The goal of my research is to develop a method for mapping spontaneous activity throughout the whole brain with high spatial and temporal resolution, with the intention of using this technique to characterize alterations in dynamic neural activity linked to dysfunction and to identify potential targets for intervention. My primary expertise is in fMRI and functional connectivity mapping, and since my lab was established at Emory, we have focused on obtaining information about the dynamic activity of functional networks from the BOLD signal. Despite BOLD’s indirect relationship to neural signals, evidence is growing that the BOLD fluctuations provide information about behaviorally relevant network activity. We take a two-pronged approach to the problem, combining MRI with direct neural measures like electrophysiology and optical imaging in the rodent, or with EEG and behavioral outputs in the human. Our effort to understand the relationship between BOLD and electrical or optical recordings (very different signals that cover very different spatial and temporal scales) has led us to develop new approaches to data analysis that include spectral, spatial, and temporal information. To better understand the large-scale dynamics of brain activity, we have become fluent in network modeling, nonlinear dynamics, and machine learning.
Dr. Pardue’s lab is focused on developing treatments for people with vision loss. Steps to successful treatment require understanding the mechanisms of the disease and characterizing temporal changes to identify therapeutic windows, with the ultimate goal of rehabilitation of visual function. She uses behavioral electrophysiological, morphological, molecular, and imaging approaches to evaluate changes in retinal function and structure. Her research is guided by applying knowledge of retinal circuits and visual processing, often leading to studies of cognition and the interaction of retinal and visual circuits during health and disease. Her studies start in animal models and move to human trials when possible.
His early research efforts focused on how to use viruses to transfer genes to cells for the purpose of human gene therapy. More recently, he has shifted his research to the learning sciences, focusing on understanding how the generation of engineering diagrams is used to support problem-solving, both by novice and expert engineers.
Professor James Rains is a faculty member of the Wallace H. Coulter Department of Biomedical Engineering. He has 13 years of product development experience working for industry leaders Stryker and Smith&Nephew. Professor Rains has ten issued patents and readily works with industry professionals and clinicians to solve healthcare issues.
Design, medical device development, and entrepreneurship.
Work with physicians and industry to develop solutions to unmet clinical needs. If you have a problem that needs to be addressed, we can help you solve it.
Predictive medicine, health informatics, data analytics, modeling, biocuration, neuropathology, neuroengineering
Cassie Mitchell’s research goal centers around expediting clinical translation from bench to bedside using data-enabled prediction. Akin to data-based models used to forecast weather, Cassie’s research integrates disparate, multi-scalar experimental and clinical data sets to dynamically forecast disease. Cassie is the principal investigator of the Laboratory for Pathology Dynamics, which uses a combination of computational, analytical, and informatics-based techniques to identify complex disease etiology, predict new therapeutics, and optimize current interventions. Cassie’s research has predominantly targeted neuropathology, but her research applications in predictive medicine expand across all clinical specialties.
Our lab studies the response of bacteria to antibiotics in order to develop new methods for eradicating persistent bacteria. Bacterial persistence is a form antibiotic resistance in which a transient fraction of bacterial cells tolerates severe antibiotic treatment while the majority of the population is eliminated. These ‘persisters’ can contribute to chronic infections and are a major medical problem. Despite their medical and scientific importance, presistence is not fully understood. A crucial challenge in studying bacterial persistence results from a lack of methods to isolate persisters from the heterogeneous populations in which they occur. As a result, systems-level analysis of persisters is beyond current techniques, and fundamental questions regarding their physiological diversity remain unanswered. Our lab seeks to develop methods to isolate persisters and study them with systems-wide, molecular techniques. The resulting findings will be used to engineer improved antibiotic therapies. Dr. Allison’s previous research included development of a novel method to eradicate pathogenic bacteria, including Escherichia coli and Staphylococcus aureus, by metabolic stimulation and the finding that bacteria communicate with each other to alter their tolerance to antibiotics.
The lab is actively developing data analysis methods for learning cytoarchitectonics (layers), mapping brain areas, and distributed segmentation and analysis of large-scale neuroimaging data.
Low-dimensional signal models
Unions of subspaces (UoS) are a generalization of single subspace models that approximate data points as living on multiple subspaces, rather than assuming a global low-dimensional model (as in PCA). Modeling data with mixtures of subspaces provides a more compact and simple representation of the data, and thus can lead to better partitioning (clustering) of the data and help in compression and denoising.
Analyzing the activity of neuronal populations
Advances in monitoring the activity of large populations of neurons has provided new insights into the collective dynamics of neurons. The lab is working on methods that learn and exploit low-dimensional structure in neural activity for decoding, classification, denoising, and deconvolution.
Optimization problems are ubiquitous in machine learning and neuroscience. The lab works on a few different topics in the areas of non-convex optimization and distributed machine learning.
Biomechanics of brain injury, pediatric head injury, soft tissue mechanics, ventilator-induced lung injury, lung mechanics, pathways of cellular mechanotransduction, and tissue injury thresholds.
My research in head injury will continue to focus on how and why head injuries occur in adults and children and to improve detection and treatment strategies. At Georgia Tech, I will be continuing that research, looking at innovative biomarkers and new devices to detect mild traumatic brain injuries. At Emory, my research will be focused on animal models for diffuse as well as focal brain injuries—incorporating developments at Georgia Tech into our preclinical model. I also look forward to close collaborations with Children’s Healthcare of Atlanta and Emory University faculty to improve the outcomes after traumatic brain injuries.