Effect of Object Texture and Weight on Ipsilateral Corticospinal Influences During Bimanual Holding in Humans

in Motor Control
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  • 1 Department of Neuroscience, University of Montreal, Montreal, Quebec, Canada
  • | 2 IRGLM, Centre for Interdisciplinary Research in Rehabilitation (CRIR), Montreal, Quebec, Canada
  • | 3 Institut für Neuroinformatik, Ruhr-Universität Bochum, Bochum, Germany
  • | 4 Faculty of Medicine, McGill University, Montreal, Quebec, Canada
  • | 5 Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
  • | 6 École de Réadaptation, University of Montreal, Montreal, Quebec, Canada
  • | 7 School of Physical & Occupational Therapy, McGill University, Montreal, Quebec, Canada

We tested the hypothesis that the ipsilateral corticospinal system, like the contralateral corticospinal system, controls the threshold muscle length at which wrist muscles and the stretch reflex begin to act during holding tasks. Transcranial magnetic stimulation was applied over the right primary motor cortex in 21 healthy subjects holding a smooth or coarse block between the hands. Regardless of the lifting force, motor evoked potentials in right wrist flexors were larger for the smooth block. This result was explained based on experimental evidence that motor actions are controlled by shifting spatial stretch reflex thresholds. Thus, the ipsilateral corticospinal system is involved in threshold position control by modulating facilitatory influences of hand skin afferents on motoneurons of wrist muscles during bimanual object manipulation.

Contralateral (c) and ipsilateral (i) corticospinal (CS) projections from the primary motor cortex (M1) mediate facilitatory and/or inhibitory influences to α-motoneurons (MNs) of arm muscles (Rosenzweig et al., 2009). Mono- or polysynaptic iCS pathways (Chen et al., 2003; Ziemann et al., 1999) are involved in planning and coordination of fine and complex movements and in the production of uni- and bimanual movements (Chen et al., 1997; Schrafl-Altermatt & Easthope, 2018; Zhang et al., 2020). There is a gap in knowledge about how sensory input influences iCS output. Previous studies have shown that cutaneous afferents play a major role in cCS modulation during force maintenance and scaling when holding an object in one hand (Johansson & Westling, 1984; Monzée et al., 2003; Tokimura et al., 2000; Witney et al., 2004). However, how sensory information influences iCS output for bimanual tasks is unknown. This information is of particular interest in determining the mechanisms of motor recovery after brain lesions affecting sensory pathways (Biernaskie et al., 2005; Bradnam et al., 2013).

Each hemisphere can receive both contralateral and ipsilateral information from skin receptors (Schnitzler et al., 1995). Integration of bilateral somesthetic inputs during bimanual tasks may be achieved by projections of ipsilateral and contralateral tactile information to somatosensory cortices (S1) of both hemispheres and via transcallosal pathways (Schnitzler et al., 1995). The secondary somatosensory cortex (S2) also receives cutaneous inputs from S1 and cutaneous receptors of both hands as stimulation of one hand elicits bilateral activation of S2 (Burton, 1986; Schrafl-Altermatt & Easthope, 2018). Both S1 and S2 send projections to M1 and premotor cortices (Burton, 1986; Dea et al., 2016). M1 also receives somatosensory inputs, possibly from the thalamus (Asanuma & Mackel, 1989). Furthermore, numerous inputs to M1 come from Area 5 in the superior parietal lobule (Dea et al., 2016; Premji et al., 2011) and from Area 7b (Godschalk et al., 1984) involved in somatosensory and associative processing. M1 activity correlates with different biomechanical variables characterizing motor outcome, such as electromyographic (EMG) signals, that is, motor commands to muscles or muscle forces (Evarts, 1968). However, correlations do not necessarily imply causation, and without additional tests, conclusions derived from observations of correlations can be unreliable (Feldman, 2019; Shah et al., 2004). Therefore, there is a persistent controversy in the understanding of how influences from M1 to MNs transmitted by the CS and other descending and spinal systems control motor actions.

This controversy results from the existence of two alternative theoretical frameworks for action and perception. One is based on the equilibrium point (EP) hypothesis. Attempts to discredit the EP hypothesis have been unsuccessful as they were based on misinterpretation of its basic concepts (see Feldman et al., 1998; Feldman and Latash, 2005). The EP hypothesis has now been further advanced to the theory of referent control of action and perception. Several recent papers have continued to demonstrate its explanatory and predictive power (Feldman, 2019; Feldman and Zhang, 2020; Latash, 2019). The second, hitherto dominant framework in behavioral neuroscience was established after Hollerbach (1982), inspired by computational control schemes for motion of robots, and suggested that the brain uses laws of mechanics and neural emulators of the properties of the neuromuscular system—hypothetical internal models—to precompute and directly specify motor commands to muscles (EMG patterns) to elicit the desired motor outcome. This view has been generally unconditionally accepted (e.g., Fodor, 1975; Ghez et al., 1995; Todorov and Jordan, 2002) but has recently been questioned (Arshavsky, 2017; Feldman, 2019). Moreover, studies (Feldman 2015; Feldman, 2019; Latash, 2019; Feldman and Zhang, 2020: Feldman et al., 2021) have found that proponents of the internal model and computational theories in general overlooked the experimental fact that the input/output functions of MNs are irreversible (Feldman, 2019) and that the system cannot compute and specify input signals that could compel MNs to generate the predetermined EMG patterns or muscle forces/torques, which makes computational theories unfeasible. In addition, laws of mechanics imply that living systems do not need to make specific efforts to obey these laws: all physical actions of living systems are unavoidably consistent with these laws, and the idea of using internal models of mechanical laws for computation of motor behavior is also dubious.

The most serious drawback of computational theories is that they disregard an important physical principle identified in experiments that resulted in the formulation of the original EP hypothesis. According to this principle, the nervous system cannot directly predetermine the motor outcome and can control motor actions only indirectly by changing or maintaining, depending on the motor goal, parameters of physical and physiological laws. Parameters influence the motor outcome but can, although not necessarily always, remain independent of biomechanical variables characterizing this outcome. Therefore, parameters do not change the causality inherent in these laws. For example, the simplest case of parametric control is when we throw different stones in the air. The stone mass (m) can be considered as a parameter in Newton’s second law (F = m·a). This law implies causality in the relationship between variables F (force) and a (acceleration): in inertial systems of coordinates, it is force that causes changes in the acceleration, not the other way around, and this causality is preserved regardless of the mass of the stone. In contrast to computational theories, the EP hypothesis respects the fact that input/output functions of MNs are irreversible and has no problem explaining how muscle forces are controlled. Specifically, experimental data show that MNs of human muscles are recruited gradually, depending on the difference between the current muscle length, x, and the threshold muscle length, λ, also called the threshold of the tonic stretch reflex (Feldman, 1986, 2019). In dynamics, the stretch reflex threshold, λ*, decreases with increasing stretch velocity (v). To a first approximation,
λ*=λμν,
where μ is time-dimensional parameter likely controlled by dynamic γ-MNs (Feldman, 1986). Thus, the muscle is active if
xλ*>0
and it is silent otherwise.
The MN recruitment and active muscle force increases with the increasing difference between x and λ*. Different spinal and descending systems, including the corticospinal system, control λ by influencing α-MNs directly, monosynaptically or indirectly, pre- or postsynaptically, via spinal interneurons and γ-MNs (Capaday, 1995; Feldman and Orlovsky, 1972; Matthews, 1959). In the presence of length-dependent afferent influences on α-MNs, changes in the membrane potential of α-MNs ΔV of a broad set of muscles (human oculomotor muscles, arm and leg muscles of humans and cats) are transformed into changes (Δ λ) in the threshold muscle length (λ) at which the MN begins to be recruited (Feldman & Zhang, 2020; Raptis et al., 2010; Zhang et al., 2017):
sΔλ=ΔV.
The minus sign indicates that ΔV and Δλ change in opposite directions; s is the sensitivity of the membrane potential to synaptic inputs. For example, MN depolarization (when ΔV is positive) elicits a decrease in the threshold λ; α-MNs are recruited depending on the difference between the actual muscle length (x) and the threshold muscle length (λ). Changes in the membrane potential can result from pre- and postsynaptic influences either directly, monosynoptically, or polysynaptically, via spinal interneurons, γ-MNs, and intermuscular and cutaneous reflexes (Feldman, 2019).

Based on these properties, one can say that MNs of a single muscle function in a one-dimensional spatial frame of reference. The current muscle length x is the coordinate, and λ* is the origin or referent point in this frame of reference, and muscle activation or relaxation is controlled by shifts in the referent point of the frame of reference, and this modus operandi is called referent control.

Descending systems, including those originating from M1, influence EMG patterns indirectly by specifying the threshold muscle length (λ) or, in angular coordinates, the threshold position called the referent position (R) of the body segments. Muscle activation increases with increasing deflection of the actual position (Q) from the referent position (R) at which muscles begin to be recruited and proprioceptive and cutaneous reflexes are predetermined to act (Feldman & Zhang, 2020).

To produce, say, a desired isometric muscle force, it is sufficient to decrease the stretch reflex threshold (λ) to a value below the isometric muscle length x until the condition (2) of muscle activation is met and the desired or predetermined isometric force is achieved without any internal model (Figure 1). Also note that when isometric force is produced with the hand pressing against a motionless object, the threshold hand position at which muscles begin to generate active force is virtually located inside the object (figuratively speaking, the hand “virtually penetrates” the object; Figure 1). This concept is essential for the explanation of how grip forces or forces holding an object are produced (Pilon et al., 2007). In other words, these forces are generated due to the stretch reflex since the object prevents the hand from reaching the threshold position at which muscles can be deactivated.

Figure 1
Figure 1

—A comparison of two alternative theoretical frameworks for motor control based on their ability to explain isometric muscle force production. Suppose we press the arm against a wall such that the length x of, say, elbow flexors remains unchanged (isometric condition). In the context of the EP hypothesis, the system can gradually diminish the threshold muscle length λ (horizontal arrow) until the required isometric muscle force and the final EP occurs inside the predetermined window (white). In the context of internal model theory and in computational theories in general, the system should precompute and specify the desired muscle force. However, the input/output functions of MNs are irreversible (Feldman, 2019), and the system cannot compute and specify input signals that could compel MNs to generate predetermined muscle forces, not only in isometric but in any other condition, which makes computational theories unfeasible. Note that, in the EP hypothesis, to produce the required force, the system sets the threshold length λ below x, that is, the λ is found behind the wall. The wall prevents the arm from reaching the threshold length, and the stretch reflex activating muscles, depending on the difference between x and λ, produce isometric muscle force compensated by the wall reaction. EP = equilibrium–point; MNs = motoneurons.

Citation: Motor Control 26, 1; 10.1123/mc.2021-0096

The controversy between the two frameworks has also been resolved in several empirical studies (Foisy and Feldman, 2010; Piscitelli et al., 2020; Raptis et al., 2010). The activation threshold was identified for two elbow flexors and two extensors, 10 arm muscles, and wrist flexors and extensors, respectively. In particular, in Raptis et al. (2010), the EMG levels of wrist muscles before and after changes in the limb position were similar. This methodology decorrelated the changes in limb position and EMG levels such that the intentional choice of the arm position became independent of EMG activity. By mechanically perturbing the arm at the initial and final positions, initially silent MNs were ready to be activated in response to passive deflections due to the stretch reflex (Raptis et al., 2010). In other words, changes in the arm position were associated with resetting of the threshold muscle length (λ) at which the wrist flexor and extensor muscles begin to be activated for each muscle involved (see also Foisy & Feldman, 2006; Ostry & Feldman, 2003). As predicted from the EP hypothesis, cCS influences on MNs of muscles were substantially different when tested by transcranial magnetic stimulation (TMS) at these threshold positions. For example, in elbow flexion, flexor MNs were facilitated and extensor MNs were inhibited (reciprocal pattern) and vice versa for elbow extension (Piscitelli et al., 2020; Raptis et al., 2010). The cCS and iCS influences were also involved in threshold control during bilateral wrist movements.

In the present study, we investigated the role of iCS and cutaneous afferents on the modulation of force output and scaling for a bimanual task in which subjects held an object between the hands (Figure 2). We tested whether MNs of the wrist flexor muscle, flexor carpi radialis (FCR), would be facilitated by iCS when the ipsilateral wrist was in the flexed position. This would imply that the iCS system, like the cCS system (Raptis et al., 2010), is involved in parametric, referent control of wrist position. In addition, by varying object texture, we investigated whether referent control relies on cutaneous inputs to α-MNs. By assuming that iCS influences are modulated similarly in bimanual tasks, we assessed iCS influences by analyzing ipsilateral motor evoked potentials (iMEPs) elicited in wrist muscles by iTMS in tasks in which subjects held a block between the hands. The weight and textural properties of the block were varied, thus changing the friction, which required a change in the magnitude of holding forces. We hypothesized that bimanually holding a smooth object would require more ipsilateral facilitation, evidenced by a larger flexor iMEP, than a coarse object, even if the object weight was not supported by the table. If confirmed, one can conclude that iCS facilitation results in a positive change in the membrane potential of wrist flexor MNs (ΔV > 0) and, according to Equation 3, in a decrease in the threshold muscle length (λ), which would mean that the iCS system is involved in threshold position control in the task of bimanual holding of an object. We also address the question of whether or not iCS influences are affected by the lifted weight. Preliminary results have appeared in abstract form (Duval et al., 2021).

Figure 2
Figure 2

—Bimanual tasks. Task 1 with the SB, LF—F0 = 0; Task 2 with the CB, LF—F0 = 0; Task 3 with the SB, LF—F1 = 0.3 kg; Task 4 with the CB, LF—F1 = 0.3 kg. In all tasks, the right wrist was in flexion, and the left wrist was in extension. F0 = lifting force—0; F1 = lifting force—0.3 kg; SB = smooth block; CB = coarse block; LF = lifting force.

Citation: Motor Control 26, 1; 10.1123/mc.2021-0096

Methods

Subjects

Right-handed healthy subjects with no history of orthopedic or neurological disorders and who did not take psychoactive or other drugs that could affect cortical excitability participated in this study. All subjects signed an informed consent form approved by the Ethics Committee of the Centre for Interdisciplinary Research in Rehabilitation in accordance with the 1964 Declaration of Helsinki. Initially, 23 subjects were included, but only data of 21 male and female subjects (age = 23.0 ± 5.0 years) were analyzed as responses to TMS in two subjects could not be identified because of background EMG noise.

Experimental Procedures

Subjects sat in a chair with back support in front of a table. The table height was adjusted to allow subjects to sit comfortably with both forearms placed on the table in a semisupinated position. For all subjects, the wrist flexion–extension axes were directed vertically to the table. In all subjects and tasks, elbow flexion was about 145°, and horizontal shoulder abduction was about 45°. Throughout the TMS testing, subjects maintained a right wrist flexion of 45° (neutral wrist position = 0°) while holding a rectangular wooden block (4 × 6 × 20 cm; weight 0.3 kg) between the hands (Figure 1). In Tasks 1 and 2, the block was placed on the table. Subjects were instructed to press the block with both hands with a moderate force (i.e., 20% of MVC) without lifting it (i.e., lifting force [LF] = 0 [F0]) while maintaining the wrist position. The surface of the block was either smooth or coarse. The block was polished with fine sandpaper to make the surface smooth. For the coarse surface, the block was covered with the hooked side of Velcro tape (Model S-15760, hook size 1 mm), thus substantially increasing the friction between the object and hands. The smooth block (SB) was used in Task 1, and the coarse block (CB) was used in Task 2. In Tasks 3 (SB) and 4 (CB), a narrower table was used such that the hands and the block were not supported. This allowed subjects to flex and extend the wrist around the vertical axis while holding the block (weight = 0.3 kg) in the air (LF = 0.3 kg; Figure 1). Task order was randomized across subjects.

We tested whether there were condition-related changes in iCS influences on MNs of the right wrist FCR by recording EMG responses to TMS of the right M1. Since MEPs elicited by TMS depend on both motor cortex and spinal MN excitability (Raptis et al., 2010; Zhang et al., 2017), we equalized the tonic EMG activity of the right FCR in each condition by asking subjects to press on the block in the flexion direction to maintain an FCR activity in the window of 20% ± 2SD of their MVC. In each task, 20 real and five sham (noise) pulses of TMS were delivered. To minimize the influences of neck position on iMEPs (Tazoe & Perez, 2014; Ziemann et al., 1999), head position and gaze were maintained during each trial by asking subjects to look directly at the screen displaying the EMG activity in front of them without moving the trunk.

The EMG level associated with the MVC of wrist flexors was measured by asking participants to flex their right wrist against the table as strongly as possible without involving elbow or shoulder muscles or the trunk with the wrist in the neutral position. The average EMG value of three attempts was computed with a 15-s rest period between attempts.

Data Recording

The EMG activity of the right and left FCR was recorded with wireless Trigno™ Mini Sensor Delsys (Natick, MA) electrodes placed on the muscle belly. Prior to electrode application, the skin was cleaned with alcohol. Rectified EMG signals (root mean square values, time constant 100 ms) of both wrist flexors were recorded and displayed on a screen via the Cambridge Electronic Design box and Signal software (CED Ltd., Cambridge, United Kingdom; sampling rate = 2,000 Hz).

TMS

Single TMS pulses (5–10 s between pulses) were delivered via a cone-shaped figure-eight coil (110° between two cones and 70 mm outer diameter) connected to a Magstim 200 system (Whitland, United Kingdom). The coil was positioned over the wrist area of the subject’s right M1 (2 cm anterior and 6 cm lateral to the vertex). The TMS induced a posterior–anterior directed current. The optimal stimulation site was located by moving the coil in small discrete steps on the scalp until the cMEPs in the left FCR remained stable for five consecutive trials. The TMS intensity was then decreased to determine the resting motor threshold when MEPs just began to exceed the background EMG activity in at least three of five consecutive trials. The TMS intensity was then increased to 1.5× above the threshold. Stimulus intensity ranged from 45% to 57% of maximal Magstim output. For each subject, TMS intensity was maintained constant during the whole experiment. Subjects wore a swimming cap on which the optimal coil position was marked to maintain this position throughout the experiment.

Data Analysis

MATLAB software (MathWorks, version R2019b, Natick, MA) was used for all offline data analysis. Raw EMG signals were filtered offline by a band-pass filter (45–500 Hz). The EMG baseline was defined as the mean rectified EMG level averaged over 50 ms before TMS onset. For group comparisons, individual EMG levels were normalized with respect to their baselines. Responses to TMS (cMEP and iMEP) were characterized by onset time, duration, amplitude, and area.

An iMEP was considered present if the poststimulus EMG exceeded the baseline by 1SD (Chen et al., 2003) for 5 ms. The iMEP duration was defined as the time between the point when the EMG began to exceed the baseline by 1SD to the point when the EMG returned to its baseline level. The iMEP amplitude was defined as the maximal deflection of rectified iMEP from the baseline. The iMEP area was defined as the area between the rectified iMEP and the EMG baseline. For each subject, we ensured that iMEPs were acquired at similar baseline EMG across conditions. The iMEPs were averaged across trials for each condition and in each subject.

Statistics

A two-factor repeated-measures analysis of variance determined the effect of friction (SB or CB) and lift force on iMEP amplitude, area, onset, and duration using IBM SPSS Statistics (version 25.0; IBM Corp., Armonk, NY). Significant results are reported together with the partial eta-squared (ηp2) for the effect size measure. The significance level was set at p < .05. Group data are presented as the mean ± SE in the text and figures.

Results

Characteristics of cMEPs and iMEPs

Figure 3 shows that in contralateral muscles, TMS elicits a facilitatory short latency cMEP followed by a silent period (c) with a subsequent excitatory rebound (c). Similar EMG components (iMEP, ipsilateral silent period, and ipsilateral rebound) can be produced in response to iTMS in ipsilateral muscles (Chen et al., 2003; Zhang et al., 2020).

Figure 3
Figure 3

—Effects of block surface texture and lifting force on iCS influences. (a) Average EMG responses of wrist flexor (n = 21, rectified) to iTMS for each condition, (b) amplitude of iMEP (normalized to the background EMG level before TMS for each condition), (c) area of iMEP for each condition—note texture-related changes in ipsilateral responses to TMS between Tasks 1 and 2 (b: p = .001, ηp2=.419; c: p = .037, ηp2=.2) and Tasks 3 and 4 (b: p = .001, ηp2=.419; c: p = .037, ηp2=.2)—and (d) iMEP baseline for each condition. *Significant effects. EMG = electromyography; F0 = lifting force—0; F1 = lifting force—0.3 kg; SB = smooth block; CB = coarse block; FCR = flexor carpi radialis; iMEP = ipsilateral motor evoked potential; iCS = ipsilateral corticospinal.

Citation: Motor Control 26, 1; 10.1123/mc.2021-0096

Although TMS intensities were lower than those used in other studies (e.g., Tazoe & Perez, 2014), responses to TMS were evoked in both the left, contralateral FCR (cMEP), and right ipsilateral FCR (iMEP) in 21 subjects. Due to noise in EMG recordings, responses to TMS in two additional subjects were not analyzed.

Effect of Texture on iCS Influences

The effect of texture on iCS influences was assessed by comparing iMEP amplitudes and areas in bimanual holding tasks in which the block surface was either smooth or coarse with lift forces of 0 kg (Figure 1; Tasks 1 and 2) or 0.3 kg (Tasks 3 and 4). Regardless of lift force, iMEP amplitudes (Figure 2b), F(1, 20) = 14.419, p = .001, ηp2=.419, and areas (Figure 2c), F(1, 20) = 4.999, p = .037, ηp2=.2, were larger for tasks with the smooth compared with the CB (Table 1).

Table 1

Characteristics of Components of TMS Responses in Tasks 1–4 in the Wrist Flexion Position

MEP characteristicsSB, F0 (n = 21)CB, F0 (n = 21)SB, F1 (n = 21)CB, F1 (n = 21)
cMEP
 Onset (ms)23.10 ± 0.5523.10 ± 0.3223.20 ± 0.3223.40 ± 0.40
 Duration (ms)20.07 ± 1.1021.0 ± 0.7220.0 ± 0.9019.90 ± 0.77
 Relative amplitude23.21 ± 5.1624.48 ± 5.2916.06 ± 3.8215.36 ± 3.12
 Area0.16 ± 0.040.16 ± 0.030.10 ± 0.030.10 ± 0.02
iMEP
 Onset (ms)27.30 ± 0.7127.10 ± 0.4427.10 ± 0.4227.30 ± 0.59
 Duration (ms)11.40 ± 0.5710.90 ± 0.6312.10 ± 0.6411.80 ± 0.72
 Relative amplitude3.50 ± 0.22*3.03 ± 0.10*3.55 ± 0.13*3.05 ± 0.12*
 Area0.01 ± 0.0007*0.01 ± 0.00040.01 ± 0.0010*0.01 ± 0.0007*

Note. All EMG components were normalized to the mean EMG level before TMS. There was a significant difference in amplitude and area for block texture (SB and CB) but not lifting force. The values are presented as mean ± SE. cMEP = contralateral motor evoked potential; iMEP = ipsilateral motor evoked potential; F0 = lifting force—0; F1 = lifting force—0.3 kg; SB = smooth block; CB = coarse block; EMG = electromyography.

*p < .05.

Ipsilateral MEPs were compared in bimanual holding tasks with two lifting forces (0 and 0.3 kg) and two textural surfaces (SB vs. CB). No significant effects of LF were observed in iMEP amplitudes, F(1, 20) = 0.063, p = .804, or areas, F(1, 20) = 2.969, p = .1, for either surface texture.

Discussion

We found that the iMEP amplitudes and areas were larger during bimanual tasks when participants held the SB compared with the CB regardless of the weight of the object. The MNs of wrist flexors were facilitated by the iCS system when subjects held the SB with two hands, thus confirming our hypothesis. Moreover, according to Equation 3, the change in the membrane potential of wrist flexor MNs resulting from iCS influences implies that the ipsilateral CS system is involved in threshold control as well as the contralateral CS system as previously shown. This implies that in contrast to computational approaches, the EP hypothesis advances our understanding of how motor actions are controlled.

Effects of Surface Texture and Lifting Force on iCS Influences During Bimanual Holding Tasks

The larger iMEP amplitudes and areas during bimanual holding of the SB suggests that facilitatory iCS influences on wrist MNs are modulated by signals from hand cutaneous afferents. They may participate in the maintenance and scaling of bimanual holding forces. This is consistent with the data by Witney et al. (2004), who found a modulation of whole-arm EMG activity by afferents from fingertip pads.

Cutaneous inputs generated in a finger loading task elicited an increase in both cCS and iCS influences on MNs (Shibuya & Ohki, 2004). This supports the notion that both hemispheres cooperate during holding, especially in bimanual tasks. It is consistent with the observation that the two hands are controlled as a single unit during holding tasks. The perturbation of motion of one arm elicits reflex reactions in both arms (Gorniak et al., 2009; see also Ustinova et. al., 2013).

To explain our finding, one can assume that the CB was perceived as less slippery and, thus, required less holding force. This is consistent with our result of increased iCS facilitation when holding the SB. When lifting forces are greater than zero, both fast- and slow-adapting cutaneous mechanoreceptors should be active (Westling & Johansson, 1987). In contrast, when stationary lifting force equals zero, the perception of holding an object likely relies on slowly adapting receptors (Phillips & Johnson, 1985). Slowly adapting receptors can better sense uneven surfaces if surface gratings are separated by a distance exceeding 2 mm (Phillips & Johnson, 1981). In our study, the Velcro used for the coarse surface might not have been appropriately perceived due to the small separation between the Velcro hooks (1 mm). Thus, the activation of cutaneous receptors may have been diminished when holding the CB by the static nature of the task and the physical characteristics of the block. This resulted in reduced cortical excitability compared with the SB.

Another explanation is based on the notion that the task of object holding is associated with its affordances (Gibson, 1966), such as its size, shape, and orientation, as well as with the anticipation of what one intends to do with the object. One can suggest that when intending to lift an object, cutaneous information from the object surface informs the nervous system of the amount of force needed to prevent object slippage during arm motion. Thus, several explanations are consistent with our finding of increasing iCS facilitation when holding the SB.

Considering Results Based on the Notion of Indirect, Parametric Control of Motor Actions and Sensory Influences by the M1

Our results can be interpreted according to indirect, parametric control, which states that, like other motor actions (Feldman, 2019; Feldman & Zhang, 2020), bimanual holding is controlled indirectly by changing the threshold muscle length (λ) that indicates the spatial muscle length range in which MNs and proprioceptive reflexes are predetermined to act.

Rather than directly predetermining motor commands to muscles, studies suggest that M1 controls motor actions indirectly by resetting the joint threshold position at which MNs begin to be activated and proprioceptive reflexes, including the stretch and tactile reflexes, become functional (Figure 3; Raptis et al., 2010). Resetting of threshold length (λ) is characteristic of a broad set of muscles (human oculomotor muscles and arm and leg muscles of humans and cats; for review, see Feldman & Zhang, 2020). Not surprisingly, many descending systems, in addition to the CS and the spinal systems mediating intermuscular and cutaneous reflexes, are involved in λ control (Feldman & Orlovsky, 1972; Matthews, 1959). For example, by changing the threshold muscle lengths of numerous body muscles, descending influences mediated by the vestibular system predetermine the body orientation in the gravitational field, for example, while leaning the body forward (Zhang et al., 2018). For bimanual wrist movements, Zhang et al. (2020) showed that both iCS and cCS influences are involved in threshold control.

Based on these notions, the production of forces during unimanual and bimanual holding tasks can be described in the following way (Figure 4). During unimanual gripping of a solid object, the actual hand aperture (Q) is defined by the object’s size. The referent aperture (R) is smaller such that at position R, the fingers virtually penetrate the object (Figure 4a; see also Frenkel-Toledo et al., 2019). The grip force is defined by the difference between Q and R. The smaller the R, the larger the grip force. To observe R, one can grip the computer mouse between the thumb and index finger and forcefully slide the mouse off the fingers. The fingers will move to position R (Figure 4b). The forces holding an object between two hands are produced similarly with R being specified as the referent distance between the hands (Figure 4c).

Figure 4
Figure 4

—Indirect, referent control of unimanual grip force and bimanual holding force. (a) In unimanual gripping of an object, the aperture Q between the fingers is defined by the object’s size, whereas at R, the fingers virtually penetrate the object. Grip force results from the difference between Q and R, increasing with decreasing R. (b) When the object is suddenly removed, the fingers move to the R position. (c) Forces holding an object bimanually are produced similarly, depending on the difference between Q and R. Modified with permission from Zhang et al. (2020). Q = actual aperture; R = referent aperture.

Citation: Motor Control 26, 1; 10.1123/mc.2021-0096

According to Equation 3, MNs of the wrist flexor muscle (FCR) would be facilitated by iCS when the ipsilateral wrist is in the flexed position (Figure 2). Thus, together with previous work (Zhang et al., 2020), our results imply that the iCS system, like the cCS system, is involved in parametric, referent control of wrist position by modulating cutaneous influences on MNs of wrist muscles.

Limitations and Future Work

We investigated relatively small changes in lifting force, which may explain why iCS facilitation was insensitive to this force. In future studies, the effects of a broader range of object surface textures and lifting forces on iCS influences should be investigated.

Control of threshold λ is initiated in advance of changes in the motor outcome (Feldman, 2019). Therefore, based on the control schemas in Figure 4, one can suggest that forces are generated in advance of object lifting not only during unimanual grip tasks (Flanagan et al., 1995; Westling & Johansson, 1984) but also during bimanual holding tasks, which can also be investigated in future studies. Schemas in Figure 4 can be used in future studies in which the role of different descending systems during unimanual and bimanual tasks can be compared.

Conclusions

We investigated iCS influences on MNs of wrist flexor muscles and their modulation in a bimanual holding task. Results are consistent with the hypothesis that facilitatory influences from cutaneous afferents modulate iCS influences on MNs and, thus, may participate in scaling and maintaining grip forces. Results support that bimanual object holding is controlled indirectly by changing parameters that indicate the spatial muscle range in which MNs and proprioceptive reflexes are predetermined to act. In addition, cutaneous influences from hand receptors are involved in the parametric control of holding forces.

Results provide important new information about the role of left and right cortices in the control of bimanual tasks involving grasping of an object between the hands, with the possible participation of mono- and polysynaptic (corticoreticulospinal, corticopropriospinal, and transcallosal) projections to MNs as well as spinal and transcortical stretch reflexes. These results may be essential for understanding the role of interhemispheric interaction in healthy subjects and neurological patients.

References

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Duval is now at the Department of Health Sciences, The University of Auckland, Auckland, New Zealand.

Feldman (feldman@med.umontreal.ca) is corresponding author.
  • View in gallery

    —A comparison of two alternative theoretical frameworks for motor control based on their ability to explain isometric muscle force production. Suppose we press the arm against a wall such that the length x of, say, elbow flexors remains unchanged (isometric condition). In the context of the EP hypothesis, the system can gradually diminish the threshold muscle length λ (horizontal arrow) until the required isometric muscle force and the final EP occurs inside the predetermined window (white). In the context of internal model theory and in computational theories in general, the system should precompute and specify the desired muscle force. However, the input/output functions of MNs are irreversible (Feldman, 2019), and the system cannot compute and specify input signals that could compel MNs to generate predetermined muscle forces, not only in isometric but in any other condition, which makes computational theories unfeasible. Note that, in the EP hypothesis, to produce the required force, the system sets the threshold length λ below x, that is, the λ is found behind the wall. The wall prevents the arm from reaching the threshold length, and the stretch reflex activating muscles, depending on the difference between x and λ, produce isometric muscle force compensated by the wall reaction. EP = equilibrium–point; MNs = motoneurons.

  • View in gallery

    —Bimanual tasks. Task 1 with the SB, LF—F0 = 0; Task 2 with the CB, LF—F0 = 0; Task 3 with the SB, LF—F1 = 0.3 kg; Task 4 with the CB, LF—F1 = 0.3 kg. In all tasks, the right wrist was in flexion, and the left wrist was in extension. F0 = lifting force—0; F1 = lifting force—0.3 kg; SB = smooth block; CB = coarse block; LF = lifting force.

  • View in gallery

    —Effects of block surface texture and lifting force on iCS influences. (a) Average EMG responses of wrist flexor (n = 21, rectified) to iTMS for each condition, (b) amplitude of iMEP (normalized to the background EMG level before TMS for each condition), (c) area of iMEP for each condition—note texture-related changes in ipsilateral responses to TMS between Tasks 1 and 2 (b: p = .001, ηp2=.419; c: p = .037, ηp2=.2) and Tasks 3 and 4 (b: p = .001, ηp2=.419; c: p = .037, ηp2=.2)—and (d) iMEP baseline for each condition. *Significant effects. EMG = electromyography; F0 = lifting force—0; F1 = lifting force—0.3 kg; SB = smooth block; CB = coarse block; FCR = flexor carpi radialis; iMEP = ipsilateral motor evoked potential; iCS = ipsilateral corticospinal.

  • View in gallery

    —Indirect, referent control of unimanual grip force and bimanual holding force. (a) In unimanual gripping of an object, the aperture Q between the fingers is defined by the object’s size, whereas at R, the fingers virtually penetrate the object. Grip force results from the difference between Q and R, increasing with decreasing R. (b) When the object is suddenly removed, the fingers move to the R position. (c) Forces holding an object bimanually are produced similarly, depending on the difference between Q and R. Modified with permission from Zhang et al. (2020). Q = actual aperture; R = referent aperture.

  • Arshavsky, Y.I. (2017). Neurons versus networks: The interplay between individual neurons and neural networks in cognitive functions. Neuroscientist, 23(4), 341355. https://doi.org/10.1177/1073858416670124

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Asanuma, H., & Mackel, R. (1989). Direct and indirect sensory input pathways to the motor cortex; its structure and function in relation to learning of motor skills. The Japanese Journal of Physiology, 39(1), 119. https://doi.org/10.2170/jjphysiol.39.1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Biernaskie, J., Szymanska, A., Windle, V., & Corbett, D. (2005). Bi-hemispheric contribution to functional motor recovery of the affected forelimb following focal ischemic brain injury in rats. European Journal of Neuroscience, 21(4), 989999.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bradnam, L.V., Stinear, C.M., & Byblow, W.D. (2013). Ipsilateral motor pathways after stroke: Implications for non-invasive brain stimulation. Frontiers in Human Neuroscience, 7, 184. https://doi.org/10.3389/fnhum.2013.00184

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burton, H. (1986). Second somatosensory cortex and related areas. In E. G. Jones & A. Peters (Eds.), Sensory-motor areas and aspects of cortical connectivity (pp. 3198). SpringerNature.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Capaday, C. (1995). The effects of baclofen on the stretch reflex parameters of the cat. Experimental Brain Research, 104(2), 287296. https://doi.org/10.1007/BF00242014

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, R., Cohen, L.G., & Hallett, M. (1997). Role of the ipsilateral motor cortex in voluntary movement. Canadian Journal of Neurological Sciences, 24(4), 284291. https://doi.org/10.1017/S0317167100032947

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, R., Yung, D., & Li, J.-Y. (2003). Organization of ipsilateral excitatory and inhibitory pathways in the human motor cortex. Journal of Neurophysiology, 89(3), 12561264. https://doi.org/10.1152/jn.00950.2002

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dea, M., Hamadjida, A., Elgbeili, G., Quessy, S., & Dancause, N. (2016). Different patterns of cortical inputs to subregions of the primary motor cortex hand representation in Cebus apella. Cerebral Cortex, 26(4), 17471761. https://doi.org/10.1093/cercor/bhv324

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duval, L., Zhang, L., Lauzé, A.S., Zhu, Y., Barthélemy, D., Dancause, N., Levin M.F., Feldman, A.G. (2021, June 17th). Ipsi- and Contralateral Corticospinal Influences in Uni- and Bimanual Movements in Humans [Poster Abstract]. Presented at the 2021 NeuroSymposium. https://mjm.mcgill.ca/article/view/923/680

    • Search Google Scholar
    • Export Citation
  • Evarts, E.V. (1968). Relation of pyramidal tract activity to force exerted during voluntary movement. Journal of Neurophysiology, 31(1), 1427. https://doi.org/10.1152/jn.1968.31.1.14

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feldman, A.G. (1986). Once more on the equilibrium-point hypothesis (λ model) for motor control. Journal of Motor Behavior, 18(1), 1754. https://doi.org/10.1080/00222895.1986.10735369

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feldman, A.G. (2015). Referent control of action and perception: Challenging conventional theories in behavioral neuroscience. Springer.

  • Feldman, A.G. (2019). Indirect, referent control of motor actions underlies directional tuning of neurons. Journal of Neurophysiology, 121(3), 823841. https://doi.org/10.1152/jn.00575.2018

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feldman, A.G., & Latash, M.L. (2005). Testing hypotheses and the advancement of science: Recent attempts to falsify the equilibrium point hypothesis. Experimental Brain Research, 161(1), 91103. https://doi.org/10.1007/s00221-004-2049-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feldman, A.G., Levin, M.F., Garofolini, A., Piscitelli, D., & Zhang, L. (2021). Central pattern generator and human locomotion in the context of referent control of motor actions. Clinical Neurophysiology, 132(11), 28702889. https://doi.org/10.1016/j.clinph.2021.08.016

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feldman, A.G., & Orlovsky, G.N. (1972). The influence of different descending systems on the tonic stretch reflex in the cat. Experimental Neurology, 37(3), 481494. https://doi.org/10.1016/0014-4886(72)90091-X

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feldman, A.G., Ostry, D.J., Levin, M.F., Gribble, P.L., & Mitnitski, A.B. (1998). Recent tests of the equilibrium-point hypothesis (lambda model). Motor Control, 2(3), 189205. https://doi.org/10.1123/mcj.2.3.189

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feldman, A.G., & Zhang, L. (2020). Eye and head movements and vestibulo-ocular reflex in the context of indirect, referent control of motor actions. Journal of Neurophysiology, 124(1), 115133. https://doi.org/10.1152/jn.00076.2020

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flanagan, J.R., Wing, A.M., Allison, S., & Spenceley, A. (1995). Effects of surface texture on weight perception when lifting objects with a precision grip. Perception & Psychophysics, 57(3), 282290. https://doi.org/10.3758/BF03213054

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fodor, J.A. (1975). The language of thought. Thomas Crowell.

  • Foisy, M., & Feldman, A.G. (2006). Threshold control of arm posture and movement adaptation to load. Experimental Brain Research, 175(4), 726744. https://doi.org/10.1007/s00221-006-0591-7

    • Crossref
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