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Lowery, Madeleine M.
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Lowery, Madeleine M.
Official Name
Lowery, Madeleine M.
Research Output
Now showing 1 - 10 of 44
- PublicationExploration of the Generation and Suppression of Pathological Oscillatory Neural Activity in a Model of Deep Brain Stimulation in Parkinsons disease(2015-01-17)
; ; ; This study explores possible mechanisms for the generation of pathological neural oscillatory activity associated with Parkinson’s disease in theoretical models. The suppression of the model oscillations with high frequency stimulation, analogous to the use of deep brain stimulation (DBS) in the treatment of Parkinson's disease, is also examined. The relationship between oscillation amplitude and the amplitude of the applied stimulation is explored theoretically and then compared with experimental data recorded in patients.54 - PublicationAnalysis of Parkinsonian Surface Electomyography Through Advanced Signal Processing and Nonlinear Methods(2016-01-23)
; Parkinson’s disease (PD) is a neurodegenerative disease that affects approx. 4% of people over 80 years of age [4]. The result of depleted dopaminergic neurons in the substantia nigra, PD is characterised with symptoms such as muscle rigidity, bradykinetic gait, and severe tremor. To distinguish Parkinsonian electromyographic (EMG) signals from those of healthy controls, recent studies have employed nonlinear methods which can capture the underlying activity of the neuromuscular system. Recurrence quantification analysis (RQA) has been shown to effectively characterise the degree of repeated synchronous structure in non-linear dynamical systems including parkinsonian EMG, through parameters such as determinism (%DET) and recurrence rate (%REC) [1]. Additional parameters such as intermuscular coherence and kurtosis have also been used to observe changes in EMG signals under various conditions [2,3]. To date, limited research has examined the potential to discern EMG of individuals with PD from healthy controls using RQA and intermuscular coherence. The work presented here aims to examine differences in Parkinsonian EMG from that of healthy controls using these measures.254 - PublicationAlterations in Motor Unit Firing Rate and Action Potential Properties during Isometric Fatigue in Stroke Survivors(2016-07-08)
; ; ; ; The limited number of studies that have investigated fatigue in chronic stroke survivors during voluntary contr actions to the endurance limit have reported relatively higher central fatigue and lower peripher al fatigue on the affected side when compared to the less-affected side and healthy controls (Riley and Bilodeau, 2002; Knorr et al., 2011). Although these changes have been investigated using global indices of motor unit (MU) activation, alterations at th e level of the single motor unit have not yet been examined.97 - PublicationEMG Driven Model of the Lumbar Spine during Flexion, Bending and Rotation Using Opensim(2013-08-09)
; ; ; This study utilised the OpenSim platform to develop an EMG driven model of the lumbar spine by expanding an existing model and incorporating a plugin to represent intervertebral stiffness. Subject-specific kinematic data and surface EMG activity were recorded from 4 subjects during flexion and extension, lateral bending, and axial rotation. The model was used to predict muscle excitation patterns necessary to produce the recorded motions, and the patterns were compared with the recorded EMG data. The model was then driven with the recorded EMG data, and new excitation patterns were calculated for the deep muscles for which EMG data was not available. Simulations were conducted for intervertebral lumbar stiffness corresponding to preloading of 0N, 250N and 500N. The model-predicted excitation patterns were most comparable to recorded EMG data for the flexion and extension motions. Excitation levels predicted for all motions were sensitive to the applied preload. Although activation patterns remained similar, there was a substantial variation in model-predicted muscle excitation levels with change in intervertebral stiffness.118 - PublicationNumerical Identification of Motor Units Using an Optimal Control Approach(International Federation of Automatic Control, 2018-02-23)
; ; A numerical approach to locate motor units in human muscles by big density surface EMG measurements is presented. For this purpose a mathematical model has been derived which can be evaluated by finite element computations. On that basis an optimal control problem is specified that can be solved by a function space oriented optimization method. Numerical results are reported for a test problem.342 - PublicationWhole body oxygen uptake and evoked knee torque in response to low frequency electrical stimulation of the quadriceps muscles: V O2 frequency response to NMESBackground: There is emerging evidence that isometric Neuromuscular Electrical Stimulation (NMES) may offer a way to elicit therapeutically significant increases in whole-body oxygen uptake in order to deliver aerobic exercise to patients unable to exercise volitionally, with consequent gains in cardiovascular health. The optimal stimulation frequency to elicit a significant and sustained pulmonary oxygen uptake has not been determined. The aim of this study was to examine the frequency response of the oxygen uptake and evoked torque due to NMES of the quadriceps muscles across a range of low frequencies spanning the twitch to tetanus transition. Methods: Ten healthy male subjects underwent bilateral NMES of the quadriceps muscles comprising eight 4 minute bouts of intermittent stimulation at selected frequencies in the range 1 to 12 Hz, interspersed with 4 minutes rest periods. Respiratory gases and knee extensor torque were simultaneously monitored throughout. Multiple linear regression was used to fit the resulting data to an energetic model which expressed the energy rate in terms of the pulse frequency, the torque time integral and a factor representing the accumulated force developed per unit time. Results: Additional oxygen uptake increased over the frequency range to a maximum of 564 (SD 114) ml min-1 at 12 Hz, and the respiratory exchange ratio was close to unity from 4 to 12 Hz. While the highest induced torque occurred at 12 Hz, the peak of the force development factor occurred at 6 Hz. The regression model accounted for 88% of the variability in the observed energetic response. Conclusions: Taking into account the requirement to avoid prolonged tetanic contractions and to minimize evoked torque, the results suggest that the ideal frequency for sustainable aerobic exercise is 4 to 5 Hz, which coincided in this study with the frequency above which significant twitch force summation occurred.
487Scopus© Citations 12 - PublicationRegression-based analysis of front crawl swimming using upper-arm mounted accelerometersWearable accelerometers can be used to quantify movement during swimming, enabling objective performance analysis. This study examined arm acceleration during front crawl swimming, and investigated how accelerometer-derived features change with lap times. Thirteen participants swam eight 50m laps using front crawl with a tri-axial accelerometer attached to each upper arm. Data were segmented into individual laps; lap times estimated and individual strokes extracted. Stroke times, root mean squared (RMS) acceleration, RMS jerk and spectral edge frequencies (SEF) were calculated for each stroke. Movement symmetry was assessed as the ratio of the minimum to maximum feature value for left and right arms. A regularized multivariate regression model was developed to estimate lap time using a subset of the accelerometer-derived features. Mean lap time was 56.99±11.99s. Fifteen of the 42 derived features were significantly correlated with lap time. The regression model included 5 features (stroke count, mean SEF of the X and Z axes, stroke count symmetry, and the coefficient of variation of stroke time symmetry) and estimated 50m lap time with a correlation coefficient of 0.86, and a cross-validated RMS error of 6.38s. The accelerometer-derived features and developed regression model may provide a useful tool to quantitatively evaluate swimming performance.
265 - PublicationUsing the Root Locus Method to Analyze Pathological Oscillations in Neurological DiseasesIn recent years the authors have developed what appears to be a very successful phenomenological model for analyzing the role of deep brain stimulation (DBS) in alleviating the symptoms of Parkinson's disease. In this paper, we extend the scope of the model by using it to predict the generation of new frequencies from networks tuned to a specific frequency, or indeed not self-oscillatory at all.We have discussed two principal cases: firstly where the constituent systems are coupled in an excitatory-excitatory fashion, which we designate by “+/+”; and secondly where the constituent systems are coupled in an excitatory-inhibitory fashion, which we designate “+/-”. The model predicts that from a basic system tuned to tremor frequency we can generate an unlimited range of frequencies. We illustrate in particular, starting from systems which are initially non-oscillatory, that when the coupling coefficient exceeds a certain value, the system begins to oscillate at an amplitude which increases with the coupling strength. Another very interesting feature, which has been shown by colleagues of ours to arise through the coupling of complicated networks based on the physiology of the basal ganglia, can be illustrated by the root locus method which shows that increasing and decreasing frequencies of oscillation, existing simultaneously, have the property that their geometric mean remains substantially constant as the coupling strength is varied. We feel that with the present approach, we have provided another tool for understanding the existence and interaction of pathological oscillations which underlie, not only Parkinson's disease, but other conditions such as Tourette's syndrome, depression and epilepsy.
223 - PublicationSimulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinsons DiseaseThis study presents a computational model of closed-loop control of deep brain stimulation (DBS) for Parkinson’s disease (PD) to investigate clinically viable control schemes for suppressing pathological beta-band activity. Closed-loop DBS for PD has shown promising results in preliminary clinical studies and offers the potential to achieve better control of patient symptoms and side effects with lower power consumption than conventional open-loop DBS. However, extensive testing of algorithms in patients is difficult. The model presented provides a means to explore a range of control algorithms in silico and optimize control parameters before preclinical testing. The model incorporates (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The performance of on-off and dual-threshold controllers for suppressing beta-band activity by modulating the DBS amplitude were first verified, showing levels of beta suppression and reductions in power consumption comparable with previous clinical studies. Proportional (P) and proportional-integral (PI) closed-loop controllers for amplitude and frequency modulation were then investigated. A simple tuning rule was derived for selecting effective PI controller parameters to target long duration beta bursts while respecting clinical constraints that limit the rate of change of stimulation parameters. Of the controllers tested, PI controllers displayed superior performance for regulating network beta-band activity whilst accounting for clinical considerations. Proportional controllers resulted in undesirable rapid fluctuations of the DBS parameters which may exceed clinically tolerable rate limits. Overall, the PI controller for modulating DBS frequency performed best, reducing the mean error by 83% compared to DBS off and the mean power consumed to 25% of that utilized by open-loop DBS. The network model presented captures sufficient physiological detail to act as a surrogate for preclinical testing of closed-loop DBS algorithms using a clinically accessible biomarker, providing a first step for deriving and testing novel, clinically suitable closed-loop DBS controllers.
182Scopus© Citations 32 - PublicationChanges in Neuronal Entropy in a Network Model of the Cortico-Basal Ganglia during Deep Brain StimulationNeuronal entropy changes are observed in the basal ganglia circuit in Parkinson’s disease (PD). These changes are observed in both single unit recordings from globus pallidus (GP) neurons and in local field potential (LFP) recordings from the subthalamic nucleus (STN). These changes are hypothesized as representing changes in the information coding capacity of the network, with PD resulting in a reduction in the coding capacity of the basal ganglia network. Entropy changes in the LFP and in single unit recordings are investigated in a detailed physiological model of the cortico-basal ganglia network during STN deep brain stimulation (DBS). The model incorporates extracellular stimulation of STN afferent fibers, with both orthodromic and antidromic activation, and simulation of the LFP detected at a differential recording electrode. LFP sample entropy and beta-band oscillation power were found to be altered following the application of DBS. The ring pattern entropy of GP neurons in the network were observed to decrease during high frequency stimulation and increase during low frequency stimulation. Simulation results were consistent with experimentally reported changes in neuronal entropy during DBS.
277Scopus© Citations 3