Elvira Pirondini

A Transition in Brain State during Propofol-Induced Unconsciousness

Rhythmic oscillations shape cortical dynamics during active behavior, sleep, and general anesthesia. Cross-frequency phase-amplitude coupling is a prominent feature of cortical oscillations, but its role in organizing conscious and unconscious brain states is poorly understood. Using high-density EEG and intracranial electrocorticography during gradual induction of propofol general anesthesia in humans, we discovered a rapid drug-induced transition between distinct states with opposite phase-amplitude coupling and different cortical source distributions. One state occurs during unconsciousness and may be similar to sleep slow oscillations. A second state occurs at the loss or recovery of consciousness and resembles an enhanced slow cortical potential. These results provide objective electrophysiological landmarks of distinct unconscious brain states, and could be used to help improve EEG-based monitoring for general anesthesia.

Evaluation of the effects of the Arm Light Exoskeleton on movement execution and muscle activities: a pilot study on healthy subjects

BackgroundExoskeletons for lower and upper extremities have been introduced in neurorehabilitation because they can guide the patient’s limb following its anatomy, covering many degrees of freedom and most of its natural workspace, and allowing the control of the articular joints. The aims of this study were to evaluate the possible use of a novel exoskeleton, the Arm Light Exoskeleton (ALEx), for robot-aided neurorehabilitation and to investigate the effects of some rehabilitative strategies adopted in robot-assisted training.MethodsWe studied movement execution and muscle activities of 16 upper limb muscles in six healthy subjects, focusing on end-effector and joint kinematics, muscle synergies, and spinal maps. The subjects performed three dimensional point-to-point reaching movements, without and with the exoskeleton in different assistive modalities and control strategies.ResultsThe results showed that ALEx supported the upper limb in all modalities and control strategies: it reduced the muscular activity of the shoulder’s abductors and it increased the activity of the elbow flexors. The different assistive modalities favored kinematics and muscle coordination similar to natural movements, but the muscle activity during the movements assisted by the exoskeleton was reduced with respect to the movements actively performed by the subjects. Moreover, natural trajectories recorded from the movements actively performed by the subjects seemed to promote an activity of muscles and spinal circuitries more similar to the natural one.ConclusionsThe preliminary analysis on healthy subjects supported the use of ALEx for post-stroke upper limb robotic assisted rehabilitation, and it provided clues on the effects of different rehabilitative strategies on movement and muscle coordination.

EMG-based decoding of grasp gestures in reaching-to-grasping motions

Predicting the grasping function during reach-to-grasp motions is essential for controlling a prosthetic hand or a robotic assistive device. An early accurate prediction increases the usability and the comfort of a prosthetic device. This work proposes an electromyographic-based learning approach that decodes the grasping intention at an early stage of reach-to-grasp motion, i.e.before the final grasp/hand pre-shape takes place. Superficial electrodes and a Cyberglove were used to record the arm muscle activity and the finger joints during reach-to-grasp motions. Our results showed a 90% accuracy for the detection of the final grasp about 0.5 s after motion onset. This paper also examines the effect of different objects distances and different motion speeds on the detection time and accuracy of the classifier. The use of our learning approach to control a 16-degrees of freedom robotic hand confirmed the usability of our approach for the real-time control of robotic devices. Decode of the grasp type by Electromyography on the early stages of the reach-to-grasp motion.Possible to decode the grasp type during the preshaping of the hand.Possible generalization over different positions of the object near the training position.A robotic implementation showed that it possible to express accurately the intention of the grasp type through a robotic hand during the reaching motion and before grasping the object.

EEG topographies provide subject-specific correlates of motor control

Electroencephalography (EEG) of brain activity can be represented in terms of dynamically changing topographies (microstates). Notably, spontaneous brain activity recorded at rest can be characterized by four distinctive topographies. Despite their well-established role during resting state, their implication in the generation of motor behavior is debated. Evidence of such a functional role of spontaneous brain activity would provide support for the design of novel and sensitive biomarkers in neurological disorders. Here we examined whether and to what extent intrinsic brain activity contributes and plays a functional role during natural motor behaviors. For this we first extracted subject-specific EEG microstates and muscle synergies during reaching-and-grasping movements in healthy volunteers. We show that, in every subject, well-known resting-state microstates persist during movement execution with similar topographies and temporal characteristics, but are supplemented by novel task-related microstates. We then show that the subject-specific microstates’ dynamical organization correlates with the activation of muscle synergies and can be used to decode individual grasping movements with high accuracy. These findings provide first evidence that spontaneous brain activity encodes detailed information about motor control, offering as such the prospect of a novel tool for the definition of subject-specific biomarkers of brain plasticity and recovery in neuro-motor disorders.

Model-based variables for the kinematic assessment of upper-extremity impairments in post-stroke patients.

BackgroundCommon scales for clinical evaluation of post-stroke upper-limb motor recovery are often complemented with kinematic parameters extracted from movement trajectories. However, there is no a general consensus on which parameters to use. Moreover, the selected variables may be redundant and highly correlated or, conversely, may incompletely sample the kinematic information from the trajectories. Here we sought to identify a set of clinically useful variables for an exhaustive but yet economical kinematic characterization of upper limb movements performed by post-stroke hemiparetic subjects.MethodsFor this purpose, we pursued a top-down model-driven approach, seeking which kinematic parameters were pivotal for a computational model to generate trajectories of point-to-point planar movements similar to those made by post-stroke subjects at different levels of impairment.ResultsThe set of kinematic variables used in the model allowed for the generation of trajectories significantly similar to those of either sub-acute or chronic post-stroke patients at different time points during the therapy. Simulated trajectories also correctly reproduced many kinematic features of real movements, as assessed by an extensive set of kinematic metrics computed on both real and simulated curves. When inspected for redundancy, we found that variations in the variables used in the model were explained by three different underlying and unobserved factors related to movement efficiency, speed, and accuracy, possibly revealing different working mechanisms of recovery.ConclusionThis study identified a set of measures capable of extensively characterizing the kinematics of upper limb movements performed by post-stroke subjects and of tracking changes of different motor improvement aspects throughout the rehabilitation process.

How are Muscle Synergies Affected by Electromyography Pre-Processing?

Muscle synergies have been used for decades to explain a variety of motor behaviors, both in humans and animals and, more recently, to steer rehabilitation strategies. However, many sources of variability such as factorization algorithms, criteria for dimensionality reduction and data pre-processing constitute a major obstacle to the successful comparison of the results obtained by different research groups. Starting from the canonical EMG processing we determined how variations in filter cut-off frequencies and normalization methods, commonly found in literature, affect synergy weights and inter-subject similarity (ISS) using experimental data related to a 15-muscles upper-limb reaching task. Synergy weights were not significantly altered by either normalization (maximum voluntary contraction – MVC – or maximum amplitude of the signal - SELF) or band-pass filter ([20–500 Hz] or [50–500] Hz). Normalization did, however, alter the amount of variance explained by a set of synergies, which is a criterion often used for model order selection. Comparing different low-pass (LP) filters (0.5 Hz, 4 Hz, 10 Hz, 20 Hz cut-offs) we showed that increasing the low pass filter cut-off had the effect of decreasing the variance accounted for by a set number of synergies and affected individual muscle contributions. Extreme smoothing (i.e., LP cut-off 0.5 Hz) enhanced the contrast between active and inactive muscles but had an unpredictable effect on the ISS. The results presented here constitute a further step towards a thoughtful EMG pre-processing for the extraction of muscle synergies.

Evaluation of a New Exoskeleton for Upper Limb Post-stroke Neuro-rehabilitation: Preliminary Results

Exoskeletons are becoming very popular for the rehabilitative treatment of post-stroke subjects. The aim of this study was to characterize the effect of a new light upper limb exoskeleton on the movement execution and muscular activity during reaching movements in healthy subjects. The results show that the exoskeleton used in the passive modality supports the upper limb reducing the muscular activity of the shoulder’s muscles and increasing the activity of the elbow flexors, without interfering with the movement execution. Our preliminary analysis on healthy subjects supports the use of this new exoskeleton for post-stroke robotic-rehabilitation.

How the selection of muscles influences their synergies? A preliminary study using real data

Muscle synergies have been proposed as building blocks that simplify the construction of movements, and as a method to study motor behavior. However, the pre-processing of the EMG signals and the factorization algorithm may impact on their extraction and meaning. This preliminary work aimed at investigating the influence of the selection of the muscles on muscle synergies analysis. In particular, muscle synergies have been extracted from datasets including different upper limb muscles, recorded during the execution of planar reaching and grasping movements. Results suggested that the amount of degrees of freedom activated by the muscles considered in the analysis determines the number of muscle synergies, while muscle redundancy does not affect muscle synergies extraction. In conclusion, the number of muscle synergies is influenced not only by the task constraints and the performance of the movement, but also by the choice of the muscles. Therefore, both aspects have to be carefully considered to design muscle synergies analysis and to compare results from different studies.

A spatially-regularized dynamic source localization algorithm for EEG

Cortical activity can be estimated from electroencephalogram (EEG) or magnetoencephalogram (MEG) data by solving an ill-conditioned inverse problem that is regularized using neuroanatomical, computational, and dynamic constraints. Recent methods have incorporated spatio-temporal dynamics into the inverse problem framework. In this approach, spatio-temporal interactions between neighboring sources enforce a form of spatial smoothing that enhances source localization quality. However, spatial smoothing could also occur by way of correlations within the state noise process that drives the underlying dynamic model. Estimating the spatial covariance structure of this state noise is challenging, particularly in EEG and MEG data where the number of underlying sources is far greater than the number of sensors. However, the EEG/MEG data are sparse compared to the large number of sources, and thus sparse constraints could be used to simplify the form of the state noise spatial covariance. In this work, we introduce an empirically tailored basis to represent the spatial covariance structure within the state noise processes of a cortical dynamic model for EEG source localization. We augment the method presented in Lamus, et al. (2011) to allow for sparsity enforcing priors on the covariance parameters. Simulation studies as well as analysis of real data reveal significant gains in the source localization performance over existing algorithms.

Effect of handedness on muscle synergies during upper limb planar movements

Handedness is a prominent but poorly understood aspect of human motor performances. Despite it is generally accepted that it results from differences in the neural control of the arm, the mechanisms at the origin of the side-difference in motor performances are still unknown. In this work, we propose to deepen this aspect by investigating muscle synergies organization. We obtained muscle synergies through the factorization of the superficial electromyographical (EMG) activity related to fifteen upper limb muscles in the dominant and non-dominant side of 5 healthy young right and left dominant subjects, while executing planar wide and tight circular trajectories. Our preliminary results showed that right and left handed subjects performed the circular trajectories with a different muscle organization. Moreover, a task-related side-difference in muscle synergies was observed. Further investigations in a larger cohort of individuals are necessary to determine the neural mechanisms generating the differences in number and organization of muscle synergies between left and right handed individuals.