Only the Fastest Corticospinal Fibers Contribute to β Corticomuscular Coherence

Abstract

Human corticospinal transmission is commonly studied using brain stimulation. However, this approach is biased to activity in the fastest conducting axons. It is unclear whether conclusions obtained in this context are representative of volitional activity in mild-to-moderate contractions. An alternative to overcome this limitation may be to study the corticospinal transmission of endogenously generated brain activity. Here, we investigate in humans (N = 19; of either sex), the transmission speeds of cortical b rhythms (;20 Hz) traveling to arm (first dorsal interosseous) and leg (tibialis anterior; TA) muscles during tonic mild contractions. For this purpose, we propose two improvements for the estimation of corticomuscular b transmission delays. First, we show that the cumulant density (cross-covariance) is more accurate than the commonly-used directed coherence to estimate transmission delays in bidirectional systems transmitting band-limited signals. Second, we show that when spiking motor unit activity is used instead of interference electromyography, corticomuscular transmission delay estimates are unaffected by the shapes of the motor unit action potentials (MUAPs). Applying these improvements, we show that descending corticomuscular b transmission is only 1-2 ms slower than expected from the fastest corticospinal pathways. In the last part of our work, we show results from simulations using estimated distributions of the conduction velocities for descending axons projecting to lower motoneurons (from macaque histologic measurements) to suggest two scenarios that can explain fast corticomuscular transmission: either only the fastest corticospinal axons selectively transmit b activity, or else the entire pool does. The implications of these two scenarios for our understanding of corticomuscular interactions are discussed.

Reading and Modulating Cortical β Bursts from Motor Unit Spiking Activity

“…Moreover, the common input to the MU pool inside the β range and the resulting MU β activity were time-locked and followed cortical β rhythms by tens of milliseconds. Although determining the transmission delay by only analyzing the β power is not robust against noise that may mask the underlying shape of β bursts, our observation is in strong agreement with previous investigations using the averaged CMC ( Mima et al, 2000 ; Ibáñez et al, 2021 ). When we asked subjects to perform volitional modulations of the β activity present in the MUs via a novel neurofeedback paradigm ( Bräcklein et al, 2021 ), changes in the cortical β power were shown to be coherent with those induced in the periphery.…”

“…Neural oscillations of brain activity in the β range (13–30 Hz) are ubiquitous in the motor nervous system ( Kilavik et al, 2013 ). Alongside their pervasive appearance in the brain, β oscillations with cortical origin are transmitted linearly and at fast and stable speeds to tonically active muscles ( Witham et al, 2011 ; Ibáñez et al, 2021 ). β Activity can indeed represent an important portion of the neural inputs received by spinal motor neurons and their innervated muscle fibers, i.e., motor units (MUs; Grosse et al, 2002 ; Farina et al, 2014 ; Dideriksen et al, 2018 ).…”

The control and training of single motor units in isometric tasks are constrained by a common input signal

“…One possible strategy consistent with such state-dependent control of individual MUs could be relying on an input signal to MUs not directly linked to motor function and non-homogeneously distributed among the MU pool. For example, cortical oscillations could meet these criteria if descending projections of this activity to large and small MUs in a pool differed ( Ibáñez et al, 2021 ; Bräcklein et al, 2021 ; Bräcklein et al, 2022 ). A different alternative that may allow for flexible MU control after initial recruitment is volitional modulation of reciprocal inhibition ( Chen et al, 2006 ; Thompson et al, 2013 ) presumably via direct descending commands ( Jankowska, 1992 ; Nielsen et al, 1995 ).…”

“…Here, we extracted single MU activity from the TA since this muscle has properties (e.g. muscle fibres arrangement, proximity to the skin, distribution of innervation zones) that facilitate a reliable decomposition of MU activity using surface recordings ( Barsakcioglu et al, 2021 ; Bräcklein et al, 2021 ; Bräcklein et al, 2022 ; Negro et al, 2016a ; Dideriksen et al, 2018 ; Del Vecchio et al, 2020 ) and a relatively large number of monosynaptic connections with the motor cortex that could potentially be leveraged for direct independent MU control ( Ibáñez et al, 2021 ; Dideriksen et al, 2018 ). Our results strongly suggest that subjects do not tend to find or opt for a control strategy that relies on flexible MU recruitment order under constraint isometric conditions.…”

Neuromechanical Biomarkers for Robotic Neurorehabilitation

“…CMC has been explored using different neuroimaging techniques, namely MEG and EEG, but can also be computed by using EEG, sEMG and electrocorticography (Gerloff et al, 2006). Other methods such as mutual information and transfer entropy have also been explored to overcome the limitations of linear methods and to characterize non-linear correlations (Liang et al, 2020) State of the art: Currently, the study of CMC is mainly focused on how different brain areas control and modulate the activation of muscles, how the feedback from the muscles is received and processed (Sinha et al, 2020;Ibáñez et al, 2021), and how CMC can be altered due to different conditions (in particular, its modulation by fatigue (Martínez-Aguilar and Gutiérrez, 2019; dos Santos et al, 2020;Padalino et al, 2021). Current literature has established CMC as a biomarker of neurophysiology in healthy subjects (Franco-Alvarenga et al, 2019;Liu et al, 2019b) and sport conditions (Ushiyama et al, 2010).…”