Browsing by Subject "Parkinson's disease"

Recently, several mass spectrometry-based proteomics techniques have been developed for the large-scale analysis of thermodynamic measurements of protein stability. This has created the possibility of characterizing disease states via differential thermodynamic stability profiles. Described here is the application of the Stability of Proteins from Rates of Oxidation (SPROX) technique to characterize mouse models of disease. The mouse models studied here are of normal aging and two genetically induced Parkinson’s Disease (PD) models.

Thermodynamic stability profiles were generated for 809 proteins in brain cell lysates from C57BL/6 mice at age 6- (n=7) and 18-months (n=9). The biological variability of the protein stability measurements was low, and within the experimental error of the SPROX technique. Remarkably, the large majority of the 83 brain protein hits were destabilized in the old mice, and the hits were enriched in proteins that have slow turnover rates (p<0.07). Furthermore, 70% of the hits have been previously linked to aging or age-related disease.

One of the PD mouse models involved characterizing the protein interactions induced by mutated leuceine-rich repeat kinase 2 (LRRK2) at a pre-symptomatic time point (3 months old). The models used were a control, overexpressed wildtype LRRK2, and overexpressed R1441G mutated LRRK2 (n=2 for all models). Comparative analyses on thermodynamic stability profiles of ~470 proteins revealed relatively few differences. In fact, the observed hit rate in each comparative analysis was close to that associated with the biological variability of the mice. However, four protein hits, dihydropyrimidinase-related protein 2, eukaryotic translation initiation factor 4A2, Rap1 GTP-GDP dissociation stimulator 1 and myelin basic protein, were identified with consistent thermodynamic stability in multiple mice within a biological state and as hits in multiple comparisons suggesting they are the most likely to be true positives.

The second PD mouse model studied was one in which the human α-synuclein protein, containing the known PD mutation A53T, was overexpressed. To characterize the disease progression of PD induced by this mutation, mice were sacrificed at 1 month (n=4), 6 months (n=4) and when they became symptomatic at 10-16 months (n=3). Thermodynamic stability profiles were generated for >850 proteins at each time point. The relative stabilities of these proteins were assayed in a series of comparative analyses involving mice at the different time points and the normally aged mice from above. In total 244 peptides were found to be differentially stabilized during PD progression. A subset of 52 peptide hits was identified to be of particular interest. Of these 52 peptides 22 were identified with early disease progression, 5 peptides showed late disease progression, 5 peptides reported a gradual difference in stability over disease progression and 20 peptides indicated no disease progression trend. More than 90% of the 32 peptides indicating a trend in disease progression showed progression related destabilization.

The results of this thesis help validate the use of thermodynamic stability measurements to capture disease-related proteomic differences in mice. Furthermore, these results establish a new biophysical link between the hit proteins identified and their role in aging, LRRK2 protein interactions, and PD progression.

Continuous deep brain stimulation (cDBS) of either subthalamic nucleus (STN) or globus pallidus (GP) is an effective therapy in Parkinson’s Disease (PD) but is inherently limited by lack of responsiveness to dynamic, fluctuating symptoms intrinsic to the disease. Adaptive DBS (aDBS) adjusts stimulation in response to neural biomarkers to improve both efficacy and battery life. This thesis discusses 1) the development of dual target STN+GP aDBS with a novel, external adaptive controller and 2) the outcomes from a first in-human clinical trial in PD patients (n = 6; NCT #03815656) in order to assess efficacy of the aDBS controller.We performed random amplitude experiments to probe system dynamics and thus estimated initial aDBS parameters. We then implemented an innovative proportional plus integral (PI) aDBS using a novel distributed architecture. The PI aDBS controller was first evaluated in the clinic settings and then compared to cDBS in the home settings. The results showed that the PI aDBS control reduced average power delivered while preserving improved Unified Parkinson’s Disease Rating Scale (UPDRS) III scores in the clinic and reduced beta oscillations during blinded testing in the home setting. Thus, we demonstrated that the novel PI aDBS may enhance chronic, symptomatic treatment of PD.

Volumetric segmentation of brain sub-cortical structures within the basal ganglia and thalamus from Magnetic Resonance Image (MRI) is necessary for non-invasive diagnosis and neurosurgery planning. This is a challenging problem due in part to limited boundary information between structures, similar intensity profiles across the different structures, and low contrast data. With recent advances in ultrahigh-field MR technology, direct identification and clear visualization of such brain sub-cortical structures are facilitated. This dissertation first presents a semi-automatic segmentation system exploiting the visual benefits of ultrahigh-field MRI. The proposed approach utilizes the complementary edge information in the multiple structural MRI modalities. It combines optimally selected two modalities from susceptibility-weighted, T2-weighted, and diffusion MRI, and introduces a tailored new edge indicator function. In addition to this, prior shape and configuration knowledge of the sub-cortical structures are employed in order to guide the evolution of geometric active surfaces. Neighboring structures are segmented iteratively, constraining over-segmentation at their borders with a non-overlapping penalty. Experiments with data acquired on a 7 Tesla (T) MRI scanner demonstrate the feasibility and power of the approach for the segmentation of basal ganglia components critical for neurosurgery applications such as Deep Brain Stimulation (DBS) surgery.

DBS surgery on brain sub-cortical regions within the Basal ganglia and thalamus is an effective treatment to alleviate symptoms of neuro-degenerative diseases. Particularly, the DBS of subthalamic nucleus (STN) has shown important clinical efficacy for Parkinson’s disease (PD). While accurate localization of the STN and its substructures is critical for precise DBS electrode placement, direct visualization of the STN in current standard clinical MR imaging (e.g., 1.5-3T) is still elusive. Therefore, to locate the target, DBS surgeons today often rely on consensus coordinates, lengthy and risky micro-electrode recording (MER), and patient’s behavioral feedback. Recently, ultrahigh-field MR imaging allows direct visualization of brain sub-cortical structures. However, such high fields are not clinically available in practice. This dissertation also introduces a non-invasive automatic localization method of the STN which is one of the critical targets for DBS surgery in a standard clinical scenario (1.5T MRI). The spatial dependency between the STN and potential predictor structures from 7T MR training data is first learned using the regression models in a bagging way. Then, given automatically detected such predictors on the clinical patient data, the complete region of the STN is predicted as a probability map using learned high quality information from 7T. Furthermore, a robust framework is proposed to properly weight different training subsets, estimating their influence in the prediction accuracy. The STN prediction on the clinical 1.5T MR datasets from 15 PD patients is performed within the proposed approach. Experimental results demonstrate that the developed framework enables accurate prediction of the STN, closely matching the 7T ground truth.

The spinal cord is an information highway continuously transmitting afferent and efferent signals to and from the brain. Although spinal cord stimulation has been used for the treatment of chronic pain for decades, its potential has not been fully explored. Spinal cord stimulation has never been used with the aim to transmit relevant information to the brain. Although, various locations along the sensory pathway have been explored for generating electrical stimulation induced sensory percepts, right from peripheral nerves, to thalamus to primary somatosensory cortex, the role of spinal cord has been largely neglected. In this dissertation, I have attempted to investigate if, electrical stimulation of dorsal columns of spinal cord called as Dorsal Column Stimulation (DCS) can be used as an effective technique to communicate therapeutic and somatosensory information to the brain.

To study the long term effects of DCS, I employed the 6-hydroxydopamine (6-OHDA) rodent model of Parkinson’s Disease (PD). Twice a week DCS for 30 minutes resulted in a dramatic recovery of weight and behavioral symptoms in rats treated with striatal infusions of 6-OHDA. The improvement in motor symptoms was accompanied by higher dopaminergic innervation in the striatum and increased cell count of dopaminergic neurons in the substantia nigra pars compacta (SNc). These results suggest that DCS has a chronic therapeutic and neuroprotective effect, increasing its potential as a new clinical option for treating PD patients. Thus, I was able to demonstrate the long-term efficacy of DCS, as a technique for therapeutic intervention.

Subsequently, I investigated if DCS can be used as a technique to transmit artificial somatosensory information to the cortex and trained rats to discriminate multiple artificial tactile sensations. Rats were able to successfully differentiate 4 different tactile percepts generated by varying temporal patterns of DCS. As the rats learnt the task, significant changes in the encoding of this artificial information were observed in multiple brain areas. Finally, I created a Brainet that interconnected two rats: an encoder and a decoder, whereby, cortical signals from the encoder rat were processed by a neural decoder while it performed a tactile discrimination task and transmitted to the spinal cord of the decoder using DCS. My study demonstrated for the first time, a cortico-spinal communication between different organisms.

My obtained results suggest that DCS, a semi-invasive technique, can be used in the future to send prosthetic somatosensory information to the brain or to enable a healthy brain to directly modulate neural activity in the nervous system of a patient, facilitating plasticity mechanism needed for efficient recovery.

Deep brain stimulation (DBS) is an effective therapy for motor symptoms in Parkinson's disease (PD). DBS efficacy depends on the stimulation parameters, and the current gold standard therapy is high-frequency stimulation (>100 Hz) with constant interpulse intervals and short pulse widths (<210 μs). However, the temporal pattern of stimulation is a novel parameter dimension that has not been thoroughly explored. We used non-regular temporal patterns of DBS to pursue two goals: to better understand the mechanisms of DBS, and to increase the efficacy and efficiency of DBS for PD.

First, we designed high frequency patterns of non-regular stimulation with distinct features proposed to be important for efficacy and evaluated these patterns in human subjects with PD. Unexpectedly, some non-regular patterns of stimulation improved performance of an alternating finger-tapping task-a proxy for bradykinesia-compared to high frequency regular stimulation. Performance in the motor task was correlated with suppression of beta band power in a computational model of the basal ganglia suggesting a possible mechanism for effective stimulation patterns.

Inspired by the increased clinical efficacy of non-regular patterns of stimulation with high average frequencies, we developed a non-regular pattern of stimulation that reduced motor symptoms in PD using a low average stimulation frequency. Since the number of potential combinations of interpulse intervals is exceedingly large and it is unclear how such timing should be selected, we applied computational evolution to design an optimal temporal pattern of deep brain stimulation to treat the symptoms of PD. Next, we demonstrated the efficacy of the resulting pattern of stimulation in hemi-parkinsonian rats and humans with PD. Both the optimized stimulation pattern and high frequency stimulation suppressed abnormal oscillatory activity in the basal ganglia in the rat and human, providing a shared mechanism of action for effective stimulation patterns. This innovation could allow patients to achieve battery life savings compared to traditional high frequency stimulation, which will reduce the costs and risks of frequent battery replacement procedures. Further, our approach can be used to design novel temporal patterns of stimulation in other applications of neural stimulation.

Finally, we explored evoked field potentials in the subthalamic nucleus (STN) in response to DBS. These potentials were evoked by stimulation through one of the contacts on the DBS lead and recorded from the two surrounding contacts. Subthalamic DBS local evoked potentials (DLEPs) have never before been recorded. We characterized the DLEPs, differences across DBS frequencies and time, their relationship to beta frequency oscillations and phase-amplitude coupling, and their dependence on electrode contact location.

A 3-dimensional biophysical model of DBS in the subthalamic nucleus-globus pallidus externus (GPe) subcircuit was built to explore the neural origin of the DLEPs. The computational model could reproduce the DLEP signal, and it revealed that the quasi-periodic DLEP oscillations are caused by excitatory synaptic currents in STN interrupted periodically by inhibition from GPe.

DLEP power was correlated with beta band oscillation power in the recordings without DBS, and significant phase-amplitude coupling was observed in a subset of subjects with robust DLEP responses. Together, all available evidence suggested the contact location was an important determinant for the presence and characteristics of DLEP signals. Predictions were made concerning contact location relative to the boundaries of the STN based on the DLEP recordings and insights gained using the computational model, and the predictions were in agreement with blinded post hoc imaging based contact localization for ~70% of contacts predicted to be within STN.

DLEPs are an exciting new signal with several useful applications. DLEPs could help neurosurgeons verify accurate DBS lead placement or optimal stimulation parameters, probe the pathological basal ganglia, and elucidate the mechanisms of DBS.

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The loss of nigrostriatal dopaminergic neurons is the fundamental hallmark of Parkinson’s disease (PD). In early PD stages, this is ameliorated by dopamine (DA) supplementation; however, as the disease progresses, the complete loss of this key dopaminergic pathway forces the central nervous system to find alternative routes to regain motor control. It has previously been shown that serotonergic routes must take on the role of the failed dopaminergic system throughout the progression of the disease. Previously studied 5-HT1A anxiolytic and anti-depressive therapeutics have yet to be successfully repurposed for Parkinson’s disease patients. Herein is described the current efforts towards the employment non-dopaminergic agonists in the investigation of motor control in Parkinson’s disease. This research outlines the development of non-dopaminergic therapeutics inspired by the core structure of the clinically approved 5-HT1A agonist Befiradol. This motif has been infused with a trans-2-arylcyclopropylamine moiety which has been independently shown to reduce motor symptoms in Parkinson’s disease via a prior collaboration from the McCafferty lab. While it was originally hypothesized that these therapeutics would act as bifunctional agonists at the 5-HT1A and M4 GPCRs, affinity assays reveal dualistic agonism at the 5-HT1A and 1 receptors, offering a new class of potential bifunctional therapeutics.

Background

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Conclusions

Substantial correlative evidence links the synchronized, oscillatory neural firing patterns that emerge in Parkinson’s disease (PD) in the frequency range of 13-30Hz (termed “beta band”) with the development of bradykinesia and akinesia. Yet, a causal link between these beta frequency oscillations and symptoms of bradykinesia has not been demonstrated. I tested the hypothesis that the synchronized beta oscillations that emerge in PD are causal of symptoms of bradykinesia/akinesia through studies in intact and parkinsonian animals as well as PD patients.

Regarding the rat studies, I designed novel stimulation patterns to mimic the temporal characteristics of the beta oscillatory bursting pattern seen in single units in PD rats and patients. I applied these beta frequency patterned stimulus trains along with continuous frequency controls over a range of amplitudes via stimulating electrodes implanted unilaterally into the subthalamic nucleus (STN) of healthy and PD rats and assessed the effects on unit activity in the substantia nigra reticulata (SNr) and performance in motor tasks designed to assess forelimb bradykinesia and gross locomotor activity. I quantified the degree of unit entrainment in the SNr as a function of pattern and amplitude by calculation of the excitatory effective pulse fraction (eEPF) [1]. I further quantified the increase in SNr unit spectral beta frequency power due to the applied stimulation paradigms. I found that the beta-patterned paradigms were superior to low frequency controls at entrainment and induction of beta power in downstream substantia nigra reticulata (SNr) neurons. However, I found no deleterious effects on motor performance across a wide battery of validated behavioral tasks.

In PD patients, my objective was to determine how beta frequency oscillatory activity varies with disease progression and severity in human PD patients using cortical electroencephalogram (EEG). I recorded EEG in twenty-five PD patients of varying disease severity after overnight abstinence from PD medication. I recorded EEG at rest (eyes open and closed) and while patients performed various hand motor tasks. These tasks included one-handed isometric grip and rapid open/close movements. For each EEG channel data-stream for each patient, I calculated the total percent of spectral power in the beta band (PSP-β) with respect to movement state. I used stepwise regression to predict UPDRSIII scores from the normalized PSP-β values calculated for each channel during the ‘Eyes Open Rest’ state and found a significant, predictive regression equation. I assessed the relationship between UPDRSIII score and cortical coherence using linear regression and found significant, positive correlations between UPDRSIII score and coherence at beta band frequencies between pre-motor-motor and motor-somatosensory cortical areas. I observed phase amplitude coupling (PAC) between beta and gamma (30-200Hz) frequencies at rest and found it to be significantly altered by task, but found no effect of motor symptom progression on mean PAC.

My data suggest that certain metrics of beta band activity in pre-motor, motor, and somatosensory brain regions at rest may serve as a marker for degree of motor impairment, but that beta frequency oscillations may be an epiphenomenon and not necessary or sufficient for the generation of bradykinesia/akinesia in PD.

This paper implements the first closed-loop adaptive deep brain stimulation (DBS) system for Parkinson’s patients that updates with multiple input streams from disparate data sources analyzed in real time. Input data streams include the brain's local field potential (LFP) from DBS leads, hand tremors, and heart rate. This approach is designed to be evaluated on 6 patients with Parkinson's disease implanted with the Medtronic Summit™ RC+S systems and has the potential to be more effective in simultaneously controlling multiple symptoms commonly presented in Parkinson’s patients. In the case of the DBS control of both the bradykinesia and tremor, the system demonstrated in this paper has the ability to overcome the challenge of the “breakout tremor” presented in previous studies. The system in-lab testing characterization results indicated that the system could be used to control closed-loop deep brain stimulation systems with a high degree of accuracy and robustness.