Localized Misfolding Within Broca’s Area as a Distinctive Feature of Autistic Disorder

  • Lucile Brun
    Affiliations
    Institut de Neurosciences de la Timone, Unite Mixte de Recherche 7289, Aix-Marseille Université, Centre National de la Recherche Scientifique, Marseille, France
    Search for articles by this author
  • Guillaume Auzias
    Affiliations
    Institut de Neurosciences de la Timone, Unite Mixte de Recherche 7289, Aix-Marseille Université, Centre National de la Recherche Scientifique, Marseille, France
    Search for articles by this author
  • Marine Viellard
    Affiliations
    Institut de Neurosciences de la Timone, Unite Mixte de Recherche 7289, Aix-Marseille Université, Centre National de la Recherche Scientifique, Marseille, France

    Centre de Ressource Autisme, Service de Pédopsychiatrie, Assistance Publique-Hôpitaux de Marseille, Hôpital Ste Marguerite, Marseille, France
    Search for articles by this author
  • Nathalie Villeneuve
    Affiliations
    Centre de Ressource Autisme, Service de Pédopsychiatrie, Assistance Publique-Hôpitaux de Marseille, Hôpital Ste Marguerite, Marseille, France
    Search for articles by this author
  • Nadine Girard
    Affiliations
    Centre de Résonance Magnétique Biologique et Médicale, Unite Mixte de Recherche 7339, Aix-Marseille Université, Centre National de la Recherche Scientifique, Marseille, France

    Assistance Publique-Hôpitaux de Marseille Timone, Service de Neuroradiologie Diagnostique et Interventionnelle, Marseille, France
    Search for articles by this author
  • François Poinso
    Affiliations
    Institut de Neurosciences de la Timone, Unite Mixte de Recherche 7289, Aix-Marseille Université, Centre National de la Recherche Scientifique, Marseille, France

    Centre de Ressource Autisme, Service de Pédopsychiatrie, Assistance Publique-Hôpitaux de Marseille, Hôpital Ste Marguerite, Marseille, France
    Search for articles by this author
  • David Da Fonseca
    Affiliations
    Institut de Neurosciences de la Timone, Unite Mixte de Recherche 7289, Aix-Marseille Université, Centre National de la Recherche Scientifique, Marseille, France

    Service de Pédopsychiatrie, Assistance Publique-Hôpitaux de Marseille, Hôpital Salvator, Marseille, France
    Search for articles by this author
  • Christine Deruelle
    Correspondence
    Address correspondence to Christine Deruelle, Ph.D., Institut de Neurosciences de la Timone, Faculté de Médecine, 27 Boulevard Jean Moulin, cedex 5 Marseille, Marseille 13005, France.
    Affiliations
    Institut de Neurosciences de la Timone, Unite Mixte de Recherche 7289, Aix-Marseille Université, Centre National de la Recherche Scientifique, Marseille, France
    Search for articles by this author
Published:November 23, 2015DOI:https://doi.org/10.1016/j.bpsc.2015.11.003

      Abstract

      Background

      Recent neuroimaging studies suggest that autism spectrum disorder results from abnormalities in the cortical folding pattern. Usual morphometric measurements have failed to provide reliable neuroanatomic markers. Here, we propose that sulcal pits, which are the deepest points in each fold, are suitable candidates to uncover this atypical cortical folding.

      Methods

      Sulcal pits were extracted from a magnetic resonance imaging database of 102 children (1.5–10 years old) distributed in three groups: children with autistic disorder (n = 59), typically developing children (n = 22), and children with pervasive developmental disorder not otherwise specified (n = 21). The geometrical properties of sulcal pits were compared between these three groups.

      Results

      Fold-level analyses revealed a reduced pit depth in the left ascending ramus of the Sylvian fissure in children with autistic disorder only. The depth of this central fold of Broca’s area was correlated with the social communication impairments that are characteristic of the pathology.

      Conclusions

      Our findings support an atypical gyrogenesis of this specific fold in autistic disorder that could be used for differential diagnosis. Sulcal pits constitute valuable markers of the cortical folding dynamics and could help for the early detection of atypical brain maturation.

      Keywords

      To read this article in full you will need to make a payment

      References

        • American Psychiatric Association
        The Diagnostic and Statistical Manual of Mental Disorders: DSM 5.
        American Psychiatric Publishing, Arlington, VA2013
        • Walsh P.
        • Elsabbagh M.
        • Bolton P.F.
        • Singh I.
        In search of biomarkers for autism: Scientific, social and ethical challenges.
        Nat Rev Neurosci. 2011; 12: 603-612
        • Wallace G.L.
        • Robustelli B.
        • Dankner N.
        • Kenworthy L.
        • Giedd J.N.
        • Martin A.
        Increased gyrification, but comparable surface area in adolescents with autism spectrum disorders.
        Brain. 2013; 136: 1956-1967
        • Auzias G.
        • Viellard M.
        • Takerkart S.
        • Villeneuve N.
        • Poinso F.
        • Fonséca D.D.
        • et al.
        Atypical sulcal anatomy in young children with autism spectrum disorder.
        Neuroimage Clin. 2014; 4: 593-603
        • Belmonte M.K.
        • Allen G.
        • Beckel-Mitchener A.
        • Boulanger L.M.
        • Carper R.A.
        • Webb S.J.
        Autism and abnormal development of brain connectivity.
        J Neurosci. 2004; 24: 9228-9231
        • Lenroot R.K.
        • Yeung P.K.
        Heterogeneity within autism spectrum disorders: What have we learned from neuroimaging studies?.
        Front Hum Neurosci. 2013; 7: 733
        • Anagnostou E.
        • Taylor M.J.
        Review of neuroimaging in autism spectrum disorders: What have we learned and where we go from here.
        Mol Autism. 2011; 2: 4
        • Stanfield A.C.
        • McIntosh A.M.
        • Spencer M.D.
        • Philip R.C.M.
        • Gaur S.
        • Lawrie S.M.
        Towards a neuroanatomy of autism: A systematic review and meta-analysis of structural magnetic resonance imaging studies.
        Eur Psychiatry. 2008; 23: 289-299
        • Amaral D.G.
        • Schumann C.M.
        • Nordahl C.W.
        Neuroanatomy of autism.
        Trends Neurosci. 2008; 31: 137-145
        • Ecker C.
        • Bookheimer S.Y.
        • Murphy D.G.
        Neuroimaging in autism spectrum disorder: Brain structure and function across the lifespan.
        Lancet Neurol. 2015; 4422: 1-14
        • Stevenson R.A.
        Using functional connectivity analyses to investigate the bases of autism spectrum disorders and other clinical populations.
        J Neurosci. 2012; 32: 17933-17934
        • Hadjikhani N.
        • Joseph R.M.
        • Snyder J.
        • Tager-Flusberg H.B.
        Anatomical differences in the mirror neuron system and social cognition network in autism.
        Cereb Cortex. 2006; 16: 1276-1282
        • Ecker C.
        • Marquand A.
        • Mourao-Miranda J.
        • Johnston P.
        • Daly E.M.
        • Brammer M.J.
        • et al.
        Describing the brain in autism in five dimensions--magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach.
        J Neurosci. 2010; 30: 10612-10623
        • Hyde K.L.
        • Samson F.
        • Evans A.C.
        • Mottron L.
        Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel-based morphometry.
        Hum Brain Mapp. 2010; 31: 556-566
        • Wallace G.L.
        • Dankner N.
        • Kenworthy L.
        • Giedd J.N.
        • Martin A.
        Age-related temporal and parietal cortical thinning in autism spectrum disorders.
        Brain. 2010; 133: 3745-3754
        • Raznahan A.
        • Lenroot R.K.
        • Thurm A.
        • Gozzi M.
        • Hanley A.
        • Spence S.J.
        • et al.
        Mapping cortical anatomy in preschool aged children with autism using surface-based morphometry.
        Neuroimage Clin. 2013; 2: 111-119
        • Doyle-Thomas K.A.
        • Duerden E.G.
        • Taylor M.J.
        • Lerch J.P.
        • Soorya L.V.
        • Wang A.T.
        • et al.
        Effects of age and symptomatology on cortical thickness in autism spectrum disorders.
        Res Autism Spectr Disord. 2013; 7: 141-150
        • Misaki M.
        • Wallace G.L.
        • Dankner N.
        • Martin A.
        • Bandettini P.A.
        Characteristic cortical thickness patterns in adolescents with autism spectrum disorders: Interactions with age and intellectual ability revealed by canonical correlation analysis.
        Neuroimage. 2012; 60: 1890-1901
        • Sussman D.
        • Leung R.C.
        • Vogan V.M.
        • Lee W.
        • Trelle S.
        • Lin S.
        • et al.
        The autism puzzle: Diffuse but not pervasive neuroanatomical abnormalities in children with ASD.
        Neuroimage Clin. 2015; 8: 170-179
        • Valk S.L.
        • Di Martino A.
        • Milham M.P.
        • Bernhardt B.C.
        Multicenter mapping of structural network alterations in autism.
        Hum Brain Mapp. 2015; 36: 2364-2373
        • Hardan A.Y.
        • Jou R.J.
        • Keshavan M.S.
        • Varma R.
        • Minshew N.J.
        Increased frontal cortical folding in autism: A preliminary MRI study.
        Psychiatry Res. 2004; 131: 263-268
        • Nordahl C.W.
        • Dierker D.L.
        • Mostafavi I.
        • Schumann C.M.
        • Rivera S.M.
        • Amaral D.G.
        • Van Essen D.C.
        Cortical folding abnormalities in autism revealed by surface-based morphometry.
        J Neurosci. 2007; 27: 11725-11735
        • Dierker D.L.
        • Feczko E.
        • Pruett J.R.
        • Petersen S.E.
        • Schlaggar B.L.
        • Constantino J.N.
        • et al.
        Analysis of cortical shape in children with simplex autism.
        Cereb Cortex. 2015; 25: 1042-1051
        • Auzias G.
        • Takerkart S.
        • Deruelle C.
        On the influence of confounding factors in multi-site brain morphometry studies of developmental pathologies: Application to autism spectrum disorder.
        IEEE J Biomed Health Inform. 2015; 99: 1
        • Lohmann G.
        • von Cramon D.Y.
        • Colchester A.C.F.
        Deep sulcal landmarks provide an organizing framework for human cortical folding.
        Cereb Cortex. 2008; 18: 1415-1420
        • Rakic P.
        Specification of cerebral cortical areas.
        Science. 1988; 241: 170-176
        • Régis J.
        • Mangin J.-F.
        • Ochiai T.
        • Frouin V.
        • Rivière D.
        • Cachia A.
        • et al.
        “Sulcal Root” generic model: A hypothesis to overcome the variability of the human cortex folding patterns.
        Neurol Med Chir (Tokyo). 2005; 45: 1-17
        • Meng Y.
        • Li G.
        • Lin W.
        • Gilmore J.H.
        • Shen D.
        Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants.
        Neuroimage. 2014; 100: 206-218
        • Im K.
        • Pienaar R.
        • Lee J.-M.
        • Seong J.-K.
        • Choi Y.Y.
        • Lee K.H.
        • Grant P.E.
        Quantitative comparison and analysis of sulcal patterns using sulcal graph matching: A twin study.
        Neuroimage. 2011; 57: 1077-1086
        • Le Goualher G.
        • Procyk E.
        • Collins D.L.
        • Venugopal R.
        • Barillot C.
        • Evans A.C.
        Automated extraction and variability analysis of sulcal neuroanatomy.
        IEEE Trans Med Imaging. 1999; 18: 206-217
        • Lohmann G.
        • von Cramon D.Y.
        • Steinmetz H.
        Sulcal variability of twins.
        Cereb Cortex. 1999; 9: 754-763
        • Leroy F.
        • Cai Q.
        • Bogart S.
        • Dubois J.
        • Coulon O.
        • Monzalvo K.
        • et al.
        New human-specific brain landmark: The depth asymmetry of superior temporal sulcus.
        Proc Natl Acad Sci U S A. 2015; 112: 1208-1213
        • Im K.
        • Jo H.J.
        • Mangin J.-F.
        • Evans A.C.
        • Kim S.I.
        • Lee J.-M.
        Spatial distribution of deep sulcal landmarks and hemispherical asymmetry on the cortical surface.
        Cereb Cortex. 2010; 20: 602-611
        • Auzias G.
        • Brun L.
        • Deruelle C.
        • Coulon O.
        Deep sulcal landmarks: Algorithmic and conceptual improvements in the definition and extraction of sulcal pits.
        Neuroimage. 2015; 111: 12-25
        • Im K.
        • Choi Y.Y.
        • Yang J.-J.
        • Lee K.H.
        • Kim S.I.
        • Grant P.E.
        • Lee J.-M.
        The relationship between the presence of sulcal pits and intelligence in human brains.
        Neuroimage. 2011; 55: 1490-1496
        • Im K.
        • Raschle N.M.
        • Smith S.A.
        • Ellen Grant P.
        • Gaab N.
        Atypical sulcal pattern in children with developmental dyslexia and at-risk kindergarteners [published online ahead of print January 9].
        Cereb Cortex. 2015;
        • Im K.
        • Lee J.-M.
        • Jeon S.
        • Kim J.-H.
        • Seo S.W.
        • Na D.L.
        • Grant P.E.
        Reliable identification of deep sulcal pits: The effects of scan session, scanner, and surface extraction tool.
        PLoS One. 2013; 8: e53678
        • Li G.
        • Wang L.
        • Shi F.
        • Lyall A.E.
        • Lin W.
        • Gilmore J.H.
        • Shen D.
        Mapping longitudinal development of local cortical gyrification in infants from birth to 2 years of age.
        J Neurosci. 2014; 34: 4228-4238
        • White T.
        • Su S.
        • Schmidt M.
        • Kao C.-Y.
        • Sapiro G.
        The development of gyrification in childhood and adolescence.
        Brain Cogn. 2010; 72: 36-45
        • American Psychiatric Association
        Diagnostic and Statistical Manual of Mental Disorders: DSM-IV-TR.
        Text Rev. 4th ed. American Psychiatric Association, Washington, DC2000
        • Schaer M.
        • Kochalka J.
        • Padmanabhan A.
        • Supekar K.
        • Menon V.
        Sex differences in cortical volume and gyrification in autism.
        Mol Autism. 2015; 6: 42
        • Takerkart S.
        • Auzias G.
        • Brun L.
        • Coulon O.
        Mapping cortical shape differences using a searchlight approach based on classification of sulcal pit graphs.
        Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on Biomedical Imaging. 2015; : 1514-1517
        • Raznahan A.
        • Shaw P.
        • Lalonde F.
        • Stockman M.
        • Wallace G.L.
        • Greenstein D.K.
        • et al.
        How does your cortex grow?.
        J Neurosci. 2011; 31: 7174-7177
        • Lord C.
        • Rutter M.
        • Le Couteur A.
        Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders.
        J Autism Dev Disord. 1994; 24: 659-685
        • Lord C.
        • Risi S.
        • Lambrecht L.
        • Cook E.H.
        • Leventhal B.L.
        • DiLavore P.C.
        • et al.
        The autism diagnostic observation schedule-generic: A standard measure of social and communication deficits associated with the spectrum of autism.
        J Autism Dev Disord. 2000; 30: 205-223
      1. Schopler E, Reichler R, Renner B (1986): The Childhood Autism Rating Scale (CARS) for Diagnostic Screening and Classification of Autism. New York: Irvington Publishers

        • Sparrow S.
        • Balla D.
        • Cicchetti D.
        Vineland Adaptive Behavior Scales: Interview Edition.
        Survey Form Manual. American Guidance Service, Circle Pines, MN1984
        • Fischl B.
        • Sereno M.I.
        • Tootell R.B.
        • Dale A.M.
        High-resolution intersubject averaging and a coordinate system for the cortical surface.
        Hum Brain Mapp. 1999; 8: 272-284
        • Dubois J.
        • Benders M.
        • Borradori-Tolsa C.
        • Cachia A.
        • Lazeyras F.
        • Ha-Vinh Leuchter R.
        • et al.
        Primary cortical folding in the human newborn: An early marker of later functional development.
        Brain. 2008; 131: 2028-2041
        • Nichols T.E.
        • Holmes A.P.
        Nonparametric permutation tests for functional neuroimaging: A primer with examples.
        Hum Brain Mapp. 2002; 15: 1-25
        • Ebeling U.
        • Steinmetz H.
        • Huang Y.
        • Kahn T.
        Topography and identification of the inferior precentral sulcus in MR imaging.
        AJNR Am J Neuroradiol. 1989; 10: 937-942
        • Liao S-F
        • Liu J-C
        • Hsu C-L
        • Chang M-Y
        • Chang T-M
        • Cheng H.
        Cognitive development in children with language impairment, and correlation between language and intelligence development in kindergarten children with developmental delay.
        J Child Neurol. 2015; 30: 42-47
        • Dubois J.
        • Benders M.
        • Cachia A.
        • Lazeyras F.
        • Ha-Vinh Leuchter R.
        • Sizonenko S.V.
        • et al.
        Mapping the early cortical folding process in the preterm newborn brain.
        Cereb Cortex. 2008; 18: 1444-1454
        • Habas P.A.
        • Kim K.
        • Corbett-Detig J.M.
        • Rousseau F.
        • Glenn O.A.
        • Barkovich A.J.
        • Studholme C.
        A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation.
        Neuroimage. 2010; 53: 460-470
        • Akshoomoff N.
        • Lord C.
        • Lincoln A.J.
        • Courchesne R.Y.
        • Carper R.A.
        • Townsend J.
        • Courchesne E.
        Outcome classification of preschool children with autism spectrum disorders using MRI brain measures.
        J Am Acad Child Adolesc Psychiatry. 2004; 43: 349-357
        • Sparks B.F.
        • Friedman S.
        • Shaw D.
        • Aylward E.
        • Echelard D.
        • Artru A.A.
        • et al.
        Brain structural abnormalities in young children with autism spectrum disorder.
        Neurology. 2002; 59: 184-192
        • Courchesne E.
        • Carper R.A.
        • Akshoomoff N.
        Evidence of brain overgrowth in the first year of life in autism.
        JAMA. 2003; 290: 337
        • Yamasaki S.
        • Yamasue H.
        • Abe O.
        • Suga M.
        • Yamada H.
        • Inoue H.
        • et al.
        Reduced gray matter volume of pars opercularis is associated with impaired social communication in high-functioning autism spectrum disorders.
        Biol Psychiatry. 2010; 68: 1141-1147
        • Libero L.E.
        • DeRamus T.P.
        • Deshpande H.D.
        • Kana R.K.
        Surface-based morphometry of the cortical architecture of autism spectrum disorders: Volume, thickness, area, and gyrification.
        Neuropsychologia. 2014; 62: 1-10
        • Clark M.M.
        • Plante E.
        Morphology of the inferior frontal gyrus in developmentally language-disordered adults.
        Brain Lang. 1998; 61: 288-303
        • Tomaiuolo F.
        • MacDonald J.D.
        • Caramanos Z.
        • Posner G.
        • Chiavaras M.
        • Evans A.C.
        • Petrides M.
        Morphology, morphometry and probability mapping of the pars opercularis of the inferior frontal gyrus: An in?vivo MRI analysis.
        Eur J Neurosci. 1999; 11: 3033-3046
        • Suga M.
        • Yamasue H.
        • Abe O.
        • Yamasaki S.
        • Yamada H.
        • Inoue H.
        • et al.
        Reduced gray matter volume of Brodmann’s area 45 is associated with severe psychotic symptoms in patients with schizophrenia.
        Eur Arch Psychiatry Clin Neurosci. 2010; 260: 465-473
        • Petrides M.
        • Pandya D.N.
        Comparative architectonic analysis of the human and the macaque frontal cortex.
        in: Handbook of Neuropsychology. vol. 9. Elsevier, Amsterdam1994: 17-58
        • Amunts K.
        • Schleicher A.
        • Zilles K.
        Outstanding language competence and cytoarchitecture in Broca’s speech region.
        Brain Lang. 2004; 89: 346-353
        • Friederici A.D.
        The brain basis of language processing: From structure to function.
        Physiol Rev. 2011; 91: 1357-1392
        • Anwander A.
        • Tittgemeyer M.
        • von Cramon D.Y.
        • Friederici A.
        • Knosche T.
        Connectivity-based parcellation of Broca’s area.
        Cereb Cortex. 2006; 17: 816-825
        • Price C.J.
        The anatomy of language: A review of 100 fMRI studies published in 2009.
        Ann N Y Acad Sci. 2010; 1191: 62-88
        • Frith U.
        A new look at language and communication in autism.
        Br J Disord Commun. 1989; 24: 123-150
        • Knaus T.A.
        • Silver A.M.
        • Dominick K.C.
        • Schuring M.D.
        • Shaffer N.
        • Lindgren K.A.
        • et al.
        Age-related changes in the anatomy of language regions in autism spectrum disorder.
        Brain Imaging Behav. 2009; 3: 51-63
        • Just M.A.
        • Cherkassky V.L.
        • Keller T.A.
        • Minshew N.J.
        Cortical activation and synchronization during sentence comprehension in high-functioning autism: Evidence of underconnectivity.
        Brain. 2004; 127: 1811-1821
        • Molko N.
        • Cachia A.
        • Rivière D.
        • Mangin J.-F.
        • Bruandet M.
        • Le Bihan D.
        • et al.
        Functional and structural alterations of the intraparietal sulcus in a developmental dyscalculia of genetic origin.
        Neuron. 2003; 40: 847-858
        • Libero L.E.
        • DeRamus T.P.
        • Lahti A.C.
        • Deshpande G.
        • Kana R.K.
        Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates.
        Cortex. 2015; 66: 46-59