Description: Despite the existence of a broad spectrum of anti-epileptic drugs (AEDs), fully 1/3 of the world’s 60 million patients with epilepsy have uncontrollable seizures and life-threatening emergencies such as status epilepticus (SE), as well as sudden unexpected death in epilepsy (SUDEP), indicative of the need for better understanding of the disorder and subsequent development of new treatments for it. We apply novel information concepts and multi-modal spatio-temporal signal processing technologies that hold great promise for better understanding of the mechanisms and diagnosis of epilepsy, and the development and evaluation of the efficacy of new anti-epileptic treatments. The REU trainees will be able to analyze electrochemical recordings, long-term electroencephalographic (EEG), and short-term magnetoencephalographic (MEG) recordings, in simulation models, animal models and patients with epilepsy. Examples include improved identification of the epileptogenic focus from seizure-free periods, differential diagnosis (e.g. epilepsy vs. metabolic encephalopathy), real-time seizure prediction and closed- loop seizure control via neuromodulation, real-time evaluation of AEDs in emergency situations (e.g. SE), as well as susceptibility to SUDEP by shedding light on the impairment of the communication of the epileptic brain with other vital organs (e.g. the heart).
Description: This project includes the experimental acquisition of intracellular calcium changes in brain cells via fluorescence microscopy, and subsequent molecular and cellular mathematical modeling, analysis, and simulation using appropriate methods. A student on this project will utilize acquired data sets, and integrate newly collected data into a growing database of cellular responses in a brain cell library as part of this project. One-two positions are open for this project. The student will gain an appreciation of live-imaging microscopy, image analysis, and cellular-biochemical signalling in brain cells.
Description: This project seeks to develop a highly multiplexed microwire microbiosensor array to detect a suite of neurochemicals of importance in brain slice model. The specific aim of the project is to engineer a metal microwire based sensor that can detect GABA, glutamate, dopamine and field potentials simultaneously in real time. Using various electrochemical techniques (voltammetry, amperometry, impedance spectroscopy), the various sensor metrics such as sensitivity, selectivity and reproducibility will be investigated. The student participating in this project will learn about surface modifications of microelectrodes, electrochemical techniques, surface characterization techniques, engineering aspects of designing and microfabricating sensors and signal analysis.
Description: This project will provide opportunities to (1) fabricate electrodes to record electroencephalographic (EEG) activity from inside the brains of epileptic rats, (2) assist with surgeries to implant these electrodes, (3) acquire EEG and neurochemical recordings from inside the brain. Additional opportunities for hands-on experience are available, including rodent behavioral experiments, multiphoton microscopy imaging of brain cells, and immunohistochemistry. REU students will also attend lab meetings to gain a more complete experience of working in a research lab. These experiences are designed to introduce undergraduates to a variety of useful methods and advanced tools in brain research.
Description: The post-ictal period is generally accompanied by a suppression of cortical EEG, during which recent studies suggest single neuron firing rates in both penumbral and core epileptic zones return to pre-ictal firing patterns. Little attention has been paid to the post-ictal period’s potential for impacting cognitive function, although MRI studies have suggested that post-ictal periods can be accompanied by reversible abnormalities in areas of the brain important to memory function such as the hippocampus, basal ganglia and cerebellum. This project will investigate this issue using both clinical and hdEEG together with ECoG when available.
Description: Sleep is a reversible period of disengagement from and lack of response to the external environment. While there is still disagreement over the fundamental reason for sleep, it is clear that sleep loss frequently leads to decreased cognitive function, and increased morbidity. Sleep disorders are common in epilepsy patients, with insomnia reported by up to 52% and daytime sleepiness by up to 70% of subjects. This project will analyze results of a self-report assessment of sleep quality pre- and post-surgically in subjects enrolled in the EPSCoR project interested in participating in this adjunct study, assessing the association of sleep loss and post-surgical cognitive function based upon experimental tasks.
Description: This project will evaluate memory function in patients with epilepsy and healthy controls as we acquire hdEEG data. The goal of the project is to determine differences in brain activity during performance of memory tasks between epilepsy patients and controls where the degree to which patients are well controlled on their medications differs between individuals. It will include participation in data acquisition in enrolled subjects and analysis of evoked brain responses to the presentation of memory tasks. This study will also collect a brief neuropsychological battery that will be used in combination with the EEG data to evaluate memory performance in these two groups. Data will be acquired at two time points for epilepsy patients (study entry and 3 months later) and at one time point for healthy controls (study entry).
Description: This project will focus on obtaining and analyzing image data containing electrode positions in patients undergoing intracranial monitoring. The purpose of this project is to localize electrode positions so as to define the anatomical substrates of iEEG signals obtained during monitoring periods. This project requires programming experience.
Description: Studies have shown that EEG data can be modeled as a set of repeating quasi-stable spatial topographies known as microstates that occur at a rate compatible with the speed of human brain information processing. These EEG scalp topographies are a much more direct measure of the momentary global state of the brain than the frequency power over a certain scalp area. Students will learn the pre-processing techniques using the open source software Brainstorm, which is a suite for processing EEG/MEG data, with integration of MRI information. Students will learn to identify pre-ictal and post-ictal periods and the calculation of microstates in different ictal periods using a Matlab toolbox.
Description: Studies have been shown that brain functions are achieved with simultaneous oscillations in different frequency bands. This interaction between oscillations is also known as cross-frequency coupling (CFC) and can be useful in understanding brain function. Phase amplitude coupling (PAC) is when the phase of the lower frequency oscillation drives the power of the coupled higher frequency oscillation. Electrophysiological studies in humans and animals have shown a role of oscillatory activity and cross-frequency interactions for both motor control and memory functions. The students will learn the pre-processing techniques for artifacts removal and calculation of temporal PAC maps during pre-ictal, ictal and post-ictal periods from MEG data.
Description: Measures of functional connectivity quantify statistical dependencies between neuronal signals. Phase amplitude coupling (PAC) is when the phase of the lower frequency oscillation drives the power of the coupled higher frequency oscillation. The purpose of this research is to calculate and correlated the connectivity and phase amplitude coupling (PAC) metrics during pre-ictal and post-ictal periods from intracranial EEG data. The students will learn the pre-processing, connectivity and PAC techniques using the open source software Brainstorm, which is a suite for processing EEG/MEG data, with integration of MRI information.
Description: This study will examine the predictive value of both CVLT-II and the verbal paired associates task from WMS to determine which measure best predicts left hemisphere function in epilepsy patients who are candidates for respective surgery. The study will then examine the correlation of predicted function with the clinical determination of the seizure focus, and effects of resection on that correlation.
Description: It is not often appreciated, but the neural retina is part of the central nervous system. It is literally part of the brain. Increasingly many researchers are looking at how the neural retina functions in various cognitive disorders - such as Alzheimer’s and schizophrenia - and there are many correlates of central cognitive dysfunction that can be seen in the retina. Most such studies use the electrical response of the retina: the electroretinogram ("ERG"). This study will explore retinal function via magnetoencephalography (“MEG”), a method using superconducting sensors to record the tiny magnetic fields that the neurons inside the brain generate when they are activated. MEG is usually used to record activity in the cerebral cortex, but can also pick up retinal signals. Stimulus-evoked MEG retinal and visual cortical signals will be recorded from normal volunteers, and possibly also from patients with schizophrenia. The use of MEG to explore retinal function is relatively novel and may lead to advances in our understanding of brain function in health and disease.
Description: Towards meeting the training goals of the EPSCoR program and NeuroNEM project, interested students will have the opportunity for both clinical and research experiences in the area of neuropsychology specifically related to epilepsy. There will be ample opportunity to learn clinical neuropsychological assessment and conceptualization approaches in epilepsy pre- surgery evaluation. Students will have the opportunity to participate in a NeuroNEM related research project examining cognition and memory function in our UAB NeuroNEM study presurgery participants.
Description: Activation of nerve cells in different regions of the brain occur when a person performs any cognitive task such as reading, memory or attention. These areas are activated sequentially and in parallel over milliseconds in a way that is specific to the task performed. This study uses magnetoencephalography ("MEG"), a method using superconducting sensors to record the tiny magnetic fields that the neurons inside the brain generate when they are activated. MEG data from normal volunteers and patients with epilepsy will be analyzed to explore the dynamics of brain activation during resting state and cognitive processing. Understanding of cognitive brain processes requires information about activation timing within and between different brain sites. Such data can be obtained by MEG that tracks cortical activation sequences with a millisecond temporal accuracy. This method can also be used for localizing important brain functions, such as speech centers in patients with medication-resistant epilepsy to avoid injury to these areas during epilepsy surgery.
Description: Epilepsy is a neurological disorder in which excessive and synchronous neuronal activity in the brain causes episodes of disrupted movement, memory, and consciousness, known as seizures. Although a strong genetic component for epilepsy is known, stroke or traumatic brain injury (TBI) can also lead to epilepsy. Likewise, TBI alone (i.e., without epilepsy) is often characterized by alterations in memory and the ability to regulate top-down processes (i.e., cognitive control). This study uses functional ("fMRI") and diffusion (“dMRI”) magnetic resonance imaging to compare brain function and structure between 1) TBI alone and 2) TBI with epilepsy groups to assess changes within information processing networks that subserve memory and cognitive functions. The students will learn pre-processing, connectivity, and statistical techniques using FSL, SPM, and AFNI software, which are suites for processing and comparing the MRI data.