Skip to main content

Advertisement

Log in

Structural connectivity changes in the cerebral pain matrix in burning mouth syndrome: a multi-shell, multi-tissue-constrained spherical deconvolution model analysis

  • Diagnostic Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

Burning mouth syndrome (BMS) is a chronic intraoral pain syndrome. Previous studies have attempted to determine the brain connectivity features in BMS using functional and structural magnetic resonance imaging. However, no study has investigated the structural connectivity using multi-shell, multi-tissue-constrained spherical deconvolution (MSMT-CSD), anatomically constrained tractography (ACT), and spherical deconvolution informed filtering of tractograms (SIFT). Therefore, this study aimed to assess the differences in brain structural connectivity of patients with BMS and healthy controls using probabilistic tractography with these methods, and graph analysis.

Methods

Fourteen patients with BMS and 11 age- and sex-matched healthy volunteers underwent 3-T magnetic resonance imaging. MSMT-CSD-based probabilistic structural connectivity was computed using the second-order integration over fiber orientation distributions algorithm based on nodes set in 84 anatomical cortical regions with ACT and SIFT. A t-test was performed for comparisons between the BMS and healthy control brain networks.

Results

The betweenness centrality was significantly higher in the left insula, right amygdala, and right lateral orbitofrontal cortex and significantly lower in the right inferotemporal cortex in the BMS group than that in healthy controls. However, no significant difference was found in the clustering coefficient, node degree, and small-worldness between the two groups.

Conclusion

Graph analysis of brain probabilistic structural connectivity, based on diffusion imaging using an MSMT-CSD model with ACT and SIFT, revealed alterations in the regions comprising the pain matrix and medial pain ascending pathway. These results highlight the emotional-affective profile of BMS, which is a type of chronic pain syndrome.

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.

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

Data cannot be shared publicly because of hospital regulations. Data are available from the University of Tokyo Hospital Institutional Data Access for researchers who meet the criteria for access to confidential data. Please contact the Graduate School of Medicine and Faculty of Medicine, The University of Tokyo Research Ethics Committee (ethics@m.u-tokyo.ac.jp) if you ask for our original data.

Abbreviations

5TT:

Five-tissue-type

ACT:

Anatomically constrained tractography

BC:

Betweenness centrality

BMS:

Burning mouth syndrome

CSD:

Constrained spherical deconvolution

EPI:

Echo planar imaging

iFOD2:

A second-order integration over fiber orientation distributions

MRI:

Magnetic resonance imaging

MSMT:

Multi-tissue-constrained spherical deconvolution

PTSD:

Post-traumatic stress disorder

ROI:

Region of interest

SIFT:

Spherical deconvolution informed filtering of tractograms

TE:

Echo time

TR:

Repetition time

References

  1. Khan SA, Keaser ML, Meiller TF, Seminowicz DA (2014) Altered structure and function in the hippocampus and medial prefrontal cortex in patients with burning mouth syndrome. Pain 155:1472–1480. https://doi.org/10.1016/j.pain.2014.04.022

    Article  PubMed  Google Scholar 

  2. Grushka M, Epstein JB, Gorsky M (2002) Burning mouth syndrome. Am Fam Physician 65:615–620

    PubMed  Google Scholar 

  3. Tan Y, Wu X, Chen J, Kong L, Qian Z (2019) Structural and functional connectivity between the amygdala and orbital frontal cortex in burning mouth syndrome: an fMRI study. Front Psychol 10:1700. https://doi.org/10.3389/fpsyg.2019.01700

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wada A, Shizukuishi T, Kikuta J, Yamada H, Watanabe Y, Imamura Y et al (2017) Altered structural connectivity of pain-related brain network in burning mouth syndrome-investigation by graph analysis of probabilistic tractography. Neuroradiology 59:525–532. https://doi.org/10.1007/s00234-017-1830-2

    Article  PubMed  Google Scholar 

  5. Behrens TEJ, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34:144–155. https://doi.org/10.1016/j.neuroimage.2006.09.018

    Article  CAS  PubMed  Google Scholar 

  6. Jeurissen B, Leemans A, Tournier JD, Jones DJ, Sikbers J (2013) Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum Brain Mapp 34:2747–2766. https://doi.org/10.1002/hbm.22099

    Article  PubMed  Google Scholar 

  7. Tuch DS (2004) Q-Ball imaging. Magn Reson Med 52:1358–1372. https://doi.org/10.1002/mrm.20279

    Article  PubMed  Google Scholar 

  8. Tournier JD, Calamante F, Gadian DG, Connelly A (2004) Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 23:1176–1185. https://doi.org/10.1016/j.neuroimage.2004.07.037

    Article  PubMed  Google Scholar 

  9. Tournier JD, Calamante F, Connelly A (2007) Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35:1459–1472. https://doi.org/10.1016/j.neuroimage.2007.02.016

    Article  PubMed  Google Scholar 

  10. Wedeen VJ, Hagmann P, Tseng WY, Reese TG, Weisskoff RM (2005) Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson Med 54:1377–1386. https://doi.org/10.1002/mrm.20642

    Article  PubMed  Google Scholar 

  11. Yeh CH, Cho KH, Lin HC, Wang JJ, Lin CP (2008) Reduced encoding diffusion spectrum imaging implemented with a bi-Gaussian model. IEEE Trans Med Imaging 27:1415–1424. https://doi.org/10.1109/TMI.2008.922189

    Article  PubMed  Google Scholar 

  12. Canales-Rodríguez EJ, Melie-García L, Iturria-Medina Y (2009) Mathematical description of q-space in spherical coordinates: exact q-ball imaging. Magn Reson Med 61:1350–1367. https://doi.org/10.1002/mrm.21917

    Article  PubMed  Google Scholar 

  13. Wilkins B, Lee N, Gajawelli N, Law M, Leporé N (2015) Fiber estimation and tractography in diffusion MRI: development of simulated brain images and comparison of multi-fiber analysis methods at clinical b-values. Neuroimage 109:341–356. https://doi.org/10.1016/j.neuroimage.2014.12.060

    Article  PubMed  Google Scholar 

  14. Tur C, Grussu F, Prados F, Charalambous T, Collorone S, Kanber B et al (2020) A multi-shell multi-tissue diffusion study of brain connectivity in early multiple sclerosis. Mult Scler 26(7):774–785. https://doi.org/10.1177/1352458519845105

    Article  PubMed  Google Scholar 

  15. Calamante F, Jeurissen B, Smith RE, Tournier JD, Connelly A (2018) The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density. Magn Reson Med 79(5):2738–2744. https://doi.org/10.1002/mrm.26917

    Article  CAS  PubMed  Google Scholar 

  16. Jeurissen B, Tournier JD, Dhollander T, Connelly A, Sijbers J (2014) Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. Neuroimage 103:411–426. https://doi.org/10.1016/j.neuroimage.2014.07.061

    Article  PubMed  Google Scholar 

  17. Kamagata K, Zalesky A, Hatano T, Di Biase MA, El Samad O, Saiki S et al (2017) Connectome analysis with diffusion MRI in idiopathic Parkinson’s disease: evaluation using multi-shell, multi-tissue, constrained spherical deconvolution. Neuroimage Clin 17:518–529. https://doi.org/10.1016/j.nicl.2017.11.007

    Article  PubMed  PubMed Central  Google Scholar 

  18. Smith RE, Tournier J-D, Calamante F, Connelly A (2013) SIFT: Spherical-deconvolution informed filtering of tractograms. Neuroimage 67:298–312. https://doi.org/10.1016/j.neuroimage.2012.11.049

    Article  PubMed  Google Scholar 

  19. Sinke MRT, Otte WM, Christiaens D, Schmitt O, Leemans A, van der Toom A et al (2018) Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics. Brain Struct Funct 223:2269–2285. https://doi.org/10.1007/s00429-018-1628-y

    Article  PubMed  PubMed Central  Google Scholar 

  20. Smith RE, Tournier JD, Calamante F, Connelly A (2012) Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage 62:1924–1938. https://doi.org/10.1016/j.neuroimage.2012.06.005

    Article  PubMed  Google Scholar 

  21. Horbruegger M, Loewe K, Kaufmann J, Wagner M, Schippling S, Pawlitzki M et al (2019) Anatomically constrained tractography facilitates biologically plausible fiber reconstruction of the optic radiation in multiple sclerosis. Neuroimage Clin 22:101740. https://doi.org/10.1016/j.nicl.2019.101740

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Smith RE, Tournier JD, Calamante F, Connelly A (2015) The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. Neuroimage 104:253–265. https://doi.org/10.1016/j.neuroimage.2014.10.004

    Article  PubMed  Google Scholar 

  23. Headache Classification Committee of the International Headache Society (IHS) (2013) The International classification of headache disorders, 3rd edition (beta version). Cephalalgia 33:629–808. https://doi.org/10.1177/0333102413485658

    Article  Google Scholar 

  24. Mugler JP III, Brookeman JR (1990) Three-dimensional magnetization-prepared rapid gradient-echo imaging (3D MP RAGE). Magn Reson Med 15:152–157. https://doi.org/10.1002/mrm.1910150117

    Article  PubMed  Google Scholar 

  25. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) FSL Neuroimage 62:782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015

    Article  PubMed  Google Scholar 

  26. Tournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M et al (2019) MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202:116137. https://doi.org/10.1016/j.neuroimage.2019.116137

    Article  PubMed  Google Scholar 

  27. Veraart J, Novikov DS, Christiaens D, Ades-Aron B, Sijbers J, Fieremans E (2016) Denoising of diffusion MRI using random matrix theory. Neuroimage 142:394–406. https://doi.org/10.1016/j.neuroimage.2016.08.016

    Article  PubMed  Google Scholar 

  28. Kellner E, Dhital B, Kiselev VG, Reisert M (2016) Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med 76:1574–1581. https://doi.org/10.1002/mrm.26054

    Article  PubMed  Google Scholar 

  29. Andersson JLR, Sotiropoulos SN (2016) An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125:1063–1078. https://doi.org/10.1016/j.neuroimage.2015.10.019

    Article  PubMed  Google Scholar 

  30. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320. https://doi.org/10.1109/TMI.2010.2046908

    Article  PubMed  PubMed Central  Google Scholar 

  31. Dhollander T, Raffelt D, Connelly A (2016) Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image. In: ISMRM Workshop on Breaking the Barriers of Diffusion MRI: 5

  32. Dhollander T, Mito R, Raffelt D, Connelly A (2019) Improved white matter response function estimation for 3-tissue constrained spherical deconvolution. Proc Intl Soc Mag Reson Med: 555

  33. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D et al (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021

    Article  PubMed  Google Scholar 

  34. Roine T, Jeurissen B, Perrone D, Aelterman J, Philips W, Sijbers J et al (2019) Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks. Med Image Anal 52:56–67. https://doi.org/10.1016/j.media.2018.10.009

    Article  PubMed  Google Scholar 

  35. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003

    Article  PubMed  Google Scholar 

  36. Rubinov M, Sporns O (2011) Weight-conserving characterization of complex functional brain networks. Neuroimage 56:2068–2079. https://doi.org/10.1016/j.neuroimage.2011.03.069

    Article  PubMed  Google Scholar 

  37. Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25:163–177. https://doi.org/10.1080/0022250X.2001.9990249

    Article  Google Scholar 

  38. Onnela JP, Saramäki J, Kertész J, Kaski K (2005) Intensity and coherence of motifs in weighted complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 71:065103. https://doi.org/10.1103/PhysRevE.71.065103

    Article  CAS  PubMed  Google Scholar 

  39. Watts DJ, Strogatz SH (1998) Collective dynamics of “small-world” networks. Nature 393:440–442. https://doi.org/10.1038/30918

    Article  CAS  PubMed  Google Scholar 

  40. Price DD (2000) Psychological and neural mechanisms of the affective dimension of pain. Science 288:1769–1772. https://doi.org/10.1126/science.288.5472.1769

    Article  CAS  PubMed  Google Scholar 

  41. Apkarian AV, Bushnell MC, Treede RD, Zubieta JK (2005) Human brain mechanisms of pain perception and regulation in health and disease. Eur J Pain 9:463–484. https://doi.org/10.1016/j.ejpain.2004.11.001

    Article  PubMed  Google Scholar 

  42. Veinante P, Yalcin I, Barrot M (2013) The amygdala between sensation and affect: a role in pain. J Mol Psychiatry 1:9. https://doi.org/10.1186/2049-9256-1-9

    Article  PubMed  PubMed Central  Google Scholar 

  43. Neugebauer V (2015) Amygdala pain mechanisms. Handb Exp Pharmacol 227:261–284. https://doi.org/10.1007/978-3-662-46450-2_13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Fanselow MS, LeDoux JE (1999) Why we think plasticity underlying Pavlovian fear conditioning occurs in the basolateral amygdala. Neuron 23:229–232. https://doi.org/10.1016/s0896-6273(00)80775-8

    Article  CAS  PubMed  Google Scholar 

  45. Shin LM, Wright CI, Cannistraro PA, Wedig MM, McMullin K, Martis B et al (2005) A functional magnetic resonance imaging study of amygdala and medial prefrontal cortex responses to overtly presented fearful faces in posttraumatic stress disorder. Arch Gen Psychiatry 62:273–281. https://doi.org/10.1001/archpsyc.62.3.273

    Article  PubMed  Google Scholar 

  46. Pitman RK, Rasmusson AM, Koenen KC, Shin LM, Orr SP, Gilbertson MW et al (2012) Biological studies of post-traumatic stress disorder. Nat Rev Neurosci 13:769–787. https://doi.org/10.1038/nrn3339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Rauch SL, Whalen PJ, Shin LM, McInerney SC, Macklin ML, Lasko NB et al (2000) Exaggerated amygdala response to masked facial stimuli in posttraumatic stress disorder: a functional MRI study. Biol Psychiatry 47:769–776. https://doi.org/10.1016/s0006-3223(00)00828-3

    Article  CAS  PubMed  Google Scholar 

  48. Williams LM, Kemp AH, Felmingham K, Barton M, Olivieri G, Peduto A et al (2006) Trauma modulates amygdala and medial prefrontal responses to consciously attended fear. Neuroimage 29:347–357. https://doi.org/10.1016/j.neuroimage.2005.03.047

    Article  PubMed  Google Scholar 

  49. Shin LM, Kosslyn SM, McNally RJ, Alpert NM, Thompson WL, Rauch SL et al (1997) Visual imagery and perception in posttraumatic stress disorder. A positron emission tomographic investigation. Arch Gen Psychiatry 54:233–241. https://doi.org/10.1001/archpsyc.1997.01830150057010

    Article  CAS  PubMed  Google Scholar 

  50. Rauch SL, van der Kolk BA, Fisler RE, Alpert NM, Orr SP, Savage CR et al (1996) A symptom provocation study of posttraumatic stress disorder using positron emission tomography and script-driven imagery. Arch Gen Psychiatry 53:380–387. https://doi.org/10.1001/archpsyc.1996.01830050014003

    Article  CAS  PubMed  Google Scholar 

  51. Liberzon I, Taylor SF, Amdur R, Jung TD, Chamberlain KR, Minoshima S et al (1999) Brain activation in PTSD in response to trauma-related stimuli. Biol Psychiatry 45:817–826. https://doi.org/10.1016/s0006-3223(98)00246-7

    Article  CAS  PubMed  Google Scholar 

  52. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H et al (2007) Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 27:2349–2356. https://doi.org/10.1523/JNEUROSCI.5587-06.2007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Eraly SA, Nievergelt CM, Maihofer AX, Barkauskas DA, Biswas N, Agorastos A et al (2014) Assessment of plasma C-reactive protein as a biomarker of posttraumatic stress disorder risk. JAMA Psychiat 71:423–431. https://doi.org/10.1001/jamapsychiatry.2013.4374

    Article  CAS  Google Scholar 

  54. Minassian A, Maihofer AX, Baker DG, Nievergelt CM, Geyer MA, Risbrough VB et al (2015) Association of predeployment heart rate variability with risk of postdeployment posttraumatic stress disorder in active-duty marines. JAMA Psychiat 72:979–986. https://doi.org/10.1001/jamapsychiatry.2015.0922

    Article  Google Scholar 

  55. Feller L, Fourie J, Bouckaert M, Khammissa RAG, Ballyram R, Lemmer J (2017) Burning mouth syndrome: aetiopathogenesis and principles of management. Pain Res Manag 2017:1926269. https://doi.org/10.1155/2017/1926269

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Jääskeläinen SK (2018) Is burning mouth syndrome a neuropathic pain condition? Pain 159:610–613. https://doi.org/10.1097/j.pain.0000000000001090

    Article  PubMed  Google Scholar 

  57. Jääskeläinen SK, Woda A (2017) Burning mouth syndrome. Cephalalgia 37:627–647. https://doi.org/10.1177/0333102417694883

    Article  PubMed  Google Scholar 

  58. Gonçalves L, Dickenson AH (2012) Asymmetric time-dependent activation of right central amygdala neurons in rats with peripheral neuropathy and pregabalin modulation: central amygdala and pregabalin in rat neuropathy. Eur J Neurosci 36:3204–3213. https://doi.org/10.1111/j.1460-9568.2012.08235.x

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We appreciate Dr. Yoshiki Imamura (Department of Oral Diagnostic Sciences, Nihon University School of Dentistry, Tokyo, Japan) for his professional advice on burning mouth syndrome. We would like to thank Editage (http://www.editage.com) for editing and reviewing this manuscript for English language.

Funding

This work was supported by JSPS KAKENHI Grant Number 16K10330.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: Ryo Kurokawa, Kouhei Kamiya, Shiori Amemiya, Osamu Abe. Data curation: Ryo Kurokawa, Shimpei Kato, Fumio Suzuki, Shiori Amemiya, Takahiro Shinozaki, Daiki Takanezawa, Ryutarou Kohashi, Osamu Abe. Methodology: Ryo Kurokawa, Kouhei Kamiya, Shiori Amemiya, Takahiro Shinozaki. Formal analysis and investigation: Ryo Kurokawa, Osamu Abe. Writing – original draft preparation: Ryo Kurokawa, Shohei Inui. Writing – review and editing: Kouhei Kamiya, Shimpei Kato, Fumio Suzuki, Shiori Amemiya. Funding acquisition: Osamu Abe. Resources: Takahiro Shinozaki, Daiki Takanezawa, Ryutarou Kohashi. Supervision: Osamu Abe.

Corresponding author

Correspondence to Ryo Kurokawa.

Ethics declarations

Conflict of interest

We have a grant from Siemens Healthcare K.K.

Ethics approval

This multi-institutional prospective study was approved by the local ethics committees [IRB 11742, 2016–21] of the hospitals and performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Consent to participate

Written informed consent was obtained by all study participants.

Consent for publication

All the authors consent to the publication of the manuscript in Neuroradiology.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 15 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kurokawa, R., Kamiya, K., Inui, S. et al. Structural connectivity changes in the cerebral pain matrix in burning mouth syndrome: a multi-shell, multi-tissue-constrained spherical deconvolution model analysis. Neuroradiology 63, 2005–2012 (2021). https://doi.org/10.1007/s00234-021-02732-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00234-021-02732-9

Keywords

Navigation