Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
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
butson

Chris Butson

Neuromodulation
Deep Brain Simulation
Transcranial Magnetic
Stimulation (TMS)

bidone

Tamara Bidone

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

   

Associated Centers:


Publications in Biomedical Computing:


Interactive computation and visualization of deep brain stimulation effects using Duality,
J. Vorwerk, D. McCann, J. Krüger, C.R. Butson. In Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Taylor & Francis, 2019.

Deep brain stimulation (DBS) is an established treatment for movement disorders such as Parkinson’s disease or essential tremor. Currently, the selection of optimal stimulation settings is performed by iteratively adjusting the stimulation parameters and is a time consuming procedure that requires multiple clinic visits of several hours. Recently, computational models to predict and visualize the effect of DBS have been developed with the goal to simplify and accelerate this procedure by providing visual guidance and such models have been made available also on mobile devices. However, currently available visualization software still either lacks mobility, i.e. it is running on desktop computers and no easily available in clinical praxis, or flexibility, as the simulations that are visualized on mobile devices have to be precomputed. The goal of the pipeline presented in this paper is to close this gap: Using Duality, a newly developed software for the interactive visualization of simulation results, we implemented a pipeline that allows to compute DBS simulations in near-real time and instantaneously visualize the result on a tablet computer. We carry out a performance analysis and present the results of a case study in which the pipeline was applied.



A retrospective evaluation of automated optimization of deep brain stimulation parameters
J. Vorwerk, A. Brock, D.N. Anderson, J.D. Rolston, C.R. Butson. In Journal of Neural Engineering, 2019.
DOI: 10.1088/1741-2552/ab35b1

Objective: We performed a retrospective analysis of an optimization algorithm for the computation of patient-specific multipolar stimulation configurations employing multiple independent current/voltage sources. We evaluated whether the obtained stimulation configurations align with clinical data and whether the optimized stimulation configurations have the potential to lead to an equal or better stimulation of the target region as manual programming, while reducing the time required for programming sessions. Methods: For three patients (five electrodes) diagnosed with essential tremor, we derived optimized multipolar stimulation configurations using an approach that is suitable for the application in clinical practice. To evaluate the automatically derived stimulation settings, we compared them to the results of the monopolar review. Results: We observe a good agreement between the findings of the monopolar review and the optimized stimulation configurations, with the algorithm assigning the maximal voltage in the optimized multipolar pattern to the contact that was found to lead to the best therapeutic effect in the clinical monopolar review in all cases. Additionally, our simulation results predict that the optimized stimulation settings lead to the activation of an equal or larger volume fraction of the target compared to the manually determined settings in all cases. Conclusions: Our results demonstrate the feasibility of an automatic determination of optimal DBS configurations and motivate a further evaluation of the applied optimization algorithm.



Interleaved deep brain stimulation for dyskinesia management in Parkinson's disease
C. C. Aquino, G. Duffley, D. M. Hedges, J. Vorwerk, P. A. House, H. B. Ferraz, J. D. Rolston, C. R. Butson, L. E. Schrock. In Movement Disorders, 2019.
DOI: 10.1002/mds.27839

Background

In patients with Parkinson's disease, stimulation above the subthalamic nucleus (STN) may engage the pallidofugal fibers and directly suppress dyskinesia.

Objectives

The objective of this study was to evaluate the effect of interleaving stimulation through a dorsal deep brain stimulation contact above the STN in a cohort of PD patients and to define the volume of tissue activated with antidyskinesia effects.

Methods

We analyzed the Core Assessment Program for Surgical Interventional Therapies dyskinesia scale, Unified Parkinson's Disease Rating Scale parts III and IV, and other endpoints in 20 patients with interleaving stimulation for management of dyskinesia. Individual models of volume of tissue activated and heat maps were used to identify stimulation sites with antidyskinesia effects.

Results

The Core Assessment Program for Surgical Interventional Therapies dyskinesia score in the on medication phase improved 70.9 ± 20.6% from baseline with noninterleaved settings (P < 0.003). With interleaved settings, dyskinesia improved 82.0 ± 27.3% from baseline (P < 0.001) and 61.6 ± 39.3% from the noninterleaved phase (P = 0.006). The heat map showed a concentration of volume of tissue activated dorsally to the STN during the interleaved setting with an antidyskinesia effect.

Conclusion

Interleaved deep brain stimulation using the dorsal contacts can directly suppress dyskinesia, probably because of the involvement of the pallidofugal tract, allowing more conservative medication reduction. © 2019 International Parkinson and Movement Disorder Society



Evaluation of methodologies for computing the deep brain stimulation volume of tissue activated
G. Duffley, D. N. Anderson, J. Vorwerk, A. C. Dorval, C. R. Butson. In Journal of Neural Engineering, Aug, 2019.
DOI: 10.1088/1741-2552/ab3c95

Computational models are a popular tool for predicting the effects of deep brain stimulation (DBS) on neural tissue. One commonly used model, the volume of tissue activated (VTA), is computed using multiple methodologies. We quantified differences in the VTAs generated by five methodologies: the traditional axon model method, the electric field norm, and three activating function based approaches - the activating function at each grid point in the tangential direction (AF-Tan) or in the maximally activating direction (AF-3D), and the maximum activating function along the entire length of a tangential fiber (AF-Max).

Approach: We computed the VTA using each method across multiple stimulation settings. The resulting volumes were compared for similarity, and the methodologies were analyzed for their differences in behavior.

Main Results: Activation threshold values for both the electric field norm and the activating function vary with regards to electrode configuration, pulse width, and frequency. All methods produced highly similar volumes for monopolar stimulation. For bipolar electrode configurations, only the maximum activating function along the tangential axon method, AF-Max, produced similar volumes to those produced by the axon model method. Further analysis revealed that both of these methods are biased by their exclusive use of tangential fiber orientations. In contrast, the activating function in the maximally activating direction method, AF-3D, produces a VTA that is free of axon orientation and projection bias.

Significance: Simulating tangentially oriented axons, the standard approach of computing the VTA, is too computationally expensive for widespread implementation and yields results biased by the assumption of tangential fiber orientation. In this work, we show that a computationally efficient method based on the activating function, AF-Max, reliably reproduces the VTAs generated by direct axon modeling. Further, we propose another method, AF-3D as a potentially superior model for representing generic neural tissue activation.



A statistical framework for quantification and visualisation of positional uncertainty in deep brain stimulation electrodes,
T. M. Athawale, K. A. Johnson, C. R. Butson, C. R. Johnson. In Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol. 7, No. 4, Taylor & Francis, pp. 438-449. 2019.
DOI: 10.1080/21681163.2018.1523750

Deep brain stimulation (DBS) is an established therapy for treating patients with movement disorders such as Parkinson’s disease. Patient-specific computational modelling and visualisation have been shown to play a key role in surgical and therapeutic decisions for DBS. The computational models use brain imaging, such as magnetic resonance (MR) and computed tomography (CT), to determine the DBS electrode positions within the patient’s head. The finite resolution of brain imaging, however, introduces uncertainty in electrode positions. The DBS stimulation settings for optimal patient response are sensitive to the relative positioning of DBS electrodes to a specific neural substrate (white/grey matter). In our contribution, we study positional uncertainty in the DBS electrodes for imaging with finite resolution. In a three-step approach, we first derive a closed-form mathematical model characterising the geometry of the DBS electrodes. Second, we devise a statistical framework for quantifying the uncertainty in the positional attributes of the DBS electrodes, namely the direction of longitudinal axis and the contact-centre positions at subvoxel levels. The statistical framework leverages the analytical model derived in step one and a Bayesian probabilistic model for uncertainty quantification. Finally, the uncertainty in contact-centre positions is interactively visualised through volume rendering and isosurfacing techniques. We demonstrate the efficacy of our contribution through experiments on synthetic and real datasets. We show that the spatial variations in true electrode positions are significant for finite resolution imaging, and interactive visualisation can be instrumental in exploring probabilistic positional variations in the DBS lead.



Image-based analysis and long-term clinical outcomes of deep brain stimulation for Tourette syndrome: a multisite study
K. A. Johnson, P. T. Fletcher, D. Servello, A. Bona, M. Porta, J. L. Ostrem, E. Bardinet, M. Welter, A. M. Lozano, J. C. Baldermann, J. Kuhn, D. Huys, T. Foltynie, M. Hariz, E. M. Joyce, L. Zrinzo, Z. Kefalopoulou, J. Zhang, F. Meng, C. Zhang, Z. Ling, X. Xu, X. Yu, A. YJM Smeets, L. Ackermans, V. Visser-Vandewalle, A. Y. Mogilner, M. H. Pourfar, L. Almeida, A. Gunduz, W. Hu, K. D. Foote, M. S. Okun, C. R. Butson. In Journal of Neurology, Neurosurgery & Psychiatry, BMJ Publishing Group, 2019.
DOI: 10.1136/jnnp-2019-320379

BACKGROUND:
Deep brain stimulation (DBS) can be an effective therapy for tics and comorbidities in select cases of severe, treatment-refractory Tourette syndrome (TS). Clinical responses remain variable across patients, which may be attributed to differences in the location of the neuroanatomical regions being stimulated. We evaluated active contact locations and regions of stimulation across a large cohort of patients with TS in an effort to guide future targeting.

METHODS:
We collected retrospective clinical data and imaging from 13 international sites on 123 patients. We assessed the effects of DBS over time in 110 patients who were implanted in the centromedial (CM) thalamus (n=51), globus pallidus internus (GPi) (n=47), nucleus accumbens/anterior limb of the internal capsule (n=4) or a combination of targets (n=8). Contact locations (n=70 patients) and volumes of tissue activated (n=63 patients) were coregistered to create probabilistic stimulation atlases.
RESULTS:
Tics and obsessive-compulsive behaviour (OCB) significantly improved over time (p<0.01), and there were no significant differences across brain targets (p>0.05). The median time was 13 months to reach a 40% improvement in tics, and there were no significant differences across targets (p=0.84), presence of OCB (p=0.09) or age at implantation (p=0.08). Active contacts were generally clustered near the target nuclei, with some variability that may reflect differences in targeting protocols, lead models and contact configurations. There were regions within and surrounding GPi and CM thalamus that improved tics for some patients but were ineffective for others. Regions within, superior or medial to GPi were associated with a greater improvement in OCB than regions inferior to GPi.
CONCLUSION:
The results collectively indicate that DBS may improve tics and OCB, the effects may develop over several months, and stimulation locations relative to structural anatomy alone may not predict response. This study was the first to visualise and evaluate the regions of stimulation across a large cohort of patients with TS to generate new hypotheses about potential targets for improving tics and comorbidities.



Neural Selectivity, Efficiency, and Dose Equivalence in Deep Brain Stimulation through Pulse Width Tuning and Segmented Electrodes
C.J. Anderson, D.N. Anderson, S.M. Pulst, C.R. Butson, A.D. Dorval. In bioRxiv, Cold Spring Harbor Laboratory, April, 2019.
DOI: 10.1101/613133

Background
Achieving deep brain stimulation (DBS) dose equivalence is challenging, especially with pulse width tuning and directional contacts. Further, the precise effects of pulse width tuning are unknown.

Methods
We created multicompartment neuron models for two axon diameters and used finite element modeling to determine extracellular influence from standard and segmented electrodes. We analyzed axon activation profiles and calculated volumes of tissue activated.

Results
Long pulse widths focus the stimulation effect on small, nearby fibers, suppressing white matter tract activation (responsible for some DBS side effects) and improving battery utilization. Directional leads enable similar benefits to a greater degree. We derive equations for equivalent activation with pulse width tuning and segmented contacts.

Interpretations
We find agreement with classic studies and reinterpret recent articles concluding that short pulse widths focus the stimulation effect on small, nearby fibers, decrease side effects, and improve power consumption. Our field should reconsider shortened pulse widths.



Anodic stimulation misunderstood: preferential activation of fiber orientations with anodic waveforms in deep brain stimulation
D.N. Anderson, G. Duffley, J. Vorwerk, A.D. Dorval, C.R. Butson. In Journal of Neural Engineering, Vol. 16, No. 1, IOP Publishing, pp. 016026. Jan, 2019.
DOI: 10.1088/1741-2552/aae590

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.



Effect of STN DBS on vesicular monoamine transporter 2 and glucose metabolism in Parkinson's disease
G. S. Smith, K. A. Mills, G. M. Pontone, W. S. Anderson, K. M. Perepezko, J. Brasic, Y. Zhou, J. Brandt, C. R. Butson, D. P. Holt, W. B. Mathews, R. F. Dannals, D. F. Wong, Z. Mari. In Parkinsonism and Related Disorders, Elsevier, 2019.

Introduction

Deep brain stimulation (DBS) is an established treatment Parkinson's Disease (PD). Despite the improvement of motor symptoms in most patients by sub-thalamic nucleus (STN) DBS and its widespread use, the neurobiological mechanisms are not completely understood. The objective of the present study was to elucidate the effects of STN DBS in PD on the dopamine system and neural circuitry employing high-resolution positron emission tomography (PET) imaging. The hypotheses tested were that STN DBS would decrease striatal VMAT2, secondary to an increase in dopamine concentrations, and would decrease striatal cerebral metabolism and increase cortical metabolism.

Methods

PET imaging of the vesicular monoamine transporter (VMAT2) and cerebral glucose metabolism was performed prior to DBS surgery and after 4–6 months of STN stimulation in seven PD patients (mean age 67 ± 7).
Results

The patients demonstrated significant improvement in motor and neuropsychiatric symptoms after STN DBS. Decreased VMAT2 was observed in the caudate, putamen and associative striatum and in extra-striatal, cortical and limbic regions. Cerebral glucose metabolism was decreased in striatal sub-regions and increased in temporal and parietal cortices and the cerebellum. Decreased striatal VMAT2 was correlated with decreased striatal and increased cortical and limbic metabolism. Improvement of depressive symptoms was correlated with decreased VMAT2 in striatal and extra-striatal regions and with striatal decreases and cortical increases in metabolism.
Conclusions

The present results support further investigation of the role of VMAT2, and associated changes in neural circuitry in the improvement of motor and non-motor symptoms with STN DBS in PD.



A High-Resolution Head and Brain Computer Model for Forward and Inverse EEG Simulation
A. Warner, J. Tate, B. Burton,, C.R. Johnson. In bioRxiv, Cold Spring Harbor Laboratory, Feb, 2019.
DOI: 10.1101/552190

To conduct computational forward and inverse EEG studies of brain electrical activity, researchers must construct realistic head and brain computer models, which is both challenging and time consuming. The availability of realistic head models and corresponding imaging data is limited in terms of imaging modalities and patient diversity. In this paper, we describe a detailed head modeling pipeline and provide a high-resolution, multimodal, open-source, female head and brain model. The modeling pipeline specifically outlines image acquisition, preprocessing, registration, and segmentation; three-dimensional tetrahedral mesh generation; finite element EEG simulations; and visualization of the model and simulation results. The dataset includes both functional and structural images and EEG recordings from two high-resolution electrode configurations. The intermediate results and software components are also included in the dataset to facilitate modifications to the pipeline. This project will contribute to neuroscience research by providing a high-quality dataset that can be used for a variety of applications and a computational pipeline that may help researchers construct new head models more efficiently.



Anodic Stimulation Misunderstood: Preferential Activation of Fiber Orientations with Anodic Waveforms in Deep Brain Stimulation
D. N. Anderson, G. Duffley, J. Vorwerk, A. Dorval, C. R. Butson. In Journal of Neural Engineering, IOP Publishing, Oct, 2018.
DOI: 10.1088/1741-2552/aae590

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.



A statistical framework for quantification and visualisation of positional uncertainty in deep brain stimulation electrodes,
T. M. Athawale, K. A. Johnson, C. R. Butson, C. R. Johnson. In Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Taylor & Francis, pp. 1-12. 2018.
DOI: doi:10.1080/21681163.2018.1523750

Deep brain stimulation (DBS) is an established therapy for treating patients with movement disorders such as Parkinson's disease. Patient-specific computational modelling and visualisation have been shown to play a key role in surgical and therapeutic decisions for DBS. The computational models use brain imaging, such as magnetic resonance (MR) and computed tomography (CT), to determine the DBS electrode positions within the patient's head. The finite resolution of brain imaging, however, introduces uncertainty in electrode positions. The DBS stimulation settings for optimal patient response are sensitive to the relative positioning of DBS electrodes to a specific neural substrate (white/grey matter). In our contribution, we study positional uncertainty in the DBS electrodes for imaging with finite resolution. In a three-step approach, we first derive a closed-form mathematical model characterising the geometry of the DBS electrodes. Second, we devise a statistical framework for quantifying the uncertainty in the positional attributes of the DBS electrodes, namely the direction of longitudinal axis and the contact-centre positions at subvoxel levels. The statistical framework leverages the analytical model derived in step one and a Bayesian probabilistic model for uncertainty quantification. Finally, the uncertainty in contact-centre positions is interactively visualised through volume rendering and isosurfacing techniques. We demonstrate the efficacy of our contribution through experiments on synthetic and real datasets. We show that the spatial variations in true electrode positions are significant for finite resolution imaging, and interactive visualisation can be instrumental in exploring probabilistic positional variations in the DBS lead.



Mathematically universal and biologically consistent astrocytoma genotype encodes for transformation and predicts survival phenotype
K. A. Aiello, S. P. Ponnapalli, O. Alter. In APL Bioengineering, Vol. 2, No. 3, AIP Publishing, pp. 031909. September, 2018.
DOI: 10.1063/1.5037882

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.



Validation and Opportunities of Electrocardiographic Imaging: From Technical Achievements to Clinical Applications
M. Cluitmans, D. H. Brooks, R. MacLeod, O. Dössel, M. S. Guillem, P. M. van Dam, J. Svehlikova, B. He, J. Sapp, L. Wang, L. Bear. In Frontiers in Physiology, Vol. 9, Frontiers Media SA, pp. 1305. 2018.
ISSN: 1664-042X
DOI: 10.3389/fphys.2018.01305

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.



Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia,
A. Prakosa, H. J. Arevalo, D. Deng, P. M. Boyle, P. P. Nikolov, H. Ashikaga, J. J. E. Blauer, E. Ghafoori, C. J. Park, R. C. Blake, F. T. Han, R. S. MacLeod, H. R. Halperin, D. J. Callans, R. Ranjan, J. Chrispin, S. Nazarian, N. A. Trayanova. In Nature Biomedical Engineering, Springer Nature America, Inc, September, 2018.
DOI: 10.1038/s41551-018-0282-2

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.



Fully Automatic Left Atrium Segmentation from Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network
Z. Xiong, V. V. Fedorov, X. Fu, E. Cheng, R. Macleod, J. Zhao. In IEEE Transactions on Medical Imaging, IEEE, pp. 1--1. 2018.
DOI: 10.1109/tmi.2018.2866845

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.



Deep brain stimulation for the treatment of disorders of consciousness and cognition in traumatic brain injury patients: a review
B. Kundu, A. A. Brock, D. J. Englot, C. R. Butson, J. D. Rolston. In Neurosurgical Focus, Vol. 45, No. 2, Journal of Neurosurgery Publishing Group (JNSPG), pp. E14. Aug, 2018.
DOI: 10.3171/2018.5.focus18168

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.



Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models,
A. Janson, C. Butson. In Journal of Visualized Experiments, No. 138, MyJove Corporation, Aug, 2018.
DOI: 10.3791/57292

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.



Fabrication of dense anisotropic collagen scaffolds using biaxial compression
J.L. Zitnay, S.P. Reese, G. Tran, N. Farhang, R.D. Bowles, J.A. Weiss. In Acta Biomaterialia, Vol. 65, Elsevier BV, pp. 76--87. Jan, 2018.
DOI: 10.1016/j.actbio.2017.11.017

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.



PFEIFER: Preprocessing Framework for Electrograms Intermittently Fiducialized from Experimental Recordings
A. Rodenhauser, W.W. Good, B. Zenger, J. Tate, K. Aras, B. Burton, R.S. Macleod. In The Journal of Open Source Software, Vol. 3, No. 21, The Open Journal, pp. 472. Jan, 2018.
DOI: 10.21105/joss.00472

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.