Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 3 of 3
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- All Subjects: Bioengineering
- Creators: Santello, Marco
Description
Neural interfacing applications have advanced in complexity, with needs for increasingly high degrees of freedom in prosthetic device control, sharper discrimination in sensory percepts in bidirectional interfaces, and more precise localization of functional connectivity in the brain. As such, there is a growing need for reliable neurophysiological recordings at a fine spatial scale matching that of cortical columnar processing. Penetrating microelectrodes provide localization sufficient to isolate action potential (AP) waveforms, but often suffer from recorded signal deterioration linked to foreign body response. Micro-Electrocorticography (μECoG) surface electrodes elicit lower foreign body response and show greater chronic stability of recorded signals, though they typically lack the signal localization necessary to isolate individual APs. This dissertation validates the recording capacity of a novel, flexible, large area μECoG array with bilayer routing in a feline implant, and explores the ability of conventional μECoG arrays to detect features of neuronal activity in a very high frequency band associated with AP waveforms.
Recordings from both layers of the flexible μECoG array showed frequency features typical of cortical local field potentials (LFP) and were shown to be stable in amplitude over time. Recordings from both layers also showed consistent, frequency-dependent modulation after induction of general anesthesia, with large increases in beta and gamma band and decreases in theta band observed over three experiments. Recordings from conventional μECoG arrays over human cortex showed robust modulation in a high frequency (250-2000 Hz) band upon production of spoken words. Modulation in this band was used to predict spoken words with over 90% accuracy. Basal Ganglia neuronal AP firing was also shown to significantly correlate with various cortical μECoG recordings in this frequency band. Results indicate that μECoG surface electrodes may detect high frequency neuronal activity potentially associated with AP firing, a source of information previously unutilized by these devices.
Recordings from both layers of the flexible μECoG array showed frequency features typical of cortical local field potentials (LFP) and were shown to be stable in amplitude over time. Recordings from both layers also showed consistent, frequency-dependent modulation after induction of general anesthesia, with large increases in beta and gamma band and decreases in theta band observed over three experiments. Recordings from conventional μECoG arrays over human cortex showed robust modulation in a high frequency (250-2000 Hz) band upon production of spoken words. Modulation in this band was used to predict spoken words with over 90% accuracy. Basal Ganglia neuronal AP firing was also shown to significantly correlate with various cortical μECoG recordings in this frequency band. Results indicate that μECoG surface electrodes may detect high frequency neuronal activity potentially associated with AP firing, a source of information previously unutilized by these devices.
ContributorsBarton, Cody David (Author) / Greger, Bradley (Thesis advisor, Committee member) / Santello, Marco (Committee member) / Buneo, Christopher (Committee member) / Graudejus, Oliver (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2018
Description
Transcranial focused ultrasound (tFUS) is a unique neurostimulation modality with potential to develop into a highly sophisticated and effective tool. Unlike any other noninvasive neurostimulation technique, tFUS has a high spatial resolution (on the order of millimeters) and can penetrate across the skull, deep into the brain. Sub-thermal tFUS has been shown to induce changes in EEG and fMRI, as well as perception and mood. This study investigates the possibility of using tFUS to modulate brain networks involved in attention and cognitive control.Three different brain areas linked to saliency, cognitive control, and emotion within the cingulo-opercular network were stimulated with tFUS while subjects performed behavioral paradigms. The first study targeted the dorsal anterior cingulate cortex (dACC), which is associated with performance on cognitive attention tasks, conflict, error, and, emotion. Subjects performed a variant of the Erikson Flanker task in which emotional faces (fear, neutral or scrambled) were displayed in the background as distractors. tFUS significantly reduced the reaction time (RT) delay induced by faces; there were significant differences between tFUS and Sham groups in event related potentials (ERP), event related spectral perturbation (ERSP), conflict and error processing, and heart rate variability (HRV).
The second study used the same behavioral paradigm, but targeted tFUS to the right anterior insula/frontal operculum (aIns/fO). The aIns/fO is implicated in saliency, cognitive control, interoceptive awareness, autonomic function, and emotion. tFUS was found to significantly alter ERP, ERSP, conflict and error processing, and HRV responses.
The third study targeted tFUS to the right inferior frontal gyrus (rIFG), employing the Stop Signal task to study inhibition. tFUS affected ERPs and improved stopping speed. Using network modeling, causal evidence is presented for rIFG influence on subcortical nodes in stopping.
This work provides preliminarily evidence that tFUS can be used to modulate broader network function through a single node, affecting neurophysiological processing, physiologic responses, and behavioral performance. Additionally it can be used as a tool to elucidate network function. These studies suggest tFUS has the potential to affect cognitive function as a clinical tool, and perhaps even enhance wellbeing and expand conscious awareness.
The second study used the same behavioral paradigm, but targeted tFUS to the right anterior insula/frontal operculum (aIns/fO). The aIns/fO is implicated in saliency, cognitive control, interoceptive awareness, autonomic function, and emotion. tFUS was found to significantly alter ERP, ERSP, conflict and error processing, and HRV responses.
The third study targeted tFUS to the right inferior frontal gyrus (rIFG), employing the Stop Signal task to study inhibition. tFUS affected ERPs and improved stopping speed. Using network modeling, causal evidence is presented for rIFG influence on subcortical nodes in stopping.
This work provides preliminarily evidence that tFUS can be used to modulate broader network function through a single node, affecting neurophysiological processing, physiologic responses, and behavioral performance. Additionally it can be used as a tool to elucidate network function. These studies suggest tFUS has the potential to affect cognitive function as a clinical tool, and perhaps even enhance wellbeing and expand conscious awareness.
ContributorsFini, Maria Elizabeth (Author) / Tyler, William J (Thesis advisor) / Greger, Bradley (Committee member) / Santello, Marco (Committee member) / Kleim, Jeffrey (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2020
Description
Antibiotic resistance is a very important issue that threatens mankind. As bacteria
are becoming resistant to multiple antibiotics, many common antibiotics will soon
become ineective. The ineciency of current methods for diagnostics is an important
cause of antibiotic resistance, since due to their relative slowness, treatment plans
are often based on physician's experience rather than on test results, having a high
chance of being inaccurate or not optimal. This leads to a need of faster, pointof-
care (POC) methods, which can provide results in a few hours. Motivated by
recent advances on computer vision methods, three projects have been developed
for bacteria identication and antibiotic susceptibility tests (AST), with the goal of
speeding up the diagnostics process. The rst two projects focus on obtaining features
from optical microscopy such as bacteria shape and motion patterns to distinguish
active and inactive cells. The results show their potential as novel methods for AST,
being able to obtain results within a window of 30 min to 3 hours, a much faster
time frame than the gold standard approach based on cell culture, which takes at
least half a day to be completed. The last project focus on the identication task,
combining large volume light scattering microscopy (LVM) and deep learning to
distinguish bacteria from urine particles. The developed setup is suitable for pointof-
care applications, as a large volume can be viewed at a time, avoiding the need
for cell culturing or enrichment. This is a signicant gain compared to cell culturing
methods. The accuracy performance of the deep learning system is higher than chance
and outperforms a traditional machine learning system by up to 20%.
are becoming resistant to multiple antibiotics, many common antibiotics will soon
become ineective. The ineciency of current methods for diagnostics is an important
cause of antibiotic resistance, since due to their relative slowness, treatment plans
are often based on physician's experience rather than on test results, having a high
chance of being inaccurate or not optimal. This leads to a need of faster, pointof-
care (POC) methods, which can provide results in a few hours. Motivated by
recent advances on computer vision methods, three projects have been developed
for bacteria identication and antibiotic susceptibility tests (AST), with the goal of
speeding up the diagnostics process. The rst two projects focus on obtaining features
from optical microscopy such as bacteria shape and motion patterns to distinguish
active and inactive cells. The results show their potential as novel methods for AST,
being able to obtain results within a window of 30 min to 3 hours, a much faster
time frame than the gold standard approach based on cell culture, which takes at
least half a day to be completed. The last project focus on the identication task,
combining large volume light scattering microscopy (LVM) and deep learning to
distinguish bacteria from urine particles. The developed setup is suitable for pointof-
care applications, as a large volume can be viewed at a time, avoiding the need
for cell culturing or enrichment. This is a signicant gain compared to cell culturing
methods. The accuracy performance of the deep learning system is higher than chance
and outperforms a traditional machine learning system by up to 20%.
ContributorsIriya, Rafael (Author) / Turaga, Pavan (Thesis advisor) / Wang, Shaopeng (Committee member) / Grys, Thomas (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2020