Brain Topography

, Volume 32, Issue 3, pp 461–471 | Cite as

Characterization of Autism Spectrum Disorder across the Age Span by Intrinsic Network Patterns

  • Benjamin R. MorganEmail author
  • George M. Ibrahim
  • Vanessa M. Vogan
  • Rachel C. Leung
  • Wayne Lee
  • Margot J. Taylor
Original Paper


Autism spectrum disorder (ASD) is characterized by abnormal functional organization of brain networks, which may underlie the cognitive and social impairments observed in affected individuals. The present study characterizes unique intrinsic connectivity within- and between- neural networks in children through to adults with ASD, relative to controls. Resting state fMRI data were analyzed in 204 subjects, 102 with ASD and 102 age- and sex-matched controls (ages 7–40 years), acquired on a single scanner. ASD was assessed using the autism diagnostic observation schedule (ADOS). BOLD correlations were calculated between 47 regions of interest, spanning seven resting state brain networks. Partial least squares (PLS) analyses evaluated the association between connectivity patterns and ASD diagnosis as well as ASD severity scores. PLS demonstrated dissociable connectivity patterns in those with ASD, relative to controls. Similar patterns were observed in the whole cohort and in a subgroup analysis of subjects under 18 years of age. Greater inter-network connectivity was seen in ASD with greater intra-network connectivity in controls. In conclusion, stronger inter-network and weaker intra-network resting state-fMRI BOLD correlations characterize ASD and may differentiate control and ASD cohorts. These findings are relevant to understanding ASD as a disruption of network topology.


Autism spectrum disorder fMRI brain Multivariate analysis Child Adult 



  1. Assaf M, Jagannathan K, Calhoun VD, Miller L, Stevens MC, Sahl R, O’boyle JG, Schultz RT, Pearlson GD (2010) Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. NeuroImage 53(1):247–256. CrossRefGoogle Scholar
  2. Beckmann CF, Beckmann CF, Smith SM, Smith SM (2004) Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging 23(2):137–152. CrossRefGoogle Scholar
  3. Bernas A, Barendse EM, Aldenkamp AP, Backes WH, Hofman PAM, Hendriks MPH, Kessels RP, Willems FM, de With PH, Zinger S, Jansen JFA (2018) Brain resting-state networks in adolescents with high-functioning autism: analysis of spatial connectivity and temporal neurodynamics. Brain Behav 8(2):1–10. CrossRefGoogle Scholar
  4. Birn RM, Molloy EK, Patriat R, Parker T, Meier TB, Kirk GR, Nair VA, Meyerand ME, Prabhakaran V (2013) The effect of scan length on the reliability of resting-state fMRI connectivity estimates. NeuroImage 83:550–558. CrossRefGoogle Scholar
  5. Brier MR, Thomas JB, Snyder AZ, Benzinger TL, Zhang D, Raichle ME, Holtzman DM, Morris JC, Ances BM (2012) Loss of intra- and inter-network resting state functional connections with alzheimer’s disease progression. J Neurosci 32(26):8890–8899. CrossRefGoogle Scholar
  6. Carper RA, Solders S, Treiber JM, Fishman I, Müller RA (2015) Corticospinal tract anatomy and functional connectivity of primary motor cortex in autism. J Am Acad Child Adolesc Psychiatry 54(10):859–867. CrossRefGoogle Scholar
  7. Chen CP, Keown CL, Jahedi A, Nair A, Pflieger ME, Bailey BA, Müller R-A (2015) Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. NeuroImage Clin 8:238–245. CrossRefGoogle Scholar
  8. Chien Y-L, Gau SS-F, Shang C-Y, Chiu Y-N, Tsai W-C, Wu Y-Y (2015) Visual memory and sustained attention impairment in youths with autism spectrum disorders. Psychol Med 45(11):2263–2273. CrossRefGoogle Scholar
  9. Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res Int J, 29(3), 162–173. Retrieved from
  10. De Pasquale F, Della Penna S, Snyder AZ, Marzetti L, Pizzella V, Luca G (2000) Supplemental information a cortical core for dynamic integration of functional networks in the resting human brain. Neuron 74, 1–21Google Scholar
  11. de Pasquale F, Della Penna S, Snyder AZ, Marzetti L, Pizzella V, Romani GL, Corbetta M (2012) A cortical core for dynamic integration of functional networks in the resting human brain. Neuron 74(4):753–764. CrossRefGoogle Scholar
  12. Dosenbach NUF, Nardos B, Cohen AL, Fair DA, Power D, Church JA, Nelson SM, Wig GS, Vogel AC, Lessov-Schlaggar CN, Barnes KA, Dubis JW, Feczko E, Coalson RS, Pruett JR Jr, Barch DM, Petersen SE, Schlaggar BL (2011) Prediction of individual brain maturity using fMRI. Science 329(5997):1358–1361. CrossRefGoogle Scholar
  13. Doyle-Thomas KaR, Lee W, Foster NEV, Tryfon A, Ouimet T, Hyde KL, Evans AC, Lewis J, Zwaigenbaum L, Anagnostou E (2015) Atypical functional brain connectivity during rest in autism spectrum disorders. Ann Neurol, 866–876.
  14. Fair DA, Cohen AL, Power JD, Dosenbach NUF, Church J, Miezin FM, Schlaggar BL, Petersen SE (2009) Functional brain networks develop from a “local to distributed” organization. PLoS Comput Biol 5(5):e1000381. CrossRefGoogle Scholar
  15. Fishman I, Keown CL, Lincoln AJ, Pineda J, Müller R-A (2014) Atypical cross talk between mentalizing and mirror neuron networks in autism spectrum disorder. JAMA Psychiatr 71(7):751–760. CrossRefGoogle Scholar
  16. Floris DL, Barber AD, Nebel MB, Martinelli M, Lai M-C, Crocetti D, Baron-Cohen S, Suckling J, Pekar JJ, Mostofsky SH (2016) Atypical lateralization of motor circuit functional connectivity in children with autism is associated with motor deficits. Mol Autism 7(1):35. CrossRefGoogle Scholar
  17. Gotham K, Pickles A, Lord C (2009) Standardizing ADOS scores for a measure of severity in autism spectrum disorders. J Autism Dev Disord 39(5):693–705. CrossRefGoogle Scholar
  18. Hahamy A, Behrmann M, Malach R (2015) The idiosyncratic brain: distortion of spontaneous connectivity patterns in autism spectrum disorder. Nat Neurosci, 18(2).
  19. Ho DE, Imai K, King G, Stuart EA (2011) MatchIt: nonparametric preprocessing for parametric causal inference. J Stat Softw 42(8):1–28. CrossRefGoogle Scholar
  20. Hus V, Lord C (2014) Effects of child characteristics on the Autism diagnostic interview-revised: implications for use of scores as a measure of ASD severity. J Autism Dev Disord 43(2):371–381. CrossRefGoogle Scholar
  21. Ibrahim GM, Morgan BR, Lee W, Smith ML, Donner EJ, Wang F, Beers CA, Federico P, Taylor MJ, Doesburg SM, Rutka JT, Carter Snead O (2014) Impaired development of intrinsic connectivity networks in children with medically intractable localization-related epilepsy. Hum Brain Mapp 35(11):5686–5700. CrossRefGoogle Scholar
  22. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM (2012) FSL NeuroImage 62(2):782–790. CrossRefGoogle Scholar
  23. Kana RK, Libero LE, Moore MS (2011) Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders. Phys Life Rev 8(4):410–437. CrossRefGoogle Scholar
  24. Kennedy DP, Redcay E, Courchesne E (2006) Failing to deactivate: resting functional abnormalities in autism. Proc Natl Acad Sci USA 103(21):8275–8280. CrossRefGoogle Scholar
  25. Keown C, Shih P, Nair A, Peterson N, Mulvey M, Müller RA (2013) Local functional overconnectivity in posterior brain regions is associated with symptom severity in autism spectrum disorders. Cell Rep 5(3):567–572. CrossRefGoogle Scholar
  26. Loomes R, Hull L, Mandy WPL (2017) What is the male-to-female ratio in autism spectrum disorder? a systematic review and meta-analysis. J Am Acad Child Psy 56(6):466–474. CrossRefGoogle Scholar
  27. Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, Dilavore PC, Pickles A, Rutter M (2000) The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord 30(3):205–23CrossRefGoogle Scholar
  28. McIntosh AR, Lobaugh NJ (2004) Partial least squares analysis of neuroimaging data: applications and advances. NeuroImage, 23(Suppl 1):S250–63. CrossRefGoogle Scholar
  29. McIntosh AR, Mišić B (2013) Multivariate statistical analyses for neuroimaging data. Annu Rev Psychol 64:499–525. CrossRefGoogle Scholar
  30. McKinnon CJ, Eggebrecht AT, Todorov A, Wolff JJ, Elison JT, Adams CM, Snyder AZ, Estes AM, Zwaigenbaum L, Botteron KN, McKinstry RC, Marrus N, Evans A, Hazlett HC, Dager SR, Paterson SJ, Pandey J, Schultz RT, Styner MA, Gerig G, Schlaggar BL, Petersen SE, Piven J, Pruett JR (2018) Restricted and repetitive behavior and brain functional connectivity in infants at risk for developing autism spectrum disorder. Biol Psychiatr: Cognit Neurosci Neuroimaging, 1–12.
  31. Menon V (2011) Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cognit Sci 15:483–506CrossRefGoogle Scholar
  32. Murphy K, Fox MD (2017) Towards a consensus regarding global signal regression for resting state functional connectivity MRI. NeuroImage 154(November 2016), 169–173. CrossRefGoogle Scholar
  33. Nair A, Keown CL, Datko M, Shih P, Keehn B, Müller RA (2014) Impact of methodological variables on functional connectivity findings in autism spectrum disorders. Hum Brain Mapp 35(8):4035–4048. CrossRefGoogle Scholar
  34. Nebel MB, Eloyan A, Barber AD, Mostofsky SH (2014) Precentral gyrus functional connectivity signatures of autism. Front Syst Neurosci 8(May):80. Google Scholar
  35. Nebel MB, Eloyan A, Nettles CA, Sweeney KL, Ament K, Ward RE, Choe AS, Barber AD, Pekar JJ, Mostofsky SH (2016) Intrinsic visual-motor synchrony correlates with social deficits in autism. Biol Psychiatr 79(8):633–641. CrossRefGoogle Scholar
  36. Padmanabhan A, Lynch CJ, Schaer M, Menon V (2017) Review the default mode network in autism. Biol Psychiatr Cogn Neurosci Neuroimaging. 2(6):476–486. CrossRefGoogle Scholar
  37. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3):1059–1069. CrossRefGoogle Scholar
  38. Rutter M, Dilavore PC, Risi S, Gotham K, Bishop S (2012) Autism diagnostic observation schedule: ADOS-2. Western Psychological Services, TorranceGoogle Scholar
  39. Schäfer CB, Morgan BR, Ye AX, Taylor MJ, Doesburg SM (2014) Oscillations, networks, and their development: MEG connectivity changes with age. Hum Brain Mapp 5261(February):5249–5261. CrossRefGoogle Scholar
  40. Uddin LQ, Supekar K, Lynch CJ, Khouzam A, Phillips J, Feinstein C, Ryali S, Menon V (2013) Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatr 70(8):869–879. CrossRefGoogle Scholar
  41. Vasa RA, Mostofsky SH, Ewen JB (2016) The disrupted connectivity hypothesis of autism spectrum disorders: time for the next phase in research. Biol Psychiat Cogn Neurosci Neuroimaging 1(3):245–252. Google Scholar
  42. Verly M, Verhoeven J, Zink I, Mantini D, Van Oudenhove L, Lagae L, Sunaert S, Rommel N (2014a) Structural and functional underconnectivity as a negative predictor for language in autism. Hum Brain Mapp 35(8):3602–3615. CrossRefGoogle Scholar
  43. Verly M, Verhoeven J, Zink I, Mantini D, Peeters R, Deprez S, Emsell L, Boets B, Noens I, Steyaert J, Lagae L, De Cock P, Rommel N, Sunaert S (2014b) Altered functional connectivity of the language network in ASD: role of classical language areas and cerebellum. NeuroImage Clin 4:374–382. CrossRefGoogle Scholar
  44. 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. Neurosci Biobehav Rev 36(1):604–625. CrossRefGoogle Scholar
  45. Washington SD, Gordon EM, Brar J, Warburton S, Sawyer T, Wolfe A, Mease-Ference ER, Girton L, Hailu A, Mbwana J, Gaillard WD, Kalbfleisch ML, Vanmeter JW (2014) Dysmaturation of the Default Mode Network in Autism. Hum Brain Mapp 35(4):1284–1296. CrossRefGoogle Scholar
  46. Wechsler D (2002) Wechsler Abbreviated Scales of Intelligence. Psychological Corporation, San AntonioGoogle Scholar
  47. Zöller D, Schaer M, Scariati E, Padula MC, Eliez S, Van De Ville D (2017) Disentangling resting-state BOLD variability and PCC functional connectivity in 22q11.2 deletion syndrome. NeuroImage 149(January):85–97. CrossRefGoogle Scholar
  48. Zöller D, Padula MC, Sandini C, Schneider M, Scariati E, Van De Ville D, Schaer M, Eliez S (2018) Psychotic symptoms influence the development of anterior cingulate BOLD variability in 22q11.2 deletion syndrome. Schizophr Res 193:319–328. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Diagnostic ImagingHospital for Sick ChildrenTorontoCanada
  2. 2.Division of Neurosurgery, Department of SurgeryUniversity of TorontoTorontoCanada
  3. 3.Applied Psychology and Human Development, Ontario Institute for Studies in EducationUniversity of TorontoTorontoCanada
  4. 4.Departments of Medical Imaging and PsychologyUniversity of TorontoTorontoCanada

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