Modeling Basal Ganglia for understanding Parkinsonian Reaching Movements

We present a computational model that highlights the role of basal ganglia (BG) in generating simple reaching movements. The model is cast within the reinforcement learning (RL) framework with the correspondence between RL components and neuroanatomy as follows: dopamine signal of substantia nigra pars compacta as the Temporal Difference error, striatum as the substrate for the Critic, and the motor cortex as the Actor. A key feature of this neurobiological interpretation is our hypothesis that the indirect pathway is the Explorer. Chaotic activity, originating from the indirect pathway part of the model, drives the wandering, exploratory movements of the arm. Thus the direct pathway subserves exploitation while the indirect pathway subserves exploration. The motor cortex becomes more and more independent of the corrective influence of BG, as training progresses. Reaching trajectories show diminishing variability with training. Reaching movements associated with Parkinson's disease (PD) are simulated by (a) reducing dopamine and (b) degrading the complexity of indirect pathway dynamics by switching it from chaotic to periodic behavior. Under the simulated PD conditions, the arm exhibits PD motor symptoms like tremor, bradykinesia and undershoot. The model echoes the notion that PD is a dynamical disease.Comment: Neural Computation, In Pres

Basal ganglia role in learning rewarded actions and executing previously learned choices: Healthy and diseased states

The basal ganglia (BG) is a collection of nuclei located deep beneath the cerebral cortex that is involved in learning and selection of rewarded actions. Here, we analyzed BG mechanisms that enable these functions. We implemented a rate model of a BG-thalamo-cortical loop and simulated its performance in a standard action selection task. We have shown that potentiation of corticostriatal synapses enables learning of a rewarded option. However, these synapses became redundant later as direct connections between prefrontal and premotor cortices (PFC-PMC) were potentiated by Hebbian learning. After we switched the reward to the previously unrewarded option (reversal), the BG was again responsible for switching to the new option. Due to the potentiated direct cortical connections, the system was biased to the previously rewarded choice, and establishing the new choice required a greater number of trials. Guided by physiological research, we then modified our model to reproduce pathological states of mild Parkinson's and Huntington's diseases. We found that in the Parkinsonian state PMC activity levels become extremely variable, which is caused by oscillations arising in the BG-thalamo-cortical loop. The model reproduced severe impairment of learning and predicted that this is caused by these oscillations as well as a reduced reward prediction signal. In the Huntington state, the potentiation of the PFC-PMC connections produced better learning, but altered BG output disrupted expression of the rewarded choices. This resulted in random switching between rewarded and unrewarded choices resembling an exploratory phase that never ended. Along with other computational studies, our results further reconcile the apparent contradiction between the critical involvement of the BG in execution of previously learned actions and yet no impairment of these actions after BG output is ablated by lesions or deep brain stimulation. We predict that the cortico-BG-thalamo-cortical loop conforms to previously learned choice in healthy conditions, but impedes those choices in disease states

Pathological Tremor as a Mechanical System: Modeling and Control of Artificial Muscle-Based Tremor Suppression

Central nervous system disorders produce the undesired, approximately rhythmic movement of body parts known as pathological tremor. This undesired motion inhibits the patient\u27s ability to perform tasks of daily living and participate in society. Typical treatments are medications and deep brain stimulation surgery, both of which include risks, side effects, and varying efficacy. Since the pathophysiology of tremor is not well understood, empirical investigation drives tremor treatment development. This dissertation explores tremor from a mechanical systems perspective to work towards theory-driven treatment design. The primary negative outcome of pathological tremor is the undesired movement of body parts: mechanically suppressing this motion provides effective tremor treatment by restoring limb function. Unlike typical treatments, the mechanisms for mechanical tremor suppression are well understood: applying joint torques that oppose tremor-producing muscular torques will reduce tremor irrespective of central nervous system pathophysiology. However, a tremor suppression system must also consider voluntary movements. For example, mechanically constraining the arm in a rigid cast eliminates tremor motion, but also eliminates the ability to produce voluntary motions. Indeed, passive mechanical systems typically reduce tremor and voluntary motions equally due to the close proximity of their frequency content. Thus, mechanical tremor suppression requires active actuation to reduce tremor with minimal influence on voluntary motion. However, typical engineering actuators are rigid and bulky, preventing clinical implementations. This dissertation explores dielectric elastomers as tremor suppression actuators to improve clinical implementation potential of mechanical tremor suppression. Dielectric elastomers are often called artificial muscles due to their similar mechanical properties as human muscle; these similarities may enable relatively soft, low-profile implementations. The primary drawback of dielectric elastomers is their relatively low actuation levels compared to typical actuators. This research develops a tremor-active approach to dielectric elastomer-based tremor suppression. In a tremor-active approach, the actuators only actuate to oppose tremor, while the human motor system must overcome the passive actuator dynamics. This approach leverages the low mechanical impedance of dielectric elastomers to overcome their low actuation levels. Simulations with recorded tremor datasets demonstrate excellent and robust tremor suppression performance. Benchtop experiments validate the control approach on a scaled system. Since dielectric elastomers are not yet commercially available, this research quantifies the necessary dielectric elastomer parameters to enable clinical implementations and evaluates the potential of manufacturing approaches in the literature to achieve these parameters. Overall, tremor-active control using dielectric elastomers represents a promising alternative to medications and surgery. Such a system may achieve comparable tremor reduction as medications and deep brain stimulation with minimal risks and greater efficacy, but at the cost of increased patient effort to produce voluntary motions. Parallel advances in scaled dielectric elastomer manufacturing processes and high-voltage power electronics will enable consumer implementations. In addition to tremor suppression, this dissertation investigates the mechanisms of central nervous system tremor generation from a control systems perspective. This research investigates a delay-based model for parkinsonian tremor. Besides tremor, Parkinson\u27s disease generally inhibits movement, with typical symptoms including rigidity, bradykinesia, and increased reaction times. This fact raises the question as to how the same disease produces excessive movement (tremor) despite characteristically inhibiting movement. One possible answer is that excessive central nervous system inhibition produces unaccounted feedback delays that cause instability. This dissertation develops an optimal control model of human motor control with an unaccounted delay between the state estimator and controller. This delay represents the increased inhibition projected from the basal ganglia to the thalamus, delaying signals traveling from the cerebellum (estimator) to the primary motor cortex (controller). Model simulations show increased delays decrease tremor frequency and increase tremor amplitude, consistent with the evolution of tremor as the disease progresses. Simulations that incorporate tremor resetting and random variation in control saturation produce simulated tremor with similar characteristics as recorded tremor. Delay-induced tremor explains the effectiveness of deep brain stimulation in both the thalamus and basal ganglia since both regions contribute to the presence of feedback delay. Clinical evaluation of mechanical tremor suppression may provide clinical evidence for delay-induced tremor: unlike state-independent tremor, suppression of delay-induced tremor increases tremor frequency. Altogether, establishing the mechanisms for tremor generation will facilitate pathways towards improved treatments and cure development

Simulating Idiopathic Parkinson\u27s Disease by In Vitro and Computational Models

In general there is a wide gap between experimental animal results, especially with respect to neuroanatomical data, and computational modeling. In order to be able to investigate the anatomical and functional properties of afferent and efferent connections between the different nuclei of the basal ganglia, similar studies need to be performed as described in this review for the Substantia Nigra. These studies, though very time-consuming, are essential to decide which pathways play important roles in normal functioning and therefore need to be included in modeling studies. In addition, it should be known what neuroanatomical changes take place resulting from the neurodegeneration associated with Parkinson’s disease and how they affect network behavior. For instance, the direct effects of DBS on motor control are of interest, but since DBS has a low threshold to side effects, additional non-motor pathways are expected to be involved. Including these pathways in network models may shed light on the extent and effect of stimulation. Similarly, as PPN stimulation may have a beneficial influence on gait and balance, different pathways are important regarding the different motor symptoms of Parkinson’s disease

PERIORAL BIOMECHANICS, KINEMATICS, AND ELECTROPHYSIOLOGY IN PARKINSON'S DISEASE

This investigation quantitatively characterized the orofacial biomechanics, labial kinematics, and associated electromyography (EMG) patterns in individuals with Parkinson's disease (PD) as a function of anti-PD medication state. Passive perioral stiffness, a clinical correlate of rigidity, was sampled using a face-referenced OroSTIFF system in 10 mildly diagnosed PD and 10 age/sex-matched control elderly. Labial movement amplitudes and velocities were evaluated using a 4-dimensional computerized motion capture system. Associated perioral EMG patterns were sampled to examine the characteristics of perioral muscles and compensatory muscular activation patterns during repetitive syllable productions. This study identified several trends that reflect various characteristics of perioral system differences between PD and control subjects: 1. The presence of high tonic EMG patterns after administration of dopaminergic treatment indicated an up-regulation of the central mechanism, which may serve to regulate orofacial postural control. 2. Multilevel regression modeling showed greater perioral stiffness in PD subjects, confirming the clinical correlate of rigidity in these patients. 3. Similar to the clinical symptoms in the upper and lower limb, a reduction of range of motion (hypokinesia) and velocity (bradykinesia) was evident in the PD orofacial system. Administration of dopaminergic treatment improved hypokinesia and bradykinesia. 4. A significant correlation was found between perioral stiffness and the range of labial movement, indicating these two symptoms may result in part from a common neural substrate. 5. As speech rate increased, PD speakers down-scaled movement amplitude and velocity compared to the control subjects, reflecting a compensatory mechanism to maintain target speech rates. 6. EMG from orbicularis oris inferior (OOIm) and depressor labii inferioris (DLIm) muscles revealed a limited range of muscle activation level in PD speakers, reflecting the underlying changes in motor unit firing behavior due to basal ganglia dysfunction. The results of this investigation provided a quantitative description of the perioral stiffness, labial kinematics, and EMG patterns in PD speakers. These findings indicate that perioral stiffness may provide clinicians a quantitative biomechanical correlate to medication response, movement aberrations, and EMG compensatory patterns in PD. The utilization of these objective assessments will be helpful in diagnosing, assessing, and monitoring the progression of PD to examine the efficacy of pharmacological, neurosurgical, and behavioral interventions

Circuit-level analyses of cortico-basal ganglia-thalamic networks. Effects of dopamine dysregulation and experience dependent plasticity.

The cortico-basal ganglia-thalamic (CBT) circuit is thought to be involved in control of voluntary and goal-directed movements and action selection. Dopamine is known to play a crucial role in this circuit and regulating its activity. The important role of dopamine is particularly evident in Parkinson’s patients, where dopaminergic cells are dying and motor impairments follow. While dopamine replacement is an effective therapy, satisfactory alleviation only lasts for a limited number of years, after which patients frequently develop side-effects in the form of levodopa-induced dyskinesia. In order to clarify the neurophysiological consequences of dopamine dysregulation we have here investigated the electrophysiological activity of each part of the CBT-loop in rats during different experimental conditions, using custom made multi-channel electrodes. Neuronal activity changes in 16 CBT structures were characterized upon acute pharmacological dopaminergic manipulations and firing rate changes of subgroup of cells within different structures in the CBT circuit were shown to potentially be responsible for the severe akinesia induced by the drugs. We have also developed a novel method to monitor the global state of the CBT circuit in a rat model of levodopa-induced dyskinesia and showed how this approach can be used to help developing new pharmacological therapies. Lastly, to investigate how somatosensory input is affecting motor circuits, we have recorded activity of the whole CBT-loop in rats before and after extensive skilled forelimb reaching and grasping training. Preliminary results show that only the motor cortex display experience-dependent changes due to the reaching training

Functional Anatomy: Dynamic States in Basal Ganglia Circuits

The most appealing models of how the basal ganglia function propose distributed patterns of cortical activity selectively interacting with striatal networks to yield the execution of context-dependent movements. If movement is encoded by patterns of activity then these may be disrupted by influences at once more subtle and more devastating than the increase or decrease of neuronal firing that dominate the usual models of the circuit. In the absence of dopamine the compositional capabilities of cell assemblies in the network could be disrupted by the generation of dominant synchronous activity that engages most of the system. Experimental evidence about Parkinson's disease suggests that dopamine loss produces abnormal patterns of activity in different nuclei. For example, increased oscillatory activity arises in the GPe, GPi, and STN and is reflected as increased cortical beta frequency coherence disrupting the ability to produce motor sequences. When the idea of deep brain stimulation was proposed – it was supported by the information that lesions of the subthalamus reversed the effects of damage to the dopamine input to the system. However, it seems increasingly unlikely that the stimulation acts by silencing the nucleus as was at first proposed. Perhaps the increased cortical beta activity caused by the lack of dopamine could have disabled the patterning of network activity. Stimulation of the subthalamic nucleus disrupts the on-going cortical rhythms. Subsequently asynchronous firing is reinstated and striatal cell assemblies and the whole basal ganglia circuit engage in a more normal pattern of activity. We will review the different variables involved in the generation of sequential activity patterns, integrate our data on deep brain stimulation and network population dynamics, and thus provide a novel interpretation of functional aspects of basal ganglia circuitry

The computational neurology of movement under active inference

We propose a computational neurology of movement based on the convergence of theoretical neurobiology and clinical neurology. A significant development in the former is the idea that we can frame brain function as a process of (active) inference, in which the nervous system makes predictions about its sensory data. These predictions depend upon an implicit predictive (generative) model used by the brain. This means neural dynamics can be framed as generating actions to ensure sensations are consistent with these predictions-and adjusting predictions when they are not. We illustrate the significance of this formulation for clinical neurology through simulating a clinical examination of the motor system; i.e. an upper limb coordination task. Specifically, we show how tendon reflexes emerge naturally under the right kind of generative model. Through simulated perturbations, pertaining to prior probabilities of this model's variables, we illustrate the emergence of hyperreflexia and pendular reflexes, reminiscent of neurological lesions in the corticospinal tract and cerebellum. We then turn to the computational lesions causing hypokinesia and deficits of coordination. This in silico lesion-deficit analysis provides an opportunity to revisit classic neurological dichotomies (e.g. pyramidal versus extrapyramidal systems) from the perspective of modern approaches to theoretical neurobiology-and our understanding of the neurocomputational architecture of movement control based on first principles