Biomedical Computing

Objective: During deep brain stimulation (DBS), it is well understood that extracellular cathodic stimulation can cause activation of passing axons. Activation can be predicted from the second derivative of the electric potential along an axon, which depends on axonal orientation with respect to the stimulation source. We hypothesize that fiber orientation influences activation thresholds and that fiber orientations can be selectively targeted with DBS waveforms. Approach: We used bioelectric field and multicompartment NEURON models to explore preferential activation based on fiber orientation during monopolar or bipolar stimulation. Preferential fiber orientation was extracted from the principal eigenvectors and eigenvalues of the Hessian matrix of the electric potential. We tested cathodic, anodic, and charge-balanced pulses to target neurons based on fiber orientation in general and clinical scenarios. Main Results: Axons passing the DBS lead have positive second derivatives around a cathode, whereas orthogonal axons have positive second derivatives around an anode, as indicated by the Hessian. Multicompartment NEURON models confirm that passing fibers are activated by cathodic stimulation, and orthogonal fibers are activated by anodic stimulation. Additionally, orthogonal axons have lower thresholds compared to passing axons. In a clinical scenario, fiber pathways associated with therapeutic benefit can be targeted with anodic stimulation at 50% lower stimulation amplitudes. Significance: Fiber orientations can be selectively targeted with simple changes to the stimulus waveform. Anodic stimulation preferentially activates orthogonal fibers, approaching or leaving the electrode, at lower thresholds for similar therapeutic benefit in DBS with decreased power consumption.

DNA alterations have been observed in astrocytoma for decades. A copy-number genotype predictive of a survival phenotype was only discovered by using the generalized singular value decomposition (GSVD) formulated as a comparative spectral decomposition. Here, we use the GSVD to compare whole-genome sequencing (WGS) profiles of patient-matched astrocytoma and normal DNA. First, the GSVD uncovers a genome-wide pattern of copy-number alterations, which is bounded by patterns recently uncovered by the GSVDs of microarray-profiled patient-matched glioblastoma (GBM) and, separately, lower-grade astrocytoma and normal genomes. Like the microarray patterns, the WGS pattern is correlated with an approximately one-year median survival time. By filling in gaps in the microarray patterns, the WGS pattern reveals that this biologically consistent genotype encodes for transformation via the Notch together with the Ras and Shh pathways. Second, like the GSVDs of the microarray profiles, the GSVD of the WGS profiles separates the tumor-exclusive pattern from normal copy-number variations and experimental inconsistencies. These include the WGS technology-specific effects of guanine-cytosine content variations across the genomes that are correlated with experimental batches. Third, by identifying the biologically consistent phenotype among the WGS-profiled tumors, the GBM pattern proves to be a technology-independent predictor of survival and response to chemotherapy and radiation, statistically better than the patient's age and tumor's grade, the best other indicators, and MGMT promoter methylation and IDH1 mutation. We conclude that by using the complex structure of the data, comparative spectral decompositions underlie a mathematically universal description of the genotype-phenotype relations in cancer that other methods miss.

Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and noninvasively reconstructed in a digitized model of the patient’s three-dimensional heart, which has led to clinical interest in ECGI’s ability to personalize diagnosis and guide therapy.
Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI’s ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this  review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice.

Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose ‘best’ practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique towards a useful role in clinical practice.

Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radio-frequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here, we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (five swine) and human studies (21 patients), as well as in a prospective feasibility study (five patients). We first assessed, using retrospective studies (one of which included a proportion of clinical images with artefacts), the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart before the clinical procedure.

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atrial structures in 3D, especially from late gadolinium-enhanced (LGE)-MRIs, rely heavily on manual segmentation methods which are extremely labor-intensive and prone to errors. As a result, a robust and automated method for analyzing atrial structures in 3D is of high interest. We have therefore developed AtriaNet, a 16-layer convolutional neural network (CNN), on 154 3D LGE-MRIs with a spatial resolution of 0.625 mm × 0.625 mm × 1.25 mm from patients with AF, to automatically segment the left atrial (LA) epicardium and endocardium. AtriaNet consists of a multi-scaled, dual pathway architecture that captures both the local atrial tissue geometry, and the global positional information of LA using 13 successive convolutions, and 3 further convolutions for merging. By utilizing computationally efficient batch prediction, AtriaNet was able to successfully process each 3D LGE-MRI within one minute. Furthermore, benchmarking experiments showed that AtriaNet outperformed state-of-the-art CNNs, with a DICE score of 0.940 and 0.942 for the LA epicardium and endocardium respectively, and an inter-patient variance of <0.001. The estimated LA diameter and volume computed from the automatic segmentations were accurate to within 1.59 mm and 4.01 cm³ of the ground truths. Our proposed CNN was tested on the largest known dataset for LA segmentation, and to the best of our knowledge, it is the most robust approach that has ever been developed for segmenting LGE-MRIs. The increased accuracy of atrial reconstruction and analysis could potentially improve the understanding and treatment of AF.

Traumatic brain injury (TBI) is a looming epidemic, growing most rapidly in the elderly population. Some of the most devastating sequelae of TBI are related to depressed levels of consciousness (e.g., coma, minimally conscious state) or deficits in executive function. To date, pharmacological and rehabilitative therapies to treat these sequelae are limited. Deep brain stimulation (DBS) has been used to treat a number of pathologies, including Parkinson disease, essential tremor, and epilepsy. Animal and clinical research shows that targets addressing depressed levels of consciousness include components of the ascending reticular activating system and areas of the thalamus. Targets for improving executive function are more varied and include areas that modulate attention and memory, such as the frontal and prefrontal cortex, fornix, nucleus accumbens, internal capsule, thalamus, and some brainstem nuclei. The authors review the literature addressing the use of DBS to treat higher-order cognitive dysfunction and disorders of consciousness in TBI patients, while also offering suggestions on directions for future research.

Deep brain stimulation (DBS), which involves insertion of an electrode to deliver stimulation to a localized brain region, is an established therapy for movement disorders and is being applied to a growing number of disorders. Computational modeling has been successfully used to predict the clinical effects of DBS; however, there is a need for novel modeling techniques to keep pace with the growing complexity of DBS devices. These models also need to generate predictions quickly and accurately. The goal of this project is to develop an image processing pipeline to incorporate structural magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) into an interactive, patient specific model to simulate the effects of DBS. A virtual DBS lead can be placed inside of the patient model, along with active contacts and stimulation settings, where changes in lead position or orientation generate a new finite element mesh and solution of the bioelectric field problem in near real-time, a timespan of approximately 10 seconds. This system also enables the simulation of multiple leads in close proximity to allow for current steering by varying anodes and cathodes on different leads. The techniques presented in this paper reduce the burden of generating and using computational models while providing meaningful feedback about the effects of electrode position, electrode design, and stimulation configurations to researchers or clinicians who may not be modeling experts.

We developed a new method to manufacture dense, aligned, and porous collagen scaffolds using biaxial plastic compression of type I collagen gels. Using a novel compression apparatus that constricts like an iris diaphragm, low density collagen gels were compressed to yield a permanently densified, highly aligned collagen material. Micro-porosity scaffolds were created using hydrophilic elastomer porogens that can be selectively removed following biaxial compression, with porosity modulated by using different porogen concentrations. The resulting scaffolds exhibit collagen densities that are similar to native connective tissues (∼10% collagen by weight), pronounced collagen alignment across multiple length scales, and an interconnected network of pores, making them highly relevant for use in tissue culture, the study of physiologically relevant cell-matrix interactions, and tissue engineering applications. The scaffolds exhibited highly anisotropic material behavior, with the modulus of the scaffolds in the fiber direction over 100 times greater than the modulus in the transverse direction. Adipose-derived mesenchymal stem cells were seeded onto the biaxially compressed scaffolds with minimal cell death over seven days of culture, along with cell proliferation and migration into the pore spaces. This fabrication method provides new capabilities to manufacture structurally and mechanically relevant cytocompatible scaffolds that will enable more physiologically relevant cell culture studies. Further improvement of manufacturing techniques has the potential to produce engineered scaffolds for direct replacement of dense connective tissues such as meniscus and annulus fibrosus.

Preprocessing Framework for Electrograms Intermittently Fiducialized from Experimental Recordings (PFEIFER) is a MATLAB Graphical User Interface designed to process bioelectric signals acquired from experiments.

PFEIFER was specifically designed to process electrocardiographic recordings from electrodes placed on or around the heart or on the body surface. Specific steps included in PFEIFER allow the user to remove some forms of noise, correct for signal drift, and mark specific instants or intervals in time (fiducialize) within all of the time sampled channels. PFEIFER includes many unique features that allow the user to process electrical signals in a consistent and time efficient manner, with additional options for advanced user configurations and input. PFEIFER is structured as a consolidated framework that provides many standard processing pipelines but also has flexibility to allow the user to customize many of the steps. PFEIFER allows the user to import time aligned cardiac electrical signals, semi-automatically determine fiducial markings from those signals, and perform computational tasks that prepare the signals for subsequent display and analysis.

The mechanics of biological fluids is an important topic in biomechanics, often requiring the use of computational tools to analyze problems with realistic geometries and material properties. This study describes the formulation and implementation of a finite element framework for computational fluid dynamics (CFD) in FEBio, a free software designed to meet the computational needs of the biomechanics and biophysics communities. This formulation models nearly incompressible flow with a compressible isothermal formulation that uses a physically realistic value for the fluid bulk modulus. It employs fluid velocity and dilatation as essential variables: The virtual work integral enforces the balance of linear momentum and the kinematic constraint between fluid velocity and dilatation, while fluid density varies with dilatation as prescribed by the axiom of mass balance. Using this approach, equal-order interpolations may be used for both essential variables over each element, contrary to traditional mixed formulations that must explicitly satisfy the inf-sup condition. The formulation accommodates Newtonian and non-Newtonian viscous responses as well as inviscid fluids. The efficiency of numerical solutions is enhanced using Broyden's quasi-Newton method. The results of finite element simulations were verified using well-documented benchmark problems as well as comparisons with other free and commercial codes. These analyses demonstrated that the novel formulation introduced in FEBio could successfully reproduce the results of other codes. The analogy between this CFD formulation and standard finite element formulations for solid mechanics makes it suitable for future extension to fluid–structure interactions (FSIs).

The role of computational modeling for biomechanics research and related clinical care will be increasingly prominent. The biomechanics community has been developing computational models routinely for exploration of the mechanics and mechanobiology of diverse biological structures. As a result, a large array of models, data, and discipline-specific simulation software has emerged to support endeavors in computational biomechanics. Sharing computational models and related data and simulation software has first become a utilitarian interest, and now, it is a necessity. Exchange of models, in support of knowledge exchange provided by scholarly publishing, has important implications. Specifically, model sharing can facilitate assessment of reproducibility in computational biomechanics and can provide an opportunity for repurposing and reuse, and a venue for medical training. The community's desire to investigate biological and biomechanical phenomena crossing multiple systems, scales, and physical domains, also motivates sharing of modeling resources as blending of models developed by domain experts will be a required step for comprehensive simulation studies as well as the enhancement of their rigor and reproducibility. The goal of this paper is to understand current perspectives in the biomechanics community for the sharing of computational models and related resources. Opinions on opportunities, challenges, and pathways to model sharing, particularly as part of the scholarly publishing workflow, were sought. A group of journal editors and a handful of investigators active in computational biomechanics were approached to collect short opinion pieces as a part of a larger effort of the IEEE EMBS Computational Biology and the Physiome Technical Committee to address model reproducibility through publications. A synthesis of these opinion pieces indicates that the community recognizes the necessity and usefulness of model sharing. There is a strong will to facilitate model sharing, and there are corresponding initiatives by the scientific journals. Outside the publishing enterprise, infrastructure to facilitate model sharing in biomechanics exists, and simulation software developers are interested in accommodating the community's needs for sharing of modeling resources. Encouragement for the use of standardized markups, concerns related to quality assurance, acknowledgement of increased burden, and importance of stewardship of resources are noted. In the short-term, it is advisable that the community builds upon recent strategies and experiments with new pathways for continued demonstration of model sharing, its promotion, and its utility. Nonetheless, the need for a long-term strategy to unify approaches in sharing computational models and related resources is acknowledged. Development of a sustainable platform supported by a culture of open model sharing will likely evolve through continued and inclusive discussions bringing all stakeholders at the table, e.g., by possibly establishing a consortium.

Purpose

Develop a framework to quantify the size, location and severity of femoral and acetabular-sided cartilage and labral damage observed in patients undergoing hip arthroscopy, and generate a database of individual defect parameters to facilitate future research and treatment efforts.

Methods

The size, location, and severity of cartilage and labral damage were prospectively collected using a custom, standardized post-operative template for 100 consecutive patients with femoroacetabular impingement syndrome. Chondrolabral junction damage, isolated intrasubstance labral damage, isolated acetabular cartilage damage and femoral cartilage damage were quantified and recorded using a combination of Beck and ICRS criteria. Radiographic measurements including alpha angle, head–neck offset, lateral centre edge angle and acetabular index were calculated and compared to the aforementioned chondral data using a multivariable logistic regression model and adjusted odd's ratio. Reliability among measurements were assessed using the kappa statistic and intraclass coefficients were used to evaluate continuous variables.

Results

Damage to the acetabular cartilage originating at the chondrolabral junction was the most common finding in 97 hips (97%) and was usually accompanied by labral damage in 65 hips (65%). The width (p = 0.003) and clock-face length (p = 0.016) of the damaged region both increased alpha angle on anteroposterior films. 10% of hips had femoral cartilage damage while only 2 (2%) of hips had isolated defects to either the acetabular cartilage or labrum. The adjusted odds of severe cartilage (p = 0.022) and labral damage (p = 0.046) increased with radiographic cam deformity but was not related to radiographic measures of acetabular coverage.

Conclusions

Damage at the chondrolabral junction was very common in this hip arthroscopy cohort, while isolated defects to the acetabular cartilage or labrum were rare. These data demonstrate that the severity of cam morphology, quantified through radiographic measurements, is a primary predictor of location and severity of chondral and labral damage and focal chondral defects may represent a unique subset of patients that deserve further study.

Osteoarthritis of the hip can result from mechanical factors, which can be studied using finite element (FE) analysis. FE studies of the hip often assume there is no significant loss of fluid pressurization in the articular cartilage during simulated activities and approximate the material as incompressible and elastic. This study examined the conditions under which interstitial fluid load support remains sustained during physiological motions, as well as the role of the labrum in maintaining fluid load support and the effect of its presence on the solid phase of the surrounding cartilage. We found that dynamic motions of gait and squatting maintained consistent fluid load support between cycles, while static single-leg stance experienced slight fluid depressurization with significant reduction of solid phase stress and strain. Presence of the labrum did not significantly influence fluid load support within the articular cartilage, but prevented deformation at the cartilage edge, leading to lower stress and strain conditions in the cartilage. A morphologically accurate representation of collagen fibril orientation through the thickness of the articular cartilage was not necessary to predict fluid load support. However, comparison with simplified fibril reinforcement underscored the physiological importance. The results of this study demonstrate that an elastic incompressible material approximation is reasonable for modeling a limited number of cyclic motions of gait and squatting without significant loss of accuracy, but is not appropriate for static motions or numerous repeated motions. Additionally, effects seen from removal of the labrum motivate evaluation of labral reattachment strategies in the context of labral repair.

Background: Noninvasive localization of premature ventricular complexes (PVCs) to guide ablation therapy is one of the emerging applications of electrocardiographic imaging (ECGI). Because of its increasing clinical use, it is essential to compare the many implementations of ECGI that exist to understand the specific characteristics of each approach.

Objective: Our consortium is a community of researchers aiming to collaborate in the field of ECGI, and to objectively compare and improve methods. Here, we will compare methods to localize the origin of PVCs with ECGI.

Methods: Our consortium hosts a repository of ECGI data on its website. For the current study, participants analysed simulated electrocardiograms from premature beats, freely available on that website. These PVCs were simulated to originate from eight ventricular locations and the resulting body-surface potentials were computed. These body-surface electrocardiograms (and the torso-heart geometry) were then provided to the study participants to apply their ECGI algorithms to determine the origin of the PVCs. Participants could choose freely among four different source models, i.e., representations of the bioelectric fields reconstructed from ECGI: 1) epicardial potentials (POTepi), 2) epicardial & endocardial potentials (POTepi&endo), 3) transmembrane potentials on the endocardium and epicardium (TMPepi&endo) and 4) transmembrame potentials throughout the myocardium (TMPmyo). Participants were free to employ any software implementation of ECGI and were blinded to the ground truth data.

Results: Four research groups submitted 11 entries for this study. The figure shows the localization error between the known and reconstructed origin of each PVC for each submission, categorized per source model. Each colour represents one research group and some groups submitted results using different approaches. These results demonstrate that the variation of accuracy was larger among research groups than among the source models. Most submissions achieved an error below 2 cm, but none performed with a consistent sub-centimetre accuracy.

Conclusion: This study demonstrates a successful community-based approach to study different ECGI methods for PVC localization. The goal was not to rank research groups but to compare both source models and numerical implementations. PVC localization with these methods was not as dependent on the source representation as it was on the implementation of ECGI. Consequently, ECGI validation should not be performed on generic methods, but should be specifically performed for each lab's implementation. The novelty of this study is that it achieves this in the first open, international comparison of approaches using a common set of gold standards. Continued collaborative validation is essential to understand the effect of implementation differences, in order to reach significant improvements and arrive at clinically-relevant sub-centimetre accuracy of PVC localization.

Background

Computational models of myocardial ischemia often use oversimplified ischemic source representations to simulate epicardial potentials. The purpose of this study was to explore the influence of biophysically justified, subject-specific ischemic zone representations on epicardial potentials.

Methods

We developed and implemented an image-based simulation pipeline, using intramural recordings from a canine experimental model to define subject-specific ischemic regions within the heart. Static epicardial potential distributions, reflective of ST segment deviations, were simulated and validated against measured epicardial recordings.

Results

Simulated epicardial potential distributions showed strong statistical correlation and visual agreement with measured epicardial potentials. Additionally, we identified and described in what way border zone parameters influence epicardial potential distributions during the ST segment.

Conclusion

From image-based simulations of myocardial ischemia, we generated subject-specific ischemic sources that accurately replicated epicardial potential distributions. Such models are essential in understanding the underlying mechanisms of the bioelectric fields that arise during ischemia and are the basis for more sophisticated simulations of body surface ECGs.

1 Introduction

Reversible edema is a part of any radiofrequency ablation but its relationship with contact force is unknown. The goal of this study was to characterize through histology and MRI, acute and chronic ablation lesions and reversible edema with contact force.

2 Methods and results

In a canine model (n = 14), chronic ventricular lesions were created with a 3.5‐mm tip ThermoCool SmartTouch (Biosense Webster) catheter at 25 W or 40 W for 30 seconds. Repeat ablation was performed after 3 months to create a second set of lesions (acute). Each ablation procedure was followed by in vivo T2‐weighted MRI for edema and late‐gadolinium enhancement (LGE) MRI for lesion characterization. For chronic lesions, the mean scar volumes at 25 W and 40 W were 77.8 ± 34.5 mm³ (n = 24) and 139.1 ± 69.7 mm3 (n = 12), respectively. The volume of chronic lesions increased (25 W: P < 0.001, 40 W: P < 0.001) with greater contact force. For acute lesions, the mean volumes of the lesion were 286.0 ± 129.8 mm³ (n = 19) and 422.1 ± 113.1 mm³ (n = 16) for 25 W and 40 W, respectively (P < 0.001 compared to chronic scar). On T2‐weighted MRI, the acute edema volume was on average 5.6–8.7 times higher than the acute lesion volume and increased with contact force (25 W: P = 0.001, 40 W: P = 0.011).

3 Conclusion

With increasing contact force, there is a marginal increase in lesion size but accompanied with a significantly larger edema. The reversible edema that is much larger than the chronic lesion volume may explain some of the chronic procedure failures.

The biophysical basis for electrocardiographic evaluation of myocardial ischemia stems from the notion that ischemic tissues develop, with relative uniformity, along the endocardial aspects of the heart. These injured regions of subendocardial tissue give rise to intramural currents that lead to ST segment deflections within electrocardiogram (ECG) recordings. The concept of subendocardial ischemic regions is often used in clinical practice, providing a simple and intuitive description of ischemic injury; however, such a model grossly oversimplifies the presentation of ischemic disease—inadvertently leading to errors in ECG-based diagnoses. Furthermore, recent experimental studies have brought into question the subendocardial ischemia paradigm suggesting instead a more distributed pattern of tissue injury. These findings come from experiments and so have both the impact and the limitations of measurements from living organisms. Computer models have often been employed to overcome the constraints of experimental approaches and have a robust history in cardiac simulation. To this end, we have developed a computational simulation framework aimed at elucidating the effects of ischemia on measurable cardiac potentials. To validate our framework, we simulated, visualized, and analyzed 226 experimentally derived acute myocardial ischemic events. Simulation outcomes agreed both qualitatively (feature comparison) and quantitatively (correlation, average error, and significance) with experimentally obtained epicardial measurements, particularly under conditions of elevated ischemic stress. Our simulation framework introduces a novel approach to incorporating subject-specific, geometric models and experimental results that are highly resolved in space and time into computational models. We propose this framework as a means to advance the understanding of the underlying mechanisms of ischemic disease while simultaneously putting in place the computational infrastructure necessary to study and improve ischemia models aimed at reducing diagnostic errors in the clinic.

Direct stimulation of the cortical surface is used clinically for cortical mapping and modulation of local activity. Future applications of cortical modulation and brain-computer interfaces may also use cortical stimulation methods. One common method to deliver current is through electrocorticography (ECoG) stimulation in which a dense array of electrodes are placed subdurally or epidurally to stimulate the cortex. However, proximity to cortical tissue limits the amount of current that can be delivered safely. It may be desirable to deliver higher current to a specific local region of interest (ROI) while limiting current to other local areas more stringently than is guaranteed by global safety limits. Two commonly used global safety constraints bound the total injected current and individual electrode currents. However, these two sets of constraints may not be sufficient to prevent high current density locally (hot-spots). In this work, we propose an efficient approach that prevents current density hot-spots in the entire brain while optimizing ECoG stimulus patterns for targeted stimulation. Specifically, we maximize the current along a particular desired directional field in the ROI while respecting three safety constraints: one on the total injected current, one on individual electrode currents, and the third on the local current density magnitude in the brain. This third set of constraints creates a computational barrier due to the huge number of constraints needed to bound the current density at every point in the entire brain. We overcome this barrier by adopting an efficient two-step approach. In the first step, the proposed method identifies the safe brain region, which cannot contain any hot-spots solely based on the global bounds on total injected current and individual electrode currents. In the second step, the proposed algorithm iteratively adjusts the stimulus pattern to arrive at a solution that exhibits no hot-spots in the remaining brain. We report on simulations on a realistic finite element (FE) head model with five anatomical ROIs and two desired directional fields. We also report on the effect of ROI depth and desired directional field on the focality of the stimulation. Finally, we provide an analysis of optimization runtime as a function of different safety and modeling parameters. Our results suggest that optimized stimulus patterns tend to differ from those used in clinical practice.

Objective. Deep brain stimulation (DBS) is a growing treatment option for movement and psychiatric disorders. As DBS technology moves toward directional leads with increased numbers of smaller electrode contacts, trial-and-error methods of manual DBS programming are becoming too time-consuming for clinical feasibility. We propose an algorithm to automate DBS programming in near real-time for a wide range of DBS lead designs. Approach. Magnetic resonance imaging and diffusion tensor imaging are used to build finite element models that include anisotropic conductivity. The algorithm maximizes activation of target tissue and utilizes the Hessian matrix of the electric potential to approximate activation of neurons in all directions. We demonstrate our algorithm's ability in an example programming case that targets the subthalamic nucleus (STN) for the treatment of Parkinson's disease for three lead designs: the Medtronic 3389 (four cylindrical contacts), the direct STNAcute (two cylindrical contacts, six directional contacts), and the Medtronic-Sapiens lead (40 directional contacts). Main results. The optimization algorithm returns patient-specific contact configurations in near real-time—less than 10 s for even the most complex leads. When the lead was placed centrally in the target STN, the directional leads were able to activate over 50% of the region, whereas the Medtronic 3389 could activate only 40%. When the lead was placed 2 mm lateral to the target, the directional leads performed as well as they did in the central position, but the Medtronic 3389 activated only 2.9% of the STN. Significance. This DBS programming algorithm can be applied to cylindrical electrodes as well as novel directional leads that are too complex with modern technology to be manually programmed. This algorithm may reduce clinical programming time and encourage the use of directional leads, since they activate a larger volume of the target area than cylindrical electrodes in central and off-target lead placements.

This report presents challenges, opportunities and directions for computational science and engineering (CSE) research and education for the next decade. Over the past two decades the field of CSE has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers with algorithmic inventions and software systems that transcend disciplines and scales. CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments—including the architectural complexity of extreme-scale computing, the data revolution and increased attention to data-driven discovery, and the specialization required to follow the applications to new frontiers—is redefining the scope and reach of the CSE endeavor. With these many current and expanding opportunities for the CSE field, there is a growing demand for CSE graduates and a need to expand CSE educational offerings. This need includes CSE programs at both the undergraduate and graduate levels, as well as continuing education and professional development programs, exploiting the synergy between computational science and data science. Yet, as institutions consider new and evolving educational programs, it is essential to consider the broader research challenges and opportunities that provide the context for CSE education and workforce development.