Experimental Brain Research最新文献

This study aimed to utilize the nonnegative matrix factorization (NNMF) algorithm for muscle synergy analysis, extracting synergy structures and muscle weightings and mining biomarkers reflecting changes in muscle fatigue from these synergy structures. A leg press exercise to induce fatigue was performed by 11 participants. Surface electromyography (sEMG) data from seven muscles, electrocardiography (ECG) data, Borg CR-10 scale scores, and the z-axis acceleration of the weight block were simultaneously collected. Three indices were derived from the synergy structures: activation phase difference, coactivation area, and coactivation time. The indicators were further validated for single-leg landing. Differences in heart rate (HR) and heart rate variability (HRV) were observed across different fatigue levels, with varying degrees of disparity. The median frequency (MDF) exhibited a consistent decline in the primary working muscle groups. Significant differences were noted in activation phase difference, coactivation area, and coactivation time before and after fatigue onset. Moreover, a significant correlation was found between the activation phase difference and the coactivation area with fatigue intensity. The further application of single-leg landing demonstrated the effectiveness of the coactivation area. These indices can serve as biomarkers reflecting simultaneous alterations in the central nervous system and muscle activity post-exertion.

Whole-body vestibular-evoked balance responses decrease following ~ 55 min of normobaric hypoxia. It is unclear how longer durations of hypoxia affect the vestibular control of balance at the muscle and whole-body levels. This study examined how four hours of normobaric hypoxia influenced the vestibular control of balance. Fifteen participants (4 females; 11 males) stood on a force plate with vision occluded and head rotated rightward while subjected to three blocks of binaural, bipolar stochastic electrical vestibular stimulation (EVS; 0-25 Hz, root mean square amplitude = 1.1 mA) consisting of two, 90-s trials. The relationship between EVS and anteroposterior (AP) forces or medial gastrocnemius (MG) electromyography (EMG) was estimated in the time and frequency domains at baseline (BL; 0.21 fraction of inspired oxygen-F_IO₂) and following two (H2) and four (H4) hours of normobaric hypoxia (0.11 F_IO₂). The EVS-MG EMG short-latency peak and peak-to-peak amplitudes were smaller than BL at H2 and H4, but the medium-latency peak amplitude was only lower at H4. The EVS-AP force medium-latency peak amplitude was lower than BL at H4, but the short-latency peak and peak-to-amplitudes were unchanged. The EVS-MG EMG coherence and gain were reduced compared to BL at H2 and H4 across multiple frequencies ≥ 7 Hz, whereas EVS-AP force coherence was blunted at H4 (≤ 4 Hz), but gain was unaffected. Overall, the central nervous system's response to vestibular-driven signals during quiet standing was decreased for up to four hours of normobaric hypoxia, and vestibular-evoked responses recorded within postural muscles may be more sensitive than the whole-body response.

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21-42.59%.

The aim of this paper is to investigate the impact of observing affordance-driven action during motor imagery. Affordance-driven action refers to actions that are initiated based on the properties of objects and the possibilities they offer for interaction. Action observation (AO) and motor imagery (MI) are two forms of motor simulation that can influence motor responses. We examined combined AO + MI, where participants simultaneously engaged in AO and MI. Two different kinds of combined AO + MI were employed. Participants imagined and observed the same affordance-driven action during congruent AO + MI, whereas in incongruent AO + MI, participants imagined the actual affordance-driven action while observing a distracting affordance involving the same object. EEG data were analyzed for the N2 component of event-related potential (ERP). Our study found that the N2 ERP became more negative during congruent AO + MI, indicating strong affordance-related activity. The maximum source current density (0.00611 $\mu$ A/mm $_{}^2$ ) using Low-Resolution Electromagnetic Tomography (LORETA) was observed during congruent AO + MI in brain areas responsible for planning motoric actions. This is consistent with prefrontal cortex and premotor cortex activity for AO + MI reported in the literature. The stronger neural activity observed during congruent AO + MI suggests that affordance-driven actions hold promise for neurorehabilitation.

We explored in 75 s long trials the effects of visually induced self-rotation and displacement (SR&D) on the horizontally extended right arm of standing subjects (N = 12). A “tool condition” was included in which subjects held a long rod. The extent of arm movement was contingent on whether the arm was extended out Freely or Pointing at a briefly proprioceptively specified target position. The results were nearly identical when subjects held the rod. Subjects in the Free conditions showed significant unintentional arm deviations, averaging 55° in the direction opposite the induced illusory self-motion. Deviations in the Pointing conditions were on average a fifth of those in the Free condition. Deviations of head and torso positions also occurred in all conditions. Total arm and head deviations were the sum of deviations of the arm and head with respect to the torso and deviations of the torso with respect to space. Pointing subjects were able to detect and correct for arm and head deviations with respect to the torso but not for the arm and head deviations with respect to space due to deviations of the torso. In all conditions, arm, head, and torso deviations began before subjects experienced SR&D. We relate our findings to being an extension of the manual following response (MFR) mechanism to influence passive arm control and arm target maintenance as well. Visual-vestibular convergence at vestibular nuclei cells and multiple cortical movement related areas can explain our results, MFR results, and classical Pass Pointing. We distinguish two Phases in the induction of SR&D. In Phase 1, the visual stimulation period prior to SR&D onset, the arm, head, and torso deviations are first apparent, circa < 1 s after stimulus begins. They are augmented at the onset of Phase 2 that starts when SR&D is first sensed. In Phase 2, reaching movements first show curved paths that are compensatory for the Coriolis forces that would be generated on the reaching arm were subjects actually physically rotating. These movement deviations are in the opposite direction to the MFR and the arm, head, and torso deviations reported here. Our results have implications for vehicle control in environments that can induce illusory self motion and displacement.

Sleep deprivation alters cognitive and sensorimotor function, but its effects on the control of standing balance are inconclusive. The vestibular system is critical for standing balance, and is modified by sleep deprivation; however, how sleep deprivation affects vestibular-evoked balance responses is unknown. Thus, this study aimed to examine the effect of 24 h of sleep deprivation on the vestibular control of standing balance. During both a well-rested (i.e., control) and sleep deprivation condition, nine females completed two 90-s trials of bilateral, binaural stochastic electrical vestibular stimulation (EVS) and two 120-s trials of quiet stance on a force plate. Quiet stance performance was assessed by center of pressure displacement parameters. Mediolateral ground reaction force (ML force) and surface electromyography (EMG) of the right medial gastrocnemius (MG) were sampled simultaneously with the EVS signal to quantify vestibular control of balance within the frequency (gain and coherence) and time (cumulant density) domains. Twenty-four hours of sleep deprivation did not affect quiet stance performance. Sleep deprivation also had limited effect on EVS-MG EMG and EVS-ML Force coherence (less than control at 8–10.5 Hz, greater at ~ 16 Hz); however, gain of EVS-MG EMG (< 8, 11–24 Hz) and EVS-ML force (0.5–9 Hz) was greater for sleep deprivation than control. Sleep deprivation did not alter peak-to-peak amplitude of EVS-MG EMG (p = 0.51) or EVS-ML force (p = 0.06) cumulant density function responses. Despite no effect on quiet stance parameters, the observed increase in vestibular-evoked balance response gain suggests 24-h sleep deprivation may lead to greater sensitivity of the central nervous system when transforming vestibular-driven signals for standing balance control.

Individuals with subclinical neck pain (SCNP) exhibit altered cerebellar processing, likely due to disordered sensorimotor integration of inaccurate proprioceptive input. This association between proprioceptive feedback and SMI has been captured in cervico-ocular reflex (COR) differences where SCNP showed higher gain than healthy participants. Previous neurophysiological research demonstrated improved cerebellar processing in SCNP participants following a single treatment session, but it is unknown whether these neurophysiological changes transfer to cerebellar function. In a parallel group, randomized control trial conducted at Ontario Tech University, 27 right-hand dominant SCNP participants were allocated to the 8-week chiropractic care (n = 15; 7M & 8 F) or 8-week control (n = 12; 6M & 6 F) group. COR gain (ratio of eye movement to trunk movement) was assessed using an eye-tracking device at baseline and at post 8-weeks (treatment vs. no treatment). COR gain (10 trials): participants gazed at a circular target that disappeared after 3 s, while a motorized chair rotated their trunk at a frequency of 0.04 Hz, with an amplitude of 5º, for 2 minutes. A 2 × 2 repeated measures ANOVA was performed. COR gain was significantly reduced following 8-weeks of chiropractic care compared to the SCNP control (8-weeks of no treatment) group (p = 0.012, η_p² = 0.237). The decrease in COR gain following treatment is likely due to normalized proprioceptive feedback from the neck, enabling improved processing and integration within the flocculonodular lobe of the cerebellum.

In Magnetic Resonance Imaging scanner environments, the continuous Lorentz Force is a potent vestibular stimulation. It is nowadays so well known that it is now identified as Magnetic vestibular stimulation (MVS). Alongside MVS, some authors argue that through induced electric fields, electromagnetic induction could also trigger the vestibular system. Indeed, for decades, vestibular-specific electric stimulations (EVS) have been known to precisely impact all vestibular pathways. Here, we go through the literature, looking at potential time varying magnetic field induced vestibular outcomes in MRI settings and comparing them with EVS-known outcomes. To date, although theoretically induction could trigger vestibular responses the behavioral evidence remains poor. Finally, more vestibular-specific work is needed.

A higher level of education was correlated with less severe motor impairment in Parkinson's Disease (PD). Nevertheless, there is limited evidence on the relationship between cognitive reserve and motor performance in complex situations in PD. To investigate the association between cognitive reserve and the dual-task gait effect in PD. Additionally, we examined the relationship between executive function, clinical and sociodemographic variables and, dual-task gait effects. We conducted a cross-sectional study with 44 PD participants. We evaluated dual-task effect on cadence, stride length, and gait velocity. Dual-task effects were correlated with neurophysiological factors, including cognitive reserve (Cognitive Reserve Index Questionnaire), overall cognitive performance of executive functions, a specific executive function domain (Trail Making Test), and the global cognitive status (Montreal Cognitive Assessment and Mini-Mental State Examination). Age, gender, and disease severity were considered as variables to be examined for correlation. We found that cognitive reserve did not influence gait performance under dual-task conditions in this sample. However, executive functions, age, and disease severity were associated with the dual-task effect on gait. The overall cognitive performance with respect to the Trail Making Test showed an inverse relationship in the dual-task gait effect on cadence. Our study's findings have important implications for understanding the association between executive functions, age, and disease severity with the dual-task effect on gait in PD. Pre-life factors, such as education, occupation, and leisure activity, did not contribute to coping with complex gait situations in PD.

The reticular thalamic nucleus (RTN) is a thin shell that covers the dorsal thalamus and controls the overall information flow from the thalamus to the cerebral cortex through GABAergic projections that contact thalamo-cortical neurons (TC). RTN neurons receive glutamatergic afferents fibers from neurons of the sixth layer of the cerebral cortex and from TC collaterals. The firing mode of RTN neurons facilitates the generation of sleep-wake cycles; a tonic mode or desynchronized mode occurs during wake and REM sleep and a burst-firing mode or synchronized mode is associated with deep sleep. Despite the presence of cannabinoid receptors CB1 (CB1Rs) and mRNA that encodes these receptors in RTN neurons, there are few works that have analyzed the participation of endocannabinoid-mediated transmission on the electrical activity of RTN. Here, we locally blocked or activated CB1Rs in ketamine anesthetized rats to analyze the spontaneous extracellular spiking activity of RTN neurons. Our results show the presence of a tonic endocannabinoid input, since local infusion of AM 251, an antagonist/inverse agonist, modifies RTN neurons electrical activity; furthermore, local activation of CB1Rs by anandamide or WIN 55212-2 produces heterogeneous effects in the basal spontaneous spiking activity, where the main effect is an increase in the spiking rate accompanied by a decrease in bursting activity in a dose-dependent manner; this effect is inhibited by AM 251. In addition, previous activation of GABA-A receptors suppresses the effects of CB1Rs on reticular neurons. Our results show that local activation of CB1Rs primarily diminishes the burst firing mode of RTn neurons.