RESUMO
Assessment of corticospinal excitability (CSE) is an essential component of experiments designed to induce or study neuronal plasticity in the motor system. Common examples are paired associative stimulation (PAS), theta-burst stimulation (TBS), intensive motor training, or any methods aimed at potentiating the corticomotor system in the hope of promoting better recovery after neurological insult. To date, rodent models of CSE assessment have mostly been completed under anaesthesia, which greatly affects the level of CSE, as well as the mechanisms of plasticity. Experiments in awake animals are difficult because the ongoing state of behavior affects the excitability of the motor system and complicates the assessment of CSE. To address this issue, we have designed a novel approach for CSE assessment in awake behaving rodents, enabling a reliable measure of evoked motor responses obtained from cortical microstimulation in repeatable conditions of ongoing motor activity. The system relies on chronically implanted intracortical and intramuscular electrodes and a custom-made software control system, enabling the user to require that precise parameters of EMG activity be met before cortical stimulation probes are delivered. This approach could be used for further studies of PAS, TBS or other interventions requiring the assessment of CSE under repeatable conditions. We provide fabrication schematics and a list of materials for the implant, as well as instructions for running a custom-made MATLAB codebase, customizing the PAS protocol, and performing the complete analysis of experimental data. We hope these tools can further facilitate animal research in the field of neuroplasticity and neurorehabilitation.
RESUMO
OBJECTIVEArtificial neural networks (ANNs) have shown considerable promise as decision support tools in medicine, including neurosurgery. However, their use in concussion and postconcussion syndrome (PCS) has been limited. The authors explore the value of using an ANN to identify patients with concussion/PCS based on their antisaccade performance.METHODSStudy participants were prospectively recruited from the emergency department and head injury clinic of a large teaching hospital in Toronto. Acquaintances of study participants were used as controls. Saccades were measured using an automated, portable, head-mounted device preprogrammed with an antisaccade task. Each participant underwent 100 trials of the task and 11 saccade parameters were recorded for each trial. ANN analysis was performed using the MATLAB Neural Network Toolbox, and individual saccade parameters were further explored with receiver operating characteristic (ROC) curves and a logistic regression analysis.RESULTSControl (n = 15), concussion (n = 32), and PCS (n = 25) groups were matched by age and level of education. The authors examined 11 saccade parameters and found that the prosaccade error rate (p = 0.04) and median antisaccade latency (p = 0.02) were significantly different between control and concussion/PCS groups. When used to distinguish concussion and PCS participants from controls, the neural networks achieved accuracies of 67% and 72%, respectively. This method was unable to distinguish study patients with concussion from those with PCS, suggesting persistence of eye movement abnormalities in patients with PCS. The authors' observations also suggest the potential for improved results with a larger training sample.CONCLUSIONSThis study explored the utility of ANNs in the diagnosis of concussion/PCS based on antisaccades. With the use of an ANN, modest accuracy was achieved in a small cohort. In addition, the authors explored the pearls and pitfalls of this novel approach and identified important future directions for this research.
