Investigating Different Levels of Bimanual Interaction With a Novel Motor Learning Task: A Behavioural and Transcranial Alternating Current Stimulation Study

Many tasks require the skilled interaction of both hands, such as eating with knife and fork or keyboard typing. However, our understanding of the behavioural and neurophysiological mechanisms underpinning bimanual motor learning is still sparse. Here, we aimed to address this by first characterising learning-related changes of different levels of bimanual interaction and second investigating how beta tACS modulates these learning-related changes. To explore early bimanual motor learning, we designed a novel bimanual motor learning task. In the task, a force grip device held in each hand (controlling x- and y-axis separately) was used to move a cursor along a path of streets at different angles (0°, 22.5°, 45°, 67.5°, and 90°). Each street corresponded to specific force ratios between hands, which resulted in different levels of hand interaction, i.e., unimanual (Uni, i.e., 0°, 90°), bimanual with equal force (Bieq, 45°), and bimanual with unequal force (Biuneq 22.5°, 67.5°). In experiment 1, 40 healthy participants performed the task for 45 min with a minimum of 100 trials. We found that the novel task induced improvements in movement time and error, with no trade-off between movement time and error, and with distinct patterns for the three levels of bimanual interaction. In experiment 2, we performed a between-subjects, double-blind study in 54 healthy participants to explore the effect of phase synchrony between both sensorimotor cortices using tACS at the individual’s beta peak frequency. The individual’s beta peak frequency was quantified using electroencephalography. 20 min of 2 mA peak-to-peak amplitude tACS was applied during task performance (40 min). Participants either received in-phase (0° phase shift), out-of-phase (90° phase shift), or sham (3 s of stimulation) tACS. We replicated the behavioural results of experiment 1, however, beta tACS did not modulate motor learning. Overall, the novel bimanual motor task allows to characterise bimanual motor learning with different levels of bimanual interaction. This should pave the way for future neuroimaging studies to further investigate the underlying mechanism of bimanual motor learning

Event-related desynchronization in motor imagery with EEG neurofeedback in the context of declarative interference and sleep

Motor imagery (MI) in combination with neurofeedback (NF) is a promising supplement to facilitate the acquisition of motor abilities and the recovery of impaired motor abilities following brain injuries. However, the ability to control MI NF is subject to a wide range of inter-individual variability. A substantial number of users experience difficulties in achieving good results, which compromises their chances to benefit from MI NF in a learning or rehabilitation context. It has been suggested that context factors, that is, factors outside the actual motor task, can explain individual differences in motor skill acquisition. Retrospective declarative interference and sleep have already been identified as critical factors for motor execution (ME) and MI based practice. Here, we investigate whether these findings generalize to practicing MI NF. Three groups underwent three blocks of practicing MI with NF, each on two subsequent days. In two of the groups, MI NF blocks were followed by either immediate or delayed declarative memory tasks. The control group performed only MI NF and no specific interference tasks. Two of the MI NF blocks were run on the first day of the experiment, the third in the morning of the second day. Significant within-block NF gains in mu and beta frequency event-related desynchronization (ERD) where evident for all groups. However, data did not provide evidence for an impact of immediate or delayed declarative interference on MI NF ERD. Also, MI NF ERD remained unchanged after a night of sleep. We did not observe the expected pattern of results for MI NF ERD with regard to declarative interference and a night of sleep. This is discussed in the context of variable experimental task designs, inter-individual differences, and performance measures

Post-stroke upper limb recovery is correlated with dynamic resting-state network connectivity

Motor recovery is still limited for people with stroke especially those with greater functional impairments. In order to improve outcome, we need to understand more about the mechanisms underpinning recovery. Task-unbiased, blood-flow independent post-stroke neural activity can be acquired from resting brain electrophysiological recordings, and offers substantial promise to investigate physiological mechanisms, but behaviourally-relevant features of resting-state sensorimotor network dynamics have not yet been identified. Thirty-seven people with subcortical ischemic stroke and unilateral hand paresis of any degree were longitudinally evaluated at 3 weeks (early subacute) and 12 weeks (late subacute) after stroke. Resting-state magnetoencephalography and clinical scores of motor function were recorded and compared with matched controls. Magnetoencephalography data were decomposed using a data-driven Hidden Markov Model into 10 time-varying resting-state networks. People with stroke showed statistically significantly improved Action Research Arm Test and Fugl-Meyer upper extremity scores between 3 weeks and 12 weeks after stroke (both p < 0.001). Hidden Markov Model analysis revealed a primarily alpha-band ipsilesional resting-state sensorimotor network which had a significantly increased life-time (the average time elapsed between entering and exiting the network) and fractional occupancy (the occupied percentage among all networks) at 3 weeks after stroke when compared to controls. The life-time of the ipsilesional resting-state sensorimotor network positively correlated with concurrent motor scores in people with stroke who had not fully recovered. Specifically, this relationship was observed only in ipsilesional rather in contralesional sensorimotor network, default mode network or visual network. The ipsilesional sensorimotor network metrics were not significantly different from controls at 12 weeks after stroke. The increased recruitment of alpha-band ipsilesional resting-state sensorimotor network at subacute stroke served as functionally correlated biomarkers exclusively in people with stroke with not fully recovered hand paresis, plausibly reflecting functional motor recovery processes

The GLM-spectrum:A multilevel framework for spectrum analysis with covariate and confound modelling

The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling, and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multilevel analysis of power spectra, with adaptive covariate and confound modelling

Repeated unilateral handgrip contractions alter functional connectivity and improve contralateral limb response times

In humans, motor learning is underpinned by changes in sensorimotor network functional connectivity (FC). Unilateral contractions increase FC in the ipsilateral primary motor cortex (M1) and supplementary motor area (SMA); areas involved in motor planning and execution of the contralateral hand. Therefore, unilateral contractions are a promising approach to augment motor performance in the contralateral hand. In a within-participant, randomized, cross-over design, 15 right-handed adults had two magnetic resonance imaging (MRI) sessions, where functional-MRI and MR-Spectroscopic Imaging were acquired before and after repeated right-hand contractions at either 5% or 50% maximum voluntary contraction (MVC). Before and after scanning, response times (RTs) were determined in both hands. Nine minutes of 50% MVC contractions resulted in decreased handgrip force in the contracting hand, and decreased RTs and increased handgrip force in the contralateral hand. This improved motor performance in the contralateral hand was supported by significant neural changes: increased FC between SMA-SMA and increased FC between right M1 and right Orbitofrontal Cortex. At a neurochemical level, the degree of GABA decline in left M1, left and right SMA correlated with subsequent behavioural improvements in the left-hand. These results support the use of repeated handgrip contractions as a potential modality for improving motor performance in the contralateral hand

An exploration of function analysis and function allocation in the commercial flight domain

The applicability is explored of functional analysis methods to support cockpit design. Specifically, alternative techniques are studied for ensuring an effective division of responsibility between the flight crew and automation. A functional decomposition is performed of the commercial flight domain to provide the information necessary to support allocation decisions and demonstrate methodology for allocating functions to flight crew or to automation. The function analysis employed 'bottom up' and 'top down' analyses and demonstrated the comparability of identified functions, using the 'lift off' segment of the 'take off' phase as a test case. The normal flight mission and selected contingencies were addressed. Two alternative methods for using the functional description in the allocation of functions between man and machine were investigated. The two methods were compared in order to ascertain their relative strengths and weaknesses. Finally, conclusions were drawn regarding the practical utility of function analysis methods

Probing of InAs/AlSb double barrier heterostructures by ballistic electron emission spectroscopy

InAs/AlSb resonant tunneling heterostructures have been studied by ballistic electron emission spectroscopy. Current thresholds attributed to quasibound states in the quantum well and emission over the AlSb barriers are observed. The observed shape of thresholds is consistent with inelastic processes in the InAs layers of the structures, where a high number of electron–hole pairs are generated. A threshold consistent with the generation of electron–hole pairs in quantum well states is observed

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning