Evolution of cerebral cortico-cortical communication during visuomotor adaptation to a cognitive-motor executive challenge.

Cortical dynamics were examined during a cognitive-motor adaptation task that required inhibition of a familiar motor plan. EEG coherence between the motor planning (Fz) and left hemispheric region was progressively reduced over trials (low-beta, high-beta, gamma bands) along with faster, straighter reaching movements during both planning and execution. The major reduction in coherence (delta, low/high-theta, low/high-alpha bands) between Fz and the left prefrontal region during both movement planning and execution suggests gradual disengagement of frontal executive following its initial role in the suppression of established visuomotor maps.

Design principles for noninvasive brain-machine interfaces.

With the advent of sophisticated prosthetic limbs, the challenge is now to develop and demonstrate optimal closed-loop control of the these limbs using neural measurements from single/multiple unit activity (SUA/MUA), electrocorticography (ECoG), local field potentials (LFP), scalp electroencephalography (EEG) or even electromyography (EMG) after targeted muscle reinnervation (TMR) in subjects with upper limb disarticulation. In this paper we propose design principles for developing a noninvasive EEG-based brain-machine interface (BMI) for dexterous control of a high degree-of-freedom, biologically realistic limb.

Selective left, right and bilateral stimulation of subthalamic nuclei in Parkinson's disease: differential effects on motor, speech and language function.

Deep brain stimulation (DBS) of the subthalamic nucleus improves the motor symptoms of Parkinson's disease, but may produce a worsening of speech and language performance at rates and amplitudes typically selected in clinical practice. The possibility that these dissociated effects might be modulated by selective stimulation of left and right STN has never been systematically investigated. To address this issue, we analyzed motor, speech and language functions of 12 patients implanted with bilateral stimulators configured for optimal motor responses.

Common and unique responses to dopamine agonist therapy and deep brain stimulation in Parkinson's disease: an H(2)(15)O PET study.

Background: Dopamine agonist therapy and deep brain stimulation (DBS) of the subthalamic nucleus (STN) are antiparkinsonian treatments that act on a different part of the basal ganglia-thalamocortical motor circuitry, yet produce similar symptomatic improvements.

Objective/hypothesis: The purpose of this study was to identify common and unique brain network features of these standard treatments.

Methods: We analyzed images produced by H(2)(15)O positron emission tomography (PET) of patients with Parkinson's disease (PD) at rest.

Fast attainment of computer cursor control with noninvasively acquired brain signals.

Brain-computer interface (BCI) systems are allowing humans and non-human primates to drive prosthetic devices such as computer cursors and artificial arms with just their thoughts. Invasive BCI systems acquire neural signals with intracranial or subdural electrodes, while noninvasive BCI systems typically acquire neural signals with scalp electroencephalography (EEG). Some drawbacks of invasive BCI systems are the inherent risks of surgery and gradual degradation of signal integrity.

Movement decoding from noninvasive neural signals.

It is generally assumed that noninvasively-acquired neural signals contain an insufficient level of information for decoding or reconstructing detailed kinematics of natural, multi-joint limb movements and hand gestures. Here, we review recent findings from our laboratory at the University of Maryland showing that noninvasive scalp electroencephalography (EEG) or magnetoencephalography (MEG) can be used to continuously decode the kinematics of 2D 'center-out' drawing, unconstrained 3D 'center-out' reaching and 3D finger gesturing. These findings suggest that these 'far-field', extra-cranial neural signals contain rich information about the neural representation of movement at the macroscale, and thus these neural representations provide alternative methods for developing noninvasive brain-machine interfaces with wide-ranging clinical relevance and for understanding functional and pathological brain states at various stages of development and aging.

Cerebral cortical dynamics during visuomotor transformation: adaptation to a cognitive-motor executive challenge.

EEG was employed during cognitive-motor adaptation to a visuomotor transformation that required inhibition of an established motor plan. Performance was positively related to frontal alpha and theta power during both planning and execution of reaching movements to visual targets. EEG changes suggest initial involvement of frontal executive functioning to suppress established visuomotor mappings followed by progressive idling (i.

Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals.

It is generally thought that the signal-to-noise ratio, the bandwidth, and the information content of neural data acquired via noninvasive scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multijoint movements of the upper limb. Here, we challenge this assumption by continuously decoding three-dimensional (3D) hand velocity from neural data acquired from the scalp with 55-channel EEG during a 3D center-out reaching task. To preserve ecological validity, five subjects self-initiated reaches and self-selected targets.

Brain biomarkers of motor adaptation using phase synchronization.

A growing number of brain monitoring tools for medical and biomedical applications such as surgery have been developed. Although many assistive technologies (e.g.

Decoding three-dimensional hand kinematics from electroencephalographic signals.

The capacity to decode kinematics of intended movement from neural activity is necessary for the development of neuromotor prostheses such as smart artificial arms. Thus far, most of the progress in the development of neuromotor prostheses has been achieved by decoding kinematics of the hand from intracranial neural activity. The comparatively low signal-to-noise ratio and spatial resolution of neural data acquired non-invasively from the scalp via electroencephalography (EEG) have been presumed to prohibit the extraction of detailed information about hand kinematics.

Decoding center-out hand velocity from MEG signals during visuomotor adaptation.

During reaching or drawing, the primate cortex carries information about the current and upcoming position of the hand. Researchers have decoded hand position, velocity, and acceleration during center-out reaching or drawing tasks from neural recordings acquired invasively at the microscale and mesoscale levels. Here we report that we can continuously decode information about hand velocity at the macroscale level from magnetoencephalography (MEG) data acquired from the scalp during a center-out drawing task with an imposed hand-cursor rotation.

Decoding hand and cursor kinematics from magnetoencephalographic signals during tool use.

The ability to decode kinematics of intended movement from neural activity is essential for the development of prosthetic devices, such as artificial arms, that can aid motor-disabled persons. To date, most of the progress in the development of neuromotor prostheses has been obtained by decoding neural activity acquired through invasive means, such as microelectrode arrays seated into motor cortical tissue. In this study, we demonstrate the feasibility of decoding both hand position and velocity from non-invasive magnetoencephalographic signals during a center-out drawing task in familiar and novel environments.