Biology subjects

Adcock, A.

Publications and source records attributed to Adcock, A..

3 recordsLinked to original sources

bioRxiv2025

Stroke-Related Changes in Tonic and Phasic Muscle Recruitment During Reaching Reveal Pathway-Specific Motor Deficits

Upper limb motor deficits are common after stroke and often persist despite rehabilitation. While clinical assessments emphasize movement quality, they do not capture the underlying neuromuscular impairments, particularly in individuals with mild deficits. This study aimed to characterize stroke-related changes in muscle recruitment during reaching by separating tonic (gravity-compensating) and phasic (intersegmental dynamics-related) components of EMG activity. We recorded surface EMG from 12 upper limb muscles during goal-directed reaching in 8 individuals with unilateral ischemic stroke and 9 controls. Using principal component analysis, we extracted tonic and phasic components and compared their amplitude, directional tuning, and coactivation patterns across groups. Group differences were evaluated with generalized linear mixed-effects models, regression, and correlation analyses. Even individuals with mild stroke exhibited abnormal muscle recruitment. Proximal muscles were over-recruited in directions that typically require less activation, indicating altered directional tuning. Phasic activation of distal muscles was significantly reduced and worsened with time post-stroke (R2 = 0.52, p = 0.002). Tonic overactivation of proximal muscles was present across all stroke participants. Muscle coactivation patterns were hemisphere-specific: right-hemisphere stroke reduced tonic coactivation in contralateral arms, whereas left-hemisphere stroke increased it. Abnormal phasic coactivation between proximal and distal muscles correlated with impaired intersegmental dynamics compensation (R2 = 0.67, p = 0.013). Tonic and phasic impairments were often correlated, suggesting shared disruption of corticospinal and reticulospinal pathways. These findings reveal distinct yet interacting deficits in muscle recruitment following stroke, supporting the development of neuromechanically-informed tools for individualized rehabilitation.

neuroscience↗

bioRxiv2020

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

neuroscience↗

bioRxiv2019

Force-based assessment captures unseen changes in normal and abnormal arm motion

The whole repertoire of complex human motion is enabled by forces applied by our muscles and controlled by the nervous system. The of stroke on the complex multi-joint motor control is difficult to quantify in a meaningful way that informs about the underlying deficit in the active motor control and intersegmental coordination. We tested the idea that post-stroke deficit can be quantified with high sensitivity using motion capture and inverse modeling of a broad range of reaching movements. Our hypothesis is that muscle moments estimated based on active joint torques provide a more sensitive measure of post-stroke motor deficits than joint angle and angular velocity. The motion of twenty-two participants was captured while performing reaching movements in a center-out task, presented in virtual reality. We used inverse dynamics analysis to derive active joint torques that were the result of muscle contractions, termed muscle torques, that caused the recorded multi-joint motion. We then applied a novel analysis to separate the component of muscle torque related to gravity compensation from that related to intersegmental dynamics. Our results show that individual reaching movements can be characterized with higher information content using muscle torques rather than joint angles. Moreover, muscle torques allow for distinguishing between the individual motor deficits due to aging or stroke from the typical differences in reaching between healthy individuals. This novel quantitative assessment method may be used in conjunction with home-based gaming motion-capture technology for remote monitoring of motor deficits and inform the development of evidence-based robotic therapy interventions. New and NoteworthyFunctional deficits seen in task performance have biomechanical underpinnings, seen only through the analysis of forces. Our study has shown that estimating muscle moments can quantify with high sensitivity post-stroke deficits in intersegmental coordination. An assessment developed based on this method could help quantify less observable deficits in mildly affected stroke patients. It may also bridge the gap between evidence from studies of constrained or robotically manipulated movements and research with functional and unconstrained movements.

neuroscience↗