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Key points Gait‐related arm swing in humans supports efficient lower limb muscle activation, indicating a neural coupling between the upper and lower limbs during gait.Intermuscular coherence analyses of gait‐related electromyography from upper and lower limbs in 20 healthy participants identified significant coherence in alpha and beta/gamma bands indicating that upper and lower limbs share common subcortical and cortical drivers that coordinate the rhythmic four‐limb gait pattern.Additional directed connectivity analyses revealed that upper limb muscles drive and shape lower limb muscle activity during gait via subcortical and cortical pathways and to a lesser extent vice versa.The results provide a neural underpinning that arm swing may serve as an effective rehabilitation therapy concerning impaired gait in neurological diseases. Abstract Human gait benefits from arm swing, as it enhances efficient lower limb muscle activation in healthy participants as well as patients suffering from neurological impairment. The underlying neuronal mechanisms of such coupling between upper and lower limbs remain poorly understood. The aim of the present study was to examine this coupling by intermuscular coherence analysis during gait. Additionally, directed connectivity analysis of this coupling enabled assessment of whether gait‐related arm swing indeed drives lower limb muscles. To that end, electromyography recordings were obtained from four lower limb muscles and two upper limb muscles bilaterally, during gait, of 20 healthy participants (mean (SD) age 67 (6.8) years). Intermuscular coherence analysis revealed functional coupling between upper and lower limb muscles in the alpha and beta/gamma band during muscle specific periods of the gait cycle. These effects in the alpha and beta/gamma bands indicate involvement of subcortical and cortical sources, respectively, that commonly drive the rhythmic four‐limb gait pattern in an efficiently coordinated fashion. Directed connectivity analysis revealed that upper limb muscles drive and shape lower limb muscle activity during gait via subcortical and cortical pathways and to a lesser extent vice versa. This indicates that gait‐related arm swing reflects the recruitment of neuronal support for optimizing the cyclic movement pattern of the lower limbs. These findings thus provide a neural underpinning for arm swing to potentially serve as an effective rehabilitation therapy concerning impaired gait in neurological diseases., Key points Gait‐related arm swing in humans supports efficient lower limb muscle activation, indicating a neural coupling between the upper and lower limbs during gait.Intermuscular coherence analyses of gait‐related electromyography from upper and lower limbs in 20 healthy participants identified significant coherence in alpha and beta/gamma bands indicating that upper and lower limbs share common subcortical and cortical drivers that coordinate the rhythmic four‐limb gait pattern.Additional directed connectivity analyses revealed that upper limb muscles drive and shape lower limb muscle activity during gait via subcortical and cortical pathways and to a lesser extent vice versa.The results provide a neural underpinning that arm swing may serve as an effective rehabilitation therapy concerning impaired gait in neurological diseases.

It is now known that astrocytes modulate the activity at the tripartite synapses where indirect signaling via the retrograde messengers, endocannabinoids, leads to a localized self-repairing capability. In this paper, a self-repairing spiking astrocyte neural network (SANN) is proposed to demonstrate a distributed self-repairing capability at the network level. The SANN uses a novel learning rule that combines the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules (hereafter referred to as the BSTDP rule). In this learning rule, the synaptic weight potentiation is not only driven by the temporal difference between the presynaptic and postsynaptic neuron firing times but also by the postsynaptic neuron activity. We will show in this paper that the BSTDP modulates the height of the plasticity window to establish an input–output mapping (in the learning phase) and also maintains this mapping (via self-repair) if synaptic pathways become dysfunctional. It is the functional dependence of postsynaptic neuron firing activity on the height of the plasticity window that underpins how the proposed SANN self-repairs on the fly. The SANN also uses the coupling between the tripartite synapses and $\gamma $ -GABAergic interneurons. This interaction gives rise to a presynaptic neuron frequency filtering capability that serves to route information, represented as spike trains, to different neurons in the subsequent layers of the SANN. The proposed SANN follows a feedforward architecture with multiple interneuron pathways and astrocytes modulate synaptic activity at the hidden and output neuronal layers. The self-repairing capability will be demonstrated in a robotic obstacle avoidance application, and the simulation results will show that the SANN can maintain learned maneuvers at synaptic fault densities of up to 80% regardless of the fault locations.

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.

BACKGROUND: Human bipedal gait benefits from arm swing, as it drives and shapes lower limb muscle activity in healthy participants as well as patients suffering from neurological impairment. Also during gait initiation, arm swing instructions were found to facilitate leg muscle recruitment.RESEARCH QUESTION: The aim of the present study is to exploit the directional decomposition of coherence to examine to what extent forward and backward arm swing contribute to leg muscle recruitment during gait initiation.METHODS: Ambulant electromyography (EMG) from shoulder muscles (deltoideus anterior and posterior) and upper leg muscles (biceps femoris and rectus femoris) was analysed during gait initiation in nineteen healthy participants (median age of 67 ± 12 (IQR) years). To assess to what extent either deltoideus anterior or posterior muscles were able to drive upper leg muscle activity during distinct stages of the gait initiation process, time dependent intermuscular coherence was decomposed into directional components based on their time lag (i.e. forward, reverse and zero-lag).RESULTS: Coherence from the forward directed components, representing shoulder muscle signals leading leg muscle signals, revealed that deltoideus anterior (i.e. forward arm swing) and deltoideus posterior (i.e. backward arm swing) equally drive upper leg muscle activity during the gait initiation process.SIGNIFICANCE: The presently demonstrated time dependent directional intermuscular coherence analysis could be of use for future studies examining directional coupling between muscles or brain areas relative to certain gait (or other time) events. In the present study, this analysis provided neural underpinning that both forward and backward arm swing can provide neuronal support for leg muscle recruitment during gait initiation and can therefore both serve as an effective gait rehabilitation method in patients with gait initiation difficulties.

Background‘Non-parametric directionality’ (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015)MethodsThis work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive model to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) and parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording.ResultsWe demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD metric is compared with non-parametric Granger causality (NPG), a well-established methodology for model free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate degrees of instantaneous signal mixing.ConclusionsThe results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging.HighlightsNon-parametric directionality (NPD) is a novel directed connectivity metric.NPD estimates are equivalent to Granger causality but more robust to signal confounds.Multivariate extensions of NPD can correctly identify signal routing.AbbreviationsdFCDirected functional connectivityEEGElectroencephalogramLFPLocal field potentialMEGMagnetoencephalogramMVARMultivariate autoregressive (model)NPDNon-parametric directionalityNPGNon-parametric Granger (causality)SMASupplementary motor areaSNRSignal-to-noise ratioSTNSubthalamic Nucleus

Objective. Graphical networks and network metrics are widely used to understand and characterise brain networks and brain function. These methods can be applied to a range of electrophysiological data including electroencephalography, local field potential and single unit recordings. Functional networks are often constructed using pair-wise correlation between variables. The objective of this study is to demonstrate that functional networks can be more accurately estimated using partial correlation than with pair-wise correlation. Approach. We compared network metrics derived from unconditional and conditional graphical networks, obtained using coherence and multivariate partial coherence (MVPC), respectively. Graphical networks were constructed using coherence and MVPC estimates, and binary and weighted network metrics derived from these: node degree, path length, clustering coefficients and small-world index. Main results. Network metrics were applied to simulated and experimental single unit spike train data. Simulated data used a 10x10 grid of simulated cortical neurons with centre-surround connectivity. Conditional network metrics gave a more accurate representation of the known connectivity: Numbers of excitatory connections had range 3–11, unconditional binary node degree had range 6–80, conditional node degree had range 2–13. Experimental data used multi-electrode array recording with 19 single-units from left and right hippocampal brain areas in a rat model for epilepsy. Conditional network analysis showed similar trends to simulated data, with lower binary node degree and longer binary path lengths compared to unconditional networks. Significance. We conclude that conditional networks, where common dependencies are removed through partial coherence analysis, give a more accurate representation of the interactions in a graphical network model. These results have important implications for graphical network analyses of brain networks and suggest that functional networks should be derived using partial correlation, based on MVPC estimates, as opposed to the common approach of pair-wise correlation.