Neurodevelopmental and Psychiatric Symptoms in Patients with a Cyst Compressing the Cerebellum: an Ongoing Enigma

  • Xavier GuellEmail author
  • Sheeba A. Anteraper
  • Satrajit S. Ghosh
  • John D. E. Gabrieli
  • Jeremy D. Schmahmann
Original Paper


A patient diagnosed with developmental delay, intellectual disability, and autistic and obsessive-compulsive symptoms was found to have a posterior fossa arachnoid cyst (PFAC) compressing the cerebellum. The patient was referred to our Ataxia Unit for consideration of surgical drainage of the cyst to improve his clinical constellation. This scenario led to an in-depth analysis including a literature review, functional resting-state MRI analysis of our patient compared to a group of controls, and genetic testing. While it is reasonable to consider that there may be a causal relationship between PFAC and neurodevelopmental or psychiatric symptoms in some patients, there is also a nontrivial prevalence of PFAC in the asymptomatic population and a significant possibility that many PFAC are incidental findings in the context of primary cognitive or psychiatric symptoms. Our functional MRI analysis is the first to examine brain function, and to report cerebellar dysfunction, in a patient presenting with cognitive/psychiatric symptoms found to have a structural abnormality compressing the cerebellum. These neuroimaging findings are inherently limited due to their correlational nature but provide unprecedented evidence suggesting that cerebellar compression may be associated with cerebellar dysfunction. Exome gene sequencing revealed additional etiological possibilities, highlighting the complexity of this field of cerebellar clinical and scientific practice. Our findings and discussion may guide future investigations addressing an important knowledge gap—namely, is there a link between cerebellar compression (including arachnoid cysts and possibly other forms of cerebellar compression such as Chiari malformation), cerebellar dysfunction (including fMRI abnormalities reported here), and neuropsychiatric symptoms?


Cerebellum fMRI Arachnoid cyst Neuropsychiatry Behavioral neurology 



This work was supported in part by the MINDlink foundation (JDS), La Caixa Banking Foundation (XG), MGH ECOR Fund for Medical Discovery Postdoctoral Fellowship Award (XG), and NIH grant R01 EB020740 (SSG). The control participant data collection was supported by the McGovern Institute Neurotechnology Fund and MIT Lincoln Laboratory. This research was carried out at the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research, Massachusetts Institute of Technology, using resources provided by the Center for Functional Neuroimaging Technologies, P41EB015896, a P41 Biotechnology Resource Grant supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health. This work also involved the use of MR instrumentation supported by the NIH Shared Instrumentation Grant Program and/or High-End Instrumentation Grant Program, specifically grant numbers S10RR022976 and S10RR019933. The authors want to thank Mathias Goncalves, Kevin R Sitek, Gregory Ciccarelli, Yoel Sanchez, Carlo de los Angeles, and Jakub R Kaczmarzyk for their participation in MRI control data collection.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest. JDS consults for Bayer, Biogen, Biohaven, and Cadent Pharmaceuticals and has no conflicts of interest to declare that may be perceived as biasing the content of this manuscript.

Supplementary material

12311_2019_1050_MOESM1_ESM.png (553 kb)
ESM 1 Top row: structural standard space (SPM single subject MNI brain) and its relationship with our cerebellum MVPA cluster (red). Bottom row: structural scan of the patient registered to standard space. While presence of the cyst is not eliminated from cerebellar territory (yellow arrow), our cerebellum MVPA cluster (red) is in lobules I-VI which were correctly registered (see cerebellar structures surrounding our MVPA cluster in the top and bottom images). MVPA findings are thus not a result of erroneous registration.(PNG 553 kb)
12311_2019_1050_Fig5_ESM.png (3.2 mb)
Fig. 5

Supplementary analysis repeating our main MVPA and post-hoc seed-to-voxel analyses including age and gender as additional regressors of no interest. The results of this supplementary analysis are virtually identical to those of our main analysis (compare panels a, b, and c here to panels a, b, and c of Figure 2), indicating that fMRI differences between our patient and controls as reported in our main analysis are not driven by age or gender.(PNG 3.17 mb)

12311_2019_1050_MOESM2_ESM.tif (3.2 mb)
Fig. 5

Supplementary analysis repeating our main MVPA and post-hoc seed-to-voxel analyses including age and gender as additional regressors of no interest. The results of this supplementary analysis are virtually identical to those of our main analysis (compare panels a, b, and c here to panels a, b, and c of Figure 2), indicating that fMRI differences between our patient and controls as reported in our main analysis are not driven by age or gender.(PNG 3.17 mb)

12311_2019_1050_MOESM3_ESM.png (7.9 mb)
ESM 3 Region-based volumetric analysis in our patient (red dots) compared to controls (black dots). Left: for each individual participant, each parcel’s volume is divided by the sum of all grey matter parcel’s volume, as a way to normalize for overall larger or smaller brain sizes between participants. Right: z scores in our patient and controls are shown for each parcel X as follows: ((normalized brain volume of X in one single participant) – (mean of normalized volume of X across all controls)) / (standard deviation of normalized volume of X across all controls). No differences between our patient and controls survived statistical significance testing (Crawford’s t-test, two-sided, with FDR correction for multiple comparisons and a threshold of p> 0.05). Abbreviations correspond to terminology of neuromorphometrics atlas (, as integrated in SPM’s CAT12 toolbox.(PNG 7.86 mb)


  1. 1.
    Schmahmann JD, Gardner R, MacMore J, Vangel MG. Development of a brief ataxia rating scale (BARS) based on a modified form of the ICARS. Mov Disord. 2009;24(12):1820–8.Google Scholar
  2. 2.
    Hoche F, Guell X, Vangel M, Sherman J, Schmahmann J. The cerebellar cognitive affective/Schmahmann syndrome scale. Brain. 2018;141(1):248–70.Google Scholar
  3. 3.
    Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62(2):782–90.Google Scholar
  4. 4.
    Whitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2(3):125–41.Google Scholar
  5. 5.
    Worsley KJ, Friston KJ. Analysis of fMRI time-series revisited—again. Neuroimage. 1995;2:173–81.Google Scholar
  6. 6.
    Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage. 2007;37(1):90–101.Google Scholar
  7. 7.
    Arnold Anteraper S, Guell X, D’Mello A, Joshi N, Whitfield-Gabrieli S, Joshi G. Disrupted cerebro-cerebellar intrinsic functional connectivity in young adults with high-functioning autism spectrum disorder: a data-driven, whole-brain, high temporal resolution fMRI study. Brain Connect 2019;9(1):48–59Google Scholar
  8. 8.
    Thompson WH, Thelin EP, Lilja A, Bellander BM, Fransson P. Functional resting-state fMRI connectivity correlates with serum levels of the S100B protein in the acute phase of traumatic brain injury. NeuroImage Clin. 2016;12:1004-1012Google Scholar
  9. 9.
    Eklund A, Nichols TE, Knutsson H. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc Natl Acad Sci. 2016;113(28):7900–5.Google Scholar
  10. 10.
    Diedrichsen J, Zotow E. Surface-based display of volume-averaged cerebellar imaging data. PLoS One. 2015;10(7):e0133402.Google Scholar
  11. 11.
    Guell X, Schmahmann J, Gabrieli J, Ghosh S. Functional gradients of the cerebellum. Elife. 2018;7:e36652.Google Scholar
  12. 12.
    Crawford JR, Howell DC. Comparing an Individual’s test score against norms derived from small samples. Clin Neuropsychol (Neuropsychology, Dev Cogn Sect D). 1998;12(4):482–6.Google Scholar
  13. 13.
    Sokal RR, Rohlf FJ. Biometry. 4th ed. Rolfes M, editor. Biometry. New York: W.H. Freeman and Company; 2011.Google Scholar
  14. 14.
    Gaser C, Dahnke R. CAT—a computational anatomy toolbox for the analysis of structural MRI data. HBM [Internet]. 2016; Available from: Accessed 4/20/2019
  15. 15.
    Ajtai B, Bertelson JA. Imaging of intracranial cysts. CONTINUUM Lifelong Learning in Neurology. 2016;22:1553–73.Google Scholar
  16. 16.
    Al-Holou WN, Yew AY, Boomsaad ZE, Garton HJL, Muraszko KM, Maher CO. Prevalence and natural history of arachnoid cysts in children. J Neurosurg Pediatr. 2010;5(6):578–85.Google Scholar
  17. 17.
    Boltshauser E, Martin F, Altermatt S. Outcome in children with space-occupying posterior fossa arachnoid cysts. Neuropediatrics. 2002;33(3):118–21.Google Scholar
  18. 18.
    Al-Holou WN, Terman S, Kilburg C, Garton HJL, Muraszko KM, Maher CO. Prevalence and natural history of arachnoid cysts in adults. J Neurosurg. 2013;118(2):222–31.Google Scholar
  19. 19.
    Morris Z, Weber F, Lee Y-C, Tsushima Y, Alphs H, Ladd SC, et al. Incidental findings on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ. 2000;339(September):b3016.Google Scholar
  20. 20.
    Galassi E, Tognetti F, Frank F, Fagioli L, Nasi M, Gaist G. Infratentorial arachnoid cysts. J Neurosurg. 1985;63:210–7.Google Scholar
  21. 21.
    Vernooij MW, Ikram MA, Tanghe HL, Vincent AJPE, Hofman A, Krestin GP, et al. Incidental findings on brain MRI in the general population. N Engl J Med. 2007;357(18):1821–8.Google Scholar
  22. 22.
    Srinivasan U, Lawrence R. Posterior fossa arachnoid cysts in adults: surgical strategy: case series. Asian J Neurosurg 2015;10(1):47.Google Scholar
  23. 23.
    Cuny ML, Pallone M, Piana H, Boddaert N, Sainte-Rose C, Vaivre-Douret L, et al. Neuropsychological improvement after posterior fossa arachnoid cyst drainage. Childs Nerv Syst. 2017;33(1):135–41.Google Scholar
  24. 24.
    Maner F, Babalioglu M, Cetinkaya O, Ipekçioglu D, Ergen N, Yesil R, et al. The coexistence of arachnoid cyst with first episode psychosis: four cases. J Neurol Disord. 2014;2(6).Google Scholar
  25. 25.
    Gewirtz G, Squires-Wheeler E, Sharif Z, Honer WG. Results of computerised tomography during first admission for psychosis. Br J Psychiatry. 1994;164(JUNE):789–95.Google Scholar
  26. 26.
    Kohn R, Lilly RB, Sokol MS, Malloy PF. Psychiatric presentations of intracranial cysts. J Neuropsychiatry Clin Neurosci. 1989;1(1):60–6.Google Scholar
  27. 27.
    Helland CA, Wester K. A population based study of intracranial arachnoid cysts: clinical and neuroimaging outcomes following surgical cyst decompression in adults. J Neurol Neurosurg Psychiatry. 2007;78(10):1129–35.Google Scholar
  28. 28.
    Huang JH, Mei WZ, Chen Y, Chen JW, Lin ZX. Analysis on clinical characteristics of intracranial arachnoid cysts in 488 pediatric cases. Int J Clin Exp Med. 2015;8(10):18343–50.Google Scholar
  29. 29.
    Marin-Sanabria EA, Yamamoto H, Nagashima T, Kohmura E. Evaluation of the management of arachnoid cyst of the posterior fossa in pediatric population: experience over 27 years. Childs Nerv Syst. 2007;23(5):535–42.Google Scholar
  30. 30.
    Arai H, Sato K. Posterior fossa cysts: clinical, neuroradiological and surgical features. Childs Nerv Syst. 1991;7(3):156–64.Google Scholar
  31. 31.
    Haberkamp TJ, Monsell EM, House WF, Levine SC, Piazza L. Diagnosis and treatment of arachnoid cysts of the posterior fossa. Otolaryngol Head Neck Surg. 1990;103(4):610–4.Google Scholar
  32. 32.
    Spansdahl T, Solheim O. Quality of life in adult patients with primary intracranial arachnoid cysts. Acta Neurochir. 2007;149(10):1025–31.Google Scholar
  33. 33.
    Gjerde PB, Schmid M, Hammar A, Wester K. Intracranial arachnoid cysts: impairment of higher cognitive functions and postoperative improvement. J Neurodev Disord. 2013;5(1):21.Google Scholar
  34. 34.
    Wester K, Hugdahl K. Arachnoid cysts of the left temporal fossa: impaired preoperative cognition and postoperative improvement. J Neurol Neurosurg Psychiatry. 1995;59(3):293–8.Google Scholar
  35. 35.
    Wester K, Hugdahl K. Verbal laterality and handedness in patients with intracranial arachnoid cysts. J Neurol. 2003;250(1):36–41.Google Scholar
  36. 36.
    Raeder MB, Helland CA, Hugdahl K, Wester K. Arachnoid cysts cause cognitive deficits that improve after surgery. Neurology. 2005;64(1):160–2.Google Scholar
  37. 37.
    Gundersen H, Helland CA, Raeder MB, Hugdahl K, Wester K. Visual attention in patients with intracranial arachnoid cysts. J Neurol. 2007;254(1):60–6.Google Scholar
  38. 38.
    Schmahmann J, Guell X, Stoodley C, Halko M. The theory and neuroscience of cerebellar cognition. Annu Rev Neurosci 2019 [Epub ahead of print].Google Scholar
  39. 39.
    Schmahmann JD, Pandya DN. Projections to the basis pontis from the superior temporal sulcus and superior temporal region in the rhesus monkey. J Comp Neurol. 1991;308(2):224–48.Google Scholar
  40. 40.
    Schmahmann JD. From movement to thought: anatomic substrates of the cerebellar contribution to cognitive processing. Hum Brain Mapp. 1996;4(3):174–98.Google Scholar
  41. 41.
    Schmahmann JD, Pandya DN. Anatomic organization of the basilar pontine projections from prefrontal cortices in rhesus monkey. J Neurosci. 1997;17(1):438–58.Google Scholar
  42. 42.
    Schmahmann JD, Pandya DN. The cerebrocerebellar system. Int Rev Neurobiol. 1997;41:31–60.Google Scholar
  43. 43.
    Kelly RM, Strick PL. Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. J Neurosci. 2003;23(23):8432–44.Google Scholar
  44. 44.
    F a M, Strick PL. Anatomical evidence for cerebellar and basal ganglia involvement in higher cognitive function. Science (80- ). 1994;266(5184):458–61.Google Scholar
  45. 45.
    Schmahmann JD, Sherman JC. The cerebellar cognitive affective syndrome. Brain. 1998;121(4):561–79.Google Scholar
  46. 46.
    Hoche F, Guell X, Sherman JC, Vangel MG, Schmahmann JD. Cerebellar contribution to social cognition. Cerebellum. 2016;15(6):732–43.Google Scholar
  47. 47.
    Guell X, Hoche F, Schmahmann JD. Metalinguistic deficits in patients with cerebellar dysfunction: empirical support for the dysmetria of thought theory. Cerebellum. 2015;14(1):50–8.Google Scholar
  48. 48.
    Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage. 2009;44(2):489–501.Google Scholar
  49. 49.
    Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BTT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(5):2322–45.Google Scholar
  50. 50.
    Habas C, Kamdar N, Nguyen D, Prater K, Beckmann CF, Menon V, et al. Distinct cerebellar contributions to intrinsic connectivity networks. J Neurosci. 2009;29(26):8586–94.Google Scholar
  51. 51.
    Guell X, Gabrieli JDE, Schmahmann JD. Triple representation of language, working memory, social and emotion processing in the cerebellum: convergent evidence from task and seed-based resting-state fMRI analyses in a single large cohort. Neuroimage. 2018;172:437–49.Google Scholar
  52. 52.
    Phillips JR, Hewedi DH, Eissa AM, Moustafa AA. The cerebellum and psychiatric disorders. Front Public Heal 2015;3:66.Google Scholar
  53. 53.
    Wang T, Liu J, Zhang J, Zhan W, Li L, Wu M, et al. Altered resting-state functional activity in posttraumatic stress disorder: a quantitative meta-analysis. Sci Rep 2016;6:27131.Google Scholar
  54. 54.
    Kim H, Kim J, Loggia ML, Cahalan C, Garcia RG, Vangel MG, et al. Fibromyalgia is characterized by altered frontal and cerebellar structural covariance brain networks. NeuroImage Clin. 2015;7:667–77.Google Scholar
  55. 55.
    Guo CC, Tan R, Hodges JR, Hu X, Sami S, Hornberger M. Network-selective vulnerability of the human cerebellum to Alzheimer’s disease and frontotemporal dementia. Brain. 2016;139(5):1527–38.Google Scholar
  56. 56.
    Bastos Leite AJ, Van Der Flier WM, Van Straaten ECW, Scheltens P, Barkhof F. Infratentorial abnormalities in vascular dementia. Stroke. 2006;37(1):105–10.Google Scholar
  57. 57.
    Wilkins A. Cerebellar dysfunction in multiple sclerosis. Front Neurol. 2017;8.Google Scholar
  58. 58.
    Wolf RC, Thomann PA, Sambataro F, Wolf ND, Vasic N, Landwehrmeyer GB, et al. Abnormal cerebellar volume and corticocerebellar dysfunction in early manifest Huntington’s disease. J Neurol. 2015;262(4):859–69.Google Scholar
  59. 59.
    Wu T, Hallett M. The cerebellum in Parkinson’s disease. Brain. 2013;136(3):696–709.Google Scholar
  60. 60.
    Stoodley CJ, Schmahmann JD. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex. 2010;46(7):831–44.Google Scholar
  61. 61.
    Guell X, Goncalves M, Kaczmarzyk J, Gabrieli J, Schmahmann J, Ghosh S. LittleBrain: a gradient-based tool for the topographical interpretation of cerebellar neuroimaging findings. PLoS One. 2019;14(1):e0210028.Google Scholar
  62. 62.
    Tham E, Lindstrand A, Santani A, Malmgren H, Nesbitt A, Dubbs HA, et al. Dominant mutations in KAT6A cause intellectual disability with recognizable syndromic features. Am J Hum Genet. 2015;96(3):507–13.Google Scholar
  63. 63.
    Arboleda VA, Lee H, Dorrani N, Zadeh N, Willis M, Macmurdo CF, et al. De novo nonsense mutations in KAT6A, a lysine acetyl-transferase gene, cause a syndrome including microcephaly and global developmental delay. Am J Hum Genet. 2015;96(3):498–506.Google Scholar
  64. 64.
    Millan F, Cho MT, Retterer K, Monaghan KG, Bai R, Vitazka P, et al. Whole exome sequencing reveals de novo pathogenic variants in KAT6A as a cause of a neurodevelopmental disorder. Am J Med Genet Part A. 2016;170(7):1791–8.Google Scholar
  65. 65.
    Reijnders MRF, Zachariadis V, Latour B, Jolly L, Mancini GM, Pfundt R, et al. De novo loss-of-function mutations in USP9X cause a female-specific recognizable syndrome with developmental delay and congenital malformations. Am J Hum Genet. 2016;98(2):373–81.Google Scholar
  66. 66.
    Tarpey PS, Smith R, Pleasance E, Whibley A, Edkins S, Hardy C, et al. A systematic, large-scale resequencing screen of X-chromosome coding exons in mental retardation. Nat Genet. 2009;41(5):535–43.Google Scholar
  67. 67.
    de Ligt J, Willemsen MH, van Bon BWM, Kleefstra T, Yntema HG, Kroes T, et al. Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med. 2012;367(20):1921–9.Google Scholar
  68. 68.
    Nambot S, Thevenon J, Kuentz P, Duffourd Y, Tisserant E, Bruel A-L, et al. Clinical whole-exome sequencing for the diagnosis of rare disorders with congenital anomalies and/or intellectual disability: substantial interest of prospective annual reanalysis. Genet Med 2017.Google Scholar
  69. 69.
    Schmahmann JD, Weilburg JB, Sherman JC. The neuropsychiatry of the cerebellum - insights from the clinic. Cerebellum. 2007;6(3):254–67.Google Scholar
  70. 70.
    Chisholm BT, Velamoor R, Chandarana PC, Cochrane DK. Anxiety disorder in a case of Arnold-Chiari malformation. J Psychiatry Neurosci. 1993;18(2):67–8.Google Scholar
  71. 71.
    Bakim B, Goksan Y, Yilmaz A, Karamustafalioglu O, Akbiyik M, Yayla M, et al. The quality of life and psychiatric morbidity in patients operated for Arnold-Chiari malformation type I. Int J Psychiatry Clin Pract. 2013;17:259–63.Google Scholar
  72. 72.
    Del Casale A, Serata D, Rapinesi C, Simonetti A, Tamorri SM, Comparelli A, et al. Psychosis risk syndrome comorbid with panic attack disorder in a cannabis-abusing patient affected by Arnold-Chiari malformation type I. Gen Hosp Psychiatry. 2012;34(6):702.e5–7.Google Scholar
  73. 73.
    Wicklund MR, Mokri B, Drubach DA, Boeve BF, Parisi JE, Josephs KA. Frontotemporal brain sagging syndrome: an SIH-like presentation mimicking FTD. Neurology. 2011;76(16):1377–82.Google Scholar
  74. 74.
    Guell X, Goncalves M, Kaczmarzyk JR, Gabrieli JDE, Schmahmann JD, Ghosh SS, Margulies DS. LittleBrain: A gradient-based tool for the topographical interpretation of cerebellar neuroimaging findings. PLOS ONE 2019;14(1):e0210028Google Scholar

Copyright information

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

Authors and Affiliations

  • Xavier Guell
    • 1
    • 2
    • 3
    Email author
  • Sheeba A. Anteraper
    • 1
    • 4
    • 5
  • Satrajit S. Ghosh
    • 1
    • 6
  • John D. E. Gabrieli
    • 1
  • Jeremy D. Schmahmann
    • 3
    • 7
  1. 1.McGovern Institute for Brain ResearchMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of NeurologyHarvard Medical School and Massachusetts General HospitalCambridgeUSA
  3. 3.Laboratory for Neuroanatomy and Cerebellar Neurobiology, Department of Neurology, Massachusetts General HospitalHarvard Medical SchoolBostonUSA
  4. 4.Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum DisorderMassachusetts General HospitalBostonUSA
  5. 5.PEN LaboratoryNortheastern UniversityBostonUSA
  6. 6.Department of OtolaryngologyHarvard Medical SchoolBostonUSA
  7. 7.Ataxia Unit, Cognitive Behavioral Neurology Unit, Department of Neurology, Massachusetts General HospitalHarvard Medical SchoolBostonUSA

Personalised recommendations