Angela M. Noecker

Reversing cognitive–motor impairments in Parkinson’s disease patients using a computational modelling approach to deep brain stimulation programming

Deep brain stimulation in the subthalamic nucleus is an effective and safe surgical procedure that has been shown to reduce the motor dysfunction of patients with advanced Parkinsons disease. Bilateral subthalamic nucleus deep brain stimulation, however, has been associated with declines in cognitive and cognitive-motor functioning. It has been hypothesized that spread of current to nonmotor areas of the subthalamic nucleus may be responsible for declines in cognitive and cognitive-motor functioning. The aim of this study was to assess the cognitive-motor performance in advanced Parkinsons disease patients with subthalamic nucleus deep brain stimulation parameters determined clinically (Clinical) to settings derived from a patient-specific computational model (Model). Data were collected from 10 patients with advanced Parkinsons disease bilaterally implanted with subthalamic nucleus deep brain stimulation systems. These patients were assessed off medication and under three deep brain stimulation conditions: Off, Clinical or Model based stimulation. Clinical stimulation parameters had been determined based on clinical evaluations and were stable for at least 6 months prior to study participation. Model-based parameters were selected to minimize the spread of current to nonmotor portions of the subthalamic nucleus using Cicerone Deep Brain Stimulation software. For each stimulation condition, participants performed a working memory (n-back task) and motor task (force tracking) under single- and dual-task settings. During the dual-task, participants performed the n-back and force-tracking tasks simultaneously. Clinical and Model parameters were equally effective in improving the Unified Parkinsons disease Rating Scale III scores relative to Off deep brain stimulation scores. Single-task working memory declines, in the 2-back condition, were significantly less under Model compared with Clinical deep brain stimulation settings. Under dual-task conditions, force tracking was significantly better with Model compared with Clinical deep brain stimulation. In addition to better overall cognitive-motor performance associated with Model parameters, the amount of power consumed was on average less than half that used with the Clinical settings. These results indicate that the cognitive and cognitive-motor declines associated with bilateral subthalamic nucleus deep brain stimulation may be reversed, without compromising motor benefits, by using model-based stimulation parameters that minimize current spread into nonmotor regions of the subthalamic nucleus.

Development of patient-specific three-dimensional pediatric cardiac models

The availability of algorithms to create three-dimensional (3D) models from medical images has made it possible to render and build patient-specific reconstructions of individual body parts. In the present study, this technology was used to create 3D models of pediatric hearts for use in medical device development. Digital models were created using CT datasets of pediatric hearts and commercially available 3D image processing software. Using this software, stacked CT data were viewed, and pixels representing the heart and rib cage were selected and rendered as 3D models. Stereolithography and 3D printing technology were used to create rigid and flexible physical heart models (biomodels) from the digital models. Twelve on-screen models of the thorax and cardiac structures were created from cardiac CT scans obtained from 11 patients with and without congenital heart disease (median age, 3 years; range, 2 days to 13 years). Rigid and flexible physical heart models were generated from the digital models to provide tactile and visual information. 3D models of pediatric cardiac and chest anatomy provide enhanced understanding and tactile representation of complex anatomy. Precise representation of the spatial relationships between anatomic structures is particularly useful during the development and placement of medical devices.

Patient-Specific Analysis of the Relationship Between the Volume of Tissue Activated During DBS and Verbal Fluency

Deep brain stimulation (DBS) for the treatment of advanced Parkinsons disease involves implantation of a lead with four small contacts usually within the subthalamic nucleus (STN) or globus pallidus internus (GPi). While generally safe from a cognitive standpoint, STN DBS has been commonly associated with a decrease in the speeded production of words, a skill referred to as verbal fluency. Virtually all studies comparing presurgical to postsurgical verbal fluency performance have detected a decrease with DBS. The decline may be attributable in part to the surgical procedures, yet the relative contributions of stimulation effects are not known. In the present study, we used patient-specific DBS computer models to investigate the effects of stimulation on verbal fluency performance. Specifically, we investigated relationships of the volume and locus of activated STN tissue to verbal fluency outcome. Stimulation of different electrode contacts within the STN did not affect total verbal fluency scores. However, models of activation revealed subtle relationships between the locus and volume of activated tissue and verbal fluency performance. At ventral contacts, more tissue activation inside the STN was associated with decreased letter fluency performance. At optimal contacts, more tissue activation within the STN was associated with improved letter fluency performance. These findings suggest subtle effects of stimulation on verbal fluency performance, consistent with the functional nonmotor subregions/somatotopy of the STN.

Automated 3-dimensional brain atlas fitting to microelectrode recordings from deep brain stimulation surgeries.

Objective: Deep brain stimulation (DBS) surgeries commonly rely on brain atlases and microelectrode recordings (MER) to help identify the target location for electrode implantation. We present an automated method for optimally fitting a 3-dimensional brain atlas to intraoperative MER and predicting a target DBS electrode location in stereotactic coordinates for the patient. Methods: We retrospectively fit a 3-dimensional brain atlas to MER points from 10 DBS surgeries targeting the subthalamic nucleus (STN). We used a constrained optimization algorithm to maximize the MER points correctly fitted (i.e., contained) within the appropriate atlas nuclei. We compared our optimization approach to conventional anterior commissure-posterior commissure (AC/PC) scaling, and to manual fits performed by four experts. A theoretical DBS electrode target location in the dorsal STN was customized to each patient as part of the fitting process and compared to the location of the clinically defined therapeutic stimulation contact. Results: The human expert and computer optimization fits achieved significantly better fits than the AC/PC scaling (80, 81, and 41% of correctly fitted MER, respectively). However, the optimization fits were performed in less time than the expert fits and converged to a single solution for each patient, eliminating interexpert variance. Conclusions and Significance: DBS therapeutic outcomes are directly related to electrode implantation accuracy. Our automated fitting techniques may aid in the surgical decision-making process by optimally integrating brain atlas and intraoperative neurophysiological data to provide a visual guide for target identification.

Optimizing Deep Brain Stimulation Parameter Selection with Detailed Models of the Electrode-Tissue Interface

Deep brain stimulation (DBS) is an established clinical therapy for the treatment of Parkinsons disease. However, selecting stimulation parameters for maximal clinical benefit can be a difficult and time consuming process that typically requires a highly trained and experienced individual to achieve acceptable results. To address this limitation we developed a Windows-based software package (StimExplorer) intended to aid the clinical implementation of DBS technology. StimExplorer uses detailed computer models to provide a quantitative description of the 3D volume of tissue activated (VTA) by DBS as a function of the stimulation parameters and electrode location within the brain. The DBS electric field models explicitly incorporate the capacitance of the electrode-tissue interface, tissue encapsulation of the electrode, and diffusion-tensor based 3D tissue anisotropy and inhomogeneity. The VTA is predicted with models of axonal activation resulting from the applied field. The stimulation models are tailored to the individual patient by reading in their magnetic resonance imaging (MRI) data and interactively scaling 3D anatomical nuclei to fit the patient anatomy. The user also inputs the DBS electrode orientation, location, and impedance data. The software then provides theoretically optimal stimulation parameter suggestions, intended to represent the start point for clinical programming of the DBS device. The software system is packaged into a clinician-friendly graphical user interface that allows for interactive 3D visualization. The goals of the StimExplorer system are to educate clinicians on the impact of stimulation parameter manipulation, and improve the customization of DBS to individual patients

Defining a therapeutic target for pallidal deep brain stimulation for dystonia

To create a data‐driven computational model that identifies brain regions most frequently influenced by successful deep brain stimulation (DBS) of the globus pallidus (GP) for advanced, medication‐resistant, generalized dystonia.

The PediPump: development status of a new pediatric ventricular assist device: update II.

The PediPump is a new ventricular assist device with a hydraulic output range designed for children from newborn infants to adolescents. The design is based on a mixed-flow rotary pump; the rotating assembly consists of a front impeller, front and rear radial magnetic bearings, and a central motor magnet. Two different implantable pumps were designed initially: an intravascular pump measuring 7 × 75 mm and an extravascular pump measuring 14 × 85 mm. Current prototypes are substantially smaller: The current intravascular version measures 4.5 × 55 mm, whereas the current extravascular version measures 11 × 70 mm. Both devices provide pressure and flows capable of supporting adults, far exceeding the initially defined physiologic requirements for children weighing 2 to 25 kg. This basic pump design may be used in acute or chronic clinical settings to provide right ventricular, left ventricular, or biventricular support. There are three objectives for the PediPump development program: 1) determination of basic engineering requirements for hardware and control logic including design analysis for system sizing, evaluation of control concepts, and bench testing of prototypes; 2) performance of preclinical anatomic fitting studies using CT-based 3D modeling; and 3) animal studies to provide characterization and reliability testing of the device.

The Pedipump: Development Status of a New Pediatric Ventricular Assist Device

The PediPump is a new rotary dynamic ventricular assist device designed specifically for pediatric applications. Although it is capable of providing support for adults, the small size of the PediPump makes it suitable for newborn circulatory support while retaining excellent hemodynamics. Current and future development plans include: 1) determination of the basic engineering requirements for hardware and control logic, including design analysis for system sizing, evaluation of control concepts and bench testing of prototypes; 2) performance of preclinical anatomical fitting studies using computed tomography–based three-dimensional modeling; and, 3) evaluation with animal studies to provide characterization and reliability testing of the device.

Customizing deep brain stimulation to the patient using computational models

Bilateral subthalamic (STN) deep brain stimulation (DBS) is effective in improving the cardinal motor signs of advanced Parkinson’s disease (PD); however declines in cognitive function have been associated with this procedure. The aim of this study was to assess cognitive-motor performance of 10 PD patients implanted with STN DBS systems during either clinically determined stimulation settings or settings derived from a computational model. Cicerone DBS software was used to define the model parameters such that current spread to non-motor areas of the STN was minimized. Clinically determined and model defined parameters were equally effective in improving motor scores on the traditional clinical rating scale (UPDRS-III). Under modest dual-task conditions, cognitive-motor performance was worse with clinically determined compared to model derived parameters. In addition, the model parameters provided a 66% reduction in power consumption. These results indicate that the cognitive-motor declines associated with bilateral STN can be mitigated, without compromising motor benefits, utilizing stimulation parameters that minimize current spread into non-motor regions of the STN.

Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease

BACKGROUND Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinsons disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization. OBJECTIVE Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication. METHODS Clinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: (1) information retrieval; (2) visualization of treatment, and; (3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest. RESULTS Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P < 0.05) and their effect on outcomes was of similar magnitude to that of DBS. Using those results, the combined machine learning algorithms were able to accurately predict 86% (12/14) of the motor improvement scores at one year after surgery. CONCLUSIONS Using patient-specific details, an appropriately parameterized CDSS could help select theoretically optimal DBS parameter settings and medication dosages that have potential to improve the clinical management of PD patients.