Intermuscular coherence explained

Intermuscular Coherence is a measure to quantify correlations between the activity of two muscles, which is often assessed using electromyography. The correlations in muscle activity are quantified in frequency domain,[1] and therefore referred to as intermuscular coherence.[2]

History

The synchronisation of motor units of a single muscle in animals and humans are known for decades. The early studies that investigated the relationship of EMG activity used time-domain cross-correlation to quantify common input.[3] [4] The explicit notion of presence of synchrony between motor units of two different muscles was reported at a later time.[5] In the 1990s, coherence analysis was introduced to examine in frequency content of common input.

Physiology

Intermuscular coherence can be used to investigate the neural circuitry involved in motor control. Correlated muscle activity indicates common input to the motor unit pools of both muscles[6] [7] and reflects shared neural pathways (including cortical, subcortical and spinal) that contribute to muscle activity and movement.[8] The strength of intermuscular coherence is dependent on the relationship between muscles and is generally stronger between muscle pairs that are anatomically and functionally closely related.[9] [10] Intermuscular coherence can therefore be used to identify impairments in motor pathways.[11] [12]

See also

External links

Notes and References

  1. Rosenberg, J. R., Amjad, A. M., Breeze, P., Brillinger, D. R., & Halliday, D. M. (1989). The Fourier approach to the identification of functional coupling between neuronal spike trains. Progress in Biophysics and Molecular Biology, 53(1), 1–31.
  2. Farmer, S. F., Bremner, F. D., Halliday, D. M., Rosenberg, J. R., & Stephens, J. A. (1993). The frequency content of common synaptic inputs to motoneurones studied during voluntary isometric contraction in man. The Journal of Physiology, 470(1), 127–155
  3. Person. R. S.. Kudina. L. P.. 1968. Cross-correlation of electromyograms showing interference patterns. Electroencephalography and Clinical Neurophysiology. 25. 1. 58–68. 0013-4694. 4174784. 10.1016/0013-4694(68)90087-4.
  4. Kirkwood. P. A.. Sears. T. A.. 1978. The synaptic connexions to intercostal motoneurones as revealed by the average common excitation potential. The Journal of Physiology. 275. 103–134. 0022-3751. 1282535. 633094. 10.1113/jphysiol.1978.sp012180.
  5. Bremner, F. D., Baker, J. R., & Stephens, J. A. (1991). Correlation between the discharges of motor units recorded from the same and from different finger muscles in man. The Journal of Physiology, 432(1), 355–380. http://doi.org/10.1113/jphysiol.1991.sp018389
  6. Negro. Francesco. Farina. Dario. 2011-01-28. Linear transmission of cortical oscillations to the neural drive to muscles is mediated by common projections to populations of motoneurons in humans. The Journal of Physiology. en. 589. 3. 629–637. 10.1113/jphysiol.2010.202473. 0022-3751. 3055547. 21135042.
  7. Boonstra. Tjeerd W.. Michael Breakspear. Breakspear. Michael. 2012. Neural mechanisms of intermuscular coherence: implications for the rectification of surface electromyography. Journal of Neurophysiology. 107. 3. 796–807. 10.1152/jn.00066.2011. 1522-1598. 22072508.
  8. Boonstra. Tjeerd W.. Farmer. Simon F.. Breakspear. Michael. 2016. Using Computational Neuroscience to Define Common Input to Spinal Motor Neurons. Frontiers in Human Neuroscience. English. 10. 313. 10.3389/fnhum.2016.00313. 27445753. 4914567. 1662-5161. free.
  9. Gibbs. J.. Harrison. L. M.. Stephens. J. A.. 1995-05-15. Organization of inputs to motoneurone pools in man. The Journal of Physiology. 485 (Pt 1). 245–256. 0022-3751. 1157987. 7658378. 10.1113/jphysiol.1995.sp020727.
  10. Kerkman. Jennifer N.. Daffertshofer. Andreas. Gollo. Leonardo L.. Breakspear. Michael. Boonstra. Tjeerd W.. 2018. Network structure of the human musculoskeletal system shapes neural interactions on multiple time scales. Science Advances. 4. 6. eaat0497. 10.1126/sciadv.aat0497. 2375-2548. 6021138. 29963631. 2018SciA....4..497K.
  11. Nishimura. Yukio. Morichika. Yosuke. Isa. Tadashi. 2009-03-01. A subcortical oscillatory network contributes to recovery of hand dexterity after spinal cord injury. Brain. 132. 3. 709–721. 10.1093/brain/awn338. 0006-8950. 2664448. 19155271.
  12. Fisher. Karen M.. Zaaimi. Boubker. Williams. Timothy L.. Baker. Stuart N.. Baker. Mark R.. 2012-09-01. Beta-band intermuscular coherence: a novel biomarker of upper motor neuron dysfunction in motor neuron disease. Brain. 135. 9. 2849–2864. 10.1093/brain/aws150. 0006-8950. 3437020. 22734124.

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