Hostname: page-component-cb9f654ff-fg9bn Total loading time: 0 Render date: 2025-08-02T20:57:44.366Z Has data issue: false hasContentIssue false
  • English
  • Français

Shared atypical brain anatomy and intrinsic functional architecture in male youth with autism spectrum disorder and their unaffected brothers

Published online by Cambridge University Press:  09 November 2016

H.-Y. Lin ,
W.-Y. I. Tseng ,
M.-C. Lai ,
Y.-T. Chang  and
S. S.-F. Gau
H.-Y. Lin
Affiliation:
Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
W.-Y. I. Tseng
Affiliation:
Institute of Medical Devices and Imaging System, National Taiwan University College of Medicine, Taipei, Taiwan Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan
M.-C. Lai
Affiliation:
Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan Department of Psychiatry, Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health and The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, UK
Y.-T. Chang
Affiliation:
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
S. S.-F. Gau*
Affiliation:
Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan
*
*Address for correspondence: S. S.-F. Gau, Department of Psychiatry, National Taiwan University Hospital and College of Medicine, No. 7, Chung-Shan South Road, Taipei, 10002, Taiwan. (Email: gaushufe@ntu.edu.tw)
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder, yet the search for definite genetic etiologies remains elusive. Delineating ASD endophenotypes can boost the statistical power to identify the genetic etiologies and pathophysiology of ASD. We aimed to test for endophenotypes of neuroanatomy and associated intrinsic functional connectivity (iFC) via contrasting male youth with ASD, their unaffected brothers and typically developing (TD) males.

Method

The 94 participants (aged 9–19 years) – 20 male youth with ASD, 20 unaffected brothers and 54 TD males – received clinical assessments, and undertook structural and resting-state functional magnetic resonance imaging scans. Voxel-based morphometry was performed to obtain regional gray and white matter volumes. A seed-based approach, with seeds defined by the regions demonstrating atypical neuroanatomy shared by youth with ASD and unaffected brothers, was implemented to derive iFC. General linear models were used to compare brain structures and iFC among the three groups. Assessment of familiality was investigated by permutation tests for variance of the within-family pair difference.

Results

We found that atypical gray matter volume in the mid-cingulate cortex was shared between male youth with ASD and their unaffected brothers as compared with TD males. Moreover, reduced iFC between the mid-cingulate cortex and the right inferior frontal gyrus, and increased iFC between the mid-cingulate cortex and bilateral middle occipital gyrus were the shared features of male ASD youth and unaffected brothers.

Conclusions

Atypical neuroanatomy and iFC surrounding the mid-cingulate cortex may be a potential endophenotypic marker for ASD in males.

Keywords

Autism spectrum disorderendophenotypesintrinsic functional connectivityresting-state functional magnetic resonance imagingunaffected siblings

Information

Type
Original Articles
Information
Psychological Medicine , Volume 47 , Issue 4 , March 2017 , pp. 639 - 654
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Amaral, DG, Schumann, CM, Nordahl, CW (2008). Neuroanatomy of autism. Trends in Neurosciences 31, 137–145.Google Scholar
Ashburner, J (2007). A fast diffeomorphic image registration algorithm. NeuroImage 38, 95–113.Google Scholar
Baron-Cohen, S, Wheelwright, S, Skinner, R, Martin, J, Clubley, E (2001). The Autism-Spectrum Quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders 31, 5–17.Google Scholar
Behzadi, Y, Restom, K, Liau, J, Liu, TT (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37, 90–101.Google Scholar
Belmonte, MK, Allen, G, Beckel-Mitchener, A, Boulanger, LM, Carper, RA, Webb, SJ (2004). Autism and abnormal development of brain connectivity. Journal of Neuroscience 24, 9228–9231.CrossRefGoogle ScholarPubMed
Belton, E, Salmond, CH, Watkins, KE, Vargha-Khadem, F, Gadian, DG (2003). Bilateral brain abnormalities associated with dominantly inherited verbal and orofacial dyspraxia. Human Brain Mapping 18, 194–200.Google Scholar
Blokland, GA, de Zubicaray, GI, McMahon, KL, Wright, MJ (2012). Genetic and environmental influences on neuroimaging phenotypes: a meta-analytical perspective on twin imaging studies. Twin Research and Human Genetics: the Official Journal of the International Society for Twin Studies 15, 351–371.Google Scholar
Branchini, LA, Lindgren, KA, Tager-Flusberg, H (2009). MRI analysis of the corpus callosum in siblings of children with autism spectrum disorder. Neurology 72, A136–A136.Google Scholar
Chen, G, Saad, ZS, Britton, JC, Pine, DS, Cox, RW (2013). Linear mixed-effects modeling approach to fMRI group analysis. NeuroImage 73, 176–190.Google Scholar
Chien, HY, Lin, HY, Lai, MC, Gau, SS, Tseng, WY (2015). Hyperconnectivity of the right posterior temporo-parietal junction predicts social difficulties in boys with autism spectrum disorder. Autism Research 8, 427–441.CrossRefGoogle ScholarPubMed
Chiu, PH, Kayali, MA, Kishida, KT, Tomlin, D, Klinger, LG, Klinger, MR, Montague, PR (2008). Self responses along cingulate cortex reveal quantitative neural phenotype for high-functioning autism. Neuron 57, 463–473.Google Scholar
Cieslik, EC, Mueller, VI, Eickhoff, CR, Langner, R, Eickhoff, SB (2015). Three key regions for supervisory attentional control: evidence from neuroimaging meta-analyses. Neuroscience and Biobehavioral Reviews 48, 22–34.Google Scholar
Cole, DM, Smith, SM, Beckmann, CF (2010). Advances and pitfalls in the analysis and interpretation of resting-state fMRI data. Frontiers in Systems Neuroscience 4, 8.Google Scholar
Colvert, E, Tick, B, McEwen, F, Stewart, C, Curran, SR, Woodhouse, E, Gillan, N, Hallett, V, Lietz, S, Garnett, T, Ronald, A, Plomin, R, Rijsdijk, F, Happe, F, Bolton, P (2015). Heritability of autism spectrum disorder in a UK population-based twin sample. JAMA Psychiatry 72, 415–423.CrossRefGoogle Scholar
Courchesne, E, Pierce, K, Schumann, CM, Redcay, E, Buckwalter, JA, Kennedy, DP, Morgan, J (2007). Mapping early brain development in autism. Neuron 56, 399–413.CrossRefGoogle ScholarPubMed
D'Mello, AM, Crocetti, D, Mostofsky, SH, Stoodley, CJ (2015). Cerebellar gray matter and lobular volumes correlate with core autism symptoms. NeuroImage: Clinical 7, 631–639.CrossRefGoogle ScholarPubMed
Dalton, KM, Nacewicz, BM, Alexander, AL, Davidson, RJ (2007). Gaze-fixation, brain activation, and amygdala volume in unaffected siblings of individuals with autism. Biological Psychiatry 61, 512–520.Google Scholar
Di Martino, A, Kelly, C, Grzadzinski, R, Zuo, XN, Mennes, M, Mairena, MA, Lord, C, Castellanos, FX, Milham, MP (2011). Aberrant striatal functional connectivity in children with autism. Biological Psychiatry 69, 847–856.Google Scholar
Di Martino, A, Yan, CG, Li, Q, Denio, E, Castellanos, FX, Alaerts, K, Anderson, JS, Assaf, M, Bookheimer, SY, Dapretto, M, Deen, B, Delmonte, S, Dinstein, I, Ertl-Wagner, B, Fair, DA, Gallagher, L, Kennedy, DP, Keown, CL, Keysers, C, Lainhart, JE, Lord, C, Luna, B, Menon, V, Minshew, NJ, Monk, CS, Mueller, S, Müller, RA, Nebel, MB, Nigg, JT, O'Hearn, K, Pelphrey, KA, Peltier, SJ, Rudie, JD, Sunaert, S, Thioux, M, Tyszka, JM, Uddin, LQ, Verhoeven, JS, Wenderoth, N, Wiggins, JL, Mostofsky, SH, Milham, MP (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry 19, 659–667.Google Scholar
Dziuk, MA, Gidley Larson, JC, Apostu, A, Mahone, EM, Denckla, MB, Mostofsky, SH (2007). Dyspraxia in autism: association with motor, social, and communicative deficits. Developmental Medicine and Child Neurology 49, 734–739.CrossRefGoogle ScholarPubMed
Ecker, C, Bookheimer, SY, Murphy, DG (2015). Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan. Lancet Neurology 14, 1121–1134.Google Scholar
Ersche, KD, Jones, PS, Williams, GB, Turton, AJ, Robbins, TW, Bullmore, ET (2012). Abnormal brain structure implicated in stimulant drug addiction. Science 335, 601–604.Google Scholar
Fan, YT, Chen, C, Chen, SC, Decety, J, Cheng, Y (2014). Empathic arousal and social understanding in individuals with autism: evidence from fMRI and ERP measurements. Social Cognitive and Affective Neuroscience 9, 1203–1213.Google Scholar
Fishman, I, Keown, CL, Lincoln, AJ, Pineda, JA, Müller, RA (2014). Atypical cross talk between mentalizing and mirror neuron networks in autism spectrum disorder. JAMA Psychiatry 71, 751–760.Google Scholar
Fornito, A, Zalesky, A, Bassett, DS, Meunier, D, Ellison-Wright, I, Yucel, M, Wood, SJ, Shaw, K, O'Connor, J, Nertney, D, Mowry, BJ, Pantelis, C, Bullmore, ET (2011). Genetic influences on cost-efficient organization of human cortical functional networks. Journal of Neuroscience 31, 3261–3270.Google Scholar
Friston, KJ, Holmes, AP, Price, CJ, Buchel, C, Worsley, KJ (1999). Multisubject fMRI studies and conjunction analyses. NeuroImage 10, 385–396.Google Scholar
Friston, KJ, Penny, WD, Glaser, DE (2005). Conjunction revisited. NeuroImage 25, 661–667.Google Scholar
Gau, SS, Chong, MY, Chen, TH, Cheng, AT (2005). A 3-year panel study of mental disorders among adolescents in Taiwan. American Journal of Psychiatry 162, 1344–1350.Google Scholar
Gau, SSF, Chou, MC, Lee, JC, Wong, CC, Chou, WJ, Chen, MF, Soong, WT, Wu, YY (2010). Behavioral problems and parenting style among Taiwanese children with autism and their siblings. Psychiatry and Clinical Neurosciences 64, 70–78.Google Scholar
Gau, SS-F, Lee, C-M, Lai, M-C, Chiu, Y-N, Huang, Y-F, Kao, J-D, Wu, Y-Y (2011). Psychometric properties of the Chinese version of the Social Communication Questionnaire. Research in Autism Spectrum Disorders 5, 809–818.Google Scholar
Gau, SS-F, Liu, L-T, Wu, Y-Y, Chiu, Y-N, Tsai, W-C (2013). Psychometric properties of the Chinese version of the Social Responsiveness Scale. Research in Autism Spectrum Disorders 7, 349–360.CrossRefGoogle Scholar
Geschwind, DH, State, MW (2015). Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurology 14, 1109–1120.Google Scholar
Giedd, JN, Stockman, M, Weddle, C, Liverpool, M, Alexander-Bloch, A, Wallace, GL, Lee, NR, Lalonde, F, Lenroot, RK (2010). Anatomic magnetic resonance imaging of the developing child and adolescent brain and effects of genetic variation. Neuropsychology Review 20, 349–361.Google Scholar
Glahn, DC, Knowles, EE, McKay, DR, Sprooten, E, Raventos, H, Blangero, J, Gottesman, II, Almasy, L (2014). Arguments for the sake of endophenotypes: examining common misconceptions about the use of endophenotypes in psychiatric genetics. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics 165B, 122–130.Google Scholar
Glahn, DC, Winkler, AM, Kochunov, P, Almasy, L, Duggirala, R, Carless, MA, Curran, JC, Olvera, RL, Laird, AR, Smith, SM, Beckmann, CF, Fox, PT, Blangero, J (2010). Genetic control over the resting brain. Proceedings of the National Academy of Sciences USA 107, 1223–1228.Google Scholar
Gottesman, II, Gould, TD (2003). The endophenotype concept in psychiatry: etymology and strategic intentions. American Journal of Psychiatry 160, 636–645.Google Scholar
Hadjikhani, N, Zurcher, NR, Rogier, O, Hippolyte, L, Lemonnier, E, Ruest, T, Ward, N, Lassalle, A, Gillberg, N, Billstedt, E, Helles, A, Gillberg, C, Solomon, P, Prkachin, KM, Gillberg, C (2014). Emotional contagion for pain is intact in autism spectrum disorders. Translational Psychiatry 4, e343.Google Scholar
Hallquist, MN, Hwang, K, Luna, B (2013). The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. NeuroImage 82, 208–225.Google Scholar
Han, S, Ma, Y (2015). A culture–behavior–brain loop model of human development. Trends in Cognitive Sciences 19, 666–676.Google Scholar
Harms, MB, Martin, A, Wallace, GL (2010). Facial emotion recognition in autism spectrum disorders: a review of behavioral and neuroimaging studies. Neuropsychology Review 20, 290–322.Google Scholar
Hayasaka, S, Nichols, TE (2003). Validating cluster size inference: random field and permutation methods. NeuroImage 20, 2343–2356.Google Scholar
Heller, R, Golland, Y, Malach, R, Benjamini, Y (2007). Conjunction group analysis: an alternative to mixed/random effect analysis. NeuroImage 37, 1178–1185.Google Scholar
Hibar, DP, Stein, JL, Renteria, ME, Arias-Vasquez, A, Desrivières, S, Jahanshad, N, Toro, R, Wittfeld, K, Abramovic, L, Andersson, M, Aribisala, BS, Armstrong, NJ, Bernard, M, Bohlken, MM, Boks, MP, Bralten, J, Brown, AA, Chakravarty, MM, Chen, Q, Ching, CR, Cuellar-Partida, G, den Braber, A, Giddaluru, S, Goldman, AL, Grimm, O, Guadalupe, T, Hass, J, Woldehawariat, G, Holmes, AJ, Hoogman, M, Janowitz, D, Jia, T, Kim, S, Klein, M, Kraemer, B, Lee, PH, Olde Loohuis, LM, Luciano, M, Macare, C, Mather, KA, Mattheisen, M, Milaneschi, Y, Nho, K, Papmeyer, M, Ramasamy, A, Risacher, SL, Roiz-Santiañez, R, Rose, EJ, Salami, A, Sämann, PG, Schmaal, L, Schork, AJ, Shin, J, Strike, LT, Teumer, A, van Donkelaar, MM, van Eijk, KR, Walters, RK, Westlye, LT, Whelan, CD, Winkler, AM, Zwiers, MP, Alhusaini, S, Athanasiu, L, Ehrlich, S, Hakobjan, MM, Hartberg, CB, Haukvik, UK, Heister, AJ, Hoehn, D, Kasperaviciute, D, Liewald, DC, Lopez, LM, Makkinje, RR, Matarin, M, Naber, MA, McKay, DR, Needham, M, Nugent, AC, Pütz, B, Royle, NA, Shen, L, Sprooten, E, Trabzuni, D, van der Marel, SS, van Hulzen, KJ, Walton, E, Wolf, C, Almasy, L, Ames, D, Arepalli, S, Assareh, AA, Bastin, ME, Brodaty, H, Bulayeva, KB, Carless, MA, Cichon, S, Corvin, A, Curran, JE, Czisch, M, de Zubicaray, GI, Dillman, A, Duggirala, R, Dyer, TD, Erk, S, Fedko, IO, Ferrucci, L, Foroud, TM, Fox, PT, Fukunaga, M, Gibbs, JR, Göring, HH, Green, RC, Guelfi, S, Hansell, NK, Hartman, CA, Hegenscheid, K, Heinz, A, Hernandez, DG, Heslenfeld, DJ, Hoekstra, PJ, Holsboer, F, Homuth, G, Hottenga, JJ, Ikeda, M, Jack, CR Jr., Jenkinson, M, Johnson, R, Kanai, R, Keil, M, Kent, JW Jr., Kochunov, P, Kwok, JB, Lawrie, SM, Liu, X, Longo, DL, McMahon, KL, Meisenzahl, E, Melle, I, Mohnke, S, Montgomery, GW, Mostert, JC, Mühleisen, TW, Nalls, MA, Nichols, TE, Nilsson, LG, Nöthen, MM, Ohi, K, Olvera, RL, Perez-Iglesias, R, Pike, GB, Potkin, SG, Reinvang, I, Reppermund, S, Rietschel, M, Romanczuk-Seiferth, N, Rosen, GD, Rujescu, D, Schnell, K, Schofield, PR, Smith, C, Steen, VM, Sussmann, JE, Thalamuthu, A, Toga, AW, Traynor, BJ, Troncoso, J, Turner, JA, Valdés Hernández, MC, van 't Ent, D, van der Brug, M, van der Wee, NJ, van Tol, MJ, Veltman, DJ, Wassink, TH, Westman, E, Zielke, RH, Zonderman, AB, Ashbrook, DG, Hager, R, Lu, L, McMahon, FJ, Morris, DW, Williams, RW, Brunner, HG, Buckner, RL, Buitelaar, JK, Cahn, W, Calhoun, VD, Cavalleri, GL, Crespo-Facorro, B, Dale, AM, Davies, GE, Delanty, N, Depondt, C, Djurovic, S, Drevets, WC, Espeseth, T, Gollub, RL, Ho, BC, Hoffmann, W, Hosten, N, Kahn, RS, Le Hellard, S, Meyer-Lindenberg, A, Müller-Myhsok, B, Nauck, M, Nyberg, L, Pandolfo, M, Penninx, BW, Roffman, JL, Sisodiya, SM, Smoller, JW, van Bokhoven, H, van Haren, NE, Völzke, H, Walter, H, Weiner, MW, Wen, W, White, T, Agartz, I, Andreassen, OA, Blangero, J, Boomsma, DI, Brouwer, RM, Cannon, DM, Cookson, MR, de Geus, EJ, Deary, IJ, Donohoe, G, Fernández, G, Fisher, SE, Francks, C, Glahn, DC, Grabe, HJ, Gruber, O, Hardy, J, Hashimoto, R, Hulshoff Pol, HE, Jönsson, EG, Kloszewska, I, Lovestone, S, Mattay, VS, Mecocci, P, McDonald, C, McIntosh, AM, Ophoff, RA, Paus, T, Pausova, Z, Ryten, M, Sachdev, PS, Saykin, AJ, Simmons, A, Singleton, A, Soininen, H, Wardlaw, JM, Weale, ME, Weinberger, DR, Adams, HH, Launer, LJ, Seiler, S, Schmidt, R, Chauhan, G, Satizabal, CL, Becker, JT, Yanek, L, van der Lee, SJ, Ebling, M, Fischl, B, Longstreth, WT Jr, Greve, D, Schmidt, H, Nyquist, P, Vinke, LN, van Duijn, CM, Xue, L, Mazoyer, B, Bis, JC, Gudnason, V, Seshadri, S, Ikram, MA; Alzheimer's Disease Neuroimaging Initiative; CHARGE Consortium; EPIGEN; IMAGEN; SYS, Martin, NG, Wright, MJ, Schumann, G, Franke, B, Thompson, PM, Medland, SE (2015). Common genetic variants influence human subcortical brain structures. Nature 520, 224–229.CrossRefGoogle ScholarPubMed
Hsiao, MN, Tseng, WL, Huang, HY, Gau, SS (2013). Effects of autistic traits on social and school adjustment in children and adolescents: the moderating roles of age and gender. Research in Developmental Disabilities 34, 254–265.CrossRefGoogle ScholarPubMed
Hua, K, Zhang, J, Wakana, S, Jiang, H, Li, X, Reich, DS, Calabresi, PA, Pekar, JJ, van Zijl, PC, Mori, S (2008). Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. NeuroImage 39, 336–347.CrossRefGoogle ScholarPubMed
Jenkinson, M, Bannister, P, Brady, M, Smith, S (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825–841.Google Scholar
Kaiser, MD, Hudac, CM, Shultz, S, Lee, SM, Cheung, C, Berken, AM, Deen, B, Pitskel, NB, Sugrue, DR, Voos, AC, Saulnier, CA, Ventola, P, Wolf, JM, Klin, A, Vander Wyk, BC, Pelphrey, KA (2010). Neural signatures of autism. Proceedings of the National Academy of Sciences USA 107, 21223–21228.Google Scholar
Kim, YS, Leventhal, BL (2015). Genetic epidemiology and insights into interactive genetic and environmental effects in autism spectrum disorders. Biological Psychiatry 77, 66–74.Google Scholar
Krach, S, Kamp-Becker, I, Einhauser, W, Sommer, J, Frassle, S, Jansen, A, Rademacher, L, Muller-Pinzler, L, Gazzola, V, Paulus, FM (2015). Evidence from pupillometry and fMRI indicates reduced neural response during vicarious social pain but not physical pain in autism. Human Brain Mapping 36, 4730–4744.Google Scholar
Kuo, C-C, Liang, K-C, Tseng, CC, Gau, SS-F (2014). Comparison of the cognitive profiles and social adjustment between mathematically and scientifically talented students and students with Asperger's syndrome. Research in Autism Spectrum Disorders 8, 838–850.Google Scholar
Kuo, PH, Chuang, LC, Su, MH, Chen, CH, Chen, CH, Wu, JY, Yen, CJ, Wu, YY, Liu, SK, Chou, MC, Chou, WJ, Chiu, YN, Tsai, WC, Gau, SS (2015). Genome-wide association study for autism spectrum disorder in Taiwanese Han population. PLOS ONE 10, e0138695.CrossRefGoogle ScholarPubMed
Lai, MC, Lombardo, MV, Auyeung, B, Chakrabarti, B, Baron-Cohen, S (2015). Sex/gender differences and autism: setting the scene for future research. Journal of the American Academy of Child and Adolescent Psychiatry 54, 11–24.CrossRefGoogle ScholarPubMed
Lai, MC, Lombardo, MV, Suckling, J, Ruigrok, AN, Chakrabarti, B, Ecker, C, Deoni, SC, Craig, MC, Murphy, DG, Bullmore, ET; MRC AIMS Consortium, Baron-Cohen, S (2013). Biological sex affects the neurobiology of autism. Brain 136, 2799–2815.Google Scholar
Lamm, C, Decety, J, Singer, T (2011). Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain. NeuroImage 54, 2492–2502.Google Scholar
Lau, WY, Gau, SS, Chiu, YN, Wu, YY (2014). Autistic traits in couple dyads as a predictor of anxiety spectrum symptoms. Journal of Autism and Developmental Disorders 44, 2949–2963.Google Scholar
Lau, WYP, Gau, SSF, Chiu, YN, Wu, YY, Chou, WJ, Liu, SK, Chou, MC (2013). Psychometric properties of the Chinese version of the Autism Spectrum Quotient (AQ). Research in Developmental Disabilities 34, 294–305.Google Scholar
Lin, H-Y, Ni, H-C, Lai, M-C, Tseng, W-YI, Gau, SSF (2015). Regional brain volume differences between males with and without autism spectrum disorder are highly age-dependent. Molecular Autism 6, 29.CrossRefGoogle ScholarPubMed
Liu, X, Shimada, T, Otowa, T, Wu, YY, Kawamura, Y, Tochigi, M, Iwata, Y, Umekage, T, Toyota, T, Maekawa, M, Iwayama, Y, Suzuki, K, Kakiuchi, C, Kuwabara, H, Kano, Y, Nishida, H, Sugiyama, T, Kato, N, Chen, CH, Mori, N, Yamada, K, Yoshikawa, T, Kasai, K, Tokunaga, K, Sasaki, T, Gau, SS (2016). Genome-wide association study of autism spectrum disorder in the East Asian populations. Autism Research 9, 340–349.Google Scholar
Marsh, LE, Hamilton, AF (2011). Dissociation of mirroring and mentalising systems in autism. NeuroImage 56, 1511–1519.Google Scholar
Mechelli, A, Price, CJ, Friston, KJ, Ashburner, J (2005). Voxel-based morphometry of the human brain: methods and applications. Current Medical Imaging Reviews 1, 105–113.CrossRefGoogle Scholar
Menzies, L, Achard, S, Chamberlain, SR, Fineberg, N, Chen, CH, del Campo, N, Sahakian, BJ, Robbins, TW, Bullmore, E (2007). Neurocognitive endophenotypes of obsessive–compulsive disorder. Brain 130, 3223–3236.CrossRefGoogle ScholarPubMed
Minio-Paluello, I, Baron-Cohen, S, Avenanti, A, Walsh, V, Aglioti, SM (2009). Absence of embodied empathy during pain observation in Asperger syndrome. Biological Psychiatry 65, 55–62.Google Scholar
Morecraft, RJ, Tanji, J (2009). Cingulofrontal interactions and the cingulate motor areas. In Cingulate Neurobiology and Disease (ed. Vogt, BA), pp. 113–144. Oxford University Press: Oxford.Google Scholar
Moseley, RL, Ypma, RJ, Holt, RJ, Floris, D, Chura, LR, Spencer, MD, Baron-Cohen, S, Suckling, J, Bullmore, E, Rubinov, M (2015). Whole-brain functional hypoconnectivity as an endophenotype of autism in adolescents. NeuroImage: Clinical 9, 140–152.Google Scholar
Mueller, S, Keeser, D, Samson, AC, Kirsch, V, Blautzik, J, Grothe, M, Erat, O, Hegenloh, M, Coates, U, Reiser, MF, Hennig-Fast, K, Meindl, T (2013). Convergent findings of altered functional and structural brain connectivity in individuals with high functioning autism: a multimodal MRI study. PLOS ONE 8, e67329.Google Scholar
Müller, RA, Shih, P, Keehn, B, Deyoe, JR, Leyden, KM, Shukla, DK (2011). Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders. Cerebral Cortex 21, 2233–2243.Google Scholar
Nickl-Jockschat, T, Habel, U, Michel, TM, Manning, J, Laird, AR, Fox, PT, Schneider, F, Eickhoff, SB (2012). Brain structure anomalies in autism spectrum disorder – a meta-analysis of VBM studies using anatomic likelihood estimation. Human Brain Mapping 33, 1470–1489.Google Scholar
Oldfield, RC (1971). The assessment and analysis of handedness: the Edinburgh Inventory. Neuropsychologia 9, 97–113.Google Scholar
Palmen, SJ, Hulshoff Pol, HE, Kemner, C, Schnack, HG, Sitskoorn, MM, Appels, MC, Kahn, RS, Van Engeland, H (2005). Brain anatomy in non-affected parents of autistic probands: a MRI study. Psychological Medicine 35, 1411–1420.Google Scholar
Peterson, E, Schmidt, GL, Tregellas, JR, Winterrowd, E, Kopelioff, L, Hepburn, S, Reite, M, Rojas, DC (2006). A voxel-based morphometry study of gray matter in parents of children with autism. Neuroreport 17, 1289–1292.Google Scholar
Philip, RC, Dauvermann, MR, Whalley, HC, Baynham, K, Lawrie, SM, Stanfield, AC (2012). A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders. Neuroscience and Biobehavioral Reviews 36, 901–942.Google Scholar
Pironti, VA, Lai, MC, Muller, U, Dodds, CM, Suckling, J, Bullmore, ET, Sahakian, BJ (2014). Neuroanatomical abnormalities and cognitive impairments are shared by adults with attention-deficit/hyperactivity disorder and their unaffected first-degree relatives. Biological Psychiatry 76, 639–647.CrossRefGoogle ScholarPubMed
Power, JD, Barnes, KA, Snyder, AZ, Schlaggar, BL, Petersen, SE (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 2142–2154.Google Scholar
Power, JD, Schlaggar, BL, Petersen, SE (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. NeuroImage 105, 536–551.CrossRefGoogle ScholarPubMed
Pruim, RH, Mennes, M, Buitelaar, JK, Beckmann, CF (2015 a). Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI. NeuroImage 112, 278–287.Google Scholar
Pruim, RH, Mennes, M, van Rooij, D, Llera, A, Buitelaar, JK, Beckmann, CF (2015 b). ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage 112, 267–277.CrossRefGoogle ScholarPubMed
Reuter, M, Tisdall, MD, Qureshi, A, Buckner, RL, van der Kouwe, AJ, Fischl, B (2015). Head motion during MRI acquisition reduces gray matter volume and thickness estimates. NeuroImage 107, 107–115.CrossRefGoogle ScholarPubMed
Rojas, DC, Smith, JA, Benkers, TL, Camou, SL, Reite, ML, Rogers, SJ (2004). Hippocampus and amygdala volumes in parents of children with autistic disorder. American Journal of Psychiatry 161, 2038–2044.CrossRefGoogle ScholarPubMed
Rorden, C, Karnath, HO, Bonilha, L (2007). Improving lesion–symptom mapping. Journal of Cognitive Neuroscience 19, 1081–1088.Google Scholar
Rutter, M, Le Couteur, A, Lord, C (2003). Autism Diagnostic Interview-Revised. Western Psychological Services: Los Angeles, CA.Google Scholar
Sandin, S, Lichtenstein, P, Kuja-Halkola, R, Larsson, H, Hultman, CM, Reichenberg, A (2014). The familial risk of autism. JAMA 311, 1770–1777.CrossRefGoogle ScholarPubMed
Schaer, M, Kochalka, J, Padmanabhan, A, Supekar, K, Menon, V (2015). Sex differences in cortical volume and gyrification in autism. Molecular Autism 6, 42.Google Scholar
Schipul, SE, Keller, TA, Just, MA (2011). Inter-regional brain communication and its disturbance in autism. Frontiers in Systems Neuroscience 5, 10.Google Scholar
Segovia, F, Holt, R, Spencer, M, Gorriz, JM, Ramirez, J, Puntonet, CG, Phillips, C, Chura, L, Baron-Cohen, S, Suckling, J (2014). Identifying endophenotypes of autism: a multivariate approach. Frontiers in Computational Neuroscience 8, 60.Google Scholar
Shackman, AJ, Salomons, TV, Slagter, HA, Fox, AS, Winter, JJ, Davidson, RJ (2011). The integration of negative affect, pain and cognitive control in the cingulate cortex. Nature Reviews Neuroscience 12, 154–167.Google Scholar
Song, XW, Dong, ZY, Long, XY, Li, SF, Zuo, XN, Zhu, CZ, He, Y, Yan, CG, Zang, YF (2011). REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS ONE 6, e25031.Google Scholar
Spencer, MD, Chura, LR, Holt, RJ, Suckling, J, Calder, AJ, Bullmore, ET, Baron-Cohen, S (2012). Failure to deactivate the default mode network indicates a possible endophenotype of autism. Molecular Autism 3, 15.Google Scholar
Suckling, J (2011). Correlated covariates in ANCOVA cannot adjust for pre-existing differences between groups. Schizophrenia Research 126, 310–311.Google Scholar
Sucksmith, E, Roth, I, Hoekstra, RA (2011). Autistic traits below the clinical threshold: re-examining the broader autism phenotype in the 21st century. Neuropsychology Review 21, 360–389.Google Scholar
Supekar, K, Menon, V (2015). Sex differences in structural organization of motor systems and their dissociable links with repetitive/restricted behaviors in children with autism. Molecular Autism 6, 50.Google Scholar
Tzourio-Mazoyer, N, Landeau, B, Papathanassiou, D, Crivello, F, Etard, O, Delcroix, N, Mazoyer, B, Joliot, M (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15, 273–289.Google Scholar
Uddin, LQ, Supekar, K, Menon, V (2013). Reconceptualizing functional brain connectivity in autism from a developmental perspective. Frontiers in Human Neuroscience 7, 458.Google Scholar
Uljarevic, M, Hamilton, A (2013). Recognition of emotions in autism: a formal meta-analysis. Journal of Autism and Developmental Disorders 43, 1517–1526.Google Scholar
Uppal, N, Wicinski, B, Buxbaum, JD, Heinsen, H, Schmitz, C, Hof, PR (2014). Neuropathology of the anterior midcingulate cortex in young children with autism. Journal of Neuropathology and Experimental Neurology 73, 891–902.Google Scholar
Urbain, CM, Pang, EW, Taylor, MJ (2015). Atypical spatiotemporal signatures of working memory brain processes in autism. Translational Psychiatry 5, e617.Google Scholar
Van Dijk, KR, Hedden, T, Venkataraman, A, Evans, KC, Lazar, SW, Buckner, RL (2010). Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. Journal of Neurophysiology 103, 297–321.Google Scholar
Vissers, ME, Cohen, MX, Geurts, HM (2012). Brain connectivity and high functioning autism: a promising path of research that needs refined models, methodological convergence, and stronger behavioral links. Neuroscience and Biobehavioral Reviews 36, 604–625.CrossRefGoogle ScholarPubMed
Vogt, BA (2009). Regions and subregions of the cingulate cortex. In Cingulate Neurobiology and Disease (ed. Vogt, BA), pp. 3–30. Oxford University Press: Oxford.Google Scholar
Wakana, S, Caprihan, A, Panzenboeck, MM, Fallon, JH, Perry, M, Gollub, RL, Hua, K, Zhang, J, Jiang, H, Dubey, P, Blitz, A, van Zijl, P, Mori, S (2007). Reproducibility of quantitative tractography methods applied to cerebral white matter. NeuroImage 36, 630–644.Google Scholar
Wass, S (2011). Distortions and disconnections: disrupted brain connectivity in autism. Brain and Cognition 75, 18–28.Google Scholar
Wechsler, D (1991). Wechsler Intelligence Scale for Children – Third Edition (WISC-III) . Psychological Corporation: San Antonio, TX.Google Scholar
Wechsler, D (1997). Wechsler Adult Intelligence Scale – Third Edition (WAIS-III). Psychological Corporation: San Antonio, TX.Google Scholar
Whitfield-Gabrieli, S, Nieto-Castanon, A (2012). Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity 2, 125–141.Google Scholar
Wilke, M, Holland, SK, Altaye, M, Gaser, C (2008). Template-O-Matic: a toolbox for creating customized pediatric templates. NeuroImage 41, 903–913.Google Scholar
Xia, M, Wang, J, He, Y (2013). BrainNet Viewer: a network visualization tool for human brain connectomics. PLOS ONE 8, e68910.Google Scholar
Yan, CG, Cheung, B, Kelly, C, Colcombe, S, Craddock, RC, Di Martino, A, Li, Q, Zuo, XN, Castellanos, FX, Milham, MP (2013 a). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 76, 183–201.Google Scholar
Yan, CG, Craddock, RC, Zuo, XN, Zang, YF, Milham, MP (2013 b). Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. NeuroImage 80, 246–262.Google Scholar
Yan, CG, Zang, YF (2010). DPARSF: a MATLAB toolbox for “Pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience 4, 13.Google Scholar
Yarkoni, T, Poldrack, RA, Nichols, TE, Van Essen, DC, Wager, TD (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods 8, 665–670.Google Scholar
Supplementary material: Image

Lin supplementary material S1

Supplementary Figure

Download Lin supplementary material S1(Image)
Image 1.5 MB
Supplementary material: File

Lin supplementary material S2

Lin supplementary material

Download Lin supplementary material S2(File)
File 2.4 MB
Supplementary material: File

Lin supplementary material S3

Lin supplementary material

Download Lin supplementary material S3(File)
File 100.4 KB