Early Development of Locomotor Patterns and Motor Control in Very Young Children at High Risk of Cerebral Palsy, a Longitudinal Case Series

The first years of life might be critical for encouraging independent walking in children with cerebral palsy (CP). We sought to identify mechanisms that may underlie the impaired development of walking in three young children with early brain lesions, at high risk of CP, via comprehensive instrumented longitudinal assessments of locomotor patterns and muscle activation during walking. We followed three children (P1–P3) with early brain lesions, at high risk of CP, during five consecutive gait analysis sessions covering a period of 1 to 2 years, starting before the onset of independent walking, and including the session during the first independent steps. In the course of the study, P1 did not develop CP, P2 was diagnosed with unilateral and P3 with bilateral CP. We monitored the early development of locomotor patterns over time via spatiotemporal gait parameters, intersegmental coordination (estimated via principal component analysis), electromyography activity, and muscle synergies (determined from 11 bilateral muscles via nonnegative matrix factorization). P1 and P2 started to walk independently at the corrected age of 14 and 22 months, respectively. In both of them, spatiotemporal gait parameters, intersegmental coordination, muscle activation patterns, and muscle synergy structure changed from supported to independent walking, although to a lesser extent when unilateral CP was diagnosed (P2), especially for the most affected leg. The child with bilateral CP (P3) did not develop independent walking, and all the parameters did not change over time. Our exploratory longitudinal study revealed differences in maturation of locomotor patterns between children with divergent developmental trajectories. We succeeded in identifying mechanisms that may underlie impaired walking development in very young children at high risk of CP. When verified in larger sample sizes, our approach may be considered a means to improve prognosis and to pinpoint possible targets for early intervention.Biomechatronics & Human-Machine Contro

EEG as an imaging tool: which inverse method can successfully disentangle sources in proximity?

The accuracy of EEG source localization depends on the choice of the inverse method, the resolution of the forward model, and the signal to noise ratio (SNR) of the recordings. Since we are interested in disentangling sources in proximity, the goal of our study is to examine the sensitivity of spatial resolution of EEG source reconstruction to a wide variety of factors like reconstruction method, SNR, orientation, inter-dipole distance and depth of the simulated dipoles, etc.We simulated time series to resemble waveforms of somatosensory evoked potentials. Inter-dipole distances and different dipole orientations were investigated as well as the effect of (realistic) noise. We employed both spherical and realistic head models. Source reconstruction was realized using a conventional stationary dipole model, MUSIC, self-consistent MUSIC (SC-MUSIC) algorithm, and e-LORETA. In addition to the above mentioned methods, a new approach is tested building upon the e-LORETA solution: the topography of the maximum of the e-LORETA distribution is projected out of the data before calculating the next e-LORETA inverse solution in a iterative process. The quality of fit (or localization) was defined as the distance between the simulated point- sources and either the estimated point-sources or the activity distributions by means of the Euclidean distance or of the Earth Mover’’s Distance, respectively. As expected, inter-dipole distances played an important role in the ability of every method to disentangle the simulated sources. Overall, SC-MUSIC appeared best suited for disentangling the two simulated sources even at high-noise simulations.Biomechatronics & Human-Machine Contro

Interlayer connectivity reconstruction for multilayer brain networks using phase oscillator models

Large-scale neurophysiological networks are often reconstructed from band-pass filtered time series derived from magnetoencephalography (MEG) data. Common practice is to reconstruct these networks separately for different frequency bands and to treat them independently. Recent evidence suggests that this separation may be inadequate, as there can be significant coupling between frequency bands (interlayer connectivity). A multilayer network approach offers a solution to analyze frequency-specific networks in one framework. We propose to use a recently developed network reconstruction method in conjunction with phase oscillator models to estimate interlayer connectivity that optimally fits the empirical data. This approach determines interlayer connectivity based on observed frequency-specific time series of the phase and a connectome derived from diffusion weighted imaging. The performance of this interlayer reconstruction method was evaluated in-silico. Our reconstruction of the underlying interlayer connectivity agreed to very high degree with the ground truth. Subsequently, we applied our method to empirical resting-state MEG data obtained from healthy subjects and reconstructed two-layered networks consisting of either alpha-to-beta or theta-to-gamma band connectivity. Our analysis revealed that interlayer connectivity is dominated by a multiplex structure, i.e. by one-to-one interactions for both alpha-to-beta band and theta-to-gamma band networks. For theta-gamma band networks, we also found a plenitude of interlayer connections between distant nodes, though weaker connectivity relative to the one-to-one connections. Our work is an stepping stone towards the identification of interdependencies across frequency-specific networks. Our results lay the ground for the use of the promising multilayer framework in this field with more-informed and justified interlayer connections. Network Architectures and Service

Neuromuscular control before and after independent walking onset in children with cerebral palsy

Early brain lesions which produce cerebral palsy (CP) may affect the development of walking. It is unclear whether or how neuromuscular control, as evaluated by muscle synergy analysis, differs in young children with CP compared to typically developing (TD) children with the same walking ability, before and after the onset of independent walking. Here we grouped twenty children with (high risk of) CP and twenty TD children (age 6.5–52.4 months) based on their walking ability, supported or independent walking. Muscle synergies were extracted from electromyography data of bilateral leg muscles using non-negative matrix factorization. Number, synergies’ structure and variability accounted for when extracting one (VAF1 ) or two (VAF2 ) synergies were compared between CP and TD. Children in the CP group recruited fewer synergies with higher VAF1 and VAF2 compared to TD children in the supported and independent walking group. The most affected side in children with asymmetric CP walking independently recruited fewer synergies with higher VAF1 compared to the least affected side. Our findings suggest that early brain lesions result in early alterations of neuromuscular control, specific for the most affected side in asymmetric CP.Biomechatronics & Human-Machine Contro