Medicine (Emery Brown, organizer)

Loren Frank (UCSF)
Adaptive Analysis of Neural Plasticity in the Hippocampal System

Thursday 4:00-4:30, Fountain I

Abstract:

The ability to use experience to guide behavior (to learn) is one of the central functions of the brain. The hippocampal formation, a set of structures in the medial temporal lobe, is particularly important for learning about complex relationships, and is essential for creating new memories about facts and events in humans. We have combined spatial tasks, large scale multielectrode recordings from awake, behaving animals and advanced analytical techniques to understand how neural activity evolves during learning. In particular, we studied the formation of new place representations in rats by examining the changes in place-specific firing of neurons in the CA1 region of the hippocampus and the relationship between these changes and behavioral change across multiple days of exposure to novel places. Using adaptive analysis techniques that make it possible to quantify the moment-by-moment changes in neural activity, we found that many neurons showed very rapid changes on the first day of exposure to the novel place, including many cases in which a previously silent neuron developed place specific activity over the course of a single pass through the environment. Across the population, the largest changes in neural activity occurred on day 2 of exposure to a novel place, but only if the animal had little experience (< 4 min) in that location on day 1. Longer exposures on day 1 were associated with smaller changes on day 2, suggesting that hippocampal neurons required five to six minutes of experience to form a stable spatial representation. Even after the representation stabilized, the animals' behavior remained different in the novel places, suggesting that other brain regions continued to distinguish novel from familiar locations. These results show that the hippocampus can form new spatial representations quickly but that stable hippocampal representations are not sufficient for a place to be treated as familiar. Our findings also illustrate how advanced analytical techniques can be used to quantify dynamic neural activity in the brain.


Matti Hamalainen (Massachusetts General Hospital)
Dynamic Imaging of Brain Function with MEG and EEG

Thursday 4:30-5:00, Fountain I

Abstract:

Timing is essential for proper brain functioning. Magneto-encephalo-graphy (MEG) and electroencephalo-graphy (EEG) are at present the only noninvasive human brain imaging tools that provide submillisecond temporal accuracy and thus help to unravel dynamics of cortical function. MEG and EEG reflect the electrical currents in neurons directly, rather than the associated hemodynamic or metabolic effects. Unfortunately, the underlying current distribution cannot be recovered uniquely, even if the magnetic field (MEG) and the electric potential (EEG) were precisely known everywhere outside the head. Therefore, appropriate constraints must be applied to facilitate the solution. The current distribution can be modelled either by a constellation of discrete focal sources or by a continuous distribution. In both approaches an accurate forward field computation model is required to predict signals generated by a given source cur-rent distribution. The computational complexity of the MEG and EEG source estimation problem is due to two basic factors. First, the data to be modelled consist of 300-400 channels sampled at a rate of 200-2000 Hz acquired for a period of 1-60 minutes. In evoked response studies, averaging is often employed to reduce the number of data samples to less 10,000. However, analysis of continuous raw data is becoming more and common to reveal fine details of brain dynamics and to compute the statistics of the current estimates. Second, an accurate forward field and potential calculation requires numerical solution of the Maxwell's equations using either the Boundary-Element Method (BEM), the Finite-Element Method (FEM), or the Finite-Difference Method (FDM) and has to be repeated for hundreds or thousands of elementary sources.


Nicho Hatsopoulos (University of Chicago)
Current Developments in a Cortically Controlled Brain Machine Interface

Thursday 5:00-5:30, Fountain I

Abstract:

Over the past ten years, we have tested and helped develop a multi-electrode array for chronic cortical recordings in behaving non-human primates. We have found that it is feasible to record from dozens of single units in the motor cortex for extended periods of time and that these signals can be decoded in a closed-loop, real-time system to generate goal-directed behavior of external devices. This work has culminated in a FDA clinical trial that has demonstrated that a tetraplegic patient can voluntarily modulate motor cortical activity in order to move a computer cursor to visual targets. Further advances in BMI technology using non-human primates have focused on using multiple modes of control from signals in different cortical areas. We demonstrate that primary motor cortical activity may be optimized for continuous movement control whereas signals from the premotor cortex may be better suited for discrete target selection. We propose a hybrid BMI whereby decoding can be voluntarily switched from discrete to continuous control modes.