Characteristics of electrophysiological activity of the cerebral cortex in children with arthrogryposis

Background. Arthrogryposis is one of the most severe congenital abnormalities of the musculoskeletal system characterized by 2 or more contractures of the large joints, muscle and anterior grey column pathology. One of the main problems making selfcare limited or impossible for the patients is an absence of the active movements in the joints of the upper extremities which can be restored through autologous transplantation from the various donor areas. Processes of the rehabilitation after these operations are associated with neuronal remodeling in the central nervous system both in the spinal cord and the brain, in the cortial regions in particular.

The objective is to evaluate possible reflection of arthrogryposis in the amplitude and neurodynamical characteristics of the electroencephalogram (EEG) in children.

Materials and methods. Electrophysiological characteristics of the cerebral cortex in children with arthrogryposis and healthy children of the same age were examined. Such EEG characteristics as power and long-range temporal correlations (evaluation of the neuronal activity dynamics) in ranges of 4–8, 8–12, and 12–16 Hz were measured. The results were evaluated in accordance with clinical scales.

Results. Data analysis has shown that children with arthrogryposis have significantly decreased EEG power in all of the studied ranges compared to healthy children. Additionally, a significant correlation between EEG power and the level of restoration of motor functions in the upper extremities after autologous transplantation of various muscle groups in the position of the biceps was observed. The obtained results reflect correlation between the electrophysiological parameters of the cerebral cortex and processes associated with arthrogryposis pathology. However, neurodynamical parameters in children with arthrogryposis are similar to those in healthy children. The results allow to state that arthrogryposis is reflected through decreased electrical activity of the cerebral cortex in 4–16 Hz range with preservation of neurodynamic characteristics typical for disease-free children.

Conclusion. In this study, a significant difference in EEG power in 4–8, 8–12, and 12–16 Hz ranges between children with arthrogryposis and healthy children was demonstrated. However, there was no difference in such an important neurodynamical characteristic as longrange temporal correlations. It is possible that decreased amplitude of EEG rhythms in children with arthrogryposis is caused by their lower motor activity in general.

1. Bamshad M., Van Heest A.E., Pleasure D. Arthrogryposis: a review and update. J Bone Joint Surg Am 2009;91:40–6. DOI: 10.2106/JBJS.I.00281. PMID: 19571066.

2. Hall J.G. Arthrogryposis multiplex congenita: etiology, genetics, classification, diagnostic approach, and general aspects. J Pediatr Orthop B 1997;6(3):159–66. DOI: 10.1097/01202412199707000-00002. PMID: 9260643.

3. Zargarbashi R., Nabian M.H., Werthel J.D., Valenti P. Is bipolar latissimus dorsi transfer a reliable option to restore elbow flexion in children with arthrogryposis? A review of 13 tendon transfers. J Shoulder Elbow Surg 2017;26(11):2004–9. DOI: 10.1016/j.jse.2017.04.002. PMID: 28689830.

4. Mikati M.A. Arthrogryposis, renal tubular acidosis and cholestasis syndrome: spectrum of the clinical manifestations. Clin Dysmorphol 2007;16(1):71. DOI: 10.1097/01.mcd.0000220607.32531.1b. PMID: 17159523.

5. Hirata H., Nanda I., van Riesen A. et al. ZC4H2 mutations are associated with arthrogryposis multiplex congenita and intellectual disability through impairment of central and peripheral synaptic plasticity. Am J Hum Genet 2013;92(5):681–95. DOI: 10.1016/j.ajhg.2013.03.021. PMID: 23623388.

6. Başar E., Güntekin B. Review of delta, theta, alpha, beta, and gamma response oscillations in neuropsychiatric disorders. Suppl Clin Neurophysiol 2013;62:303–41. DOI: 10.1016/b978-0-7020-53078.00019-3. PMID: 24053047.

7. Yener G.G., Başar E. Brain oscillations as biomarkers in neuropsychiatric disorders: following an interactive panel discussion and synopsis. Suppl Clin Neurophysiol 2013;62:343–63. DOI: 10.1016/b978-07020-5307-8.00016-8. PMID: 24053048.

8. Van Der Naalt J. Resting functional imaging tools (MRS, SPECT, PET and PCT). Handb Clin Neurol 2015;127:295–308. DOI: 10.1016/B978-0-444-528926.00019-2. PMID: 25702224.

9. Hardstone R., Poil S.S., Schiavone G. et al. Detrended fluctuation analysis: a scale-free view on neuronal oscillations. Front Physiol 2012;3:450. DOI: 10.3389/fphys.2012.00450. PMID: 23226132.

10. Palva J.M., Zhigalov A., Hirvonen J. et al. Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws. Proc Natl Acad Sci USA 2013;110(9):3585–90. DOI: 10.1073/pnas.1216855110. PMID: 23401536.

11. Samek W., Blythe D.A.J., Curio G. et al. Multiscale temporal neural dynamics predict performance in a complex sensorimotor task. Neuroimage 2016;141:291–303. DOI: 10.1016/j.neuroimage.2016.06.056. PMID: 27402598.

12. Poil S.S., Hardstone R., Mansvelder H.D., Linkenkaer-Hansen K. Critical-state dynamics of avalanches and oscillations jointly emerge from balanced excitation/ inhibition in neuronal networks. J Neurosci 2012;32(29):9817–23. DOI: 10.1523/JNEUROSCI.5990-11.2012. PMID: 22815496.

13. Fedele T., Blagovechtchenski E., Nazarova M. et al. Long-Range Temporal Correlations in the amplitude of alpha oscillations predict and reflect strength of intracortical facilitation: combined TMS and EEG study. Neuroscience 2016;331:109–19. DOI: 10.1016/j.neuroscience.2016.06.015. PMID: 27318302.

14. Mumtaz W., Malik A.S., Ali S.S. et al. Detrended fluctuation analysis for major depressive disorder. Conf Proc IEEE Eng Med Biol Soc 2015;2015:4162–5. DOI: 10.1109/EMBC.2015.7319311. PMID: 26737211.

15. Agranovich O.E., Lakhina O.L. Clinical variants of upper limbs deformities in children with arthrogryposis multiplex congenita. Traumatol Orthop Russ 2013:125–9. DOI: 10.21823/2311-29052013--3-125-129.

16. Morrey B.F., Bryan R.S., Dobyns J.H., Linscheid R.L. Total elbow arthroplasty. A five-year experience at the Mayo Clinic. J Bone Joint Surg Am 1981;63(7): 1050–63. DOI: 10.2106/00004623198163070-00002. PMID: 7276042.

17. Van Heest A., Waters P.M., Simmons B.P. Surgical treatment of arthrogryposis of the elbow. J Hand Surg Am 1998;23(6):1063–0. DOI: 10.1016/S0363-5023(98)80017-8. PMID: 9848560.

18. Friston K.J. The labile brain. I. Neuronal transients and nonlinear coupling. Philos Trans R Soc Lond B Biol Sci 2000;355(1394):215–36. DOI: 10.1098/rstb.2000.0560. PMID: 10724457.

19. Lemon R.N. Descending pathways in motor control. Annu Rev Neurosci 2008;31:195–218. DOI: 10.1146/annurev. neuro.31.060407.125547. PMID: 18558853.

20. Lemon R.N., Baker S.N., Davis J.A. et al. The importance of the cortico-motoneuronal system for control of grasp. Novartis Found Symp 1998;218:202–15; discussion 215–8. DOI: 10.1002/9780470515563.ch11. PMID: 9949822.

21. Pettersson L.G., Alstermark B., Blagovechtchenski E. et al. Skilled digit movements in feline and primate – recovery after selective spinal cord lesions. Acta Physiol 2007;189(2):141–54. DOI: 10.1111/j.17481716.2006.01650.x. PMID: 17250565.

22. Blagovechtchenski E., Pettersson L.G., Perfiliev S. et al. Control of digits via C3– C4 propriospinal neurones in cats; recovery after lesions. Neurosci Res 2000;38(1):103–7. DOI: 10.1016/s01680102(00)00147-4. PMID: 10997583.

23. Wilson J.M., Blagovechtchenski E., Brownstone R.M. Genetically defined inhibitory neurons in the mouse spinal cord dorsal horn: a possible source of rhythmic inhibition of motoneurons during fictive locomotion. J Neurosci 2010;30(3): 1137–48. DOI: 10.1523/JNEUROSCI.1401-09.2010. PMID: 20089922.

24. Vasen A.P., Lacey S.H., Keith M.W., Shaffer J.W. Functional range of motion of the elbow. J Hand Surg Am 1995;20(2):288–92. DOI: 10.1016/S0363-5023(05)80028-0. PMID: 7775772.

25. Eyre J.A. Corticospinal tract development and its plasticity after perinatal injury. Neurosci Biobehav Rev 2007;31(8): 1136–49. DOI: 10.1016/j.neubiorev.2007.05.011. PMID: 18053875.

26. Lardon M.T., Polich J. EEG changes from long-term physical exercise. Biol Psychol 1996;44(1):19–30. DOI: 10.1016/s03010511(96)05198-8. PMID: 8906355.

27. Cho S.H. Effects of horseback riding exercise on the relative alpha power spectrum in the elderly. Arch Gerontol Geriatr 2017;70:141–7. DOI: 10.1016/j.archger.2017.01.011. PMID: 28135668.

28. Moraes H., Deslandes A., Silveira H. et al. The effect of acute effort on EEG in healthy young and elderly subjects. Eur J Appl Physiol 2011;111(1):67–75. DOI: 10.1007/s00421-010-1627-z. PMID: 20809229.

29. Pfurtscheller G., Lopes da Silva F.H. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 1999;110(11): 1842–57. DOI: 10.1016/s13882457(99)00141-8. PMID: 10576479.

30. Nikulin V.V., Hohlefeld F.U., Jacobs A.M., Curio G. Quasi-movements: a novel motorcognitive phenomenon. Neuropsychologia 2008;46(2):727–42. DOI: 10.1016/j.neuropsychologia.2007.10.008. PMID: 18035381.

31. Nikulin V.V., Jönsson E.G., Brismar T. Attenuation of long-range temporal correlations in the amplitude dynamics of alpha and beta neuronal oscillations in patients with schizophrenia. Neuroimage 2012;61(1):162–9. DOI: 10.1016/j.neuroimage.2012.03.008. PMID: 22430497.

32. Monto S., Vanhatalo S., Holmes M.D., Palva J.M. Epileptogenic neocortical networks are revealed by abnormal temporal dynamics in seizure-free subdural EEG. Cereb Cortex 2007;17(6):1386–93. DOI: 10.1093/cercor/bhl049. PMID: 16908492.

33. Montez T., Poil S.S., Jones B.F. et al. Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease. Proc Natl Acad Sci USA 2009;106(5):1614–9. DOI: 10.1073/pnas.0811699106. PMID: 19164579.

34. Hohlefeld F.U., Ehlen F., Tiedt H.O. et al. Correlation between cortical and subcortical neural dynamics on multiple time scales in Parkinson’s disease. Neuroscience 2015;298:145–60. DOI: 10.1016/j.neuroscience.2015.04.013. PMID: 25881724.

35. Nikulin V.V., Brismar T. Long-range temporal correlations in electroencephalographic oscillations: relation to topography, frequency band, age and gender. Neuroscience 2005;130(2):549–58. 2’2018 DOI: 10.1016/j.neuroscience.2004.10.007. PMID: 15664711.

36. Smit D.J., de Geus E.J., van de Nieuwenhuijzen M.E. et al. Scale-free modulation of resting-state neuronal oscillations reflects prolonged brain maturation in humans. J Neurosci 2011;31(37):13128–36. DOI: 10.1523/JNEURO-SCI.1678-11.2011. PMID: 21917796.