Self-modulation of rectus femoris reflex excitability in humans

Management Approaches to Spastic Gait Disorders

Spastic gait presents clinically as the net mechanical consequence of neurological impairments of spasticity, weakness, and abnormal synergies and their interactions with the ground reaction force in patients with upper motor neuron syndromes and with some neuromuscular diseases. It is critical to differentiate whether the primary problem is weakness or spasticity, thus better understanding different phenotypes of spastic gait disorders. Pelvic girdle abnormality plays a pivotal role in determining the clinical presentation of gait disorders, since it determines the body vector and compensatory kinetic chain reactions in the knee and ankle joints. Knee joint abnormality can be a mechanical compensation for hip and/or ankle and foot abnormality. Diagnostic nerve blocks and instrumented gait analysis may be needed for diagnosing the underlying problems and developing an individualized plan of care. A wide spectrum of treatment options has been used to manage spastic gait disorders. Some are in early and investigational stages, such as neuromodulation modalities, while others are well-developed, such as therapeutic exercise, ankle-foot orthoses, botulinum toxin treatment, and surgical interventions. Physicians and other healthcare providers who manage spastic gait disorders should be familiar with these treatment options and should employ appropriate interventions concurrently rather than serially. The most effective treatments can be selected based on careful evaluation, inputs from patients, family, and therapists, along with appropriate goal setting. Treatment plans need to be re-evaluated for effectiveness, relevance, and in concordance with disease progress. This is particularly important for patients with progressive neuromuscular diseases such as amyotrophic lateral sclerosis.

Soleus H-reflex size versus stimulation rate in the presence of background muscle activity: A methodological study

Introduction

Hoffmann reflex (HR) operant conditioning has emerged as an important intervention in neurorehabilitation. During conditioning, the HR is elicited at low rates (∼0.2 Hz) to avoid the initial reduction in HR size that can occur over repeated stimulation, i.e., rate-dependent depression (RDD), thereby maintaining reflex size. This study investigated the impact of higher stimulation rates on HR size, where a stable, low-level, background electromyographic (EMG) signal is maintained over 225 conditioning trials in each of 30 sessions. A higher rate could shorten session length and/or number.

Methods

Fifteen healthy participants maintained low background soleus EMG (5-18 µV, ∼1-3% of the maximum stimulation evoked direct muscle (M-wave) EMG response (M max ) while standing. Soleus HR and M-wave recruitment curves were obtained at rates of 0.2, 1, and 2 Hz, from which M max and H max were calculated. Seventy-five HR trials (HRT) were collected for each stimulation rate at a target M-wave size (∼10-20% of M max ).

Results

There was no evidence of RDD at higher stimulation rates. In addition, the mean HR over trials was reliable across participants and rates. The Intraclass Correlation Coefficient (ICC) was 0.965 (95%CI:0.915, 0.987).

Discussion

This study shows that H-reflex conditioning might be performed at rates up to 2 Hz with no RDD and with consistent HR values. A faster rate could increase the number of conditioning trials per session, reduce session duration, and/or reduce the number of sessions. It could thereby accelerate the conditioning process and make the process less demanding for participants.

Support

NIH Grant P41 EB018783 (Wolpaw), NYS Spinal Cord Injury Research Board C37714GG (Gupta) and C38338GG (Wolpaw), VA SPiRE NCT05880251 (Brangaccio), Stratton Veterans Affairs Medical Center.

Simulated operant reflex conditioning environment reveals effects of feedback parameters

Operant conditioning of neural activation has been researched for decades in humans and animals. Many theories suggest two parallel learning processes, implicit and explicit. The degree to which feedback affects these processes individually remains to be fully understood and may contribute to a large percentage of non-learners. Our goal is to determine the explicit decision-making processes in response to feedback representing an operant conditioning environment. We developed a simulated operant conditioning environment based on a feedback model of spinal reflex excitability, one of the simplest forms of neural operant conditioning. We isolated the perception of the feedback signal from self-regulation of an explicit unskilled visuomotor task, enabling us to quantitatively examine feedback strategy. Our hypothesis was that feedback type, biological variability, and reward threshold affect operant conditioning performance and operant strategy. Healthy individuals (N = 41) were instructed to play a web application game using keyboard inputs to rotate a virtual knob representative of an operant strategy. The goal was to align the knob with a hidden target. Participants were asked to "down-condition" the amplitude of the virtual feedback signal, which was achieved by placing the knob as close as possible to the hidden target. We varied feedback type (knowledge of performance, knowledge of results), biological variability (low, high), and reward threshold (easy, moderate, difficult) in a factorial design. Parameters were extracted from real operant conditioning data. Our main outcomes were the feedback signal amplitude (performance) and the mean change in dial position (operant strategy). We observed that performance was modulated by variability, while operant strategy was modulated by feedback type. These results show complex relations between fundamental feedback parameters and provide the principles for optimizing neural operant conditioning for non-responders.

OPERANT REFLEX CONDITIONING SIMULATION ENVIRONMENT REVEALS EFFECTS OF FEEDBACK PARAMETERS

Operant conditioning of neural activation has been researched for decades in humans and animals. Many theories suggest two parallel learning processes, implicit and explicit. The degree to which feedback affects these processes individually remains to be fully understood and may contribute to a large percentage of non-learners. Our goal is to determine the explicit decision-making processes in response to feedback representing an operant conditioning environment. We developed a simulated operant conditioning environment based on a feedback model of spinal reflex excitability, one of the simplest forms of neural operant conditioning. We isolated the perception of the feedback signal from self-regulation of an explicit unskilled visuomotor task, enabling us to quantitatively examine feedback strategy. Our hypothesis was that feedback type, signal quality and success threshold affect operant conditioning performance and operant strategy. Healthy individuals (N = 41) were instructed to play a web application game using keyboard inputs to rotate a virtual knob representative of an operant strategy. The goal was to align the knob with a hidden target. Participants were asked to "down-condition" the amplitude of the virtual feedback signal, which was achieved by placing the knob as close as possible to the hidden target. We varied feedback type (knowledge of performance, knowledge of results), success threshold (easy, moderate, difficult), and biological variability (low, high) in a factorial design. Parameters were extracted from real operant conditioning data. Our main outcomes were the feedback signal amplitude (performance) and the mean change in dial position (operant strategy). We observed that performance was modulated by variability, while operant strategy was modulated by feedback type. These results show complex relations between fundamental feedback parameters and provide the principles for optimizing neural operant conditioning for non-responders.