The role of tactile sensation in online and offline hierarchical control of multi-finger force synergy

Interpersonal motor synergy: coworking strategy depends on task constraints

We investigated the role of task constraints on interpersonal interactions. Twenty-one pairs of coworkers performed a finger force production task on force sensors placed at two ends of a seesaw-like apparatus and matched a combined target force of 20 N for 23 s over 10 trials. There were two experimental conditions: 1) FIXED: the seesaw apparatus was mechanically held in place so that the only task constraint was to match the 20 N resultant force, and 2) MOVING: the lever in the apparatus was allowed to rotate freely around its fulcrum, acting like a seesaw, so an additional task constraint to (implicitly) balance the resultant moment was added. We hypothesized that the additional task constraint of moment stabilization imposed on the MOVING condition would deteriorate task performance compared with the FIXED condition; however, this was rejected, as the performance of the force matching task was similar between two conditions. We also hypothesized that the central nervous systems (CNSs) would employ distinct coworking strategies or interpersonal motor synergy (IPMS) between conditions to satisfy different task constraints, which was supported by our results. Negative covariance between coworker's forces in the FIXED condition suggested a force stabilization strategy, whereas positive covariance in the MOVING condition suggested a moment stabilization strategy, implying that independent CNSs adopt distinct IPMSs depending on task constraints. We speculate that in the absence of a central neural controller, shared visual and mechanical connections between coworkers may suffice to trigger modulations in the cerebellum of each CNS to satisfy competing task constraints.NEW & NOTEWORTHY To the best of our knowledge, this is the first study to investigate the coworking behavior or IPMS when an additional task constraint is imposed. Our proposed analytical framework quantifies IPMS and allows for investigating variability in offline (i.e., across multiple repetitions) and online (i.e., across time) control, which is novel in coworking research. Understanding variability while performing a task is essential, as repeating a task is not always possible, as in therapeutic contexts.

Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

Unveiling the neuromechanical mechanisms underlying the synergistic interactions in human sensorimotor system

Motor synergies are neural organizations of a set of redundant motor effectors that interact with one another to compensate for each other’s error and ensure the stabilization of a performance variable. Recent studies have demonstrated that central nervous system synergistically coordinates its numerous motor effectors through Bayesian multi-sensory integration. Deficiency in sensory synergy weakens the synergistic interaction between the motor effectors. Here, we scrutinize the neuromechanical mechanism underlying this phenomenon through spectral analysis and modeling. We validate our model-generated results using experimental data reported in the literature collected from participants performing a finger force production task with and without tactile feedback (manipulated through injection of anesthetic in fingers). Spectral analysis reveals that the error compensation feature of synergies occurs only at low frequencies. Modeling suggests that the neurophysiological structures involving short-latency back-coupling loops similar to the well-known Renshaw cells explain the deterioration of synergy due to sensory deprivation.

Force-stabilizing synergies can be retained by coordinating sensory-blocked and sensory-intact digits

The present study examined the effects of selective digital deafferentation on the multi-finger synergies as a function of total force requirement and the number of digits involved in isometric pressing. 12 healthy adults participated in maximal and sub-maximal isometric pressing tasks with or without digital anesthesia to selective digits from the right hand. Our results indicate that selective anesthesia paradigm induces changes in both anesthetized (local) and non-anesthetized (non-local) digits’ performance, including: (1) decreased maximal force abilities in both local and non-local digits; (2) reduced force share during multi-finger tasks from non-local but not local digits; (3) decreased force error-making; and (4) marginally increased motor synergies. These results reinforce the contribution of somatosensory feedback in the process of maximal voluntary contraction force, motor performance, and indicate that somatosensation may play a role in optimizing secondary goals during isometric force production rather than ensuring task performance.

Inter-dependence between mathematically independent variability components in human multi-finger force control

In human movement control, inherent error or uncertainty in the controller, motor system, and sensory system causes variability in motor outcomes. Previous studies have suggested different methods to quantify and examine independent components of the motor variability of motor outputs in a redundant motor system. While these motor variability components are mathematically independent, it is unknown if these components are behaviorally independent among subjects. The aim of this study was to investigate inter-relations between mathematically independent motor variability components in multi-finger force control among subjects. Nineteen healthy subjects performed two tasks, producing a constant force and a sinusoidal force for 12 s over 12 trials. We used the hierarchical variability decomposition (HVD) model to quantify mathematically independent variability components, i.e., online task-relevant variance (onTRV), online task-irrelevant variance (onTIV), offline task-relevant variance (offTRV), and offline task-irrelevant variance (offTIV) for each subject. The correlation analysis performed among all subjects showed that online and offline motor variability components were positively correlated, while task-relevant and -irrelevant variability components were not. The results indicate that these mathematically independent motor variability components existing in multi-finger force space are dependently scaled among subjects, more noticeably between online and offline controls, suggesting a non-independent behavioral relationship between these mathematically independent components among subjects.

Intra-auditory integration between pitch and loudness in humans: Evidence of super-optimal integration at moderate uncertainty in auditory signals

When a person plays a musical instrument, sound is produced and the integrated frequency and intensity produced are perceived aurally. The central nervous system (CNS) receives defective afferent signals from auditory systems and delivers imperfect efferent signals to the motor system due to the noise in both systems. However, it is still little known about auditory-motor interactions for successful performance. Here, we investigated auditory-motor interactions as multi-sensory input and multi-motor output system. Subjects performed a constant force production task using four fingers in three different auditory feedback conditions, where either the frequency (F), intensity (I), or both frequency and intensity (FI) of an auditory tone changed with sum of finger forces. Four levels of uncertainty (high, moderate-high, moderate-low, and low) were conditioned by manipulating the feedback gain of the produced force. We observed performance enhancement under the FI condition compared to either F or I alone at moderate-high uncertainty. Interestingly, the performance enhancement was greater than the prediction of the Bayesian model, suggesting super-optimality. We also observed deteriorated synergistic multi-finger interactions as the level of uncertainty increased, suggesting that the CNS responded to increased uncertainty by changing control strategy of multi-finger actions.

Aging differentially affects online control and offline control in finger force production

Human central nervous system (CNS) undergoes neurological changes during the aging process, leading to declines in hand and finger functions. Previous studies have shown that the CNS can independently process multi-finger force control and moment of force control. However, if both force and moment control are simultaneously imposed by motor task constraints, the CNS needs to resolve competing interests of generating negative and positive covariances between fingers, respectively, which causes "conflict of interest or COI". Here, we investigated how aging affects the CNS's abilities to solve COI through a new experimental paradigm. Both elderly and young subjects performed a constant force production task using index and middle fingers under two conditions, multi-finger pressing with no COI and with COI. We found that the elderly increased variance of a virtual finger (VF: an imagined finger producing the same mechanical effect as both fingers together) in time-to-time basis (i.e. online control), while increasing covariance between individual fingers (IF) forces in trial-to-trial basis (i.e. offline control) with COI than no COI. Aging affects the CNS's abilities to solve COI by deteriorating VF actions in online control and IF actions in offline control.

Examining impairment of adaptive compensation for stabilizing motor repetitions in stroke survivors

The hand, one of the most versatile but mechanically redundant parts of the human body, suffers more and longer than other body parts after stroke. One of the rehabilitation paradigms, task-oriented rehabilitation, encourages motor repeatability, the ability to produce similar motor performance over repetitions through compensatory strategies while taking advantage of the motor system's redundancy. The previous studies showed that stroke survivors inconsistently performed a given motor task with limited motor solutions. We hypothesized that stroke survivors would exhibit deficits in motor repeatability and adaptive compensation compared to healthy controls in during repetitive force-pulse (RFP) production tasks using multiple fingers. Seventeen hemiparetic stroke survivors and seven healthy controls were asked to repeatedly press force sensors as fast as possible using the four fingers of each hand. The hierarchical variability decomposition model was employed to compute motor repeatability and adaptive compensation across finger-force impulses, respectively. Stroke survivors showed decreased repeatability and adaptive compensation of force impulses between individual fingers as compared to the control (p < 0.05). The stroke survivors also showed decreased pulse frequency and greater peak-to-peak time variance than the control (p < 0.05). Force-related variables, such as mean peak force and peak force interval variability, demonstrated no significant difference between groups. Our findings indicate that stroke-induced brain injury negatively affects their ability to exploit their redundant or abundant motor system in an RFP task.

Temporary Nerve Block at Selected Digits Revealed Hand Motor Deficits in Grasping Tasks

Peripheral sensory feedback plays a crucial role in ensuring correct motor execution throughout hand grasp control. Previous studies utilized local anesthesia to deprive somatosensory feedback in the digits or hand, observations included sensorimotor deficits at both corticospinal and peripheral levels. However, the questions of how the disturbed and intact sensory input integrate and interact with each other to assist the motor program execution, and whether the motor coordination based on motor output variability between affected and non-affected elements (e.g., digits) becomes interfered by the local sensory deficiency, have not been answered. The current study aims to investigate the effect of peripheral deafferentation through digital nerve blocks at selective digits on motor performance and motor coordination in grasp control. Our results suggested that the absence of somatosensory information induced motor deficits in hand grasp control, as evidenced by reduced maximal force production ability in both local and non-local digits, impairment of force and moment control during object lift and hold, and attenuated motor synergies in stabilizing task performance variables, namely the tangential force and moment of force. These findings implied that individual sensory input is shared across all the digits and the disturbed signal from local sensory channel(s) has a more comprehensive impact on the process of the motor output execution in the sensorimotor integration process. Additionally, a feedback control mechanism with a sensation-based component resides in the formation process for the motor covariation structure.

Intra-Auditory Integration Improves Motor Performance and Synergy in an Accurate Multi-Finger Pressing Task

Humans detect changes in the air pressure and understand the surroundings through the auditory system. The sound humans perceive is composed of two distinct physical properties, frequency and intensity. However, our knowledge is limited how the brain perceives and combines these two properties simultaneously (i.e., intra-auditory integration), especially in relation to motor behaviors. Here, we investigated the effect of intra-auditory integration between the frequency and intensity components of auditory feedback on motor outputs in a constant finger-force production task. The hierarchical variability decomposition model previously developed was used to decompose motor performance into mathematically independent components each of which quantifies a distinct motor behavior such as consistency, repeatability, systematic error, within-trial synergy, or between-trial synergy. We hypothesized that feedback on two components of sound as a function of motor performance (frequency and intensity) would improve motor performance and multi-finger synergy compared to feedback on just one component (frequency or intensity). Subjects were instructed to match the reference force of 18 N with the sum of all finger forces (virtual finger or VF force) while listening to auditory feedback of their accuracy. Three experimental conditions were used: (i) condition F, where frequency changed; (ii) condition I, where intensity changed; (iii) condition FI, where both frequency and intensity changed. Motor performance was enhanced for the FI conditions as compared to either the F or I condition alone. The enhancement of motor performance was achieved mainly by the improved consistency and repeatability. However, the systematic error remained unchanged across conditions. Within- and between-trial synergies were also improved for the FI condition as compared to either the F or I condition alone. However, variability of individual finger forces for the FI condition was not significantly decreased as compared to I condition alone. This result indicates an improvement in motor performance is consistent with Bayesian estimation, and changes in multi-finger interaction mostly result in the enhanced motor performance. These findings provide evidence that the central nervous system can take advantage of the intra-auditory integration in a statistically optimal (Bayesian) fashion to enhance motor performance by improving multi-finger synergy.

Deficits in motor abilities for multi-finger force control in hemiparetic stroke survivors

The ability to control redundant motor effectors is one of hallmarks in human motor control, and the topic has been studied extensively over several decades since the initial inquiries proposed by Nicholi Bernstein. However, our understanding of the influence of stroke on the control of redundant motor systems is very limited. This study aimed to investigate the effect of stroke-related constraints on multi-finger force control abilities in a visuomotor task. Impaired (IH) and less-impaired hands (LH) of 19 hemiparetic stroke survivors and 19 age-matched control subjects were examined. Each hand repeatedly produced isometric forces to match a target force of 5 N shown on a computer screen using all four fingers. The hierarchical variability decomposition (HVD) model was used to separate force-matching errors (motor performance) into task-relevant measures (accuracy, steadiness, and reproducibility). Task-irrelevant sources of variability in individual finger force profiles within and between trials (flexibility and multiformity) were also quantified. The IH in the stroke survivors showed deficits in motor performance attributed mainly to lower accuracy and reproducibility as compared to control hands (p < 0.05). The LH in stroke survivors showed lower reproducibility and both hands in stroke also had higher multiformity than the control hands (p < 0.05). The findings from our HVD model suggest that accuracy, reproducibility, and multiformity were mainly impaired during force-matching task in the stroke survivors. The specific motor deficits identified through the HVD model with the new conceptual framework may be considered as critical factors for scientific investigation on stroke and evidence-based rehabilitation of this population.