Dataset Information

Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array.

ABSTRACT: The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.

SUBMITTER: Simeral JD

PROVIDER: S-EPMC3715131 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array.

Simeral J D JD Kim S-P SP Black M J MJ Donoghue J P JP Hochberg L R LR

Journal of neural engineering 20110324 2

The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assisti ...[more]

PMID: 21436513

Similar Datasets

Project description:Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities-specifically, the changes in the statistics of the data between training and testing data sets. This includes changes to the neural tuning profiles and baseline shifts in firing rates of recorded neurons, as well as nonphysiological noise. While progress has been made toward providing long-term user control via decoder recalibration, relatively little work has been dedicated to making the decoding algorithm more resilient to signal nonstationarities. Here, we describe how principled kernel selection with gaussian process regression can be used within a Bayesian filtering framework to mitigate the effects of commonly encountered nonstationarities. Given a supervised training set of (neural features, intention to move in a direction)-pairs, we use gaussian process regression to predict the intention given the neural data. We apply kernel embedding for each neural feature with the standard radial basis function. The multiple kernels are then summed together across each neural dimension, which allows the kernel to effectively ignore large differences that occur only in a single feature. The summed kernel is used for real-time predictions of the posterior mean and variance under a gaussian process framework. The predictions are then filtered using the discriminative Kalman filter to produce an estimate of the neural intention given the history of neural data. We refer to the multiple kernel approach combined with the discriminative Kalman filter as the MK-DKF. We found that the MK-DKF decoder was more resilient to nonstationarities frequently encountered in-real world settings yet provided similar performance to the currently used Kalman decoder. These results demonstrate a method by which neural decoding can be made more resistant to nonstationarities.

Project description:Brain-implanted microelectrode arrays show promise as future clinical devices. However, biological responses to various designs, compositions and locations of these implants have not been fully characterized, and may impact the long-term functionality of these devices. In order to improve our understanding of the tissue conditions at the interface of chronic brain-implanted microdevices, we proposed utilizing advanced histology and microscopy techniques to image implanted devices and surrounding tissue intact within brain slices. We then proposed utilizing these methods to examine whether depth within the cerebral cortex affected tissue conditions around implants.Histological data was collected from rodent brain slices containing intact, intracortical microdevices four weeks after implantation surgery. Thick tissue sections containing the chronic implants were processed with fluorescent antibody labels, and imaged in an optical clearing solution using laser confocal microscopy.Tissue surrounding microdevices exhibited two major depth-related phenomena: a non-uniform microglial coating along the device length and a dense mass of cells surrounding the implant in cerebral cortical layers I and II. Detailed views of the monocyte-derived immune cells improve our understanding of the close and complex association that immune cells have with chronic brain implants, and illuminated a possible relationship between cortical depth and the intensity of a chronic monocyte response around penetrating microdevices. The dense mass of cells contained vimentin, a protein not typically expressed highly in CNS cells, evidence that non-CNS cells likely descended down the face of the penetrating devices from the pial surface.Image data of highly non-uniform and depth-dependent biological responses along a device provides novel insight into the complexity of the tissue response to penetrating brain-implanted microdevices. The presented work also demonstrates the value of in situ histological collection of brain implants for studying the complex tissue changes that occur, and the utility of pairing thick-tissue histology with appropriate optical clearing solutions.

Dataset Information

Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array.

Publications

Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets