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Description
The basal ganglia are four sub-cortical nuclei associated with motor control and reward learning. They are part of numerous larger mostly segregated loops where the basal ganglia receive inputs from specific regions of cortex. Converging on these inputs are dopaminergic neurons that alter their firing based on received and/or predicted

The basal ganglia are four sub-cortical nuclei associated with motor control and reward learning. They are part of numerous larger mostly segregated loops where the basal ganglia receive inputs from specific regions of cortex. Converging on these inputs are dopaminergic neurons that alter their firing based on received and/or predicted rewarding outcomes of a behavior. The basal ganglia's output feeds through the thalamus back to the areas of the cortex where the loop originated. Understanding the dynamic interactions between the various parts of these loops is critical to understanding the basal ganglia's role in motor control and reward based learning. This work developed several experimental techniques that can be applied to further study basal ganglia function. The first technique used micro-volume injections of low concentration muscimol to decrease the firing rates of recorded neurons in a limited area of cortex in rats. Afterwards, an artificial cerebrospinal fluid flush was injected to rapidly eliminate the muscimol's effects. This technique was able to contain the effects of muscimol to approximately a 1 mm radius volume and limited the duration of the drug effect to less than one hour. This technique could be used to temporarily perturb a small portion of the loops involving the basal ganglia and then observe how these effects propagate in other connected regions. The second part applied self-organizing maps (SOM) to find temporal patterns in neural firing rate that are independent of behavior. The distribution of detected patterns frequency on these maps can then be used to determine if changes in neural activity are occurring over time. The final technique focused on the role of the basal ganglia in reward learning. A new conditioning technique was created to increase the occurrence of selected patterns of neural activity without utilizing any external reward or behavior. A pattern of neural activity in the cortex of rats was selected using an SOM. The pattern was then reinforced by being paired with electrical stimulation of the medial forebrain bundle triggering dopamine release in the basal ganglia. Ultimately, this technique proved unsuccessful possibly due to poor selection of the patterns being reinforced.
ContributorsBaldwin, Nathan Aaron (Author) / Helms Tillery, Stephen I (Thesis advisor) / Castaneda, Edward (Committee member) / Buneo, Christopher A (Committee member) / Muthuswamy, Jitendran (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Learning by trial-and-error requires retrospective information that whether a past action resulted in a rewarded outcome. Previous outcome in turn may provide information to guide future behavioral adjustment. But the specific contribution of this information to learning a task and the neural representations during the trial-and-error learning process is not

Learning by trial-and-error requires retrospective information that whether a past action resulted in a rewarded outcome. Previous outcome in turn may provide information to guide future behavioral adjustment. But the specific contribution of this information to learning a task and the neural representations during the trial-and-error learning process is not well understood. In this dissertation, such learning is analyzed by means of single unit neural recordings in the rats' motor agranular medial (AGm) and agranular lateral (AGl) while the rats learned to perform a directional choice task. Multichannel chronic recordings using implanted microelectrodes in the rat's brain were essential to this study. Also for fundamental scientific investigations in general and for some applications such as brain machine interface, the recorded neural waveforms need to be analyzed first to identify neural action potentials as basic computing units. Prior to analyzing and modeling the recorded neural signals, this dissertation proposes an advanced spike sorting system, the M-Sorter, to extract the action potentials from raw neural waveforms. The M-Sorter shows better or comparable performance compared with two other popular spike sorters under automatic mode. With the sorted action potentials in place, neuronal activity in the AGm and AGl areas in rats during learning of a directional choice task is examined. Systematic analyses suggest that rat's neural activity in AGm and AGl was modulated by previous trial outcomes during learning. Single unit based neural dynamics during task learning are described in detail in the dissertation. Furthermore, the differences in neural modulation between fast and slow learning rats were compared. The results show that the level of neural modulation of previous trial outcome is different in fast and slow learning rats which may in turn suggest an important role of previous trial outcome encoding in learning.
ContributorsYuan, Yu'an (Author) / Si, Jennie (Thesis advisor) / Buneo, Christopher (Committee member) / Santello, Marco (Committee member) / Chae, Junseok (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Animals learn to choose a proper action among alternatives according to the circumstance. Through trial-and-error, animals improve their odds by making correct association between their behavioral choices and external stimuli. While there has been an extensive literature on the theory of learning, it is still unclear how individual neurons and

Animals learn to choose a proper action among alternatives according to the circumstance. Through trial-and-error, animals improve their odds by making correct association between their behavioral choices and external stimuli. While there has been an extensive literature on the theory of learning, it is still unclear how individual neurons and a neural network adapt as learning progresses. In this dissertation, single units in the medial and lateral agranular (AGm and AGl) cortices were recorded as rats learned a directional choice task. The task required the rat to make a left/right side lever press if a light cue appeared on the left/right side of the interface panel. Behavior analysis showed that rat's movement parameters during performance of directional choices became stereotyped very quickly (2-3 days) while learning to solve the directional choice problem took weeks to occur. The entire learning process was further broken down to 3 stages, each having similar number of recording sessions (days). Single unit based firing rate analysis revealed that 1) directional rate modulation was observed in both cortices; 2) the averaged mean rate between left and right trials in the neural ensemble each day did not change significantly among the three learning stages; 3) the rate difference between left and right trials of the ensemble did not change significantly either. Besides, for either left or right trials, the trial-to-trial firing variability of single neurons did not change significantly over the three stages. To explore the spatiotemporal neural pattern of the recorded ensemble, support vector machines (SVMs) were constructed each day to decode the direction of choice in single trials. Improved classification accuracy indicated enhanced discriminability between neural patterns of left and right choices as learning progressed. When using a restricted Boltzmann machine (RBM) model to extract features from neural activity patterns, results further supported the idea that neural firing patterns adapted during the three learning stages to facilitate the neural codes of directional choices. Put together, these findings suggest a spatiotemporal neural coding scheme in a rat AGl and AGm neural ensemble that may be responsible for and contributing to learning the directional choice task.
ContributorsMao, Hongwei (Author) / Si, Jennie (Thesis advisor) / Buneo, Christopher (Committee member) / Cao, Yu (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Control engineering offers a systematic and efficient approach to optimizing the effectiveness of individually tailored treatment and prevention policies, also known as adaptive or ``just-in-time'' behavioral interventions. These types of interventions represent promising strategies for addressing many significant public health concerns. This dissertation explores the development of decision algorithms for

Control engineering offers a systematic and efficient approach to optimizing the effectiveness of individually tailored treatment and prevention policies, also known as adaptive or ``just-in-time'' behavioral interventions. These types of interventions represent promising strategies for addressing many significant public health concerns. This dissertation explores the development of decision algorithms for adaptive sequential behavioral interventions using dynamical systems modeling, control engineering principles and formal optimization methods. A novel gestational weight gain (GWG) intervention involving multiple intervention components and featuring a pre-defined, clinically relevant set of sequence rules serves as an excellent example of a sequential behavioral intervention; it is examined in detail in this research.

 

A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.

 

Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework.

The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.
ContributorsDong, Yuwen (Author) / Rivera, Daniel E (Thesis advisor) / Dai, Lenore (Committee member) / Forzani, Erica (Committee member) / Rege, Kaushal (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Interictal spikes, together with seizures, have been recognized as the two hallmarks of epilepsy, a brain disorder that 1% of the world's population suffers from. Even though the presence of spikes in brain's electromagnetic activity has diagnostic value, their dynamics are still elusive. It was an objective of this dissertation

Interictal spikes, together with seizures, have been recognized as the two hallmarks of epilepsy, a brain disorder that 1% of the world's population suffers from. Even though the presence of spikes in brain's electromagnetic activity has diagnostic value, their dynamics are still elusive. It was an objective of this dissertation to formulate a mathematical framework within which the dynamics of interictal spikes could be thoroughly investigated. A new epileptic spike detection algorithm was developed by employing data adaptive morphological filters. The performance of the spike detection algorithm was favorably compared with others in the literature. A novel spike spatial synchronization measure was developed and tested on coupled spiking neuron models. Application of this measure to individual epileptic spikes in EEG from patients with temporal lobe epilepsy revealed long-term trends of increase in synchronization between pairs of brain sites before seizures and desynchronization after seizures, in the same patient as well as across patients, thus supporting the hypothesis that seizures may occur to break (reset) the abnormal spike synchronization in the brain network. Furthermore, based on these results, a separate spatial analysis of spike rates was conducted that shed light onto conflicting results in the literature about variability of spike rate before and after seizure. The ability to automatically classify seizures into clinical and subclinical was a result of the above findings. A novel method for epileptogenic focus localization from interictal periods based on spike occurrences was also devised, combining concepts from graph theory, like eigenvector centrality, and the developed spike synchronization measure, and tested very favorably against the utilized gold rule in clinical practice for focus localization from seizures onset. Finally, in another application of resetting of brain dynamics at seizures, it was shown that it is possible to differentiate with a high accuracy between patients with epileptic seizures (ES) and patients with psychogenic nonepileptic seizures (PNES). The above studies of spike dynamics have elucidated many unknown aspects of ictogenesis and it is expected to significantly contribute to further understanding of the basic mechanisms that lead to seizures, the diagnosis and treatment of epilepsy.
ContributorsKrishnan, Balu (Author) / Iasemidis, Leonidas (Thesis advisor) / Tsakalis, Kostantinos (Committee member) / Spanias, Andreas (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2012
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Description
The ability to plan, execute, and control goal oriented reaching and grasping movements is among the most essential functions of the brain. Yet, these movements are inherently variable; a result of the noise pervading the neural signals underlying sensorimotor processing. The specific influences and interactions of these noise processes remain

The ability to plan, execute, and control goal oriented reaching and grasping movements is among the most essential functions of the brain. Yet, these movements are inherently variable; a result of the noise pervading the neural signals underlying sensorimotor processing. The specific influences and interactions of these noise processes remain unclear. Thus several studies have been performed to elucidate the role and influence of sensorimotor noise on movement variability. The first study focuses on sensory integration and movement planning across the reaching workspace. An experiment was designed to examine the relative contributions of vision and proprioception to movement planning by measuring the rotation of the initial movement direction induced by a perturbation of the visual feedback prior to movement onset. The results suggest that contribution of vision was relatively consistent across the evaluated workspace depths; however, the influence of vision differed between the vertical and later axes indicate that additional factors beyond vision and proprioception influence movement planning of 3-dimensional movements. If the first study investigated the role of noise in sensorimotor integration, the second and third studies investigate relative influence of sensorimotor noise on reaching performance. Specifically, they evaluate how the characteristics of neural processing that underlie movement planning and execution manifest in movement variability during natural reaching. Subjects performed reaching movements with and without visual feedback throughout the movement and the patterns of endpoint variability were compared across movement directions. The results of these studies suggest a primary role of visual feedback noise in shaping patterns of variability and in determining the relative influence of planning and execution related noise sources. The final work considers a computational approach to characterizing how sensorimotor processes interact to shape movement variability. A model of multi-modal feedback control was developed to simulate the interaction of planning and execution noise on reaching variability. The model predictions suggest that anisotropic properties of feedback noise significantly affect the relative influence of planning and execution noise on patterns of reaching variability.
ContributorsApker, Gregory Allen (Author) / Buneo, Christopher A (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Santello, Marco (Committee member) / Santos, Veronica (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Division of labor is a hallmark for social insects and is closely related to honey bee morphology and physiology. Vitellogenin (Vg), a precursor protein in insect egg yolk, has several known functions apart from serving as a nutrient source for developing eggs. Vg is a component in the royal jelly

Division of labor is a hallmark for social insects and is closely related to honey bee morphology and physiology. Vitellogenin (Vg), a precursor protein in insect egg yolk, has several known functions apart from serving as a nutrient source for developing eggs. Vg is a component in the royal jelly produced in the hypopharyngeal glands (HPG) of worker bees which is used to feed both the developing brood and the queen. The HPG is closely associated with divisions of labor as the peak in its development corresponds with the nursing behavior. Independent of the connection between Vg and the HPG, Vg has been seen to play a fundamental role in divisions of labor by affecting worker gustatory responses, age of onset of foraging, and foraging preferences. Similar to Vg, the number of ovarioles in worker ovaries is also associated with division of labor as bees with more ovarioles tend to finish tasks in the hive and become foragers faster. This experiment aims to connect HPGs, ovaries, and Vg by proposing a link between them in the form of ecdysone (20E). 20E is a hormone produced by the ovaries and is linked to ovary development and Vg by tyramine titers. By treating young emerged bees with ecdysone and measuring HPG and ovary development over a trial period, this experiment seeks to determine whether 20E affects division of labor through Vg. We found that though the stress of injection caused a significant decrease in development of both the ovaries and HPG, there was no discernable effect of 20E on either of these organs.
ContributorsChin, Elijah Seth (Author) / Wang, Ying (Thesis director) / Page, Robert (Committee member) / Cook, Chelsea (Committee member) / School of Molecular Sciences (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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Description
Brain-machine interfaces (BMIs) were first imagined as a technology that would allow subjects to have direct communication with prosthetics and external devices (e.g. control over a computer cursor or robotic arm movement). Operation of these devices was not automatic, and subjects needed calibration and training in order to master this

Brain-machine interfaces (BMIs) were first imagined as a technology that would allow subjects to have direct communication with prosthetics and external devices (e.g. control over a computer cursor or robotic arm movement). Operation of these devices was not automatic, and subjects needed calibration and training in order to master this control. In short, learning became a key component in controlling these systems. As a result, BMIs have become ideal tools to probe and explore brain activity, since they allow the isolation of neural inputs and systematic altering of the relationships between the neural signals and output. I have used BMIs to explore the process of brain adaptability in a motor-like task. To this end, I trained non-human primates to control a 3D cursor and adapt to two different perturbations: a visuomotor rotation, uniform across the neural ensemble, and a decorrelation task, which non-uniformly altered the relationship between the activity of particular neurons in an ensemble and movement output. I measured individual and population level changes in the neural ensemble as subjects honed their skills over the span of several days. I found some similarities in the adaptation process elicited by these two tasks. On one hand, individual neurons displayed tuning changes across the entire ensemble after task adaptation: most neurons displayed transient changes in their preferred directions, and most neuron pairs showed changes in their cross-correlations during the learning process. On the other hand, I also measured population level adaptation in the neural ensemble: the underlying neural manifolds that control these neural signals also had dynamic changes during adaptation. I have found that the neural circuits seem to apply an exploratory strategy when adapting to new tasks. Our results suggest that information and trajectories in the neural space increase after initially introducing the perturbations, and before the subject settles into workable solutions. These results provide new insights into both the underlying population level processes in motor learning, and the changes in neural coding which are necessary for subjects to learn to control neuroprosthetics. Understanding of these mechanisms can help us create better control algorithms, and design training paradigms that will take advantage of these processes.
ContributorsArmenta Salas, Michelle (Author) / Helms Tillery, Stephen I (Thesis advisor) / Si, Jennie (Committee member) / Buneo, Christopher (Committee member) / Santello, Marco (Committee member) / Kleim, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Recent new experiments showed that wide-field imaging at millimeter scale is capable of recording hundreds of neurons in behaving mice brain. Monitoring hundreds of individual neurons at a high frame rate provides a promising tool for discovering spatiotemporal features of large neural networks. However, processing the massive data sets is

Recent new experiments showed that wide-field imaging at millimeter scale is capable of recording hundreds of neurons in behaving mice brain. Monitoring hundreds of individual neurons at a high frame rate provides a promising tool for discovering spatiotemporal features of large neural networks. However, processing the massive data sets is impossible without automated procedures. Thus, this thesis aims at developing a new tool to automatically segment and track individual neuron cells. The new method used in this study employs two major ideas including feature extraction based on power spectral density of single neuron temporal activity and clustering tree to separate overlapping cells. To address issues associated with high-resolution imaging of a large recording area, focused areas and out-of-focus areas were analyzed separately. A static segmentation with a fixed PSD thresholding method is applied to within focus visual field. A dynamic segmentation by comparing maximum PSD with surrounding pixels is applied to out-of-focus area. Both approaches helped remove irrelevant pixels in the background. After detection of potential single cells, some of which appeared in groups due to overlapping cells in the image, a hierarchical clustering algorithm is applied to separate them. The hierarchical clustering uses correlation coefficient as a distance measurement to group similar pixels into single cells. As such, overlapping cells can be separated. We tested the entire algorithm using two real recordings with the respective truth carefully determined by manual inspections. The results show high accuracy on tested datasets while false positive error is controlled within an acceptable range. Furthermore, results indicate robustness of the algorithm when applied to different image sequences.
ContributorsWu, Ruofan (Author) / Si, Jennie (Thesis advisor) / Sadleir, Rosalind (Committee member) / Crook, Sharon (Committee member) / Arizona State University (Publisher)
Created2016
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Description
Stroke remains a leading cause of adult disability in the United States. In recent studies, chronic vagus nerve stimulation (VNS) has been proven to enhance functional recovery when paired with motor rehabilitation training after stroke. Other studies have also demonstrated that delivering VNS during the onset of a

Stroke remains a leading cause of adult disability in the United States. In recent studies, chronic vagus nerve stimulation (VNS) has been proven to enhance functional recovery when paired with motor rehabilitation training after stroke. Other studies have also demonstrated that delivering VNS during the onset of a stroke may elicit some neuroprotective effects as observed in remaining neural tissue and motor function. While these studies have demonstrated the benefits of VNS as a treatment or therapy in combatting stroke damage, the mechanisms responsible for these effects are still not well understood or known. The aim of this research was to further investigate the mechanisms underlying the efficacy of acute VNS treatment of stroke by observing the effect of VNS when applied after the onset of stroke. Animals were randomly assigned to three groups: Stroke animals received cortical ischemia (ET-1 injection), VNS+Stroke animals received acute VNS starting within 48 hours after cortical ischemia and continuing once per day for three days, or Control animals which received neither the injury nor stimulation. Results showed that stroke animals receiving acute VNS had smaller lesion volumes and larger motor cortical maps than those in the Stroke group. The results suggest VNS may confer neuroprotective effects when delivered within the first 96 hours of stroke.
ContributorsOkada, Kristen Yuri (Author) / Kleim, Jeffrey A (Thesis advisor) / Si, Jennie (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2019