Cerebellar abnormalities, particularly in Right Crus I (RCrusI), are consistently reported in autism spectrum disorders (ASD). Although RCrusI is functionally connected with ASD-implicated circuits, the contribution of RCrusI dysfunction to ASD remains unclear. Here neuromodulation of RCrusI in neurotypical humans resulted in altered functional connectivity with the inferior parietal lobule, and children with ASD showed atypical functional connectivity in this circuit. Atypical RCrusI–inferior parietal lobule structural connectivity was also evident in the Purkinje neuron (PN) TscI ASD mouse model. Additionally, chemogenetically mediated inhibition of RCrusI PN activity in mice was sufficient to generate ASD-related social, repetitive, and restricted behaviors, while stimulation of RCrusI PNs rescued social impairment in the PN TscI ASD mouse model. Together, these studies reveal important roles for RCrusI in ASD-related behaviors. Further, the rescue of social behaviors in an ASD mouse model suggests that investigation of the therapeutic potential of cerebellar neuromodulation in ASD may be warranted.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

  • 16 March 2018

    In the version of this article initially published, the Simons Foundation was missing from the list of sources of support to P.T.T. in the Acknowledgments. The error has been corrected in the HTML and PDF versions of the article.


  1. 1.

    Wingate, M. et al. Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators. Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2010. MMWR Surveill. Summ. 63, 1–21 (2014).

  2. 2.

    Skefos, J. et al. Regional alterations in purkinje cell density in patients with autism. PLoS One 9, e81255 (2014).

  3. 3.

    Whitney, E. R., Kemper, T. L., Bauman, M. L., Rosene, D. L. & Blatt, G. J. Cerebellar Purkinje cells are reduced in a subpopulation of autistic brains: a stereological experiment using calbindin-D28k. Cerebellum 7, 406–416 (2008).

  4. 4.

    Limperopoulos, C. et al. Does cerebellar injury in premature infants contribute to the high prevalence of long-term cognitive, learning, and behavioral disability in survivors? Pediatrics 120, 584–593 (2007).

  5. 5.

    Bolduc, M. E. & Limperopoulos, C. Neurodevelopmental outcomes in children with cerebellar malformations: a systematic review. Dev. Med. Child Neurol 51, 256–267 (2009).

  6. 6.

    Catsman-Berrevoets, C. E. & Aarsen, F. K. The spectrum of neurobehavioural deficits in the posterior fossa syndrome in children after cerebellar tumour surgery. Cortex 46, 933–946 (2010).

  7. 7.

    Tsai, P. T. et al. Autistic-like behaviour and cerebellar dysfunction in Purkinje cell Tsc1 mutant mice. Nature 488, 647–651 (2012).

  8. 8.

    D’Mello, A. M., Crocetti, D., Mostofsky, S. H. & Stoodley, C. J. Cerebellar gray matter and lobular volumes correlate with core autism symptoms. Neuroimage Clin 7, 631–639 (2015).

  9. 9.

    D’Mello, A. M. & Stoodley, C. J. Cerebro-cerebellar circuits in autism spectrum disorder. Front. Neurosci 9, 408 (2015).

  10. 10.

    Mosconi, M. W., Wang, Z., Schmitt, L. M., Tsai, P. & Sweeney, J. A. The role of cerebellar circuitry alterations in the pathophysiology of autism spectrum disorders. Front. Neurosci 9, 296 (2015).

  11. 11.

    Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C. & Yeo, B. T. The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 2322–2345 (2011).

  12. 12.

    Stoodley, C. J. & Schmahmann, J. D. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex 46, 831–844 (2010).

  13. 13.

    Stoodley, C. J. & Schmahmann, J. D. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage 44, 489–501 (2009).

  14. 14.

    Strick, P. L., Dum, R. P. & Fiez, J. A. Cerebellum and nonmotor function. Annu. Rev. Neurosci. 32, 413–434 (2009).

  15. 15.

    Grimaldi, G. et al. Cerebellar transcranial direct current stimulation (ctDCS): a novel approach to understanding cerebellar function in health and disease. Neuroscientist 22, 83–97 (2016).

  16. 16.

    De Luca, M., Beckmann, C. F., De Stefano, N., Matthews, P. M. & Smith, S. M. fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage 29, 1359–1367 (2006).

  17. 17.

    Deshpande, G., Santhanam, P. & Hu, X. Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data. Neuroimage 54, 1043–1052 (2011).

  18. 18.

    Williams, J. H. et al. Neural mechanisms of imitation and ‘mirror neuron’ functioning in autistic spectrum disorder. Neuropsychologia 44, 610–621 (2006).

  19. 19.

    Clower, D. M., West, R. A., Lynch, J. C. & Strick, P. L. The inferior parietal lobule is the target of output from the superior colliculus, hippocampus, and cerebellum. J. Neurosci. 21, 6283–6291 (2001).

  20. 20.

    Alexander, G. M. et al. Remote control of neuronal activity in transgenic mice expressing evolved G protein-coupled receptors. Neuron 63, 27–39 (2009).

  21. 21.

    Armbruster, B. N., Li, X., Pausch, M. H., Herlitze, S. & Roth, B. L. Evolving the lock to fit the key to create a family of G protein-coupled receptors potently activated by an inert ligand. Proc. Natl. Acad. Sci. USA 104, 5163–5168 (2007).

  22. 22.

    Arancillo, M., White, J. J., Lin, T., Stay, T. L. & Sillitoe, R. V. In vivo analysis of Purkinje cell firing properties during postnatal mouse development. J. Neurophysiol. 113, 578–591 (2015).

  23. 23.

    Peter, S. et al. Dysfunctional cerebellar Purkinje cells contribute to autism-like behaviour in Shank2-deficient mice. Nat. Commun. 7, 12627 (2016).

  24. 24.

    Zhou, H. et al. Cerebellar modules operate at different frequencies. eLife 3, e02536 (2014).

  25. 25.

    Khan, A. J. et al. Cerebro-cerebellar resting-state functional connectivity in children and adolescents with autism spectrum disorder. Biol. Psychiatry 78, 625–634 (2015).

  26. 26.

    Mesulam, M. M. From sensation to cognition. Brain 121, 1013–1052 (1998).

  27. 27.

    Reep, R. L. & Corwin, J. V. Posterior parietal cortex as part of a neural network for directed attention in rats. Neurobiol. Learn. Mem. 91, 104–113 (2009).

  28. 28.

    Clayden, J. D. Imaging connectivity: MRI and the structural networks of the brain. Funct. Neurol. 28, 197–203 (2013).

  29. 29.

    Lerch, J. P. et al. Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage 31, 993–1003 (2006).

  30. 30.

    Asano, E. et al. Autism in tuberous sclerosis complex is related to both cortical and subcortical dysfunction. Neurology 57, 1269–1277 (2001).

  31. 31.

    Ryu, Y. H. et al. Perfusion impairments in infantile autism on technetium-99m ethyl cysteinate dimer brain single-photon emission tomography: comparison with findings on magnetic resonance imaging. Eur. J. Nucl. Med. 26, 253–259 (1999).

  32. 32.

    Moy, S. S. et al. Social approach in genetically engineered mouse lines relevant to autism. Genes Brain Behav 8, 129–142 (2009).

  33. 33.

    Cupolillo, D. et al. Autistic-like traits and cerebellar dysfunction in Purkinje cell PTEN knock-out mice. Neuropsychopharmacology 41, 1457–1466 (2016).

  34. 34.

    Reith, R. M. et al. Loss of Tsc2 in Purkinje cells is associated with autistic-like behavior in a mouse model of tuberous sclerosis complex. Neurobiol. Dis. 51, 93–103 (2013).

  35. 35.

    Gottlieb, J. From thought to action: the parietal cortex as a bridge between perception, action, and cognition. Neuron 53, 9–16 (2007).

  36. 36.

    Fogassi, L. et al. Parietal lobe: from action organization to intention understanding. Science 308, 662–667 (2005).

  37. 37.

    Marko, M. K. et al. Behavioural and neural basis of anomalous motor learning in children with autism. Brain 138, 784–797 (2015).

  38. 38.

    Nebel, M. B. et al. Intrinsic visual-motor synchrony correlates with social deficits in autism. Biol. Psychiatry 79, 633–641 (2016).

  39. 39.

    Haswell, C. C., Izawa, J., Dowell, L. R., Mostofsky, S. H. & Shadmehr, R. Representation of internal models of action in the autistic brain. Nat. Neurosci. 12, 970–972 (2009).

  40. 40.

    Mostofsky, S. H. et al. Developmental dyspraxia is not limited to imitation in children with autism spectrum disorders. J. Int. Neuropsychol. Soc. 12, 314–326 (2006).

  41. 41.

    Stoodley, C. J. & Limperopoulos, C. Structure-function relationships in the developing cerebellum: evidence from early-life cerebellar injury and neurodevelopmental disorders. Semin. Fetal Neonatal Med. 21, 356–364 (2016).

  42. 42.

    Van Overwalle, F., Baetens, K., Mariën, P. & Vandekerckhove, M. Social cognition and the cerebellum: a meta-analysis of over 350 fMRI studies. Neuroimage 86, 554–572 (2014).

  43. 43.

    Jack, A., Englander, Z. A. & Morris, J. P. Subcortical contributions to effective connectivity in brain networks supporting imitation. Neuropsychologia 49, 3689–3698 (2011).

  44. 44.

    Jack, A. & Pelphrey, K. A. Neural correlates of animacy attribution include neocerebellum in healthy adults. Cereb. Cortex 25, 4240–4247 (2015).

  45. 45.

    Deeley, Q. et al. An event related functional magnetic resonance imaging study of facial emotion processing in Asperger syndrome. Biol. Psychiatry 62, 207–217 (2007).

  46. 46.

    LeBlanc, J. J. & Fagiolini, M. Autism: a “critical period” disorder? Neural Plast. 2011, 921680 (2011).

  47. 47.

    Bryant, J. L., Boughter, J. D., Gong, S., LeDoux, M. S. & Heck, D. H. Cerebellar cortical output encodes temporal aspects of rhythmic licking movements and is necessary for normal licking frequency. Eur. J. Neurosci 32, 41–52 (2010).

  48. 48.

    Boggio, P. S., Asthana, M. K., Costa, T. L., Valasek, C. A. & Osório, A. A. Promoting social plasticity in developmental disorders with non-invasive brain stimulation techniques. Front. Neurosci 9, 294 (2015).

  49. 49.

    Demirtas-Tatlidede, A. et al. Safety and proof of principle study of cerebellar vermal theta burst stimulation in refractory schizophrenia. Schizophr. Res. 124, 91–100 (2010).

  50. 50.

    Amadi, U., Ilie, A., Johansen-Berg, H. & Stagg, C. J. Polarity-specific effects of motor transcranial direct current stimulation on fMRI resting state networks. Neuroimage 88, 155–161 (2014).

  51. 51.

    Wechsler, D. Wechsler Intelligence Scale for Children (4th edn.). The Psychological Corporation (San Antonio, Texas, 2003).

  52. 52.

    Wechsler, D. Wechsler Intelligence Scale for Children (5th edn.). The Psychological Corporation (San Antonio, Texas, 2014).

  53. 53.

    Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2, 125–141 (2012).

  54. 54.

    Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37, 90–101 (2007).

  55. 55.

    Muschelli, J. et al. Reduction of motion-related artifacts in resting state fMRI using aCompCor. Neuroimage 96, 22–35 (2014).

  56. 56.

    Hollingshead, A.B. Four factor index of social status. Yale University Department of Sociology (New Haven, Connecticut, 1975).

  57. 57.

    Ho, D. E., Imai, K., King, G. & Stuart, E. A. MatchIt: nonparameteric preprocessing for parametric causal inference. J. Stat. Softw. 42, 1–28 (2011).

  58. 58.

    Stuart, E. A. & Ialongo, N. S. Matching methods for selection of subjects for follow-up. Multivariate Behav. Res. 45, 746–765 (2010).

  59. 59.

    Diedrichsen, J. A spatially unbiased atlas template of the human cerebellum. Neuroimage 33, 127–138 (2006).

  60. 60.

    Diedrichsen, J., Balsters, J. H., Flavell, J., Cussans, E. & Ramnani, N. A probabilistic MR atlas of the human cerebellum. Neuroimage 46, 39–46 (2009).

  61. 61.

    Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002).

  62. 62.

    Barski, J. J., Dethleffsen, K. & Meyer, M. Cre recombinase expression in cerebellar Purkinje cells. Genesis 28, 93–98 (2000).

  63. 63.

    Kwiatkowski, D. J. et al. A mouse model of TSC1 reveals sex-dependent lethality from liver hemangiomas, and up-regulation of p70S6 kinase activity in Tsc1 null cells. Hum. Mol. Genet 11, 525–534 (2002).

  64. 64.

    Márquez-Ruiz, J. & Cheron, G. Sensory stimulation-dependent plasticity in the cerebellar cortex of alert mice. PLoS One 7, e36184 (2012).

  65. 65.

    Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C. & Fried, I. Invariant visual representation by single neurons in the human brain. Nature 435, 1102–1107 (2005).

  66. 66.

    Quiroga, R. Q., Nadasdy, Z. & Ben-Shaul, Y. Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16, 1661–1687 (2004).

  67. 67.

    Quiroga, R. Q. Spike sorting. Curr. Biol. 22, R45–R46 (2012).

  68. 68.

    Bock, N. A., Nieman, B. J., Bishop, J. B. & Mark Henkelman, R. In vivo multiple-mouse MRI at 7 Tesla. Magn. Reson. Med. 54, 1311–1316 (2005).

  69. 69.

    Lerch, J. P. et al. Automated cortical thickness measurements from MRI can accurately separate Alzheimer’s patients from normal elderly controls. Neurobiol. Aging 29, 23–30 (2008).

  70. 70.

    Nieman, B. J., Flenniken, A. M., Adamson, S. L., Henkelman, R. M. & Sled, J. G. Anatomical phenotyping in the brain and skull of a mutant mouse by magnetic resonance imaging and computed tomography. Physiol. Genomics 24, 154–162 (2006).

  71. 71.

    Dorr, A. E., Lerch, J. P., Spring, S., Kabani, N. & Henkelman, R. M. High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice. Neuroimage 42, 60–69 (2008).

  72. 72.

    Steadman, P. E. et al. Genetic effects on cerebellar structure across mouse models of autism using a magnetic resonance imaging atlas. Autism Res 7, 124–137 (2014).

  73. 73.

    Ullmann, J. F., Watson, C., Janke, A. L., Kurniawan, N. D. & Reutens, D. C. A segmentation protocol and MRI atlas of the C57BL/6J mouse neocortex. Neuroimage 78, 196–203 (2013).

  74. 74.

    Alexander-Bloch, A., Giedd, J. N. & Bullmore, E. Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 14, 322–336 (2013).

  75. 75.

    Bohbot, V. D., Lerch, J., Thorndycraft, B., Iaria, G. & Zijdenbos, A. P. Gray matter differences correlate with spontaneous strategies in a human virtual navigation task. J. Neurosci. 27, 10078–10083 (2007).

  76. 76.

    Bozzali, M. et al. Anatomical connectivity mapping: a new tool to assess brain disconnection in Alzheimer’s disease. Neuroimage 54, 2045–2051 (2011).

  77. 77.

    Evans, A. C. Networks of anatomical covariance. Neuroimage 80, 489–504 (2013).

  78. 78.

    Kelly, C. et al. A convergent functional architecture of the insula emerges across imaging modalities. Neuroimage 61, 1129–1142 (2012).

  79. 79.

    Spreng, R. N. & Turner, G. R. Structural covariance of the default network in healthy and pathological aging. J. Neurosci. 33, 15226–15234 (2013).

  80. 80.

    Zielinski, B. A. et al. scMRI reveals large-scale brain network abnormalities in autism. PLoS One 7, e49172 (2012).

  81. 81.

    The Mouse Nervous System. (eds. Watson C., Paxinos, G. & Peulles, L.) (Academic Press, London, 2011).

  82. 82.

    Yang, M., Silverman, J.L. & Crawley, J.N. Automated three-chambered social approach task for mice. Curr Protoc. Neurosci. 56, 8.26.1–8.26.16 (2011).

  83. 83.

    Yuan, E. et al. Graded loss of tuberin in an allelic series of brain models of TSC correlates with survival, and biochemical, histological and behavioral features. Hum. Mol. Genet 21, 4286–4300 (2012).

  84. 84.

    Holmes, A. et al. Behavioral characterization of dopamine D5 receptor null mutant mice. Behav. Neurosci. 115, 1129–1144 (2001).

  85. 85.

    Silverman, J. L. et al. Sociability and motor functions in Shank1 mutant mice. Brain Res. 1380, 120–137 (2011).

  86. 86.

    Bednar, I. et al. Selective nicotinic receptor consequences in APP(SWE) transgenic mice. Mol. Cell. Neurosci. 20, 354–365 (2002).

  87. 87.

    Yang, M. & Crawley, J. N. Simple behavioral assessment of mouse olfaction. Curr. Protoc. Neurosci. 48, 8.24.1–8.24.12 (2009).

  88. 88.

    Buitrago, M. M., Schulz, J. B., Dichgans, J. & Luft, A. R. Short and long-term motor skill learning in an accelerated rotarod training paradigm. Neurobiol. Learn. Mem. 81, 211–216 (2004).

  89. 89.

    Hayar, A., Bryant, J. L., Boughter, J. D. & Heck, D. H. A low-cost solution to measure mouse licking in an electrophysiological setup with a standard analog-to-digital converter. J. Neurosci. Methods 153, 203–207 (2006).

Download references


A.M.D. and C.J.S. acknowledge the support of P. Turkeltaub, C. Barrett, B. Drury, and S. Martin in neuroimaging data collection and analysis. All neurobehavioral experiments were performed at the Department of Psychiatry Rodent Behavioral Core, and the authors thank the Director for Core services, S. Birnbaum, for her assistance. The authors also appreciate assistance from the UT Southwestern Whole Brain Microscopy Facility, and from B. Nieman and L. Spencer Noakes for their work on the MRI sequence used. P.T.T. acknowledges support from the National Institute of Neurologic Disorders and Stroke of the NIH (K08 NS083733) the Child Neurology Foundation, the Tuberous Sclerosis Alliance, the Simons Foundation, and the University of Texas Southwestern Medical Center Disease Oriented Clinical Scholar Award. C.J.S. acknowledges funding from the National Institute of Mental Health of the NIH (R15 MH106957), pilot research funds from the Department of Psychology, and institutional startup funds from American University. J.P.L. and J.E. acknowledge support from the Canadian Institute for Health Research (CIHR) and the Ontario Brain Institute (OBI). S.H.M. acknowledges support from Autism Speaks, the National Institute of Mental Health (R01 MH085328-09, R01 MH078160-07, and K01 MH109766), and the National Institute of Neurological Disorders and Stroke (R01 NS048527-08). E.K. acknowledges funding from National Institute of Drug Abuse T32 training grant (T32 DA007290-24).

Author information


  1. Department of Psychology and Center for Behavioral Neuroscience, American University, Washington, DC, USA

    • Catherine J. Stoodley
    •  & Anila M. D’Mello
  2. Toronto Mouse Imaging Centre, Hospital for Sick Kids, Toronto, Canada

    • Jacob Ellegood
    •  & Jason P. Lerch
  3. The Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, Texas, USA

    • Vikram Jakkamsetti
    • , Pei Liu
    • , Jennifer M. Gibson
    • , Elyza Kelly
    • , Fantao Meng
    • , Christopher A. Cano
    • , Juan M. Pascual
    •  & Peter T. Tsai
  4. Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland, USA

    • Mary Beth Nebel
    •  & Stewart H. Mostofsky


  1. Search for Catherine J. Stoodley in:

  2. Search for Anila M. D’Mello in:

  3. Search for Jacob Ellegood in:

  4. Search for Vikram Jakkamsetti in:

  5. Search for Pei Liu in:

  6. Search for Mary Beth Nebel in:

  7. Search for Jennifer M. Gibson in:

  8. Search for Elyza Kelly in:

  9. Search for Fantao Meng in:

  10. Search for Christopher A. Cano in:

  11. Search for Juan M. Pascual in:

  12. Search for Stewart H. Mostofsky in:

  13. Search for Jason P. Lerch in:

  14. Search for Peter T. Tsai in:


C.J.S. and P.T.T. formulated human experiments and analysis, while P.T.T. formulated experiments in mice. P.T.T., J.M.G., F.M., and C.A.C. carried out mouse experiments. A.M.D. and C.J.S. carried out human studies and analysis. J.E. and J.P.L. performed mouse MRI and analysis. C.J.S., A.M.D., M.B.N., and S.H.M. designed the human ASD analysis, and S.H.M. and M.B.N. provided the human ASD data. V.J., P.L., E.K., and J.M.P. performed electrophysiology experiments and analysis. C.J.S., A.M.D., and P.T.T. prepared the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Catherine J. Stoodley or Peter T. Tsai.

Supplementary information

About this article

Publication history






Further reading