No abstract

Neural populations produce complex oscillatory patterns thought to implement brain function. The dominant rhythm in the healthy adult human brain is formed by alpha oscillations with a typical power peak most commonly found between 8 and 12Hz. This alpha peak frequency has been repeatedly discussed as a highly heritable and stable neurophysiological "trait" marker reflecting anatomical properties of the brain, and individuals' general cognitive capacity. However, growing evidence suggests that the alpha peak frequency is highly volatile at shorter time scales, dependent on the individuals' "state". Based on the converging experimental and theoretical results from numerous recent studies, here we propose that alpha frequency variability forms the basis of an adaptive mechanism mirroring the activation level of neural populations which has important functional implications. We here integrate experimental and computational perspectives to shed new light on the potential role played by shifts in alpha peak frequency and discuss resulting implications. We further propose a potential mechanism by which alpha oscillations are regulated in a noisy network of spiking neurons in presence of delayed feedback.

The compensation for a sudden balance perturbation, unpracticed and unpredictable in timing and magnitude is accompanied by pronounced postural instability that is suggested to be causal to falls. However, subsequent presentations of an identical perturbation are characterized by a marked decrease of the amplitude of postural reactions; a phenomenon called adaptation or habituation. This study aimed to identify cortical characteristics associated with adaptive behavior during repetitive balance perturbations based on single-trial analyses of the P1 and N1 perturbation-evoked potentials. Thirty-seven young men were exposed to ten transient balance perturbations while balancing on the dominant leg. Thirty two-channel electroencephalography (EEG), surface electromyography (EMG) of the ankle plantar flexor muscles and postural sway (i.e., Euclidean distance of the supporting platform) were recorded simultaneously. The P1 and N1 potentials were localized and the amplitude/latency was analyzed trial by trial. The best match sources for P1 and N1 potentials were located in the parietal (Brodmann area (BA) 5) and midline fronto-central cortex (BA 6), respectively. The amplitude and latency of the P1 potential remained unchanged over trials. In contrast, a significant adaptation of the N1 amplitude was observed. Similar adaptation effects were found with regard to postural sway and ankle plantarflexors EMG activity of the non-dominant (free) leg; i.e., an indicator for reduced muscular co-contraction and/or less temporary bipedal stance to regain stability. Significant but weak correlations were found between N1 amplitude and postural sway as well as EMG activity. These results highlight the important role of the midline fronto-central cortex for adaptive behavior associated with balance control.

The athletes' superior visuomotor performance in response to visual motion is primarily related to visual perception and, to a minor degree, to motor-related processes.

No abstract

Balance control is fundamental for most daily motor activities, and its impairment is associated with an increased risk of falling. Growing evidence suggests the human cortex is essentially contributing to the control of standing balance. However, the exact mechanisms remain unclear and need further investigation. In a previous study, we introduced a new protocol to identify electrocortical activity associated with performance of different continuous balance tasks with the eyes opened. The aim of this study was to extend our previous results by investigating the individual alpha peak frequency (iAPF), a neurophysiological marker of thalamo-cortical information transmission, which remained unconsidered so far in balance research. Thirty-seven subjects completed nine balance tasks varying in surface stability and base of support. Electroencephalography (EEG) was recorded from 32 scalp locations throughout balancing with the eyes closed to ensure reliable identification of the iAPF. Balance performance was quantified as the sum of anterior-posterior and medio-lateral movements of the supporting platform. The iAPF, as well as power in the theta, lower alpha and upper alpha frequency bands were determined for each balance task after applying an ICA-based artifact rejection procedure. Higher demands on balance control were associated with a global increase in iAPF and a decrease in lower alpha power. These results may indicate increased thalamo-cortical information transfer and general cortical activation, respectively. In addition, a significant increase in upper alpha activity was observed in the fronto-central region whereas it decreased in the centro-parietal region. Furthermore, midline theta increased with higher task demands probably indicating activation of error detection/processing mechanisms. IAPF as well as theta and alpha power were correlated with platform movements. The results provide new insights into spectral and spatial characteristics of cortical oscillations subserving balance control. This information may be particularly useful in a clinical context as it could be used to reveal cortical contributions to balance dysfunction in specific populations such as Parkinson’s or vestibular loss. However, this should be addressed in future studies.

Balance control is a fundamental component of human every day motor activities such as standing or walking, and its impairment is associated with an increased risk of falling. However, in humans the exact neurobiological mechanisms underlying balance control are still unclear. Specifically, although previous studies have identified a number of cortical regions that become significantly activated during real or imagined balancing, the interactions within and between the relevant cortical regions remain to be investigated. The working hypothesis of this study is that cortical networks contribute to an optimization of balance control, and that this contribution can be revealed by partial directed coherence—a time-variant, frequency-selective and directed functional connectivity analysis tool. Electroencephalographic activity was recorded in 37 subjects during single-leg balancing on a stable as well as an unstable surface. Results of this study show that in the transition from balancing on a stable surface to an unstable surface, two topographically delimitable connectivity networks (weighted directed networks) are established; one associated with the alpha and one with the theta frequency band. The theta network sequence can be described as a set of subnetworks (modules) comprising the frontal, central and parietal cortex with individual temporal and spatial developments within and between those modules. In the alpha network, the occipital electrodes O1 and O2 act as a source, and the interactions propagate predominantly in the directions from occipital to parietal and to centro-parietal areas. These important findings indicate that balance control is supported by at least two functional cortical networks.Electronic supplementary materialThe online version of this article (doi:10.1007/s10548-017-0567-x) contains supplementary material, which is available to authorized users.

Previous research has shown that both acute and chronic physical exercises can induce positive effects on brain function and this is associated with improvements in cognitive performance. However, the neurophysiological mechanisms underlying the beneficial effects of exercise on cognitive processing are not well understood. This study examined the effects of an acute bout of physical exercise as well as four weeks of exercise training on the individual resting state electroencephalographic (EEG) alpha peak frequency (iAPF), a neurophysiological marker of the individual's state of arousal and attention, in healthy young adults. The subjects completed a steady state exercise (SSE) protocol or an exhaustive exercise (EE) protocol, respectively, on two separate days. EEG activity was recorded for 2 min before exercise, immediately after exercise, and after 10 min of rest. All assessments were repeated following four weeks of exercise training to investigate whether an improvement in physical fitness modulates the resting state iAPF and/or the iAPF response to an acute bout of SSE and EE. The iAPF was significantly increased following EE (P = 0.012) but not following SSE. It is concluded that the iAPF is increased following intense exercise, indicating a higher level of arousal and preparedness for external input.