs (n = 24,183) between patients and control subjects, and overlapping brain regions between bvFTD and each PPD were examined.

      Results

      We found that GMV alterations in the prefrontal and anterior cingulate cortices, temporal lobe, amygdala, and insula comprise the transdiagnostic brain alterations in bvFTD and PPD.

      Conclusions

      Our meta-analysis revealed significant anatomical overlap that paves the way for future investigations of shared pathophysiological pathways, and our cross-disorder approach would provide new insights to better understand the relationship between bvFTD and PPD.

      Keywords

      Frontotemporal dementia (FTD) is a neurodegenerative disorder that predominantly affects the frontal and/or temporal lobes (
      • Neary D.
      • Snowden J.S.
      • Gustafson L.
      • Passant U.
      • Stuss D.
      • Black S.
      • et al.
      Frontotemporal lobar degeneration: A consensus on clinical diagnostic criteria.
      ,
      • Rascovsky K.
      • Hodges J.R.
      • Knopman D.
      • Mendez M.F.
      • Kramer J.H.
      • Neuhaus J.
      • et al.
      Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia.
      ). The most common subtype is the behavioral variant (bvFTD) that presents with behavioral disturbances such as disinhibition, social awkwardness, loss of insight, apathy, loss of empathy, stereotypical behavior, and changes in eating habits (
      • Rascovsky K.
      • Hodges J.R.
      • Knopman D.
      • Mendez M.F.
      • Kramer J.H.
      • Neuhaus J.
      • et al.
      Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia.
      ). One of the earliest and core symptoms of bvFTD is a gradual loss of social cognition (
      • Johnen A.
      • Bertoux M.
      Psychological and cognitive markers of behavioral variant frontotemporal dementia-A clinical neuropsychologist’s view on diagnostic criteria and beyond.
      ), which in turn interferes with behavioral and personality aspects.
      From a clinical perspective, a number of major primary psychiatric disorders (PPDs) such as schizophrenia (SZ), bipolar disorder (BD), and autism spectrum disorder (ASD) strongly resemble bvFTD (
      • Woolley J.D.
      • Khan B.K.
      • Murthy N.K.
      • Miller B.L.
      • Rankin K.P.
      The diagnostic challenge of psychiatric symptoms in neurodegenerative disease: Rates of and risk factors for prior psychiatric diagnosis in patients with early neurodegenerative disease.
      ,
      • Pose M.
      • Cetkovich M.
      • Gleichgerrcht E.
      • Ibáñez A.
      • Torralva T.
      • Manes F.
      The overlap of symptomatic dimensions between frontotemporal dementia and several psychiatric disorders that appear in late adulthood.
      ). More specifically, impaired social cognition is one of the core features of PPD (
      • Cotter J.
      • Granger K.
      • Backx R.
      • Hobbs M.
      • Looi C.Y.
      • Barnett J.H.
      Social cognitive dysfunction as a clinical marker: A systematic review of meta-analyses across 30 clinical conditions.
      ). Therefore, both bvFTD and major PPDs might be considered as social brain disorders (
      • Kennedy D.P.
      • Adolphs R.
      The social brain in psychiatric and neurological disorders.
      ). In addition, in daily clinical practice, the elated mood and lack of insight in mania can strongly resemble bvFTD (
      • Mendez M.F.
      • Parand L.
      • Akhlaghipour G.
      Bipolar disorder among patients diagnosed with frontotemporal dementia.
      ). Finally, both the positive and negative symptoms of SZ (e.g., delusions and hallucinations vs. social withdrawal, paucity of spontaneous speech, and concreteness, respectively) are very similar to what is seen in bvFTD (
      • Cipriani G.
      • Danti S.
      • Nuti A.
      • Di Fiorino M.
      • Cammisuli D.M.
      Is that schizophrenia or frontotemporal dementia? Supporting clinicians in making the right diagnosis.
      ). Not surprisingly, approximately 50% of patients with bvFTD receive a prior psychiatric diagnosis (
      • Woolley J.D.
      • Khan B.K.
      • Murthy N.K.
      • Miller B.L.
      • Rankin K.P.
      The diagnostic challenge of psychiatric symptoms in neurodegenerative disease: Rates of and risk factors for prior psychiatric diagnosis in patients with early neurodegenerative disease.
      ) owing to similar and overlapping diagnostic criteria for bvFTD and various PPDs (
      • Rascovsky K.
      • Hodges J.R.
      • Knopman D.
      • Mendez M.F.
      • Kramer J.H.
      • Neuhaus J.
      • et al.
      Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia.
      ,
      • Psychological Association American
      Diagnostic and Statistical Manual of Mental Disorders.
      ). The relationship between psychiatric symptoms and neurodegenerative disease becomes particularly evident in carriers of a C9orf72 repeat expansion. It has been shown that family members of C9orf72 mutation carriers have a higher prevalence of SZ and BD, whereas C9orf72-related FTD can present with SZ, BD, or ASD symptoms (
      • Ducharme S.
      • Bajestan S.
      • Dickerson B.C.
      • Voon V.
      Psychiatric presentations of C9orf72 mutation: What are the diagnostic implications for clinicians?.
      ,
      • Sellami L.
      • St-Onge F.
      • Poulin S.
      • Laforce Jr., R.
      Schizophrenia phenotype preceding behavioral variant frontotemporal dementia related to C9orf72 repeat expansion.
      ,
      • Devenney E.M.
      • Ahmed R.M.
      • Halliday G.
      • Piguet O.
      • Kiernan M.C.
      • Hodges J.R.
      Psychiatric disorders in C9orf72 kindreds: Study of 1,414 family members.
      ,
      • Meisler M.H.
      • Grant A.E.
      • Jones J.M.
      • Lenk G.M.
      • He F.
      • Todd P.K.
      • et al.
      C9ORF72 expansion in a family with bipolar disorder.
      ,
      • Silverman H.E.
      • Goldman J.S.
      • Huey E.D.
      Links between the C9orf72 repeat expansion and psychiatric symptoms.
      ). Moreover, young cases with a diagnosis of SZ and BD may have underlying FTD neuropathology (
      • Velakoulis D.
      • Walterfang M.
      • Mocellin R.
      • Pantelis C.
      • McLean C.
      Frontotemporal dementia presenting as schizophrenia-like psychosis in young people: Clinicopathological series and review of cases.
      ). Based on this empirical overlap, a potential shared neurobiological background between bvFTD and SZ (
      • Velakoulis D.
      • Walterfang M.
      • Mocellin R.
      • Pantelis C.
      • McLean C.
      Frontotemporal dementia presenting as schizophrenia-like psychosis in young people: Clinicopathological series and review of cases.
      ,
      • Cooper J.J.
      • Ovsiew F.
      The relationship between schizophrenia and frontotemporal dementia.
      ,
      • Harciarek M.
      • Malaspina D.
      • Sun T.
      • Goldberg E.
      Schizophrenia and frontotemporal dementia: Shared causation?.
      ), BD (
      • Mendez M.F.
      • Parand L.
      • Akhlaghipour G.
      Bipolar disorder among patients diagnosed with frontotemporal dementia.
      ), and ASD (
      • Devenney E.M.
      • Ahmed R.M.
      • Halliday G.
      • Piguet O.
      • Kiernan M.C.
      • Hodges J.R.
      Psychiatric disorders in C9orf72 kindreds: Study of 1,414 family members.
      ) has been postulated by independent authors. Their hypotheses, however, remain to be tested.
      Based on the clinical overlap and given the significant structural alterations in the frontotemporal brain regions in patients with a PPD in large-scale studies yielded by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium (
      • Thompson P.M.
      • Jahanshad N.
      • Ching C.R.K.
      • Salminen L.E.
      • Thomopoulos S.I.
      • Bright J.
      • et al.
      ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.
      ), in this cross-disorder analysis, we hypothesize that bvFTD and PPDs share a biological vulnerability of specific neuroanatomical networks. The identification of shared neuroanatomical vulnerabilities between bvFTD and PPDs is important because such a finding may support a conceptual framework of how these disorders are related and whether they have common pathophysiological pathways that could be targeted by treatment. Voxel-based morphometry (VBM) is a commonly used neuroimaging method that measures gray matter (GM) structure (
      • Davatzikos C.
      Why voxel-based morphometric analysis should be used with great caution when characterizing group differences.
      ). In this cross-disorder comparison, we aimed to identify the overlapping GM differences of bvFTD and PPD including SZ, BD, and ASD by using a voxelwise, coordinate-based meta-analytic approach.

      Methods and Materials

      Search Strategy

      This meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (
      • Moher D.
      • Liberati A.
      • Tetzlaff J.
      • Altman D.G.
      PRISMA Group
      Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA statement.
      ). Studies included in this meta-analysis were collected by using MEDLINE (PubMed), Embase, and BrainMap databases, covering the literature until April 2020. We registered our meta-analysis on the Open Science Framework (https://osf.io/9kxrb).
      Because different methodologies can affect the volumetric results, we avoided combining various analyses. We, therefore, focused on whole-brain VBM analysis to measure GM volume (GMV) that is widely used within the neuroimaging scientific community. Therefore, to avoid any misinterpretation, we excluded GM alterations when investigated using cortical thickness measurements (using FreeSurfer).

      PubMed and Embase

      Relevant structural GM neuroimaging studies were retrieved using keywords in the form of medical subject heading or Emtree terms and free text terms in the title and abstract, as follows: “Voxel based Morphometry” OR “VBM” OR “gray matter” in combination with either 1) “Frontotemporal Dementia” OR “Pick disease” OR “Frontotemporal Lobar Degeneration,” 2) “Schizophrenia,” 3) “Bipolar Disorder” OR “Manic Depression,” and 4) “autism spectrum disorder” OR “Asperger syndrome.” For overview of the full electronic search strategy, see the Supplement.

      BrainMap

      A search for BrainMap was conducted by using Sleuth 3.0.3 software to retrieve structural neuroimaging studies (
      • Fox P.T.
      • Lancaster J.L.
      Opinion: Mapping context and content: The BrainMap model.
      ). Studies were selected from the BrainMap’s Voxel-Based Morphometry database. Inclusion criteria for selecting structural GM neuroimaging studies were set as follows: “Experiment + Contrast + Gray Matter” and “Experiments + Imaging Modality + MRI” in combination with 1) “Subjects + Diagnosis + Frontotemporal Dementia” or “Subjects + Diagnosis + Frontotemporal Lobar Degeneration,” 2) “Subjects + Diagnosis + Schizophrenia,” 3)” “Subjects + Diagnosis + Bipolar Disorder,” and 4)” “Subjects + Diagnosis + Autism Spectrum Disorders” or “Subjects + Diagnosis + Asperger’s Syndrome.”

      Study Selection

      To be included in our meta-analysis, studies had to fulfill the following inclusion criteria: 1) conducted structural neuroimaging analysis comparing patients with healthy control subjects; 2) used the Rascovsky (
      • Rascovsky K.
      • Hodges J.R.
      • Knopman D.
      • Mendez M.F.
      • Kramer J.H.
      • Neuhaus J.
      • et al.
      Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia.
      ), Neary (
      • Neary D.
      • Snowden J.S.
      • Gustafson L.
      • Passant U.
      • Stuss D.
      • Black S.
      • et al.
      Frontotemporal lobar degeneration: A consensus on clinical diagnostic criteria.
      ), and McKhann (
      • McKhann G.M.
      • Albert M.S.
      • Grossman M.
      • Miller B.
      • Dickson D.
      • Trojanowski J.Q.
      Work Group on Frontotemporal Dementia and Pick’s Disease
      Clinical and pathological diagnosis of frontotemporal dementia: Report of the Work Group on Frontotemporal Dementia and Pick’s Disease.
      ) diagnostic criteria for bvFTD; used the Autism Diagnostic Interview-Revised for ASD; or used the DSM-III, DSM-IV, DSM-5 (
      • Psychological Association American
      Diagnostic and Statistical Manual of Mental Disorders.
      ), or ICD-10 for SZ, BD, and ASD; 3) conducted a VBM analysis for GMV; 4) reported coordinates in Montreal Neurological Institute (MNI) or Talairach stereotactic standard space; 5) only included patients over age 16 years; 6) reported GM alterations, which reported peak coordinates of statistical significance at the whole-brain level; and 7) were written in English. Studies were excluded when 1) no original data were reported, for example, letters to the editor, meta-analyses, or review studies, and 2) the study sample overlapped with those of another publication. In case of sample duplication, the studies from the same institution/cohort at the same period of time were identified, and the study with the largest sample size was selected.
      Endnote database (version 9) was used to register all citations in our search. Duplicated studies were removed based on overlapping authorship, study description, year of publication, and journal. The titles and abstracts of the citations were then screened by 2 independent authors (CT and HU) to determine their relevance for inclusion. Disagreements between authors were resolved through consensus or through the decision of a third author (YALP). Full-text articles of the relevant citations were then assessed to determine whether the study met the predefined inclusion criteria (see PRISMA flow charts in Figures S1--S4).

      Patient Selection

      In the selection of patients from the included studies, we used several diagnostic criteria. For bvFTD, we only selected subjects who had been diagnosed with bvFTD; subjects with other FTD subtypes such as semantic, logopenic, or nonfluent variant primary progressive aphasia were excluded. Studies including subjects with SZ, psychosis, or schizophreniform disorder were included in the SZ diagnostic group. This included patients with either chronic or first-onset psychosis. Subjects diagnosed with BD type I or II or first-episode mania were included in the BD diagnostic group. For the ASD diagnostic group, subjects were included when diagnosed with ASD, Asperger syndrome, or pervasive developmental disorder (not otherwise specified). Finally, some studies included multiple diagnostic groups. For this case, we included separate patient groups in comparison with healthy control subjects.

      Data Recorded in the Database

      The following data of study characteristics were extracted from full-text articles: sample size and percentage of females in the group, age of the subjects at the time of magnetic resonance imaging (MRI) (mean age and standard deviation), diagnostic criteria used, global IQ or full-scale IQ (autism studies), mood state at time of MRI (BD studies), field strength of the MRI scanner, slice thickness (in mm), smoothing applied (full width at half maximum in mm), threshold p value, software used for analysis, and nuisance covariates (Tables S1–S8).

      Data Extraction and Analysis of VBM Studies

      Separate analyses were performed comparing clusters of voxels with alterations in regional GMVs between patient groups and healthy control subjects. The analyses were performed at a cluster-forming threshold (reported with each p value and activation likelihood estimation (ALE) thresholds in the results; clusters with greater ALE values than this threshold were considered statistically significant) computed using a p < .05, false discovery rate corrected (with no assumptions to correlations within the dataset), and a conservative minimum cluster volume of 200 mm3 using BrainMap’s GingerALE (version 3.0.2). Peak coordinates for GMV were extracted from eligible studies and were converted to MNI152 template using the Lancaster transformation before analysis (
      • Lancaster J.L.
      • Tordesillas-Gutiérrez D.
      • Martinez M.
      • Salinas F.
      • Evans A.
      • Zilles K.
      • et al.
      Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template.
      ). The data in MNI coordinates were entered in BrainMap’s GingerALE 3.0.2. The details of the procedure can be found on the website (http://brainmap.org/ale/index.html). In brief, meta-analysis calculations were performed using the latest ALE algorithm in GingerALE. The likelihood of anatomical differences between groups was estimated on the basis of the coordinates reported by the included studies in this meta-analysis (
      • Turkeltaub P.E.
      • Eden G.F.
      • Jones K.M.
      • Zeffiro T.A.
      Meta-analysis of the functional neuroanatomy of single-word reading: Method and validation.
      ). A modeled map was constructed by combining foci at each voxel. The statistical maps were thresholded using a cluster-level, familywise error–corrected p < .05. The coordinate of the weighed center was generated for each cluster. Within the cluster, the maximum ALE value and its coordinates were identified, which was then assigned to the MNI location of the cluster in the MNI152 atlas. Based on the collected coordinates, single datasets were created by GingerALE for each diagnostic group. Separated single-dataset analyses were conducted to investigate GM alterations within each disorder group. After analyzing the single dataset for each diagnostic group, we performed 3 pairwise conjunction analyses to study the overlap between 1) bvFTD and SZ, 2) bvFTD and BD, and 3) bvFTD and ASD. For the conjunction analyses, we used a voxel threshold of p < .05 and a cluster-forming threshold of p < .001 (
      • Eickhoff S.B.
      • Bzdok D.
      • Laird A.R.
      • Kurth F.
      • Fox P.T.
      Activation likelihood estimation meta-analysis revisited.
      ).

      Quality Assessment

      The study quality of all included articles was assessed using the Joanna Briggs Institute quality assessment tool (
      • Peters M.
      • Godfrey C.
      • McInerney P.
      • Soares C.
      • Khalil H.
      • Parker D.
      The Joanna Briggs Institute Reviewers’ Manual 2015: Methodology for JBI Scoping Reviews.
      ). The 9-point checklist assesses the rigor of inclusion criteria, subject selection, measurement of exposure, measurement of condition, identification of confounders, strategies for confounders, measurement of outcome, and statistical analysis (Tables S5–S8). Owing to the methodology of our included studies, measurement of exposure has not been taken into account in the final quality assessment. The Joanna Briggs Institute quality assessment tool is a recommended methodological quality (risk of bias) assessment tool and is widely used in VBM studies (
      • Ma L.L.
      • Wang Y.Y.
      • Yang Z.H.
      • Huang D.
      • Weng H.
      • Zeng X.T.
      Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: What are they and which is better?.
      ,
      • Keeler J.
      • Patsalos O.
      • Thuret S.
      • Ehrlich S.
      • Tchanturia K.
      • Himmerich H.
      • Treasure J.
      Hippocampal volume, function, and related molecular activity in anorexia nervosa: A scoping review.
      ,
      • Vieira de Melo B.B.
      • Trigueiro M.J.
      • Rodrigues P.P.
      Systematic overview of neuroanatomical differences in ADHD: Definitive evidence.
      ).

      Results

      Search Results

      A total of 13,205 studies were retrieved following our systematic search strategy for VBM studies contrasting GMV alterations, of which 2225 were for bvFTD, 7135 were for SZ, 2079 were for BD, and 1766 were for ASD. Ultimately, 258 studies met the final inclusion criteria and were included in our review. Of these, 24 studies concerned bvFTD (patients n = 496, control subjects n = 602), 150 SZ (patients n = 7094, control subjects n = 7332), 64 BD (patients n = 3127, control subjects n = 4248), and 20 ASD (patients n = 643, control subjects n = 641) (Table 1; Figures S1–S4).
      Table 1Demographic Data of the Diagnostic Groups
      DemographicsDiagnosis
      bvFTDSZBDASD
      No. of Subjects109814,42673751284
       Patients (female)496 (∼160)7094 (∼2617)3127 (∼1820)643 (101)
       Healthy control subjects (female)602 (∼250)7332 (∼2986)4248 (∼2380)641 (∼100)
      No. of Studies241506420
      Age, Mean, Years
       Patients62.0∼32.839.528.9
       Healthy control subjects∼64.2∼32.5∼37.929.1
      The number of female subjects and the mean age are approximations because these values were not provided in some studies.
      ASD, autism spectrum disorder; BD, bipolar disorder; bvFTD, behavioral variant frontotemporal dementia; SZ, schizophrenia.
      Overall, our search yielded a sample size of 11,360 patients and 12,823 control subjects in VBM studies. The details of the collected demographical data are displayed in Table 1, and demographics for each separate study can be found in Tables S1–S4. Of note, some studies lacked information on sex distribution or age. In these cases, estimates of missing values for sex distribution and age were imputed with weighted means.
      In addition, PRISMA flowcharts (Figures S1–S4), characteristics of the included studies (Tables S1–S4), images (axial MRI slides) for each analysis (Figures S5–S11), peak coordinates (Tables S9–S15), reported region of interest coordinates for lower and higher values, and related brain regions are displayed in the Supplement, and GingerALE results in NIfTI file format are added as Supplemental Files.

      Quality Assessment

      The overall scores of the quality assessment for each diagnostic group were displayed in Table 2. Between 93% and 97% of the included studies, for each diagnostic group, fulfilled the quality criteria of the Joanna Briggs Institute quality assessment tool, indicating that the included studies were of high quality. A detailed overview of the quality assessment for each item per study can be found in the Supplement (Tables S5–S8).
      Table 2Quality Assessment of Voxel-Based Morphometry Studies of Gray Matter Included in the Meta-analysis
      StudyInclusion CriteriaStudy SubjectsExposureMeasurement of ConditionConfounding FactorsStrategies for Confounding FactorsOutcome MeasurementStatistical AnalysisFinal Score
      Because of the methodology of our included studies, “exposure” has not been taken into account in the final score.
      bvFTD100%83%NA100%100%92%100%100%96%
      SZ97%77%NA100%100%75%100%100%93%
      BD100%85%NA100%100%97%100%100%97%
      ASD100%85%NA100%100%90%100%100%96%
      ASD, autism spectrum disorder; BD, bipolar disorder; bvFTD, behavioral variant frontotemporal dementia; NA, not applicable; SZ, schizophrenia.
      a Because of the methodology of our included studies, “exposure” has not been taken into account in the final score.

      Single-Dataset Analysis

      Behavioral Variant Frontotemporal Dementia

      The number of the studies and sample demographics are displayed in Table 1. Whole-brain coordinate-based meta-analysis of VBM studies demonstrated lower GMV in patients with bvFTD than in control subjects in the brain areas involving the bilateral frontal areas (superior, medial, inferior), bilateral cingulate (especially anterior part), bilateral caudate, putamen, globus pallidus, bilateral insula, temporal cortex (superior, medial, fusiform), amygdala, hippocampus, parahippocampus, and right uncus (Figure 1; Table S9 and Figure S5). No larger GMV in the patient group was detected.
      Figure thumbnail gr1
      Figure 1Meta-analytic results of regional gray matter alterations in the diagnostic groups. All results were thresholded at clusterwise threshold p < .05 (familywise error–corrected). The activation likelihood estimation (ALE) scores are demonstrated. For the coordinates, brain regions, and detailed presentation of each axial slide for each disorder, see the . A, axial; ASD, autism spectrum disorder; BD, bipolar disorder; bvFTD, behavioral variant frontotemporal dementia; I, inferior; L, left; R, right; S, sagittal; SZ, schizophrenia.

      Schizophrenia

      The number of the studies and sample demographics are displayed in Table 1. Regions of smaller GMV than those of healthy control subjects were observed in the bilateral cingulate (especially anterior cingulate), bilateral frontal and temporal lobes (superior, medial, inferior), insular, parietal areas (left predominant), bilateral caudate, bilateral thalamus, bilateral amygdala, left hippocampus, and left uncus. Only 1 statistically significant cluster was detected as larger GMV in SZ, pointing out the right precentral gyrus (Table S10 and Figure S6). When all volumetric alterations in SZ were combined, GMVs in the anterior cingulate; frontal, temporal, and insular lobes; thalamus; caudate; amygdala; and hippocampus were significantly different compared with healthy control subjects (Figure 1; Table S10 and Figure S6).

      Bipolar Disorder

      The number of the studies and sample demographics are displayed in Table 1. Significantly smaller GMV was found in the bilateral frontal lobes (superior, medial, inferior), bilateral cingulate (especially anterior cingulate), bilateral insula, bilateral temporal lobes (superior and medial), amygdala, and hippocampus. Although the larger volumes in the putamen were highly reported in the studies on BD, none of those clusters were significant in our analysis. The combination of smaller and larger GMVs in BD revealed that volumetric brain alterations in BD were related to the bilateral prefrontal areas, anterior cingulate, insula, amygdala, hippocampus, and temporal lobes (Figure 1; Table S11 and; Figure S7).

      Autism Spectrum Disorder

      The number of the studies and sample demographics are displayed in Table 1. Patients with ASD showed significantly smaller GMV, predominantly in the temporal areas. Lower GMV was observed both in cortical areas including the temporal (especially the fusiform gyrus) and insular areas, and in the subcortical areas including the amygdala, putamen, and hippocampus. Although some studies reported larger GMVs especially in the frontal areas, no significant cluster was detected in the separate analysis of larger GMVs in ASD. The combined analysis pointed out the putamen and the temporal areas including the cortical temporal, fusiform, amygdala, and parahippocampal areas. (Figure 1; Table S12 and Figure S8).

      Conjunction Analysis

      Across all studies, the clear majority of peak voxels represented GM volumetric changes in patients (bvFTD, SZ, BD, and ASD) compared with control individuals. Consistent GM alterations across all diagnostic groups highlighted included the amygdala, insula, cingulate cortex, and medial prefrontal cortex. (Figure 2; Tables S13–S15 and Figures S9–S11). Although we did not conduct a direct volumetric analysis, basal ganglia involvement including the caudate, putamen, and globus pallidus was more eminent in bvFTD. While GM alterations in the caudate were recorded also in SZ and the putamen in ASD, GM alterations in the globus pallidus were not one of the statistically significant clusters in SZ, BD, and ASD compared with their respective healthy control subjects. Of note, GM changes in thalamic area were more prominent in SZ, whereas statistically significant clusters in this area were not observed in other diagnostic groups.
      Figure thumbnail gr2
      Figure 2Meta-analytic results of overlapping gray matter alterations among the diagnostic groups. Brain regions involved in the conjunction analysis of behavioral variant frontotemporal dementia (bvFTD) and each psychiatric diagnostic group. All results were thresholded at clusterwise threshold p < .05 (familywise error–corrected). The activation likelihood estimation (ALE) scores are demonstrated. For the coordinates, brain regions and detailed presentation of each axial slide for each disease group, see the . ASD, autism spectrum disorder; BD, bipolar disorder; L, left; SZ, schizophrenia.

      Overlapping Structural Brain Abnormalities Between bvFTD and SZ

      GM differences were indicated by conjunction analysis in the bilateral prefrontal areas (medial and inferior), anterior cingulate, insula, amygdala, hippocampus, caudate, and superior temporal lobe in both bvFTD and SZ compared with control subjects (Figure 2; Table S13 and Figure S9).

      Overlapping Structural Brain Abnormalities Between bvFTD and BD

      Overlapping GM alterations between bvFTD and BD were observed in the medial and inferior prefrontal areas as well as the insula, anterior cingulate, and left superior temporal lobe (Figure 2; Table S14 and Figure S10).

      Overlapping Structural Brain Abnormalities Between bvFTD and ASD

      Conjunction analysis revealed overlapping areas with GM alterations between bvFTD and ASD in the temporal medial and inferior area, amygdala, uncus, putamen, and insula (Figure 2; Table S15 and Figure S11).

      Discussion

      In this cross-disorder analysis, we aimed to identify the overlapping anatomical correlates of bvFTD and PPD. We conducted a meta-analysis of structural neuroimaging studies in bvFTD, SZ, BD, and ASD by using an unbiased technique, anatomical likelihood estimation. Brain GM volumetric alterations in the prefrontal, temporal, insular, and the limbic areas were observed in bvFTD, SZ, and BD, whereas GMV changes prominently in the temporal regions were detected in ASD. Our results identified the prefrontal cortex, temporal lobe, amygdala, insula, and anterior cingulate cortex as overlapping brain areas with structural alterations in bvFTD and PPD, especially in SZ and BD. This shared morphometric signature might explain the overlapping clinical phenotypes of those disorders and open the doors for the study of common pathophysiological pathways in both types of disorders.
      Brain structural abnormalities have been widely reported in SZ (
      • van Erp T.G.M.
      • Walton E.
      • Hibar D.P.
      • Schmaal L.
      • Jiang W.
      • Glahn D.C.
      • et al.
      Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) consortium.
      ), BD (
      • Hibar D.P.
      • Westlye L.T.
      • Doan N.T.
      • Jahanshad N.
      • Cheung J.W.
      • Ching C.R.K.
      • et al.
      Cortical abnormalities in bipolar disorder: An MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group.
      ), and ASD (
      • van Rooij D.
      • Anagnostou E.
      • Arango C.
      • Auzias G.
      • Behrmann M.
      • Busatto G.F.
      • et al.
      Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: Results from the ENIGMA ASD working group.
      ), but there is no published meta-analysis reporting the overlapping structural brain abnormalities between bvFTD and those found in PPD, despite their significant clinical overlap. In line with the literature, beyond the frontotemporal cortical areas, the anterior cingulate, insular, and subcortical areas including the caudate, putamen, globus pallidum, and amygdala were affected in bvFTD (
      • Luo C.
      • Hu N.
      • Xiao Y.
      • Zhang W.
      • Gong Q.
      • Lui S.
      Comparison of gray matter atrophy in behavioral variant frontal temporal dementia and amyotrophic lateral sclerosis: A coordinate-based meta-analysis.
      ,
      • Pan P.L.
      • Song W.
      • Yang J.
      • Huang R.
      • Chen K.
      • Gong Q.Y.
      • et al.
      Gray matter atrophy in behavioral variant frontotemporal dementia: A meta-analysis of voxel-based morphometry studies.
      ,
      • Gordon E.
      • Rohrer J.D.
      • Fox N.C.
      Advances in neuroimaging in frontotemporal dementia.
      ). Remarkably, regional volume differences were observed in the same areas in SZ and BD as well. Conjunction analysis confirmed that the prefrontal, cingulate, insular lobes, and amygdala were the shared regions with GM alterations, showing structural alterations in bvFTD and SZ and BD. Not surprisingly, these results have already been published as the overlapping brain areas between SZ and BD (
      • Yu K.
      • Cheung C.
      • Leung M.
      • Li Q.
      • Chua S.
      • McAlonan G.
      Are bipolar disorder and schizophrenia neuroanatomically distinct? An anatomical likelihood meta-analysis.
      ), but it has not been associated with bvFTD before. Interestingly, over the years, the same anatomical areas have been reported by different authors using different terms such as the neuroanatomical localizations of psychiatric disorders (
      • Ebert A.
      • Bär K.J.
      Emil Kraepelin: A pioneer of scientific understanding of psychiatry and psychopharmacology.
      ,
      • Arzy S.
      • Danziger S.
      • M.A
      The science of neuropsychiatry: Past, present, and future.
      ) and brain morphometric changes in SZ (
      • van Erp T.G.M.
      • Walton E.
      • Hibar D.P.
      • Schmaal L.
      • Jiang W.
      • Glahn D.C.
      • et al.
      Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) consortium.
      ,
      • van Erp T.G.M.
      • Hibar D.P.
      • Rasmussen J.M.
      • Glahn D.C.
      • Pearlson G.D.
      • Andreassen O.A.
      • et al.
      Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium.
      ), BD, (
      • Hibar D.P.
      • Westlye L.T.
      • van Erp T.G.
      • Rasmussen J.
      • Leonardo C.D.
      • Faskowitz J.
      • et al.
      Subcortical volumetric abnormalities in bipolar disorder.
      ,
      • Bora E.
      • Fornito A.
      • Yücel M.
      • Pantelis C.
      Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder.
      ), and ASD (
      • van Rooij D.
      • Anagnostou E.
      • Arango C.
      • Auzias G.
      • Behrmann M.
      • Busatto G.F.
      • et al.
      Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: Results from the ENIGMA ASD working group.
      ,
      • Lange N.
      • Travers B.G.
      • Bigler E.D.
      • Prigge M.B.D.
      • Froehlich A.L.
      • Nielsen J.A.
      • et al.
      Longitudinal volumetric brain changes in autism spectrum disorder ages 6–35 years.
      ). In addition, similar areas have been reported as the atrophy pattern of bvFTD (
      • Luo C.
      • Hu N.
      • Xiao Y.
      • Zhang W.
      • Gong Q.
      • Lui S.
      Comparison of gray matter atrophy in behavioral variant frontal temporal dementia and amyotrophic lateral sclerosis: A coordinate-based meta-analysis.
      ,
      • Pan P.L.
      • Song W.
      • Yang J.
      • Huang R.
      • Chen K.
      • Gong Q.Y.
      • et al.
      Gray matter atrophy in behavioral variant frontotemporal dementia: A meta-analysis of voxel-based morphometry studies.
      ), anatomical model of apathy (
      • Le Heron C.
      • Apps M.A.J.
      • Husain M.
      The anatomy of apathy: A neurocognitive framework for amotivated behaviour.
      ,
      • Bortolon C.
      • Macgregor A.
      • Capdevielle D.
      • Raffard S.
      Apathy in schizophrenia: A review of neuropsychological and neuroanatomical studies.
      ,
      • Levy R.
      • Dubois B.
      Apathy and the functional anatomy of the prefrontal cortex-basal ganglia circuits.
      ), disinhibition (
      • Hornberger M.
      • Geng J.
      • Hodges J.R.
      Convergent grey and white matter evidence of orbitofrontal cortex changes related to disinhibition in behavioural variant frontotemporal dementia.
      ,
      • Krueger C.E.
      • Laluz V.
      • Rosen H.J.
      • Neuhaus J.M.
      • Miller B.L.
      • Kramer J.H.
      Double dissociation in the anatomy of socioemotional disinhibition and executive functioning in dementia.
      ), loss of empathy (
      • Rankin K.P.
      • Gorno-Tempini M.L.
      • Allison S.C.
      • Stanley C.M.
      • Glenn S.
      • Weiner M.W.
      • Miller B.L.
      Structural anatomy of empathy in neurodegenerative disease.
      ,
      • Lockwood P.L.
      The anatomy of empathy: Vicarious experience and disorders of social cognition.
      ), emotion regulation system (
      • Le Heron C.
      • Apps M.A.J.
      • Husain M.
      The anatomy of apathy: A neurocognitive framework for amotivated behaviour.
      ,
      • Levy R.
      • Dubois B.
      Apathy and the functional anatomy of the prefrontal cortex-basal ganglia circuits.
      ,
      • Lockwood P.L.
      The anatomy of empathy: Vicarious experience and disorders of social cognition.
      ), social cognition (
      • Cotter J.
      • Granger K.
      • Backx R.
      • Hobbs M.
      • Looi C.Y.
      • Barnett J.H.
      Social cognitive dysfunction as a clinical marker: A systematic review of meta-analyses across 30 clinical conditions.
      ,
      • Van Overwalle F.
      Social cognition and the brain: A meta-analysis.
      ), and limbic-thalamo-prefrontal cortical circuitry (
      • Catani M.
      • Howard R.J.
      • Pajevic S.
      • Jones D.K.
      Virtual in vivo interactive dissection of white matter fasciculi in the human brain.
      ,
      • Wakana S.
      • Jiang H.
      • Nagae-Poetscher L.M.
      • van Zijl P.C.
      • Mori S.
      Fiber tract-based atlas of human white matter anatomy.
      ). Another important point is that bvFTD (
      • Seelaar H.
      • Rohrer J.D.
      • Pijnenburg Y.A.
      • Fox N.C.
      • van Swieten J.C.
      Clinical, genetic and pathological heterogeneity of frontotemporal dementia: A review.
      ,
      • Rohrer J.D.
      • Guerreiro R.
      • Vandrovcova J.
      • Uphill J.
      • Reiman D.
      • Beck J.
      • et al.
      The heritability and genetics of frontotemporal lobar degeneration.
      ) and the psychiatric disorders studied here are heritable disorders with variable genetic architectures (
      • Sullivan P.F.
      • Geschwind D.H.
      Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders.
      ,
      • Stahl E.A.
      • Breen G.
      • Forstner A.J.
      • McQuillin A.
      • Ripke S.
      • Trubetskoy V.
      • et al.
      Genome-wide association study identifies 30 loci associated with bipolar disorder.
      ,
      • Reay W.R.
      • Cairns M.J.
      Pairwise common variant meta-analyses of schizophrenia with other psychiatric disorders reveals shared and distinct gene and gene-set associations.
      ,
      • Grove J.
      • Ripke S.
      • Als T.D.
      • Mattheisen M.
      • Walters R.K.
      • Won H.
      • et al.
      Identification of common genetic risk variants for autism spectrum disorder.
      ,
      • Ripke S.
      • O’Dushlaine C.
      • Chambert K.
      • Moran J.L.
      • Kähler A.K.
      • Akterin S.
      • et al.
      Genome-wide association analysis identifies 13 new risk loci for schizophrenia.
      ,
      Cross-Disorder Group of the Psychiatric Genomics Consortium
      Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders.
      ). Whereas monogenetic causes underlie bvFTD in 20% to 30% of cases (
      • Mol M.O.
      • van Rooij J.G.J.
      • Wong T.H.
      • Melhem S.
      • Verkerk A.J.M.H.
      • Kievit A.J.A.
      • et al.
      Underlying genetic variation in familial frontotemporal dementia: Sequencing of 198 patients.
      ), SZ and BD are highly polygenic (
      • Stahl E.A.
      • Breen G.
      • Forstner A.J.
      • McQuillin A.
      • Ripke S.
      • Trubetskoy V.
      • et al.
      Genome-wide association study identifies 30 loci associated with bipolar disorder.
      ,
      • Ripke S.
      • O’Dushlaine C.
      • Chambert K.
      • Moran J.L.
      • Kähler A.K.
      • Akterin S.
      • et al.
      Genome-wide association analysis identifies 13 new risk loci for schizophrenia.
      ). SZ and BD share polygenic overlap, whereas ASD is characterized by both polygenicity and a low percentage (<5%) of rare mutations (
      • Stahl E.A.
      • Breen G.
      • Forstner A.J.
      • McQuillin A.
      • Ripke S.
      • Trubetskoy V.
      • et al.
      Genome-wide association study identifies 30 loci associated with bipolar disorder.
      ,
      • Grove J.
      • Ripke S.
      • Als T.D.
      • Mattheisen M.
      • Walters R.K.
      • Won H.
      • et al.
      Identification of common genetic risk variants for autism spectrum disorder.
      ,
      • Ripke S.
      • O’Dushlaine C.
      • Chambert K.
      • Moran J.L.
      • Kähler A.K.
      • Akterin S.
      • et al.
      Genome-wide association analysis identifies 13 new risk loci for schizophrenia.
      ,
      Cross-Disorder Group of the Psychiatric Genomics Consortium
      Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders.
      ,
      • Lee S.H.
      • Ripke S.
      • Neale B.M.
      • Faraone S.V.
      • Purcell S.M.
      • et al.
      Cross-Disorder Group of the Psychiatric Genomics Consortium
      Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs.
      ). It is conceivable that through various mechanisms of action, these social brain disorders affect the same neuroanatomical networks (
      • Sullivan P.F.
      • Geschwind D.H.
      Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders.
      ,
      Cross-Disorder Group of the Psychiatric Genomics Consortium
      Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders.
      ). Our radiological approach is pertinent because neuroimaging studies may offer clues about the effects of the potential shared genetic etiology. Recent ENIGMA-genome-wide association study collaborations have hypothesized that if some brain regions show volumetric case-control differences and others not, these areas may be more vulnerable to the genetic and environmental risk factors, and they have termed it selective brain region vulnerability (
      • Radonjić N.V.
      • Hess J.L.
      • Rovira P.
      • Andreassen O.
      • Buitelaar J.K.
      • Ching C.R.K.
      • et al.
      Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders.
      ). Indeed, it was found that selective brain region vulnerability overlapped between SZ and BD and was positively associated with their respective genetic background (
      • Radonjić N.V.
      • Hess J.L.
      • Rovira P.
      • Andreassen O.
      • Buitelaar J.K.
      • Ching C.R.K.
      • et al.
      Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders.
      ). Consistent with these results, a large body of literature has reported substantial genetic etiologic overlap between SZ and BD (
      • Berrettini W.
      Bipolar disorder and schizophrenia: Convergent molecular data.
      ,
      • Lizano P.
      • Bannai D.
      • Lutz O.
      • Kim L.A.
      • Miller J.
      • Keshavan M.
      A meta-analysis of retinal cytoarchitectural abnormalities in schizophrenia and bipolar disorder.
      ,
      • Dezhina Z.
      • Ranlund S.
      • Kyriakopoulos M.
      • Williams S.C.R.
      • Dima D.
      A systematic review of associations between functional MRI activity and polygenic risk for schizophrenia and bipolar disorder.
      ,
      • Bora E.
      • Akgül Ö.
      • Ceylan D.
      • Özerdem A.
      Neurological soft signs in bipolar disorder in comparison to healthy controls and schizophrenia: A meta-analysis.
      ). The results of the present study raise the question whether an etiologic overlap between SZ, BD, and bvFTD might exist.
      Apart from the overlapping areas, our separate group analyses were in line with previous meta-analyses focusing on the GM morphometric changes in bvFTD (
      • Luo C.
      • Hu N.
      • Xiao Y.
      • Zhang W.
      • Gong Q.
      • Lui S.
      Comparison of gray matter atrophy in behavioral variant frontal temporal dementia and amyotrophic lateral sclerosis: A coordinate-based meta-analysis.
      ), SZ (
      • van Erp T.G.M.
      • Walton E.
      • Hibar D.P.
      • Schmaal L.
      • Jiang W.
      • Glahn D.C.
      • et al.
      Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) consortium.
      ,
      • van Erp T.G.M.
      • Hibar D.P.
      • Rasmussen J.M.
      • Glahn D.C.
      • Pearlson G.D.
      • Andreassen O.A.
      • et al.
      Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium.
      ,
      • Glahn D.C.
      • Laird A.R.
      • Ellison-Wright I.
      • Thelen S.M.
      • Robinson J.L.
      • Lancaster J.L.
      • et al.
      Meta-analysis of gray matter anomalies in schizophrenia: Application of anatomic likelihood estimation and network analysis.
      ), and BD (
      • Hibar D.P.
      • Westlye L.T.
      • van Erp T.G.
      • Rasmussen J.
      • Leonardo C.D.
      • Faskowitz J.
      • et al.
      Subcortical volumetric abnormalities in bipolar disorder.
      ,
      • Bora E.
      • Fornito A.
      • Yücel M.
      • Pantelis C.
      Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder.
      ,
      • Wang X.
      • Luo Q.
      • Tian F.
      • Cheng B.
      • Qiu L.
      • Wang S.
      • et al.
      Brain grey-matter volume alteration in adult patients with bipolar disorder under different conditions: A voxel-based meta-analysis.
      ). However, there was a discrepancy between our results and a large ENIGMA study suggesting larger frontal lobe volumes in ASD (
      • van Rooij D.
      • Anagnostou E.
      • Arango C.
      • Auzias G.
      • Behrmann M.
      • Busatto G.F.
      • et al.
      Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: Results from the ENIGMA ASD working group.
      ). The potential explanations of this inconsistency might be the use of different volumetric-analysis techniques. In this mentioned study (
      • van Rooij D.
      • Anagnostou E.
      • Arango C.
      • Auzias G.
      • Behrmann M.
      • Busatto G.F.
      • et al.
      Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: Results from the ENIGMA ASD working group.
      ), FreeSurfer cortical thickness analysis has been used, whereas we only included VBM studies in our meta-analysis to avoid the effect of the different neuroimaging data processing techniques on the results. Second, the effect might be explained by the fact that their sample size was younger than the study populations we included in our meta-analysis. Because our approach is centered on bvFTD, which is an adult-onset disorder, we excluded the pediatric population in our study. Consistent with our interpretation, a large longitudinal neuroimaging study on ASD has shown abnormally high volumes (especially in frontal areas) in early childhood, typical values between ages 10 and 15 years, and then further abnormal decline into adulthood (
      • Lange N.
      • Travers B.G.
      • Bigler E.D.
      • Prigge M.B.D.
      • Froehlich A.L.
      • Nielsen J.A.
      • et al.
      Longitudinal volumetric brain changes in autism spectrum disorder ages 6–35 years.
      ). Although numerous explanations such as age and medication effect have been proposed, the mechanism of increased/larger volumes in PPD remains unclear (
      • Thompson P.M.
      • Jahanshad N.
      • Ching C.R.K.
      • Salminen L.E.
      • Thomopoulos S.I.
      • Bright J.
      • et al.
      ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.
      ). However, this discussion is beyond the scope of this study. Nevertheless, abnormal cortical brain volumes (smaller or larger) in the frontotemporal areas occur in ASD, which supports our argument that ASD is also a frontotemporal lobe disorder.
      This is the first study focusing on the overlapping neuroanatomical signatures in bvFTD and PPD. Although our study contains the largest sample size in the literature, there are some limitations that should be addressed. First, we included the studies that reported significant clusters and displayed the region of interest coordinates. Therefore, other large sample size neuroimaging studies that did not display the region of interest coordinates were excluded. Second, we included only VBM studies for the GM structural brain changes analysis. Even though it excluded a large number of studies, we restricted ourselves to those methodologies because variability in the neuroimaging data acquisition, processing, and analysis protocols can affect the sensitivity and apparent variability of other brain imaging measures, making it challenging to compare different studies. Because negative results might likely have not been published, another strong concern in all meta-analyses is publication bias. In contrast, our results were in line with large sample size studies such as ENIGMA that collects and assesses extracted data and other meta-analyses, suggesting that a potential publication bias or our exclusion criteria did not create a major bias. In addition, because the prevalence of bvFTD is lower than that of PPD and owing to our strict inclusion criteria, the bvFTD sample size was smaller than those of SZ and BD. Moreover, because we could not use individual data, we were unable to conduct direct volume comparisons between diagnostic groups. Our methodology provided the statistically significant clusters only between patient groups and their respective age- and sex-matched/corrected control groups. Therefore, the design of the current study does not provide any data to directly compare atrophy severity between bvFTD and PPD. However, we observed GM differences between bvFTD and PPD especially in basal ganglia areas that need to be tested by future better designed methodologies. Moreover, although we cannot generalize our results to all genetic or sporadic subtypes of FTD, this novel approach could initiate more detailed studies in the future focusing on the relationship between bvFTD and PPD.
      To conclude, we found considerable overlap in neuroanatomical involvement between 2 diagnostic groups classified as neurodegenerative (bvFTD) versus non-neurodegenerative (PPD), pointing to shared genetic or environmental selective brain region vulnerability that can explain their clinical overlap. We believe that such a cross-disorder point of view might allow identification of shared disease mechanisms and development of analogous disease modifying treatments.

      Acknowledgments and Disclosures

      Research of the Alzheimer Centre Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. The Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting VUmc fonds. FB is supported by the National Institute for Health and Care Research Biomedical Research Center at University College London Hospitals. YALP is recipient of the JPND-funded project (FTD-DIPPA).
      The authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

      References

        • Neary D.
        • Snowden J.S.
        • Gustafson L.
        • Passant U.
        • Stuss D.
        • Black S.
        • et al.
        Frontotemporal lobar degeneration: A consensus on clinical diagnostic criteria.
        Neurology. 1998; 51: 1546-1554
        • Rascovsky K.
        • Hodges J.R.
        • Knopman D.
        • Mendez M.F.
        • Kramer J.H.
        • Neuhaus J.
        • et al.
        Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia.
        Brain. 2011; 134: 2456-2477
        • Johnen A.
        • Bertoux M.
        Psychological and cognitive markers of behavioral variant frontotemporal dementia-A clinical neuropsychologist’s view on diagnostic criteria and beyond.
        Front Neurol. 2019; 10 (594–594)
        • Woolley J.D.
        • Khan B.K.
        • Murthy N.K.
        • Miller B.L.
        • Rankin K.P.
        The diagnostic challenge of psychiatric symptoms in neurodegenerative disease: Rates of and risk factors for prior psychiatric diagnosis in patients with early neurodegenerative disease.
        J Clin Psychiatry. 2011; 72: 126-133
        • Pose M.
        • Cetkovich M.
        • Gleichgerrcht E.
        • Ibáñez A.
        • Torralva T.
        • Manes F.
        The overlap of symptomatic dimensions between frontotemporal dementia and several psychiatric disorders that appear in late adulthood.
        Int Rev Psychiatry. 2013; 25: 159-167
        • Cotter J.
        • Granger K.
        • Backx R.
        • Hobbs M.
        • Looi C.Y.
        • Barnett J.H.
        Social cognitive dysfunction as a clinical marker: A systematic review of meta-analyses across 30 clinical conditions.
        Neurosci Biobehav Rev. 2018; 84: 92-99
        • Kennedy D.P.
        • Adolphs R.
        The social brain in psychiatric and neurological disorders.
        Trends Cogn Sci. 2012; 16: 559-572
        • Mendez M.F.
        • Parand L.
        • Akhlaghipour G.
        Bipolar disorder among patients diagnosed with frontotemporal dementia.
        J Neuropsychiatry Clin Neurosci. 2020; 32: 376-384
        • Cipriani G.
        • Danti S.
        • Nuti A.
        • Di Fiorino M.
        • Cammisuli D.M.
        Is that schizophrenia or frontotemporal dementia? Supporting clinicians in making the right diagnosis.
        Acta Neurol Belg. 2020; 120: 799-804
        • Psychological Association American
        Diagnostic and Statistical Manual of Mental Disorders.
        5th ed. American Psychiatric Publishing, Arlington2013
        • Ducharme S.
        • Bajestan S.
        • Dickerson B.C.
        • Voon V.
        Psychiatric presentations of C9orf72 mutation: What are the diagnostic implications for clinicians?.
        J Neuropsychiatry Clin Neurosci. 2017; 29: 195-205
        • Sellami L.
        • St-Onge F.
        • Poulin S.
        • Laforce Jr., R.
        Schizophrenia phenotype preceding behavioral variant frontotemporal dementia related to C9orf72 repeat expansion.
        Cogn Behav Neurol. 2019; 32: 120-123
        • Devenney E.M.
        • Ahmed R.M.
        • Halliday G.
        • Piguet O.
        • Kiernan M.C.
        • Hodges J.R.
        Psychiatric disorders in C9orf72 kindreds: Study of 1,414 family members.
        Neurology. 2018; 91: e1498-e1507
        • Meisler M.H.
        • Grant A.E.
        • Jones J.M.
        • Lenk G.M.
        • He F.
        • Todd P.K.
        • et al.
        C9ORF72 expansion in a family with bipolar disorder.
        Bipolar Disord. 2013; 15: 326-332
        • Silverman H.E.
        • Goldman J.S.
        • Huey E.D.
        Links between the C9orf72 repeat expansion and psychiatric symptoms.
        Curr Neurol Neurosci Rep. 2019; 19: 93
        • Velakoulis D.
        • Walterfang M.
        • Mocellin R.
        • Pantelis C.
        • McLean C.
        Frontotemporal dementia presenting as schizophrenia-like psychosis in young people: Clinicopathological series and review of cases.
        Br J Psychiatry. 2009; 194: 298-305
        • Cooper J.J.
        • Ovsiew F.
        The relationship between schizophrenia and frontotemporal dementia.
        J Geriatr Psychiatry Neurol. 2013; 26: 131-137
        • Harciarek M.
        • Malaspina D.
        • Sun T.
        • Goldberg E.
        Schizophrenia and frontotemporal dementia: Shared causation?.
        Int Rev Psychiatry. 2013; 25: 168-177
        • Thompson P.M.
        • Jahanshad N.
        • Ching C.R.K.
        • Salminen L.E.
        • Thomopoulos S.I.
        • Bright J.
        • et al.
        ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.
        Transl Psychiatry. 2020; 10: 100
        • Davatzikos C.
        Why voxel-based morphometric analysis should be used with great caution when characterizing group differences.
        NeuroImage. 2004; 23: 17-20
        • Moher D.
        • Liberati A.
        • Tetzlaff J.
        • Altman D.G.
        • PRISMA Group
        Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA statement.
        PLoS Med. 2009; 6e1000097
        • Fox P.T.
        • Lancaster J.L.
        Opinion: Mapping context and content: The BrainMap model.
        Nat Rev Neurosci. 2002; 3: 319-321
        • McKhann G.M.
        • Albert M.S.
        • Grossman M.
        • Miller B.
        • Dickson D.
        • Trojanowski J.Q.
        • Work Group on Frontotemporal Dementia and Pick’s Disease
        Clinical and pathological diagnosis of frontotemporal dementia: Report of the Work Group on Frontotemporal Dementia and Pick’s Disease.
        Arch Neurol. 2001; 58: 1803-1809
        • Lancaster J.L.
        • Tordesillas-Gutiérrez D.
        • Martinez M.
        • Salinas F.
        • Evans A.
        • Zilles K.
        • et al.
        Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template.
        Hum Brain Mapp. 2007; 28: 1194-1205
        • Turkeltaub P.E.
        • Eden G.F.
        • Jones K.M.
        • Zeffiro T.A.
        Meta-analysis of the functional neuroanatomy of single-word reading: Method and validation.
        Neuroimage. 2002; 16: 765-780
        • Eickhoff S.B.
        • Bzdok D.
        • Laird A.R.
        • Kurth F.
        • Fox P.T.
        Activation likelihood estimation meta-analysis revisited.
        Neuroimage. 2012; 59: 2349-2361
        • Peters M.
        • Godfrey C.
        • McInerney P.
        • Soares C.
        • Khalil H.
        • Parker D.
        The Joanna Briggs Institute Reviewers’ Manual 2015: Methodology for JBI Scoping Reviews.
        The Joanna Briggs Institute, Adelaide, Australia2015
        • Ma L.L.
        • Wang Y.Y.
        • Yang Z.H.
        • Huang D.
        • Weng H.
        • Zeng X.T.
        Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: What are they and which is better?.
        Mil Med Res. 2020; 7: 7
        • Keeler J.
        • Patsalos O.
        • Thuret S.
        • Ehrlich S.
        • Tchanturia K.
        • Himmerich H.
        • Treasure J.
        Hippocampal volume, function, and related molecular activity in anorexia nervosa: A scoping review.
        Expert Rev Clin Pharmacol. 2020; 13: 1367-1387
        • Vieira de Melo B.B.
        • Trigueiro M.J.
        • Rodrigues P.P.
        Systematic overview of neuroanatomical differences in ADHD: Definitive evidence.
        Dev Neuropsychol. 2018; 43: 52-68
        • van Erp T.G.M.
        • Walton E.
        • Hibar D.P.
        • Schmaal L.
        • Jiang W.
        • Glahn D.C.
        • et al.
        Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) consortium.
        Biol Psychiatry. 2018; 84: 644-654
        • Hibar D.P.
        • Westlye L.T.
        • Doan N.T.
        • Jahanshad N.
        • Cheung J.W.
        • Ching C.R.K.
        • et al.
        Cortical abnormalities in bipolar disorder: An MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group.
        Mol Psychiatry. 2018; 23: 932-942
        • van Rooij D.
        • Anagnostou E.
        • Arango C.
        • Auzias G.
        • Behrmann M.
        • Busatto G.F.
        • et al.
        Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: Results from the ENIGMA ASD working group.
        Am J Psychiatry. 2018; 175: 359-369
        • Luo C.
        • Hu N.
        • Xiao Y.
        • Zhang W.
        • Gong Q.
        • Lui S.
        Comparison of gray matter atrophy in behavioral variant frontal temporal dementia and amyotrophic lateral sclerosis: A coordinate-based meta-analysis.
        Front Aging Neurosci. 2020; 12: 14
        • Pan P.L.
        • Song W.
        • Yang J.
        • Huang R.
        • Chen K.
        • Gong Q.Y.
        • et al.
        Gray matter atrophy in behavioral variant frontotemporal dementia: A meta-analysis of voxel-based morphometry studies.
        Dement Geriatr Cogn Disord. 2012; 33: 141-148
        • Gordon E.
        • Rohrer J.D.
        • Fox N.C.
        Advances in neuroimaging in frontotemporal dementia.
        J Neurochem. 2016; 138: 193-210
        • Yu K.
        • Cheung C.
        • Leung M.
        • Li Q.
        • Chua S.
        • McAlonan G.
        Are bipolar disorder and schizophrenia neuroanatomically distinct? An anatomical likelihood meta-analysis.
        Front Hum Neurosci. 2010; 4 (189–189)
        • Ebert A.
        • Bär K.J.
        Emil Kraepelin: A pioneer of scientific understanding of psychiatry and psychopharmacology.
        Indian J Psychiatry. 2010; 52: 191-192
        • Arzy S.
        • Danziger S.
        • M.A
        The science of neuropsychiatry: Past, present, and future.
        J Neuropsychiatry Clin Neurosci. 2014; 26: 392-395
        • van Erp T.G.M.
        • Hibar D.P.
        • Rasmussen J.M.
        • Glahn D.C.
        • Pearlson G.D.
        • Andreassen O.A.
        • et al.
        Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium.
        Mol Psychiatry. 2016; 21: 547-553
        • Hibar D.P.
        • Westlye L.T.
        • van Erp T.G.
        • Rasmussen J.
        • Leonardo C.D.
        • Faskowitz J.
        • et al.
        Subcortical volumetric abnormalities in bipolar disorder.
        Mol Psychiatry. 2016; 21: 1710-1716
        • Bora E.
        • Fornito A.
        • Yücel M.
        • Pantelis C.
        Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder.
        Biol Psychiatry. 2010; 67: 1097-1105
        • Lange N.
        • Travers B.G.
        • Bigler E.D.
        • Prigge M.B.D.
        • Froehlich A.L.
        • Nielsen J.A.
        • et al.
        Longitudinal volumetric brain changes in autism spectrum disorder ages 6–35 years.
        Autism Res. 2015; 8: 82-93
        • Le Heron C.
        • Apps M.A.J.
        • Husain M.
        The anatomy of apathy: A neurocognitive framework for amotivated behaviour.
        Neuropsychologia. 2018; 118: 54-67
        • Bortolon C.
        • Macgregor A.
        • Capdevielle D.
        • Raffard S.
        Apathy in schizophrenia: A review of neuropsychological and neuroanatomical studies.
        Neuropsychologia. 2018; 118: 22-33
        • Levy R.
        • Dubois B.
        Apathy and the functional anatomy of the prefrontal cortex-basal ganglia circuits.
        Cereb Cortex. 2006; 16: 916-928
        • Hornberger M.
        • Geng J.
        • Hodges J.R.
        Convergent grey and white matter evidence of orbitofrontal cortex changes related to disinhibition in behavioural variant frontotemporal dementia.
        Brain. 2011; 134: 2502-2512
        • Krueger C.E.
        • Laluz V.
        • Rosen H.J.
        • Neuhaus J.M.
        • Miller B.L.
        • Kramer J.H.
        Double dissociation in the anatomy of socioemotional disinhibition and executive functioning in dementia.
        Neuropsychology. 2011; 25: 249-259
        • Rankin K.P.
        • Gorno-Tempini M.L.
        • Allison S.C.
        • Stanley C.M.
        • Glenn S.
        • Weiner M.W.
        • Miller B.L.
        Structural anatomy of empathy in neurodegenerative disease.
        Brain. 2006; 129: 2945-2956
        • Lockwood P.L.
        The anatomy of empathy: Vicarious experience and disorders of social cognition.
        Behav Brain Res. 2016; 311: 255-266
        • Van Overwalle F.
        Social cognition and the brain: A meta-analysis.
        Hum Brain Mapp. 2009; 30: 829-858
        • Catani M.
        • Howard R.J.
        • Pajevic S.
        • Jones D.K.
        Virtual in vivo interactive dissection of white matter fasciculi in the human brain.
        Neuroimage. 2002; 17: 77-94
        • Wakana S.
        • Jiang H.
        • Nagae-Poetscher L.M.
        • van Zijl P.C.
        • Mori S.
        Fiber tract-based atlas of human white matter anatomy.
        Radiology. 2004; 230: 77-87
        • Seelaar H.
        • Rohrer J.D.
        • Pijnenburg Y.A.
        • Fox N.C.
        • van Swieten J.C.
        Clinical, genetic and pathological heterogeneity of frontotemporal dementia: A review.
        J Neurol Neurosurg Psychiatry. 2011; 82: 476-486
        • Rohrer J.D.
        • Guerreiro R.
        • Vandrovcova J.
        • Uphill J.
        • Reiman D.
        • Beck J.
        • et al.
        The heritability and genetics of frontotemporal lobar degeneration.
        Neurology. 2009; 73: 1451-1456
        • Sullivan P.F.
        • Geschwind D.H.
        Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders.
        Cell. 2019; 177: 162-183
        • Stahl E.A.
        • Breen G.
        • Forstner A.J.
        • McQuillin A.
        • Ripke S.
        • Trubetskoy V.
        • et al.
        Genome-wide association study identifies 30 loci associated with bipolar disorder.
        Nat Genet. 2019; 51: 793-803
        • Reay W.R.
        • Cairns M.J.
        Pairwise common variant meta-analyses of schizophrenia with other psychiatric disorders reveals shared and distinct gene and gene-set associations.
        Transl Psychiatry. 2020; 10: 134
        • Grove J.
        • Ripke S.
        • Als T.D.
        • Mattheisen M.
        • Walters R.K.
        • Won H.
        • et al.
        Identification of common genetic risk variants for autism spectrum disorder.
        Nat Genet. 2019; 51: 431-444
        • Ripke S.
        • O’Dushlaine C.
        • Chambert K.
        • Moran J.L.
        • Kähler A.K.
        • Akterin S.
        • et al.
        Genome-wide association analysis identifies 13 new risk loci for schizophrenia.
        Nat Genet. 2013; 45: 1150-1159
        • Cross-Disorder Group of the Psychiatric Genomics Consortium
        Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders.
        Cell. 2019; 179: 1469-1482.e1411
        • Mol M.O.
        • van Rooij J.G.J.
        • Wong T.H.
        • Melhem S.
        • Verkerk A.J.M.H.
        • Kievit A.J.A.
        • et al.
        Underlying genetic variation in familial frontotemporal dementia: Sequencing of 198 patients.
        Neurobiol Aging. 2021; 97: 148.e9-148.e16
        • Lee S.H.
        • Ripke S.
        • Neale B.M.
        • Faraone S.V.
        • Purcell S.M.
        • et al.
        • Cross-Disorder Group of the Psychiatric Genomics Consortium
        Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs.
        Nat Genet. 2013; 45: 984-994
        • Radonjić N.V.
        • Hess J.L.
        • Rovira P.
        • Andreassen O.
        • Buitelaar J.K.
        • Ching C.R.K.
        • et al.
        Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders.
        Mol Psychiatry. 2021; 26: 2101-2110
        • Berrettini W.
        Bipolar disorder and schizophrenia: Convergent molecular data.
        NeuroMolecular Med. 2004; 5: 109-117
        • Lizano P.
        • Bannai D.
        • Lutz O.
        • Kim L.A.
        • Miller J.
        • Keshavan M.
        A meta-analysis of retinal cytoarchitectural abnormalities in schizophrenia and bipolar disorder.
        Schizophr Bull. 2020; 46: 43-53
        • Dezhina Z.
        • Ranlund S.
        • Kyriakopoulos M.
        • Williams S.C.R.
        • Dima D.
        A systematic review of associations between functional MRI activity and polygenic risk for schizophrenia and bipolar disorder.
        Brain Imaging Behav. 2019; 13: 862-877
        • Bora E.
        • Akgül Ö.
        • Ceylan D.
        • Özerdem A.
        Neurological soft signs in bipolar disorder in comparison to healthy controls and schizophrenia: A meta-analysis.
        Eur Neuropsychopharmacol. 2018; 28: 1185-1193
        • Glahn D.C.
        • Laird A.R.
        • Ellison-Wright I.
        • Thelen S.M.
        • Robinson J.L.
        • Lancaster J.L.
        • et al.
        Meta-analysis of gray matter anomalies in schizophrenia: Application of anatomic likelihood estimation and network analysis.
        Biol Psychiatry. 2008; 64: 774-781
        • Wang X.
        • Luo Q.
        • Tian F.
        • Cheng B.
        • Qiu L.
        • Wang S.
        • et al.
        Brain grey-matter volume alteration in adult patients with bipolar disorder under different conditions: A voxel-based meta-analysis.
        J Psychiatry Neurosci. 2019; 44: 89-101