The critical stability task: quantifying sensory-motor control during ongoing movement in nonhuman primates.

Everyday behaviors require that we interact with the environment, using sensory information in an ongoing manner to guide our actions. Yet, by design, many of the tasks used in primate neurophysiology laboratories can be performed with limited sensory guidance. As a consequence, our knowledge about the neural mechanisms of motor control is largely limited to the feedforward aspects of the motor command. To study the feedback aspects of volitional motor control, we adapted the critical stability task (CST) from the human performance literature (Jex H, McDonnell J, Phatak A. IEEE Trans Hum Factors Electron 7: 138-145, 1966). In the CST, our monkey subjects interact with an inherently unstable (i.e., divergent) virtual system and must generate sensory-guided actions to stabilize it about an equilibrium point. The difficulty of the CST is determined by a single parameter, which allows us to quantitatively establish the limits of performance in the task for different sensory feedback conditions. Two monkeys learned to perform the CST with visual or vibrotactile feedback. Performance was better under visual feedback, as expected, but both monkeys were able to utilize vibrotactile feedback alone to successfully perform the CST. We also observed changes in behavioral strategy as the task became more challenging. The CST will have value for basic science investigations of the neural basis of sensory-motor integration during ongoing actions, and it may also provide value for the design and testing of bidirectional brain computer interface systems. NEW & NOTEWORTHY Currently, most behavioral tasks used in motor neurophysiology studies require primates to make short-duration, stereotyped movements that do not necessitate sensory feedback. To improve our understanding of sensorimotor integration, and to engineer meaningful artificial sensory feedback systems for brain-computer interfaces, it is crucial to have a task that requires sensory feedback for good control. The critical stability task demands that sensory information be used to guide long-duration movements.

Histological evaluation of a chronically-implanted electrocorticographic electrode grid in a non-human primate.

OBJECTIVE: Electrocorticography (ECoG), used as a neural recording modality for brain-machine interfaces (BMIs), potentially allows for field potentials to be recorded from the surface of the cerebral cortex for long durations without suffering the host-tissue reaction to the extent that it is common with intracortical microelectrodes. Though the stability of signals obtained from chronically implanted ECoG electrodes has begun receiving attention, to date little work has characterized the effects of long-term implantation of ECoG electrodes on underlying cortical tissue. APPROACH: We implanted and recorded from a high-density ECoG electrode grid subdurally over cortical motor areas of a Rhesus macaque for 666 d. MAIN RESULTS: Histological analysis revealed minimal damage to the cortex underneath the implant, though the grid itself was encapsulated in collagenous tissue. We observed macrophages and foreign body giant cells at the tissue-array interface, indicative of a stereotypical foreign body response. Despite this encapsulation, cortical modulation during reaching movements was observed more than 18 months post-implantation. SIGNIFICANCE: These results suggest that ECoG may provide a means by which stable chronic cortical recordings can be obtained with comparatively little tissue damage, facilitating the development of clinically viable BMI systems.

Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters.

OBJECTIVE: A traditional goal of neural recording with extracellular electrodes is to isolate action potential waveforms of an individual neuron. Recently, in brain-computer interfaces (BCIs), it has been recognized that threshold crossing events of the voltage waveform also convey rich information. To date, the threshold for detecting threshold crossings has been selected to preserve single-neuron isolation. However, the optimal threshold for single-neuron identification is not necessarily the optimal threshold for information extraction. Here we introduce a procedure to determine the best threshold for extracting information from extracellular recordings. We apply this procedure in two distinct contexts: the encoding of kinematic parameters from neural activity in primary motor cortex (M1), and visual stimulus parameters from neural activity in primary visual cortex (V1). APPROACH: We record extracellularly from multi-electrode arrays implanted in M1 or V1 in monkeys. Then, we systematically sweep the voltage detection threshold and quantify the information conveyed by the corresponding threshold crossings. MAIN RESULTS: The optimal threshold depends on the desired information. In M1, velocity is optimally encoded at higher thresholds than speed; in both cases the optimal thresholds are lower than are typically used in BCI applications. In V1, information about the orientation of a visual stimulus is optimally encoded at higher thresholds than is visual contrast. A conceptual model explains these results as a consequence of cortical topography. SIGNIFICANCE: How neural signals are processed impacts the information that can be extracted from them. Both the type and quality of information contained in threshold crossings depend on the threshold setting. There is more information available in these signals than is typically extracted. Adjusting the detection threshold to the parameter of interest in a BCI context should improve our ability to decode motor intent, and thus enhance BCI control. Further, by sweeping the detection threshold, one can gain insights into the topographic organization of the nearby neural tissue.