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Home / Equine Research Bank / PLoS One / EEG individual power profiles correlate with tension along s...
PloS one2020; 15(12); e0243970; doi: 10.1371/journal.pone.0243970

EEG individual power profiles correlate with tension along spine in horses.

Abstract: Assessing chronic pain is a challenge given its subjective dimension. In humans, resting state electroencephalography (EEG) is a promising tool although the results of various studies are contradictory. Spontaneous chronic pain is understudied in animals but could be of the highest interest for a comparative study. Riding horses show a very high prevalence of back disorders thought to be associated with chronic pain. Moreover, horses with known back problems show cognitive alterations, such as a lower attentional engagement. Therefore, we hypothesized that the individual EEG power profiles resting state (i.e. quiet standing) of different horses could reflect the state of their back, that we measured using static sEMG, a tool first promoted to assess lower back pain in human patients. Results show that 1) EEG profiles are highly stable at the intra-individual level, 2) horses with elevated back tension showed resting state EEG profiles characterized by more fast (beta and gamma) and less slow (theta and alpha) waves. The proportion of theta waves was particularly negatively correlated with muscular tension along the spine. Moreover, elevated back tension was positively correlated with the frequency of stereotypic behaviours (an "addictive- like" repetitive behavior) performed by the horses in their stall. Resting state quantitative EEG appears therefore as a very promising tool that may allow to assess individual subjective chronic pain experience, beyond more objective measures of tension. These results open new lines of research for a multi-species comparative approach and might reveal very important in the context of animal welfare.
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Publication Date: 2020-12-14 PubMed ID: 33315932PubMed Central: PMC7735639DOI: 10.1371/journal.pone.0243970Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The study utilizes electroencephalography (EEG) to track brain activity in horses with back tension and disorders, showing a correlation between EEG power profiles and muscular tension along the spine, potentially suggesting an approach to the assessment of chronic pain in animals.

Study Background

  • The research takes into account the challenges in determining the presence of chronic pain in animals due to its inherent subjective aspect.
  • It acknowledges the potential of resting state EEG as a tool for this purpose, even as it acknowledges the non-uniformity of findings on the subject in human-focused studies.
  • It highlights the prevalence of back disorders – typically associated with chronic pain – in riding horses, as well as the cognitive disturbances that these disorders cause, such as lower levels of attention.

Hypothesis and Methodology

  • The researchers hypothesized that the state of a horse’s back can be reflected in the resting state EEG power profiles of different horses.
  • To test this, they applied static surface electromyography (sEMG) to assess back tension in horses. This is a technique initially used to diagnose lower back pain in humans.

Key Findings

  • Firstly, the EEG profiles of individuals demonstrated a high level of stability at the intra-individual level.
  • Secondly, horses with higher levels of back tension exhibited resting state EEG profiles that were characterized by a greater number of fast (beta and gamma) waves and fewer slow (theta and alpha) waves.
  • There was a specific negative correlation between the proportion of theta waves and muscular tension along the spine.
  • Furthermore, an increase in back tension was observed to be directly related to the frequency of stereotypic behaviors – repetitive, “addict-like” actions – performed by the horses in their stables.

Significance and Future Applications

  • The study establishes resting state quantitative EEG as a promising tool that can potentially assess chronic pain in individual animals, offering a more subjective understanding of pain in addition to measuring tension.
  • This opens up new possibilities for research into a multi-species comparative approach.
  • It also offers potential benefits for animal welfare if chronic pain can be better diagnosed and treated as a result of these findings.

Cite This Article

APA
Stomp M, d'Ingeo S, Henry S, Lesimple C, Cousillas H, Hausberger M. (2020). EEG individual power profiles correlate with tension along spine in horses. PLoS One, 15(12), e0243970. https://doi.org/10.1371/journal.pone.0243970

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 15
Issue: 12
Pages: e0243970

Researcher Affiliations

Stomp, Mathilde
  • Univ Rennes, Normandie Univ, CNRS, EthoS (Éthologie animale et humaine)-UMR 6552, Paimpont, France.
d'Ingeo, Serenella
  • Univ Rennes, Normandie Univ, CNRS, EthoS (Éthologie animale et humaine)-UMR 6552, Paimpont, France.
  • Department of Veterinary Medicine, Section of Animal Physiology and Behaviour, University of Bari "Aldo Moro", Bari, Italy.
Henry, Séverine
  • Univ Rennes, Normandie Univ, CNRS, EthoS (Éthologie animale et humaine)-UMR 6552, Paimpont, France.
Lesimple, Clémence
  • Univ Rennes, Normandie Univ, CNRS, EthoS (Éthologie animale et humaine)-UMR 6552, Paimpont, France.
Cousillas, Hugo
  • Univ Rennes, Normandie Univ, CNRS, EthoS (Éthologie animale et humaine)-UMR 6552, Paimpont, France.
Hausberger, Martine
  • Univ Rennes, Normandie Univ, CNRS, EthoS (Éthologie animale et humaine)-UMR 6552, Paimpont, France.

MeSH Terms

  • Animal Welfare
  • Animals
  • Attention / physiology
  • Chronic Pain / complications
  • Chronic Pain / diagnostic imaging
  • Chronic Pain / physiopathology
  • Chronic Pain / veterinary
  • Cognitive Dysfunction / complications
  • Cognitive Dysfunction / diagnostic imaging
  • Cognitive Dysfunction / physiopathology
  • Electroencephalography
  • Female
  • Horses / physiology
  • Male
  • Spine / diagnostic imaging
  • Spine / physiopathology

Conflict of Interest Statement

The authors have declared that no competing interests exist.

References

This article includes 57 references
  1. Reddan MC, Wager TD. Brain systems at the intersection of chronic pain and self-regulation. Neuroscience Letters Vol. 702, Elsevier Ireland Ltd; 2019. p. 24–33.
    doi: 10.1016/j.neulet.2018.11.047pubmed: 30503923google scholar: lookup
  2. Levitt J, Saab CY. What does a pain ‘biomarker’ mean and can a machine be taught to measure pain?. Neuroscience Letters Vol. 702, Elsevier Ireland Ltd; 2019. p. 40–3.
    doi: 10.1016/j.neulet.2018.11.038pubmed: 30503919google scholar: lookup
  3. Koyama S, LeBlanc BW, Smith KA, Roach C, Levitt J, Edhi MM. An electroencephalography bioassay for preclinical testing of analgesic efficacy. Sci Rep 2018. December 1;8(1).
    doi: 10.1038/s41598-018-34594-2pmc: PMC6219560pubmed: 30401974google scholar: lookup
  4. May ES, Nickel MM, Ta Dinh S, Tiemann L, Heitmann H, Voth I. Prefrontal gamma oscillations reflect ongoing pain intensity in chronic back pain patients. Hum Brain Mapp 2019. January 10;40(1):293–305.
    doi: 10.1002/hbm.24373pmc: PMC6585682pubmed: 30260531google scholar: lookup
  5. Putman P, van Peer J, Maimari I, van der Werff S. EEG theta/beta ratio in relation to fear-modulated response-inhibition, attentional control, and affective traits. Biol Psychol 2010. February 1;83(2):73–8.
    doi: 10.1016/j.biopsycho.2009.10.008pubmed: 19897008google scholar: lookup
  6. Hughes JR, Roy John E. Conventional and Quantitative Electroencephalography in Psychiatry. The Journal of Neuropsychiatry and Clinical Neurosciences Vol. 11. 1999.
    pubmed: 10333991
  7. Nickel MM, May ES, Tiemann L, Schmidt P, Postorino M, Ta Dinh S. Brain oscillations differentially encode noxious stimulus intensity and pain intensity. Neuroimage 2017. March 1;148:141–7.
    doi: 10.1016/j.neuroimage.2017.01.011pmc: PMC5349759pubmed: 28069543google scholar: lookup
  8. Schulz E, May ES, Postorino M, Tiemann L, Nickel MM, Witkovsky V. Prefrontal Gamma Oscillations Encode Tonic Pain in Humans. Cereb Cortex 2015. November;25(11):4407–14.
    doi: 10.1093/cercor/bhv043pmc: PMC4816790pubmed: 25754338google scholar: lookup
  9. Ong R, Morris J, O’Dwyer J, Barnett J, Hemsworth P, Clarke I. Behavioural and EEG changes in sheep in response to painful acute electrical stimuli. Aust Vet J 1997. March 1;75(3):189–93.
    doi: 10.1111/j.1751-0813.1997.tb10064.xpubmed: 9088510google scholar: lookup
  10. Harris C, White PJ, Mohler VL, Lomax S. Electroencephalography can distinguish between pain and anaesthetic intervention in conscious lambs undergoing castration. Animals 2020. March 4;10(3):428.
    pmc: PMC7142543pubmed: 32143285
  11. Johnson C, Gibson T, Stafford K, Mellor D. Pain perception at slaughter. Anim Welf 2012. 21(S2):113–22.
  12. Murrell JC, Johnson CB, White KL, Taylor PM, Haberham ZL, Waterman-Pearson AE. Changes in the EEG during castration in horses and ponies anaesthetized with halothane. Vet Anaesth Analg 2003. July 1;30(3):138–46.
    doi: 10.1046/j.1467-2995.2003.00138.xpubmed: 14498845google scholar: lookup
  13. Jeffcott LB. Back problems in the horse-A look at past, present and future progress. Equine Vet J 1979. July 1;11(3):129–36.
    doi: 10.1111/j.2042-3306.1979.tb01324.xpubmed: 158523google scholar: lookup
  14. Jeffcott LB. Disorders of the thoracolumbar spine of the horse—a survey of 443 cases. Equine Vet J 1980. October;12(4):197–210.
    doi: 10.1111/j.2042-3306.1980.tb03427.xpubmed: 7439145google scholar: lookup
  15. Fonseca BPA, Alves ALG, Nicoletti JLM, Thomassian A, Hussni CA, Mikail S. Thermography and ultrasonography in back pain diagnosis of equine athletes. J Equine Vet Sci 2006. November;26(11):507–16.
  16. Visser EK, Neijenhuis F, de Graaf-Roelfsema E, Wesselink HGM, de Boer J, van Wijhe-Kiezebrink MC. Risk factors associated with health disorders in sport and leisure horses in the Netherlands1. J Anim Sci 2014. February 1;92(2):844–55.
    pubmed: 24352963
  17. Fureix C, Menguy H, Hausberger M. Partners with bad temper: reject or cure? A study of chronic pain and aggression in horses. PLoS One 2010. August 26;5(8):e12434.
    doi: 10.1371/journal.pone.0012434pmc: PMC2928779pubmed: 20865160google scholar: lookup
  18. Lesimple C, Fureix C, Aubé L, Hausberger M. Detecting and measuring back disorders in nonverbal individuals: the example of domestic horses. Anim Behav Cogn 2016. 3(3):159–79.
  19. Haussler KK. The lower back and pelvis of performance horses receive a closer look. J Equine Vet Sci 1996. July 1;16(7):279–81.
  20. Stubbs NC, Hodges PW, Jeffcott LB, Cowin G, Hodgson DR, McGowan CM. Functional anatomy of the caudal thoracolumbar and lumbosacral spine in the horse. Equine Vet J 2006. August;38(S36):393–9.
    doi: 10.1111/j.2042-3306.2006.tb05575.xpubmed: 17402454google scholar: lookup
  21. Fureix C, Hausberger M, Seneque E, Morisset S, Baylac M, Cornette R. Geometric morphometrics as a tool for improving the comparative study of behavioural postures. Naturwissenschaften 2011;98(7):583.
    doi: 10.1007/s00114-011-0803-2pubmed: 21573691google scholar: lookup
  22. Lesimple C, Fureix C, De Margerie E, Sénèque E, Menguy H, Hausberger M. Towards a postural indicator of back pain in horses (Equus caballus). PLoS One 2012. September 7;7(9):e44604.
    doi: 10.1371/journal.pone.0044604pmc: PMC3436792pubmed: 22970261google scholar: lookup
  23. Rochais C, Fureix C, Lesimple C, Hausberger M. Lower attention to daily environment: a novel cue for detecting chronic horses’ back pain?. Sci Rep 2016. January 29;6:20117.
    doi: 10.1038/srep20117pmc: PMC4731760pubmed: 26823123google scholar: lookup
  24. Dyson S, Berger JM, Ellis AD, Mullard J. Behavioral observations and comparisons of nonlame horses and lame horses before and after resolution of lameness by diagnostic analgesia. J Vet Behav 2018. July 1;26:64–70.
  25. Hausberger M, Fureix C, Lesimple C. Detecting horses’ sickness: in search of visible signs. Appl Anim Behav Sci 2016. February 1;175:41–9.
  26. Arena JG, Sherman RA, Bruno GM, Young TR. Electromyographic recordings of 5 types of low back pain subjects and non-pain controls in different positions. Pain 1989;37(1):57–65.
    doi: 10.1016/0304-3959(89)90153-xpubmed: 2524711google scholar: lookup
  27. Arena JG, Sherman RA, Bruno GM, Young TR. Electromyographic recordings of low back pain subjects and non-pain controls in six different positions: effect of pain levels. Pain 1991;45(1):23–8.
    doi: 10.1016/0304-3959(91)90160-ypubmed: 1830645google scholar: lookup
  28. Greve L, Dyson SJ. The interrelationship of lameness, saddle slip and back shape in the general sports horse population. Equine Vet J 2014. November 1;46(6):687–94.
    doi: 10.1111/evj.12222pubmed: 24372949google scholar: lookup
  29. Cauvin E. Assessment of back pain in horses. In Pract 1997. November;19(10):522–33.
  30. Haussler KK, Erb HN. Pressure algometry for the detection of induced back pain in horses: a preliminary study. Equine Vet J 2006. January 5;38(1):76–81.
    doi: 10.2746/042516406775374225pubmed: 16411591google scholar: lookup
  31. Cousillas H, Oger M, Rochais C, Pettoello C, Ménoret M, Henry S. An ambulatory electroencephalography system for freely moving horses: an innovating approach. Front Vet Sci 2017. May 2;4:57.
    doi: 10.3389/fvets.2017.00057/fullpmc: PMC5411420pubmed: 28512633google scholar: lookup
  32. Broom DM. Indicators of poor welfare. Br Vet J 1986;142(6):524–6.
    doi: 10.1016/0007-1935(86)90109-0pubmed: 3594185google scholar: lookup
  33. Wickens C. Investigating specific stereotypic behaviors in horses. Michigan State University, East Lansing; 2009.
  34. Hausberger M, Gautier E, Biquand V, Lunel C, Jégo P. Could work be a source of behavioural disorders? A Study in horses. PLoS One 2009. October 28;4(10):e7625.
    doi: 10.1371/journal.pone.0007625pmc: PMC2763287pubmed: 19862328google scholar: lookup
  35. Mills DS. Repetitive movement problems in the horse. In: The domestic horse: the evolution, development and management of its behaviour. Cambridge University Press; 2005. p. 212–27.
  36. McGreevy PD, Cripps PJ, French NP, Green LE, Nicol CJ. Management factors associated with stereotypic and redirected behaviour in the Thoroughbred horse. Equine Vet J 1995. March 1;27(2):86–91.
    doi: 10.1111/j.2042-3306.1995.tb03041.xpubmed: 7607155google scholar: lookup
  37. Hausberger M, Gautier E, Müller C, Jego P. Lower learning abilities in stereotypic horses. Appl Anim Behav Sci 2007. November 1;107(3–4):299–306.
  38. Parker M, Redhead ES, Goodwin D, McBride SD. Impaired instrumental choice in crib-biting horses (Equus caballus). Behav Brain Res 2008. August 5;191(1):137–40.
    doi: 10.1016/j.bbr.2008.03.009pubmed: 18430476google scholar: lookup
  39. Benhajali H, Ezzaouia M, Lunel C, Charfi F, Hausberger M. Stereotypic behaviours and mating success in domestic mares. Appl Anim Behav Sci 2014. April 1;153:36–42.
  40. Rochais C, Sébilleau M, Menoret M, Oger M, Henry S, Hausberger M. Attentional state and brain processes: state-dependent lateralization of EEG profiles in horses. Sci Rep 2018. December 5;8(1):10153.
    doi: 10.1038/s41598-018-28334-9pmc: PMC6033862pubmed: 29976936google scholar: lookup
  41. Hausberger M, Stomp M, Sankey C, Brajon S, Lunel C, Henry S. Mutual interactions between cognition and welfare: The horse as an animal model. Neurosci Biobehav Rev 2019.
    doi: 10.1016/j.neubiorev.2019.08.022pubmed: 31491471google scholar: lookup
  42. Rapin V, Poncet PA, Burger D, Mermod C, Hausberger M, Richard M-A. Mesure de la durée d’attention chez le cheval. Schweiz Arch Tierheilkd 2007. February 1;149(2):77–83.
    pubmed: 17343134
  43. Fureix C, Jego P, Henry S, Lansade L, Hausberger M. Towards an ethological animal model of depression? A study on horses. PLoS One 2012. June 28;7(6):e39280.
    doi: 10.1371/journal.pone.0039280pmc: PMC3386251pubmed: 22761752google scholar: lookup
  44. Rochais C, Henry S, Hausberger M. Spontaneous attention-capture by auditory distractors as predictor of distractibility: a study of domestic horses (Equus caballus). Sci Rep 2017. December 10;7(1):15283.
    doi: 10.1038/s41598-017-15654-5pmc: PMC5681571pubmed: 29127367google scholar: lookup
  45. Kujala M V., Törnqvist H, Somppi S, Hänninen L, Krause CM, Vainio O. Reactivity of dogs’ brain oscillations to visual stimuli measured with non-invasive electroencephalography. PLoS One 2013;8(5):1–8.
    doi: 10.1371/journal.pone.0061818pmc: PMC3641087pubmed: 23650504google scholar: lookup
  46. Shearar KA, Colloca CJ, White HL. A randomized clinical trial of manual versus mechanical force manipulation in the treatment of sacroiliac joint syndrome. J Manipulative Physiol Ther 2005. September 1;28(7):493–501.
    doi: 10.1016/j.jmpt.2005.07.006pubmed: 16182023google scholar: lookup
  47. Siegel S, Castellan J. Nonparametric statistics for the behavioral sciences. 2nd ed. New York: McGraw-Hill international; 1988.
  48. Xiangjun S, Li H, Du W, Chen W, Zhou F, Wang L. Analysis of Electroencephalogram of patients with specific low back pain with the massage treatment. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Institute of Electrical and Electronics Engineers Inc.; 2017. p. 479–83.
    pubmed: 29059914
  49. Reddan MC, Wager TD. Modeling Pain Using fMRI: From Regions to Biomarkers. Neuroscience Bulletin Vol. 34, Springer; 2018. p. 208–15.
    doi: 10.1007/s12264-017-0150-1pmc: PMC5799128pubmed: 28646349google scholar: lookup
  50. Aftanas LI, Golocheikine SA. Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: high-resolution EEG investigation of meditation. Neurosci Lett 2001. September 7;310(1):57–60.
    doi: 10.1016/s0304-3940(01)02094-8pubmed: 11524157google scholar: lookup
  51. Başar E, Başar-Eroglu C, Karakaş S, Schürmann M. Gamma, alpha, delta, and theta oscillations govern cognitive processes. Int J Psychophysiol 2001. January 1;39(2–3):241–8.
    doi: 10.1016/s0167-8760(00)00145-8pubmed: 11163901google scholar: lookup
  52. Apkarian AV, Hashmi JA, Baliki MN. Pain and the brain: Specificity and plasticity of the brain in clinical chronic pain. Pain Vol. 152, 2011.
    doi: 10.1016/j.pain.2010.11.010pmc: PMC3045648pubmed: 21146929google scholar: lookup
  53. Dunnet S, Whishaw I, Jones G, Bunch S. Behavioural, biochemical and histochemical effects of different neurotoxic amino acids injected into nucleus basalis magnocellularis of rats. Neuroscience 1987;20:653–9.
    doi: 10.1016/0306-4522(87)90117-5pubmed: 3295586google scholar: lookup
  54. Lesimple C, Poissonnet A, Hausberger M. How to keep your horse safe? An epidemiological study about management practices. Appl Anim Behav Sci 2016. August 1;181:105–14.
  55. Davidson RJ. Emotion and Affective Style: Hemispheric Substrates. Psychol Sci 1992. January 25;3(1):39–43.
    doi: 10.1111/j.1467-9280.1992.tb00254.xgoogle scholar: lookup
  56. Basile M, Boivin S, Boutin A, Blois-Heulin C, Hausberger M, Lemasson A. Socially dependent auditory laterality in domestic horses (Equus caballus). Anim Cogn 2009. 12(4):611–9.
    doi: 10.1007/s10071-009-0220-5pubmed: 19283416google scholar: lookup
  57. Andrew R, Rogers L. The nature of lateralization in tetrapods. In: Comparative Vertebrate Lateralization. 2002. p. 94–127.

Citations

This article has been cited 5 times.
  1. d'Ingeo S, Siniscalchi M, Quaranta A, Cousillas H, Hausberger M. Chronic State and Relationship to Humans Influence How Horses Decode Emotions in Human Voices: A Brain and Behavior Study. Animals (Basel) 2025 Nov 5;15(21).
    doi: 10.3390/ani15213217pubmed: 41227547google scholar: lookup
  2. Grandgeorge M, Lerch N, Delarue A, Hausberger M. From Human Perception of Good Practices to Horse (Equus Caballus) Welfare: Example of Equine-Assisted Activities. Animals (Basel) 2024 Sep 2;14(17).
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  3. Gueguen L, Henry S, Delbos M, Lemasson A, Hausberger M. Selected Acoustic Frequencies Have a Positive Impact on Behavioural and Physiological Welfare Indicators in Thoroughbred Racehorses. Animals (Basel) 2023 Sep 20;13(18).
    doi: 10.3390/ani13182970pubmed: 37760370google scholar: lookup
  4. Rochais C, Stomp M, Sébilleau M, Houdebine M, Henry S, Hausberger M. Horses' attentional characteristics differ according to the type of work. PLoS One 2022;17(7):e0269974.
    doi: 10.1371/journal.pone.0269974pubmed: 35877616google scholar: lookup
  5. Lerch N, Cirulli F, Rochais C, Lesimple C, Guilbaud E, Contalbrigo L, Borgi M, Grandgeorge M, Hausberger M. Interest in Humans: Comparisons between Riding School Lesson Equids and Assisted-Intervention Equids. Animals (Basel) 2021 Aug 28;11(9).
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