Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Biomedical Computing

Biomedical computing combines the diagnostic and investigative aspects of biology and medical science with the power and problem-solving capabilities of modern computing. Computers are used to accelerate research learning, simulate patient behavior and visualize complex biological models.


chris

Chris Johnson

Inverse Problems
Computational Electrophysiology
rob

Rob MacLeod

ECG Imaging
Cardiac Disease
Computational Electrophysiology
jeff

Jeff Weiss

Computational Biomechanics
orly

Orly Alter

Computational Biology
bidone

Tamara Bidone

Computational Models
Simulations of Biological Systems
Multi-Physics Models of Cancer Cells


Centers and Labs:


Funded Research Projects:



Publications in Biomedical Computing:


Treatment Planning for Atrial Fibrillation Using Patient-Specific Models Showing the Importance of Fibrillatory-Areas
R. Kamali, K. Gillete, J. Tate, D. A. Abhyankar, D. J. Dosdall, G. Plank, T. J. Bunch, R. S. Macleod & R. Ranjan . In Annals of Biomedical Engineering, Springer, 2022.
DOI: https://doi.org/10.1007/s10439-022-03029-5

Computational models have made it possible to study the effect of fibrosis and scar on atrial fibrillation (AF) and plan future personalized treatments. Here, we study the effect of area available for fibrillatory waves to sustain AF. Then we use it to plan for AF ablation to improve procedural outcomes. CARPentry was used to create patient-specific models to determine the association between the size of residual contiguous areas available for AF wavefronts to propagate and sustain AF [fibrillatory area (FA)] after ablation with procedural outcomes. The FA was quantified in a novel manner accounting for gaps in ablation lines. We selected 30 persistent AF patients with known ablation outcomes. We divided the atrial surface into five areas based on ablation scar pattern and anatomical landmarks and calculated the FAs. We validated the models based on clinical outcomes and suggested future ablation lines that minimize the FAs and terminate rotor activities in simulations. We also simulated the effects of three common antiarrhythmic drugs. In the patient-specific models, the predicted arrhythmias matched the clinical outcomes in 25 of 30 patients (accuracy 83.33%). The average largest FA (FAmax) in the recurrence group was 8517 ± 1444 vs. 6772 ± 1531 mm2 in the no recurrence group (p < 0.004). The final FAs after adding the suggested ablation lines in the AF recurrence group reduced the average FAmax from 8517 ± 1444 to 6168 ± 1358 mm2 (p < 0.001) and stopped the sustained rotor activity. Simulations also correctly anticipated the effect of antiarrhythmic drugs in 5 out of 6 patients who used drug therapy post unsuccessful ablation (accuracy 83.33%). Sizes of FAs available for AF wavefronts to propagate are important determinants for ablation outcomes. FA size in combination with computational simulations can be used to direct ablation in persistent AF to minimize the critical mass required to sustain recurrent AF.



Relating Metopic Craniosynostosis Severity to Intracranial Pressure,
J.D. Blum, J. Beiriger, C. Kalmar, R.A. Avery, S. Lang, D.F. Villavisanis, L. Cheung, D.Y. Cho, W. Tao, R. Whitaker, S.P. Bartlett, J.A. Taylor, J.A. Goldstein, J.W. Swanson. In The Journal of Craniofacial Surgery, 2022.
DOI: 10.1097/SCS.0000000000008748

Purpose:

A subset of patients with metopic craniosynostosis are noted to have elevated intracranial pressure (ICP). However, it is not known if the propensity for elevated ICP is influenced by the severity of metopic cranial dysmorphology.

Methods:

Children with nonsyndromic single-suture metopic synostosis were prospectively enrolled and underwent optical coherence tomography to measure optic nerve head morphology. Preoperative head computed tomography scans were assessed for endocranial bifrontal angle as well as scaled metopic synostosis severity score (MSS) and cranial morphology deviation score determined by CranioRate, an automated severity classifier.
Results:

Forty-seven subjects were enrolled between 2014 and 2019, at an average age of 8.5 months at preoperative computed tomography and 11.8 months at index procedure. Fourteen patients (29.7%) had elevated optical coherence tomography parameters suggestive of elevated ICP at the time of surgery. Ten patients (21.3%) had been diagnosed with developmental delay, eight of whom demonstrated elevated ICP. There were no significant associations between measures of metopic severity and ICP. Metopic synostosis severity score and endocranial bifrontal angle were inversely correlated, as expected (r=−0.545, P<0.001). A negative correlation was noted between MSS and formally diagnosed developmental delay (r=−0.387, P=0.008). Likewise, negative correlations between age at procedure and both MSS and cranial morphology deviation was observed (r=−0.573, P<0.001 and r=−0.312, P=0.025, respectively).
Conclusions:

Increased metopic severity was not associated with elevated ICP at the time of surgery. Patients who underwent later surgical correction showed milder phenotypic dysmorphology with an increased incidence of developmental delay.



Shorter Distance Between The Esophagus And The Left Atrium Is Associated With Higher Rates Of Esophageal Thermal Injury After Radiofrequency Ablation,
Y. Ishidoya, E. Kwan, D. J. Dosdall, R. S. Macleod, L. Navaravong, B. A. Steinberg, T. J. Bunch, R. Ranjan. In Journal of Cardiovascular Electrophysiology, Wiley, 2022.
DOI: 10.1111/jce.15554

Background
Esophageal thermal injury (ETI) is a known and potentially serious complication of catheter ablation for atrial fibrillation. We intended to evaluate the distance between the esophagus and the left atrium posterior wall (LAPW) and its association with esophageal thermal injury.

Methods
A retrospective analysis of 73 patients who underwent esophagogastroduodenoscopy (EGD) after LA radiofrequency catheter ablation for symptomatic atrial fibrillation and pre-ablation magnetic resonance imaging (MRI) was used to identify the minimum distance between the inner lumen of the esophagus and the ablated atrial endocardium (pre-ablation atrial esophageal distance; pre-AED) and occurrence of ETI. Parameters of ablation index (AI, Visitag Surpoint) were collected in 30 patients from the CARTO3 system and compared to assess if ablation strategies and AI further impacted risk of ETI.
Results
Pre-AED was significantly larger in patients without ETI than those with ETI (5.23 ± 0.96 mm vs 4.31 ± 0.75 mm, p < 0.001). Pre-AED showed high accuracy for predicting ETI with the best cutoff value of 4.37 mm. AI was statistically comparable between Visitag lesion markers with and without associated esophageal late gadolinium enhancement (LGE) detected by post-ablation MRI in the low-power long-duration ablation group (LPLD, 25-40W for 10 to 30 s, 393.16 [308.62, 408.86] versus 406.58 [364.38, 451.22], p = 0.16) and high-power short-duration group (HPSD, 50W for 5-10 s, 336.14 [299.66, 380.11] versus 330.54 [286.21, 384.71], p = 0.53), respectively.
Conclusion
Measuring the distance between the LA and the esophagus in pre-ablation LGE-MRI could be helpful in predicting ETI after LAPW ablation.



Short-Term Natural Course of Esophageal Thermal Injury After Ablation for Atrial Fibrillation,
Y. Ishidoya, E. Kwan, D. J. Dosdall, R. S. Macleod, L. Navaravong, B. A. Steinberg, T. J. Bunch, R. Ranjan. In Journal of Cardiovascular Electrophysiology, Wiley, 2022.
DOI: 10.1111/jce.15553

Purpose
To provide insight into the short-term natural history of esophageal thermal injury (ETI) after radiofrequency catheter ablation (RFCA) for atrial fibrillation (AF) by esophagogastroduodenoscopy (EGD).

Methods
We screened patients who underwent RFCA for AF and EGD based on esophageal late gadolinium enhancement (LGE) in post ablation MRI. Patients with ETI diagnosed with EGD were included. We defined severity of ETI according to Kansas City classification (KCC): type 1: erythema; type 2: ulcers (2a: superficial; 2b deep); type 3 perforation (3a: perforation; 3b: perforation with atrioesophageal fistula). Repeated EGD was performed within 1-14 days after the last EGD if recommended and possible until any certain healing signs (visible reduction in size without deepening of ETI or complete resolution) were observed.
Results
ETI was observed in 62 of 378 patients who underwent EGD after RFCA. Out of these 62 patients with ETI, 21% (13) were type 1, 50% (31) were type 2a and 29% (18) were type 2b at the initial EGD. All esophageal lesions, but one type 2b lesion that developed into an atrioesophageal fistula (AEF), showed signs of healing in repeated EGD studies within 14 days after the procedure. The one type 2b lesion developing into an AEF showed an increase in size and ulcer deepening in repeat EGD 8 days after the procedure.
Conclusion
We found that all ETI which didn't progress to AEF presented healing signs within 14 days after the procedure and that worsening ETI might be an early signal for developing esophageal perforation.



Reconstruction of cardiac position using body surface potentials
J. A. Bergquist, J. Coll-Font, B. Zenger, L. C. Rupp, W. W. Good, D. H. Brooks, R. S. MacLeod. In Computers in Biology and Medicine, Vol. 142, pp. 105174. 2022.
DOI: https://doi.org/10.1016/j.compbiomed.2021.105174

Electrocardiographic imaging (ECGI) is a noninvasive technique to assess the bioelectric activity of the heart which has been applied to aid in clinical diagnosis and management of cardiac dysfunction. ECGI is built on mathematical models that take into account several patient specific factors including the position of the heart within the torso. Errors in the localization of the heart within the torso, as might arise due to natural changes in heart position from respiration or changes in body position, contribute to errors in ECGI reconstructions of the cardiac activity, thereby reducing the clinical utility of ECGI. In this study we present a novel method for the reconstruction of cardiac geometry utilizing noninvasively acquired body surface potential measurements. Our geometric correction method simultaneously estimates the cardiac position over a series of heartbeats by leveraging an iterative approach which alternates between estimating the cardiac bioelectric source across all heartbeats and then estimating cardiac positions for each heartbeat. We demonstrate that our geometric correction method is able to reduce geometric error and improve ECGI accuracy in a wide range of testing scenarios. We examine the performance of our geometric correction method using different activation sequences, ranges of cardiac motion, and body surface electrode configurations. We find that after geometric correction resulting ECGI solution accuracy is improved and variability of the ECGI solutions between heartbeats is substantially reduced.



Translational computer science at the scientific computing and imaging institute
C. R. Johnson. In Journal of Computational Science, Vol. 52, pp. 101217. 2021.
ISSN: 1877-7503
DOI: https://doi.org/10.1016/j.jocs.2020.101217

The Scientific Computing and Imaging (SCI) Institute at the University of Utah evolved from the SCI research group, started in 1994 by Professors Chris Johnson and Rob MacLeod. Over time, research centers funded by the National Institutes of Health, Department of Energy, and State of Utah significantly spurred growth, and SCI became a permanent interdisciplinary research institute in 2000. The SCI Institute is now home to more than 150 faculty, students, and staff. The history of the SCI Institute is underpinned by a culture of multidisciplinary, collaborative research, which led to its emergence as an internationally recognized leader in the development and use of visualization, scientific computing, and image analysis research to solve important problems in a broad range of domains in biomedicine, science, and engineering. A particular hallmark of SCI Institute research is the creation of open source software systems, including the SCIRun scientific problem-solving environment, Seg3D, ImageVis3D, Uintah, ViSUS, Nektar++, VisTrails, FluoRender, and FEBio. At this point, the SCI Institute has made more than 50 software packages broadly available to the scientific community under open-source licensing and supports them through web pages, documentation, and user groups. While the vast majority of academic research software is written and maintained by graduate students, the SCI Institute employs several professional software developers to help create, maintain, and document robust, tested, well-engineered open source software. The story of how and why we worked, and often struggled, to make professional software engineers an integral part of an academic research institute is crucial to the larger story of the SCI Institute’s success in translational computer science (TCS).



Prediction of Femoral Head Coverage from Articulated Statistical Shape Models of Patients with Developmental Dysplasia of the Hip
P. R. Atkins, P. Agrawal, J. D. Mozingo, K. Uemura, K. Tokunaga, C. L. Peters, S. Y. Elhabian, R. T. Whitaker, A. E. Anderson. In Journal of Orthopaedic Research, Wiley, 2021.
DOI: 10.1002/jor.25227

Developmental dysplasia of the hip (DDH) is commonly described as reduced femoral head coverage due to anterolateral acetabular deficiency. Although reduced coverage is the defining trait of DDH, more subtle and localized anatomic features of the joint are also thought to contribute to symptom development and degeneration. These features are challenging to identify using conventional approaches. Herein, we assessed the morphology of the full femur and hemi-pelvis using an articulated statistical shape model (SSM). The model determined the morphological and pose-based variations associated with DDH in a population of Japanese females and established which of these variations predict coverage. Computed tomography images of 83 hips from 47 patients were segmented for input into a correspondence-based SSM. The dominant modes of variation in the model initially represented scale and pose. After removal of these factors through individual bone alignment, femoral version and neck-shaft angle, pelvic curvature, and acetabular version dominated the observed variation. Femoral head oblateness and prominence of the acetabular rim and various muscle attachment sites of the femur and hemi-pelvis were found to predict 3D CT-based coverage measurements (R2=0.5-0.7 for the full bones, R2=0.9 for the joint).



Uncertainty Quantification in Brain Stimulation using UncertainSCI
J. Tate, S. Rampersad, C. Charlebois, Z. Liu, J. Bergquist, D. White, L. Rupp, D. Brooks, A. Narayan, R. MacLeod. In Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, Vol. 14, No. 6, Elsevier, pp. 1659-1660. 2021.

Predicting the effects of brain stimulation with computer models presents many challenges, including estimating the possible error from the propagation of uncertain input parameters through the model. Quantification and control of these errors through uncertainty quantification (UQ) provide statistics on the likely impact of parameter variation on solution accuracy, including total variance and sensitivity associated to each parameter. While the need and importance of UQ in clinical modeling is generally accepted, tools for implementing UQ techniques remain limited or inaccessible for many researchers.



Combining endocardial mapping and electrocardiographic imaging (ECGI) for improving PVC localization: A feasibility study
W. W. Good, B. Zenger, J. A. Bergquist, L. C. Rupp, K. Gillett, N. Angel, D. Chou, G. Plank, R. S. MacLeod. In Journal of Electrocardiology, 2021.
ISSN: 0022-0736
DOI: https://doi.org/10.1016/j.jelectrocard.2021.08.013

Introduction

Accurate reconstruction of cardiac activation wavefronts is crucial for clinical diagnosis, management, and treatment of cardiac arrhythmias. Furthermore, reconstruction of activation profiles within the intramural myocardium has long been impossible because electrical mapping was only performed on the endocardial surface. Recent advancements in electrocardiographic imaging (ECGI) have made endocardial and epicardial activation mapping possible. We propose a novel approach to use both endocardial and epicardial mapping in a combined approach to reconstruct intramural activation times.

Objective

To implement and validate a combined epicardial/endocardial intramural activation time reconstruction technique.
Methods

We used 11 simulations of ventricular activation paced from sites throughout myocardial wall and extracted endocardial and epicardial activation maps at approximate clinical resolution. From these maps, we interpolated the activation times through the myocardium using thin-plate-spline radial basis functions. We evaluated activation time reconstruction accuracy using root-mean-squared error (RMSE) of activation times and the percent of nodes within 1 ms of the ground truth.
Results

Reconstructed intramural activation times showed an RMSE and percentage of nodes within 1 ms of the ground truth simulations of 3 ms and 70%, respectively. In the worst case, the RMSE and percentage of nodes were 4 ms and 60%, respectively.
Conclusion

We showed that a simple, yet effective combination of clinical endocardial and epicardial activation maps can accurately reconstruct intramural wavefronts. Furthermore, we showed that this approach provided robust reconstructions across multiple intramural stimulation sites.



A Nonparametric Approach for Estimating Three-Dimensional Fiber Orientation Distribution Functions (ODFs) in Fibrous Materials
A. Rauff, L.H. Timmins, R.T. Whitaker, J.A. Weiss. In IEEE Transactions on Medical Imaging, 2021.
DOI: 10.1109/TMI.2021.3115716

Many biological tissues contain an underlying fibrous microstructure that is optimized to suit a physiological function. The fiber architecture dictates physical characteristics such as stiffness, diffusivity, and electrical conduction. Abnormal deviations of fiber architecture are often associated with disease. Thus, it is useful to characterize fiber network organization from image data in order to better understand pathological mechanisms. We devised a method to quantify distributions of fiber orientations based on the Fourier transform and the Qball algorithm from diffusion MRI. The Fourier transform was used to decompose images into directional components, while the Qball algorithm efficiently converted the directional data from the frequency domain to the orientation domain. The representation in the orientation domain does not require any particular functional representation, and thus the method is nonparametric. The algorithm was verified to demonstrate its reliability and used on datasets from microscopy to show its applicability. This method increases the ability to extract information of microstructural fiber organization from experimental data that will enhance our understanding of structure-function relationships and enable accurate representation of material anisotropy in biological tissues.



Integrin-Based Mechanosensing through Conformational Deformation
T.P. Driscoll, T.C. Bidone, S.J. Ahn, A. Yu, A. Groisman, G.A. Voth, M.A. Schwartz. In Biophysical Journal, 2021.
DOI: https://doi.org/10.1016/j.bpj.2021.09.010

Conversion of integrins from low to high affinity states, termed activation, is important in biological processes including immunity, hemostasis, angiogenesis and embryonic development. Integrin activation is regulated by large-scale conformational transitions from closed, low affinity states to open, high affinity states. While it has been suggested that substrate stiffness shifts the conformational equilibrium of integrin and governs its unbinding, here we address the role of integrin conformational activation in cellular mechanosensing. Comparison of WT vs activating mutants of integrin αVβ3 show that activating mutants shift cell spreading, FAK activation, traction stress and force on talin toward high stiffness values at lower stiffness. Although all activated integrin mutants showed equivalent binding affinity for soluble ligands, the β3 S243E mutant showed the strongest shift in mechanical responses. To understand this behavior, we used coarse-grained computational models derived from molecular level information. The models predicted that wild type integrin αVβ3 displaces under force, and that activating mutations shift the required force toward lower values, with S243E showing the strongest effect. Cellular stiffness sensing thus correlates with computed effects of force on integrin conformation. Together, these data identify a role for force-induced integrin conformational deformation in cellular mechanosensing.



Reducing Line-of-block Artifacts in Cardiac Activation Maps Estimated Using ECG Imaging: A Comparison of Source Models and Estimation Methods
A.S. Rababah, L.R. Bear, Y.S. Dogrusoz, W. Good, J. Bergquist, J. Stoks, R. MacLeod, K. Rjoob, M. Jennings, J. Mclaughlin, D. D. Finlay. In Computers in Biology and Medicine, Vol. 136, pp. 104666. 2021.

Electrocardiographic imaging is an imaging modality that has been introduced recently to help in visualizing the electrical activity of the heart and consequently guide the ablation therapy for ventricular arrhythmias. One of the main challenges of this modality is that the electrocardiographic signals recorded at the torso surface are contaminated with noise from different sources. Low amplitude leads are more affected by noise due to their low peak-to-peak amplitude. In this paper, we have studied 6 datasets from two torso tank experiments (Bordeaux and Utah experiments) to investigate the impact of removing or interpolating these low amplitude leads on the inverse reconstruction of cardiac electrical activity. Body surface potential maps used were calculated by using the full set of recorded leads, removing 1, 6, 11, 16, or 21 low amplitude leads, or interpolating 1, 6, 11, 16, or 21 low amplitude leads using one of the three interpolation methods – Laplacian interpolation, hybrid interpolation, or the inverse-forward interpolation. The epicardial potential maps and activation time maps were computed from these body surface potential maps and compared with those recorded directly from the heart surface in the torso tank experiments. There was no significant change in the potential maps and activation time maps after the removal of up to 11 low amplitude leads. Laplacian interpolation and hybrid interpolation improved the inverse reconstruction in some datasets and worsened it in the rest. The inverse forward interpolation of low amplitude leads improved it in two out of 6 datasets and at least remained the same in the other datasets. It was noticed that after doing the inverse-forward interpolation, the selected lambda value was closer to the optimum lambda value that gives the inverse solution best correlated with the recorded one.



Transient recovery of epicardial and torso ST-segment ischemic signals during cardiac stress tests: A possible physiological mechanism
B. Zenger, W. W. Good, J. A. Bergquist, L. C. Rupp, M. Perez, G. J. Stoddard, V. Sharma, R. S. MacLeod. In Journal of Electrocardiology, Churchill Livingstone, 2021.

Background

Acute myocardial ischemia has several characteristic ECG findings, including clinically detectable ST-segment deviations. However, the sensitivity and specificity of diagnosis based on ST-segment changes are low. Furthermore, ST-segment deviations have been shown to be transient and spontaneously recover without any indication the ischemic event has subsided.

Objective

Assess the transient recovery of ST-segment deviations on remote recording electrodes during a partial occlusion cardiac stress test and compare them to intramyocardial ST-segment deviations.

Methods

We used a previously validated porcineBZ experimental model of acute myocardial ischemia with controllable ischemic load and simultaneous electrical measurements within the heart wall, on the epicardial surface, and on the torso surface. Simulated cardiac stress tests were induced by occluding a coronary artery while simultaneously pacing rapidly or infusing dobutamine to stimulate cardiac function. Postexperimental imaging created anatomical models for data visualization and quantification. Markers of ischemia were identified as deviations in the potentials measured at 40% of the ST-segment. Intramural cardiac conduction speed was also determined using the inverse gradient method. We assessed changes in intramyocardial ischemic volume proportion, conduction speed, clinical presence of ischemia on remote recording arrays, and regional changes to intramyocardial ischemia. We defined the peak deviation response time as the time interval after onset of ischemia at which maximum ST-segment deviation was achieved, and ST-recovery time was the interval when ST deviation returned to below thresholded of ST elevation.

Results

In both epicardial and torso recordings, the peak ST-segment deviation response time was 4.9±1.1 min and the ST-recovery time was approximately 7.9±2.5 min, both well before the termination of the ischemic stress. At peak response time, conduction speed was reduced by 50% and returned to near baseline at ST-recovery. The overall ischemic volume proportion initially increased, on average, to 37% at peak response time; however, it recovered only to 30% at the ST-recovery time. By contrast, the subepicardial region of the myocardial wall showed 40% ischemic volume at peak response time and recovered much more strongly to 25% as epicardial ST-segment deviations returned to baseline.

Conclusion

Our data show that remote ischemic signal recovery correlates with a recovery of the subepicardial myocardium, while subendocardial ischemic development persists.



Deep Adaptive Electrocardiographic Imaging with Generative Forward Model for Error Reduction,
X. Jiang, J. C. Font, J. A. Bergquist, B. Zenger, W. W. Good, D. H. Brooks, R. S. MacLeod, L. Wang. In Functional Imaging and Modeling of the Heart: 11th International Conference, In Functional Imaging and Modeling of the Heart: 11th International Conference, Vol. 12738, Springer Nature, pp. 471. 2021.

Accuracy of estimating the heart’s electrical activity with Electrocardiographic Imaging (ECGI) is challenging due to using an error-prone physics-based model (forward model). While getting better results than the traditional numerical methods following the underlying physics, modern deep learning approaches ignore the physics behind the electrical propagation in the body and do not allow the use of patientspecific geometry. We introduce a deep-learning-based ECGI framework capable of understanding the underlying physics, aware of geometry, and adjustable to patient-specific data. Using a variational autoencoder (VAE), we uncover the forward model’s parameter space, and when solving the inverse problem, these parameters will be optimized to reduce the errors in the forward model. In both simulation and real data experiments, we demonstrated the ability of the presented framework to provide accurate reconstruction of the heart’s electrical potentials and localization of the earliest activation sites.



Uncertainty Quantification of the Effects of Segmentation Variability in ECGI,
J. D. Tate, W. W. Good, N. Zemzemi, M. Boonstra, P. van Dam, D. H. Brooks, A. Narayan, R. S. MacLeod. In Functional Imaging and Modeling of the Heart, Springer International Publishing, pp. 515--522. 2021.
DOI: 10.1007/978-3-030-78710-3_49

Despite advances in many of the techniques used in Electrocardiographic Imaging (ECGI), uncertainty remains insufficiently quantified for many aspects of the pipeline. The effect of geometric uncertainty, particularly due to segmentation variability, may be the least explored to date. We use statistical shape modeling and uncertainty quantification (UQ) to compute the effect of segmentation variability on ECGI solutions. The shape model was made with Shapeworks from nine segmentations of the same patient and incorporated into an ECGI pipeline. We computed uncertainty of the pericardial potentials and local activation times (LATs) using polynomial chaos expansion (PCE) implemented in UncertainSCI. Uncertainty in pericardial potentials from segmentation variation mirrored areas of high variability in the shape model, near the base of the heart and the right ventricular outflow tract, and that ECGI was less sensitive to uncertainty in the posterior region of the heart. Subsequently LAT calculations could vary dramatically due to segmentation variability, with a standard deviation as high as 126ms, yet mainly in regions with low conduction velocity. Our shape modeling and UQ pipeline presented possible uncertainty in ECGI due to segmentation variability and can be used by researchers to reduce said uncertainty or mitigate its effects. The demonstrated use of statistical shape modeling and UQ can also be extended to other types of modeling pipelines.



The Electrocardiographic Forward Problem: A Benchmark Study
J. A. Bergquist, W. W. Good, B. Zenger, J. D. Tate, L. C. Rupp, R. S. MacLeod. In Computers in Biology and Medicine, Vol. 134, Pergamon, pp. 104476. 2021.
DOI: https://doi.org/10.1016/j.compbiomed.2021.104476

Background
Electrocardiographic forward problems are crucial components for noninvasive electrocardiographic imaging (ECGI) that compute torso potentials from cardiac source measurements. Forward problems have few sources of error as they are physically well posed and supported by mature numerical and computational techniques. However, the residual errors reported from experimental validation studies between forward computed and measured torso signals remain surprisingly high.

Objective
To test the hypothesis that incomplete cardiac source sampling, especially above the atrioventricular (AV) plane is a major contributor to forward solution errors.

Methods
We used a modified Langendorff preparation suspended in a human-shaped electrolytic torso-tank and a novel pericardiac-cage recording array to thoroughly sample the cardiac potentials. With this carefully controlled experimental preparation, we minimized possible sources of error, including geometric error and torso inhomogeneities. We progressively removed recorded signals from above the atrioventricular plane to determine how the forward-computed torso-tank potentials were affected by incomplete source sampling.

Results
We studied 240 beats total recorded from three different activation sequence types (sinus, and posterior and anterior left-ventricular free-wall pacing) in each of two experiments. With complete sampling by the cage electrodes, all correlation metrics between computed and measured torso-tank potentials were above 0.93 (maximum 0.99). The mean root-mean-squared error across all beat types was also low, less than or equal to 0.10 mV. A precipitous drop in forward solution accuracy was observed when we included only cage measurements below the AV plane.

Conclusion
First, our forward computed potentials using complete cardiac source measurements set a benchmark for similar studies. Second, this study validates the importance of complete cardiac source sampling above the AV plane to produce accurate forward computed torso potentials. Testing ECGI systems and techniques with these more complete and highly accurate datasets will improve inverse techniques and noninvasive detection of cardiac electrical abnormalities.



Proceedings of the Eighth Annual Deep Brain Stimulation Think Tank: Advances in Optogenetics, Ethical Issues Affecting DBS Research, Neuromodulatory Approaches for Depression, Adaptive Neurostimulation, and Emerging DBS Technologies
V. Vedam-Mai, K. Deisseroth, J. Giordano, G. Lazaro-Munoz, W. Chiong, N. Suthana, J. Langevin, J. Gill, W. Goodman, N. R. Provenza, C. H. Halpern, R. S. Shivacharan, T. N. Cunningham, S. A. Sheth, N. Pouratian, K. W. Scangos, H. S. Mayberg, A. Horn, K. A. Johnson, C. R. Butson, R. Gilron, C. de Hemptinne, R. Wilt, M. Yaroshinsky, S. Little, P. Starr, G. Worrell, P. Shirvalkar, E. Chang, J. Volkmann, M. Muthuraman, S. Groppa, A. A. Kühn, L. Li, M. Johnson, K. J. Otto, R. Raike, S. Goetz, C. Wu, P. Silburn, B. Cheeran, Y. J. Pathak, M. Malekmohammadi, A. Gunduz, J. K. Wong, S. Cernera, A. W. Shukla, A. Ramirez-Zamora, W. Deeb, A. Patterson, K. D. Foote, M. S. Okun. In Frontiers in Human Neuroscience, Vol. 15, pp. 169. 2021.
ISSN: 1662-5161
DOI: 10.3389/fnhum.2021.644593

We estimate that 208,000 deep brain stimulation (DBS) devices have been implanted to address neurological and neuropsychiatric disorders worldwide. DBS Think Tank presenters pooled data and determined that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. The DBS Think Tank was founded in 2012 providing a space where clinicians, engineers, researchers from industry and academia discuss current and emerging DBS technologies and logistical and ethical issues facing the field. The emphasis is on cutting edge research and collaboration aimed to advance the DBS field. The Eighth Annual DBS Think Tank was held virtually on September 1 and 2, 2020 (Zoom Video Communications) due to restrictions related to the COVID-19 pandemic. The meeting focused on advances in: (1) optogenetics as a tool for comprehending neurobiology of diseases and on optogenetically-inspired DBS, (2) cutting edge of emerging DBS technologies, (3) ethical issues affecting DBS research and access to care, (4) neuromodulatory approaches for depression, (5) advancing novel hardware, software and imaging methodologies, (6) use of neurophysiological signals in adaptive neurostimulation, and (7) use of more advanced technologies to improve DBS clinical outcomes. There were 178 attendees who participated in a DBS Think Tank survey, which revealed the expansion of DBS into several indications such as obesity, post-traumatic stress disorder, addiction and Alzheimer’s disease. This proceedings summarizes the advances discussed at the Eighth Annual DBS Think Tank.



Identification of Deep Brain Stimulation Targets for Neuropathic Pain After Spinal Cord Injury Using Localized Increases in White Matter Fiber Cross‐Section
S. R. Black, A. Janson, M. Mahan, J. Anderson, C. R. Butson. In Neuromodulation: Technology at the Neural Interface, John Wiley & Sons, Inc., 2021.

Objectives
The spinal cord injury (SCI) patient population is overwhelmingly affected by neuropathic pain (NP), a secondary condition for which therapeutic options are limited and have a low degree of efficacy. The objective of this study was to identify novel deep brain stimulation (DBS) targets that may theoretically benefit those with NP in the SCI patient population. We hypothesize that localized changes in white matter identified in SCI subjects with NP compared to those without NP could be used to develop an evidence‐based approach to DBS target identification.

Materials and Methods
To classify localized neurostructural changes associated with NP in the SCI population, we compared white matter fiber density (FD) and cross‐section (FC) between SCI subjects with NP (N = 17) and SCI subjects without NP (N = 15) using diffusion‐weighted magnetic resonance imaging (MRI). We then identified theoretical target locations for DBS using fiber bundles connected to significantly altered regions of white matter. Finally, we used computational models of DBS to determine if our theoretical target locations could be used to feasibly activate our fiber bundles of interest.
Results
We identified significant increases in FC in the splenium of the corpus callosum in pain subjects when compared to controls. We then isolated five fiber bundles that were directly connected to the affected region of white matter. Our models were able to predict that our fiber bundles of interest can be feasibly activated with DBS at reasonable stimulation amplitudes and with clinically relevant implantation approaches.
Conclusions
Altogether, we identified neuroarchitectural changes associated with NP in the SCI cohort and implemented a novel, evidence‐driven target selection approach for DBS to guide future research in neuromodulation treatment of NP after SCI.



3D Model of Cell Migration and Proliferation in a Tissue Scaffold,
S. H. Campbell, T. Bidone. In Biophysical Journal, Vol. 120, No. 3, Elsevier, pp. 265a. 2021.

Tissue scaffolds restore tissue functionality without the limitations of transplants. However, successful tissue growth depends on the interplay between scaffold properties and cell activities. It has been previously reported that scaffold porosity and Young's modulus affect cell migration and tissue generation. However, how the geometrical and mechanical properties of a scaffold exactly interplay with cell processes remain poorly understood and are essential for successful tissue growth. We developed a 3D computational model that simulates cell migration and proliferation on a scaffold. The model generates an adjustable 3D porous scaffold environment with a defined pore size and Young modulus. Cells are treated as explicit spherical particles comparable in size to bone-marrow cells and are initially seeded randomly throughout the scaffold. Cells can create adhesions, proliferate, and independently migrate across pores in a random walk. Cell adhesions during migration follow the molecular-clutch mechanism, where traction force from the cells against the scaffold stiffness reinforces adhesions lifetime up to a threshold. We used the model to test how variations in cell proliferation rate, scaffold Young's modulus, and porosity affect cell migration speed. At a low proliferation rate (1 x 10−7 s−1), the spread of cell speeds is larger than at a high replication rate (1 x 10−6 s−1). A biphasic relation between Young's modulus and cell speed is also observed reflecting the molecular-clutch mechanism at the level of individual adhesions. These observations are consistent with previous reports regarding fibroblast migration on collagen-glycosaminoglycan scaffolds. Additionally, our model shows that similar cell diameters and pore diameter induces a crowding effect decreasing cell speed. The results from our study provide important insights about biophysical mechanisms that govern cell motility on scaffolds with different properties for tissue engineering applications.



Prestin Generates Instantaneous Force in Outer Hair Cell Membranes,
J. Sandhu, T. Bidone, R. D. Rabbitt. In Biophysical Journal, Vol. 120, No. 3, 2021.

Hearing occurs from sound reaching the inner ear cochlea, where electromotile Outer Hair Cells (OHCs) amplify vibrations by elongating and contracting rapidly in response to auditory frequency changes in membrane potential. OHCs can generate force cycle-by-cycle at frequencies exceeding 50kHz, but precisely how this is achieved is unclear. Electromotility requires expression of the transmembrane protein, prestin, which facilitates the electromechanical conversion through action of the Coulomb force acting on the anion Cl- bound at the core of the protein. However, recent experimental data suggests the charge displacement is too slow to support sound amplification at auditory frequencies. As a consequence, prestin electromechanics remain unclear at the molecular level. We hypothesize that prestin instantaneously transmits stress to the membrane, which subsequently drives charge displacement, membrane deformation, and OHC shape changes. To test the hypothesis, we examined the conformational dynamics of prestin and its effects on the motion of lipids under: (1) isometric conditions and (2) constant force conditions in order to mimic different regimes of membrane loading. All-atom molecular dynamics simulations of the prestin dimer embedded in POPC membranes were run and the trajectories analyzed. We discovered that under isometric conditions, the presence of a chloride ion in the electric field increased residue fluctuations. This trend was not observed under constant force conditions, supporting the idea that isometric conditions cause instantaneous force to be generated in the membrane. The analysis allowed us to identify the molecular mechanisms by which prestin allows electromechanical amplification by OHCs in the cochlea.