Kaitlyn Whyte, 3rd year BME student, Natalia Trayanova’s Lab
Reconstruction of Personalized Atrial Models from LGE-MRI Scans

Abstract: This presentation will be about a new methodology for creating image-based models of the human atria that include personalized geometry and the unique spatial patterns of fibrotic structural remodeling for each patient. We use late gadolinium enhanced magnetic resonance imaging (LGE-MRI) scans from patients with atrial arrhythmias (fibrillation or atypical flutter) to reconstruct patient-specific finite element models, including realistic fiber orientations. These can be used to simulate the electrophysiological behavior during normal and arrhythmia states, allowing us to non-invasively characterize the underlying mechanisms of rhythm disorder perpetuation. Insights from our simulations are expected to be helpful for clinicians conducting personalized catheter ablation procedures that attempt to cure patients from atrial arrhythmias.  

Alianna Sanzi, 2nd year BME student, Michael Miller’s Lab
Alexandra Berges, 2nd year BME student, Michael Miller’s Lab
Applying Quality Control to Automated Segmentations to Improve Biomarker Discovery

Abstract: In the progression of Huntington’s Disease, brain structures gradually atrophy before the patient experiences symptoms.  To study indications of pre-symptomatic Huntington’s Disease requires reliable imaging techniques.  At the University of Iowa, predromal individuals were assigned CAG-Age Product (CAP) scores based on the stage of their disease progression. These individuals’ brains were imaged during the progression of the disease to measure the focal atrophy of six subcortical brain structures. While automated image segmentations are faster and less expensive than manual segmentations, they can lead to inaccuracies that affect the validity of the investigation. Researchers at the Center for Imaging Science developed an algorithm to make the segmentations more accurate. The goal of this study is to verify the efficacy of this new segmentation method. Six subcortical regions of the brain were segmented in presymptomatic Huntington’s Disease patients including caudate, putamen, globus, accumbens, hippocampus, and thalamus. Common errors in the segmentations were identified, and a quantitative evaluation was developed to assess the efficacy of the new segmentation method.

Austin Jordan, 2^nd year BME student, Sarma Lab
Sandya Subamanian, 4^th year BME student, Sarma Lab
Seizure Localization in Medically Refractory Epilepsy Patients

Abstract: Epilepsy is a neurological disorder characterized by abnormal electrical activity in the brain, called seizures. The region of the brain that causes the seizures is called the epileptogenic zone (EZ), and may differ for each patient. Epilepsy affects 60 million people worldwide, of whom over 30% of cases do not respond to medication or have medically refractory epilepsy (MRE). There are currently two treatments for patients with focal MRE: surgical resection, in which the EZ is removed in hopes of stopping seizures, or neurostimulation, in which the EZ is electrically stimulated to suppress seizures. Both treatments depend on accurately localizing the EZ, and when successful, both treatments are life-changing. EZTrack generates a simple-to-read heat map overlaid over the patient’s brain scan that displays to clinicians which regions of the brain are highly likely to be in the EZ. EZTrack was tested in a small retrospective study that included 19 patients who had resective surgeries. To test its efficacy, we compared EZTrack’s “red-hot” regions (ROI) to resected regions using electrocorticographic data from only 2 seizure events per patient. If the complete ROI was resected, then we predicted a successful surgery; else we predicted a failure. For 19 patients, EZtrack achieved a prediction accuracy of 95%. It also correctly predicted all 8 failed surgeries, which is especially important to indicate to clinicians whether to resample different areas of the brain before deciding to resect.

Kathryn Hochberg, 2^nd year BME student, Karchin Lab
Predicting Disease Severity and Mechanism From Genetic Variation

Abstract: Genetic variation influences human health, but predicting disease from genetic variation remains a vexing problem. Many computational programs exist for classifying genetic variants as benign or disease causing. This partitioning of patients into either a diseased or healthy class ignores the fact that health problems often occupy a spectrum of severities. Thus, predicting whether or not a patient is strictly positive or negative for a disease limits the understanding of the health impacts of the genetic variation. Members of Rachel Karchin’s lab have developed the phenotype-optimized sequence ensemble (POSE) algorithm, which can predict disease severity and mechanism from genetic variation. We are currently improving the POSE algorithm, and collaborating with clinicians to predict disease severity and mechanism from genetic variation.

Richard Chen, 2^nd year BME student, Karchin Lab
Graeme Steller, 2^nd year BME student, Karchin Lab
“Exploring Cancer Genomics using the TCGA Database and MOCA”.

Abstract: Cancer is caused by the accumulation of genetic alterations. We’ve been analyzing data from the TCGA database, a large NIH initiative that has done comprehensive genomic characterization of 1000s of patient tumors across more than 25 distinct cancer types. By analyzing data from different cancer patients within the database, we’ve been able to gain a better understanding of how various genetic alterations impact the cancer tumor. Using MOCA, an algorithm developed in Dr. Karchin’s lab, we’ve been able to gain a better understanding of how specific combinations of genetic alterations can tell us more about noninvasive, genetic markers, which in turn contributes to the process of predicting cancer phenotypes.

Edric Tam, 3rd year BME student, Miller Lab
A Computational Anatomical Approach to Study Alzheimer’s Disease

Abstract: Alzheimer’s disease is one of the most common degenerative neurological diseases. Atrophy and the associated changes in the shape of the hippocampus are some of the most important markers for Alzheimer’s. We apply a combination of diffeomorphometry and Bayesian statistics to a large longitudinal dataset. From the data, we explore how the shape of the hippocampus changes with Alzheimer’s and try to extract useful information from that transformation for potential diagnostic and predictive applications.

Vignesh Ramchandran, 2nd year BME student, Sarma Lab
Ionic Mechanisms That Underlie Ventricular Action Potential Prolongation Following Loss of Caveolin-3 in Adult Transgenic Mice

Abstract: Caveolin proteins are involved in establishing membrane microstructure, lipid raft organization, and cell signaling. In the heart, caveolin-3 (Cav3) predominates. Inherited or disease-induced Cav3 loss increases risk of sudden cardiac death (SCD). We aimed to explore connections between Cav3 loss and arrhythmogenic changes in the ventricular action potential (AP) by investigating the Cav3 dependence of ionic currents. Drugs commonly used to disrupt or remove Cav3 in cultured cells exclude any compensatory process likely to occur in vivo. This motivated us to engineer a novel conditional Cav3 knockout (Cav3-/-) mouse that survives to adulthood. We isolated ventricular cells for electrophysiological experimentation. AP duration (APD90) was prolonged from 24±4 ms in WT to 96±9 ms in Cav3-/-, and several currents were affected. Reduced peak: L-type Ca2+ current (ICaL), 21%; slow K+ current, 81%; transient outward K+ current, 57%; steady state outward K+ current (Iss), 43%. Late Na+ current was enhanced ~10-fold. These changes were partially offsetting – preventing a simple account for the APD90 increase. To relate changes in currents to changes in the AP, we developed a computational representation of Cav3-/- based on the Morotti et al. mouse ventricular cell model and defined by fractional change in currents. Unexpectedly, the relatively small change in relatively small Iss caused 33% of total simulated AP prolongation. Though Iss conductance was reduced, peak Iss actually increased in the dynamic setting of the simulated AP. Early in the AP, lower Iss indirectly enhanced inward currents (importantly late ICaL) by extending the plateau phase, which in turn allowed Iss to more fully activate. This Iss/ICaL process largely accounted for the pro-arrhythmic APD90 increase following Cav3 loss and is therefore a candidate target for normalizing SCD risk.

• Food will be served in Hackerman 306
• Open labs to Explore: Hackerman 218, 219, 220, 317, 318, and 319