Using fMRI, EEG, neurophysiologic recordings, microdialysis methods, and mathematical modeling, my laboratory collaborates with investigators from MGH, Harvard, MIT, and Boston University to use a systems neuroscience approach in studying how the state of general anesthesia is induced and maintained. The long-term goal of this research is to establish a neurophysiological definition of anesthesia; safer, site-specific anesthetic drugs; and to develop better neurophysiologically-based methods for measuring depth of anesthesia.

Recent technological and experimental advances in the capabilities to record signals from neural systems have led to an unprecedented increase in the types and volume of data collected in neuroscience experiments and hence, in the need for appropriate techniques to analyze them. Therefore, using combinations of likelihood, Bayesian, state space, time-series and point process approaches, a primary focus of the research in my laboratory is the development of statistical methods and signal-processing algorithms for neuroscience data analysis.

We have used our methods to:

• characterize how hippocampal neurons represent spatial information in their ensemble firing patterns.
• analyze formation of spatial receptive fields in the hippocampus during learning of novel environments.
• relate changes in hippocampal neural activity to changes in performance during procedural learning.
• improve signal extraction from fMR imaging time-series.
• characterize the spiking properties of neurons in primary motor cortex.
• localize dynamically sources of neural activity in the brain from EEG and MEG recordings made during cognitive, motor and somatosensory tasks.
• measure the period of the circadian pacemaker (human biological clock) and its sensitivity to light.
• characterize the dynamics of human heart beats in physiological and pathological states.