Modulation of corticospinal output during goal-directed actions: Evidence for a contingent coding hypothesis.

Abstract Seeing a person perform an action activates the observer's motor system. The present study aimed at investigating the temporal relationship between execution and observation of goal-directed actions. One possibility is that the corticospinal excitability (CSE) follows the dynamic evolution of the pattern of muscle activity in the executed action. Alternatively, CSE may anticipate the future course of the observed action, prospectively extrapolating future states. Our study was designed to test these alternative hypotheses by directly comparing the time course of muscle recruitment during the execution and observation of reach-to-grasp movements. We found that the time course of CSE during action observation followed the time course of the EMG signal during action execution. This contingent coding was observed despite the outcome of the observed motor act being predictable from the earliest phases of the movement. These findings challenge the view that CSE serves to predict the target of an observed action

Nonlinear modeling of cortical responses to mechanical wrist perturbations using the NARMAX method

Objective: Nonlinear modeling of cortical responses (EEG) to wrist perturbations allows for the quantification of cortical sensorimotor function in healthy and neurologically impaired individuals. A common model structure reflecting key characteristics shared across healthy individuals may provide a reference for future clinical studies investigating abnormal cortical responses associated with sensorimotor impairments. Thus, the goal of our study is to identify this common model structure and therefore to build a nonlinear dynamic model of cortical responses, using nonlinear autoregressivemoving-average model with exogenous inputs (NARMAX). Methods: EEG was recorded from ten participants when they were receiving continuous wrist perturbations. A common model structure detection method was developed for identifying a common NARMAX model structure across all participants, with individualized parameter values. The results were compared to conventional subject-specific models. Results: The proposed method achieved 93.91% variance accounted for (VAF) when implementing a one-step-ahead prediction and around 50% VAF for a k-step ahead prediction (k = 3), without a substantial drop of VAF as compare to subject-specific models. The estimated common structure suggests that the measured cortical response is a mixed outcome of the nonlinear transformation of external inputs and local neuronal interactions or inherent neuronal dynamics at the cortex. Conclusion: The proposed method well determined the common characteristics across subjects in the cortical responses to wrist perturbations. Significance: It provides new insights into the human sensorimotor nervous system in response to somatosensory inputs and paves the way for future translational studies on assessments of sensorimotor impairments using our modeling approach

Time-frequency optimization for discrimination between imagination of right and left hand movements based on two bipolar electroencephalography channels

International audienceTo enforce a widespread use of efficient and easy to use brain-computer interfaces (BCIs), the inter-subject robustness should be increased and the number of electrodes should be reduced. These two key issues are addressed in this contribution, proposing a novel method to identify subject-specific time-frequency characteristics with a minimal number of electrodes. In this method, two alternative criteria, time-frequency discrimination factor (TFDF) and F score, are proposed to evaluate the discriminative power of time-frequency regions. Distinct from classical measures (e.g., Fisher criterion, r 2 coefficient), the TFDF is based on the neurophysiologic phenomena, on which the motor imagery BCI paradigm relies, rather than only from statistics. F score is based on the popular Fisher's discriminant and purely data driven; however, it differs from traditional measures since it provides a simple and effective measure for quantifying the discriminative power of a multi-dimensional feature vector. The proposed method is tested on BCI competition IV datasets IIa and IIb for discriminating right and left hand motor imagery. Compared to state-of-the-art methods, our method based on both criteria led to comparable or even better classification results, while using fewer electrodes (i.e., only two bipolar channels, C3 and C4). This work indicates that time-frequency optimization can not only improve the classification performance but also contribute to reducing the number of electrodes required in motor imagery BCIs

Towards simultaneous electroencephalography and functional near-infrared spectroscopy for improving diagnostic accuracy in prolonged disorders of consciousness: a healthy cohort study

Qualitative clinical assessments of the recovery of awareness after severe brain injury require an assessor to differentiate purposeful behaviour from spontaneous behaviour. As many such behaviours are minimal and inconsistent, behavioural assessments are susceptible to diagnostic errors. Advanced neuroimaging tools such as functional magnetic resonance imaging and electroencephalography (EEG) can bypass behavioural responsiveness and reveal evidence of covert awareness and cognition within the brains of some patients, thus providing a means for more accurate diagnoses, more accurate prognoses, and, in some instances, facilitated communication. As each individual neuroimaging method has its own advantages and disadvantages (e.g., signal resolution, accessibility, etc.), this thesis studies on healthy individuals a burgeoning technique of non-invasive electrical and optical neuroimaging—simultaneous EEG and functional near-infrared spectroscopy (fNIRS)—that can be applied at the bedside. Measuring reliable covert behaviours is correlated with participant engagement, instrumental sensitivity and the accurate localisation of responses, aspects which are further addressed over three studies. Experiment 1 quantifies the typical EEG changes in response to covert commands in the absence and presence of an object. This is investigated to determine whether a goal-directed task can yield greater EEG control accuracy over simple monotonous imagined single-joint actions. Experiment 2 characterises frequency domain NIRS changes in response to overt and covert hand movements. A method for reconstructing haemodynamics using the less frequently investigated phase parameter is outlined and the impact of noise contaminated NIRS measurements are discussed. Furthermore, classification performances between frequency-domain and continuous-wave-like signals are compared. Experiment 3 lastly applies these techniques to determine the potential of simultaneous EEG-fNIRS classification. Here a sparse channel montage that would ultimately favour clinical utility is used to demonstrate whether such a hybrid method containing rich spatial and temporal information can improve the classification of covert responses in comparison to unimodal classification of signals. The findings and discussions presented within this thesis identify a direction for future research in order to more accurately translate the brain state of patients with a prolonged disorder of consciousness