Neuronal adaptation of corticospinal mechanisms of muscle contraction regulation in athletes

Fomin, Roman; Selyaev, M. V.

doi:10.1134/s0362119711060077

Cited by 3 publications

(3 citation statements)

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“…The corticospinal excitability of trained individuals compared to untrained was shown to be either lower (Pearcey, Power, & Button, 2014) or greater (Fomin & Seliaev, 2011; Monda et al., 2017). While a decrease in corticospinal excitability could reflect a neural economy within the neuronal network of athletes trained to the same task (Falvo, Sirevaag, Rohrbaugh, & Earhart, 2010), an increase can be interpreted as an extension of the cortical representation of the considered muscle with intensive skilled practice (Pearce, Thickbroom, Byrnes, & Mastaglia, 2000).…”

Section: Discussionmentioning

confidence: 99%

Cortical and spinal excitabilities are differently balanced in power athletes

Grosprêtre¹,

Bouguetoch

Martin

2019

European Journal of Sport Science

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It is recognised that power-sport practices have a particular effect on lower-limb neuromuscular parameters. Less is known about corticospinal network adaptation, however, or whether these adaptations are specific to the lower limb. In the present study, the corticospinal and spinal excitabilities of upper and lower limbs have been examined in a group of untrained participants (UT, n = 10) and compared to those of a group of well-trained athletes practicing parkour (PK, n = 10). This activity, consisting of overcoming obstacles offered by the urban environment, was chosen as a model of power activity. The motor evoked potentials (MEPs) induced by transcranial magnetic stimulations and H-reflexes and maximal M-waves evoked by peripheral nerve stimulations were elicited in both upper-(flexor carpi radialis [FCR]) and lower-limb muscles (soleus [SOL] and gastrocnemius medialis [GM]). The results tended toward an overall greater corticospinal excitability in PK than in UT (as evidenced by greater MEP/Mmax ratio) and lower spinal excitability (lower Hmax/ Mmax). H/M MAX ratio was lower for PK (0.32) than for UT (0.41) in SOL (p = 0.02), while MEP/M MAX was greater for PK than for UT in FCR (PK: 0.12; UT: 0.06; P = 0.04) and in GM (PK: 0.05, UT: 0.03, P = 0.02). In both limbs, the decrease of spinal excitability induced by parkour practice was counterbalanced by an increase in cortical excitability. Finally, the present study indicates that such long-term power practice leads to similar corticospinal plasticity in upper and lower limbs, explained by the similar solicitation of those muscles.

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Section: Discussionmentioning

confidence: 99%

Cortical and spinal excitabilities are differently balanced in power athletes

Grosprêtre¹,

Bouguetoch

Martin

2019

European Journal of Sport Science

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“…For example, corpus callosum is thicker in expert performers ( Gooijers and Swinnen, 2014 ; Meier et al, 2016 ). Concerning neurophysiological data, signals obtained from humans with, e.g., electroencephalography (EEG) or functional magnetic resonance imaging (fMRI), show activity related to movement and motor expertise, such as reduced brain activation in experts (neural efficiency) ( Guo et al, 2017 ; Del Percio et al, 2019 ) and different threshold to elicit corticospinal facilitation ( Fomin et al, 2010 ; Fomin and Selyaev, 2011 ; Wang et al, 2014 ; Wright et al, 2018 ).…”

Section: Ready Set Go! On Cognitive and Neural Features Of Athletesmentioning

confidence: 99%

Future Portrait of the Athletic Brain: Mechanistic Understanding of Human Sport Performance Via Animal Neurophysiology of Motor Behavior

Quarta

Cohen

Bravi

et al. 2020

Front. Syst. Neurosci.

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Sport performances are often showcases of skilled motor control. Efforts to understand the neural processes subserving such movements may teach us about general principles of behavior, similarly to how studies on neurological patients have guided early work in cognitive neuroscience. While investigations on non-human animal models offer valuable information on the neural dynamics of skilled motor control that is still difficult to obtain from humans, sport sciences have paid relatively little attention to these mechanisms. Similarly, knowledge emerging from the study of sport performance could inspire innovative experiments in animal neurophysiology, but the latter has been only partially applied. Here, we advocate that fostering interactions between these two seemingly distant fields, i.e., animal neurophysiology and sport sciences, may lead to mutual benefits. For instance, recording and manipulating the activity from neurons of behaving animals offer a unique viewpoint on the computations for motor control, with potentially untapped relevance for motor skills development in athletes. To stimulate such transdisciplinary dialog, in the present article, we also discuss steps for the reverse translation of sport sciences findings to animal models and the evaluation of comparability between animal models of a given sport and athletes. In the final section of the article, we envision that some approaches developed for animal neurophysiology could translate to sport sciences anytime soon (e.g., advanced tracking methods) or in the future (e.g., novel brain stimulation techniques) and could be used to monitor and manipulate motor skills, with implications for human performance extending well beyond sport.

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“…While subcortical stimulations (e.g., spinal cord stimulation) (Fomin and Selyaev, 2011) may separate divergence, transcranial cortical stimulations will have to include proper biophysical models. A step in that direction is the simulation of the induced E-field.…”

Section: Sources Of Cortical Synergymentioning

confidence: 99%

Searching for cortical synergies by mapping multiple muscles using TMS

Jin

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Ever since the 1937 publication, the proposed motor homunculus is standard in our textbooks though more recently the evidence accumulates that this picture oversimplifies reality. With my work bundled in this thesis, I added to this evidence and challenged the mere one-to-one mapping between M1 and muscles. In contrast to Penfield and Boldrey (1937), I opted for a non-invasive, secure, and painless brain stimulation method, namely transcranial magnetic stimulation (TMS) combined with neuro-navigation (nTMS). I employed nTMS to map the cortical representations of eight muscles in the hand and the forearm. Using data of N=20 subjects, I performed test-retest assessments to first establish the reliability of my procedure. I chose the size of excitable areas in M1 and their centroids as primary outcome measures and estimated the corresponding intraclass correlation coefficients. All the ICCs values demonstrated good reliability in most muscles. Random positioning of the TMS, essential to my approach, yields a substantial number of stimulations that do not elicit muscle activity, that is, motor-evoked potentials (MEPs) cannot be observed. To my surprise these non-MEP stimulations have been ignored in the literature potentially causing an overestimate of the (size of the) excitable area in M1. In the analysis of the overlap between cortical representations of different muscles. For this, I introduced how to warp the subject-specific neuroanatomy and the TMS stimulation parameters to a standardised template. Motivated by previous research that indicated the presence of overlapping cortical representations, I used this (in the field of TMS novel) approach and examined the overlap of representations within functionally relevant muscle groups (within-hand, within-forearm, and between hand and forearm). Using again an ANOVA with repeated measures, the synergistic muscle pairs turned out to have a significantly larger overlap in cortical representation than their non-synergistic counterparts. This agrees with my overall hypothesis that muscle synergies in the upper extremities are partly represented in the cortex. I devoted Chapter 4 to the quest of predicting the presence of MEPs when merely using the TMS setup parameters, stimulation intensity and position and orientation of the TMS coil. The fairly large number of stimulations in my experiment (more than 13,000) allowed for employed different supervised machine learning approaches. When predicting data within a subject, a subset of the subjects’ data served as a training set with which the machine learner could be (cross)-validated before testing on remaining subset. For the between-subject prediction, I used the data from all subjects for training and validation. The model with highest predictive capacity was a decision tree-based bagging ensemble. In the within-subject design it reached a maximum accuracy of 90% with an average of subject as high as 77%. Possible reasons for this arguably poor performance are the yet limited sample size next to a pronounced between-subject variability that was manifested is substantial differences in the predictor importance between subjects. Still, I believe that machine learning we help simplifying the motor mapping process in experimental settings and will eventually enter clinical assessments, as discussed in the final Chapter 5.

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Neuronal adaptation of corticospinal mechanisms of muscle contraction regulation in athletes

Cited by 3 publications

References 40 publications

Cortical and spinal excitabilities are differently balanced in power athletes

Cortical and spinal excitabilities are differently balanced in power athletes

Future Portrait of the Athletic Brain: Mechanistic Understanding of Human Sport Performance Via Animal Neurophysiology of Motor Behavior

Searching for cortical synergies by mapping multiple muscles using TMS

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