Intermuscular coherence

Last updated

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]

Contents

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. [2]

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

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. 1 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. doi:10.1016/0013-4694(68)90087-4. ISSN   0013-4694. PMID   4174784.
  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. doi:10.1113/jphysiol.1978.sp012180. ISSN   0022-3751. PMC   1282535 . PMID   633094.
  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. 589 (3): 629–637. doi:10.1113/jphysiol.2010.202473. ISSN   0022-3751. PMC   3055547 . PMID   21135042.
  7. Boonstra, Tjeerd W.; Breakspear, Michael (2012). "Neural mechanisms of intermuscular coherence: implications for the rectification of surface electromyography". Journal of Neurophysiology. 107 (3): 796–807. doi:10.1152/jn.00066.2011. hdl: 1959.4/unsworks_49701 . ISSN   1522-1598. PMID   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. 10: 313. doi: 10.3389/fnhum.2016.00313 . ISSN   1662-5161. PMC   4914567 . PMID   27445753.
  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) (Pt 1): 245–256. doi:10.1113/jphysiol.1995.sp020727. ISSN   0022-3751. PMC   1157987 . PMID   7658378.
  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. Bibcode:2018SciA....4..497K. doi:10.1126/sciadv.aat0497. ISSN   2375-2548. PMC   6021138 . PMID   29963631.
  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. doi:10.1093/brain/awn338. ISSN   0006-8950. PMC   2664448 . PMID   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. doi:10.1093/brain/aws150. ISSN   0006-8950. PMC   3437020 . PMID   22734124.