Modular auditory decision-making behavioral task designed for intraoperative use in humans.

BACKGROUND: Neurosurgical interventions that require active patient feedback, such as deep brain stimulation surgery, create an opportunity to conduct cognitive or behavioral experiments during the acquisition of invasive neurophysiology. Optimal design and implementation of intraoperative behavioral experiments require consideration of stimulus presentation, time and surgical constraints. We describe the use of a modular, inexpensive system that implements a decision-making paradigm, designed to overcome challenges associated with the operative environment. NEW METHOD: We have created an auditory, two-alternative forced choice (2AFC) task for intraoperative use. Behavioral responses were acquired using an Arduino based single-hand held joystick controller equipped with a 3-axis accelerometer, and two button presses, capable of sampling at 2 kHz. We include designs for all task relevant code, 3D printed components, and Arduino pin-out diagram. RESULTS: We demonstrate feasibility both in and out of the operating room with behavioral results represented by three healthy control subjects and two Parkinson's disease subjects undergoing deep brain stimulator implantation. Psychometric assessment of performance indicated that the subjects could detect, interpret and respond accurately to the task stimuli using the joystick controller. We also demonstrate, using intraoperative neurophysiology recorded during the task, that the behavioral system described here allows us to examine neural correlates of human behavior. COMPARISON WITH EXISTING METHODS: For low cost and minimal effort, any clinical neural recording system can be adapted for intraoperative behavioral testing with our experimental setup. CONCLUSION: Our system will enable clinicians and basic scientists to conduct intraoperative awake and behaving electrophysiologic studies in humans.

Cerebral mechanisms of general anesthesia.

How does general anesthesia (GA) work? Anesthetics are pharmacological agents that target specific central nervous system receptors. Once they bind to their brain receptors, anesthetics modulate remote brain areas and end up interfering with global neuronal networks, leading to a controlled and reversible loss of consciousness. This remarkable manipulation of consciousness allows millions of people every year to undergo surgery safely most of the time. However, despite all the progress that has been made, we still lack a clear and comprehensive insight into the specific neurophysiological mechanisms of GA, from the molecular level to the global brain propagation. During the last decade, the exponential progress in neuroscience and neuro-imaging led to a significant step in the understanding of the neural correlates of consciousness, with direct consequences for clinical anesthesia. Far from shutting down all brain activity, anesthetics lead to a shift in the brain state to a distinct, highly specific and complex state, which is being increasingly characterized by modern neuro-imaging techniques. There are several clinical consequences and challenges that are arising from the current efforts to dissect GA mechanisms: the improvement of anesthetic depth monitoring, the characterization and avoidance of intra-operative awareness and post-anesthesia cognitive disorders, and the development of future generations of anesthetics.

Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.

During 0.1-0.2% of operations with general anesthesia, patients become aware during surgery. Unfortunately, pharmacologically paralyzed patients cannot seek attention by moving. Their attempted movements may however induce detectable EEG changes over the motor cortex. Here, methods from the area of movement-based brain-computer interfacing are proposed as a novel direction in anesthesia monitoring. Optimal settings for development of such a paradigm are studied to allow for a clinically feasible system. A classifier was trained on recorded EEG data of ten healthy non-anesthetized participants executing 3-second movement tasks. Extensive analysis was performed on this data to obtain an optimal EEG channel set and optimal features for use in a movement detection paradigm. EEG during movement could be distinguished from EEG during non-movement with very high accuracy. After a short calibration session, an average classification rate of 92% was obtained using nine EEG channels over the motor cortex, combined movement and post-movement signals, a frequency resolution of 4 Hz and a frequency range of 8-24 Hz. Using Monte Carlo simulation and a simple decision making paradigm, this translated into a probability of 99% of true positive movement detection within the first two and a half minutes after movement onset. A very low mean false positive rate of <0.01% was obtained. The current results corroborate the feasibility of detecting movement-related EEG signals, bearing in mind the clinical demands for use during surgery. Based on these results further clinical testing can be initiated.