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- All Subjects: Biomedical Engineering
- All Subjects: Neuroprostheses
- Creators: Greger, Bradley
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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
In medical imaging, a wide variety of methods are used to interrogate structural and physiological differences between soft tissues. One of the most ubiquitous methods in clinical practice is Magnetic Resonance Imaging (MRI), which has the advantage of limited invasiveness, soft tissue discrimination, and adequate volumetric resolution. A myriad of advanced MRI methods exists to investigate the microstructural, physiologic and metabolic characteristics of tissue. For example, Dynamic Contrast Enhanced (DCE) and Dynamic Susceptibility Contrast (DSC) MRI non-invasively interrogates the dynamic passage of an exogenously administered MRI contrast agent through tissue to quantify local tracer kinetic properties like blood flow, vascular permeability and tissue compartmental volume fractions. Recently, an improved understanding of the biophysical basis of DSC-MRI signals in brain tumors revealed a new approach to derive multiple quantitative biomarkers that identify intrinsic sub-voxel cellular and vascular microstructure that can be used differentiate tumor sub-types. One of these characteristic biomarkers called Transverse Relaxivity at Tracer Equilibrium (TRATE), utilizes a combination of DCE and DSC techniques to compute a steady-state metric which is particularly sensitive to cell size, density, and packing properties. This work seeks to investigate the sensitivity and potential utility of TRATE in a range of disease states including Glioblastomas, Amyotrophic Lateral Sclerosis (ALS), and Duchenne’s Muscular Dystrophy (DMD). The MRC measures of TRATE showed the most promise in mouse models of ALS where TRATE values decreased with disease progression, a finding that correlated with reductions in myofiber size and area, as quantified by immunohistochemistry. In the animal models of cancer and DMD, TRATE results were more inconclusive, due to marked heterogeneity across animals and treatment state. Overall, TRATE seems to be a promising new biomarker but still needs further methodological refinement due to its sensitivity to contrast to noise and further characterization owing to its non-specificity with respect to multiple cellular features (e.g. size, density, heterogeneity) that complicate interpretation.
ContributorsFuentes, Alberto (Author) / Quarles, Chad C (Thesis advisor) / Kodibagkar, Vikram (Thesis advisor) / Greger, Bradley (Committee member) / Arizona State University (Publisher)
Created2018
Description
Bioimpedance measurements have been long used for monitoring tissue ischemia and blood flow. This research employs implantable microelectronic devices to measure impedance chronically as a potential way to monitor the progress of peripheral vascular disease (PVD). Ultrasonically powered implantable microdevices previously developed for the purposes of neuroelectric vasodilation for therapeutic treatment of PVD were found to also allow a secondary function of tissue bioimpedance monitoring. Having no structural differences between devices used for neurostimulation and impedance measurements, there is a potential for double functionality and closed loop control of the neurostimulation performed by these types of microimplants. The proposed technique involves actuation of the implantable microdevices using a frequency-swept amplitude modulated continuous waveform ultrasound and remote monitoring of induced tissue current. The design has been investigated using simulations, ex vivo testing, and preliminary animal experiments. Obtained results have demonstrated the ability of ultrasonically powered neurostimulators to be sensitive to the impedance changes of tissue surrounding the device and wirelessly report impedance spectra. Present work suggests the potential feasibility of wireless tissue impedance measurements for PVD applications as a complement to neurostimulation.
ContributorsCelinskis, Dmitrijs (Author) / Towe, Bruce (Thesis advisor) / Greger, Bradley (Committee member) / Sadleir, Rosalind (Committee member) / Arizona State University (Publisher)
Created2015
Description
Epilepsy is a group of disorders that cause seizures in approximately 2.2 million people in the United States. Over 30% of these patients have epilepsies that do not respond to treatment with anti-epileptic drugs. For this population, focal resection surgery could offer long-term seizure freedom. Surgery candidates undergo a myriad of tests and monitoring to determine where and when seizures occur. The “gold standard” method for focus identification involves the placement of electrocorticography (ECoG) grids in the sub-dural space, followed by continual monitoring and visual inspection of the patient’s cortical activity. This process, however, is highly subjective and uses dated technology. Multiple studies were performed to investigate how the evaluation process could benefit from an algorithmic adjust using current ECoG technology, and how the use of new microECoG technology could further improve the process.
Computational algorithms can quickly and objectively find signal characteristics that may not be detectable with visual inspection, but many assume the data are stationary and/or linear, which biological data are not. An empirical mode decomposition (EMD) based algorithm was developed to detect potential seizures and tested on data collected from eight patients undergoing monitoring for focal resection surgery. EMD does not require linearity or stationarity and is data driven. The results suggest that a biological data driven algorithm could serve as a useful tool to objectively identify changes in cortical activity associated with seizures.
Next, the use of microECoG technology was investigated. Though both ECoG and microECoG grids are composed of electrodes resting on the surface of the cortex, changing the diameter of the electrodes creates non-trivial changes in the physics of the electrode-tissue interface that need to be accounted for. Experimenting with different recording configurations showed that proper grounding, referencing, and amplification are critical to obtain high quality neural signals from microECoG grids.
Finally, the relationship between data collected from the cortical surface with micro and macro electrodes was studied. Simultaneous recordings of the two electrode types showed differences in power spectra that suggest the inclusion of activity, possibly from deep structures, by macroelectrodes that is not accessible by microelectrodes.
Computational algorithms can quickly and objectively find signal characteristics that may not be detectable with visual inspection, but many assume the data are stationary and/or linear, which biological data are not. An empirical mode decomposition (EMD) based algorithm was developed to detect potential seizures and tested on data collected from eight patients undergoing monitoring for focal resection surgery. EMD does not require linearity or stationarity and is data driven. The results suggest that a biological data driven algorithm could serve as a useful tool to objectively identify changes in cortical activity associated with seizures.
Next, the use of microECoG technology was investigated. Though both ECoG and microECoG grids are composed of electrodes resting on the surface of the cortex, changing the diameter of the electrodes creates non-trivial changes in the physics of the electrode-tissue interface that need to be accounted for. Experimenting with different recording configurations showed that proper grounding, referencing, and amplification are critical to obtain high quality neural signals from microECoG grids.
Finally, the relationship between data collected from the cortical surface with micro and macro electrodes was studied. Simultaneous recordings of the two electrode types showed differences in power spectra that suggest the inclusion of activity, possibly from deep structures, by macroelectrodes that is not accessible by microelectrodes.
ContributorsAshmont, Kari Rich (Author) / Greger, Bradley (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Buneo, Christopher (Committee member) / Adelson, P David (Committee member) / Dudek, F Edward (Committee member) / Arizona State University (Publisher)
Created2015
Description
Robust and stable decoding of neural signals is imperative for implementing a useful neuroprosthesis capable of carrying out dexterous tasks. A nonhuman primate (NHP) was trained to perform combined flexions of the thumb, index and middle fingers in addition to individual flexions and extensions of the same digits. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon action potential firing rates. The effect of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis, and Mutual Information Maximization was compared based on SVM classification performance. SVM classification was used to examine the functional parameters of (i) efficacy (ii) endurance to simulated failure and (iii) longevity of classification. The effect of using isolated-neuron and multi-unit firing rates was compared as the feature vector supplied to the SVM. The best classification performance was on post-implantation day 36, when using multi-unit firing rates the worst classification accuracy resulted from features selected with Wilcoxon signed-rank test (51.12 ± 0.65%) and the best classification accuracy resulted from Mutual Information Maximization (93.74 ± 0.32%). On this day when using single-unit firing rates, the classification accuracy from the Wilcoxon signed-rank test was 88.85 ± 0.61 % and Mutual Information Maximization was 95.60 ± 0.52% (degrees of freedom =10, level of chance =10%)
ContributorsPadmanaban, Subash (Author) / Greger, Bradley (Thesis advisor) / Santello, Marco (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2015
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
Object manipulation is a common sensorimotor task that humans perform to interact with the physical world. The first aim of this dissertation was to characterize and identify the role of feedback and feedforward mechanisms for force control in object manipulation by introducing a new feature based on force trajectories to quantify the interaction between feedback- and feedforward control. This feature was applied on two grasp contexts: grasping the object at either (1) predetermined or (2) self-selected grasp locations (“constrained” and “unconstrained”, respectively), where unconstrained grasping is thought to involve feedback-driven force corrections to a greater extent than constrained grasping. This proposition was confirmed by force feature analysis. The second aim of this dissertation was to quantify whether force control mechanisms differ between dominant and non-dominant hands. The force feature analysis demonstrated that manipulation by the dominant hand relies on feedforward control more than the non-dominant hand. The third aim was to quantify coordination mechanisms underlying physical interaction by dyads in object manipulation. The results revealed that only individuals with worse solo performance benefit from interpersonal coordination through physical couplings, whereas the better individuals do not. This work showed that naturally emerging leader-follower roles, whereby the leader in dyadic manipulation exhibits significant greater force changes than the follower. Furthermore, brain activity measured through electroencephalography (EEG) could discriminate leader and follower roles as indicated power modulation in the alpha frequency band over centro-parietal areas. Lastly, this dissertation suggested that the relation between force and motion (arm impedance) could be an important means for communicating intended movement direction between biological agents.
ContributorsMojtahedi, Keivan (Author) / Santello, Marco (Thesis advisor) / Greger, Bradley (Committee member) / Artemiadis, Panagiotis (Committee member) / Helms Tillery, Stephen (Committee member) / Buneo, Christopher (Committee member) / Arizona State University (Publisher)
Created2017
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