Mark H. Myers

Analysis of phase transitions in KIV with amygdala during simulated navigation control

A biologically inspired dynamical neural network model called KIV is used in this work to design autonomous agents. The KIV set models the vertebrate limbic system. Previous studies indicated that KIV is able to provide a control algorithm for navigation and decision-making for autonomous mobile agents. In this work we use Hilbert transform to capture global synchronized spatio-temporal patterns of amplitude modulation in KIV. We identify phase transition in the simulated amygdala and show that it shares several important features of EEC signals.

A Feedback Control Approach to Organic Drug Infusions Using Electrochemical Measurement

Goal: Target-controlled infusion of anesthesia is a closed-loop automated drug delivery method with a computer-aided control. Our goal is to design and test an automated drug infusion platform for propofol delivery in total intravenous anesthesia (TIVA) administration. Methods: In the proposed method, a dilution chamber with first-order exponential decay characteristics was used to model the pharmacodynamics decay of a drug. The dilution chamber was connected to a flow system through an electrochemical cell containing an organic film-coated glassy carbon electrode as working electrode. To set up the feedback-controlled delivery platform and optimize its parameters, ferrocene methanol was used as a proxy of the propofol. The output signal of the sensor was connected to a PI controller, which prompted a syringe pump for feedback-controlled drug infusion. Results: The result is a bench-top drug infusion platform to automate the delivery of a propofol based on the measurement of concentration with an organic film-coated voltammetric sensor. Conclusion: To evaluate the performance characteristics of the infusion platform, the propofol concentration in the dilution chamber was monitored with the organic film-coated glassy carbon electrode and the difference between the set and measured concentrations was assessed. The feasibility of measurement-based feedback-controlled propofol delivery is demonstrated and confirmed. Significance: This platform will contribute to high-performance TIVA application of intravenous propofol anesthesia.

Seizure prediction through dynamic synchronization measures of neural populations

Recent studies have focused on the phenomena of abnormal electrical brain activity which may transition into a debilitating seizure state through the entrainment of large populations of neurons. Starting from the initial epileptogenisis of a small population of abnormally firing neurons, to the mobilization of mesoscopic neuron populations behaving in a synchronous manner, a prediction methodology has been formulated that compares the initial epileptogenisis to distant neuron populations. As two neuron populations begin to operate in a synchronized manner, the respective signals phase lock, manifesting into a seizure state. The normal non-linear dynamic signal captured through an EEG enters a semi-periodic state, which can be quantified into a seizure state. A method for capturing synchronous behavior of the pathological brain state is described. An individual patient based phase-locking threshold is introduced for seizure prediction and for differentiating seizure and non-seizure states.

Ambulatory Seizure Monitoring: From Concept to Prototype Device

Background: The brain, made up of billions of neurons and synapses, is the marvelous core of human thought, action and memory. However, if neuronal activity manifests into abnormal electrical activity across the brain, neural behavior may exhibit synchronous neural firings known as seizures. If unprovoked seizures occur repeatedly, a patient may be diagnosed with epilepsy. Purpose: The scope of this project is to develop an ambulatory seizure monitoring system that can be used away from a hospital, making it possible for the user to stay at home, and primary care personnel to monitor a patients seizure activity in order to provide deeper analysis of the patients condition and apply personalized intervention techniques. Methods: The ambulatory seizure monitoring device is a research device that has been developed with the objective of acquiring a portable, clean electroencephalography (EEG) signal and transmitting it wirelessly to a handheld device for processing and notification. Result: This device is comprised of 4 phases: acquisition, transmission, processing and notification. During the acquisition stage, the EEG signal is detected using EEG electrodes; these signals are filtered and amplified before being transmitted in the second stage. The processing stage encompasses the signal processing and seizure prediction. A notification is sent to the patient and designated contacts, given an impending seizure. Each of these phases is comprised of various design components, hardware and software. The experimental findings illustrate that there may be a triggering mechanism through the phase lock value method that enables seizure prediction. Conclusion: The device addresses the need for long-term monitoring of the patients seizure condition in order to provide the clinician a better understanding of the seizures duration and frequency and ultimately provide the best remedy for the patient.

Modeling normal/epileptic brain dynamics with potential application in titration therapy

The KIV (K-4) model is based on biological attributes found in the limbic system of a salamander. Higher forms of organisms including humans have a limbic system which incorporates the sensory cortex, hippocampus and entorhinal cortex/amygdala of the brain. The KIV model has been used successfully for classification and prediction tasks. We propose the use of the KIV model as a metaphor of the limbic system of the human brain. The brain states of normal /pathological (seizure)/restoration are modeled to further understand the pathological states of the brain and propose a titration therapy through this model.

Phase cone detection optimization in EEG data

Signals measured by electroencephalogram (EEG) arrays were decomposed using Hubert Transformations to produce the spatial amplitude and phase modulation (AM and PM) patterns. Spatial PM patterns intermittently exhibit synchronization-desynchronization transitions. During desynchronization, the spatial PM patterns intermittently conform to conic shapes. These phase cones mark the onset of emergent AM patterns, which carry cognitive content. In this work, various temporal band pass filters were applied to study the frequency dependence of phase cones in the beta-gamma range (10-40 Hz). The results are interpreted in the context of the cognitive cycle of knowledge generation.

Studies on Synchronization Using KIV Model

The KIV model is a biologically inspired neural network that can exhibit non-linear electrical brain activity found in the limbic system of the brain. One such behavior exhibited in brain activity is cognitive processing. Spatial patterns of beta-gamma EEG emerged following sudden jumps in cortical activity called “phase transitions”. The interaction between entorhinal cortex (EC), amygdala, and hippocampal and cortical areas in vertebrate brains is studied using the dynamical K model approach. Other biological attributes defined by Freeman et al. [1] are applied to display the biological relevance of the KIV model.

Machine Learning Techniques in Detecting of Pulmonary Embolisms

Computer Aided Detection (CAD) systems have recently been used by physicians to help automatically detect early forms of breast cancer in X-ray images, lung nodules in lung CT images, and polyps in colon CT images. We discuss an automatic detection mechanism using a genetic algorithms (GA) approach to identify and classify Pulmonary Embolisms (PE) captured through Computed Tomography Angiography (CTA). Our method enhances the performance of the classification of diseases as compared to other methodologies discussed in this paper.

Spatial Directionality Found in Frontal-Parietal Attentional Networks

Research in last few years on neurophysiology focused on several areas across the cortex during cognitive processing to determine the dominant direction of electrical activity. However, information about the frequency and direction of episodic synchronization related to higher cognitive functions remain unclear. Our aim was to determine whether neural oscillations carry perceptual information as spatial patterns across the cortex, which could be found in the scalp EEG of human subjects while being engaged in visual sensory stimulation. Magnitude squared coherence of neural activity during task states that “finger movement with Eyes Open (EO) or Eyes Wandering (EW)” among all electrode combinations has the smallest standard deviation and variations. Additionally, the highest coherence among the electrode pairs occurred between alpha (8-12 Hz) and beta (12-16 Hz) ranges. Our results indicate that alpha rhythms seem to be regulated during activities when an individual is focused on a given task. Beta activity, which has also been implicated in cognitive processing to neural oscillations, is seen in our work as a manner to integrate external stimuli to higher cognitive activation. We have found spatial network organization which served to classify the EEG epochs in time with respect to the stimuli class. Our findings suggest that cortical neural signaling utilizes alpha-beta phase coupling during cognitive processing states, where beta activity has been implicated in shifting cognitive states. Significance. Our approach has found frontoparietal attentional mechanisms in shifting brain states which could provide new insights into understanding the global cerebral dynamics of intentional activity and reflect how the brain allocates resources during tasking and cognitive processing states.

Mesoscopic neuron population modeling of normal/epileptic brain dynamics

Simulations of EEG data provide the understanding of how the limbic system exhibits normal and abnormal states of the electrical activity of the brain. While brain activity exhibits a type of homeostasis of excitatory and inhibitory mesoscopic neuron behavior, abnormal neural firings found in the seizure state exhibits brain instability due to runaway oscillatory entrained neural behavior. We utilize a model of mesoscopic brain activity, the KIV model, where each network represents the areas of the limbic system, i.e., hippocampus, sensory cortex, and the amygdala. Our model initially demonstrates oscillatory entrained neural behavior as the epileptogenesis, and then by increasing the external weights that join the three networks that represent the areas of the limbic system, seizure activity entrains the entire system. By introducing an external signal into the model, simulating external electrical titration therapy, the modeled seizure behavior can be ‘rebalanced’ back to its normal state.