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- Creators: Abbas, James
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Description
The efficacy of deep brain stimulation (DBS) in Parkinson's disease (PD) has been convincingly demonstrated in studies that compare motor performance with and without stimulation, but characterization of performance at intermediate stimulation amplitudes has been limited. This study investigated the effects of changing DBS amplitude in order to assess dose-response characteristics, inter-subject variability, consistency of effect across outcome measures, and day-to-day variability. Eight subjects with PD and bilateral DBS systems were evaluated at their clinically determined stimulation (CDS) and at three reduced amplitude conditions: approximately 70%, 30%, and 0% of the CDS (MOD, LOW, and OFF, respectively). Overall symptom severity and performance on a battery of motor tasks - gait, postural control, single-joint flexion-extension, postural tremor, and tapping - were assessed at each condition using the motor section of the Unified Parkinson's Disease Rating Scale (UPDRS-III) and quantitative measures. Data were analyzed to determine whether subjects demonstrated a threshold response (one decrement in stimulation resulted in ≥ 70% of the maximum change) or a graded response to reduced stimulation. Day-to-day variability was assessed using the CDS data from the three testing sessions. Although the cohort as a whole demonstrated a graded response on several measures, there was high variability across subjects, with subsets exhibiting graded, threshold, or minimal responses. Some subjects experienced greater variability in their CDS performance across the three days than the change induced by reducing stimulation. For several tasks, a subset of subjects exhibited improved performance at one or more of the reduced conditions. Reducing stimulation did not affect all subjects equally, nor did it uniformly affect each subject's performance across tasks. These results indicate that altered recruitment of neural structures can differentially affect motor capabilities and demonstrate the need for clinical consideration of the effects on multiple symptoms across several days when selecting DBS parameters.
ContributorsConovaloff, Alison (Author) / Abbas, James (Thesis advisor) / Krishnamurthi, Narayanan (Committee member) / Mahant, Padma (Committee member) / Jung, Ranu (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the desired skill level. It would result in more reliable and adaptive neural interfaces that could record optimal neural activity 24/7 with high fidelity signals, high yield and increased throughput. The main contribution here is validating adaptive strategies to overcome challenges in autonomous navigation of microelectrodes inside the brain. The following issues pose significant challenges as brain tissue is both functionally and structurally dynamic: a) time varying mechanical properties of the brain tissue-microelectrode interface due to the hyperelastic, viscoelastic nature of brain tissue b) non-stationarities in the neural signal caused by mechanical and physiological events in the interface and c) the lack of visual feedback of microelectrode position in brain tissue. A closed loop control algorithm is proposed here for autonomous navigation of microelectrodes in brain tissue while optimizing the signal-to-noise ratio of multi-unit neural recordings. The algorithm incorporates a quantitative understanding of constitutive mechanical properties of soft viscoelastic tissue like the brain and is guided by models that predict stresses developed in brain tissue during movement of the microelectrode. An optimal movement strategy is developed that achieves precise positioning of microelectrodes in the brain by minimizing the stresses developed in the surrounding tissue during navigation and maximizing the speed of movement. Results of testing the closed-loop control paradigm in short-term rodent experiments validated that it was possible to achieve a consistently high quality SNR throughout the duration of the experiment. At the systems level, new generation of MEMS actuators for movable microelectrode array are characterized and the MEMS device operation parameters are optimized for improved performance and reliability. Further, recommendations for packaging to minimize the form factor of the implant; design of device mounting and implantation techniques of MEMS microelectrode array to enhance the longevity of the implant are also included in a top-down approach to achieve a reliable brain interface.
ContributorsAnand, Sindhu (Author) / Muthuswamy, Jitendran (Thesis advisor) / Tillery, Stephen H (Committee member) / Buneo, Christopher (Committee member) / Abbas, James (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. However, this process still requires that seizures are visually detected and marked by experienced and trained electroencephalographers. The motivation for the development of an automated seizure detection algorithm in this research was to assist physicians in such a laborious, time consuming and expensive task. Seizures in the EEG vary in duration (seconds to minutes), morphology and severity (clinical to subclinical, occurrence rate) within the same patient and across patients. The task of seizure detection is also made difficult due to the presence of movement and other recording artifacts. An early approach towards the development of automated seizure detection algorithms utilizing both EEG changes and clinical manifestations resulted to a sensitivity of 70-80% and 1 false detection per hour. Approaches based on artificial neural networks have improved the detection performance at the cost of algorithm's training. Measures of nonlinear dynamics, such as Lyapunov exponents, have been applied successfully to seizure prediction. Within the framework of this MS research, a seizure detection algorithm based on measures of linear and nonlinear dynamics, i.e., the adaptive short-term maximum Lyapunov exponent (ASTLmax) and the adaptive Teager energy (ATE) was developed and tested. The algorithm was tested on long-term (0.5-11.7 days) continuous EEG recordings from five patients (3 with intracranial and 2 with scalp EEG) and a total of 56 seizures, producing a mean sensitivity of 93% and mean specificity of 0.048 false positives per hour. The developed seizure detection algorithm is data-adaptive, training-free and patient-independent. It is expected that this algorithm will assist physicians in reducing the time spent on detecting seizures, lead to faster and more accurate diagnosis, better evaluation of treatment, and possibly to better treatments if it is incorporated on-line and real-time with advanced neuromodulation therapies for epilepsy.
ContributorsVenkataraman, Vinay (Author) / Jassemidis, Leonidas (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2012
Description
This report provides an overview of scramjet-powered hypersonic vehicle modeling and control challenges. Such vehicles are characterized by unstable non-minimum phase dynamics with significant coupling and low thrust margins. Recent trends in hypersonic vehicle research are summarized. To illustrate control relevant design issues and tradeoffs, a generic nonlinear 3DOF longitudinal dynamics model capturing aero-elastic-propulsive interactions for wedge-shaped vehicle is used. Limitations of the model are discussed and numerous modifications have been made to address control relevant needs. Two different baseline configurations are examined over a two-stage to orbit ascent trajectory. The report highlights how vehicle level-flight static (trim) and dynamic properties change over the trajectory. Thermal choking constraints are imposed on control system design as a direct consequence of having a finite FER margin. The implication of this state-dependent nonlinear FER margin constraint, the right half plane (RHP) zero, and lightly damped flexible modes, on control system bandwidth (BW) and FPA tracking has been discussed. A control methodology has been proposed that addresses the above dynamics while providing some robustness to modeling uncertainty. Vehicle closure (the ability to fly a trajectory segment subject to constraints) is provided through a proposed vehicle design methodology. The design method attempts to use open loop metrics whenever possible to design the vehicle. The design method is applied to a vehicle/control law closed loop nonlinear simulation for validation. The 3DOF longitudinal modeling results are validated against a newly released NASA 6DOF code.
ContributorsDickeson, Jeffrey James (Author) / Rodriguez, Armando A (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Si, Jennie (Committee member) / Wells, Valana (Committee member) / Kawski, Mattias (Committee member) / Arizona State University (Publisher)
Created2012
Description
Development of post-traumatic epilepsy (PTE) after traumatic brain injury (TBI) is a major health concern (5% - 50% of TBI cases). A significant problem in TBI management is the inability to predict which patients will develop PTE. Such prediction, followed by timely treatment, could be highly beneficial to TBI patients. Six male Sprague-Dawley rats were subjected to a controlled cortical impact (CCI). A 6mm piston was pneumatically driven 3mm into the right parietal cortex with velocity of 5.5m/s. The rats were subsequently implanted with 6 intracranial electroencephalographic (EEG) electrodes. Long-term (14-week) continuous EEG recordings were conducted. Using linear (coherence) and non-linear (Lyapunov exponents) measures of EEG dynamics in conjunction with measures of network connectivity, we studied the evolution over time of the functional connectivity between brain sites in order to identify early precursors of development of epilepsy. Four of the six TBI rats developed PTE 6 to 10 weeks after the initial insult to the brain. Analysis of the continuous EEG from these rats showed a gradual increase of the connectivity between critical brain sites in terms of their EEG dynamics, starting at least 2 weeks prior to their first spontaneous seizure. In contrast, for the rats that did not develop epilepsy, connectivity levels did not change, or decreased during the whole course of the experiment across pairs of brain sites. Consistent behavior of functional connectivity changes between brain sites and the "focus" (site of impact) over time was demonstrated for coherence in three out of the four epileptic and in both non-epileptic rats, while for STLmax in all four epileptic and in both non-epileptic rats. This study provided us with the opportunity to quantitatively investigate several aspects of epileptogenesis following traumatic brain injury. Our results strongly support a network pathology that worsens with time. It is conceivable that the observed changes in spatiotemporal dynamics after an initial brain insult, and long before the development of epilepsy, could constitute a basis for predictors of epileptogenesis in TBI patients.
ContributorsTobin, Edward (Author) / Iasemidis, Leonidas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Muthuswamy, Jitendran (Committee member) / Arizona State University (Publisher)
Created2012
Description
Spinal cord injury (SCI) disrupts the communication between supraspinal circuits and spinal circuits distal to the injury. This disruption causes changes in the motor abilities of the affected individual, but it can also be used as an opportunity to study motor control in the absence or limited presence of control from the brain. In the case of incomplete paraplegia, locomotion is impaired and often results in increased incidence of foot drag and decreased postural stability after injury. The overall goal of this work is to understand how changes in kinematics of movement and neural control of muscles effect locomotor coordination following SCI. Toward this end, we examined musculoskeletal parameters and kinematics of gait in rats with and without incomplete SCI (iSCI) and used an empirically developed computational model to test related hypotheses. The first study tested the hypothesis that iSCI causes a decrease in locomotor and joint angle movement complexity. A rat model was used to measure musculoskeletal properties and gait kinematics following mild iSCI. The data indicated joint-specific changes in kinematics in the absence of measurable muscle atrophy, particularly at the ankle as a result of the injury. Kinematic changes manifested as a decrease in complexity of ankle motion as indicated by measures of permutation entropy. In the second study, a new 2-dimensional computational model of the rat ankle combining forward and inverse dynamics was developed using the previously collected data. This model was used to test the hypothesis that altered coordination of flexor and extensor muscles (specifically alteration in burst shape and timing) acting at the ankle joint could be responsible for increases in incidence of foot drag following injury. Simulation results suggest a time course for changes in neural control following injury that begins with foot drag and decreased delay between antagonistic muscle activations. Following this, beneficial adaptations in muscle activation profile and ankle kinematics counteract the decreased delay to allow foot swing. In both studies, small changes in neural control caused large changes in behavior, particularly at the ankle. Future work will further examine the role of neural control of hindlimb in rat locomotion following iSCI.
ContributorsHillen, Brian (Author) / Jung, Ranu (Thesis advisor) / Abbas, James (Committee member) / Muthuswamy, Jit (Committee member) / Jindrich, Devin (Committee member) / Yamaguchi, Gary (Committee member) / Arizona State University (Publisher)
Created2012
Description
This thesis examines themodeling, analysis, and control system design issues for scramjet powered hypersonic vehicles. A nonlinear three degrees of freedom longitudinal model which includes aero-propulsion-elasticity effects was used for all analyses. This model is based upon classical compressible flow and Euler-Bernouli structural concepts. Higher fidelity computational fluid dynamics and finite element methods are needed for more precise intermediate and final evaluations. The methods presented within this thesis were shown to be useful for guiding initial control relevant design. The model was used to examine the vehicle's static and dynamic characteristics over the vehicle's trimmable region. The vehicle has significant longitudinal coupling between the fuel equivalency ratio (FER) and the flight path angle (FPA). For control system design, a two-input two-output plant (FER - elevator to speed-FPA) with 11 states (including 3 flexible modes) was used. Velocity, FPA, and pitch were assumed to be available for feedback. Aerodynamic heat modeling and design for the assumed TPS was incorporated to original Bolender's model to study the change in static and dynamic properties. De-centralized control stability, feasibility and limitations issues were dealt with the change in TPS elasticity, mass and physical dimension. The impact of elasticity due to TPS mass, TPS physical dimension as well as prolonged heating was also analyzed to understand performance limitations of de-centralized control designed for nominal model.
ContributorsKhatri, Jaidev (Author) / Rodriguez, Armando Antonio (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Wells, Valana (Committee member) / Arizona State University (Publisher)
Created2011
Description
Skin and muscle receptors in the leg and foot provide able-bodied humans with force and position information that is crucial for balance and movement control. In lower-limb amputees however, this vital information is either missing or incomplete. Amputees typically compensate for the loss of sensory information by relying on haptic feedback from the stump-socket interface. Unfortunately, this is not an adequate substitute. Areas of the stump that directly interface with the socket are also prone to painful irritation, which further degrades haptic feedback. The lack of somatosensory feedback from prosthetic legs causes several problems for lower-limb amputees. Previous studies have established that the lack of adequate sensory feedback from prosthetic limbs contributes to poor balance and abnormal gait kinematics. These improper gait kinematics can, in turn, lead to the development of musculoskeletal diseases. Finally, the absence of sensory information has been shown to lead to steeper learning curves and increased rehabilitation times, which hampers amputees from recovering from the trauma. In this study, a novel haptic feedback system for lower-limb amputees was develped, and studies were performed to verify that information presented was sufficiently accurate and precise in comparison to a Bertec 4060-NC force plate. The prototype device consisted of a sensorized insole, a belt-mounted microcontroller, and a linear array of four vibrotactile motors worn on the thigh. The prototype worked by calculating the center of pressure in the anteroposterior plane, and applying a time-discrete vibrotactile stimulus based on the location of the center of pressure.
ContributorsKaplan, Gabriel Benjamin (Author) / Abbas, James (Thesis director) / McDaniel, Troy (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Localization tasks using two-way ranging (TWR) are making headway in modern daynavigation applications as an alternative to legacy global navigation satellite systems
(GNSS) such as GPS. There is not currently literature that provides a closed-form
expression for estimation performance bounds on position and attitude when a TWR
system is employed. A Cramer-Rao Lower Bounds (CRLB) is derived for position
and orientation estimation using both 2-D and 3-D geometries. A literature review is
performed to give background and detail on the tools needed for a thorough analysis of
this problem. Popular Least Squares techniques and solutions to Wahba’s problem are
compared to the derived bounds as proof of correctness using Monte Carlo simulations.
A brief exploration on estimation performance using an Extended Kalman Filter for
non-stationary users is also looked at as an introduction to future extensions to this
work. The literature Applications like the CHP2 system are discussed as well to show
how secure, inexpensive and robust implementation of TWR is highly feasible.
i
ContributorsWelker, Samuel (Author) / Bliss, Daniel (Thesis advisor) / Herschfelt, Andrew (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2022
Description
Nearly one percent of the population over 65 years of age is living with Parkinson’s disease (PD) and this population worldwide is projected to be approximately nine million by 2030. PD is a progressive neurological disease characterized by both motor and cognitive impairments. One of the most serious challenges for an individual as the disease progresses is the increasing severity of gait and posture impairments since they result in debilitating conditions such as freezing of gait, increased likelihood of falls, and poor quality of life. Although dopaminergic therapy and deep brain stimulation are generally effective, they often fail to improve gait and posture deficits. Several recent studies have employed real-time feedback (RTF) of gait parameters to improve walking patterns in PD. In earlier work, results from the investigation of the effects of RTF of step length and back angle during treadmill walking demonstrated that people with PD could follow the feedback and utilize it to modulate movements favorably in a manner that transferred, at least acutely, to overground walking. In this work, recent advances in wearable technologies were leveraged to develop a wearable real-time feedback (WRTF) system that can monitor and evaluate movements and provide feedback during daily activities that involve overground walking. Specifically, this work addressed the challenges of obtaining accurate gait and posture measures from wearable sensors in real-time and providing auditory feedback on the calculated real-time measures for rehabilitation. An algorithm was developed to calculate gait and posture variables from wearable sensor measurements, which were then validated against gold-standard measurements. The WRTF system calculates these measures and provides auditory feedback in real-time. The WRTF system was evaluated as a potential rehabilitation tool for use by people with mild to moderate PD. Results from the study indicated that the system can accurately measure step length and back angle, and that subjects could respond to real-time auditory feedback in a manner that improved their step length and uprightness. These improvements were exhibited while using the system that provided feedback and were sustained in subsequent trials immediately thereafter in which subjects walked without receiving feedback from the system.
ContributorsMuthukrishnan, Niveditha (Author) / Abbas, James (Thesis advisor) / Krishnamurthi, Narayanan (Thesis advisor) / Shill, Holly A (Committee member) / Honeycutt, Claire (Committee member) / Turaga, Pavan (Committee member) / Ingalls, Todd (Committee member) / Arizona State University (Publisher)
Created2022