A study on adaptive stimulation of the basal ganglia as a treatment for Parkinsonism

The purpose of this dissertation is to design an automated system for the modification of Deep Brain Stimulation (DBS) parameters based on specific identifiers in the neuronal response of Parkinsonian patients undergoing DBS treatment. The neural response patterns are obtained from an artificial neural network consisting of dynamic neuron and synapse components and programmed to exhibit a response to pulse stimuli that resembles the activity in the subthalamic nucleus of Parkinsonian patients undergoing DBS treatment. Moreover, using pulse stimuli of varying specification, a band-pass filtered response of the network is subjected to a set of signal processing techniques including Linear Predictive Coding (LPC), Autoregressive Moving Average (ARMA) modeling, Discrete Fourier Transform (DFT), moments and higher order statistics, producing a set of results or features. Then, each feature is evaluated to determine the effectiveness, in terms of error probability, of discerning between different neuronal responses to pulse stimuli. Furthermore, a digital circuit is designed at the transistor level for computing the 1st LPC coefficient of recorded neural data and also autonomously regulating the specifications of the stimulus waveform based on the value of the computed coefficient. Also, the circuit design is optimized using a pipeline to reduce dynamic power dissipation. Moreover, it is suggested that a similar design may be useful in automating the administration of DBS as a treatment for Parkinsonism with only a minimal additional power demand.Ph.D.Includes bibliographical references (p. 181-192)

An in vitro method to manipulate the direction and functional strength between neural populations.

We report the design and application of a Micro Electro Mechanical Systems (MEMs) device that permits investigators to create arbitrary network topologies. With this device investigators can manipulate the degree of functional connectivity among distinct neural populations by systematically altering their geometric connectivity in vitro. Each polydimethylsilxane (PDMS) device was cast from molds and consisted of two wells each containing a small neural population of dissociated rat cortical neurons. Wells were separated by a series of parallel micrometer scale tunnels that permitted passage of axonal processes but not somata; with the device placed over an 8 × 8 microelectrode array, action potentials from somata in wells and axons in microtunnels can be recorded and stimulated. In our earlier report we showed that a one week delay in plating of neurons from one well to the other led to a filling and blocking of the microtunnels by axons from the older well resulting in strong directionality (older to younger) of both axon action potentials in tunnels and longer duration and more slowly propagating bursts of action potentials between wells. Here we show that changing the number of tunnels, and hence the number of axons, connecting the two wells leads to changes in connectivity and propagation of bursting activity. More specifically, the greater the number of tunnels the stronger the connectivity, the greater the probability of bursting propagating between wells, and shorter peak-to-peak delays between bursts and time to first spike measured in the opposing well. We estimate that a minimum of 100 axons are needed to reliably initiate a burst in the opposing well. This device provides a tool for researchers interested in understanding network dynamics who will profit from having the ability to design both the degree and directionality connectivity among multiple small neural populations