The Cerebellum

, Volume 15, Issue 3, pp 343–356 | Cite as

Cerebellar Clustering and Functional Connectivity During Pain Processing

  • M. Diano
  • F. D’Agata
  • F. Cauda
  • T. Costa
  • E. Geda
  • K. Sacco
  • S. Duca
  • D. M. Torta
  • G. C. Geminiani
Original Paper


The cerebellum has been traditionally considered a sensory-motor structure, but more recently has been related to other cognitive and affective functions. Previous research and meta-analytic studies suggested that it could be involved in pain processing. Our aim was to distinguish the functional networks subserved by the cerebellum during pain processing. We used functional magnetic resonance imaging (fMRI) on 12 subjects undergoing mechanical pain stimulation and resting state acquisition. For the analysis of data, we used fuzzy c-mean to cluster cerebellar activity of each participant during nociception. The mean time courses of the clusters were used as regressors in a general linear model (GLM) analysis to explore brain functional connectivity (FC) of the cerebellar clusters. We compared our results with the resting state FC of the same cluster and explored with meta-analysis the behavior profile of the FC networks. We identified three significant clusters: cluster V, involving the culmen and quadrangular lobules (vermis IV-V, hemispheres IV-V-VI); cluster VI, involving the posterior quadrangular lobule and superior semilunar lobule (hemisphere VI, crus 1, crus 2), and cluster VII, involving the inferior semilunar lobule (VIIb, crus1, crus 2). Cluster V was more connected during pain with sensory-motor areas, cluster VI with cognitive areas, and cluster VII with emotional areas. Our results indicate that during the application of mechanical punctate stimuli, the cerebellum is not only involved in sensory functions but also with areas typically associated with cognitive and affective functions. Cerebellum seems to be involved in various aspects of nociception, reflecting the multidimensionality of pain perception.


Cerebellum Pain Fuzzy clustering Functional connectivity 



We want to thank the reviewers for the help and the precious suggestions. Also, we would like to thank Dr. Rebecca Watson for her useful comments on the final revision of the manuscript.

Conflict of Interest

The authors declare no conflict of interest.

Supplementary material

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  1. 1.
    Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage. 2009;44:489–501. doi: 10.1016/j.neuroimage.2008.08.039.CrossRefPubMedGoogle Scholar
  2. 2.
    Saab CY, Willis WD. The cerebellum: organization, functions and its role in nociception. Brain Res Brain Res Rev. 2003;42:85–95.CrossRefPubMedGoogle Scholar
  3. 3.
    Ruscheweyh R, Kühnel M, Filippopulos F, Blum B, Eggert T, Straube A. Altered experimental pain perception after cerebellar infarction. Pain. 2014;155:1303–12. doi: 10.1016/j.pain.2014.04.006.CrossRefPubMedGoogle Scholar
  4. 4.
    Duerden EG, Albanese MC. Localization of pain-related brain activation: a meta-analysis of neuroimaging data. Hum Brain Mapp. 2013;34:109–49. doi: 10.1002/hbm.21416.CrossRefPubMedGoogle Scholar
  5. 5.
    Mehack R, Torgerson WS. On the language of pain. Anesthesiology. 1971;34:50–9.CrossRefGoogle Scholar
  6. 6.
    Ngamkham S, Vincent C, Finnegan L, Holden JE, Wang ZJ, Wilkie DJ. The McGill Pain Questionnaire as a multidimensional measure in people with cancer: an integrative review. Pain Manag Nurs. 2012;13:27–51. doi: 10.1016/j.pmn.2010.12.003.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    De Gagné TA, Mikail SF, D’Eon JL. Confirmatory factor analysis of a 4-factor model of chronic pain evaluation. Pain. 1995;60:195–202.CrossRefPubMedGoogle Scholar
  8. 8.
    Melzack R. The McGill Pain Questionnaire: major properties and scoring methods. Pain. 1975;1:277–99.CrossRefPubMedGoogle Scholar
  9. 9.
    Moulton EA, Schmahmann JD, Becerra L, Borsook D. The cerebellum and pain: passive integrator or active participator? Brain Res Rev. 2010;65:14–27. doi: 10.1016/j.brainresrev.2010.05.005.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Ito M. Bases and implications of learning in the cerebellum—adaptive control and internal model mechanism. Prog Brain Res. 2005;148:95–109.CrossRefPubMedGoogle Scholar
  11. 11.
    Carrive P, Morgan MM. Periaqueductal gray. In: Paxinos G, Mai J, editors. Hum. Cent. Nerv. Syst. 2nd ed., Amsterdam: Elsevier; 2004, pp. 393–423Google Scholar
  12. 12.
    Benarroch EE. Periaqueductal gray: an interface for behavioral control. Neurology. 2012;78:210–7. doi: 10.1212/WNL.0b013e31823fcdee.CrossRefPubMedGoogle Scholar
  13. 13.
    Kong J, Loggia ML, Zyloney C, Tu P, Laviolette P, Gollub RL. Exploring the brain in pain: activations, deactivations and their relation. Pain. 2010;148:257–67. doi: 10.1016/j.pain.2009.11.008.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Linnman C, Beucke J-C, Jensen KB, Gollub RL, Kong J. Sex similarities and differences in pain-related periaqueductal gray connectivity. Pain. 2012;153:444–54. doi: 10.1016/j.pain.2011.11.006.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Sillery E, Bittar RG, Robson MD, Behrens TEJ, Stein J, Aziz TZ, et al. Connectivity of the human periventricular-periaqueductal gray region. J Neurosurg. 2005;103:1030–4. doi: 10.3171/jns.2005.103.6.1030.CrossRefPubMedGoogle Scholar
  16. 16.
    Kong J, Tu P, Zyloney C, Su T. Intrinsic functional connectivity of the periaqueductal gray, a resting fMRI study. Behav Brain Res. 2010;211:215–9. doi: 10.1016/j.bbr.2010.03.042.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Linnman C, Moulton EA, Barmettler G, Becerra L, Borsook D. Neuroimaging of the periaqueductal gray: state of the field. Neuroimage. 2012;60:505–22. doi: 10.1016/j.neuroimage.2011.11.095.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Cauda F, Costa T, Diano M, Sacco K, Duca S, Geminiani G, et al. Massive modulation of brain areas after mechanical pain stimulation: a time-resolved fMRI study. Cereb Cortex. 2014;24:2991–3005. doi: 10.1093/cercor/bht153.CrossRefPubMedGoogle Scholar
  19. 19.
    Mayhew SD, Hylands-White N, Porcaro C, Derbyshire SWG, Bagshaw AP. Intrinsic variability in the human response to pain is assembled from multiple, dynamic brain processes. Neuroimage. 2013;75:68–78. doi: 10.1016/j.neuroimage.2013.02.028.CrossRefPubMedGoogle Scholar
  20. 20.
    Moulton E a, Pendse G, Becerra LR, Borsook D. BOLD responses in somatosensory cortices better reflect heat sensation than pain. J Neurosci. 2012;32:6024–31. doi: 10.1523/JNEUROSCI.0006-12.2012.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9:97–113.CrossRefPubMedGoogle Scholar
  22. 22.
    Baumgärtner U, Iannetti GD, Zambreanu L, Stoeter P, Treede R-D, Tracey I. Multiple somatotopic representations of heat and mechanical pain in the operculo-insular cortex: a high-resolution fMRI study. J Neurophysiol. 2010;104:2863–72. doi: 10.1152/jn.00253.2010.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Miezin FM, Maccotta L, Ollinger JM, Petersen SE, Buckner RL. Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing. Neuroimage. 2000;11:735–59.CrossRefPubMedGoogle Scholar
  24. 24.
    Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system: an approach to cerebral imaging. New York: Thieme; 1988.Google Scholar
  25. 25.
    Smolders A, De Martino F, Staeren N, Scheunders P, Sijbers J, Goebel R, et al. Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis. Magn Reson Imaging. 2007;25:860–8.CrossRefPubMedGoogle Scholar
  26. 26.
    Bezdek JC. FCM: the fuzzy c-means clustering algorithm. Comput Geosci. 1984;10:191–203.CrossRefGoogle Scholar
  27. 27.
    Fadili MJ, Ruan S, Bloyet D, Mazoyer B. A multistep unsupervised fuzzy clustering analysis of fMRI time series. Hum Brain Mapp. 2000;10:160–78.CrossRefPubMedGoogle Scholar
  28. 28.
    Golay X, Kollias S, Stoll G, Meier D, Valavanis A, Boesiger P. A new correlation-based fuzzy logic clustering algorithm for fMRI. Magn Reson Med. 1998;40:249–60.CrossRefPubMedGoogle Scholar
  29. 29.
    Esposito F, Scarabino T, Hyvarinen A, Himberg J, Formisano E, Comani S, et al. Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage. 2005;25:193–205.CrossRefPubMedGoogle Scholar
  30. 30.
    Cauda F, Geminiani G, D’Agata F, Sacco K, Duca S, Bagshaw AP, et al. Functional connectivity of the posteromedial cortex. PLoS One 2010;5. doi: 10.1371/journal.pone.0013107
  31. 31.
    Goebel R, Esposito F, Formisano E. Analysis of functional image analysis contest (FIAC) data with BrainVoyager QX: from single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis. Hum Brain Mapp. 2006;27:392–401.CrossRefPubMedGoogle Scholar
  32. 32.
    Forman SD, Cohen JD, Fitzgerald M, Eddy WF, Mintun MA, Noll DC. Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magn Reson Med. 1995;33:636–47.CrossRefPubMedGoogle Scholar
  33. 33.
    Cauda F, D’Agata F, Sacco K, Duca S, Geminiani G, Vercelli A. Functional connectivity of the insula in the resting brain. Neuroimage. 2011;55:8–23. doi: 10.1016/j.neuroimage.2010.11.049.CrossRefPubMedGoogle Scholar
  34. 34.
    Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE, Penny WD. Statistical parametric mapping: the analysis of functional brain images. vol. 8. Academic Press; 2007.Google Scholar
  35. 35.
    Fox PT, Lancaster JL. Opinion: mapping context and content: the BrainMap model. Nat Rev Neurosci. 2002;3:319–21.CrossRefPubMedGoogle Scholar
  36. 36.
    Lancaster JL, Laird AR, Eickhoff SB, Martinez MJ, Fox PM, Fox PT. Automated regional behavioral analysis for human brain images. Front Neuroinform. 2012;6:23.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Hum Brain Mapp. 2009;30:2907–26. doi: 10.1002/hbm.20718.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Fox PT, Laird AR, Fox SP, Fox PM, Uecker AM, Crank M, et al. BrainMap taxonomy of experimental design: description and evaluation. Hum Brain Mapp. 2005;25:185–98.CrossRefPubMedGoogle Scholar
  39. 39.
    Eickhoff SB, Bzdok D, Laird AR, Kurth F, Fox PT. Activation likelihood estimation meta-analysis revisited. Neuroimage. 2012;59:2349–61.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Kochunov P, Lancaster J, Thompson P, Toga AW, Brewer P, Hardies J, et al. An optimized individual target brain in the Talairach coordinate system. Neuroimage. 2002;17:922–7.CrossRefPubMedGoogle Scholar
  41. 41.
    Peyron R, Laurent B, García-Larrea L. Functional imaging of brain responses to pain. A review and meta-analysis (2000). Neurophysiol Clin. 2000;30:263–88.CrossRefPubMedGoogle Scholar
  42. 42.
    Tölle TR, Kaufmann T, Siessmeier T, Lautenbacher S, Berthele a, Munz F, et al. Region-specific encoding of sensory and affective components of pain in the human brain: a positron emission tomography correlation analysis. Ann Neurol. 1999;45:40–7.CrossRefPubMedGoogle Scholar
  43. 43.
    Veldhuijzen DS, Nemenov MI, Keaser M, Zhuo J, Gullapalli RP, Greenspan JD. Differential brain activation associated with laser-evoked burning and pricking pain: an event-related fMRI study. Pain. 2009;141:104–13. doi: 10.1016/j.pain.2008.10.027.CrossRefPubMedGoogle Scholar
  44. 44.
    Wager TD, Atlas LY, Lindquist MA, Roy M, Woo C-W, Kross E. An fMRI-based neurologic signature of physical pain. N Engl J Med. 2013;368:1388–97. doi: 10.1056/NEJMoa1204471.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Coghill RC, Sang CN, Maisog JM, Iadarola MJ. Pain intensity processing within the human brain: a bilateral, distributed mechanism. J Neurophysiol. 1999;82:1934–43.PubMedGoogle Scholar
  46. 46.
    Kong J, White NS, Kwong KK, Vangel MG, Rosman IS, Gracely RH, et al. Using fMRI to dissociate sensory encoding from cognitive evaluation of heat pain intensity. Hum Brain Mapp. 2006;27:715–21. doi: 10.1002/hbm.20213.CrossRefPubMedGoogle Scholar
  47. 47.
    Baliki MN, Geha PY, Apkarian A V. Parsing pain perception between nociceptive representation and magnitude estimation. J Neurophysiol. 2009; 101:875–87. doi: 10.1152/jn.91100.2008
  48. 48.
    Asplund CL, Todd JJ, Snyder AP, Marois R. A central role for the lateral prefrontal cortex in goal-directed and stimulus-driven attention. Nat Neurosci. 2010;13:507–12. doi: 10.1038/nn.2509.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007;8:700–11. doi: 10.1038/nrn2201.CrossRefPubMedGoogle Scholar
  50. 50.
    Mobbs D, Petrovic P, Marchant JL, Hassabis D, Weiskopf N, Seymour B, et al. When fear is near: threat imminence elicits prefrontal-periaqueductal gray shifts in humans. Science. 2007;317:1079–83. doi: 10.1126/science.1144298.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Craig AD. Significance of the insula for the evolution of human awareness of feelings from the body. Ann N Y Acad Sci. 2011;1225:72–82. doi: 10.1111/j.1749-6632.2011.05990.x.CrossRefPubMedGoogle Scholar
  52. 52.
    Mesmoudi S, Perlbarg V, Rudrauf D, Messe A, Pinsard B, Hasboun D, et al. Resting state networks’ corticotopy: the dual intertwined rings architecture. PLoS One. 2013;8:e67444.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Bernard JA, Seidler RD, Hassevoort KM, Benson BL, Welsh RC, Wiggins JL, et al. Resting state cortico-cerebellar functional connectivity networks: a comparison of anatomical and self-organizing map approaches. Front Neuroanat. 2012;6. doi: 10.3389/fnana.2012.00031
  54. 54.
    Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BTT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol 2011; 106:2322–45. doi: 10.1152/jn.00339.2011
  55. 55.
    Habas C, Kamdar N, Nguyen D, Prater K, Beckmann CF, Menon V, et al. Distinct cerebellar contributions to intrinsic connectivity networks. J Neurosci. 2009;29:8586–94. doi: 10.1523/JNEUROSCI.1868-09.2009.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Krienen FM, Buckner RL. Segregated fronto-cerebellar circuits revealed by intrinsic functional connectivity. Cereb Cortex. 2009;19:2485–97. doi: 10.1093/cercor/bhp135.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Stoodley CJ, Valera EM, Schmahmann JD. Functional topography of the cerebellum for motor and cognitive tasks: an fMRI study. Neuroimage. 2011;59:1560–70. doi: 10.1016/j.neuroimage.2011.08.065.CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Legrain V, Iannetti GD, Plaghki L, Mouraux A. The pain matrix reloaded: a salience detection system for the body. Prog Neurobiol. 2011;93:111–24. doi: 10.1016/j.pneurobio.2010.10.005.CrossRefPubMedGoogle Scholar
  59. 59.
    Simons LE, Moulton EA, Linnman C, Carpino E, Becerra L, Borsook D. The human amygdala and pain: evidence from neuroimaging. Hum Brain Mapp. 2014;35:527–38. doi: 10.1002/hbm.22199.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Moulton EA, Pendse G, Schmahmann J, Becerra L, Borsook D. Aversion-related circuitry in the cerebellum: responses to noxious heat and unpleasant images. J Neurosci. 2011;31:3795–804. doi: 10.1523/JNEUROSCI.6709-10.2011.CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Baumann O, Mattingley JB. Functional topography of primary emotion processing in the human cerebellum. Neuroimage. 2012;61:805–11. doi: 10.1016/j.neuroimage.2012.03.044.CrossRefPubMedGoogle Scholar
  62. 62.
    Sacchetti B, Scelfo B, Strata P. Cerebellum and emotional behavior. Neuroscience. 2009;162:756–62. doi: 10.1016/j.neuroscience.2009.01.064.CrossRefPubMedGoogle Scholar
  63. 63.
    Schienle A, Scharmüller W. Cerebellar activity and connectivity during the experience of disgust and happiness. Neuroscience. 2013;246:375–81. doi: 10.1016/j.neuroscience.2013.04.048.CrossRefPubMedGoogle Scholar
  64. 64.
    Stoodley CJ, Valera EM, Schmahmann JD. Functional topography of the cerebellum for motor and cognitive tasks: an fMRI study. Neuroimage. 2012;59:1560–70. doi: 10.1016/j.neuroimage.2011.08.065.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Adamaszek M, D’Agata F, Kirkby KC, Trenner MU, Sehm B, Steele CJ, et al. Impairment of emotional facial expression and prosody discrimination due to ischemic cerebellar lesions. Cerebellum. 2014;13:338–45. doi: 10.1007/s12311-013-0537-0.CrossRefPubMedGoogle Scholar
  66. 66.
    Damasio AR. The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philos Trans R Soc Lond B Biol Sci. 1996;351:1413–20. doi: 10.1098/rstb.1996.0125.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • M. Diano
    • 1
    • 2
  • F. D’Agata
    • 3
  • F. Cauda
    • 1
    • 2
    • 4
  • T. Costa
    • 1
    • 2
  • E. Geda
    • 5
  • K. Sacco
    • 1
    • 4
    • 5
  • S. Duca
    • 2
  • D. M. Torta
    • 1
    • 2
  • G. C. Geminiani
    • 1
    • 2
    • 4
    • 6
  1. 1.Department of PsychologyUniversity of TurinTurinItaly
  2. 2.GCS-fMRI, Koelliker HospitalTurinItaly
  3. 3.Department of Neuroscience, AOU S. Giovanni BattistaUniversity of TurinTurinItaly
  4. 4.NIT, Neuroscience Institute of TurinTurinItaly
  5. 5.Imaging and Plasticity Lab, Department of PsychologyUniversity of TurinTurinItaly
  6. 6.National Institute of Neuroscience-ItalyTurinItaly

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