Vision loss after optic neuropathy is considered irreversible. Here, repetitive transorbital alternating current stimulation (rtACS) was applied in partially blind patients with the goal of activating their residual vision.
We conducted a multicenter, prospective, randomized, double-blind, sham-controlled trial in an ambulatory setting with daily application of rtACS (n = 45) or sham-stimulation (n = 37) for 50 min for a duration of 10 week days. A volunteer sample of patients with optic nerve damage (mean age 59.1 yrs) was recruited. The primary outcome measure for efficacy was super-threshold visual fields with 48 hrs after the last treatment day and at 2-months follow-up. Secondary outcome measures were near-threshold visual fields, reaction time, visual acuity, and resting-state EEGs to assess changes in brain physiology.
The rtACS-treated group had a mean improvement in visual field of 24.0% which was significantly greater than after sham-stimulation (2.5%). This improvement persisted for at least 2 months in terms of both within- and between-group comparisons. Secondary analyses revealed improvements of near-threshold visual fields in the central 5° and increased thresholds in static perimetry after rtACS and improved reaction times, but visual acuity did not change compared to shams. Visual field improvement induced by rtACS was associated with EEG power-spectra and coherence alterations in visual cortical networks which are interpreted as signs of neuromodulation. Current flow simulation indicates current in the frontal cortex, eye, and optic nerve and in the subcortical but not in the cortical regions.
rtACS treatment is a safe and effective means to partially restore vision after optic nerve damage probably by modulating brain plasticity. This class 1 evidence suggests that visual fields can be improved in a clinically meaningful way.
Transcranial direct current stimulation (tDCS) aims to alter brain function non-invasively via electrodes placed on the scalp. Conventional tDCS uses two relatively large patch electrodes to deliver electrical current to the brain region of interest (ROI). Recent studies have shown that using dense arrays containing up to 512 smaller electrodes may increase the precision of targeting ROIs. However, this creates a need for methods to determine effective and safe stimulus patterns as the number of degrees of freedom is much higher with such arrays. Several approaches to this problem have appeared in the literature. In this paper, we describe a new method for calculating optimal electrode stimulus patterns for targeted and directional modulation in dense array tDCS which differs in some important aspects with methods reported to date.
We optimize stimulus pattern of dense arrays with fixed electrode placement to maximize the current density in a particular direction in the ROI. We impose a flexible set of safety constraints on the current power in the brain, individual electrode currents, and total injected current, to protect subject safety. The proposed optimization problem is convex and thus efficiently solved using existing optimization software to find unique and globally optimal electrode stimulus patterns.
Solutions for four anatomical ROIs based on a realistic head model are shown as exemplary results. To illustrate the differences between our approach and previously introduced methods, we compare our method with two of the other leading methods in the literature. We also report on extensive simulations that show the effect of the values chosen for each proposed safety constraint bound on the optimized stimulus patterns.
The proposed optimization approach employs volume based ROIs, easily adapts to different sets of safety constraints, and takes negligible time to compute. An in-depth comparison study gives insight into the relationship between different objective criteria and optimized stimulus patterns. In addition, the analysis of the interaction between optimized stimulus patterns and safety constraint bounds suggests that more precise current localization in the ROI, with improved safety criterion, may be achieved by careful selection of the constraint bounds.
B. Hollister, G. Duffley, C. Butson,, C.R. Johnson. Visualization for Understanding Uncertainty in Activation Volumes for Deep Brain Stimulation, In Eurographics Conference on Visualization, Edited by K.L. Ma G. Santucci, and J. van Wijk, 2016.
We have created the Neurostimulation Uncertainty Viewer (nuView or nView) tool for exploring data arising from deep brain stimulation (DBS). Simulated volume of tissue activated (VTA), using clinical electrode placements, are recorded along withpatient outcomes in the Unified Parkinson's disease rating scale (UPDRS). The data is volumetric and sparse, with multi-value patient results for each activated voxel in the simulation. nView provides a collection of visual methods to explore the activated tissue to enhance understanding of electrode usage for improved therapy with DBS.
Major depressive disorder (MDD) is a public health problem worldwide. There is increasing interest in using non-invasive therapies such as repetitive transcranial magnetic stimulation (rTMS) to treat MDD. However, the changes induced by rTMS on neural circuits remain poorly characterized. The present study aims to test whether the brain regions previously targeted by deep brain stimulation (DBS) in the treatment of MDD respond to rTMS, and whether functional connectivity (FC) measures can predict clinical response.
rTMS (20 sessions) was administered to five MDD patients at the left-dorsolateral prefrontal cortex (L-DLPFC) over 4 weeks. Magnetoencephalography (MEG) recordings and Montgomery-Asberg depression rating scale (MADRS) assessments were acquired before, during and after treatment. Our primary measures, obtained with MEG source imaging, were changes in power spectral density (PSD) and changes in FC as measured using coherence.
Of the five patients, four met the clinical response criterion (40% or greater decrease in MADRS) after 4 weeks of treatment. An increase in gamma power at the L-DLPFC was correlated with improvement in symptoms. We also found that increases in delta band connectivity between L-DLPFC/amygdala and L-DLPFC/pregenual anterior cingulate cortex (pACC), and decreases in gamma band connectivity between L-DLPFC/subgenual anterior cingulate cortex (sACC), were correlated with improvements in depressive symptoms.
Our results suggest that non-invasive intervention techniques, such as rTMS, modulate the ongoing activity of depressive circuits targeted for DBS, and that MEG can capture these changes. Gamma oscillations may originate from GABA-mediated inhibition, which increases synchronization of large neuronal populations, possibly leading to increased long-range FC. We postulate that responses to rTMS could provide valuable insights into early evaluation of patient candidates for DBS surgery.
I.A. Polejaeva, R. Ranjan, C.J. Davies, M. Regouski, J. Hall, A.L. Olsen, Q. Meng, H.M. Rutigliano, D.J. Dosdall, N.A. Angel, F.B. Sachse, T. Seidel, A.J. Thomas, R. Stott, K.E. Panter, P.M. Lee, A.J. Van Wettere, J.R. Stevens, Z. Wang, R.S. Macleod, N.F. Marrouche, K.L. White.
Increased Susceptibility to Atrial Fibrillation Secondary to Atrial Fibrosis in Transgenic Goats Expressing Transforming Growth Factor-β1, In Journal of Cardiovascular Electrophysiology, Vol. 27, No. 10, Wiley-Blackwell, pp. 1220--1229. Aug, 2016.
Large animal models of progressive atrial fibrosis would provide an attractive platform to study relationship between structural and electrical remodeling in atrial fibrillation (AF). Here we established a new transgenic goat model of AF with cardiac specific overexpression of TGF-β1 and investigated the changes in the cardiac structure and function leading to AF.
Methods and Results
Transgenic goats with cardiac specific overexpression of constitutively active TGF-β1 were generated by somatic cell nuclear transfer. We examined myocardial tissue, ECGs, echocardiographic data, and AF susceptibility in transgenic and wild-type control goats. Transgenic goats exhibited significant increase in fibrosis and myocyte diameters in the atria compared to controls, but not in the ventricles. P-wave duration was significantly greater in transgenic animals starting at 12 months of age, but no significant chamber enlargement was detected, suggesting conduction slowing in the atria. Furthermore, this transgenic goat model exhibited a significant increase in AF vulnerability. Six of 8 transgenic goats (75%) were susceptible to AF induction and exhibited sustained AF (>2 minutes), whereas none of 6 controls displayed sustained AF (P < 0.01). Length of induced AF episodes was also significantly greater in the transgenic group compared to controls (687 ± 212.02 seconds vs. 2.50 ± 0.88 seconds, P < 0.0001), but no persistent or permanent AF was observed.
A novel transgenic goat model with a substrate for AF was generated. In this model, cardiac overexpression of TGF-β1 led to an increase in fibrosis and myocyte size in the atria, and to progressive P-wave prolongation. We suggest that these factors underlie increased AF susceptibility.
M. Raj, M. Mirzargar, R. Kirby, R. Whitaker, J. Preston.
Evaluating Shape Alignment via Ensemble Visualization, In IEEE Computer Graphics and Applications, Vol. 36, No. 3, IEEE, pp. 60--71. May, 2016.
The visualization of variability in surfaces embedded in 3D, which is a type of ensemble uncertainty visualization, provides a means of understanding the underlying distribution of a collection or ensemble of surfaces. This work extends the contour boxplot technique to 3D and evaluates it against an enumeration-style visualization of the ensemble members and other conventional visualizations used by atlas builders. The authors demonstrate the efficacy of using the 3D contour boxplot ensemble visualization technique to analyze shape alignment and variability in atlas construction and analysis as a real-world application.
X. Tong, J. Edwards, C. Chen, H. Shen, C. R. Johnson, P. Wong.
View-Dependent Streamline Deformation and Exploration, In Transactions on Visualization and Computer Graphics, Vol. 22, No. 7, IEEE, pp. 1788--1801. July, 2016.
Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. We propose a new streamline exploration approach by visually manipulating the cluttered streamlines by pulling visible layers apart and revealing the hidden structures underneath. This paper presents a customized view-dependent deformation algorithm and an interactive visualization tool to minimize visual clutter in 3D vector and tensor fields. The algorithm is able to maintain the overall integrity of the fields and expose previously hidden structures. Our system supports both mouse and direct-touch interactions to manipulate the viewing perspectives and visualize the streamlines in depth. By using a lens metaphor of different shapes to select the transition zone of the targeted area interactively, the users can move their focus and examine the vector or tensor field freely.
Keywords: Context;Deformable models;Lenses;Shape;Streaming media;Three-dimensional displays;Visualization;Flow visualization;deformation;focus+context;occlusion;streamline;white matter tracts
K.K. Aras, W. Good, J. Tate, B.M. Burton, D.H. Brooks, J. Coll-Font, O. Doessel, W. Schulze, D. Patyogaylo, L. Wang, P. Van Dam,, R.S. MacLeod. Experimental Data and Geometric Analysis Repository: EDGAR, In Journal of Electrocardiology, 2015.
The "Experimental Data and Geometric Analysis Repository", or EDGAR is an Internet-based archive of curated data that are freely distributed to the international research community for the application and validation of electrocardiographic imaging (ECGI) techniques. The EDGAR project is a collaborative effort by the Consortium for ECG Imaging (CEI, ecg-imaging.org), and focused on two specific aims. One aim is to host an online repository that provides access to a wide spectrum of data, and the second aim is to provide a standard information format for the exchange of these diverse datasets.
The EDGAR system is composed of two interrelated components: 1) a metadata model, which includes a set of descriptive parameters and information, time signals from both the cardiac source and body-surface, and extensive geometric information, including images, geometric models, and measure locations used during the data acquisition/generation; and 2) a web interface. This web interface provides efficient, search, browsing, and retrieval of data from the repository.
An aggregation of experimental, clinical and simulation data from various centers is being made available through the EDGAR project including experimental data from animal studies provided by the University of Utah (USA), clinical data from multiple human subjects provided by the Charles University Hospital (Czech Republic), and computer simulation data provided by the Karlsruhe Institute of Technology (Germany).
It is our hope that EDGAR will serve as a communal forum for sharing and distribution of cardiac electrophysiology data and geometric models for use in ECGI research.
CIBC. Note: Data Sets: NCRR Center for Integrative Biomedical Computing (CIBC) data set archive. Download from: http://www.sci.utah.edu/cibc/software.html, 2015.
CIBC. Note: Cleaver: A MultiMaterial Tetrahedral Meshing Library and Application. Scientific Computing and Imaging Institute (SCI), Download from: http://www.sci.utah.edu/cibc/software.html, 2015.
Y. Gao, L. Zhu, J. Cates, R. S. MacLeod, S. Bouix,, A. Tannenbaum.
A Kalman Filtering Perspective for Multiatlas Segmentation, In SIAM J. Imaging Sciences, Vol. 8, No. 2, pp. 1007-1029. 2015.
K. Gillette, J.D. Tate, B. Kindall, P. Van Dam, E. Kholmovski, R.S. MacLeod. Generation of Combined-Modality Tetrahedral Meshes, In Computing in Cardiology, 2015.
Registering and combining anatomical components from different image modalities, like MRI and CT that have different tissue contrast, could result in patient-specific models that more closely represent underlying anatomical structures.
In this study, we combined a pair of CT and MRI scans of a pig thorax to make a tetrahedral mesh and compared different registration techniques including rigid, affine, thin plate spline morphing (TPSM), and iterative closest point (ICP), to superimpose the segmented bones from the CT scan on the soft tissues segmented from the MRI. The TPSM and affine-registered bones remained close to, but not overlapping, important soft tissue.
Simulation models, including an ECG forward model and a defibrillation model, were computed on generated multi-modality meshes after TPSM and affine registration and compared to those based on the original torso mesh.
CIBC. Note: ImageVis3D: An interactive visualization software system for large-scale volume data. Scientific Computing and Imaging Institute (SCI), Download from: http://www.imagevis3d.org, 2015.
C.R. Johnson. Computational Methods and Software for Bioelectric Field Problems, In Biomedical Engineering Handbook, 4, Vol. 1, Ch. 43, Edited by J.D. Bronzino and D.R. Peterson, CRC Press, pp. 1--28. 2015.
Computer modeling and simulation continue to become more important in the field of bioengineering. The reasons for this growing importance are manyfold. First, mathematical modeling has been shown to be a substantial tool for the investigation of complex biophysical phenomena. Second, since the level of complexity one can model parallels the existing hardware configurations, advances in computer architecture have made it feasible to apply the computational paradigm to complex biophysical systems. Hence, while biological complexity continues to outstrip the capabilities of even the largest computational systems, the computational methodology has taken hold in bioengineering and has been used successfully to suggest physiologically and clinically important scenarios and results.
This chapter provides an overview of numerical techniques that can be applied to a class of bioelectric field problems. Bioelectric field problems are found in a wide variety of biomedical applications, which range from single cells, to organs, up to models that incorporate partial to full human structures. We describe some general modeling techniques that will be applicable, in part, to all the aforementioned applications. We focus our study on a class of bioelectric volume conductor problems that arise in electrocardiography (ECG) and electroencephalography (EEG).
We begin by stating the mathematical formulation for a bioelectric volume conductor, continue by describing the model construction process, and follow with sections on numerical solutions and computational considerations. We continue with a section on error analysis coupled with a brief introduction to adaptive methods. We conclude with a section on software.
Encyclopedia of Applied and Computational Mathematics, Edited by Björn Engquist, Springer, pp. 1537-1546. 2015.
CIBC. Note: map3d: Interactive scientific visualization tool for bioengineering data. Scientific Computing and Imaging Institute (SCI), Download from: http://www.sci.utah.edu/cibc/software.html, 2015.
Research has indicated that atrial fibrillation (AF) ablation failure is related to the presence of atrial fibrosis. However it remains unclear whether this information can be successfully used in predicting the optimal ablation targets for AF termination. We aimed to provide a proof-of-concept that patient-specific virtual electrophysiological study that combines i) atrial structure and fibrosis distribution from clinical MRI and ii) modeling of atrial electrophysiology, could be used to predict: (1) how fibrosis distribution determines the locations from which paced beats degrade into AF; (2) the dynamic behavior of persistent AF rotors; and (3) the optimal ablation targets in each patient. Four MRI-based patient-specific models of fibrotic left atria were generated, ranging in fibrosis amount. Virtual electrophysiological studies were performed in these models, and where AF was inducible, the dynamics of AF were used to determine the ablation locations that render AF non-inducible. In 2 of the 4 models patient-specific models AF was induced; in these models the distance between a given pacing location and the closest fibrotic region determined whether AF was inducible from that particular location, with only the mid-range distances resulting in arrhythmia. Phase singularities of persistent rotors were found to move within restricted regions of tissue, which were independent of the pacing location from which AF was induced. Electrophysiological sensitivity analysis demonstrated that these regions changed little with variations in electrophysiological parameters. Patient-specific distribution of fibrosis was thus found to be a critical component of AF initiation and maintenance. When the restricted regions encompassing the meander of the persistent phase singularities were modeled as ablation lesions, AF could no longer be induced. The study demonstrates that a patient-specific modeling approach to identify non-invasively AF ablation targets prior to the clinical procedure is feasible.
Entropy-based particle correspondence for shape populations, In International Journal of Computer Assisted Radiology and Surgery, Springer, pp. 1-12. December, 2015.I. OguzI, J. Cates, M. Datar, B. Paniagua, T. Fletcher, C. Vachet, M. Styner, R. Whitaker.
Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies.
In this paper, we present an entropy-based correspondence framework for computing point-based correspondence among populations of surfaces in a groupwise manner. This robust framework is parameterization-free and computationally efficient. We review the core principles of this method as well as various extensions to deal effectively with surfaces of complex geometry and application-driven correspondence metrics.
We apply our method to synthetic and biological datasets to illustrate the concepts proposed and compare the performance of our framework to existing techniques.
Through the numerous extensions and variations presented here, we create a very flexible framework that can effectively handle objects of various topologies, multi-object complexes, open surfaces, and objects of complex geometry such as high-curvature regions or extremely thin features.