Too much information is no information: how machine learning and feature selection could help in understanding the motor control of pointing

© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The aim of this study was to develop the use of Machine Learning techniques as a means of multivariate analysis in studies of motor control. These studies generate a huge amount of data, the analysis of which continues to be largely univariate. We propose the use of machine learning classification and feature selection as a means of uncovering feature combinations that are altered between conditions. High dimensional electromyogram (EMG) vectors were generated as several arm and trunk muscles were recorded while subjects pointed at various angles above and below the gravity neutral horizontal plane. We used Linear Discriminant Analysis (LDA) to carry out binary classifications between the EMG vectors for pointing at a particular angle, vs. pointing at the gravity neutral direction. Classification success provided a composite index of muscular adjustments for various task constraints—in this case, pointing angles. In order to find the combination of features that were significantly altered between task conditions, we conducted a post classification feature selection i.e., investigated which combination of features had allowed for the classification. Feature selection was done by comparing the representations of each category created by LDA for the classification. In other words computing the difference between the representations of each class. We propose that this approach will help with comparing high dimensional EMG patterns in two ways; (i) quantifying the effects of the entire pattern rather than using single arbitrarily defined variables and (ii) identifying the parts of the patterns that convey the most information regarding the investigated effects.Peer reviewe

The Temporal Structure of Vertical Arm Movements

The present study investigates how the CNS deals with the omnipresent force of gravity during arm motor planning. Previous studies have reported direction-dependent kinematic differences in the vertical plane; notably, acceleration duration was greater during a downward than an upward arm movement. Although the analysis of acceleration and deceleration phases has permitted to explore the integration of gravity force, further investigation is necessary to conclude whether feedforward or feedback control processes are at the origin of this incorporation. We considered that a more detailed analysis of the temporal features of vertical arm movements could provide additional information about gravity force integration into the motor planning. Eight subjects performed single joint vertical arm movements (45° rotation around the shoulder joint) in two opposite directions (upwards and downwards) and at three different speeds (slow, natural and fast). We calculated different parameters of hand acceleration profiles: movement duration (MD), duration to peak acceleration (D PA), duration from peak acceleration to peak velocity (D PA-PV), duration from peak velocity to peak deceleration (D PV-PD), duration from peak deceleration to the movement end (D PD-End), acceleration duration (AD), deceleration duration (DD), peak acceleration (PA), peak velocity (PV), and peak deceleration (PD). While movement durations and amplitudes were similar for upward and downward movements, the temporal structure of acceleration profiles differed between the two directions. More specifically, subjects performed upward movements faster than downward movements; these direction-dependent asymmetries appeared early in the movement (i.e., before PA) and lasted until the moment of PD. Additionally, PA and PV were greater for upward than downward movements. Movement speed also changed the temporal structure of acceleration profiles. The effect of speed and direction on the form of acceleration profiles is consistent with the premise that the CNS optimises motor commands with respect to both gravitational and inertial constraints

Normalized temporal parameters and normalized acceleration profiles of arm movements.

<p>Left column: averaged (n = 8) values (± SD) of (A) relative duration to peak acceleration, (B) relative duration to peak velocity, (C) relative duration to peak deceleration; slow (S), natural (N) and fast (F) speeds. Middle column: normalized (in duration) and averaged (8 subjects) acceleration profiles in which movement start and end were determined with the ‘<i>percentage threshold method</i>’. (D) Slow movements, (E) natural movements, (G) fast movements. Right column: normalized (in duration) and averaged (n = 15 trials) acceleration profiles from a representative subject in which movement start and end were determined by a ‘<i>fix value threshold method</i>’ (1 m/s², 3 m/s² and 10 m/s² for slow, natural and fast speeds respectively). (H) Slow movements, (I) natural movements, (J) fast movements. Dashed vertical lines are depicted to compare profiles between speed conditions. Vertical arrows indicate movement directions. Stars indicate differences between directions and horizontal black arrows differences between speeds.</p

Experimental setup and data analysis.

<p>(A) Participants initial position and spatial location of the targets (right-side view). (B) We delimited movement duration by cutting velocity profiles with a 5% threshold of their peak velocity (PV). Several parameters were then determined on the corresponding acceleration profile: the peak acceleration (PA) and its time of apparition (D PA), the time between PA and PV (D PA-PV), the Acceleration Duration (time to PV), the deceleration peak (PD) and the time between PV and PD (D PV-PD); the time between PD and movement end (D PD-End); the deceleration duration (from PV to end).</p

Arm Kinematics.

<p>Typical profiles of hand position, velocity and acceleration for all experimental conditions. Vertical arrows indicate movement directions.</p

Temporal parameters of arm movements.

<p>Averaged (n = 8) values of non normalized temporal parameters (± SD) for upward and downward arm movements performed at slow (S), natural (N) and fast (F) speeds. (A) movement duration, (B) time of apparition of peak acceleration, (C) duration between peak acceleration and peak velocity, (D) duration between peak velocity and peak deceleration, (E) duration between peak deceleration and movement end, (G) acceleration duration, (H) deceleration duration, (I) peak acceleration, (J) peak velocity, (K) peak deceleration. Vertical arrows indicate movement directions. Stars indicate differences between directions and horizontal black arrows differences between speeds.</p

An acute session of motor imagery training induces use-dependent plasticity

Motor imagery, defined as the mental representation of an action without movement-related sensory inputs, is a well-known intervention to improve motor performance. In the current study, we tested whether use-dependent plasticity, a mechanism underlying motor learning, could be induced by an acute session of motor imagery. By means of transcranial magnetic stimulation (TMS) over the left primary motor cortex, we evoked isolated thumb movements in the right hand and assessed corticospinal excitability in the flexor and extensor pollicis brevis muscles. We measured the mean TMS-induced movement direction before and after an acute session of motor imagery practice. In a first experiment, participants of the imagery group were instructed to repeatedly imagine their thumb moving in a direction deviated by 90° from the pre-test movement. This group, but not the control group, deviated the post-training TMS-induced movements toward the training target direction (+44° ± 62° and −1° ± 23°, respectively). Interestingly, the deviation magnitude was driven by the corticospinal excitability increase in the agonist muscle. In a second experiment, we found that posttraining TMS-induced movements were proportionally deviated toward the trained direction and returned to baseline 30 minutes after the motor imagery training. These findings suggest that motor imagery induces use-dependent plasticity and, this neural process is accompanied by corticospinal excitability increase in the agonist muscle

Gravity-efficient motor control is associated with contraction-dependent intracortical inhibition

Summary: In humans, moving efficiently along the gravity axis requires shifts in muscular contraction modes. Raising the arm up involves shortening contractions of arm flexors, whereas the reverse movement can rely on lengthening contractions with the help of gravity. Although this control mode is universal, the neuromuscular mechanisms that drive gravity-oriented movements remain unknown. Here, we designed neurophysiological experiments that aimed to track the modulations of cortical, spinal, and muscular outputs of arm flexors during vertical movements with specific kinematics (i.e., optimal motor commands). We report a specific drop of corticospinal excitability during lengthening versus shortening contractions, with an increase of intracortical inhibition and no change in spinal motoneuron responsiveness. We discuss these contraction-dependent modulations of the supraspinal motor output in the light of feedforward mechanisms that may support gravity-tuned motor control. Generally, these results shed a new perspective on the neural policy that optimizes movement control along the gravity axis

Pain, No Gain: Acute Pain Interrupts Motor Imagery Processes and Affects Mental Training-Induced Plasticity

Abstract Pain influences both motor behavior and neuroplastic adaptations induced by physical training. Motor imagery (MI) is a promising method to recover motor functions, for instance in clinical populations with limited endurance or concomitant pain. However, the influence of pain on the MI processes is not well established. This study investigated whether acute experimental pain could modulate corticospinal excitability assessed at rest and during MI (Exp. 1) and limit the use-dependent plasticity induced by MI practice (Exp. 2). Participants imagined thumb movements without pain or with painful electrical stimulations applied either on digit V or over the knee. We used transcranial magnetic stimulation to measure corticospinal excitability at rest and during MI (Exp. 1) and to evoke involuntary thumb movements before and after MI practice (Exp. 2). Regardless of its location, pain prevented the increase of corticospinal excitability that is classically observed during MI. In addition, pain blocked use-dependent plasticity following MI practice, as testified by a lack of significant posttraining deviations. These findings suggest that pain interferes with MI processes, preventing the corticospinal excitability facilitation needed to induce use-dependent plasticity. Pain should be carefully considered for rehabilitation programs using MI to restore motor function