Description: This project will deal with the brain networks responsible for the occurrence of epileptic seizures in patients with focal epilepsy and epileptic rodents. Long-term (weeks to months) multi-electrode continuous electroencephalographic (EEG) recordings in humans and animals, and short-term (hours) magnetoencephalographic (MEG) recordings will be analyzed with state-of-the-art signal processing techniques for localization of the epileptogenic focus and its network. The relation of the epileptic networks to brain's memory networks will also be investigated. It is expected that a) the spatial overlap between the identified memory and epileptic networks would lead to useful biomarkers for prospective quantification of memory impairment for patients undergoing surgical removal of their epileptogenic focus and b) the measured spatio-temporal dynamics of the identified epileptic networks over time would contribute to improvement of long-term prediction of upcoming crises (seizures) and result to the development of responsive, truly intelligent, implanted neurostimulators for treatment of epilepsy. The student(s) participating in this project will learn about brain signal analysis in the time and frequency domain, apply commercial as well as in-house developed MatLab algorithms, aspects of epileptic brain physiology, and gain skills in data mining of unique, big, multi-dimensional physiological databases.
Description: This REU project focuses on the topic of neurochemical signaling. Dr. Iasemidis will mentor and supervise students in the analysis of chemical (Glutamate and Dopamine) signals recorded from the brain of rodents using mathematical procedures that will range from simple spectral to advanced non-linear time series analysis techniques. The neurochemical signals will be provided by the Advanced Materials Research Laboratory of Dr. Arumugam, where the student will also be familiarized with research topics related to nanomaterials, chemical sensing and microfabrication. The objective of this project is to generate new information regarding the brain's chemistry and how disease can alter this. The students participating in this project will gain knowledge of data acquisition and learn how to use mathematical and statistical procedures to do data analysis, results production, and results verification.
Description: This project includes the experimental acquisition of intracellular calcium changes in brain cells via fluorescence microscopy, and subsequent molecular and cellular mathematical modeling and simulation using appropriate numerical methods. We will apply the theory for spatially distributed dynamical systems in this project to develop system level models and perform subsequent simulation for epileptic seizure identification and prediction. A student on this project will utilize acquired data sets and fundamental mathematics from calculus and differential equations to quantify brain cell responses to neurochemical stimuli coordinating software, hardware, and mathematical methods to develop such models.
Description: This project will provide (1) training in the making of electrodes to record electroencephalographic (EEG) activity from the brains of epileptic rats, (2) an opportunity to assist with surgeries to implant these electrodes, (3) and hands-on experience in acquiring EEG recordings from inside the brain. Additional opportunities to participate in rodent behavioral experiments, multiphoton microscopy sessions to image brain cells in live rodents, examination of brain tissue (histology), and other research techniques will be provided. REU students will also attend weekly lab meetings to have 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: This project seeks to investigate the long-term performance of chemical microsensors in the detection of two neurochemicals (glutamate, GLU and dopamine, DA) in three conditions: 1) standard buffers, 2) cell culture and 3) rodents. The two specific aims of the project are to engineer (a) CNT electrode scaffolds and nafion coatings that can detect dopamine with high sensitivity and selectivity and (b) novel polymer matrix that can significantly extend the lifetime of enzyme activity, a grand hindrance in developing biosensors for chronic chemical monitoring. Using various electrochemical techniques (voltammetry, amperometry, impedance spectroscopy), GLU and DA will be detected and electrode surface fouling mechanisms that affects long-term sensor life will be investigated. The students participating in this project will learn about surface modifications of metal microelectrodes with CNTs, polymers and enzymes, electrochemical techniques, surface characterization techniques (Scanning Electron Microscopy, Raman Spectroscopy), engineering aspects of designing and fabricating chemical microsensors and signal analysis.
Description: The post-ictal (after an epileptic seizure) period is generally accompanied by a suppression of cortical EEG, during which recent studies suggest that 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 hdEEG evoked responses to memory tasks collected in enrolled subjects using classical EEG analysis methods in both sensor and source space. Changes in both behavioral and electrophysiological measures to the 3 memory tasks performed in this EPSCoR project will be assessed at 3 time points: pre-surgical, and 3 and 6 months post-surgical.
Description: Studies have 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 coupling map, also known as comodulogram, shows PAC in the frequency domain and indicates the level of coupling between a pair of low and high frequencies. The objective of this research project is to develop a methodology for representing the PAC comodulogram maps across sensors based on the maximum coupling intensity for EEG and ECoG data. The students will learn the pre-processing techniques for artifacts removal and calculation of the PAC maps. Then, each map will be normalized and a clustering technique will use to represent a single comodulogram across sensors.
Description: Measures of functional connectivity quantify statistical dependencies between neuronal signals. Several metrics for connectivity have been used in EEG- derived source space to control for effects of field spread at the sensor level. The purpose of this research is to test a technique for tackling the effects of field spread prior to performing the inversion solution for source localization, and to calculate connectivity metrics on source space from EEG data. The students will learn the pre-processing and connectivity techniques using the open source software Brainstorm, which is a suite for processing EEG/MEG data, with integration of MRI information.
Description: Memory impairment is one of the most common complaints and objective findings in individuals with epilepsy. It has negative impact on quality of life and it creates barriers in fulfilling activities of daily living. Cognitive interventions have been investigated in individuals with seizures with one approach being to teach individuals to use compensation strategies or external cues. An important contributor to successful compensation of cognitive deficits is self-motivation and active involvement via self- generation. Verbal associate learning fMRI task is one way of testing the self-generation abilities and their cortical correlates. Data in over 100 healthy normal volunteers will be made available for exploring the cortical correlates of verbal associate learning. The students will learn basic and advanced methods of individual and group fMRI data analyses.
Description: Even when a person is relaxed and apparently doing nothing, the brain is active. This is called "the resting state," and an increasing body of evidence suggests that this resting state is well organized and functionally important. This study will use magnetoencephalography ("MEG"), a method using superconducting sensors to record the tiny magnetic fields that the neurons inside the brain generate when they are activated. Resting MEG data from normal volunteers, and possibly also from patients with epilepsy and schizophrenia, will be analyzed to explore the dynamics of the resting state.
Description: When patients undergo intracranial EEG recordings from multiple cortical and subcortical structures for a clinical reason, we perform computerized cognitive and psychological tests with the goal to map the neural networks involved in supporting these tasks. Students interested in these projects will have the opportunity to see how intracranial EEG recordings are performed and how cognitive tasks are run in parallel to clinical recordings. There will be ample opportunity to learn cognitive tasks, analyzing EEG and evoked potentials, and programming in Matlab and Arduino. The students will assist the mentors and a graduate student dedicated to this research.