Feedforward Control Mechanisms Research Articles

Exploring Risk of Posture and Musculoskeletal Disorders among Smartphone Addicted Youth: a Protocol Paper

Introduction: Smartphones with rapid advancement in telecommunication technology causing high usage leads to addiction, especially among youth. Concerning health, smartphone addiction influences posture causing neck-shoulder pain. However, the mechanism of how smartphone addiction causes pain related to posture control is still doubtful. Anticipatory feed-forward motor control mechanism has proposed that smartphone usage is a static low-level activity influenced by muscle fatigue and discomfort. Therefore, this study aimed to explore neck-shoulder muscle fatigue, particularly at the upper trapezius about smartphone addiction behaviors and adopted posture while using smartphones among youth. This study aims to investigate the relationship between smartphone addiction, risk of posture and the relationship between trapezius muscle fatigue, and neck-shoulder pain among youth. Methods: A cross-sectional study design will be conducted through convenient sampling among students at secondary schools in Malaysia. The Smartphone Addiction Scale, Rapid Assessment Upper Limb, Nordic Musculoskeletal Questionnaire and Electromyogram will be used as outcomes tools. Discussion: This study investigates the risk of smartphones in terms of duration usage among adults. Fatigue over the upper trapezius muscle has not yet been investigated among youth which will provide fundamental findings on how faulty posture during smartphone usage might cause neck pain.

A mathematical model of potassium homeostasis: Effect of feedforward and feedback controls.

Maintaining normal potassium (K+) concentrations in the extra- and intracellular fluid is critical for cell function. K+ homeostasis is achieved by ensuring proper distribution between extra- and intracellular fluid compartments and by matching K+ excretion with intake. The Na+-K+-ATPase pump facilitates K+ uptake into the skeletal muscle, where most K+ is stored. Na+-K+-ATPase activity is stimulated by insulin and aldosterone. The kidneys regulate long term K+ homeostasis by controlling the amount of K+ excreted through urine. Renal handling of K+ is mediated by a number of regulatory mechanisms, including an aldosterone-mediated feedback control, in which high extracellular K+ concentration stimulates aldosterone secretion, which enhances urine K+ excretion, and a gastrointestinal feedforward control mechanism, in which dietary K+ intake increases K+ excretion. Recently, a muscle-kidney cross talk signal has been hypothesized, where the K+ concentration in skeletal muscle cells directly affects urine K+ excretion without changes in extracellular K+ concentration. To understand how these mechanisms coordinate under different K+ challenges, we have developed a compartmental model of whole-body K+ regulation. The model represents the intra- and extracellular fluid compartments in a human (male) as well as a detailed kidney compartment. We included (i) the gastrointestinal feedforward control mechanism, (ii) the effect of insulin and (iii) aldosterone on Na+-K+-ATPase K+ uptake, and (iv) aldosterone stimulation of renal K+ secretion. We used this model to investigate the impact of regulatory mechanisms on K+ homeostasis. Model predictions showed how the regulatory mechanisms synthesize to ensure that the extra- and intracelluller fluid K+ concentrations remain in normal range in times of K+ loading and fasting. Additionally, we predict that without the hypothesized muscle-kidney cross talk signal, the model was unable to predict a return to normal extracellular K+ concentration after a period of high K+ loading or depletion.

A Computational Model for Estimating the Speech Motor System's Sensitivity to Auditory Prediction Errors.

Purpose The speech motor system uses feedforward and feedback control mechanisms that are both reliant on prediction errors. Here, we developed a state-space model to estimate the error sensitivity of the control systems. We examined (a) whether the model accounts for the error sensitivity of the control systems and (b) whether the two systems have similar error sensitivity. Method Participants (N = 50) completed an adaptation paradigm, in which their first and second formants were perturbed such that a participant's /ε/ would sound like her /ӕ/. We measured adaptive responses to the perturbations at early (0-80 ms) and late (220-300 ms) time points relative to the onset of the perturbations. As data-driven correlates of the error sensitivity of the feedforward and feedback systems, we used the average early responses and difference responses (i.e., late minus early responses), respectively. We fitted the state-space model to participants' adaptive responses and used the model's parameters as model-based estimates of error sensitivity. Results We found that the late responses were larger than the early responses. Additionally, the model-based estimates of error sensitivity strongly correlated with the data-driven estimates. However, the data-driven and model-based estimates of error sensitivity of the feedforward system did not correlate with those of the feedback system. Conclusions Overall, our results suggested that the dynamics of adaptive responses as well as error sensitivity of the control systems can be accurately predicted by the model. Furthermore, our results suggested that the feedforward and feedback control systems function independently. Supplemental Material https://doi.org/10.23641/asha.14669808.

Unit cell interactions of non-local active acoustic media

Active acoustic metamaterials have greatly expanded upon the unique and unusual ways that passive metamaterials manipulate sound. Previously, we have shown how spatially non-local forces in an acoustic system can create band gaps and break acoustic reciprocity, and how such effects can be realized in practice using a non-local active acoustic metamaterial (NAM). We demonstrated experimentally the remarkable ability of a single NAM unit cell to generate large, subwavelength, broadband nonreciprocity using an open loop feedforward control mechanism where a measured local pressure is transmitted by an electronic controller downstream to actuate an acoustic source. In NAM systems with multiple unit cells, this control scheme strongly couples each cell with every other cell, and thus requires special design considerations. Here, we discuss the advantages and challenges of developing NAM systems with multiple unit cells by first considering a system with two cells, detailing the impacts of their complex interaction on both performance and stability. We validate model predictions with experiments and highlight the exciting prospects offered by larger multi-cell NAM arrays.

Speech compensation responses and sensorimotor adaptation to formant feedback perturbations.

Control of speech formants is important for the production of distinguishable speech sounds and is achieved with both feedback and learned feedforward control. However, it is unclear whether the learning of feedforward control involves the mechanisms of feedback control. Speakers have been shown to compensate for unpredictable transient mid-utterance perturbations of pitch and loudness feedback, demonstrating online feedback control of these speech features. To determine whether similar feedback control mechanisms exist in the production of formants, responses to unpredictable vowel formant feedback perturbations were examined. Results showed similar within-trial compensatory responses to formant perturbations that were presented at utterance onset and mid-utterance. The relationship between online feedback compensation to unpredictable formant perturbations and sensorimotor adaptation to consistent formant perturbations was further examined. Within-trial online compensation responses were not correlated with across-trial sensorimotor adaptation. A detailed analysis of within-trial time course dynamics across trials during sensorimotor adaptation revealed that across-trial sensorimotor adaptation responses did not result from an incorporation of within-trial compensation response. These findings suggest that online feedback compensation and sensorimotor adaptation are governed by distinct neural mechanisms. These findings have important implications for models of speech motor control in terms of how feedback and feedforward control mechanisms are implemented.

Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon

With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.

The Relation of Articulatory and Vocal Auditory-Motor Control in Typical Speakers.

Purpose The purpose of this study was to explore the relationship between feedback and feedforward control of articulation and voice by measuring reflexive and adaptive responses to first formant (F 1) and fundamental frequency (f o) perturbations. In addition, perception of F 1 and f o perturbation was estimated using passive (listening) and active (speaking) just noticeable difference paradigms to assess the relation of auditory acuity to reflexive and adaptive responses. Method Twenty healthy women produced single words and sustained vowels while the F 1 or f o of their auditory feedback was suddenly and unpredictably perturbed to assess reflexive responses or gradually and predictably perturbed to assess adaptive responses. Results Typical speakers' reflexive responses to sudden perturbation of F 1 were related to their adaptive responses to gradual perturbation of F 1. Specifically, speakers with larger reflexive responses to sudden perturbation of F 1 had larger adaptive responses to gradual perturbation of F 1. Furthermore, their reflexive responses to sudden perturbation of F 1 were associated with their passive auditory acuity to F 1 such that speakers with better auditory acuity to F 1 produced larger reflexive responses to sudden perturbations of F 1. Typical speakers' adaptive responses to gradual perturbation of F 1 were not associated with their auditory acuity to F 1. Speakers' reflexive and adaptive responses to perturbation of f o were not related, nor were their responses related to either measure of auditory acuity to f o. Conclusion These findings indicate that there may be disparate feedback and feedforward control mechanisms for articulatory and vocal error correction based on auditory feedback.

Tuning of feedforward control enables stable muscle force-length dynamics after loss of autogenic proprioceptive feedback.

Animals must integrate feedforward, feedback and intrinsic mechanical control mechanisms to maintain stable locomotion. Recent studies of guinea fowl (Numida meleagris) revealed that the distal leg muscles rapidly modulate force and work output to minimize perturbations in uneven terrain. Here we probe the role of reflexes in the rapid perturbation responses of muscle by studying the effects of proprioceptive loss. We induced bilateral loss of autogenic proprioception in the lateral gastrocnemius muscle (LG) using self-reinnervation. We compared in vivo muscle dynamics and ankle kinematics in birds with reinnervated and intact LG. Reinnervated and intact LG exhibit similar steady state mechanical function and similar work modulation in response to obstacle encounters. Reinnervated LG exhibits 23ms earlier steady-state activation, consistent with feedforward tuning of activation phase to compensate for lost proprioception. Modulation of activity duration is impaired in rLG, confirming the role of reflex feedback in regulating force duration in intact muscle.

Articulation lost in space. The effects of local orobuccal anesthesia on articulation and intelligibility of phonemes

Motor speech requires numerous neural computations including feedforward and feedback control mechanisms. A reduction of auditory or somatosensory feedback may be implicated in disorders of speech, as predicted by various models of speech control. In this paper the effects of reduced somatosensory feedback on articulation and intelligibility of individual phonemes was evaluated by using topical anesthesia of orobuccal structures in 24 healthy subjects. The evaluation was done using a combination of perceptual intelligibility estimation of consonants and vowels and acoustic analysis of motor speech. A significantly reduced intelligibility was found, with a major impact on consonant formation. Acoustic analysis demonstrated disturbed diadochokinesis. These results underscore the clinical importance of somatosensory feedback in speech control. The interpretation of these findings in the context of speech control models, neuro-anatomy and clinical neurology may have implications for subtyping of dysarthria.

Learning New Feedforward Motor Commands Based on Feedback Responses.

Learning a new motor task modifies feedforward (i.e., voluntary) motor commands and such learning also changes the sensitivity of feedback responses (i.e., reflexes) to mechanical perturbations [1-9]. For example, after people learn to generate straight reaching movements in the presence of an external force field or learn to reduce shoulder muscle activity when generating pure elbow movements with shoulder fixation, evoked stretch reflex responses to mechanical perturbations reflect the learning expressed during self-initiated reaching. Such a transfer from feedforward motor commands to feedback responses is thought to take place because of shared neural circuits at the level of the spinal cord, brainstem, and cerebral cortex [10-13]. The presence of shared neural resources also predicts the transfer from feedback responses to feedforward motor commands. Little is known about such a transfer presumably because it is relatively hard to elicit learning in reflexes without engaging associated voluntary responses following mechanical perturbations. Here, we demonstrate such transfer by leveraging two approaches to elicit stretch reflexes while minimizing engagement of voluntary motor responses in the learning process: applying very short mechanical perturbations [14-19] and instructing participants to not respond to them [20-26]. Taken together, our work shows that transfer between feedforward and feedback control is bidirectional, furthering the notion that these processes share common neural circuits that underlie motor learning and transfer.

Auditory Feedback Control Mechanisms Do Not Contribute to Cortical Hyperactivity Within the Voice Production Network in Adductor Spasmodic Dysphonia.

Purpose Adductor spasmodic dysphonia (ADSD), the most common form of spasmodic dysphonia, is a debilitating voice disorder characterized by hyperactivity and muscle spasms in the vocal folds during speech. Prior neuroimaging studies have noted excessive brain activity during speech in participants with ADSD compared to controls. Speech involves an auditory feedback control mechanism that generates motor commands aimed at eliminating disparities between desired and actual auditory signals. Thus, excessive neural activity in ADSD during speech may reflect, at least in part, increased engagement of the auditory feedback control mechanism as it attempts to correct vocal production errors detected through audition. Method To test this possibility, functional magnetic resonance imaging was used to identify differences between participants with ADSD (n = 12) and age-matched controls (n = 12) in (a) brain activity when producing speech under different auditory feedback conditions and (b) resting-state functional connectivity within the cortical network responsible for vocalization. Results As seen in prior studies, the ADSD group had significantly higher activity than the control group during speech with normal auditory feedback (compared to a silent baseline task) in three left-hemisphere cortical regions: ventral Rolandic (sensorimotor) cortex, anterior planum temporale, and posterior superior temporal gyrus/planum temporale. Importantly, this same pattern of hyperactivity was also found when auditory feedback control of speech was eliminated through masking noise. Furthermore, the ADSD group had significantly higher resting-state functional connectivity between sensorimotor and auditory cortical regions within the left hemisphere as well as between the left and right hemispheres. Conclusions Together, our results indicate that hyperactivation in the cortical speech network of individuals with ADSD does not result from hyperactive auditory feedback control mechanisms and rather is likely related to impairments in somatosensory feedback control and/or feedforward control mechanisms.

A Simple 3-Parameter Model for Examining Adaptation in Speech and Voice Production.

Sensorimotor adaptation experiments are commonly used to examine motor learning behavior and to uncover information about the underlying control mechanisms of many motor behaviors, including speech production. In the speech and voice domains, aspects of the acoustic signal are shifted/perturbed over time via auditory feedback manipulations. In response, speakers alter their production in the opposite direction of the shift so that their perceived production is closer to what they intended. This process relies on a combination of feedback and feedforward control mechanisms that are difficult to disentangle. The current study describes and tests a simple 3-parameter mathematical model that quantifies the relative contribution of feedback and feedforward control mechanisms to sensorimotor adaptation. The model is a simplified version of the DIVA model, an adaptive neural network model of speech motor control. The three fitting parameters of SimpleDIVA are associated with the three key subsystems involved in speech motor control, namely auditory feedback control, somatosensory feedback control, and feedforward control. The model is tested through computer simulations that identify optimal model fits to six existing sensorimotor adaptation datasets. We show its utility in (1) interpreting the results of adaptation experiments involving the first and second formant frequencies as well as fundamental frequency; (2) assessing the effects of masking noise in adaptation paradigms; (3) fitting more than one perturbation dimension simultaneously; (4) examining sensorimotor adaptation at different timepoints in the production signal; and (5) quantitatively predicting responses in one experiment using parameters derived from another experiment. The model simulations produce excellent fits to real data across different types of perturbations and experimental paradigms (mean correlation between data and model fits across all six studies = 0.95 ± 0.02). The model parameters provide a mechanistic explanation for the behavioral responses to the adaptation paradigm that are not readily available from the behavioral responses alone. Overall, SimpleDIVA offers new insights into speech and voice motor control and has the potential to inform future directions of speech rehabilitation research in disordered populations. Simulation software, including an easy-to-use graphical user interface, is publicly available to facilitate the use of the model in future studies.

Is postural control during unstable sitting a proxy measure for determinants associated with lumbar stability?

The aim of this study was to test whether determinants associated with lumbar stability can predict performance during unstable sitting (trunk postural control - TPC). If confirmed, unstable sitting could be viewed as a proxy measure for these determinants.Wobbling chair motion was measured in 58 subjects with an inertial sensor, and six outcomes were computed (mean frequency and velocity, frequency dispersion, two variables from the sway density analysis and Lyapunov exponent - short interval) to represent TPC performance. Subjects also performed five other trunk neuromuscular tests to quantify the thickness of back and abdominal muscles and connective tissues, lumbar proprioception, lumbar stiffness, feedforward and feedback control mechanisms, and trunk/muscle coordination.Four to five predictors explained between 36 and 47% of TPC outcomes variance, as determined with multivariate analyses. These predictors were mainly related to (1) angular kinematic parameters of the pelvis or lumbar spine following rapid arm movement, (2) lumbar intrinsic stiffness, (3) thickness of perimuscular connective tissues surrounding specific abdominal muscles, (4) activation onsets of specific trunk muscles (IO/TrA and iliocostalis lumborum) before rapid arm movement, and (5) percent thickness change of internal oblique (IO) and transversus abdominis (TrA) muscles. Lumbar proprioception and reflex responses were not predictive, possibly due to the lack of appropriate measurements.These findings support the use of TPC in unstable sitting as a proxy measure for determinants associated with lumbar stability. This might be useful in research and clinical settings, considering time and equipment constraints associated with measuring these determinants individually.

A Taxonomy of Distraction Mitigation Strategies for Operating Rooms

Operating rooms (ORs) are complex dynamic settings that require individual OR team members to conduct focused and cognitively demanding tasks. The team focus may be disrupted by distractions such as alarms, phone calls, or in-person communications irrelevant to work. Some of these distractions can result in a break in task performance which are known as interruptions (Healey, Primus, & Koutantji, 2007). Distractions are inherent in the ORs and are generally considered as harmful (Mentis, Chellali, Manser, Cao, & Schwaitzberg, 2016). Studies have shown that distractions are associated with disrupted surgical flow (Wiegmann, Elbardissi, Dearani, Daly, & Sundt III, 2007), decreased cognitive task performance (Hsu, Man, Gizicki, Feldman, & Fried, 2008), increased time taken to complete surgical tasks, impaired dexterity, and increased surgical error rates (Ahmed, Ahmad, Stewart, Francis, & Bhatti, 2015). Individuals can experience prospective memory failures after an interruption, i.e., forgetting to resume the interrupted task or resuming the task at an incorrect step (Reason & Hobbs, 2003). Negative effects of distractions have been the central concern of these studies; however, literature is increasingly agreeing on the potential benefits of distractions (e.g., communication) such as decreasing stress, boredom, and mental fatigue (Weigl, Antoniadis, Chiapponi, Bruns, & Sevdalis, 2015) and improving surgical task performance (Kyrillos & Caissie, 2017). It is important to foster positive distractions, while mitigating the negative ones. Research is limited when explaining the conditions in which OR distractions are beneficial or harmful and the consequences of removing or reducing these distractions. Despite this lack of evidence, several techniques, mostly adapted from other high risk industries such as aviation, have been suggested for mitigating OR distractions (see Feil, 2014 for a comprehansive review). We created a taxonomy to categorize OR distraction mitigation strategies that we identified through literature reviews and discussions with OR teams from our partner hospital. Systems approach in human error management expects the system to prevent errors from occurring or propagating by having several defense barriers (Reason, 2008). Hence, our taxonomy sorts mitigation strategies that act as defense barriers using three dimensions: technology type (hard, soft), management control type (feedforward, concurrent, feedback), and target (individual, team, hospital). Hard technology refers to strategies that have tangible forms (e.g., physical tools such as checklists) while soft technology refers to the ones that have intangible forms (e.g., training and policies). Feedforward control aims to mitigate potential distractions by putting in system defenses based on anticipation of OR distractions that may happen. Concurrent control introduces defenses that are active during surgery. Feedback control compares surgical events and performance with desired outcomes and provides feedback and corrective control. Finally, distraction mitigation strategies should not only target individuals but also the OR team and the hospital at large, therefore, the last dimension captures this perspective. Thorough examinations are needed before implementing different mitigation strategies since these strategies, both hard and soft, can become sources of distraction themselves if designed, implemented, or maintained poorly. A safety culture needs to be adopted and supported, which can be achieved through the inclusion of feedforward, concurrent, and feedback control mechanisms for managing OR distraction. Success of distraction mitigation efforts requires active participation from individuals, OR teams, and hospital management. This systematic approach in categorizing mitigation strategies is aimed to assist OR distraction management and to improve patient safety together with work conditions for OR team members. However, further research is needed to assess the effectiveness of different strategies in practice. Acknowledgments. Funding for this work was provided by NSERC and the Centre for Healthcare Engineering of the University of Toronto, Faculty of Applied Science and Engineering.

Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter

Distributed power inverters with inductive capacitive–inductive (LCL) filters have become popular in distributed power generation system. However, due to unknown grid impedance, the inverters are confronted with challenges of local filter resonance, poor power quality, and multiple interactive resonance. This paper proposes a low-loss active compensator that can counteract effects of the grid impedance on the current control performance of single-phase grid-connected inverter with an LCL filter. The compensator utilizes dual unit point-of-common-coupling voltage feedforward control mechanisms (VFFC), in which one is integrated with the inverter controller, and the other is generated through an extra low voltage source converter (LV-VSC) in series with the filter capacitor. The LV-VSC has no additional passive inductive-capacitive filtering elements and provides very low volt-ampere. To confirm its validity, a single-phase inverter testbed integrated with the compensator was built. The experimental results validate the current-controlled performance enhancement of the proposed inverter system operating under different grid conditions.

Design and fabrication of a linear broadband non-reciprocal acoustic waveguide using feedforward control

Acoustic metamaterials that exhibit non-reciprocal transmission have received substantial attention due to their wide range of applications such as noise control, diagnostic imaging, communications, and acoustic cloaking. Passive metamaterials achieve non-reciprocity through system resonances combined with nonlinearities or manipulation of phononic bandgap and are usually effective over a narrow band of frequencies. In this study, we describe the development of a new class of acoustic waveguides that exhibit linear non-reciprocal sound transmission over a wide range of frequencies. We use an open loop feedforward control mechanism where the measured local pressure is passed through an electronic controller and transmitted downstream to actuate an acoustic source. Analysis of the idealized one-dimensional model with a continuous distribution of probes and sources shows spatial asymmetry with attenuation in one direction and amplification in the other for frequencies ranging from one-half to five times the ratio of the active length of the segment to the acoustic wavelength. We show that such behavior can be replicated in a physical system with a finite array of acoustic probes and sources and develop the framework to compute the stability of this class of active waveguides. The experimental results for a simple feedforward model system are presented.

Expectation of movement generates contrasting changes in multifinger synergies in young and older adults.

Anticipatory synergy adjustment (ASA) is a feed-forward control mechanism that describes a continuous decrease in the stability of the current motor state beginning about 150ms prior to a state transition. Recently, we described an associated phenomenon in which the system stability was reduced solely in response to a cue that generates an expectation of a state change, independent of whether the state change actually occurs. Both phenomena are of the same kind (stability reduction), but evoked by distinct antecedent conditions. Since, logically, cuing for movement must occur before the initiation of that movement, we named this new phenomenon 'Stage-1 ASA' and rechristened the well-established version 'Stage-2 ASA'. Here, we used a four-finger, isometric force production task to explore (1) the effect of healthy aging on Stage-1 ASA, and (2) if Stage-1 ASA resulted in a more rapid state change. Young and older adult participants produced 10% of their maximal force when they did not expect to produce any change in the force, and when they expected to change their force in an unknown direction and at an unknown time. In the latter condition, the 10% constant-force phase was followed by a choice reaction time task, in which the participants rapidly changed their force to follow a moving target presented on a computer monitor. Both young and older adults displayed equivalent amount of Stage-1 ASA. This was driven by a 42% reduction in finger-force variability in young adults. In contrast, it was driven by a 38% increase in finger-force variability in older adults. We speculate that the reduction in finger force variability assists the young adults in rapid state changes via two mechanisms: (1) the finger forces occupy a restricted set of states that are optimal for quick state transitions, and (2) lower variability during steady state translates into lower self-motion during state transition. Self-motion is the covariation between finger forces that fails to change the total force. The older adults are unable to adopt this strategy, and the increase in finger-force variability arises from (1) the adoption of an alternative strategy of destabilizing the attractor associated with the current state to facilitate state transitions and (2) the inability to coordinate multiple finger forces. Finally, older adults displayed longer reaction times than young adults, but a clear relation between Stage-1 ASA and consequent behavioral benefit in terms of reduced reaction time remained elusive.

Forced use of the paretic leg induced by repeated exposure to constraint force applied to the non-paretic leg of individuals post-stroke during walking

Locomotor training has been used to improve walking function of individuals post-stroke. However, the functional gains are relatively small, which may be due to the compensatory movements of the non-paretic leg during locomotor training. Our previous study indicated that applying a constraint force to the non-paretic leg could increase muscle activations of the paretic leg during treadmill walking. It is still unclear whether muscle responses in the paretic leg are acquired through feedback correction mechanisms or feedforward control mechanisms. The goal of this study was to examine electromyography (EMG) responses in the paretic leg to a repeated constraint force applied to the non-paretic leg during treadmill walking. Fifteen individuals with chronic stroke participated in this study. Subjects walked on a treadmill with no force for 1 minute, i.e., baseline. A controlled resistance force was then applied to the non-paretic leg starting from toe-off to mid-swing through a custom designed cable-driven robotic system for 7 minutes, i.e., adaptation period. The force was released and subjects continued walking on the treadmill for another 1 minute, i.e., post-adaptation period. The magnitude of resistance force was ∼18% of MVC of the hip flexion. EMG from 8 muscles of the paretic leg were recorded using electrodes and ankle movement of both legs were measured using position sensors. Integrated EMGs of ankle plantarflexors and hip extensors during stance phase significantly increased (33–50% increase, P < 0.01) during the early and late adaptation periods, and partially retained (17–22%) during the post-adaptation period. Our results suggest that both feedback correction and feedforward control mechanisms may be involved in response to the constraint force applied to the non-paretic leg. Results from this study may be used to develop a long-term training paradigm to induce a forced use of the paretic leg and improve walking function of individuals post-stroke.

Neuroimaging of the speech network

Historically, the study of the neural underpinnings of speech has suffered from the lack of an animal model whose brain activity could be measured using invasive electrophysiological techniques. The development of non-invasive structural and functional neuroimaging techniques in the latter part of the 20th century has led to a dramatic improvement in our understanding of the network of brain regions responsible for speech production. Techniques for measuring regional cerebral blood flow, including positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), have illuminated the neural regions involved in various aspects of speech, including feedforward control mechanisms as well as auditory and somatosensory feedback control circuits. More recently, fMRI studies utilizing repetition suppression have been used to identify the neural representations used in different parts of the speech network, including the identification of a syllable representation in left ventral premotor cortex. Magnetic resonance imaging has also been used to investigate the anatomical structure of the speech network, providing crucial information regarding connectivity within the network as well as identifying anomalies in the sizes of neural regions and/or white matter pathways in speech disorders.

Sensory feedback control in speech: Neural circuits and individual differences

Speech production involves a combination of feedforward and sensory feedback-based control mechanisms. The latter have been characterized in experiments involving real-time perturbations during speech. Unexpected perturbations of auditory feedback result in corrective motor responses with a minimum latency of approximately 100 ms after perturbation onset. These responses have been shown for both pitch and formant frequency perturbations, and the responsible neural circuitry includes portions of the superior temporal gyrus and ventral premotor cortex (vPMC). Corrective responses to somatosensory perturbations (such as a downward force applied to the jaw) occur approximately 50 ms after perturbation onset and are mediated by ventral somatosensory cortex and vPMC. The degree to which an individual weights auditory versus somatosensory feedback varies substantially. Such differences rely in part on differences in sensory acuity, e.g., a speaker with relatively poor hearing is likely to rely more heavily on somatosensory feedback control mechanisms than auditory feedback control mechanisms. Additionally, reliance on feedback control may be modulated to compensate for impairments in the feedforward system for speech, e.g., adults who stutter show a reduced reliance on auditory feedback control compared to fluent speakers, perhaps because auditory feedback can have a deleterious effect on speech initiation in stuttering.