Spatial co-adaptation of cortical control columns in a micro-ECoG brain-computer interface.

OBJECTIVE: Electrocorticography (ECoG) has been used for a range of applications including electrophysiological mapping, epilepsy monitoring, and more recently as a recording modality for brain-computer interfaces (BCIs). Studies that examine ECoG electrodes designed and implanted chronically solely for BCI applications remain limited. The present study explored how two key factors influence chronic, closed-loop ECoG BCI: (i) the effect of inter-electrode distance on BCI performance and (ii) the differences in neural adaptation and performance when fixed versus adaptive BCI decoding weights are used. APPROACH: The amplitudes of epidural micro-ECoG signals between 75 and 105 Hz with 300 µm diameter electrodes were used for one-dimensional and two-dimensional BCI tasks. The effect of inter-electrode distance on BCI control was tested between 3 and 15 mm. Additionally, the performance and cortical modulation differences between constant, fixed decoding using a small subset of channels versus adaptive decoding weights using the entire array were explored. MAIN RESULTS: Successful BCI control was possible with two electrodes separated by 9 and 15 mm. Performance decreased and the signals became more correlated when the electrodes were only 3 mm apart. BCI performance in a 2D BCI task improved significantly when using adaptive decoding weights (80%-90%) compared to using constant, fixed weights (50%-60%). Additionally, modulation increased for channels previously unavailable for BCI control under the fixed decoding scheme upon switching to the adaptive, all-channel scheme. SIGNIFICANCE: Our results clearly show that neural activity under a BCI recording electrode (which we define as a 'cortical control column') readily adapts to generate an appropriate control signal. These results show that the practical minimal spatial resolution of these control columns with micro-ECoG BCI is likely on the order of 3 mm. Additionally, they show that the combination and interaction between neural adaptation and machine learning are critical to optimizing ECoG BCI performance.

A chronic generalized bi-directional brain-machine interface.

A bi-directional neural interface (NI) system was designed and prototyped by incorporating a novel neural recording and processing subsystem into a commercial neural stimulator architecture. The NI system prototype leverages the system infrastructure from an existing neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing predicate therapy capabilities, the device adds key elements to facilitate chronic research, such as four channels of electrocortigram/local field potential amplification and spectral analysis, a three-axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom-integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in vivo non-human primate model for brain control of a computer cursor (i.e. brain-machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinson's disease). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques have the potential to be generalized beyond motor prosthesis, and are being explored for unmet needs in other neurological conditions such as movement disorders, stroke and epilepsy.

A novel early-stage orthotopic model for ovarian cancer in the Fischer 344 rat.

The purpose of our study was to ascertain the progression of metastases in a novel ovarian cancer model designed to mimic early-stage disease by utilizing an orthotopic injection technique. Female Fischer 344 rats were injected with either 10(4) or 10(5) NuTu-19 cells by intraperitoneal or orthotopic injection. Peritoneal washings and histologic specimens were examined to correlate the incidence and extent of tumor growth. In a second phase, orthotopic injections of 10(2) and 10(3) cells were compared to that of 10(4) cells. Progression of ovarian cancer was observed by gross and microscopic examinations in both intraperitoneal and orthotopic models. Pelvic extension and abdominal adhesions uniquely characterized the orthotopically injected animals. Numbers of identifiable metastases declined with lower cell inocula, confirming that early-stage disease was extended to at least 14 days with 10(2) NuTu-19 cells. The orthotopic ovarian cancer model emulates early disease with the initiation of a primary tumor that is localized within the inherent microenvironment. The orthotopic model offers a clinically relevant alternative for future cancer research that allows for the investigation of therapeutic strategies against early stages of the disease process.