Unimanual sensorimotor learning-A simultaneous EEG-fMRI aging study.

Sensorimotor coordination requires orchestrated network activity in the brain, mediated by inter- and intra-hemispheric interactions that may be affected by aging-related changes. We adopted a theoretical model, according to which intra-hemispheric inhibition from premotor to primary motor cortex is mandatory to compensate for inter-hemispheric excitation through the corpus callosum. To test this as a function of age we acquired electroencephalography (EEG) simultaneously with functional magnetic resonance imaging (fMRI) in two groups of healthy adults (younger N = 13: 20-25 year and older N = 14: 59-70 year) while learning a unimanual motor task. On average, quality of performance of older participants stayed significantly below that of the younger ones. Accompanying decreases in motor-event-related EEG ß-activity were lateralized toward contralateral motor regions, albeit more so in younger participants. In this younger group, the mean ß-power during motor task execution was significantly higher in bilateral premotor areas compared to the older adults. In both groups, fMRI blood oxygen level dependent (BOLD) signals were positively correlated with source-reconstructed ß-amplitudes: positive in primary motor and negative in premotor cortex. This suggests that ß-amplitude modulation is associated with primary motor cortex "activation" (positive BOLD response) and premotor "deactivation" (negative BOLD response). Although the latter results did not discriminate between age groups, they underscore that enhanced modulation in primary motor cortex may be explained by a ß-associated excitatory crosstalk between hemispheres.

The development of mature gait patterns in children during walking and running.

PURPOSE: We sought to identify the developing maturity of walking and running in young children. We assessed gait patterns for the presence of flight and double support phases complemented by mechanical energetics. The corresponding classification outcomes were contrasted via a shotgun approach involving several potentially informative gait characteristics. A subsequent clustering turned out very effective to classify the degree of gait maturity. METHODS: Participants (22 typically developing children aged 2-9 years and 7 young, healthy adults) walked/ran on a treadmill at comfortable speeds. We determined double support and flight phases and the relationship between potential and kinetic energy oscillations of the center-of-mass. Based on the literature, we further incorporated a total of 93 gait characteristics (including the above-mentioned ones) and employed multivariate statistics comprising principal component analysis for data compression and hierarchical clustering for classification. RESULTS: While the ability to run including a flight phase increased with age, the flight phase did not reach 20% of the gait cycle. It seems that children use a walk-run-strategy when learning to run. Yet, the correlation strength between potential and kinetic energies saturated and so did the amount of recovered mechanical energy. Clustering the set of gait characteristics allowed for classifying gait in more detail. This defines a metric for maturity in terms of deviations from adult gait, which disagrees with chronological age. CONCLUSIONS: The degree of gait maturity estimated statistically using various gait characteristics does not always relate directly to the chronological age of the child.

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.

Dynamic Functional Connectivity between order and randomness and its evolution across the human adult lifespan.

Functional Connectivity (FC) during resting-state or task conditions is not static but inherently dynamic. Yet, there is no consensus on whether fluctuations in FC may resemble isolated transitions between discrete FC states rather than continuous changes. This quarrel hampers advancing the study of dynamic FC. This is unfortunate as the structure of fluctuations in FC can certainly provide more information about developmental changes, aging, and progression of pathologies. We merge the two perspectives and consider dynamic FC as an ongoing network reconfiguration, including a stochastic exploration of the space of possible steady FC states. The statistical properties of this random walk deviate both from a purely "order-driven" dynamics, in which the mean FC is preserved, and from a purely "randomness-driven" scenario, in which fluctuations of FC remain uncorrelated over time. Instead, dynamic FC has a complex structure endowed with long-range sequential correlations that give rise to transient slowing and acceleration epochs in the continuous flow of reconfiguration. Our analysis for fMRI data in healthy elderly revealed that dynamic FC tends to slow down and becomes less complex as well as more random with increasing age. These effects appear to be strongly associated with age-related changes in behavioural and cognitive performance.

The human sensorimotor cortex fosters muscle synergies through cortico-synergy coherence.

In neuromotor control, the dimensionality of complex muscular activation patterns is effectively reduced through the emergence of muscle synergies. Muscle synergies are tailored to task-specific biomechanical needs. Traditionally, they are considered as low-dimensional neural output of the spinal cord and as such their coherent cortico-muscular pathways have remained underexplored in humans. We investigated whether muscle synergies have a higher-order origin, especially, whether they are manifest in the cortical motor network. We focused on cortical muscle synergy representations involved in balance control and examined changes in cortico-synergy coherence accompanying short-term balance training. We acquired electromyography and electro-encephalography and reconstructed cortical source activity using adaptive spatial filters. The latter were based on three muscle synergies decomposed from the activity of nine unilateral leg muscles using non-negative matrix factorization. The corresponding cortico-synergy coherence displayed phase-locked activity at the Piper rhythm, i.e., cortico-spinal synchronization around 40 Hz. Our study revealed the presence of muscle synergies in the motor cortex, in particular, in the paracentral lobule, known for the representation of lower extremities. We conclude that neural oscillations synchronize between the motor cortex and spinal motor neuron pools signifying muscle synergies. The corresponding cortico-synergy coherence around the Piper rhythm decreases with training-induced balance improvement.

A combined diffusion-weighted and electroencephalography study on age-related differences in connectivity in the motor network during bimanual performance.

We studied the relationship between age-related differences in inter- and intra-hemispheric structural and functional connectivity in the bilateral motor network. Our focus was on the correlation between connectivity and declined motor performance in older adults. Structural and functional connectivity were estimated using diffusion weighted imaging and resting-state electro-encephalography, respectively. A total of 48 young and older healthy participants were measured. In addition, motor performances were assessed using bimanual coordination tasks. To pre-select regions-of-interest (ROIs), a neural model was adopted that accounts for intra-hemispheric functional connectivity between dorsal premotor area (PMd) and primary motor cortex (M1) and inter-hemispheric connections between left and right M1 (M1L and M1R ). Functional connectivity was determined via the weighted phase-lag index (wPLI) in the source-reconstructed beta activity during rest. We quantified structural connectivity using kurtosis anisotropy (KA) values of tracts derived from diffusion tensor-based fiber tractography between the aforementioned areas. In the group of older adults, wPLI values between M1L -M1R were negatively associated with the quality of bimanual motor performance. The additional association between wPLI values of PMdL --M1L and PMdR -M1L supports that functional connectivity with the left hemisphere mediated (bimanual) motor control in older adults. The correlational analysis between the selected structural and functional connections revealed a strong association between wPLI values in the left intra-hemispheric PMdL -M1L pathway and KA values in M1L -M1R and PMdR -M1L pathways in the group of older adults. This suggests that weaker structural connections in older adults correlate with stronger functional connectivity and, hence, poorer motor performance.

Distinct criticality of phase and amplitude dynamics in the resting brain.

Converging research suggests that the resting brain operates at the cusp of dynamic instability, as signified by scale-free temporal correlations. We asked whether the scaling properties of these correlations differ between amplitude and phase fluctuations, which may reflect different aspects of cortical functioning. Using source-reconstructed magneto-encephalographic signals, we found power-law scaling for the collective amplitude and for phase synchronization, both capturing whole-brain activity. The temporal changes of the amplitude comprise slow, persistent memory processes, whereas phase synchronization exhibits less temporally structured and more complex correlations, indicating a fast and flexible coding. This distinct temporal scaling supports the idea of different roles of amplitude and phase fluctuations in cortical functioning.

Scale-freeness or partial synchronization in neural mass phase oscillator networks: Pick one of two?

Modeling and interpreting (partial) synchronous neural activity can be a challenge. We illustrate this by deriving the phase dynamics of two seminal neural mass models: the Wilson-Cowan firing rate model and the voltage-based Freeman model. We established that the phase dynamics of these models differed qualitatively due to an attractive coupling in the first and a repulsive coupling in the latter. Using empirical structural connectivity matrices, we determined that the two dynamics cover the functional connectivity observed in resting state activity. We further searched for two pivotal dynamical features that have been reported in many experimental studies: (1) a partial phase synchrony with a possibility of a transition towards either a desynchronized or a (fully) synchronized state; (2) long-term autocorrelations indicative of a scale-free temporal dynamics of phase synchronization. Only the Freeman phase model exhibited scale-free behavior. Its repulsive coupling, however, let the individual phases disperse and did not allow for a transition into a synchronized state. The Wilson-Cowan phase model, by contrast, could switch into a (partially) synchronized state, but it did not generate long-term correlations although being located close to the onset of synchronization, i.e. in its critical regime. That is, the phase-reduced models can display one of the two dynamical features, but not both.

Disentangling Somatosensory Evoked Potentials of the Fingers: Limitations and Clinical Potential.

In searching for clinical biomarkers of the somatosensory function, we studied reproducibility of somatosensory potentials (SEP) evoked by finger stimulation in healthy subjects. SEPs induced by electrical stimulation and especially after median nerve stimulation is a method widely used in the literature. It is unclear, however, if the EEG recordings after finger stimulation are reproducible within the same subject. We tested in five healthy subjects the consistency and reproducibility of responses through bootstrapping as well as test-retest recordings. We further evaluated the possibility to discriminate activity of different fingers both at electrode and at source level. The lack of consistency and reproducibility suggest responses to finger stimulation to be unreliable, even with reasonably high signal-to-noise ratio and adequate number of trials. At sources level, somatotopic arrangement of the fingers representation was only found in one of the subjects. Although finding distinct locations of the different fingers activation was possible, our protocol did not allow for non-overlapping dipole representations of the fingers. We conclude that despite its theoretical advantages, we cannot recommend the use of somatosensory potentials evoked by finger stimulation to extract clinical biomarkers.

Model selection for identifying power-law scaling.

Long-range temporal and spatial correlations have been reported in a remarkable number of studies. In particular power-law scaling in neural activity raised considerable interest. We here provide a straightforward algorithm not only to quantify power-law scaling but to test it against alternatives using (Bayesian) model comparison. Our algorithm builds on the well-established detrended fluctuation analysis (DFA). After removing trends of a signal, we determine its mean squared fluctuations in consecutive intervals. In contrast to DFA we use the values per interval to approximate the distribution of these mean squared fluctuations. This allows for estimating the corresponding log-likelihood as a function of interval size without presuming the fluctuations to be normally distributed, as is the case in conventional DFA. We demonstrate the validity and robustness of our algorithm using a variety of simulated signals, ranging from scale-free fluctuations with known Hurst exponents, via more conventional dynamical systems resembling exponentially correlated fluctuations, to a toy model of neural mass activity. We also illustrate its use for encephalographic signals. We further discuss confounding factors like the finite signal size. Our model comparison provides a proper means to identify power-law scaling including the range over which it is present.

Determination of head conductivity frequency response in vivo with optimized EIT-EEG.

Electroencephalography (EEG) benefits from accurate head models. Dipole source modelling errors can be reduced from over 1cm to a few millimetres by replacing generic head geometry and conductivity with tailored ones. When adequate head geometry is available, electrical impedance tomography (EIT) can be used to infer the conductivities of head tissues. In this study, the boundary element method (BEM) is applied with three-compartment (scalp, skull and brain) subject-specific head models. The optimal injection of small currents to the head with a modular EIT current injector, and voltage measurement by an EEG amplifier is first sought by simulations. The measurement with a 64-electrode EEG layout is studied with respect to three noise sources affecting EIT: background EEG, deviations from the fitting assumption of equal scalp and brain conductivities, and smooth model geometry deviations from the true head geometry. The noise source effects were investigated depending on the positioning of the injection and extraction electrode and the number of their combinations used sequentially. The deviation from equal scalp and brain conductivities produces rather deterministic errors in the three conductivities irrespective of the current injection locations. With a realistic measurement of around 2 min and around 8 distant distinct current injection pairs, the error from the other noise sources is reduced to around 10% or less in the skull conductivity. The analysis of subsequent real measurements, however, suggests that there could be subject-specific local thinnings in the skull, which could amplify the conductivity fitting errors. With proper analysis of multiplexed sinusoidal EIT current injections, the measurements on average yielded conductivities of 340 mS/m (scalp and brain) and 6.6 mS/m (skull) at 2 Hz. From 11 to 127 Hz, the conductivities increased by 1.6% (scalp and brain) and 6.7% (skull) on the average. The proper analysis was ensured by using recombination of the current injections into virtual ones, avoiding problems in location-specific skull morphology variations. The observed large intersubject variations support the need for in vivo measurement of skull conductivity, resulting in calibrated subject-specific head models.

Functional connectivity in the neuromuscular system underlying bimanual coordination.

Neural synchrony has been suggested as a mechanism for integrating distributed sensorimotor systems involved in coordinated movement. To test the role of corticomuscular and intermuscular coherence in bimanual coordination, we experimentally manipulated the degree of coordination between hand muscles by varying the sensitivity of the visual feedback to differences in bilateral force. In 16 healthy participants, cortical activity was measured using EEG and muscle activity of the flexor pollicis brevis of both hands using high-density electromyography (HDsEMG). Using the uncontrolled manifold framework, coordination between bilateral forces was quantified by the synergy index RV in the time and frequency domain. Functional connectivity was assessed using corticomuscular coherence between muscle activity and cortical source activity and intermuscular coherence between bilateral EMG activity. The synergy index increased in the high coordination condition. RV was higher in the high coordination condition in frequencies between 0 and 0.5 Hz; for the 0.5- to 2-Hz frequency band, this pattern was inverted. Corticomuscular coherence in the beta band (16-30 Hz) was maximal in the contralateral motor cortex and was reduced in the high coordination condition. In contrast, intermuscular coherence was observed at 5-12 Hz and increased with bimanual coordination. Within-subject comparisons revealed a negative correlation between RV and corticomuscular coherence and a positive correlation between RV and intermuscular coherence. Our findings suggest two distinct neural pathways: 1) corticomuscular coherence reflects direct corticospinal projections involved in controlling individual muscles; and 2) intermuscular coherence reflects diverging pathways involved in the coordination of multiple muscles.

Ott-Antonsen attractiveness for parameter-dependent oscillatory systems.

The Ott-Antonsen (OA) ansatz [Ott and Antonsen, Chaos 18, 037113 (2008); Chaos 19, 023117 (2009)] has been widely used to describe large systems of coupled phase oscillators. If the coupling is sinusoidal and if the phase dynamics does not depend on the specific oscillator, then the macroscopic behavior of the systems can be fully described by a low-dimensional dynamics. Does the corresponding manifold remain attractive when introducing an intrinsic dependence between an oscillator's phase and its dynamics by additional, oscillator specific parameters? To answer this, we extended the OA ansatz and proved that parameter-dependent oscillatory systems converge to the OA manifold given certain conditions. Our proof confirms recent numerical findings that already hinted at this convergence. Furthermore, we offer a thorough mathematical underpinning for networks of so-called theta neurons, where the OA ansatz has just been applied. In a final step, we extend our proof by allowing for time-dependent and multi-dimensional parameters as well as for network topologies other than global coupling. This renders the OA ansatz an excellent starting point for the analysis of a broad class of realistic settings.

Fall-related gait characteristics on the treadmill and in daily life.

BACKGROUND: Body-worn sensors allow assessment of gait characteristics that are predictive of fall risk, both when measured during treadmill walking and in daily life. The present study aimed to assess differences as well as associations between fall-related gait characteristics measured on a treadmill and in daily life. METHODS: In a cross-sectional study, trunk accelerations of 18 older adults (72.3 ± 4.5 years) were recorded during walking on a treadmill (Dynaport Hybrid sensor) and during daily life (Dynaport MoveMonitor). A comprehensive set of 32 fall-risk-related gait characteristics was estimated and compared between both settings. RESULTS: For 25 gait characteristics, a systematic difference between treadmill and daily-life measurements was found. Gait was more variable, less symmetric, and less stable during daily life. Fourteen characteristics showed a significant correlation between treadmill and daily-life measurements, including stride time and regularity (0.48 < r < 0.73; p < 0.022). No correlation between treadmill and daily-life measurements was found for stride-time variability, acceleration range and sample entropy in vertical and mediolateral direction, gait symmetry in vertical direction, and stability estimated as the local divergence exponent by Rosenstein's method in mediolateral direction (r < 0.16; p > 0.25). CONCLUSIONS: Gait characteristics revealed less stable, less symmetric, and more variable gait during daily life than on a treadmill, yet about half of the characteristics were significantly correlated between conditions. These results suggest that daily-life gait analysis is sensitive to static personal factors (i.e., physical and cognitive capacity) as well as dynamic situational factors (i.e., behavior and environment), which may both represent determinants of fall risk.

Structure-function discrepancy: inhomogeneity and delays in synchronized neural networks.

The discrepancy between structural and functional connectivity in neural systems forms the challenge in understanding general brain functioning. To pinpoint a mapping between structure and function, we investigated the effects of (in)homogeneity in coupling structure and delays on synchronization behavior in networks of oscillatory neural masses by deriving the phase dynamics of these generic networks. For homogeneous delays, the structural coupling matrix is largely preserved in the coupling between phases, resulting in clustered stationary phase distributions. Accordingly, we found only a small number of synchronized groups in the network. Distributed delays, by contrast, introduce inhomogeneity in the phase coupling so that clustered stationary phase distributions no longer exist. The effect of distributed delays mimicked that of structural inhomogeneity. Hence, we argue that phase (de-)synchronization patterns caused by inhomogeneous coupling cannot be distinguished from those caused by distributed delays, at least not by the naked eye. The here-derived analytical expression for the effective coupling between phases as a function of structural coupling constitutes a direct relationship between structural and functional connectivity. Structural connectivity constrains synchronizability that may be modified by the delay distribution. This explains why structural and functional connectivity bear much resemblance albeit not a one-to-one correspondence. We illustrate this in the context of resting-state activity, using the anatomical connectivity structure reported by Hagmann and others.

Shotgun approaches to gait analysis: insights & limitations.

BACKGROUND: Identifying features for gait classification is a formidable problem. The number of candidate measures is legion. This calls for proper, objective criteria when ranking their relevance. METHODS: Following a shotgun approach we determined a plenitude of kinematic and physiological gait measures and ranked their relevance using conventional analysis of variance (ANOVA) supplemented by logistic and partial least squares (PLS) regressions. We illustrated this approach using data from two studies involving stroke patients, amputees, and healthy controls. RESULTS: Only a handful of measures turned out significant in the ANOVAs. The logistic regressions, by contrast, revealed various measures that clearly discriminated between experimental groups and conditions. The PLS regression also identified several discriminating measures, but they did not always agree with those of the logistic regression. DISCUSSION & CONCLUSION: Extracting a measure's classification capacity cannot solely rely on its statistical validity but typically requires proper post-hoc analysis. However, choosing the latter inevitably introduces some arbitrariness, which may affect outcome in general. We hence advocate the use of generic expert systems, possibly based on machine-learning.

Machine learning approaches to predict whether MEPs can be elicited via TMS.

BACKGROUND: Transcranial magnetic stimulation (TMS) is a valuable technique for assessing the function of the motor cortex and cortico-muscular pathways. TMS activates the motoneurons in the cortex, which after transmission along cortico-muscular pathways can be measured as motor-evoked potentials (MEPs). The position and orientation of the TMS coil and the intensity used to deliver a TMS pulse are considered central TMS setup parameters influencing the presence/absence of MEPs. NEW METHOD: We sought to predict the presence of MEPs from TMS setup parameters using machine learning. We trained different machine learners using either within-subject or between-subject designs. RESULTS: We obtained prediction accuracies of on average 77 % and 65 % with maxima up to up to 90 % and 72 % within and between subjects, respectively. Across the board, a bagging ensemble appeared to be the most suitable approach to predict the presence of MEPs. CONCLUSIONS: Although within a subject the prediction of MEPs via TMS setup parameter-based machine learning might be feasible, the limited accuracy between subjects suggests that the transfer of this approach to experimental or clinical research comes with significant challenges.

Lateralized modulation of cortical beta power during human gait is related to arm swing.

Human gait is a complex behavior requiring dynamic control of upper and lower extremities that is accompanied by cortical activity in multiple brain areas. We investigated the contribution of beta (15-30 Hz) and gamma (30-50 Hz) band electroencephalography (EEG) activity during specific phases of the gait cycle, comparing treadmill walking with and without arm swing. Modulations of spectral power in the beta band during early double support and swing phases source-localized to the sensorimotor cortex ipsilateral, but not contralateral, to the leading leg. The lateralization disappeared in the condition with constrained arms, together with an increase of activity in bilateral supplementary motor areas. By contrast, gamma band modulations that localized to the presumed leg area of sensorimotor cortex around the heel-strike events were unaffected by arm movement. Our findings demonstrate that arm swing is accompanied by considerable cortical activation that should not be neglected in gait-related neuroimaging studies.

The effects of augmented visual feedback during balance training in Parkinson's disease: study design of a randomized clinical trial.

BACKGROUND: Patients with Parkinson's disease often suffer from reduced mobility due to impaired postural control. Balance exercises form an integral part of rehabilitative therapy but the effectiveness of existing interventions is limited. Recent technological advances allow for providing enhanced visual feedback in the context of computer games, which provide an attractive alternative to conventional therapy. The objective of this randomized clinical trial is to investigate whether a training program capitalizing on virtual-reality-based visual feedback is more effective than an equally-dosed conventional training in improving standing balance performance in patients with Parkinson's disease. METHODS/DESIGN: Patients with idiopathic Parkinson's disease will participate in a five-week balance training program comprising ten treatment sessions of 60 minutes each. Participants will be randomly allocated to (1) an experimental group that will receive balance training using augmented visual feedback, or (2) a control group that will receive balance training in accordance with current physical therapy guidelines for Parkinson's disease patients. Training sessions consist of task-specific exercises that are organized as a series of workstations. Assessments will take place before training, at six weeks, and at twelve weeks follow-up. The functional reach test will serve as the primary outcome measure supplemented by comprehensive assessments of functional balance, posturography, and electroencephalography. DISCUSSION: We hypothesize that balance training based on visual feedback will show greater improvements on standing balance performance than conventional balance training. In addition, we expect that learning new control strategies will be visible in the co-registered posturographic recordings but also through changes in functional connectivity.

The energetic effect of hip flexion and retraction in walking at different speeds: a modeling study.

In human walking, power for propulsion is generated primarily via ankle and hip muscles. The addition of a 'passive' hip spring to simple bipedal models appears more efficient than using only push-off impulse, at least, when hip spring associated energetic costs are not considered. Hip flexion and retraction torques, however, are not 'free', as they are produced by muscles demanding metabolic energy. Studies evaluating the inclusion of hip actuation costs, especially during the swing phase, and the hip actuation's energetic benefits are few and far between. It is also unknown whether these possible benefits/effects may depend on speed. We simulated a planar flat-feet model walking stably over a range of speeds. We asked whether the addition of independent hip flexion and retraction remains energetically beneficial when considering work-based metabolic cost of transport (MCOT) with different efficiencies of doing positive and negative work. We found asymmetric hip actuation can reduce the estimated MCOT relative to ankle actuation by up to 6%, but only at medium speeds. The corresponding optimal strategy is zero hip flexion and some hip retraction actuation. The reason for this reduced MCOT is that the decrease in collision loss is larger than the associated increase in hip negative work. This leads to a reduction in total positive mechanical work, which results in an overall lower MCOT. Our study shows how ankle actuation, hip flexion, and retraction actuation can be coordinated to reduce MCOT.