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Description
During attempted fixation, the eyes are not still but continue to produce so called "fixational eye movements", which include microsaccades, drift, and tremor. Microsaccades are thought to help prevent and restore vision loss during fixation, and to correct fixation errors, but how they contribute to these functions remains a matter of debate. This dissertation presents the results of four experiments conducted to address current controversies concerning the role of microsaccades in visibility and oculomotor control.
The first two experiments set out to correlate microsaccade production with the visibility of foveal and peripheral targets of varied spatial frequencies, during attempted fixation. The results indicate that microsaccades restore the visibility of both peripheral targets and targets presented entirely within the fovea, as a function of their spatial frequency characteristics.
The last two experiments set out to determine the role of microsaccades and drifts on the correction of gaze-position errors due to blinks in human and non-human primates, and to characterize microsaccades forming square-wave jerks (SWJs) in non-human primates. The results showed that microsaccades, but not drifts, correct gaze-position errors due to blinks, and that SWJ production and dynamic properties are equivalent in human and non-human primates.
These combined findings suggest that microsaccades, like saccades, serve multiple and non-exclusive functional roles in vision and oculomotor control, as opposed to having a single specialized function.
The first two experiments set out to correlate microsaccade production with the visibility of foveal and peripheral targets of varied spatial frequencies, during attempted fixation. The results indicate that microsaccades restore the visibility of both peripheral targets and targets presented entirely within the fovea, as a function of their spatial frequency characteristics.
The last two experiments set out to determine the role of microsaccades and drifts on the correction of gaze-position errors due to blinks in human and non-human primates, and to characterize microsaccades forming square-wave jerks (SWJs) in non-human primates. The results showed that microsaccades, but not drifts, correct gaze-position errors due to blinks, and that SWJ production and dynamic properties are equivalent in human and non-human primates.
These combined findings suggest that microsaccades, like saccades, serve multiple and non-exclusive functional roles in vision and oculomotor control, as opposed to having a single specialized function.
ContributorsCostela, Francisco M (Author) / Crook, Sharon M (Committee member) / Martinez-Conde, Susana (Committee member) / Macknik, Stephen L. (Committee member) / Baer, Stephen (Committee member) / McCamy, Michael B (Committee member) / Arizona State University (Publisher)
Created2014
Description
Dopamine (DA) is a neurotransmitter involved in attention, goal oriented behavior, movement, reward learning, and short term and working memory. For the past four decades, mathematical and computational modeling approaches have been useful in DA research, and although every modeling approach has limitations, a model is an efficient way to generate and explore hypotheses. This work develops a model of DA dynamics in a representative, single DA neuron by integrating previous experimental, theoretical and computational research. The model consists of three compartments: the cytosol, the vesicles, and the extracellular space and forms the basis of a new mathematical paradigm for examining the dynamics of DA synthesis, storage, release and reuptake. The model can be driven by action potentials generated by any model of excitable membrane potential or even from experimentally induced depolarization voltage recordings. Here the model is forced by a previously published model of the excitable membrane of a mesencephalic DA neuron in order to study the biochemical processes involved in extracellular DA production. After demonstrating that the model exhibits realistic dynamics resembling those observed experimentally, the model is used to examine the functional changes in presynaptic mechanisms due to application of cocaine. Sensitivity analysis and numerical studies that focus on various possible mechanisms for the inhibition of DAT by cocaine provide insight for the complex interactions involved in DA dynamics. In particular, comparing numerical results for a mixed inhibition mechanism to those for competitive, non-competitive and uncompetitive inhibition mechanisms reveals many behavioral similarities for these different types of inhibition that depend on inhibition parameters and levels of cocaine. Placing experimental results within this context of mixed inhibition provides a possible explanation for the conflicting views of uptake inhibition mechanisms found in experimental neuroscience literature.
ContributorsTello-Bravo, David (Author) / Crook, Sharon M (Thesis advisor) / Greenwood, Priscilla E (Thesis advisor) / Baer, Steven M. (Committee member) / Castaneda, Edward (Committee member) / Castillo-Chavez, Carlos (Committee member) / Arizona State University (Publisher)
Created2012
Description
With a growing number of adults with autism spectrum disorder (ASD), more and more research has been conducted on majority male cohorts with ASD from young, adolescence, and some older age. Currently, males make up the majority of individuals diagnosed with ASD, however, recent research states that the gender gap is closing due to more advanced screening and a better understanding of how females with ASD present their symptoms. Little research has been published on the neurocognitive differences that exist between older adults with ASD compared to neurotypical (NT) counterparts, and nothing has specifically addressed older women with ASD. This study utilized neuroimaging and neuropsychological tests to examine differences between diagnosis and sex of four distinct groups: older men with ASD, older women with ASD, older NT men, and older NT women. In each group, hippocampal size (via FreeSurfer) was analyzed for differences as well as correlations with neuropsychological tests. Participants (ASD Female, n = 12; NT Female, n = 14; ASD Male, n = 30; NT Male = 22), were similar according to age, IQ, and education. The results of the study indicated that the ASD Group as a whole performed worse on executive functioning tasks (Wisconsin Card Sorting Test, Trails Making Test) and memory-related tasks (Rey Auditory Verbal Learning Test, Weschler Memory Scale: Visual Reproduction) compared to the NT Group. Interactions of sex by diagnosis approached significance only within the WCST non-perseverative errors, with the women with ASD performing worse than NT women, but no group differences between men. Effect sizes between the female groups (ASD female vs. NT female) showed more than double that of the male groups (ASD male vs. NT male) for all WCST and AVLT measures. Participants with ASD had significantly smaller right hippocampal volumes than NT participants. In addition, all older women showed larger hippocampal volumes when corrected for total intracranial volume (TIV) compared to all older men. Overall, NT Females had significant correlations across all neuropsychological tests and their hippocampal volumes whereas no other group had significant correlations. These results suggest a tighter coupling between hippocampal size and cognition in NT Females than NT Males and both sexes with ASD. This study promotes further understanding of the neuropsychological differences between older men and women, both with and without ASD. Further research is needed on a larger sample of older women with and without ASD.
ContributorsWebb, Christen Len (Author) / Braden, B. Blair (Thesis advisor) / Azuma, Tamiko (Committee member) / Dixon, Maria (Committee member) / Arizona State University (Publisher)
Created2019
Description
This pilot study evaluated whether Story Champs and Puente de Cuentos helped bilingual preschoolers increase their usage of emotional terms and ability to tell stories. Participants in this study included 10 Spanish-English bilingual preschoolers. Intervention was conducted in 9 sessions over 3 days using the Test of Narrative Retell to measure results. Results did not find significant gains in either emotional term usage or ability to tell stories, but the results were promising as a pilot study.
ContributorsSato, Leslie Mariko (Author) / Restrepo, Maria (Thesis director) / Dixon, Maria (Committee member) / Department of Speech and Hearing Science (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
The Development of the Strengthening Skills Program for Autistic Adults: Feasibility & Acceptability
Description
The study focuses on the creation of the Strengthening Skills Program (SSP) and its feasibility and acceptability among autistic adults across the lifespan. Over the course of two years, the program has been developed and delivered to autistic adults with the aim of improving quality of life. The program included adapted social skills training from the UCLA Program for the Education and Enrichment of Relational Skills (PEERS) for young adults, adapted mindfulness training from Mindfulness-Based Stress Reduction, and custom executive skills training. Pre- and post-intervention acceptability questionnaires were gathered from 42 participants. Participants were separated into three groups (SSP, PEERS, and Delayed Treatment Control [DTC]; n=14 per group) stratified by age, gender, and if the participant had a program partner who would attend the program alongside as support. All groups were administered over Zoom once per week and lasted for 16 weeks each. The SSP group met for three hours each week and the PEERS group met for an hour and a half. Qualitative analysis was implemented on participant feedback to identify thematic codes related to their experiences with the programs. Overall, results suggest the SSP intervention had significantly higher acceptability ratings compared to PEERS alone and could be a useful addition to the limited interventions available for autistic adults.
ContributorsHill, Ethan Reed (Author) / Braden, Blair (Thesis advisor) / Matthews, Nicole (Committee member) / Dixon, Maria (Committee member) / Arizona State University (Publisher)
Created2023
Description
Individuals with autism spectrum disorder (ASD) are known to show impairments in various domains of executive function (EF) such as behavioral flexibility or inhibitory control. Research suggests that EF impairment in adults with ASD may relate to ASD core symptoms, restrictive behaviors and social communication deficits. Mindfulness-based stress reduction (MBSR) has shown promise for improving EF abilities in neurotypical adults, but research has not explored its efficacy or neural mechanisms in adults with ASD. This pilot study examines the effects of an 8-week MBSR intervention on self-report measures of EF and resting-state functional connectivity in a sample of adults with ASD. Fifty-four participants were assigned either to an MBSR group (n = 29) or a social support group (n = 25). Executive function was measured using the BRIEF-2 before and after the intervention for the twenty-seven participants in the second cohort. MBSR-specific improvements in EF were found for BRIEF measures of initiation, inhibition, and working-memory. Resting-state fMRI data was analyzed using independent component analysis (ICA), and group by time resting-state functional connectivity differences were observed between the cerebellar network and frontal regions including the right frontal pole (rFP), medial frontal cortex (MFC) and left and right superior frontal gyri (SFG). The MBSR group showed increases in functional connectivity between the cerebellum and EF regions which correlated with improvements in BRIEF-2 measures. These findings suggest that MBSR may improve EF domains in adults with ASD, and that increases in functional connectivity between the cerebellum and frontal regions while at rest may be a mechanism for such improvements.
ContributorsGuerithault, Nicolas (Author) / Braden, B. Blair (Thesis advisor) / Rogalsky, Corianne (Committee member) / Dixon, Maria (Committee member) / Arizona State University (Publisher)
Created2021
Description
Electrical stimulation of the human peripheral nervous system can be a powerful tool to treat various medical conditions and provide insight into nervous system processes. A critical challenge for many applications is to selectively activate neurons that have the desired effect while avoiding the activation of neurons that produce side effects. To stimulate peripheral fibers, the longitudinal intrafascicular electrode (LIFE) targets small groups of fibers inside the fascicle using low-amplitude pulses and is well-suited for chronic use. This work aims to understand better the ability to use intrafascicular stimulation with LIFEs to activate small groups of neurons within a fascicle selectively.A hybrid workflow was developed to simulate: 1) the production/propagation of the electric field induced by the stimulation pulse and 2) the effect of the electric field on fiber activation (recruitment). To create efficient and robust strategies for the selective recruitment of axons, recognizing the effect of each parameter on their recruitment and activation pattern is essential. Thus, using this hybrid workflow, the effects of various factors such as fascicular anatomy, electrode parameters, and stimulation pulse parameters on recruitment have been characterized, and the sensitivity of the recruitment patterns to these parameters has been explored.
Results demonstrated the potential advantages of specific stimulation strategies and the sensitivity of recruitment patterns to electrode placement and tissue properties. For example, it is demonstrated: the significant effect of endoneurium conductivities on threshold levels; that a configuration with a LIFE as a local ground can be used to deselect its surrounding axons; the advantages of changing the delay between pulses in dual monopolar stimulation in targeting different axons clusters and increasing the activation frequency of some axons; how monopolar and bipolar configurations can be used to enhance spatial selectivity; the effect of longitudinal displacement of axons, electrode length and electrode movement on the recruitment and the activation pattern. In summary, this work forms the foundation for developing stimulation strategies to enhance the selectivity that can be achieved with intrafascicular stimulation.
ContributorsRouhani, Morteza (Author) / Abbas, James J (Thesis advisor) / Crook, Sharon M (Thesis advisor) / Baer, Steven M (Committee member) / Sadleir, Rosalind (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2022
Description
The increasing availability of experimental data and computational power have resulted in increasingly detailed and sophisticated models of brain structures. Biophysically realistic models allow detailed investigations of the mechanisms that operate within those structures. In this work, published mouse experimental data were synthesized to develop an extensible, open-source platform for modeling the mouse main olfactory bulb and other brain regions. A “virtual slice” model of a main olfactory bulb glomerular column that includes detailed models of tufted, mitral, and granule cells was created to investigate the underlying mechanisms of a gamma frequency oscillation pattern (“gamma fingerprint”) often observed in rodent bulbar local field potential recordings. The gamma fingerprint was reproduced by the model and a mechanistic hypothesis to explain aspects of the fingerprint was developed. A series of computational experiments tested the hypothesis. The results demonstrate the importance of interactions between electrical synapses, principal cell synaptic input strength differences, and granule cell inhibition in the formation of the gamma fingerprint. The model, data, results, and reproduction materials are accessible at https://github.com/justasb/olfactorybulb. The discussion includes a detailed description of mechanisms underlying the gamma fingerprint and how the model predictions can be tested experimentally. In summary, the modeling platform can be extended to include other types of cells, mechanisms and brain regions and can be used to investigate a wide range of experimentally testable hypotheses.
ContributorsBirgiolas, Justas (Author) / Crook, Sharon M (Thesis advisor) / Gerkin, Richard C (Committee member) / Smith, Brian H. (Committee member) / Neisewander, Janet (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2019
Description
Neuron models that behave like their biological counterparts are essential for computational neuroscience.Reduced neuron models, which abstract away biological mechanisms in the interest of speed and interpretability, have received much attention due to their utility in large scale simulations of the brain, but little care has been taken to ensure that these models exhibit behaviors that closely resemble real neurons.
In order to improve the verisimilitude of these reduced neuron models, I developed an optimizer that uses genetic algorithms to align model behaviors with those observed in experiments.
I verified that this optimizer was able to recover model parameters given only observed physiological data; however, I also found that reduced models nonetheless had limited ability to reproduce all observed behaviors, and that this varied by cell type and desired behavior.
These challenges can partly be surmounted by carefully designing the set of physiological features that guide the optimization. In summary, we found evidence that reduced neuron model optimization had the potential to produce reduced neuron models for only a limited range of neuron types.
In order to improve the verisimilitude of these reduced neuron models, I developed an optimizer that uses genetic algorithms to align model behaviors with those observed in experiments.
I verified that this optimizer was able to recover model parameters given only observed physiological data; however, I also found that reduced models nonetheless had limited ability to reproduce all observed behaviors, and that this varied by cell type and desired behavior.
These challenges can partly be surmounted by carefully designing the set of physiological features that guide the optimization. In summary, we found evidence that reduced neuron model optimization had the potential to produce reduced neuron models for only a limited range of neuron types.
ContributorsJarvis, Russell Jarrod (Author) / Crook, Sharon M (Thesis advisor) / Gerkin, Richard C (Thesis advisor) / Zhou, Yi (Committee member) / Abbas, James J (Committee member) / Arizona State University (Publisher)
Created2020
Description
It is increasingly common to see machine learning techniques applied in conjunction with computational modeling for data-driven research in neuroscience. Such applications include using machine learning for model development, particularly for optimization of parameters based on electrophysiological constraints. Alternatively, machine learning can be used to validate and enhance techniques for experimental data analysis or to analyze model simulation data in large-scale modeling studies, which is the approach I apply here. I use simulations of biophysically-realistic cortical neuron models to supplement a common feature-based technique for analysis of electrophysiological signals. I leverage these simulated electrophysiological signals to perform feature selection that provides an improved method for neuron-type classification. Additionally, I validate an unsupervised approach that extends this improved feature selection to discover signatures associated with neuron morphologies - performing in vivo histology in effect. The result is a simulation-based discovery of the underlying synaptic conditions responsible for patterns of extracellular signatures that can be applied to understand both simulation and experimental data. I also use unsupervised learning techniques to identify common channel mechanisms underlying electrophysiological behaviors of cortical neuron models. This work relies on an open-source database containing a large number of computational models for cortical neurons. I perform a quantitative data-driven analysis of these previously published ion channel and neuron models that uses information shared across models as opposed to information limited to individual models. The result is simulation-based discovery of model sub-types at two spatial scales which map functional relationships between activation/inactivation properties of channel family model sub-types to electrophysiological properties of cortical neuron model sub-types. Further, the combination of unsupervised learning techniques and parameter visualizations serve to integrate characterizations of model electrophysiological behavior across scales.
ContributorsHaynes, Reuben (Author) / Crook, Sharon M (Thesis advisor) / Gerkin, Richard C (Committee member) / Zhou, Yi (Committee member) / Baer, Steven (Committee member) / Armbruster, Hans D (Committee member) / Arizona State University (Publisher)
Created2020