How Laminar Frontal Cortex and Basal Ganglia Circuits Interact to Control Planned and Reactive Saccades

The basal ganglia and frontal cortex together allow animals to learn adaptive responses that acquire rewards when prepotent reflexive responses are insufficient. Anatomical studies show a rich pattern of interactions between the basal ganglia and distinct frontal cortical layers. Analysis of the laminar circuitry of the frontal cortex, together with its interactions with the basal ganglia, motor thalamus, superior colliculus, and inferotemporal and parietal cortices, provides new insight into how these brain regions interact to learn and perform complexly conditioned behaviors. A neural model whose cortical component represents the frontal eye fields captures these interacting circuits. Simulations of the neural model illustrate how it provides a functional explanation of the dynamics of 17 physiologically identified cell types found in these areas. The model predicts how action planning or priming (in cortical layers III and VI) is dissociated from execution (in layer V), how a cue may serve either as a movement target or as a discriminative cue to move elsewhere, and how the basal ganglia help choose among competing actions. The model simulates neurophysiological, anatomical, and behavioral data about how monkeys perform saccadic eye movement tasks, including fixation; single saccade, overlap, gap, and memory-guided saccades; anti-saccades; and parallel search among distractors.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-l-0409, N00014-92-J-1309, N00014-95-1-0657); National Science Foundation (IRI-97-20333)

Propagation of beta/gamma rhythms in the cortico-basal ganglia circuits of the Parkinsonian rat

Much of the motor impairment associated with Parkinson’s disease is thought to arise from pathological activity in the networks formed by the basal ganglia (BG) and motor cortex. To evaluate several hypotheses proposed to explain the emergence of pathological oscillations in Parkinsonism, we investigated changes to the directed connectivity in BG networks following dopamine depletion. We recorded local field potentials (LFPs) in the cortex and basal ganglia of rats rendered Parkinsonian by injection of 6-hydroxydopamine (6-OHDA) and in dopamine-intact controls. We performed systematic analyses of the networks using a novel tool for estimation of directed interactions (Non-Parametric Directionality, NPD). Additionally, we used a ‘conditioned’ version of the NPD analysis which reveals the dependence of the correlation between two signals upon a third reference signal. We find evidence of the dopamine dependency of both low beta (14-20 Hz) and high beta/low gamma (20-40 Hz) directed interactions within the network. Notably, 6-OHDA lesions were associated with enhancement of the cortical “hyper-direct” connection to the subthalamic nucleus (STN) and its feedback to the cortex and striatum. We find that pathological beta synchronization resulting from 6-OHDA lesioning is widely distributed across the network and cannot be located to any individual structure. Further, we provide evidence that high beta/gamma oscillations propagate through the striatum in a pathway that is independent of STN. Rhythms at high beta/gamma show susceptibility to conditioning that indicates a hierarchical organization when compared to low beta. These results further inform our understanding of the substrates for pathological rhythms in salient brain networks in Parkinsonism

A Demand-Controlled Application of Deep Brain Stimulation with a Portable Neurostimulator

Deep brain stimulation (DBS) is an electrical therapy for several advanced neurological disorders such as Parkinson’s disease (PD). The improvement of current DBS technique has gained more importance in order to increase the therapeutic benefits and reduce the side effects. The electrical stimulation of the deep brain is administered at standard high-frequency (HF) or by using dedicated patterns intending the modulation of pathological neuronal activity. Apart from the development of new stimulation protocols such as the desynchronizing coordinated reset (CR) DBS protocol, a continuous and appropriate adjustment of the stimulation parameters might increase the efficacy of DBS. Therapeutic benefits could be maximized by a system that automatically detects the demand for further stimulation and continuously modifies the stimulation parameters. For instance, such a system can help the clinician to find the optimal parameters easily, without lengthy test procedures. In this thesis, the technical realization of ademand-controlled application of CR DBS for Parkinson’s disease (PD) with a portable neurostimulator is investigated. The applicability of such an autonomic system is studied retrospectively using local field potential (LFP) and resting tremor recordings from PD patients, as well as LFP recordings from parkinsonian non-human primates were used. A demand-controlled application of DBS requires a real-time analysis of the on-going pathological activity. LFP recordings during DBS are normally contaminated by strong artifacts that are caused by technical drawbacks. The artifacts inhibit the examination of the presence of pathological activity.Therefore, software-based technical solutions for artifact reduction were developed and implemented to obtain clean feedback signals from the recordings. Additional tests were performed on LFP recordings in saline solution to evaluate the performance of the implemented algorithms. Results obtained from these tests indicated the efficacy of the algorithms in removing most of the artifacts. Furthermore, the biological recordings were analyzed to find biomarkers of the pathological activity that can be used as a criterion to quantify the demand for tuning the stimulation parameters. The proposed approach aims to analyze and monitor the variation in the strength of such pathological activities. A demonstration of the tuning of several HF and CR DBS parameters was performed in real-time with a digital signal processor (DSP) board using tremor-like recordings collected from a healthy subject. This thesis explores and demonstrates the design and implementation of a demand-controlled application of DBS that is adapted according to the pathological fluctuations of patients and can be more efficacious than standard continuous DBS technique

Network dynamics in the neural control of birdsong

Sequences of stereotyped actions are central to the everyday lives of humans and animals, from the kingfisher's dive to the performance of a piano concerto. Lashley asked how neural circuits managed this feat nearly 6 decades ago, and to this day it remains a fundamental question in neuroscience. Toward answering this question, vocal performance in the songbird was used as a model to study the performance of learned, stereotyped motor sequences. The first component of this work considers the song motor cortical zone HVC in the zebra finch, an area that sends precise timing signals to both the descending motor pathway, responsible for stereotyped vocal performance in the adult, and the basal ganglia, which is responsible for both motor variability and song learning. Despite intense interest in HVC, previous research has exclusively focused on describing the activity of small numbers of neurons recorded serially as the bird sings. To better understand HVC network dynamics, both single units and local field potentials were sampled across multiple electrodes simultaneously in awake behaving zebra finches. The local field potential and spiking data reveal a stereotyped spatio-temporal pattern of inhibition operating on a 30 ms time-scale that coordinates the neural sequences in principal cells underlying song. The second component addresses the resilience of the song circuit through cutting the motor cortical zone HVC in half along one axis. Despite this large-scale perturbation, the finch quickly recovers and sings a near-perfect song within a single day. These first two studies suggest that HVC is functionally organized to robustly generate neural dynamics that enable vocal performance. The final component concerns a statistical study of the complex, flexible songs of the domesticated canary. This study revealed that canary song is characterized by specific long-range correlations up to 7 seconds long-a time-scale more typical of human music than animal vocalizations. Thus, the neural sequences underlying birdsong must be capable of generating more structure and complexity than previously thought

DEVELOPMENT OF A CEREBELLAR MEAN FIELD MODEL: THE THEORETICAL FRAMEWORK, THE IMPLEMENTATION AND THE FIRST APPLICATION

Brain modeling constantly evolves to improve the accuracy of the simulated brain dynamics with the ambitious aim to build a digital twin of the brain. Specific models tuned on brain regions specific features empower the brain simulations introducing bottom-up physiology properties into data-driven simulators. Despite the cerebellum contains 80 % of the neurons and is deeply involved in a wide range of functions, from sensorimotor to cognitive ones, a specific cerebellar model is still missing. Furthermore, its quasi-crystalline multi-layer circuitry deeply differs from the cerebral cortical one, therefore is hard to imagine a unique general model suitable for the realistic simulation of both cerebellar and cerebral cortex. The present thesis tackles the challenge of developing a specific model for the cerebellum. Specifically, multi-neuron multi-layer mean field (MF) model of the cerebellar network, including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells, was implemented, and validated against experimental data and the corresponding spiking neural network microcircuit model. The cerebellar MF model was built using a system of interdependent equations, where the single neuronal populations and topological parameters were captured by neuron-specific inter- dependent Transfer Functions. The model time resolution was optimized using Local Field Potentials recorded experimentally with high-density multielectrode array from acute mouse cerebellar slices. The present MF model satisfactorily captured the average discharge of different microcircuit neuronal populations in response to various input patterns and was able to predict the changes in Purkinje Cells firing patterns occurring in specific behavioral conditions: cortical plasticity mapping, which drives learning in associative tasks, and Molecular Layer Interneurons feed-forward inhibition, which controls Purkinje Cells activity patterns. The cerebellar multi-layer MF model thus provides a computationally efficient tool that will allow to investigate the causal relationship between microscopic neuronal properties and ensemble brain activity in health and pathological conditions. Furthermore, preliminary attempts to simulate a pathological cerebellum were done in the perspective of introducing our multi-layer cerebellar MF model in whole-brain simulators to realize patient-specific treatments, moving ahead towards personalized medicine. Two preliminary works assessed the relevant impact of the cerebellum on whole-brain dynamics and its role in modulating complex responses in causal connected cerebral regions, confirming that a specific model is required to further investigate the cerebellum-on- cerebrum influence. The framework presented in this thesis allows to develop a multi-layer MF model depicting the features of a specific brain region (e.g., cerebellum, basal ganglia), in order to define a general strategy to build up a pool of biology grounded MF models for computationally feasible simulations. Interconnected bottom-up MF models integrated in large-scale simulators would capture specific features of different brain regions, while the applications of a virtual brain would have a substantial impact on the reality ranging from the characterization of neurobiological processes, subject-specific preoperative plans, and development of neuro-prosthetic devices

Doctor of Philosophy

dissertationHigh-count microelectrode arrays implanted in peripheral nerves could restore motor function after spinal cord injury or sensory function after limb loss via electrical stimulation. The same device could also help restore volitional control to a prosthesis-using amputee, or sensation to a Spinal cord Injury (SCI) patient, via recordings from the still-viable peripheral nerves. The overall objective of these dissertations studies is to improve the usefulness of intrafascicular electrodes, such as the Utah Slanted Electrode Array (USEA), for neuroprosthetic devices for limb loss or spinal cord injury patients. Previous work in cat sciatic nerve has shown that stimulation through the USEA can remain viable for months after implant. However, stimulation parameters were not stable, and recordings were lost rapidly and were subject to strong contamination by myoelectrical activity from adjacent muscles. Recent research has shown that even when mobility is restored to a patient, either through prosthesis or functional electrical stimulation, difficulties in using the affected limbs arise from the lack of sensory input. In the absence of the usual proprioceptive and cutaneous inputs from the limb, planning and executing motions can be challenging and sometimes lead to the user's abandonment of prostheses. To begin to address this need, I examined the ability of USEAs in cat hindlimb nerves to activate primary sensory fibers by monitoring evoked potentials in somatosensory cortex via skull-screw electrodes. I iv also monitored evoked EMG responses, and determined that it is possible to recruit sensory or motor responses independently of one another. In the second study of this dissertation, I sought to improve the long-term stability of USEAs in the PNS by physically and electrically stabilizing and protecting the array. To demonstrate the efficacy of the stabilization and shielding technique, I examined the recording capabilities of USEA electrodes and their selectivity of muscle activation over the long term in cat sciatic nerve. In addition to long-term viability, clinically useful neuroprosthetic devices will have to be capable of interfacing with complex motor systems such as the human hand. To extend previous results of USEAs in cat hindlimb nerves and to examine selectivity when interfacing with a complex sensorimotor system, I characterized EMG and cortical somatosensory responses to acute USEA stimulation in monkey arm nerves. Then, to demonstrate the functional usefulness of stimulation through the USEA. I used multi-array, multi-electrode stimulation to generate a natural, coordinated grasp

Neural Dynamics of Reward-Induced Response Activation and Inhibition

Reward-predictive stimuli can increase an automatic response tendency, which needs to be counteracted by effortful response inhibition when this tendency is inappropriate for the current task. Here we investigated how the human brain implements this dynamic process by adopting a reward-modulated Simon task while acquiring EEG and fMRI data in separate sessions. In the Simon task, a lateral target stimulus triggers an automatic response tendency of the spatially corresponding hand, which needs to be overcome if the activated hand is opposite to what the task requires, thereby delaying the response. We associated high or low reward with different targets, the location of which could be congruent or incongruent with the correct response hand. High-reward targets elicited larger Simon effects than low-reward targets, suggesting an increase in the automatic response tendency induced by the stimulus location. This tendency was accompanied by modulations of the lateralized readiness potential over the motor cortex, and was inhibited soon after if the high-reward targets were incongruent with the correct response hand. Moreover, this process was accompanied by enhanced theta oscillations in medial frontal cortex and enhanced activity in a frontobasal ganglia network. With dynamical causal modeling, we further demonstrated that the connection from presupplementary motor area (pre-SMA) to right inferior frontal cortex (rIFC) played a crucial role in modulating the reward-modulated response inhibition. Our results support a dynamic neural model of reward-induced response activation and inhibition, and shed light on the neural communication between reward and cognitive control in generating adaptive behaviors

ELECTROPHYSIOLOGY OF BASAL GANGLIA (BG) CIRCUITRY AND DYSTONIA AS A MODEL OF MOTOR CONTROL DYSFUNCTION

The basal ganglia (BG) is a complex set of heavily interconnected nuclei located in the central part of the brain that receives inputs from the several areas of the cortex and projects via the thalamus back to the prefrontal and motor cortical areas. Despite playing a significant part in multiple brain functions, the physiology of the BG and associated disorders like dystonia remain poorly understood. Dystonia is a devastating condition characterized by ineffective, twisting movements, prolonged co-contractions and contorted postures. Evidences suggest that it occurs due to abnormal discharge patterning in BG-thalamocortocal (BGTC) circuitry. The central purpose of this study was to understand the electrophysiology of BGTC circuitry and its role in motor control and dystonia. Toward this goal, an advanced multi-target multi-unit recording and analysis system was utilized, which allows simultaneous collection and analysis of multiple neuronal units from multiple brain nuclei. Over the cause of this work, neuronal data from the globus pallidus (GP), subthalamic nucleus (STN), entopenduncular nucleus (EP), pallidal receiving thalamus (VL) and motor cortex (MC) was collected from normal, lesioned and dystonic rats under awake, head restrained conditions. The results have shown that the neuronal population in BG nuclei (GP, STN and EP) were characterized by a dichotomy of firing patterns in normal rats which remains preserved in dystonic rats. Unlike normals, neurons in dystonic rat exhibit reduced mean firing rate, increased irregularity and burstiness at resting state. The chaotic changes that occurs in BG leads to inadequate hyperpolarization levels within the VL thalamic neurons resulting in a shift from the normal bursting mode to an abnormal tonic firing pattern. During movement, the dystonic EP generates abnormally synchronized and elongated burst duration which further corrupts the VL motor signals. It was finally concluded that the loss of specificity and temporal misalignment between motor neurons leads to corrupted signaling to the muscles resulting in dystonic behavior. Furthermore, this study reveals the importance of EP output in controlling firing modes occurring in the VL thalamus