Network Effects of Deep Brain Stimulation for Parkinson's Disease - A Computational Modeling Study

<p>Electrical stimulation of the sub-cortical regions (basal ganglia) of the brain, also known as deep brain stimulation (DBS), is an effective treatment technique for neurological diseases such as Parkinson's disease and essential tremor. Chronic high frequency stimulation in the subthalamic nucleus (STN) or globus pallidus interna (GPi) reduces motor symptoms such as bradykinesia and tremor in patients with Parkinson's disease (PD). However, the therapeutic mechanisms of DBS are not fully understood despite its clinical benefits. We developed a biophysical network model comprising of the cortico-basal ganglia-thalamic circuit representing the healthy and parkinsonian rat brain. The network properties of the model were validated by comparing the responses evoked in different basal ganglia (BG) nuclei by cortical (CTX) stimulation to published experimental results. The key emergent property of the validated model was generation of beta oscillations in the CTX and STN-GPe network (independent of synaptic inputs) during PD and propagation of that oscillatory activity to the output nucleus of the BG (GPi). Consistent with their putative pathological role, beta oscillations in the model BG neurons were exaggerated in the parkinsonian compared to the healthy condition. We used the validated model to quantify the effectiveness of STN DBS at different frequencies in suppressing pathological beta oscillations in GPe. STN DBS frequencies less than 40 Hz were ineffective in suppressing GPe beta band power. GPe beta band power decreased gradually for stimulus frequencies between 50 Hz and 135 Hz, and saturated at frequencies higher than 135 Hz. High frequency (HF) STN DBS produced an increased excitatory response in a large number of GPe neurons and a subsequent reduction in pathological oscillatory activity. Further, HF STN DBS suppressed pathological oscillations in GPi both by exciting and inhibiting the firing in GPi neurons, and the number of GPi neurons affected was greater for HF stimulation than low frequency stimulation. Therefore, therapeutic HF STN DBS effectively suppresses pathological oscillations by influencing the activity of a greater proportion of neurons in the output nucleus of the BG.</p>Thesi

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

Using Phase Response Curves to Optimize Deep Brain Stimulation

University of Minnesota Ph.D. dissertation. April 2016. Major: Neuroscience. Advisor: Theoden Netoff. 1 computer file (PDF); vii, 190 pages.Deep brain stimulation (DBS) is a neuromodulation therapy effective at treating motor symptoms of patients with Parkinson’s disease (PD). Currently, an open-loop approach is used to set stimulus parameters, where stimulation settings are programmed by a clinician using a time intensive trial-and-error process. There is a need for a systematic approach to tuning stimulation parameters based on a patient’s physiology. An effective biomarker in the recorded neural signal is needed for this approach. It is hypothesized that DBS may work by disrupting enhanced oscillatory activity seen in PD. In this thesis I propose and provide evidence for using a simple measure, called a phase response curve, to systematically tune stimulation parameters and develop novel approaches to stimulation to suppress pathological oscillations. In this work I show that PRCs can be used to optimize stimulus frequency, waveform, and stimulus phase to disrupt a pathological oscillation in a computational model of Parkinson’s disease and/or to disrupt entrainment of single neurons in vitro. This approach has the potential to improve efficacy and reduce post-operative programming time

An entropy-based investigation of underpinnings and impact of oscillations in a model of PD

The involvement of the basal ganglia in motor control has been highlighted in studies of Parkinson’s disease (PD) and other movement disorders. The loss of dopaminergic neurons in the substantia nigra pars compacta and subsequent decrease of the dopamine level in the basal ganglia is recognized as the hallmark of PD. The classical view of the architecture of the dopamine depleted basal ganglia-thalamo-cortical circuit identifies changes in firing rates as the probable cause for the motor impairments in PD. Yet, more recent findings have shown that disturbances in other intrinsic dynamical properties of these networks may also contribute to motor deficits. Electrophysiological recordings in the basal ganglia of deep brain stimulation (DBS) patients (when OFF stimulation) have found pathological oscillations at beta frequency (13-30 Hz). This abnormal oscillatory activity has also been found in basal ganglia nuclei of animal models of PD. Additionally, the beta frequency oscillations were found to decrease when the patients are on dopamine replacement therapies and as they initiate movement. Beta frequency oscillations have been identified in the firing of single neurons and in the coupling of discharges between neurons. Within the framework of information theory, we proposed a time series model to analyse and relate the effects of changes in the dynamics of individual factors – such as alterations in firing rates, oscillations and synchrony (or auto and cross-correlations) caused by dopamine depletion – on the coding capacity (i.e., entropy) of a network. We estimated the entropy of a neural network based on the probabilities of current spiking conditioned on the observation of firing rate and spiking history of the current neuron and of neighbouring neurons. Moreover, we could estimate entropies for each of these factors separately, in healthy and dopamine depleted conditions, and assess their relative contribution to the decrease of coding capacity in disease. Furthermore, the causal characteristics of the model made it possible to compare the synaptic connectivity of neuronal populations in health with that in disease, by measuring the amount of directed information transferred between populations. We employed the model to study the external globus pallidus (GPe) network in control and 6-hydroxydopamine (6-OHDA) lesioned rats – a model for PD. We found a significant decrease in the coding capacity in lesioned animals, compared to controls, and that this decrease was predominantly on account of a reduction in the GPe firing rates. Although to a lesser extent, the amplification of the oscillatory activity (mainly in the beta frequency range) observed in the lesioned animals had also a significant impact on the reduction of their coding capacity. The higher synchrony found in the 6-OHDA rats had the least effect. We also found that the levels of coding capacity in the GPe were restored to levels close to control when the lesioned animals were treated with the dopamine agonist apomorphine. In addition, we detected a stronger coupling between the subthalamic nucleus (STN) and the GPe in the dopamine depleted rats, pointing to an abnormally exaggerated transfer of information within this network. We have shown that the GPe and the STN-GPe networks in the dopamine depleted rat exhibit information processing irregularities. We believe these deficits in processing and relaying information may also be present in other structures of the basal ganglia-thalamo-cortical circuit and that they may underlie the motor impairment in PD

How sleep deprivation degrades task performance: combining experimental analysis with simulations of adenosinergic effects of basal ganglia and cortical circuits

Thesis (Ph.D.)--Boston UniversityHumans configure themselves into "neural machines" to perform optimally on distinct tasks, and they excel at maintaining such configurations for brief episodes. The neural configuration needed for peak performance, however, is subject to perturbations on multiple time scales. This thesis reports new empirical analyses and computational modeling to advance understanding of the variations in reaction time (RT) on simple RT tasks that are associated with the duration of the preceding inter-stimulus interval (order of seconds); the time-on-task duration (order of minutes); and sleep deprivation duration (order of hours to days). Responses from the psychomotor vigilance task (PVT), including anticipations (false alarms), normal RTs, and very long RTs (lapses in attention), were analyzed to discover the effects of: the 1 - 9 second inter-stimulus interval (ISI); the 10-minute task session; up to 50 hours of sleep deprivation (SD); and wake-promoting agents, caffeine and modafinil. Normal RTs and lapses in attention were negatively correlated with ISI length, whereas anticipations were positively correlated. Anticipations, normal RTs, and lapses increased as time-on-task increased, and during SD. Both caffeine and modafinil reduced lapses and anticipations during SD and decreased RT variability. A simple neural network model incorporating both a time-dependent inhibitory process and a time-dependent excitatory process was developed. The model robustly simulated the ISI effect on behavior. The SD effects were reproducible with two parameter adjustments. Informed modeling of drug effects required greater neurobiological detail. In the basal ganglia (BG), adenosine accumulation during SD has two notable effects: it antagonizes dopamine to reduce BG responsiveness to incoming cortical signals, and it reduces cholinergic transmission to parietal and prefrontal cortices, thus reducing attention to visual signals. A detailed computational model of interactions between BG and cortex during PVT was developed to simulate effects of adenosine and their amelioration by caffeine. The model simulates drug, ISI and SD effects on anticipations, RTs, and lapses. This model can be used to describe the effects of SD over a wide range of tasks requiring planned and reactive movements, and can predict and model effects of pharmacological agents acting on the adenosinergic, cholinergic and dopaminergic systems

Experimental and Model-based Approaches to Directional Thalamic Deep Brain Stimulation

University of Minnesota Ph.D. dissertation. September 2016. Major: Biomedical Engineering. Advisor: Matthew Johnson. 1 computer file (PDF); xii, 181 pages.Deep brain stimulation (DBS) is an effective surgical procedure for the treatment of several brain disorders. However, the clinical successes of DBS hinges on several factors. Here, we describe the development of tools and methodologies in the context of thalamic DBS for essential tremor (ET) to address three key challenges: 1) accurate localization of nuclei and fiber pathways for stimulation, 2) model-based programming of high-density DBS electrode arrays (DBSA) and 3) in vivo assessment of computational DBS model predictions. We approached the first challenge through a multimodal imaging approach, utilizing high-field (7T) susceptibility-weighted imaging and diffusion-weighted imaging data. A nonlinear image deformation algorithm was used in conjunction with probabilistic fiber tractography to segment individual thalamic sub-nuclei and reconstruct their afferent fiber pathways. We addressed the second challenge by developing subject-specific computational model-based algorithms built on maximizing population activating function values within a target region using convex optimization principles. The algorithms converged within seconds and only required as many finite-element simulations as the number of electrodes on the DBSA being modeled. For the third challenge, we recorded (in two non-human primates) unit-spike data from neurons in the vicinity of chronically implanted thalamic DBSAs before, during and after high-frequency stimulation. A novel entropy-based method was developed to quantify the degree and significance of stimulation-induced changes in neuronal firing pattern. Results indicated that neurons modulated by thalamic DBS were distributed and not confined to the immediate proximity of the active electrode. For those that were modulated by DBS, their responses increasingly shifted from firing rate modulation to firing pattern modulation with increased stimulation amplitude. Additionally, strong low-pass filtering effect was observed where <4% of DBS pulses produced phase-locked spikes in cells exhibiting significant excitatory firing pattern modulation. Finally, we quantified the spatial distribution of neurons modulated by DBS by developing a novel spherical statistical framework for analysis. Together, these tools and methodologies are poised to improve our understanding of DBS mechanisms and improve the efficacy and efficiency of DBS therapy

Investigating the Primate Prefrontal Cortex Correlates of Cognitive Deficits In the Ketamine Model of Schizophrenia

The World Health Organization has classified schizophrenia as one of the five leading causes of disability worldwide. Afflicting almost 1% of the world’s population, the disease’s greatest impact stems from its reduction in patients’ cognitive faculties. In order to better study these impairments, a pharmacological model has been developed using the NMDA antagonist, ketamine. This disease model successfully recreates the cognitive dysfunction of schizophrenia, allowing researchers to search for associated electrophysiological changes. In this project I examined the behavioural and neurophysiological effects of ketamine on non-human primates performing the anti-saccade task. Success in this task requires a degree of cognitive control over behaviour and previous studies have described poor performance in both patients with schizophrenia and healthy controls administered ketamine. Our intracranial recordings are localized in the prefrontal cortex (PFC), a region associated with many of the cognitive functions impaired in schizophrenia. The first study shows that neurons in the PFC exhibit selectivity for the task rule. This rule selectivity is lost after ketamine administration due to an indiscriminate increase in the neuronal firing rate. These changes were also associated with an increased error rate and longer reaction times. The second study shows that neurons in the PFC are also sensitive to the outcome of the trial, firing more for either correct or erroneous responses. Once again, selectivity is lost following ketamine administration and the neurons show increased, nonspecific activity. Lastly, we recorded the local field potential of the PFC and found changes in the oscillatory patterns during the anti-saccade task. Prior to ketamine there was a significantly stronger beta-band activity after correct trials compared to error trials, but this selective activity was lost due to an overall decrease in the outcome selective oscillatory events. These findings show that ketamine’s effect on the PFC is one of selectivity reduction. Patients with schizophrenia have been shown to require increased PFC activity but only reach moderate performance levels in cognitive challenges. It is possible that their brains suffer the same changes highlighted in this research. Although the signals are still present in their PFC, they are being lost amongst the noise

Use of functional neuroimaging and optogenetics to explore deep brain stimulation targets for the treatment of Parkinson's disease and epilepsy

Deep brain stimulation (DBS) is a neurosurgical therapy for Parkinson’s disease and epilepsy. In DBS, an electrode is stereotactically implanted in a specific region of the brain and electrical pulses are delivered using a subcutaneous pacemaker-like stimulator. DBS-therapy has proven to effectively suppress tremor or seizures in pharmaco-resistant Parkinson’s disease and epilepsy patients respectively. It is most commonly applied in the subthalamic nucleus for Parkinson’s disease, or in the anterior thalamic nucleus for epilepsy. Despite the rapidly growing use of DBS at these classic brain structures, there are still non-responders to the treatment. This creates a need to explore other brain structures as potential DBS-targets. However, research in patients is restricted mainly because of ethical reasons. Therefore, in order to search for potential new DBS targets, animal research is indispensable. Previous animal studies of DBS-relevant circuitry largely relied on electrophysiological recordings at predefined brain areas with assumed relevance to DBS therapy. Due to their inherent regional biases, such experimental techniques prevent the identification of less recognized brain structures that might be suitable DBS targets. Therefore, functional neuroimaging techniques, such as functional Magnetic Resonance Imaging and Positron Emission Tomography, were used in this thesis because they allow to visualize and to analyze the whole brain during DBS. Additionally, optogenetics, a new technique that uses light instead of electricity, was employed to manipulate brain cells with unprecedented selectivity