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- All Subjects: Neurosciences
- Creators: Greger, Bradley
Description
It is commonly known that the left hemisphere of the brain is more efficient in the processing of verbal information, compared to the right hemisphere. One proposal suggests that hemispheric asymmetries in verbal processing are due in part to the efficient use of top-down mechanisms by the left hemisphere. Most evidence for this comes from hemispheric semantic priming, though fewer studies have investigated verbal memory in the cerebral hemispheres. The goal of the current investigations is to examine how top-down mechanisms influence hemispheric asymmetries in verbal memory, and determine the specific nature of hypothesized top-down mechanisms. Five experiments were conducted to explore the influence of top-down mechanisms on hemispheric asymmetries in verbal memory. Experiments 1 and 2 used item-method directed forgetting to examine maintenance and inhibition mechanisms. In Experiment 1, participants were cued to remember or forget certain words, and cues were presented simultaneously or after the presentation of target words. In Experiment 2, participants were cued again to remember or forget words, but each word was repeated once or four times. Experiments 3 and 4 examined the influence of cognitive load on hemispheric asymmetries in true and false memory. In Experiment 3, cognitive load was imposed during memory encoding, while in Experiment 4, cognitive load was imposed during memory retrieval. Finally, Experiment 5 investigated the association between controlled processing in hemispheric semantic priming, and top-down mechanisms used for hemispheric verbal memory. Across all experiments, divided visual field presentation was used to probe verbal memory in the cerebral hemispheres. Results from all experiments revealed several important findings. First, top-down mechanisms used by the LH primarily used to facilitate verbal processing, but also operate in a domain general manner in the face of increasing processing demands. Second, evidence indicates that the RH uses top-down mechanisms minimally, and processes verbal information in a more bottom-up manner. These data help clarify the nature of top-down mechanisms used in hemispheric memory and language processing, and build upon current theories that attempt to explain hemispheric asymmetries in language processing.
ContributorsTat, Michael J (Author) / Azuma, Tamiko (Thesis advisor) / Goldinger, Stephen D (Committee member) / Liss, Julie M (Committee member) / Arizona State University (Publisher)
Created2013
Description
Distorted vowel production is a hallmark characteristic of dysarthric speech, irrespective of the underlying neurological condition or dysarthria diagnosis. A variety of acoustic metrics have been used to study the nature of vowel production deficits in dysarthria; however, not all demonstrate sensitivity to the exhibited deficits. Less attention has been paid to quantifying the vowel production deficits associated with the specific dysarthrias. Attempts to characterize the relationship between naturally degraded vowel production in dysarthria with overall intelligibility have met with mixed results, leading some to question the nature of this relationship. It has been suggested that aberrant vowel acoustics may be an index of overall severity of the impairment and not an "integral component" of the intelligibility deficit. A limitation of previous work detailing perceptual consequences of disordered vowel acoustics is that overall intelligibility, not vowel identification accuracy, has been the perceptual measure of interest. A series of three experiments were conducted to address the problems outlined herein. The goals of the first experiment were to identify subsets of vowel metrics that reliably distinguish speakers with dysarthria from non-disordered speakers and differentiate the dysarthria subtypes. Vowel metrics that capture vowel centralization and reduced spectral distinctiveness among vowels differentiated dysarthric from non-disordered speakers. Vowel metrics generally failed to differentiate speakers according to their dysarthria diagnosis. The second and third experiments were conducted to evaluate the relationship between degraded vowel acoustics and the resulting percept. In the second experiment, correlation and regression analyses revealed vowel metrics that capture vowel centralization and distinctiveness and movement of the second formant frequency were most predictive of vowel identification accuracy and overall intelligibility. The third experiment was conducted to evaluate the extent to which the nature of the acoustic degradation predicts the resulting percept. Results suggest distinctive vowel tokens are better identified and, likewise, better-identified tokens are more distinctive. Further, an above-chance level agreement between nature of vowel misclassification and misidentification errors was demonstrated for all vowels, suggesting degraded vowel acoustics are not merely an index of severity in dysarthria, but rather are an integral component of the resultant intelligibility disorder.
ContributorsLansford, Kaitlin L (Author) / Liss, Julie M (Thesis advisor) / Dorman, Michael F. (Committee member) / Azuma, Tamiko (Committee member) / Lotto, Andrew J (Committee member) / Arizona State University (Publisher)
Created2012
Description
Multisensory integration is the process by which information from different sensory modalities is integrated by the nervous system. This process is important not only from a basic science perspective but also for translational reasons, e.g., for the development of closed-loop neural prosthetic systems. A mixed virtual reality platform was developed to study the neural mechanisms of multisensory integration for the upper limb during motor planning. The platform allows for selection of different arms and manipulation of the locations of physical and virtual target cues in the environment. The system was tested with two non-human primates (NHP) trained to reach to multiple virtual targets. Arm kinematic data as well as neural spiking data from primary motor (M1) and dorsal premotor cortex (PMd) were collected. The task involved manipulating visual information about initial arm position by rendering the virtual avatar arm in either its actual position (veridical (V) condition) or in a different shifted (e.g., small vs large shifts) position (perturbed (P) condition) prior to movement. Tactile feedback was modulated in blocks by placing or removing the physical start cue on the table (tactile (T), and no-tactile (NT) conditions, respectively). Behaviorally, errors in initial movement direction were larger when the physical start cue was absent. Slightly larger directional errors were found in the P condition compared to the V condition for some movement directions. Both effects were consistent with the idea that erroneous or reduced information about initial hand location led to movement direction-dependent reach planning errors. Neural correlates of these behavioral effects were probed using population decoding techniques. For small shifts in the visual position of the arm, no differences in decoding accuracy between the T and NT conditions were observed in either M1 or PMd. However, for larger visual shifts, decoding accuracy decreased in the NT condition, but only in PMd. Thus, activity in PMd, but not M1, may reflect the uncertainty in reach planning that results when sensory cues regarding initial hand position are erroneous or absent.
ContributorsPhataraphruk, Preyaporn Kris (Author) / Buneo, Christopher A (Thesis advisor) / Zhou, Yi (Committee member) / Helms Tillery, Steve (Committee member) / Greger, Bradley (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2023
Description
Safety and efficacy of neuromodulation are influenced by abiotic factors like failure of implants, biotic factors like tissue damage, and molecular and cellular mechanisms of neuromodulation. Accelerated lifetime test (ALT) predict lifetime of implants by accelerating failure modes in controlled bench-top conditions. Current ALT models do not capture failure modes involving biological mechanisms. First part of this dissertation is focused on developing ALTs for predicting failure of chronically implanted tungsten stimulation electrodes. Three factors used in ALT are temperature, H2O2 concentration, and amount of charge delivered through electrode to develop a predictive model of lifetime for stimulation electrodes. Second part of this dissertation is focused on developing a novel method for evaluating tissue response to implants and electrical stimulation. Current methods to evaluate tissue damage in the brain require invasive and terminal procedures that have poor clinical translation. I report a novel non-invasive method that sampled peripheral blood monocytes (PBMCs) and used enzyme-linked immunoassay (ELISA) to assess level of glial fibrillary acidic protein (GFAP) expression and fluorescence-activated cell sorting (FACS) to quantify number of GFAP expressing PBMCs. Using this method, I was able to detect and quantify GFAP expression in PBMCs. However, there was no statistically significant difference in GFAP expression between stimulatory and non-stimulatory implants. Final part of this dissertation assessed molecular and cellular mechanisms of non-invasive ultrasound neuromodulation approach. Unlike electrical stimulation, cellular mechanisms of ultrasound-based neuromodulation are not fully known. Final part of this dissertation assessed role of mechanosensitive ion channels and neuronal nitric oxide production in cell cultures under ultrasound excitation. I used fluorescent imaging to quantify expression of nitric oxide in neuronal cell cultures in response to ultrasound stimulation. Results from these experiments indicate that neuronal nitric oxide production increased in response to ultrasound stimulation compared to control and decreased when mechanosensitive ion channels were suppressed. Two novel methods developed in this dissertation enable assessment of lifetime and safety of neuromodulation techniques that use electrical stimulation through implants. The final part of this dissertation concludes that non-invasive ultrasound neuromodulation may be mediated through neuronal nitric oxide even in absence of activation of mechanosensitive ion channels.
ContributorsVoziyanov, Vladislav (Author) / Muthuswamy, Jitendran (Thesis advisor) / Smith, Barbara (Committee member) / Greger, Bradley (Committee member) / Abbas, James (Committee member) / Okandan, Murat (Committee member) / Arizona State University (Publisher)
Created2022
Description
Information processing in the brain is mediated by network interactions between anatomically distant (centimeters apart) regions of cortex and network action is fundamental to human behavior. Disruptive activity of these networks may allow a variety of diseases to develop. Degradation or loss of network function in the brain can affect many aspects of the human experience; motor disorder, language difficulties, memory loss, mood swings, and more. The cortico-basal ganglia loop is a system of networks in the brain between the cortex, basal ganglia, the thalamus, and back to the cortex. It is not one singular circuit, but rather a series of parallel circuits that are relevant towards motor output, motor planning, and motivation and reward. Studying the relationship between basal ganglia neurons and cortical local field potentials may lead to insights about neurodegenerative diseases and how these diseases change the cortico-basal ganglia circuit. Speech and language are uniquely human and require the coactivation of several brain regions. The various aspects of language are spread over the temporal lobe and parts of the occipital, parietal, and frontal lobe. However, the core network for speech production involves collaboration between phonologic retrieval (encoding ideas into syllabic representations) from Wernicke’s area, and phonemic encoding (translating syllables into motor articulations) from Broca’s area. Studying the coactivation of these brain regions during a repetitive speech production task may lead to a greater understanding of their electrophysiological functional connectivity. The primary purpose of the work presented in this document is to validate the use of subdural microelectrodes in electrophysiological functional connectivity research as these devices best match the spatial and temporal scales of brain activity. Neuron populations in the cortex are organized into functional units called cortical columns. These cortical columns operate on the sub-millisecond temporal and millimeter spatial scale. The study of brain networks, both in healthy and unwell individuals, may reveal new methodologies of treatment or management for disease and injury, as well as contribute to our scientific understanding of how the brain works.
ContributorsO'Neill, Kevin John (Author) / Greger, Bradley (Thesis advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Papandreou-Suppapola, Antonia (Committee member) / Kleim, Jeffery (Committee member) / Arizona State University (Publisher)
Created2021
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
Autism spectrum disorder (ASD) is a developmental neuropsychiatric condition with early childhood onset, thus most research has focused on characterizing brain function in young individuals. Little is understood about brain function differences in middle age and older adults with ASD, despite evidence of persistent and worsening cognitive symptoms. Functional Magnetic Resonance Imaging (MRI) in younger persons with ASD demonstrate that large-scale brain networks containing the prefrontal cortex are affected. A novel, threshold-selection-free graph theory metric is proposed as a more robust and sensitive method for tracking brain aging in ASD and is compared against five well-accepted graph theoretical analysis methods in older men with ASD and matched neurotypical (NT) participants. Participants were 27 men with ASD (52 +/- 8.4 years) and 21 NT men (49.7 +/- 6.5 years). Resting-state functional MRI (rs-fMRI) scans were collected for six minutes (repetition time=3s) with eyes closed. Data was preprocessed in SPM12, and Data Processing Assistant for Resting-State fMRI (DPARSF) was used to extract 116 regions-of-interest defined by the automated anatomical labeling (AAL) atlas. AAL regions were separated into six large-scale brain networks. This proposed metric is the slope of a monotonically decreasing convergence function (Integrated Persistent Feature, IPF; Slope of the IPF, SIP). Results were analyzed in SPSS using ANCOVA, with IQ as a covariate. A reduced SIP was in older men with ASD, compared to NT men, in the Default Mode Network [F(1,47)=6.48; p=0.02; 2=0.13] and Executive Network [F(1,47)=4.40; p=0.04; 2=0.09], a trend in the Fronto-Parietal Network [F(1,47)=3.36; p=0.07; 2=0.07]. There were no differences in the non-prefrontal networks (Sensory motor network, auditory network, and medial visual network). The only other graph theory metric to reach significance was network diameter in the Default Mode Network [F(1,47)=4.31; p=0.04; 2=0.09]; however, the effect size for the SIP was stronger. Modularity, Betti number, characteristic path length, and eigenvalue centrality were all non-significant. These results provide empirical evidence of decreased functional network integration in pre-frontal networks of older adults with ASD and propose a useful biomarker for tracking prognosis of aging adults with ASD to enable more informed treatment, support, and care methods for this growing population.
ContributorsCatchings, Michael Thomas (Author) / Braden, Brittany B (Thesis advisor) / Greger, Bradley (Thesis advisor) / Schaefer, Sydney (Committee member) / Arizona State University (Publisher)
Created2019
Description
Intracranial pressure is an important parameter to monitor, and elevated intracranial pressure can be life threatening. Elevated intracranial pressure is indicative of distress in the brain attributed by conditions such as aneurysm, traumatic brain injury, brain tumor, hydrocephalus, stroke, or meningitis.
Electrocorticography (ECoG) recordings are invaluable in understanding epilepsy and detecting seizure zones. However, ECoG electrodes cause a foreign body mass effect, swelling, and pneumocephaly, which results in elevation of intracranial pressure (ICP). Thus, the aim of this work is to design an intracranial pressure monitoring system that could augment ECoG electrodes.
A minimally invasive, low-cost epidural intracranial pressure monitoring system is developed for this purpose, using a commercial pressure transducer available for biomedical applications. The system is composed of a pressure transducer, sensing cup, electronics, and data acquisition system. The pressure transducer is a microelectromechanical system (MEMS)-based die that works on piezoresistive phenomenon with dielectric isolation for direct contact with fluids.
The developed system was bench tested and verified in an animal model to confirm the efficacy of the system for intracranial pressure monitoring. The system has a 0.1 mmHg accuracy and a 2% error for the 0-10 mmHg range, with resolution of 0.01 mmHg. This system serves as a minimally invasive (2 mm burr hole) epidural ICP monitor, which could augment existing ECoG electrode arrays, to simultaneously measure intracranial pressure along with the neural signals.
This device could also be employed with brain implants that causes elevation in ICP due to tissue - implant interaction often leading to edema. This research explores the concept and feasibility for integrating the sensing component directly on to the ECoG electrode arrays.
Electrocorticography (ECoG) recordings are invaluable in understanding epilepsy and detecting seizure zones. However, ECoG electrodes cause a foreign body mass effect, swelling, and pneumocephaly, which results in elevation of intracranial pressure (ICP). Thus, the aim of this work is to design an intracranial pressure monitoring system that could augment ECoG electrodes.
A minimally invasive, low-cost epidural intracranial pressure monitoring system is developed for this purpose, using a commercial pressure transducer available for biomedical applications. The system is composed of a pressure transducer, sensing cup, electronics, and data acquisition system. The pressure transducer is a microelectromechanical system (MEMS)-based die that works on piezoresistive phenomenon with dielectric isolation for direct contact with fluids.
The developed system was bench tested and verified in an animal model to confirm the efficacy of the system for intracranial pressure monitoring. The system has a 0.1 mmHg accuracy and a 2% error for the 0-10 mmHg range, with resolution of 0.01 mmHg. This system serves as a minimally invasive (2 mm burr hole) epidural ICP monitor, which could augment existing ECoG electrode arrays, to simultaneously measure intracranial pressure along with the neural signals.
This device could also be employed with brain implants that causes elevation in ICP due to tissue - implant interaction often leading to edema. This research explores the concept and feasibility for integrating the sensing component directly on to the ECoG electrode arrays.
ContributorsSampath Kumaran, Ranjani (Author) / Christen, Jennifer Blain (Thesis advisor) / Tillery, Stephen Helms (Committee member) / Greger, Bradley (Committee member) / Arizona State University (Publisher)
Created2015
Description
In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.
ContributorsPadmanaban, Subash (Author) / Greger, Bradley (Thesis advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Crook, Sharon (Committee member) / Arizona State University (Publisher)
Created2017
Description
Prosthetic users abandon devices due to difficulties performing tasks without proper graded or interpretable feedback. The inability to adequately detect and correct error of the device leads to failure and frustration. In advanced prostheses, peripheral nerve stimulation can be used to deliver sensations, but standard schemes used in sensorized prosthetic systems induce percepts inconsistent with natural sensations, providing limited benefit. Recent uses of time varying stimulation strategies appear to produce more practical sensations, but without a clear path to pursue improvements. This dissertation examines the use of physiologically based stimulation strategies to elicit sensations that are more readily interpretable. A psychophysical experiment designed to investigate sensitivities to the discrimination of perturbation direction within precision grip suggests that perception is biomechanically referenced: increased sensitivities along the ulnar-radial axis align with potential anisotropic deformation of the finger pad, indicating somatosensation uses internal information rather than environmental. Contact-site and direction dependent deformation of the finger pad activates complimentary fast adapting and slow adapting mechanoreceptors, exhibiting parallel activity of the two associate temporal patterns: static and dynamic. The spectrum of temporal activity seen in somatosensory cortex can be explained by a combined representation of these distinct response dynamics, a phenomenon referred in this dissertation to “biphasic representation.” In a reach-to-precision-grasp task, neurons in somatosensory cortex were found to possess biphasic firing patterns in their responses to texture, orientation, and movement. Sensitivities seem to align with variable deformation and mechanoreceptor activity: movement and smooth texture responses align with potential fast adapting activation, non-movement and coarse texture responses align with potential increased slow adapting activation, and responses to orientation are conceptually consistent with coding of tangential load. Using evidence of biphasic representations’ association with perceptual priorities, gamma band phase locking is used to compare responses to peripheral nerve stimulation patterns and mechanical stimulation. Vibrotactile and punctate mechanical stimuli are used to represent the practical and impractical percepts commonly observed in peripheral nerve stimulation feedback. Standard patterns of constant parameters closely mimic impractical vibrotactile stimulation while biphasic patterns better mimic punctate stimulation and provide a platform to investigate intragrip dynamics representing contextual activation.
ContributorsTanner, Justin Cody (Author) / Helms Tillery, Stephen I (Thesis advisor) / Santos, Veronica J (Committee member) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Buneo, Christopher A (Committee member) / Arizona State University (Publisher)
Created2017