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The present study used coherence and directionality analyses to explore whether the motor cortex contributes to plantar flexor muscle activity during the stance phase and push-off phase during gait. Subjects walked on a treadmill, while EEG over the leg motorcortex area and EMG from the medial gastrocnemius and soleus muscles was recorded. Corticomuscular and intermuscular coherence were calculated from pair-wise recordings. Significant EEG-EMG and EMG-EMG coherence in the beta and gamma frequency bands was found throughout the stance phase with the largest coherence towards push-off. Analysis of directionality revealed that EEG activity preceded EMG activity throughout the stance phase until the time of push-off. These findings suggest that the motor cortex contributes to ankle plantar flexor muscle activity and forward propulsion during gait. [ABSTRACT FROM AUTHOR]

The need to determine the directionality of interactions between neural signals is a key requirement for analysis of multichannel recordings. Approaches most commonly used are parametric, typically relying on autoregressive models. A number of concerns have been expressed regarding parametric approaches, thus there is a need to consider alternatives. We present an alternative nonparametric approach for construction of directionality measures for bivariate random processes. The method combines time and frequency domain representations of bivariate data to decompose the correlation by direction. Our framework generates two sets of complementary measures, a set of scalar measures, which decompose the total product moment correlation coefficient summatively into three terms by direction and a set of functions which decompose the coherence summatively at each frequency into three terms by direction: forward direction, reverse direction and instantaneous interaction. It can be undertaken as an addition to a standard bivariate spectral and coherence analysis, and applied to either time series or point-process (spike train) data or mixtures of the two (hybrid data). In this paper, we demonstrate application to spike train data using simulated cortical neurone networks and application to experimental data from isolated muscle spindle sensory endings subject to random efferent stimulation. [ABSTRACT FROM AUTHOR]

Changes in coherence and cumulant calculated between electroencephalograph (EEG) recorded from the scalp over primary motor cortex and rectified surface electromyograph (EMG) recorded from the contralateral wrist extensor muscles have been studied during development in humans (48 subjects, age 0–59 years). Using the techniques of EEG–EMG coherence and cumulant analysis and pooled coherence and cumulant analysis we demonstrate that between childhood, adolescence and adulthood there are increases in the prevalence and magnitude of coherence at frequencies between 15 and 35 Hz with corresponding development of a tri-phasic feature in the EEG–EMG cumulant. The results show for the first time that changes in the cortical ∼20 Hz oscillatory drive to human motoneurone pools take place during motor development. [ABSTRACT FROM AUTHOR]

A method of single-trial coherence analysis is presented, through the application of continuous multiwavelets. Multiwavelets allow the construction of spectra and bivariate statistics such as coherence within single trials. Spectral estimates are made consistent through optimal time-frequency localization and smoothing. The use of multiwavelets is considered along with an alternative single-trial method prevalent in the literature, with the focus being on statistical, interpretive and computational aspects. The multiwavelet approach is shown to possess many desirable properties, including optimal conditioning, statistical descriptions and computational efficiency. The methods are then applied to bivariate surrogate and neurophysiological data for calibration and comparative study. Neurophysiological data were recorded intracellularly from two spinal motoneurones innervating the posterior biceps muscle during fictive locomotion in the decerebrated cat. [ABSTRACT FROM AUTHOR]

A comparison of previously defined spike train syncrhonization indices is undertaken within a stochastic point process framework. The second-order cumulant density (covariance density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second-order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% to 160% of the estimated value, On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a single population measure. The pooled coherence framework allows pooled time domain measures to be derived, application of this to the simulated data is illustrated. Data from the cortical neurone model is generated over a wide range of firing rates (1-250 spikes/s). The pooled coherence framework correctly characterizes the sampling variability as not significant over this wide operating range. The broader applicability of this approach to multielectrode array data is briefly discussed. [ABSTRACT FROM AUTHOR]

Abstract In adults, oscillatory activity in the sensorimotor cortex is coherent with contralateral muscle activity at beta frequencies (15–35 Hz) during tonic contraction. This functional coupling reflects the involvement of the sensorimotor cortex, the corticospinal pathway, and likely also ascending sensory feedback in the task at hand. However, little is known about the developmental trajectory of task-related corticomuscular connectivity relating to the voluntary control of the ankle muscles. To address this, we recorded electroencephalography (EEG) from the vertex (Cz) and electromyography (EMG) from ankle muscles (proximal and distal anterior tibial, TA; soleus, SOL; gastrocnemius medialis, GM) in 33 participants aged 7–23 yr during tonic dorsi- and plantar flexion requiring precise maintenance of a submaximal torque level. Coherence was calculated for Cz-TA, Cz-SOL, TA-TA, and SOL-GM signal pairs. We found strong, positive associations between age and beta band coherence for Cz-TA, Cz-SOL, and TA-TA, suggesting that oscillatory corticomuscular connectivity is strengthened during childhood development and adolescence. Directionality analysis indicated that the primary interaction underlying this age-related increase was in the descending direction. In addition, performance during dorsi- and plantar flexion tasks was positively associated with age, indicating more precise control of the ankle joint in older participants. Performance was also positively associated with beta band coherence, suggesting that participants with greater coherence also exhibited greater precision. We propose that these results indicate an age-related increase in oscillatory corticospinal input to the ankle muscle motoneuron pools during childhood development and adolescence, with possible implications for maturation of precision force control. Within the theoretical framework of predictive coding, we suggest that our results may reflect an age-related increase in reliance on feedforward control as the developing nervous system becomes better at predicting the sensory consequences of movement. These findings may contribute to the development of novel intervention strategies targeting improved sensorimotor control in children and adolescents with central motor disorders. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we test the performance of a synchronicity estimator widely applied in Neuroscience, phase lag index (PLI), for brain network inference in EEG. We implement the four sphere head model to simulate the volume conduction problem present in EEG recordings and measure the activity at the scalp of surrogate sources located at the brain level. Then, networks are estimated under the null hypothesis (independent sources) using PLI, coherence (R) and phase coherence (PC) for the volume conduction and no volume conduction (NVC) cases. It is known that R and PC are highly influenced by volume conduction, leading to the inference of clustered grid networks. PLI was designed to solve this problem. Our simulations show that PLI is partially invariant to volume conduction. The networks found by PLI show small-worldness, with a clustering coefficient higher than random networks. On the contrary, PLI-NVC obtains networks whose distribution is closer to random networks indicating that the high clustering shown by PLI networks are caused by volume conduction. The influence of volume conduction in PLI might lead to biased results in brain network inference from EEG if this behaviour is ignored. [Copyright &y& Elsevier]

Abstract: A novel method of optimal spectral tracking is presented which permits the characterisation of trial-varying parameters. Many experimental studies suffer from the limitations of available analysis methodologies, which often impose a condition of stationarity. This severely limits our ability to track slow varying or dynamic responses with any statistical certainty. Presented is a complete framework for the non-stationary analysis of trial-varying data. Theory is introduced and developed in the characterisation of speed dependent neural modulation of the locomotor drive to tibialis anterior (TA) during healthy treadmill locomotion. The approach adopts adaptive filter theory while retaining a spectral focus, thus remaining compatible with much of the current literature. Spectral tracking procedures are evaluated using both surrogate and neurophysiological time-series. Confidence intervals are derived in both empiric and numerical form. Analysis of the pre-synaptic drive to TA under the modulation of treadmill belt speed follows, with results demonstrating clear speed dependent influences on the spectral content of TA, suggesting dynamic neural modulation of the locomotor drive. Findings include speed-modulated components at 7–12Hz (early swing) and 15–20Hz (pre-stance). Speed invariant components were identified at 8–15 and 15–20Hz during early and late swing, in agreement with previous studies. Modification to the method permits a sub-optimal alternative, encouraging the exploration of short epoched data. [Copyright &y& Elsevier]