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Cerebellar Volume in Autism: Literature Meta-analysis and Analysis of the Autism Brain Imaging Data Exchange Cohort

  • Nicolas Traut
    Correspondence
    Address correspondence to Nicolas Traut, M.Sc., Human Genetics and Cognitive Functions Unit, Institut Pasteur, 25 rue du Dr Roux, Paris 75015, France.
    Affiliations
    Unité de Génétique Humaine et Fonctions Cognitives, Département de Neuroscience, Institut Pasteur, Paris, France

    Neuroscience Paris Seine, Institut de Biologie Paris Seine, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Université Pierre et Marie Curie, Sorbonne Universités, Paris, France

    Genes, Synapses and Cognition, Unité Mixte de Recherche 3571, Centre National de la Recherche Scientifique, Institut Pasteur, Paris, France

    Human Genetics and Cognitive Functions, University Paris Diderot, Sorbonne Paris Cité, Paris, France
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  • Anita Beggiato
    Affiliations
    Unité de Génétique Humaine et Fonctions Cognitives, Département de Neuroscience, Institut Pasteur, Paris, France

    Département de Psychiatrie de l’Enfant et de l’Adolescent, Hôpital Robert Debré, L’Assistance Publique-Hôpitaux de Paris, Paris, France
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  • Thomas Bourgeron
    Affiliations
    Unité de Génétique Humaine et Fonctions Cognitives, Département de Neuroscience, Institut Pasteur, Paris, France

    Genes, Synapses and Cognition, Unité Mixte de Recherche 3571, Centre National de la Recherche Scientifique, Institut Pasteur, Paris, France

    Human Genetics and Cognitive Functions, University Paris Diderot, Sorbonne Paris Cité, Paris, France

    Foundation Fondamentale, Créteil, France
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  • Richard Delorme
    Affiliations
    Unité de Génétique Humaine et Fonctions Cognitives, Département de Neuroscience, Institut Pasteur, Paris, France

    Département de Psychiatrie de l’Enfant et de l’Adolescent, Hôpital Robert Debré, L’Assistance Publique-Hôpitaux de Paris, Paris, France
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  • Laure Rondi-Reig
    Affiliations
    Neuroscience Paris Seine, Institut de Biologie Paris Seine, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Université Pierre et Marie Curie, Sorbonne Universités, Paris, France
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  • Anne-Lise Paradis
    Affiliations
    Neuroscience Paris Seine, Institut de Biologie Paris Seine, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Université Pierre et Marie Curie, Sorbonne Universités, Paris, France
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  • Roberto Toro
    Correspondence
    Roberto Toro, Ph.D., Human Genetics and Cognitive Functions Unit, Institut Pasteur, 25 rue du Dr Roux, Paris 75015, France.
    Affiliations
    Unité de Génétique Humaine et Fonctions Cognitives, Département de Neuroscience, Institut Pasteur, Paris, France

    Genes, Synapses and Cognition, Unité Mixte de Recherche 3571, Centre National de la Recherche Scientifique, Institut Pasteur, Paris, France

    Human Genetics and Cognitive Functions, University Paris Diderot, Sorbonne Paris Cité, Paris, France
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Open AccessPublished:October 10, 2017DOI:https://doi.org/10.1016/j.biopsych.2017.09.029

      Abstract

      Background

      The neuroanatomical bases of autism spectrum disorder remain largely unknown. Among the most widely discussed candidate endophenotypes, differences in cerebellar volume have been often reported as statistically significant.

      Methods

      We aimed at objectifying this possible alteration by performing a systematic meta-analysis of the literature and an analysis of the ABIDE (Autism Brain Imaging Data Exchange) cohort. Our meta-analysis sought to determine a combined effect size of autism spectrum disorder diagnosis on different measures of the cerebellar anatomy as well as the effect of possible factors of variability across studies. We then analyzed the cerebellar volume of 328 patients and 353 control subjects from the ABIDE project.

      Results

      The meta-analysis of the literature suggested a weak but significant association between autism spectrum disorder diagnosis and increased cerebellar volume (p = .049, uncorrected), but the analysis of ABIDE did not show any relationship. The studies meta-analyzed were generally underpowered; however, the number of statistically significant findings was larger than expected.

      Conclusions

      Although we could not provide a conclusive explanation for this excess of significant findings, our analyses would suggest publication bias as a possible reason. Finally, age, sex, and IQ were important sources of cerebellar volume variability, although independent of autism diagnosis.

      Keywords

      Autism spectrum disorder (ASD) affect 1% of the population and is characterized by impairments in social interactions and the variety of interests. Through the years, many reports have suggested that cerebellar abnormalities were implicated in the onset of ASD [reviewed in (
      • Wang S.S.-H.
      • Kloth A.D.
      • Badura A.
      The cerebellum, sensitive periods, and autism.
      )]. The cerebellum exhibits a highly regular arrangement of neurons and connections, supposed to support massive parallel computing capabilities, in particular through long-term synaptic plasticity (
      • D’Angelo E.
      The organization of plasticity in the cerebellar cortex: From synapses to control.
      ,
      • Dean P.
      • Porrill J.
      • Ekerot C.-F.
      • Jörntell H.
      The cerebellar microcircuit as an adaptive filter: Experimental and computational evidence.
      ). It has been traditionally involved in the performance of precise motor behavior, and patients with ASD also present with varying degrees of dyspraxia (
      • Dziuk M.A.
      • Larson J.C.G.
      • Apostu A.
      • Mahone E.M.
      • Denckla M.B.
      • Mostofsky S.H.
      Dyspraxia in autism: Association with motor, social, and communicative deficits.
      ,
      • Åhsgren I.
      • Baldwin I.
      • Goetzinger-Falk C.
      • Erikson A.
      • Flodmark O.
      • Gillberg C.
      Ataxia, autism, and the cerebellum: A clinical study of 32 individuals with congenital ataxia.
      ). There is also growing evidence for involvement of the cerebellum in cognitive and affective functions, which could be impaired in autism (
      • Leiner H.C.
      • Leiner A.L.
      • Dow R.S.
      Cognitive and language functions of the human cerebellum.
      ,
      • Manni E.
      • Petrosini L.
      A century of cerebellar somatotopy: A debated representation.
      ,
      • Rondi-Reig L.
      • Paradis A.-L.
      • Lefort J.M.
      • Babayan B.M.
      • Tobin C.
      How the cerebellum may monitor sensory information for spatial representation.
      ,
      • Schmahmann J.D.
      • Sherman J.C.
      The cerebellar cognitive affective syndrome.
      ).
      The first case report of abnormal cerebellar anatomy in autism was published in 1980 (
      • Williams R.S.
      • Hauser S.L.
      • Purpura D.P.
      • DeLong G.R.
      • Swisher C.N.
      Autism and mental retardation: Neuropathologic studies performed in four retarded persons with autistic behavior.
      ) and described a reduced number of Purkinje cells in the cerebellar vermis of a patient with autism. In 1987, Courchesne et al. (
      • Courchesne E.
      • Hesselink J.R.
      • Jernigan T.L.
      • Yeung-Courchesne R.
      Abnormal neuroanatomy in a nonretarded person with autism: Unusual findings with magnetic resonance imaging.
      ), using in vivo magnetic resonance imaging (MRI), were the first to report a cerebellar abnormality in a patient with ASD. Since then, various studies comparing the volumes and areas of cerebellar subregions between patients with ASD and control subjects have reported significant differences. However, whereas many articles report statistically significant differences, many others fail to detect differences. These discrepancies could be due to many factors, for example, the high heterogeneity in the etiology of ASD, differences in the inclusion criteria across studies, or differences in the eventual comorbidities affecting patients in different groups. The discrepancies could also reveal methodological bias, such as differences in MRI sequences, segmentation protocols, or statistical analyses. Finally, they could also result from chance when small sample sizes lead to noisy estimations of mean volumes.
      Our aim was to objectify the alterations of cerebellar volumes in ASD. In the first part of this article, we present a systematic meta-analysis of the literature, examining the differences across previous reports and determining a combined effect size. In the second part, we describe our analysis of cerebellar volume in the ABIDE (Autism Brain Imaging Data Exchange) cohort (
      • Di Martino A.
      • Yan C.-G.
      • Li Q.
      • Denio E.
      • Castellanos F.X.
      • Alaerts K.
      • et al.
      The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism.
      ) and study the consistency of these results with results from the meta-analysis. Finally, we describe our analyses of the impact of distinct sources of variability, such as sex, age at inclusion, or IQ, on the volume differences between patients with ASD and control subjects.

      Methods and Materials

      Meta-analysis of the Literature

      Collection and Selection of Articles

      We queried PubMed on October 12, 2016, for all articles that met the search criteria “cerebell* AND autis*”. We included articles reporting volumetric or area MRI measurements (mean and standard deviation) on at least one region of the cerebellum for patients with ASD and control subjects (Supplemental Methods).

      Meta-analysis

      We conducted two meta-analyses. The first compared mean cerebellar volume between patients with ASD and control subjects. Effect sizes were computed as standardized mean differences (Cohen’s d) using Hedges’ g as estimator (
      • Hedges L.V.
      Distribution theory for Glass’s estimator of effect size and related estimators.
      ). The second compared the variability of cerebellar volumes between patients and control subjects by computing the log-variance ratio (

      Traut N (2017): Meta-analysis of the variance ratio [published online ahead of print Jan 31]. bioRxiv.

      ). We combined effect sizes using a random-effects model, where the global estimate is obtained as the average of each estimate weighted by the inverse of its variance (
      • Borenstein M.
      • Hedges L.V.
      • Higgins J.P.T.
      • Rothstein H.R.
      Introduction to Meta-Analysis.
      ). The between-study variance τ2 was estimated using the restricted maximum likelihood method, from which we computed the proportion of variance imputable to heterogeneity (I2). To assess the impact of age and IQ on the effect size and thus identify possible sources of heterogeneity, we also conducted a meta-regression with average age and average IQ of patients with ASD as fixed effects (
      • Borenstein M.
      • Hedges L.V.
      • Higgins J.P.T.
      • Rothstein H.R.
      Introduction to Meta-Analysis.
      ).
      We then evaluated publication bias and p-hacking in several ways. Publication bias is the tendency to publish preferentially studies reporting statistically significant results. p-Hacking describes the different methods used to achieve significance in a statistical test, such as flexible choice of covariates, flexible inclusion/exclusion criteria, and selective reporting. First, we calculated the rate of studies showing statistically significant differences between patients and control subjects. We compared this rate with the average statistical power obtained by assuming that the actual effect was equal to the effect estimated after meta-regression. Second, we evaluated the asymmetry of the funnel plot (
      • Light R.J.
      • Pillemer D.B.
      Summing Up: The Science of Reviewing Research, Highlighting edition.
      ) using Egger’s test. In a funnel plot, the x axis represents effect size, and the y axis represents standard error. The interpretation of Egger’s test relies on the assumption that studies with small sample sizes are more affected by publication bias than studies with large sample sizes. Publication bias could then appear as an asymmetric distribution of points in the funnel plot, with an excess of small studies reporting large effect sizes (
      • Egger M.
      • Smith G.D.
      • Schneider M.
      • Minder C.
      Bias in meta-analysis detected by a simple, graphical test.
      ). Finally, we plotted the p curve, which shows the distribution of significant p values (
      • Simonsohn U.
      • Nelson L.D.
      • Simmons J.P.
      P-curve: A key to the file-drawer.
      ). We evaluated p curves for inferred power—the most likely statistical power of the studies to get the observed p curve. Computations were performed using R (https://www.r-project.org) with the packages meta (
      • Schwarzer G.
      meta: An R package for meta-analysis.
      ) and metafor (
      • Viechtbauer W.
      Conducting meta-analyses in R with the metafor package.
      ) along with the p curve app 4.0 (http://www.p-curve.com/app4/). We report statistical significance for an α level of .05. p values were not corrected for multiple comparisons.

      Analysis of ABIDE

      We analyzed data from the ABIDE I project (the original release of ABIDE), which include MRI scans for 539 patients with ASD and 573 control subjects. From the information provided in the ABIDE website, we concluded that there was no overlap between their subject groups and those included in our meta-analysis. Cerebellar volumes were automatically segmented using FreeSurfer 5.1 (https://surfer.nmr.mgh.harvard.edu). We developed a tool for visual quality control of the segmentations (Figure 1 and Supplemental Methods). We included only subjects for whom the segmentation quality was clearly good. Our quality control focused on the cerebellum, but we combined its results with our previous quality control of the whole brain (
      • Lefebvre A.
      • Beggiato A.
      • Bourgeron T.
      • Toro R.
      Neuroanatomical diversity of corpus callosum and brain volume in autism: Meta-analysis, analysis of the autism brain imaging data exchange project, and simulation.
      ) and conserved only the subjects that passed both.
      Figure thumbnail gr1
      Figure 1Quality control (QC) of the cerebellum automatic segmentation. (A) QC tool. For each subject, we evaluated the quality of the segmentation in comparison with the original image in the three different planes. The values of the different measured volumes along with their deviation from the overall mean is represented in the graph at the bottom left. (B) A segmentation evaluated as correct. (C) A segmentation with an excess of unlabeled regions. (D) A segmentation with major labeling errors.
      From the original 1112 subjects, 328 patients (61% of the subjects) and 353 control subjects (62% of the subjects) were retained for further analysis after quality control (see Table 1 for a description of selected subjects by ABIDE site). Among the excluded subjects, 411 subjects did not pass the quality control step, and 20 subjects were excluded because of unavailable full-scale IQ.
      Table 1Demographics of ABIDE Subjects Retained for Statistical Analyses (Allometry and Linear Models)
      Site/InstitutionNASD (F)
      Number of subjects retained/Number of subjects segmented (Number of female subjects retained/Number of female subjects segmented).
      NCtrl (F)
      Number of subjects retained/Number of subjects segmented (Number of female subjects retained/Number of female subjects segmented).
      AgeASD, YearsAgeCtrl, YearsIQASDIQCtrl
      Caltech/California Institute of Technology, California14/19 (3/4)14/19 (4/4)27.4 ± 10.730.1 ± 12.2108 ± 13112 ± 9
      CMU/Carnegie Mellon University, Pennsylvania0/14 (0/3)0/13 (0/3)
      KKI/Kennedy Krieger Institute, Maryland17/22 (3/4)29/33 (9/9)10.1 ± 1.510.1 ± 1.395 ± 16114 ± 9
      Leuven/University of Leuven, Belgium29/29 (3/3)32/35 (5/5)17.8 ± 5.017.8 ± 5.0101 ± 16113 ± 11
      MaxMun/Ludwig Maximilian University Munich, Germany14/24 (2/3)18/33 (4/4)23.8 ± 14.027.0 ± 10.3108 ± 15109 ± 12
      NYU/New York University Langone Medical Center, New York29/79 (5/11)53/105 (17/26)13.3 ± 5.317.1 ± 6.7111 ± 17114 ± 11
      OHSU/Oregon Health and Science University, Oregon11/13 (0/0)15/15 (0/0)11.1 ± 1.910.1 ± 1.1106 ± 22116 ± 11
      Olin/Olin, Institute of Living at Hartford Hospital, Connecticut15/20 (2/3)16/16 (2/2)16.5 ± 3.016.9 ± 3.7110 ± 18115 ± 17
      Pitt/University of Pittsburgh, School of Medicine, Pennsylvania23/30 (3/4)20/27 (4/4)19.4 ± 7.917.4 ± 5.1110 ± 15110 ± 10
      SBL/Netherlands Institute for Neurosciences, Netherlands0/15 (0/0)0/15 (0/0)
      SDSU/San Diego State University, California7/14 (1/1)17/22 (5/6)14.8 ± 1.914.1 ± 2.1111 ± 19109 ± 11
      Stanford/Stanford University, California5/20 (2/4)5/20 (2/4)10.0 ± 1.611.0 ± 2.0108 ± 26121 ± 9
      Trinity/Trinity Centre for Health Sciences, Ireland22/24 (0/0)23/25 (0/0)17.7 ± 3.417.2 ± 3.8109 ± 16112 ± 12
      UCLA/University of California, Los Angeles, California49/62 (6/7)43/47 (6/6)13.1 ± 2.413.0 ± 2.0101 ± 13107 ± 11
      UM/University of Michigan, Michigan16/68 (2/10)6/77 (2/18)13.5 ± 2.516.9 ± 1.1107 ± 20114 ± 5
      USM/University of Utah, School of Medicine, Utah52/58 (0/0)38/43 (0/0)22.6 ± 7.921.8 ± 7.9100 ± 16115 ± 13
      Yale/Yale Child Study Center, Connecticut25/28 (8/8)24/28 (8/8)13.0 ± 3.012.8 ± 2.894 ± 21106 ± 18
      Total328/539 (40/65)353/573 (68/99)16.8 ± 7.516.8 ± 7.4104 ± 17112 ± 12
      Age and IQ data are presented as mean ± SD.
      ABIDE, Autism Brain Imaging Data Exchange; ASD, patients with autism spectrum disorder; Ctrl, control subjects; F, female.
      a Number of subjects retained/Number of subjects segmented (Number of female subjects retained/Number of female subjects segmented).
      Following our previous results showing the nonlinear variation of brain anatomy relative to brain volume (
      • Lefebvre A.
      • Beggiato A.
      • Bourgeron T.
      • Toro R.
      Neuroanatomical diversity of corpus callosum and brain volume in autism: Meta-analysis, analysis of the autism brain imaging data exchange project, and simulation.
      ,
      • Toro R.
      • Perron M.
      • Pike B.
      • Richer L.
      • Veillette S.
      • Pausova Z.
      • Paus T.
      Brain size and folding of the human cerebral cortex.
      ,
      • Toro R.
      • Chupin M.
      • Garnero L.
      • Leonard G.
      • Perron M.
      • Pike B.
      • et al.
      Brain volumes and Val66Met polymorphism of the BDNF gene: Local or global effects?.
      ), we studied the allometric scaling of cerebellar volume. The division of regional volume measurements by total brain volume (normalization) is often used to control for differences in brain volume between groups. This strategy would be appropriate only if the volume of the cerebellum scaled proportionally to total brain volume. We assessed the scaling factor of the cerebellum with total brain volume using a linear regression.
      We evaluated the effect of diagnosis and other factors on total cerebellar volume, cerebellar white matter volume, and cerebellar gray matter volume with two linear models. The first model included group, age, IQ, scanning site, sex, and brain volume as fixed effects; the second model included in addition the interactions of group with age, IQ, scanning site, sex, and brain volume. Because these statistical models were not the same as the models used in the meta-analysis of the literature, we also analyzed the ABIDE data using the same meta-analytical approach as for the literature. In the meta-analysis of ABIDE, the volume estimations of each site were combined using a random-effects model. At each site, we eliminated the minimal number of subjects that would ensure that the age and sex matching were respected, as in the meta-analysis of the literature. We did this iteratively, eliminating one subject after another, until the p values on the differences of age mean, age variance, and sex ratio were each above 0.2. We used JMP Pro 12.1.0 (https://www.jmp.com) for fitting linear models and R version 3.3.1 for the meta-analytical approach. The R scripts that we used for the computation of the meta-analysis are available on the web (https://github.com/neuroanatomy/Cerebellum).

      Results

      Meta-analysis of the Literature

      Selection of Articles

      The PubMed queries (“cerebell* AND autis*”) returned 947 items that were combined with the 124 references cited by a systematic review (
      • Brambilla P.
      • Hardan A.
      • di Nemi S.U.
      • Perez J.
      • Soares J.C.
      • Barale F.
      Brain anatomy and development in autism: Review of structural MRI studies.
      ) and a meta-analysis on cerebellum in ASD (
      • Stanfield A.C.
      • McIntosh A.M.
      • Spencer M.D.
      • Philip R.
      • Gaur S.
      • Lawrie S.M.
      Towards a neuroanatomy of autism: A systematic review and meta-analysis of structural magnetic resonance imaging studies.
      ). We also added two studies (
      • Langen M.
      • Schnack H.G.
      • Nederveen H.
      • Bos D.
      • Lahuis B.E.
      • de Jonge M.V.
      • et al.
      Changes in the developmental trajectories of striatum in autism.
      ,
      • Evans D.W.
      • Lazar S.M.
      • Boomer K.B.
      • Mitchel A.D.
      • Michael A.M.
      • Moore G.J.
      Social cognition and brain morphology: Implications for developmental brain dysfunction.
      ) that we found by other means. We selected studies in two steps, first based on their titles and abstracts and then based on their full text (see Supplemental Figure S1 for the Preferred Reporting Items for Systematic Reviews and Meta-Analyses workflow). Our final analyses were based on 30 articles covering seven different regions of interest. The three most reported measures were total cerebellar volume (1050 subjects in total), vermal lobules VI–VII area (965 subjects in total), and vermal lobules I–V area (861 subjects in total) (see Table 2 for a description of the selected articles).
      Table 2Studies Included in Meta-analysis
      StudyNASD (F)NCtrl (F)AgeASD, YearsAgeCtrl, YearsIQASDIQCtrlRegions of Interest
      Hodge et al., 2010
      • Hodge S.M.
      • Makris N.
      • Kennedy D.N.
      • Caviness V.S.J.
      • Howard J.
      • McGrath L.
      • et al.
      Cerebellum, language, and cognition in autism and specific language impairment.
      22 (0)11 (0)9.4 ± 2.010.4 ± 2.787 ± 22114 ± 11Cb, WM, GM, vermis, I–V, VI–VII, VIII–X
      Scott et al., 2009
      • Scott J.A.
      • Schumann C.M.
      • Goodlin-Jones B.L.
      • Amaral D.G.
      A comprehensive volumetric analysis of the cerebellum in children and adolescents with autism spectrum disorder.
      48 (0)14 (0)12.4 ± 3.112.5 ± 3.179 ± 23113 ± 12Cb, vermis, I–V, VI–VII, VIII–X
      Hallahan et al., 2009
      • Hallahan B.
      • Daly E.M.
      • McAlonan G.
      • Loth E.
      • Toal F.
      • O’Brien F.
      • et al.
      Brain morphometry volume in autistic spectrum disorder: A magnetic resonance imaging study of adults.
      114 (18)60 (7)31.9 ± 10.132.0 ± 9.097 ± 18114 ± 12Cb
      Webb et al., 2009
      • Webb S.J.
      • Sparks B.-F.
      • Friedman S.D.
      • Shaw D.W.
      • Giedd J.
      • Dawson G.
      • Dager S.R.
      Cerebellar vermal volumes and behavioral correlates in children with autism spectrum disorder.
      45 (7)26 (8)3.9 ± 0.33.9 ± 0.559 ± 21115 ± 15
      IQ for normal control group supposed to be 115 ± 15.
      Cb, vermis, I–V, VI–VII, VIII–X
      Langen et al., 2009
      • Langen M.
      • Schnack H.G.
      • Nederveen H.
      • Bos D.
      • Lahuis B.E.
      • de Jonge M.V.
      • et al.
      Changes in the developmental trajectories of striatum in autism.
      99 (8)89 (7)12.9 ± 4.512.4 ± 4.8108 ± 14110 ± 13Cb
      Lahuis et al., 2008
      • Lahuis B.E.
      • Durston S.
      • Nederveen H.
      • Zeegers M.
      • Palmen S.J.
      • Van Engeland H.
      MRI-based morphometry in children with multiple complex developmental disorder, a phenotypically defined subtype of pervasive developmental disorder not otherwise specified.
      21 (0)21 (0)11.1 ± 2.210.4 ± 1.8107 ± 14103 ± 15Cb
      Cleavinger et al., 2008
      • Cleavinger H.B.
      • Bigler E.D.
      • Johnson J.L.
      • Lu J.
      • McMahon W.
      • Lainhart J.E.
      Quantitative magnetic resonance image analysis of the cerebellum in macrocephalic and normocephalic children and adults with autism.
      28 (0)16 (0)13.9 ± 5.313.9 ± 5.499 ± 18102 ± 14Cb, WM, GM, vermis, I–V, VI–VII, VIII–X
      Catani et al., 2008
      • Catani M.
      • Jones D.K.
      • Daly E.
      • Embiricos N.
      • Deeley Q.
      • Pugliese L.
      • et al.
      Altered cerebellar feedback projections in Asperger syndrome.
      15 (0)16 (0)31.0 ± 9.035.0 ± 11.0109 ± 17120 ± 21Cb
      Bloss and Courchesne, 2007
      • Bloss C.S.
      • Courchesne E.
      MRI neuroanatomy in young girls with autism: A preliminary study.
      9 (9)14 (14)3.7 ± 0.93.8 ± 1.183 ± 18119 ± 13Cb, WM, GM
      Hazlett et al., 2005
      • Hazlett H.C.
      • Poe M.
      • Gerig G.
      • Smith R.G.
      • Provenzale J.
      • Ross A.
      • et al.
      Magnetic resonance imaging and head circumference study of brain size in autism: Birth through age 2 years.
      51 (5)14 (4)2.7 ± 0.32.4 ± 0.454 ± 9108 ± 19Cb, WM, GM
      Palmen et al., 2004
      • Palmen S.J.
      • Hulshoff Pol H.E.
      • Kemner C.
      • Schnack H.G.
      • Janssen J.
      • Kahn R.S.
      • van Engeland H.
      Larger brains in medication naive high-functioning subjects with pervasive developmental disorder.
      21 (2)21 (1)20.1 ± 3.120.3 ± 2.2115 ± 19113 ± 10Cb
      Kates et al., 2004
      • Kates W.R.
      • Burnette C.P.
      • Eliez S.
      • Strunge L.A.
      • Kaplan D.
      • Landa R.
      • et al.
      Neuroanatomic variation in monozygotic twin pairs discordant for the narrow phenotype for autism.
      9 (1)16 (2)7.6 ± 2.48.3 ± 2.470 ± 19124 ± 10Cb, WM, GM
      Akshoomoff et al., 2004
      • 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.
      52 (0)15 (0)3.8 ± 0.83.6 ± 1.182 ± 24108 ± 18
      Mean and/or SD extrapolated from minimum and maximum.
      I–V, VI–VII
      Herbert et al., 2003
      • Herbert M.R.
      • Ziegler D.A.
      • Deutsch C.K.
      • O’Brien L.M.
      • Lange N.
      • Bakardjiev A.
      • et al.
      Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys.
      17 (0)15 (0)9.0
      Mean and/or SD extrapolated from minimum and maximum.
      ± 1.4
      Mean and/or SD extrapolated from minimum and maximum.
      9.0
      Mean and/or SD extrapolated from minimum and maximum.
      ± 1.4
      Mean and/or SD extrapolated from minimum and maximum.
      100 ± 15
      IQ for high-functioning autism group supposed to be 100 ± 15.
      115 ± 15
      IQ for normal control group supposed to be 115 ± 15.
      Cb
      Kaufmann et al., 2003
      • Kaufmann W.E.
      • Cooper K.L.
      • Mostofsky S.H.
      • Capone G.T.
      • Kates W.R.
      • Newschaffer C.J.
      • et al.
      Specificity of cerebellar vermian abnormalities in autism: A quantitative magnetic resonance imaging study.
      10 (0)22 (0)6.9 ± 2.48.3 ± 1.966 ± 14121 ± 9Vermis, I–V, VI–VII, VIII–X
      Pierce and Courchesne, 2001
      • Pierce K.
      • Courchesne E.
      Evidence for a cerebellar role in reduced exploration and stereotyped behavior in autism.
      14 (2)14 (4)3.8 ± 1.14.4 ± 1.284 ± 24110 ± 12I–V, VI–VII
      Courchesne et al., 2001
      • Courchesne E.
      • Karns C.M.
      • Davis H.R.
      • Ziccardi R.
      • Carper R.A.
      • Tigue Z.D.
      • et al.
      Unusual brain growth patterns in early life in patients with autistic disorder: An MRI study.
      60 (0)52 (0)6.2 ± 3.58.1 ± 3.579 ± 26115
      Mean and/or SD extrapolated from minimum and maximum.
      ± 15
      Mean and/or SD extrapolated from minimum and maximum.
      Cb, WM, GM
      Hardan et al., 2001
      • Hardan A.Y.
      • Minshew N.J.
      • Harenski K.
      • Keshavan M.S.
      Posterior fossa magnetic resonance imaging in autism.
      22 (0)
      Cerebellar volume was estimated for only 16 patients with ASD and 19 control subjects in Hardan et al. (46).
      22 (0)
      Cerebellar volume was estimated for only 16 patients with ASD and 19 control subjects in Hardan et al. (46).
      22.4 ± 10.122.4 ± 10.0100 ± 15100 ± 14Vermis, I–V, VI–VII, VIII–X, Cb
      Elia et al., 2000
      • Elia M.
      • Ferri R.
      • Musumeci S.A.
      • Panerai S.
      • Bottitta M.
      • Scuderi C.
      Clinical correlates of brain morphometric features of subjects with low-functioning autistic disorder.
      22 (0)11 (0)10.9 ± 4.010.9 ± 2.955 ± 10
      IQ for low-functioning autism group supposed to be 55 ± 10.
      115 ± 15
      IQ for normal control group supposed to be 115 ± 15.
      Vermis, VI–VII
      Carper and Courchesne, 2000
      • Carper R.A.
      • Courchesne E.
      Inverse correlation between frontal lobe and cerebellum sizes in children with autism.
      42 (0)29 (0)5.4 ± 1.76.0 ± 1.880 ± 22114 ± 12VI–VII
      Levitt et al., 1999
      • Levitt J.G.
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      Cerebellar vermis lobules VIII-X in autism.
      8 (NR)21 (NR)12.5 ± 2.212.0 ± 2.883 ± 12115 ± 11VIII–X
      Piven et al., 1997
      • Piven J.
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      An MRI study of autism: The cerebellum revisited.
      35 (9)36 (16)18.0 ± 4.520.2 ± 3.891 ± 20102 ± 13Cb
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      Cerebellar hypoplasia and frontal lobe cognitive deficits in disorders of early childhood.
      9 (4)10 (3)16.8 ± 5.2
      Mean and/or SD extrapolated from minimum and maximum.
      16.6 ± 5.4
      Mean and/or SD extrapolated from minimum and maximum.
      93
      Mean and/or SD extrapolated from minimum and maximum.
      ± 13
      Mean and/or SD extrapolated from minimum and maximum.
      119
      Mean and/or SD extrapolated from minimum and maximum.
      ± 10
      Mean and/or SD extrapolated from minimum and maximum.
      I–V, VI–VII
      Hashimoto et al., 1995
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      96 (24)112 (47)6.4 ± 4.97.2 ± 5.260 ± 2599 ± 18Vermis, I–V, VI–VII, VIII–X
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      Abnormality of cerebellar vermian lobules VI and VII in patients with infantile autism: Identification of hypoplastic and hyperplastic subgroups with MR imaging.
      50 (9)53 (10)16.5 ± 11.6
      Mean and/or SD extrapolated from minimum and maximum.
      18.8 ± 10.4
      Mean and/or SD extrapolated from minimum and maximum.
      81
      Mean and/or SD extrapolated from minimum and maximum.
      ± 31
      Mean and/or SD extrapolated from minimum and maximum.
      115 ± 15
      IQ for normal control group supposed to be 115 ± 15.
      I–V, VI–VII
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      15 (0)15 (0)27.7 ± 10.728.8 ± 5.692 ± 23130 ± 0I–V, VI–VII
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      Magnetic resonance imaging of the posterior fossa in autism.
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      • Garber H.J.
      • Ritvo E.R.
      Magnetic resonance imaging of the posterior fossa in autistic adults.
      12 (3)12 (4)27.2 ± 5.326.4 ± 3.695 ± 15
      IQ for medium- to high-functioning autism group supposed to be 95 ± 15.
      115 ± 15
      IQ for normal control group supposed to be 115 ± 15.
      Vermis, I–V, VI–VII
      Kleiman et al., 1992
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      13 (3)17 (8)7.7 ± 5.77.0 ± 4.3
      Mean and/or SD extrapolated from minimum and maximum.
      52 ± 17115 ± 15
      IQ for normal control group supposed to be 115 ± 15.
      I–V, VI–VII
      Ritvo et al., 1988
      • Ritvo E.R.
      • Garber H.J.
      • Reiss A.L.
      • Garcia-Bunuel L.
      • Hayden C.T.
      • Lichtenberg P.S.
      • et al.
      Cerebellar hypoplasia and autism.
      15 (4)15 (4)11.6 ± 4.111.6 ± 4.175 ± 15115 ± 15
      IQ for normal control group supposed to be 115 ± 15.
      Vermis, I–V, VI–VII
      Age and IQ data are presented as mean ± SD. Regions of interest include volumes (Cb, WM, and GM) and midsagittal areas (whole vermis and its lobules numbered I–V, anterior; VI–VII, superior-posterior; and VIII–X, inferior-posterior).
      ASD, patients with autism spectrum disorder; Cb, cerebellum; Ctrl, normal control subjects; F, female; GM, cerebellum gray matter; NR, not reported; WM, cerebellum white matter.
      a IQ for normal control group supposed to be 115 ± 15.
      b Mean and/or SD extrapolated from minimum and maximum.
      c IQ for high-functioning autism group supposed to be 100 ± 15.
      d Cerebellar volume was estimated for only 16 patients with ASD and 19 control subjects in Hardan et al.
      • Hardan A.Y.
      • Minshew N.J.
      • Harenski K.
      • Keshavan M.S.
      Posterior fossa magnetic resonance imaging in autism.
      .
      e IQ for low-functioning autism group supposed to be 55 ± 10.
      f IQ for medium- to high-functioning autism group supposed to be 95 ± 15.

      Mean Effect Size

      Significant mean effects were found for three of the seven regions studied: total cerebellum (p = .049), white matter (p = .011), and vermal lobules VI–VII (p = .022) (Table 3). Compared with control subjects, patients with ASD displayed larger cerebellar white matter volume (Cohen’s d = 0.31, 95% confidence interval [CI] [0.07, 0.55]) and smaller areas for vermal lobules VI–VII (Cohen’s d = −0.24, 95% CI [−0.44, −0.03]). The effect of diagnosis on total cerebellar volume was barely statistically significant (Cohen’s d = 0.23, 95% CI [0.00, 0.45]) (see forest plot in Figure 2A).
      Table 3Standardized Mean Difference: Combination of Effect Sizes
      RegionNumber of StudiesRandom-Effects ModelMeta-regression
      Effect Size (Cohen’s d)p ValueHeterogeneity (I2)Age Impact (Year−1)IQ Impact (IQ−1)Residual Heterogeneity (I2)
      Meta-analysis of Literature
       Cerebellum160.23 (0.00, 0.45)
      p < .05.
      .049
      p < .05.
      66% (42%, 80%)−0.026 (−0.057, 0.005)0.014 (−0.002, 0.030)57%, p = .002
      p < .05.
       White Matter60.31 (0.07, 0.55)
      p < .05.
      .011
      p < .05.
      19% (0%, 64%)−0.153 (−0.287, −0.019)
      p < .05.
      0.042 (0.007, 0.077)
      p < .05.
      0%, p = .943
       Gray Matter6−0.22 (−0.47, 0.03).08516% (0%, 79%)0.152 (0.020, 0.284)
      p < .05.
      −0.033 (−0.067, 0.002)0%, p = .837
       Whole vermis11−0.09 (−0.30, 0.13).41621% (0%, 60%)0.011 (−0.039, 0.062)0.009 (−0.010, 0.029)0%, p = .657
       Lobules I–V160.00 (−0.22, 0.23).97154% (20%, 74%)
      p < .05.
      −0.019 (−0.054, 0.016)0.020 (0.003, 0.038)29%, p = .061
       Lobules VI–VII18−0.24 (−0.44, −0.03)
      p < .05.
      .022
      p < .05.
      50% (14%, 71%)
      p < .05.
      0.009 (−0.024, 0.041)0.010 (−0.006, 0.026)36%, p = .039
      p < .05.
       Lobules VIII–X9−0.20 (−0.50, 0.10).19256% (8%, 79%)
      p < .05.
      −0.043 (−0.167, 0.080)0.008 (−0.038, 0.055)65%, p = .013
      p < .05.
      Meta-analysis of ABIDE
       Cerebellum130.06 (−0.12, 0.24).53233% (0%, 65%)0.003 (−0.045, 0.052)0.007 (−0.032, 0.046)38%, p = .063
       White Matter130.01 (−0.15, 0.17).9245% (0%, 59%)−0.014 (−0.051, 0.023)−0.003 (−0.033, 0.027)0%, p = .296
       Gray Matter130.06 (−0.11, 0.23).49731% (0%, 64%)0.006 (−0.041, 0.053)0.009 (−0.029, 0.047)34%, p = .074
      Values in parentheses represent 95% confidence intervals.
      ABIDE, Autism Brain Imaging Data Exchange.
      a p < .05.
      Figure thumbnail gr2
      Figure 2Forest plots for the cerebellar volume. For each study, the gray square is centered on the estimated standardized mean difference, the black segment illustrates the 95% confidence interval (CI), and the surface of the square is proportional to the number of subjects in the study. Standardized mean difference is positive when the cerebellar volume is greater in autism spectrum disorder. W(random) represents the weight given to each study for the combination of effect sizes. (A) Meta-analysis of the literature. Heterogeneity: I2 = 65.9%, τ2 = 0.1235, p = .0001. (B) Meta-analysis of ABIDE (Autism Brain Imaging Data Exchange) sites. Heterogeneity: I2 = 32.6%, τ2 = 0.0192, p = .1219.

      Heterogeneity and Meta-regression

      A statistically significant heterogeneity was found for four of the seven regions under study: total cerebellar volume (p = .0001), vermal lobules I–V area (p = .0049), vermal lobules VI–VII area (p = .0081), and vermal lobules VIII–X area (p = .019). Despite this high heterogeneity, our meta-regression did not show a significant impact of age or IQ on total cerebellar volume. The age of patients with ASD correlated with a reduced volume of cerebellar white matter (p = .025) and an increased volume of cerebellar gray matter (p = .024) compared with control subjects. However, the number of studies included in the meta-regression may be insufficient to obtain reliable results. The IQ of patients with ASD correlated with increased volume of the cerebellar white matter (p = .0176) and increased area of vermal lobules I–V (p = .019). Age and IQ did not seem to be the only factors producing heterogeneity: after the meta-regression, residual heterogeneity was still statistically significant for total cerebellar volume (p = .0020), vermal lobules VI–VII area (p = .039), and vermal lobules VIII–X area (p = .013). Table 3 shows the results of the random-effects models and meta-regressions for the cerebellum regions, and Supplemental Figure S2 shows the observed effect sizes versus the expected effect from the meta-regression on total cerebellar volume.

      Statistical Power

      Despite a small mean effect size estimated at Cohen’s d = 0.23 for the total cerebellar volume, 44% of the studies reported a significant result. If the actual effect size were fixed at this value, the mean statistical power for all the studies would be only 14%—that is, only 14% chances of detecting such a small effect size. The heterogeneity in age and IQ across studies appeared to limit the statistical power: mean achieved statistical power increased to 20% when taking into account the variations induced by age and IQ estimated by the meta-regression (the value is, however, still much lower than the observed 44% rate of detection, which agrees with the fact that much of the heterogeneity was not explained).

      Publication Bias and p-Hacking

      Egger’s test detected a statistically significant funnel plot asymmetry only for the whole vermis area (p = .002). None of the remaining regions presented a significant funnel plot asymmetry (Figure 3).
      Figure thumbnail gr3
      Figure 3Funnel plots for the different cerebellar regions. The dotted line represents the meta-analytic effect size, whereas the two dashed lines represent the boundaries of the 95% confidence interval for effect combined with the fixed effect model. *statistically significant asymmetry of the funnel plot (p = .002). GM, gray matter; WM, white matter.
      Our analyses of the p curves were not conclusive on the presence of p-hacking. In five of the seven regions studied, there were only two or three p values < .05, which resulted in very imprecise estimations with wide confidence intervals. Total cerebellar volume and area of lobules VI–VII each had seven p values < .05, which still produced unreliable estimations. p curve analyses suggested low statistical power for both regions (total cerebellum, 22%, 95% CI [5%, 67%], and lobules VI–VII, 38%, 95% CI [6%, 78%]). The statistical powers inferred from the p curves were not incompatible with the respective rates of significant studies, but again, confidence intervals were very wide. Supplemental Table S1 summarizes the results for the different publication bias and p-hacking analyses. Figure 4 shows the observed p curve for the total cerebellar volume.
      Figure thumbnail gr4
      Figure 4p curve of studies with a significant result for the cerebellar volume. Note the observed p curve includes seven studies with statistically significant results (p < .05). Among them, five had p < .025. Eight additional studies were excluded from the p curve analysis because their results did not pass significance (p > .05). Null of, expected p curve in case of.

      Meta-analysis of Variability (Log-Variance Ratio)

      A barely statistically significant (uncorrected) effect was found for vermal lobules VIII–X area, suggesting larger volume variations for the ASD groups (p = .049). No significant effect was found for any other region. Heterogeneity was statistically significant only for the total cerebellar volume (p = .0052, uncorrected). As for the meta-analysis of mean differences, taking into account age and IQ in the meta-regression did not significantly reduce the heterogeneity (Supplemental Table S2).

      Analysis of ABIDE Data

      Allometry

      The scaling between cerebellar volume and brain volume was not isometric, i.e., the volume of the cerebellum was not directly proportional to brain volume. Indeed, the scaling factor was estimated at 0.518 (95% CI [0.457, 0.578]), showing that large brains have a proportionally smaller cerebellum (Figure 5). This replicates a recent result by Mankiw et al. (
      • Mankiw C.
      • Park M.T.M.
      • Reardon P.K.
      • Fish A.M.
      • Clasen L.S.
      • Greenstein D.
      • et al.
      Allometric analysis detects brain size-independent effects of sex and sex chromosome complement on human cerebellar organization.
      ). Because of this nonproportional relationship, the normalization of cerebellar volumes by total brain volume should not be used to control for group differences. Instead, we used brain volume as covariate in our linear models.
      Figure thumbnail gr5
      Figure 5Log of cerebellar volume (Cb) vs. log of total brain volume (BV).

      Linear Model

      A very significant site effect was found for each volume; this effect was also different between patients and control subjects for total cerebellar volume and cerebellum gray matter volume. The effect of diagnosis group for the total cerebellar volume was not significant (estimated at −0.59 cm3, Cohen’s d = −0.04, 95% CI [−0.16, 0.09], in the first model—including group, age, IQ, scanning site, sex, and brain volume as fixed effects; estimated at −1.22 cm3, Cohen’s d = −0.08, 95% CI [−0.27, 0.11], in the second model—including in addition the interactions of group with age, IQ, sex, brain volume, and scanning site). These estimations were more precise than in the meta-analysis of the literature, with narrower confidence intervals. Despite this, we did not find any significant difference between patients and control subjects. We did not find any impact of age, sex, IQ, or brain volume on cerebellar volume that would differ by diagnosis group. The analysis of cerebellar subregions (white and gray matter volumes) did not reveal a statistically significant group effect. See Supplemental Table S3 for the results of the linear model with group as main effect and Supplemental Table S4 for the results model including the interaction of group with the other variables.

      ABIDE Meta-analysis

      The results of the analyses using the meta-analytical approach agreed with the results of the direct fit of linear models. Despite a smaller number of subjects than in the meta-analysis of the literature (Supplemental Table S5), the confidence intervals for the combined mean differences were narrower. This was due to a smaller estimated between-study variance among ABIDE sites compared with that among articles in the literature. We did not find a statistically significant standard mean difference between patients and control subjects: Cohen’s d = 0.06, 95% CI (−0.12, 0.24). In the meta-regression, no significant effect was found for age (p = .888) or IQ (p = .726). Supplemental Table S5 describes the characteristics of the subjects selected for the preservation of age and sex matching between patients and control subjects. Figure 2B shows the forest plot of ABIDE sites combined with the random-effects model. The meta-analytical approach did not show any statistically significant difference in the variability of volume measures between patients and control subjects (log-variance ratio = 0.17, 95% CI [−0.18, 0.52]). The results of the ABIDE meta-analysis on standardized mean difference are summarized in Table 3, and Supplemental Table S6 summarizes the results of the ABIDE meta-analysis on log-variance ratio.

      Discussion

      Neuroanatomical diversity appears to account for a substantial proportion of the risk for ASD (
      • Sabuncu M.R.
      • Ge T.
      • Holmes A.J.
      • Smoller J.W.
      • Buckner R.L.
      • Fischl B.
      • et al.
      Morphometricity as a measure of the neuroanatomical signature of a trait.
      ). However, and even though several candidate neuroanatomical biomarkers have been proposed, it is not yet clear exactly which neuroanatomical traits more strongly influence diagnosis. In this report, we looked at one specific structure, the cerebellum, that has been widely discussed in the literature. We performed a meta-analysis of the literature and an analysis of data from the ABIDE project.
      The meta-analysis of the literature did not show conclusive evidence for a difference between patients with ASD and control subjects either for the total cerebellar volume or for its subregions. Total cerebellar volume and cerebellar white matter volume appeared slightly larger in ASD, whereas the area of vermal lobules VI–VII was found to be slightly smaller in ASD, but the significance of these results did not survive correction for multiple comparisons. Compared with a previous meta-analysis on total cerebellar volume and vermal areas (
      • Stanfield A.C.
      • McIntosh A.M.
      • Spencer M.D.
      • Philip R.
      • Gaur S.
      • Lawrie S.M.
      Towards a neuroanatomy of autism: A systematic review and meta-analysis of structural magnetic resonance imaging studies.
      ), the effect sizes we computed were all smaller in absolute value. Specifically, Stanfield et al. (
      • Stanfield A.C.
      • McIntosh A.M.
      • Spencer M.D.
      • Philip R.
      • Gaur S.
      • Lawrie S.M.
      Towards a neuroanatomy of autism: A systematic review and meta-analysis of structural magnetic resonance imaging studies.
      ) reported a significant difference in the volume of vermal lobules VIII–X. In our analysis, despite the fact that a larger number of studies was taken into account, we did not replicate their finding.
      The combined effect sizes in the meta-analysis of the literature were small in general. The number of articles that reported statistically significant results was larger than expected given the mean achieved power. We investigated whether this excess could be due to publication bias or p-hacking. Publication bias occurs when studies with statistically significant results have higher chances to be published than studies without statistically significant results. This is more likely to happen when the finding reported is central to the hypothesis made by an article. However, the main focus of many of the articles in our meta-analysis was not cerebellar volume, which should decrease the likelihood of bias. We studied publication bias by analyzing the asymmetry of the funnel plot using Egger’s test. This type of analysis is not very sensitive and is able to detect only strong publication bias. We observed statistically significant funnel plot asymmetry only for the vermis area (uncorrected). Whereas this result suggests the presence of publication bias, it could also be due to greater interstudy heterogeneity in this specific measurement (where a few large studies reported negative effect sizes, whereas the majority of the other studies reported positive effect sizes).
      We aimed at testing for p-hacking by analyzing the p curves: the distribution of p values < .05. The numbers of significant p values reported, however, were not sufficient to draw a definite conclusion. In the case of the total cerebellar volume, where seven of 16 articles reported statistically significant findings, p curve analysis did not reveal evidence for p-hacking. Overall, the analysis of the literature alone did not allow us to provide a definitive explanation for the excess of statistically significant findings.
      One result that appeared very clearly was the strong heterogeneity of the findings in the literature, even compared with the multisite data from the ABIDE project. For cerebellar volume, p value for heterogeneity was .0001, which would remain significant even after correction for multiple testing. A certain degree of variability in the estimations of volume is expected, especially given the small sample sizes used. Heterogeneity tests aim at detecting a degree of variability that would go beyond this expectation. We found statistically significant heterogeneity for the estimations of four of the seven meta-analyzed regions (i.e., total cerebellar volume, lobules I–V, lobules VI–VII, and lobules VIII–IX) (Table 3). Heterogeneity may be due to a combination of technical and physiological causes; for example, differences in MRI equipment, acquisition sequences, and segmentation protocol as well as the age of the subjects, IQ distribution, and so forth. We analyzed the effect of two of these factors, age and IQ, using meta-regression. First, our analysis did not reveal a differential effect of age or IQ on cerebellar volume for patients and control subjects. Second, residual heterogeneity was still statistically significant for three of the seven regions studied (total cerebellar volume, lobules VI–VII, and lobules VIII–IX). This indicates that sources other than age and IQ level may be causing significant heterogeneity in the literature.
      The ABIDE project data provide a very interesting point of comparison for previous findings in the literature. The subjects in ABIDE come in most cases from research projects that had already been published and thus should be of similar characteristics as those in our literature meta-analysis. However, because the raw MRI and behavioral data are available, there is no issue of publication bias or p-hacking having an effect on cohort selection. Additionally, the availability of raw data makes it possible to run methodologically homogeneous analyses: same segmentation protocol, quality control procedures, and statistical analyses. There still remain, of course, many additional sources of heterogeneity owing to the grassroots nature of the project (some of them being currently addressed in ABIDE II through an important harmonization effort). Overall, the analyses of ABIDE data should provide a more precise, less heterogeneous estimation. The availability of raw data makes it also possible to engage a community effort to assess the impact of different methodological choices on the same dataset.
      We analyzed all 1112 subjects from ABIDE using validated automatic computational neuroanatomy tools. After quality control and additional inclusion criteria, we retained a group of 681 subjects. We had 85% power to detect the Cohen’s d = 0.23 effect obtained from our meta-analysis of the literature (two-sided t test, α = .05). We did not find any statistically significant effect of ASD diagnosis on mean volumes. Our statistical analysis here was a linear model including group, age, sex, IQ, and site as main effects, or additionally the interaction between group and the other covariates. Although the absence of group effect was clear, we repeated our analyses using the same meta-analytical procedure used to study the literature to rule out an eventual methodological artifact. Every ABIDE site was considered as a different source of data, and we computed a meta-analytical effect size using a random-effects model weighting of each site’s estimations by the inverse of the variance (thus giving more weight to sites with larger sample sizes, as in the case of the literature meta-analysis). Our meta-analysis was in agreement with the results of our linear models: a clear absence of group differences.
      Interestingly, and although the site effect was substantial, the estimations from the ABIDE data were more precise than the estimations from the literature (tighter confidence intervals) and less heterogeneous. One reason for the lack of power in the literature (in addition to the small sample sizes) could be the significant heterogeneity across studies. This may be an important source of discordant reports and makes it more difficult to draw conclusions from the literature. The public availability of raw data should greatly enhance our ability to understand neuroanatomical variability and increase our chances of detecting reliable neuroimaging phenotypes for neurodevelopmental disorders such as autism. Toward this aim, all our analysis scripts, our software for quality control, and the list of subjects we included have been made openly available on the Web to facilitate the replication, critical appraisal, and extension of our current results (https://github.com/neuroanatomy/Cerebellum).
      In conclusion, we did not find evidence, either in the literature or in the ABIDE cohort, for a difference in cerebellar volume between patients with ASD and control subjects. This result does not rule out possible involvement of the cerebellum in the etiology of ASD. In particular, ABIDE includes only subjects without intellectual disability, and a difference could still appear in a population with a wider IQ range (although the meta-analysis of the literature did not suggest such a link). Also, it is possible that some other measurement of cerebellar anatomy, more sophisticated than mere volume measurements, may be linked to autism in the future. However, our current results do not provide evidence to justify a specific focus on the study of the cerebellum instead of any other brain structure. We reached a similar conclusion after analyzing the corpus callosum (
      • Lefebvre A.
      • Beggiato A.
      • Bourgeron T.
      • Toro R.
      Neuroanatomical diversity of corpus callosum and brain volume in autism: Meta-analysis, analysis of the autism brain imaging data exchange project, and simulation.
      ), another structure that had traditionally captured the attention of the research community. Based on these experiences, we can advocate only for a broad analysis of all neuroanatomical phenotypes available. For this effort to be successful, our community needs to continue developing the data-sharing initiatives that will allow us to increase statistical power, decrease heterogeneity, and avoid the biases that prevent researchers from benefiting from the work of each other.

      Acknowledgments and Disclosures

      This work was supported by the Fondation de France project “Développement du plissement cortical dans les troubles du spectre autistique: caractérisation morphométrique et génétique” (NT, AB, TB, RD, RT). The Institut Pasteur group (NT, AB, TB, RD, RT) was funded by the European Research Area Networks Network of European Funding for Neuroscience Research Cofund Programme under “Horizon 2020” (SynPathy project), Institut Pasteur, Bettencourt-Schueller Foundation, Centre National de la Recherche Scientifique, University Paris Diderot, Agence Nationale de la Recherche programme “Investissements d’avenir,” Laboratoire d'excellence de Biologie pour la Psychiatrie (BioPsy; reference: ANR-11-IDEX-0004-02), Laboratoire d'excellence en Génomique Médicale, Conny-Maeva Charitable Foundation, Cognacq Jay Foundation, Orange Foundation, and Fondamental Foundation. The Neuroscience Paris Seine group (NT, LR-R, A-LP) is a member of BioPsy and École de Neuroscience de Paris foundation.
      The authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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