Pedunculopontine nucleus deep brain stimulation in Parkinson's disease: A clinical review|
W.Thevathasan, B. Debu, T. Aziz, B. R. Bloem, C. Blahak, C. Butson, V. Czernecki, T. Foltynie, V. Fraix, D. Grabli, C. Joint, A. M. Lozano, M. S. Okun, J. Ostrem, N. Pavese, C. Schrader, C. H. Tai, J. K. Krauss, E. Moro. In Movement Disorders, Vol. 33, No. 1, pp. 10--20. 2018.
Pedunculopontine nucleus region deep brain stimulation (DBS) is a promising but experimental therapy for axial motor deficits in Parkinson's disease (PD), particularly gait freezing and falls. Here, we summarise the clinical application and outcomes reported during the past 10 years. The published dataset is limited, comprising fewer than 100 cases. Furthermore, there is great variability in clinical methodology between and within surgical centers. The most common indication has been severe medication refractory gait freezing (often associated with postural instability). Some patients received lone pedunculopontine nucleus DBS (unilateral or bilateral) and some received costimulation of the subthalamic nucleus or internal pallidum. Both rostral and caudal pedunculopontine nucleus subregions have been targeted. However, the spread of stimulation and variance in targeting means that neighboring brain stem regions may be implicated in any response. Low stimulation frequencies are typically employed (20-80 Hertz). The fluctuating nature of gait freezing can confound programming and outcome assessments. Although firm conclusions cannot be drawn on therapeutic efficacy, the literature suggests that medication refractory gait freezing and falls can improve. The impact on postural instability is unclear. Most groups report a lack of benefit on gait or limb akinesia or dopaminergic medication requirements. The key question is whether pedunculopontine nucleus DBS can improve quality of life in PD. So far, the evidence supporting such an effect is minimal. Development of pedunculopontine nucleus DBS to become a reliable, established therapy would likely require a collaborative effort between experienced centres to clarify biomarkers predictive of response and the optimal clinical methodology.
Research and Education in Computational Science and Engineering|
Subtitled Report from a workshop sponsored by the Society for Industrial and Applied Mathematics (SIAM) and the European Exascale Software Initiative (EESI-2), August 4-6, 2014, Breckenridge, Colorado, U. Ruede, K. Willcox, L. C. McInnes, H. De Sterck, G. Biros, H. Bungartz, J. Corones, E. Cramer, J. Crowley, O. Ghattas, M. Gunzburger, M. Hanke, R. Harrison, M. Heroux, J. Hesthaven, P. Jimack, C. Johnson, K. E. Jordan, D. E. Keyes, R. Krause, V. Kumar, S. Mayer, J. Meza, K. M. Mrken, J. T. Oden, L. Petzold, P. Raghavan, S. M. Shontz, A. Trefethen, P. Turner, V. Voevodin, B. Wohlmuth,, C. S. Woodward. Vol. abs/1610.02608, 2018.
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.
FluoRender: joint freehand segmentation and visualization for many-channel fluorescence data analysis|
Y. Wan, H. Otsuna, H. A. Holman, B. Bagley, M. Ito, A. K. Lewis, M. Colasanto, G. Kardon, K. Ito, C. Hansen. In BMC Bioinformatics, Vol. 18, No. 1, Springer Nature, May, 2017.
Uncertainty Footprint: Visualization of Nonuniform Behavior of Iterative Algorithms Applied to 4D Cell Tracking|
Y. Wan, C. Hansen. In Computer Graphics Forum, Wiley, 2017.
Research on microscopy data from developing biological samples usually requires tracking individual cells over time. When cells are three-dimensionally and densely packed in a time-dependent scan of volumes, tracking results can become unreliable and uncertain. Not only are cell segmentation results often inaccurate to start with, but it also lacks a simple method to evaluate the tracking outcome. Previous cell tracking methods have been validated against benchmark data from real scans or artificial data, whose ground truth results are established by manual work or simulation. However, the wide variety of real-world data makes an exhaustive validation impossible. Established cell tracking tools often fail on new data, whose issues are also difficult to diagnose with only manual examinations. Therefore, data-independent tracking evaluation methods are desired for an explosion of microscopy data with increasing scale and resolution. In this paper, we propose the uncertainty footprint, an uncertainty quantification and visualization technique that examines nonuniformity at local convergence for an iterative evaluation process on a spatial domain supported by partially overlapping bases. We demonstrate that the patterns revealed by the uncertainty footprint indicate data processing quality in two algorithms from a typical cell tracking workflow – cell identification and association. A detailed analysis of the patterns further allows us to diagnose issues and design methods for improvements. A 4D cell tracking workflow equipped with the uncertainty footprint is capable of self diagnosis and correction for a higher accuracy than previous methods whose evaluation is limited by manual examinations.
Proceedings of the Fourth Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies|
W. Deeb, J. J. Giordano, P. J. Rossi, A. Y. Mogilner, A. Gunduz, J. W. Judy, B. T. Klassen, C. R. Butson, C. Van Horne, D. Deny, D. D. Dougherty, D. Rowell, G. A. Gerhardt, G. S. Smith, F. A. Ponce, H. C. Walker, H. M. Bronte-Stewart, H. S. Mayberg, H. J. Chizeck, J. Langevin, J. Volkmann, J. L. Ostrem, J. B. Shute, J. Jimenez-Shahed, K. D. Foote, A. W. Shukla, M. A. Rossi, M. Oh, M. Pourfar, P. B. Rosenberg, P. A. Silburn, C. de Hemptine, P. A. Starr, T. Denison, U. Akbar, W. M. Grill,, M. S. Okun. In Frontiers in Integrative Neuroscience, Vol. 10, pp. 38. 2016.
This paper provides an overview of current progress in the technological advances and the use of deep brain stimulation (DBS) to treat neurological and neuropsychiatric disorders, as presented by participants of the Fourth Annual Deep Brain Stimulation Think Tank, which was convened in March 2016 in conjunction with the Center for Movement Disorders and Neurorestoration at the University of Florida, Gainesveille FL, USA. The Think Tank discussions first focused on policy and advocacy in DBS research and clinical practice, formation of registries, and issues involving the use of DBS in the treatment of Tourette Syndrome. Next, advances in the use of neuroimaging and electrochemical markers to enhance DBS specificity were addressed. Updates on ongoing use and developments of DBS for the treatment of Parkinson’s disease, essential tremor, Alzheimer’s disease, depression, post-traumatic stress disorder, obesity, addiction were presented, and progress toward innovation(s) in closed-loop applications were discussed. Each section of these proceedings provides updates and highlights of new information as presented at this year’s international Think Tank, with a view toward current and near future advancement of the field.
Alternating Current Stimulation for Vision Restoration after Optic Nerve Damage: A Randomized Clinical Trial|
C. Gall, S. Schmidt, M.P. Schittkowski, A. Antal, G. Ambrus, W. Paulus, M. Dannhauer, R. Michalik, A. Mante, M. Bola, A. Lux, S. Kropf, S.A. Brandt, B.A. Sabel. In PLOS ONE, Vol. 11, No. 6, Public Library of Science, pp. e0156134. June, 2016.
Robust modulation of arousal regulation, performance, and frontostriatal activity through central thalamic deep brain stimulation in healthy nonhuman primates|
J.L. Baker, J. Ryou, X.F. Wei, C.R. Butson, N.D. Schiff, K.P. Purpura. In Journal of Neurophysiology, Vol. 116, No. 5, American Physiological Society, pp. 2383--2404. Aug, 2016.
The central thalamus (CT) is a key component of the brain-wide network underlying arousal regulation and sensory-motor integration during wakefulness in the mammalian brain. Dysfunction of the CT, typically a result of severe brain injury (SBI), leads to long-lasting impairments in arousal regulation and subsequent deficits in cognition. Central thalamic deep brain stimulation (CT-DBS) is proposed as a therapy to reestablish and maintain arousal regulation to improve cognition in select SBI patients. However, a mechanistic understanding of CT-DBS and an optimal method of implementing this promising therapy are unknown. Here we demonstrate in two healthy nonhuman primates (NHPs), Macaca mulatta, that location-specific CT-DBS improves performance in visuomotor tasks and is associated with physiological effects consistent with enhancement of endogenous arousal. Specifically, CT-DBS within the lateral wing of the central lateral nucleus and the surrounding medial dorsal thalamic tegmental tract (DTTm) produces a rapid and robust modulation of performance and arousal, as measured by neuronal activity in the frontal cortex and striatum. Notably, the most robust and reliable behavioral and physiological responses resulted when we implemented a novel method of CT-DBS that orients and shapes the electric field within the DTTm using spatially separated DBS leads. Collectively, our results demonstrate that selective activation within the DTTm of the CT robustly regulates endogenous arousal and enhances cognitive performance in the intact NHP; these findings provide insights into the mechanism of CT-DBS and further support the development of CT-DBS as a therapy for reestablishing arousal regulation to support cognition in SBI patients.
Longitudinal Changes in Depressive Circuitry in Response to Neuromodulation Therapy|
Y. Pathak, O. Salami, S. Baillet, Z. Li, C.R. Butson. In Frontiers in Neural Circuits, Vol. 10, rontiers Media SA, July, 2016.
Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals|
B. Erem, R.M. Orellana, D.E. Hyde, J.M. Peters, F.H. Duffy, P. Stovicek, S.K. Warfield, R.S. MacLeod, G. Tadmor, D.H. Brooks. In Physical Review E, Vol. 93, No. 4, American Physical Society, apr, 2016.
This paper addresses the challenge of extracting meaningful information from measured bioelectric signals generated by complex, large scale physiological systems such as the brain or the heart. We focus on a combination of the well-known Laplacian eigenmaps machine learning approach with dynamical systems ideas to analyze emergent dynamic behaviors. The method reconstructs the abstract dynamical system phase-space geometry of the embedded measurements and tracks changes in physiological conditions or activities through changes in that geometry. It is geared to extract information from the joint behavior of time traces obtained from large sensor arrays, such as those used in multiple-electrode ECG and EEG, and explore the geometrical structure of the low dimensional embedding of moving time windows of those joint snapshots. Our main contribution is a method for mapping vectors from the phase space to the data domain. We present cases to evaluate the methods, including a synthetic example using the chaotic Lorenz system, several sets of cardiac measurements from both canine and human hearts, and measurements from a human brain.
Optimization of focality and direction in dense electrode array transcranial direct currentstimulation (tDCS)|
S. Guler, M. Dannhauer, B. Erem, R.S. Macleod, D. Tucker, S. Turovets, P. Luu, D. Erdogmus, D. Brooks. In Journal of Neural Engineering, Vol. 13, No. 3, IOP Publishing, pp. 036020. May, 2016.
Quantitative comparison of cortical bone thickness using correspondence-based shape modeling in patients with cam femoroacetabular impingement|
P.R. Atkins, S.Y. Elhabian, P. Agrawal, M.D. Harris, R.T. Whitaker, J.A. Weiss, C.L. Peters, A.E. Anderson. In Journal of Orthopaedic Research, Wiley-Blackwell, Nov, 2016.
The proximal femur is abnormally shaped in patients with cam-type femoroacetabular impingement (FAI). Impingement
The role of blood vessels in high-resolution volume conductor head modeling of EEG|
L.D.J. Fiederer, J. Vorwerk, F. Lucka, M. Dannhauer, S. Yang, M. Dümpelmann, A. Schulze-Bonhage, A. Aertsen, O. Speck, C.H. Wolters, T. Ball. In NeuroImage, Vol. 128, Elsevier, pp. 193--208. March, 2016.
Reconstruction of the electrical sources of human EEG activity at high spatio-temporal accuracy is an important aim in neuroscience and neurological diagnostics. Over the last decades, numerous studies have demonstrated that realistic modeling of head anatomy improves the accuracy of source reconstruction of EEG signals. For example, including a cerebro-spinal fluid compartment and the anisotropy of white matter electrical conductivity were both shown to significantly reduce modeling errors. Here, we for the first time quantify the role of detailed reconstructions of the cerebral blood vessels in volume conductor head modeling for EEG. To study the role of the highly arborized cerebral blood vessels, we created a submillimeter head model based on ultra-high-field-strength (7T) structural MRI datasets. Blood vessels (arteries and emissary/intraosseous veins) were segmented using Frangi multi-scale vesselness filtering. The final head model consisted of a geometry-adapted cubic mesh with over 17×10(6) nodes. We solved the forward model using a finite-element-method (FEM) transfer matrix approach, which allowed reducing computation times substantially and quantified the importance of the blood vessel compartment by computing forward and inverse errors resulting from ignoring the blood vessels. Our results show that ignoring emissary veins piercing the skull leads to focal localization errors of approx. 5 to 15mm. Large errors (>2cm) were observed due to the carotid arteries and the dense arterial vasculature in areas such as in the insula or in the medial temporal lobe. Thus, in such predisposed areas, errors caused by neglecting blood vessels can reach similar magnitudes as those previously reported for neglecting white matter anisotropy, the CSF or the dura - structures which are generally considered important components of realistic EEG head models. Our findings thus imply that including a realistic blood vessel compartment in EEG head models will be helpful to improve the accuracy of EEG source analyses particularly when high accuracies in brain areas with dense vasculature are required.
Increased Susceptibility to Atrial Fibrillation Secondary to Atrial Fibrosis in Transgenic Goats Expressing Transforming Growth Factor-β1|
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. In Journal of Cardiovascular Electrophysiology, Vol. 27, No. 10, Wiley-Blackwell, pp. 1220--1229. Aug, 2016.
Spatial organization of acute myocardial ischemia|
K. Aras B. Burton, D. Swenson, R.S. MacLeod. In Journal of Electrocardiology, Vol. 49, No. 3, Elsevier, pp. 323–336. May, 2016.
muView: A Visual Analysis System for Exploring Uncertainty in Myocardial Ischemia Simulations|
P. Rosen, B. Burton, K. Potter, C.R. Johnson. In Visualization in Medicine and Life Sciences III, Springer Nature, pp. 49--69. 2016.
In this paper we describe the Myocardial Uncertainty Viewer (muView or µView) system for exploring data stemming from the simulation of cardiac ischemia. The simulation uses a collection of conductivity values to understand how ischemic regions effect the undamaged anisotropic heart tissue. The data resulting from the simulation is multi-valued and volumetric, and thus, for every data point, we have a collection of samples describing cardiac electrical properties. µView combines a suite of visual analysis methods to explore the area surrounding the ischemic zone and identify how perturbations of variables change the propagation of their effects. In addition to presenting a collection of visualization techniques, which individually highlight different aspects of the data, the coordinated view system forms a cohesive environment for exploring the simulations.We also discuss the findings of our study, which are helping to steer further development of the simulation and strengthening our collaboration with the biomedical engineers attempting to understand the phenomenon.
The use of stimulation field models for deep brain stimulation programming|
C. R. Butson, C. C. McIntyre. In Brain Stimulation, Vol. 8, No. 5, Elsevier BV, pp. 976--978. September, 2015.
Proceedings of the Second Annual Deep Brain Stimulation Think Tank: What's in the Pipeline|
A. Gunduz, H. Morita, P. J. Rossi, W. L. Allen, R. L. Alterman, H. Bronte-Stewart, C. R. Butson, D. Charles, S. Deckers, C. de Hemptinne, M. DeLong, D. Dougherty, J. Ellrich, K. D. Foote, J. Giordano, W. Goodman, B. D. Greenberg, D. Greene, R. Gross, J. W. Judy, E. Karst, A. Kent, B. Kopell, A. Lang, A. Lozano, C. Lungu, K. E. Lyons, A. Machado, H. Martens, C. McIntyre, H. Min, J. Neimat, J. Ostrem, S. Pannu, F. Ponce, N. Pouratian, D. Reymers, L. Schrock, S. Sheth, L. Shih, S. Stanslaski, G. K. Steinke, P. Stypulkowski, A. I. Tröster, L. Verhagen, H. Walker, M. S. Okun. In International Journal of Neuroscience, Vol. 125, No. 7, Taylor & Francis, pp. 475-485. 2015.
PubMed ID: 25526555
The proceedings of the 2nd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, and computational work on DBS for the treatment of neurological and neuropsychiatric disease and represent the insights of a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers and members of industry. Presentations and discussions covered a broad range of topics, including advocacy for DBS, improving clinical outcomes, innovations in computational models of DBS, understanding of the neurophysiology of Parkinson's disease (PD) and Tourette syndrome (TS) and evolving sensor and device technologies.
|Poor scar formation after ablation is associated with atrial fibrillation recurrence,
B.R. Parmar, T.R. Jarrett, E.G. Kholmovski, N. Hu, D. Parker, R.S. MacLeod, N.F. Marrouche, R. Ranjan. In Journal of Interventional Cardiac Electrophysiology, Vol. 44, No. 3, pp. 247-256. December, 2015.
Generation of Combined-Modality Tetrahedral Meshes|
K. Gillette, J.D. Tate, B. Kindall, P. Van Dam, E. Kholmovski, R.S. MacLeod. 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.
A Kalman Filtering Perspective for Multiatlas Segmentation|
Y. Gao, L. Zhu, J. Cates, R. S. MacLeod, S. Bouix,, A. Tannenbaum. In SIAM J. Imaging Sciences, Vol. 8, No. 2, pp. 1007-1029. 2015.
In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity—neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.