Identification of the contribution of the ankle and hip joints to multi-segmental balance control

Background\ud \ud Human stance involves multiple segments, including the legs and trunk, and requires coordinated actions of both. A novel method was developed that reliably estimates the contribution of the left and right leg (i.e., the ankle and hip joints) to the balance control of individual subjects. \ud \ud Methods\ud \ud The method was evaluated using simulations of a double-inverted pendulum model and the applicability was demonstrated with an experiment with seven healthy and one Parkinsonian participant. Model simulations indicated that two perturbations are required to reliably estimate the dynamics of a double-inverted pendulum balance control system. In the experiment, two multisine perturbation signals were applied simultaneously. The balance control system dynamic behaviour of the participants was estimated by Frequency Response Functions (FRFs), which relate ankle and hip joint angles to joint torques, using a multivariate closed-loop system identification technique. \ud \ud Results\ud \ud In the model simulations, the FRFs were reliably estimated, also in the presence of realistic levels of noise. In the experiment, the participants responded consistently to the perturbations, indicated by low noise-to-signal ratios of the ankle angle (0.24), hip angle (0.28), ankle torque (0.07), and hip torque (0.33). The developed method could detect that the Parkinson patient controlled his balance asymmetrically, that is, the right ankle and hip joints produced more corrective torque. \ud \ud Conclusion\ud \ud The method allows for a reliable estimate of the multisegmental feedback mechanism that stabilizes stance, of individual participants and of separate leg

Balance between competing spectral states in subthalamic nucleus is linked to motor impairment in Parkinson's disease

Exaggerated bursts of activity at frequencies in the low beta band are a well-established phenomenon in the subthalamic nucleus (STN) of patients with Parkinson's disease. However, such activity is only moderately correlated with motor impairment. Here we test the hypothesis that beta bursts are just one of several dynamic states in the STN local field potential (LFP) in Parkinson's disease, and that together these different states predict motor impairment with high fidelity. LFPs were recorded in 32 patients (64 hemispheres) undergoing deep brain stimulation surgery targeting the STN. Recordings were performed following overnight withdrawal of anti-parkinsonian medication, and after administration of levodopa. LFPs were analysed using Hidden Markov Modelling to identify transient spectral states with frequencies under 40 Hz. Findings in the low beta frequency band were similar to those previously reported; levodopa reduced occurrence rate and duration of low beta states, and the greater the reductions, the greater the improvement in motor impairment. However, additional LFP states were distinguished in the theta, alpha and high beta bands, and these behaved in an opposite manner. They were increased in occurrence rate and duration by levodopa, and the greater the increases, the greater the improvement in motor impairment. In addition, levodopa favoured the transition of low beta states to other spectral states. When all LFP states and corresponding features were considered in a multivariate model it was possible to predict 50% of the variance in patients' hemibody impairment OFF medication, and in the change in hemibody impairment following levodopa. This only improved slightly if signal amplitude or gamma band features were also included in the multivariate model. In addition, it compares with a prediction of only 16% of the variance when using beta bursts alone. We conclude that multiple spectral states in the STN LFP have a bearing on motor impairment, and that levodopa-induced shifts in the balance between these states can predict clinical change with high fidelity. This is important in suggesting that some states might be upregulated to improve parkinsonism and in suggesting how LFP feedback can be made more informative in closed-loop deep brain stimulation systems

L-DOPA-induced signaling pathways and neuroepigenetic mechanisms in experimental Parkinsonism and dyskinesia

In patients with Parkinson’s disease (PD), the restoration of depleted striatal dopamine by chronic administration of its precursor, L-DOPA, results in the emergence of debilitating involuntary movements. This complication, termed L-DOPA-induced dyskinesia (LID), represents a limitation to the most efficacious treatment for PD motor symptoms. LID progressively increases in severity despite the continual ability of L-DOPA to alleviate parkinsonian symptoms, suggesting divergent mechanisms of action and the potential for therapeutic intervention. Utilizing an experimental mouse model of PD, the work presented within this thesis investigates the molecular alterations underlying LID. These studies reveal that L-DOPA administration results in pathological intracellular signaling and gene expression within striatal medium spiny neurons (MSNs) expressing the dopamine D1 receptor (D1R). Following L-DOPA administration, sensitized D1R signaling results in hyperactivity of the cyclic 3’-5’ adenosine monophosphate (cAMP)/ cAMP-dependent kinase (PKA)/ dopamine- and cAMP-regulated phosphoprotein of 32 kDA (DARPP-32) pathway. This exaggerated response results in excessive activation of the downstream extracellularregulated kinases 1 and 2 (ERK1/2) and mammalian target of rapamycin complex 1 (mTORC1) cascades, both of which are implicated in LID. These data also demonstrate that nuclear events mediated by mitogen- and stress-activated kinase 1 (MSK1), a direct ERK1/2 substrate, promote the induction of the transcription factor ΔFosB, which exacerbates LID. Furthermore, the concerted activity of MSK1 and DARPP-32 promotes histone H3K27me3S28p and the dissociation from transcription start sites of Rnf2, a Polycomb group protein that represses gene expression. These events are associated with an increase in transcription. Taken together, these studies support the idea that sensitized striatal D1R signaling promotes LID by excessive activation of intracellular signaling pathways and nuclear events promoting gene expression

Sensor Approach for Brain Pathophysiology of Freezing of Gait in Parkinson\u27s Disease Patients

Parkinson\u27s Disease (PD) affects over 1% of the population over 60 years of age and is expected to reach 1 million in the USA by the year 2020, growing by 60 thousand each year. It is well understood that PD is characterized by dopaminergic loss, leading to decreased executive function causing motor symptoms such as tremors, bradykinesia, dyskinesia, and freezing of gait (FoG) as well as non-motor symptoms such as loss of smell, depression, and sleep abnormalities. A PD diagnosis is difficult to make since there is no worldwide approved test and difficult to manage since its manifestations are widely heterogeneous among subjects. Thus, understanding the patient subsets and the neural biomarkers that set them apart will lead to improved personalized care. To explore the physiological alternations caused by PD on neurological pathways and their effect on motor control, it is necessary to detect the neural activity and its dissociation with healthy physiological function. To this effect, this study presents a custom ultra-wearable sensor solution, consisting of electroencephalograph, electromyograph, ground reaction force, and symptom measurement sensors for the exploration of neural biomarkers during active gait paradigms. Additionally, this study employed novel de-noising techniques for dealing with the motion artifacts associated with active gait EEG recordings and compared time-frequency features between a group of PD with FoG and a group of age-matched controls and found significant differences between several EEG frequency bands during start and end of normal walking (with a p\u3c0.05)

Wireless tools for neuromodulation

Epilepsy is a spectrum of diseases characterized by recurrent seizures. It is estimated that 50 million individuals worldwide are affected and 30% of cases are medically refractory or drug resistant. Vagus nerve stimulation (VNS) and deep brain stimulation (DBS) are the only FDA approved device based therapies. Neither therapy offers complete seizure freedom in a majority of users. Novel methodologies are needed to better understand mechanisms and chronic nature of epilepsy. Most tools for neuromodulation in rodents are tethered. The few wireless devices use batteries or are inductively powered. The tether restricts movement, limits behavioral tests, and increases the risk of infection. Batteries are large and heavy with a limited lifetime. Inductive powering suffers from rapid efficiency drops due to alignment mismatches and increased distances. Miniature wireless tools that offer behavioral freedom, data acquisition, and stimulation are needed. This dissertation presents a platform of electrical, optical and radiofrequency (RF) technologies for device based neuromodulation. The platform can be configured with features including: two channels differential recording, one channel electrical stimulation, and one channel optical stimulation. Typical device operation consumes less than 4 mW. The analog front end has a bandwidth of 0.7 Hz - 1 kHz and a gain of 60 dB, and the constant current driver provides biphasic electrical stimulation. For use with optogenetics, the deep brain optical stimulation module provides 27 mW/mm2 of blue light (473 nm) with 21.01 mA. Pairing of stimulating and recording technologies allows closed-loop operation. A wireless powering cage is designed using the resonantly coupled filter energy transfer (RCFET) methodology. RF energy is coupled through magnetic resonance. The cage has a PTE ranging from 1.8-6.28% for a volume of 11 x 11 x 11 in3. This is sufficient to chronically house subjects. The technologies are validated through various in vivo preparations. The tools are designed to study epilepsy, SUDEP, and urinary incontinence but can be configured for other studies. The broad application of these technologies can enable the scientific community to better study chronic diseases and closed-loop therapies

Towards the Development of a Wearable Tremor Suppression Glove

Patients diagnosed with Parkinson’s disease (PD) often associate with tremor. Among other symptoms of PD, tremor is the most aggressive symptom and it is difficult to control with traditional treatments. This thesis presents the assessment of Parkinsonian hand tremor in both the time domain and the frequency domain, the performance of a tremor estimator using different tremor models, and the development of a novel mechatronic transmission system for a wearable tremor suppression device. This transmission system functions as a mechatronic splitter that allows a single power source to support multiple independent applications. Unique features of this transmission system include low power consumption and adjustability in size and weight. Tremor assessment results showed that the hand tremor signal often presents a multi-harmonics pattern. The use of a multi-harmonics tremor model produced a better estimation result than using a monoharmonic tremor model

Application of Linear Stochastic Models in the Investigation of the Effects of Parkinson’s Disease on the Cop Time Series

The primary objective of this study was to use linear stochastic modeling approach to investigate parameters which may be sensitive enough to detect and quantify the changes in postural instability (PI) related to the progression in Parkinson’s disease (PD). Data collected in a previous study were analyzed in the current study. Participants with mild PD (n=13), moderate PD (n=10) and age range match healthy controls (HC, n=21) were instructed to stand in a comfortable self-selected natural stance on a force platform in both eyes open (EO) and eyes closed (EC) conditions. The foot-floor reaction forces were used to calculate the center of pressure (COP) time series. This COP time series was fitted by two different linear stochastic models: 1) an autoregressive (AR), and 2) an autoregressive moving average (ARMA) model. The postural control system was modeled as an inverted pendulum to describe pure body mechanics and a proportional, derivative and integral (PID) strategy was assumed for balance regulation. Swiftness, damping and stiffness parameters were extracted from the AR model. Natural frequency and damping ratio were extracted from the ARMA model. The statistical analysis (ANOVA) of these parameters revealed significant differences in stiffness and swiftness parameters between the HC and moderate PD population in the EO condition. These three parameters showed trends with progression of PD. The swiftness parameter showed decreasing mean values as PD severity increased, indicating that PD caused slower reactions to small deviations from equilibrium when compared to healthy controls. The mild and moderate PD, compared to HC, demonstrated by higher mean values of stiffness, suggesting a more rigid control strategy. The analysis of damping parameter revealed that the PD, compared to HC, may have a reduced ability to attenuate sway velocity during quiet stance as indicated by lower mean values of damping parameter and damping ratio. The natural frequency did not show significant trends in EO condition, but revealed an increasing trend with progression of PD. This could indicate that the PD could have larger number of deviations of COP from equilibrium. The analysis of effect of condition (EO, EC) revealed significant differences in all the five parameters. The stiffness, damping parameter and damping ratio had higher mean values for EO, compared to the EC condition, indicating the vital role that the visual feedback plays in detecting small perturbations from equilibrium leading to a better posture regulation in EO condition. The swiftness parameter and natural frequency indicated higher mean values in EC, compared to the EO condition, suggesting that the various sensory cues might be weighted differently in EO and EC conditions. Future studies should investigate the sensitivity of these calculated parameters to changes in PI in PD using a larger sample size and longer duration of trials. Also the variations in these parameters in response to dynamic tasks such as gait initiation and balance recovery should be considered in future studies

Action selection in the rhythmic brain: The role of the basal ganglia and tremor.

Low-frequency oscillatory activity has been the target of extensive research both in cortical structures and in the basal ganglia (BG), due to numerous reports of associations with brain disorders and the normal functioning of the brain. Additionally, a plethora of evidence and theoretical work indicates that the BG might be the locus where conflicts between prospective actions are being resolved. Whereas a number of computational models of the BG investigate these phenomena, these models tend to focus on intrinsic oscillatory mechanisms, neglecting evidence that points to the cortex as the origin of this oscillatory behaviour. In this thesis, we construct a detailed neural model of the complete BG circuit based on fine-tuned spiking neurons, with both electrical and chemical synapses as well as short-term plasticity between structures. To do so, we build a complete suite of computational tools for the design, optimization and simulation of spiking neural networks. Our model successfully reproduces firing and oscillatory behaviour found in both the healthy and Parkinsonian BG, and it was used to make a number of biologically-plausible predictions. First, we investigate the influence of various cortical frequency bands on the intrinsic effective connectivity of the BG, as well as the role of the latter in regulating cortical behaviour. We found that, indeed, effective connectivity changes dramatically for different cortical frequency bands and phase offsets, which are able to modulate (or even block) information flow in the three major BG pathways. Our results indicate the existence of a multimodal gating mechanism at the level of the BG that can be entirely controlled by cortical oscillations, and provide evidence for the hypothesis of cortically-entrained but locally-generated subthalamic beta activity. Next, we explore the relationship of wave properties of entrained cortical inputs, dopamine and the transient effectiveness of the BG, when viewed as an action selection device. We found that cortical frequency, phase, dopamine and the examined time scale, all have a very important impact on the ability of our model to select. Our simulations resulted in a canonical profile of selectivity, which we termed selectivity portraits. Taking together, our results suggest that the cortex is the structure that determines whether action selection will be performed and what strategy will be utilized while the role of the BG is to perform this selection. Some frequency ranges promote the exploitation of actions of whom the outcome is known, others promote the exploration of new actions with high uncertainty while the remaining frequencies simply deactivate selection. Based on this behaviour, we propose a metaphor according to which, the basal ganglia can be viewed as the ''gearbox" of the cortex. Coalitions of rhythmic cortical areas are able to switch between a repertoire of available BG modes which, in turn, change the course of information flow back to and within the cortex. In the same context, dopamine can be likened to the ''control pedals" of action selection that either stop or initiate a decision. Finally, the frequency of active cortical areas that project to the BG acts as a gear lever, that instead of controlling the type and direction of thrust that the throttle provides to an automobile, it dictates the extent to which dopamine can trigger a decision, as well as what type of decision this will be. Finally, we identify a selection cycle with a period of around 200 ms, which was used to assess the biological plausibility of the most popular architectures in cognitive science. Using extensions of the BG model, we further propose novel mechanisms that provide explanations for (1) the two distinctive dynamical behaviours of neurons in globus pallidus external, and (2) the generation of resting tremor in Parkinson's disease. Our findings agree well with experimental observations, suggest new insights into the pathophysiology of specific BG disorders, provide new justifications for oscillatory phenomena related to decision making and reaffirm the role of the BG as the selection centre of the brain.Open Acces

Music normalizes visual and proprioceptive control of movement in Parkinson's disease

xiv, 147 leaves : ill. ; 29 cm. --The sensory control of movements has been shown to be impaired with Parkinson’s disease. I investigated the task, reach-to-eat, in which advancing of the limb towards a target is guided by vision and withdrawal of the grasped target to the mouth is guided by somatosensation (i.e., haptics and proprioception). Parkinson’s diseased subjects display an alteration in the balance of visual and proprioceptive guidance, such that they display increased visual fixation on the target prior to movement onset that persists following the grasp. Music therapy can normalize the balance between visual and proprioceptive guidance on the reach-to-eat task, as visual fixation with the target prior to movement onset is consistent with controls, and disengagement following grasp no longer differs from mild Parkinson’s disease subjects. These results are the first to demonstrate that music can have an ameliorating effect on the sensory impairments seen in the control of forelimb movements in Parkinson’s disease