Skip to main content
Log in

Cerebellar Clustering and Functional Connectivity During Pain Processing

  • Original Paper
  • Published:
The Cerebellum Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  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.

    Article  PubMed  Google Scholar 

  2. Saab CY, Willis WD. The cerebellum: organization, functions and its role in nociception. Brain Res Brain Res Rev. 2003;42:85–95.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  5. Mehack R, Torgerson WS. On the language of pain. Anesthesiology. 1971;34:50–9.

    Article  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  8. Melzack R. The McGill Pain Questionnaire: major properties and scoring methods. Pain. 1975;1:277–99.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ito M. Bases and implications of learning in the cerebellum—adaptive control and internal model mechanism. Prog Brain Res. 2005;148:95–109.

    Article  PubMed  Google Scholar 

  11. Carrive P, Morgan MM. Periaqueductal gray. In: Paxinos G, Mai J, editors. Hum. Cent. Nerv. Syst. 2nd ed., Amsterdam: Elsevier; 2004, pp. 393–423

  12. Benarroch EE. Periaqueductal gray: an interface for behavioral control. Neurology. 2012;78:210–7. doi:10.1212/WNL.0b013e31823fcdee.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9:97–113.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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. 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.

    Article  PubMed  Google Scholar 

  26. Bezdek JC. FCM: the fuzzy c-means clustering algorithm. Comput Geosci. 1984;10:191–203.

    Article  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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. 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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

  35. Fox PT, Lancaster JL. Opinion: mapping context and content: the BrainMap model. Nat Rev Neurosci. 2002;3:319–21.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  39. Eickhoff SB, Bzdok D, Laird AR, Kurth F, Fox PT. Activation likelihood estimation meta-analysis revisited. Neuroimage. 2012;59:2349–61.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. 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. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  62. Sacchetti B, Scelfo B, Strata P. Cerebellum and emotional behavior. Neuroscience. 2009;162:756–62. doi:10.1016/j.neuroscience.2009.01.064.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to F. D’Agata.

Additional information

M. Diano and F. D’Agata contributed equally to this work.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 206 kb)

ESM 2

(PDF 108 kb)

ESM 3

(PDF 109 kb)

ESM 4

(PDF 107 kb)

ESM 5

(PDF 106 kb)

ESM 6

(PDF 106 kb)

ESM 7

(PDF 107 kb)

ESM 8

(PDF 109 kb)

ESM 9

(PDF 111 kb)

ESM 10

(PDF 107 kb)

ESM 11

(PDF 114 kb)

ESM 12

(PDF 54 kb)

ESM 13

(PDF 27 kb)

ESM 14

(PDF 34 kb)

ESM 15

(PDF 29 kb)

ESM 16

(PDF 29 kb)

ESM 17

(PDF 27 kb)

ESM 18

(PDF 33 kb)

ESM 19

(PDF 29 kb)

ESM 20

(PDF 24 kb)

ESM 21

(PDF 26 kb)

ESM 22

(PDF 27 kb)

ESM 23

(PDF 30 kb)

ESM 24

(PDF 28 kb)

ESM 25

(PDF 25 kb)

ESM 26

(PDF 38 kb)

ESM 27

(PDF 40 kb)

ESM 28

(PDF 20 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Diano, M., D’Agata, F., Cauda, F. et al. Cerebellar Clustering and Functional Connectivity During Pain Processing. Cerebellum 15, 343–356 (2016). https://doi.org/10.1007/s12311-015-0706-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12311-015-0706-4

Keywords

Navigation