Yiyu Wang

This exploratory, single-session, repeated-measures study characterized human and equine trunk muscle activity and human–horse EMG covariation during physical therapy utilizing equine movement in individuals with Parkinson’s disease (PD). Nine individuals with PD and three horses completed pre-session quiet sitting (QS Pre), figure-eight riding (F8), stop-and-go riding (SNG), and post-session quiet sitting (QS Post). Bilateral surface EMG was recorded from three human and four equine trunk muscles. All analyses were exploratory and examined four outcomes: normalized EMG amplitude, zero-lag human–horse correlation, lagged human–horse correlation, and human–horse EMG-EMG coherence. Human normalized EMG amplitude did not differ across QS Pre, F8, and SNG. Equine amplitude was greater during F8 than QS Pre (difference 9.43 percentage points, 95% CI [4.27, 14.59]). SNG also showed greater amplitude than QS Pre (5.30 percentage points, 95% CI [0.32, 10.27]), although this difference was not statistically significant in the participant-level sensitivity analysis. F8 exceeded SNG in the direct comparison (4.43 percentage points, 95% CI [1.44, 7.41]) but not in the adjusted three-condition comparison. Zero-lag correlations were generally close to zero; SNG exceeded F8 by only 0.0056 Fisher’s z units (95% CI [0.0004, 0.0107]) in the direct comparison, but this difference was not statistically significant in the adjusted three-condition comparison. When temporal offsets of ±300 ms were permitted, lagged correlation strength was greater during F8 than QS Pre (difference 0.013, 95% CI [0.003, 0.024]) and during SNG than QS Pre (0.011, 95% CI [0.001, 0.021]), with no difference between riding conditions. Median wavelet coherence was greater during QS Pre than F8 (difference 0.016, 95% CI [0.013, 0.018]) or SNG (0.013, 95% CI [0.011, 0.015]), while SNG exceeded F8 overall (0.003, 95% CI [0.002, 0.004]). Simultaneous human and equine EMG was feasible during the sequential mounted conditions, but the observed signal properties differed across outcomes. Because the conditions were administered in a fixed sequence, their individual effects could not be distinguished from order-related influences. These exploratory findings require confirmation in larger studies with counterbalanced task orders, controlled gait characteristics, and relevant clinical outcomes.

Bimanual coordination is essential for many skilled everyday actions and requires maintaining specific spatiotemporal relationships between the two hands according to task demands. However, the contribution of the primary motor cortices (M1s) to learning such relationships is only partially understood. Conventional bimanual tasks necessarily engage both M1s in controlling the moving hands, making learning-related changes difficult to distinguish from those associated with movement execution. To reduce this effector-related confound, we used a unimanual and bimanual rhythmic coordination paradigm in which participants learned the same novel 90° relative-phase relationship by coordinating either hand with a virtual partner and compared these conditions with conventional bimanual practice. In three experiments, performance accuracy and stability improved during practice and improved further after a 6-h retention interval with comparable time-dependent changes across unimanual and bimanual trained groups. In Experiment 2, motor-evoked potentials measured before and after training revealed an overall increase in corticospinal excitability across both M1s under unimanual and bimanual training. In Experiment 3, bidirectional dual-site paired-pulse stimulation revealed effector-dependent changes in interhemispheric inhibition, in which bimanual practice produced no reliable directional modulation, whereas unimanual practice produced asymmetric changes that depended on the trained hand. Neither corticospinal excitability nor interhemispheric inhibition showed a consistent relationship with behavioral improvement. Together, these findings demonstrate comparable behavioral learning of a novel spatiotemporal coordination relationship across bimanual and unimanual contexts, while showing that behavioral improvement is not straightforwardly reflected in M1 neurophysiological changes. Causal intervention studies are therefore needed to determine whether and when M1 contributes directly to acquisition of novel coordination relationships.

The influence of explicit strategies on implicit recalibration during visuomotor adaptation has become a central question in motor learning. Because the two systems operate in tandem, explicit strategies could indirectly influence implicit recalibration. However, explicit strategies are not unitary: they may rely on algorithmic-based computations or memory-based retrieval of cached solutions. This raises the possibility that different strategy implementations interact with the cerebellar-based implicit recalibration system in qualitatively distinct ways, especially given that these strategies likely rely on different frontoparietal networks. Here, we tested whether the type of explicit strategy modulates implicit recalibration. Across a set of experiments, we observed subtle differences in the spatial profile of implicit generalization: the algorithmic strategy produced a broader generalization pattern than the retrieval strategy, even after controlling for intertrial decay, generalization structure, and between-target interactions. While this pattern is suggestive of greater flexibility afforded by algorithmic strategy use compared to memory-based retrieval, it could instead arise from increased variability in explicit aiming, which constitutes the input data driving implicit recalibration. Indeed, when we isolated the direct contribution of each strategy to implicit recalibration by rigorously controlling for reach variability and using error-clamp feedback to ensure uniform implicit learning conditions, we found no difference in implicit recalibration across strategies. Together, these findings suggest that while algorithmic and retrieval strategies differ in their behavioral signatures and influence the movement plan, the implicit recalibration process itself remains rigid with respect to the strategy employed.

The influence of explicit strategies on implicit recalibration during visuomotor adaptation has become a central question in motor learning. Because the two systems operate in tandem, explicit strategies could indirectly influence implicit recalibration. However, explicit strategies are not unitary: they may rely on algorithmic-based computations or memory-based retrieval of cached solutions. This raises the possibility that different strategy implementations interact with the cerebellar-based implicit recalibration system in qualitatively distinct ways, especially given that these strategies likely rely on different frontoparietal networks. Here, we tested whether the type of explicit strategy modulates implicit recalibration. Across a set of experiments, we observed subtle differences in the spatial profile of implicit generalization: the algorithmic strategy produced a broader generalization pattern than the retrieval strategy, even after controlling for intertrial decay, generalization structure, and between-target interactions. While this pattern is suggestive of greater flexibility afforded by algorithmic strategy use compared to memory-based retrieval, it could instead arise from increased variability in explicit aiming, which constitutes the input data driving implicit recalibration. Indeed, when we isolated the direct contribution of each strategy to implicit recalibration by rigorously controlling for reach variability and using error-clamp feedback to ensure uniform implicit learning conditions, we found no difference in implicit recalibration across strategies. Together, these findings suggest that while algorithmic and retrieval strategies differ in their behavioral signatures and influence the movement plan, the implicit recalibration process itself remains rigid with respect to the strategy employed. Significance Statement: Whether explicit strategies influence implicit recalibration remains debated – a question complicated by the fact that different training conditions give rise to distinct forms of strategy. Because algorithmic and retrieval-based strategies engage partially dissociable frontoparietal networks, they may differentially interface with cerebellar subregions, yielding distinct effects on implicit recalibration, potentially reconciling prior mixed findings. Our results indicate that, once the first order spatial and temporal statistics that typically covary with these different strategies are matched, the strategies themselves do not directly influence implicit recalibration. Instead, the cerebellar-based implicit recalibration system faithfully updates the sensorimotor mapping simply based on the training data it is provided. These findings support prior work suggesting strong independence and stereotyped behavior of implicit recalibration.

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder characterized by tremors, balance impairments, and mobility limitations. Innovative approaches like combining transcranial direct current stimulation (tDCS) with exercise show promise in addressing these symptoms. This study investigates the effects of exercise combined with tDCS on mobility and tremor management in PD patients. Methods: Twenty-five individuals aged 60−75 (66.6 ± 7.33), diagnosed with PD (Hoehn and Yahr stage 2−3), were assigned to three groups in a randomized controlled design: exercise with active tDCS (n = 8), exercise with sham tDCS (n = 8), and a control group (n = 9). Dual-task training sessions focusing on walking speed, balance, and force control were conducted over ten sessions. Results: No significant differences were detected across the groups for grip strength or force control measures (p > 0.05). Significant improvements were observed in the intervention group: the Timed Up and Go (TUG) test showed a significant reduction in time (mean difference = 2.498 s, p < 0.001, ηp² = 0.331); anterior–posterior displacement significantly increased (mean difference = 21.375 mm, p = 0.0269, ηp² = 0.303); and force-tremor decoupling improved, with coherence in the 1−4 Hz band significantly decreasing (p = 0.0067). Finally, changes in TUG from post- to pre-treatment values were significantly positively correlated with the changes in coherence (R = 0.468, p = 0.018). Conclusions: Combining tDCS with exercise enhances mobility and tremor management in PD patients. These findings support the potential for such interventions to improve functional outcomes and quality of life for individuals with PD.

The ability to coordinate movements between the hands is crucial for many daily tasks. However, the precise mechanisms governing the storage and utilization of bimanual movement and the distinct contributions of each limb in this process are currently not fully understood. Two key questions persist: 1) How is the neural representation of bimanual coordination stored in the brain, and 2) How is the information governing bimanual coordination shared between hemispheres? In this investigation, we used a virtual partner (VP) to systematically address these issues by allowing the same coordination pattern (CP) to be acquired with unimanual and bimanual movements. More specifically, we used four experimental groups: unimanual (left, right) VP, bimanual, and control conditions. For each condition, retention and transfer tests were administered immediately and 6 hours after the initial practice. The control condition employed the same protocol as unimanual conditions without practice. As anticipated, performance after practice and during retention sessions indicated that all groups learned to perform the target CP. Furthermore, generalization from unimanual to bimanual occurred when the same type of visual feedback (VF) was provided. Interestingly, the absence of VF impaired motor generalization from unimanual to bimanual condition unless the participants initially practiced the task bimanually. Taken together, our results demonstrated that both limbs could access the memory representation of the CP. However, this globally shared representation appeared to be encoded in the visual-spatial domain. The conditions without VF underscored the importance of proprioception in forming a motor representation in intrinsic coordinates. NEW & NOTEWORTHY Conventional views on acquiring bimanual skills stress the need for simultaneous engagement of both hands. However, our study challenges this notion by demonstrating that the coordination pattern learned in unimanual conditions significantly boosts subsequent bimanual coordination—a novel approach to skill acquisition. Yet, this advantage diminishes without visual feedback, resulting in a breakdown of the intended bimanual coordination, highlighting the limitations of relying solely on unimanual practice.

Touch sensation from the glabrous skin of the hand is essential for precisely controlling dexterous movements, yet the neural mechanisms by which tactile inputs influence motor circuits remain largely unexplored. By pairing air-puff tactile stimulation on the hand's glabrous skin with transcranial magnetic stimulation (TMS) over the primary motor cortex (M1), we examined the effects of tactile stimuli from single or multiple fingers on corticospinal excitability and M1's intracortical circuits. Our results showed that when we targeted the hand's first dorsal interosseous (FDI) muscle with TMS, homotopic (index finger) tactile stimulation, regardless of its point (fingertip or base), reduced corticospinal excitability. Conversely, heterotopic (ring finger) tactile stimulation had no such effect. Notably, stimulating all five fingers simultaneously led to a more pronounced decrease in corticospinal excitability than stimulating individual fingers. Furthermore, tactile stimulation significantly increased intracortical facilitation (ICF) and decreased long-interval intracortical inhibition (LICI) but did not affect short-interval intracortical inhibition (SICI). Considering the significant role of the primary somatosensory cortex (S1) in tactile processing, we also examined the effects of downregulating S1 excitability via continuous theta burst stimulation (cTBS) on tactile–motor interactions. Following cTBS, the inhibitory influence of tactile inputs on corticospinal excitability was diminished. Our findings highlight the spatial specificity of tactile inputs in influencing corticospinal excitability. Moreover, we suggest that tactile inputs distinctly modulate M1's excitatory and inhibitory pathways, with S1 being crucial in facilitating tactile–motor integration.

Motor learning involves acquiring new movement sequences and adapting motor commands to novel conditions. Labile motor memories, acquired through sequence learning and dynamic adaptation, undergo a consolidation process during wakefulness after initial training. This process stabilizes the new memories, leading to long-term memory formation. However, it remains unclear if the consolidation processes underlying sequence learning and dynamic adaptation are independent and if distinct neural regions underpin memory consolidation associated with sequence learning and dynamic adaptation. Here, we first demonstrated that the initially labile memories formed during sequence learning and dynamic adaptation were stabilized against interference through time-dependent consolidation processes occurring during wakefulness. Furthermore, we found that sequence learning memory was not disrupted when immediately followed by dynamic adaptation and vice versa, indicating distinct mechanisms for sequence learning and dynamic adaptation consolidation. Finally, by applying patterned transcranial magnetic stimulation to selectively disrupt the activity in the primary motor (M1) or sensory (S1) cortices immediately after sequence learning or dynamic adaptation, we found that sequence learning consolidation depended on M1 but not S1, while dynamic adaptation consolidation relied on S1 but not M1. For the first time in a single experimental framework, this study revealed distinct neural underpinnings for sequence learning and dynamic adaptation consolidation during wakefulness, with significant implications for motor skill enhancement and rehabilitation.

Previous research has indicated that neural crosstalk is asymmetric, with the dominant effector exerting a stronger influence on the non-dominant effector than vice versa. Recently, it has been hypothesized that this influence is more substantial for proximal than distal effectors. The current investigation was designed to determine the effects of distal ((First Dorsal Interosseous (FDI)) and proximal (triceps brachii (TBI)) muscle activation on neural crosstalk. Twelve right-limb dominant participants (mean age = 21.9) were required to rhythmically coordinate a 1:2 pattern of isometric force guided by Lissajous displays. Participants performed 10, 30 s trials with both distal and proximal effectors. Coherence between the two effector groups were calculated using EMG-EMG wavelet coherence. The results indicated that participants could effectively coordinate the goal coordination pattern regardless of the effectors used. However, spatiotemporal performance was more accurate when performing the task with distal than proximal effectors. Force distortion, quantified by harmonicity, indicated that more perturbations occurred in the non-dominant effector than in the dominant effector. The results also indicated significantly lower harmonicity for the non-dominant proximal effector compared to the distal effectors. The current results support the notion that neural crosstalk is asymmetric in nature and is greater for proximal than distal effectors. Additionally, the EMG-EMG coherence results indicated significant neural crosstalk was occurring in the Alpha bands (5–13 Hz), with higher values observed in the proximal condition. Significant coherence in the Alpha bands suggest that the influence of neural crosstalk is occurring at a subcortical level.