Deep Brain Stimulation (DBS) is a stimulating therapy currently used to treat the motor disabilities that occur as a result of Parkinson’s disease (PD). Previous literature has proven the DBS to be an effective treatment in the effects of PD but the mechanism to validating this phenomenon is poorly understood. In this study, an evaluation of the DBS mechanism was analyzed in patients who received both contralateral and ipsilateral stimulation by the DBS electrode in relation to the recording microelectrode. I hypothesize that the data recorded from the neural tissue of the Parkinson’s patients will exhibit increased electromagnetic field (EMF) fall-off as spatial distance increases among the DBS lead and the microelectrode within the subthalamic nucleus (STN) as a result of the interaction between the EMF exuded by DBS and the neural tissue. Results depicted that EMF fall-off values increased with distance, observable upon comparing ipsilateral and contralateral patient data. The resulting analysis supported this phenomenon evidenced by the production of greater peak voltage amplitudes in ipsilateral patient stimulation with respect to time when compared to contralateral patient stimulation. The understanding of EMF strength and the associated trends among this data are vital to the progression and continued development of the DBS field relative to future research.

Interictal spikes have been used to diagnose idiopathic seizure disorder and localize the seizure onset zone. Interictal spikes are thought to arise primarily from large excitatory postsynaptic potentials, and the role of interictal spikes in idiopathic seizure disorder and epileptogenesis remains unclear. We evaluated how local voltage changes due to interictal spikes impact action potential generation and firing using intracellular recordings from human tissue and the Hodgkin-Huxley model. During interictal spikes, bursts of action potentials underwent variable degrees of depolarization-induced inactivation in the intracellular data. Intracellular recordings in neocortical slices of human brain tissue confirmed that bursts of inactivated action potentials occurred during spontaneous paroxysmal depolarization shifts. These ex vitro findings were predicted using the Hodgkin-Huxley model and showed inactivated action potentials being generated by large depolarizations. As the amplitude of the interictal spike increased, there was a progression from low firing rate normal action potentials to higher firing rate normal action potentials to inactivated action potentials. The results show that the Hodgkin-Huxley model confirmed the effect of large interictal spike depolarizations on action potential firing and inactivation. This supports a key element in the hypothesis that interictal spikes, and the associated action potential firing, may alter the electrical environment of the brain and contribute to idiopathic seizure disorder.

Brain micromotion is a phenomenon that arises from basic physiological functions such as respiration (breathing) and vascular pulsation (pumping blood or heart rate). These physiological processes cause small micro displacements of 2-4µm for vascular pulsation and 10-30µm for respiration, in rat models. One problem related to micromotion is the instability of the probe and its ability to acquire stable neural recordings in chronic studies. It has long been thought the membrane potential (MP) changes due to micromotion in the presence of brain implants were an artefact caused by the implant. Here is shown that intracellular membrane potential changes are a consequence of the activation of mechanosensitive ion channels at the neural interface. A combination of aplysia and rat animal models were used to show activation of mechanosensitive ion channels is occurring during a neural recording. During simulated micromotion of displacements of 50μm and 100μm at a frequency of 1 Hz, showed a change of 8 and 10mV respectively and that the addition of Ethylenediaminetetraacetic acid (EDTA) inhibited the membrane potential changes. The application of EDTA showed a 71% decrease in changes in membrane potential changes due to micromotion. Simulation of breathing using periodic motion of a probe in an Aplysia model showed that there were no membrane potential changes for <1.5kPa and action potentials were observed at >3.1kPa. Drug studies utilizing 5-HT showed an 80% reduction in membrane potentials. To validate the electrophysiological changes due to micromotion in a rat model, a double barrel pipette for simultaneous recording and drug delivery was designed, the drug delivery tip was recessed from the recording tip no greater than 50μm on average. The double barrel pipette using iontophoresis was used to deliver 30 μM of Gadolinium Chloride (Gd3+) into the microenvironment of the cell. Here is shown a significant reduction in membrane potential for n = 13 cells across 4 different rats tested using Gd3+. Membrane potential changes related to breathing and vascular pulsation were reduced between approximately 0.25-2.5 mV for both breathing and heart rate after the addition of Gd3+, a known mechanosensitive ion channel blocker.

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.

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.

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.

Growing understanding of the neural code and how to speak it has allowed for notable advancements in neural prosthetics. With commercially-available implantable systems with bi- directional neural communication on the horizon, there is an increasing imperative to develop high resolution interfaces that can survive the environment and be well tolerated by the nervous system under chronic use. The sensory encoding aspect optimally interfaces at a scale sufficient to evoke perception but focal in nature to maximize resolution and evoke more complex and nuanced sensations. Microelectrode arrays can maintain high spatial density, operating on the scale of cortical columns, and can be either penetrating or non-penetrating. The non-penetrating subset sits on the tissue surface without puncturing the parenchyma and is known to engender minimal tissue response and less damage than the penetrating counterpart, improving long term viability in vivo. Provided non-penetrating microelectrodes can consistently evoke perception and maintain a localized region of activation, non-penetrating micro-electrodes may provide an ideal platform for a high performing neural prosthesis; this dissertation explores their functional capacity.

The scale at which non-penetrating electrode arrays can interface with cortex is evaluated in the context of extracting useful information. Articulate movements were decoded from surface microelectrode electrodes, and additional spatial analysis revealed unique signal content despite dense electrode spacing. With a basis for data extraction established, the focus shifts towards the information encoding half of neural interfaces. Finite element modeling was used to compare tissue recruitment under surface stimulation across electrode scales. Results indicated charge density-based metrics provide a reasonable approximation for current levels required to evoke a visual sensation and showed tissue recruitment increases exponentially with electrode diameter. Micro-scale electrodes (0.1 – 0.3 mm diameter) could sufficiently activate layers II/III in a model tuned to striate cortex while maintaining focal radii of activated tissue.

In vivo testing proceeded in a nonhuman primate model. Stimulation consistently evoked visual percepts at safe current thresholds. Tracking perception thresholds across one year reflected stable values within minimal fluctuation. Modulating waveform parameters was found useful in reducing charge requirements to evoke perception. Pulse frequency and phase asymmetry were each used to reduce thresholds, improve charge efficiency, lower charge per phase – charge density metrics associated with tissue damage. No impairments to photic perception were observed during the course of the study, suggesting limited tissue damage from array implantation or electrically induced neurotoxicity. The subject consistently identified stimulation on closely spaced electrodes (2 mm center-to-center) as separate percepts, indicating sub-visual degree discrete resolution may be feasible with this platform. Although continued testing is necessary, preliminary results supports epicortical microelectrode arrays as a stable platform for interfacing with neural tissue and a viable option for bi-directional BCI applications.

Functional electrical stimulation (FES) is a technology utilized to attempt to restore motor control in patients affected with paralysis, usually through techniques like intraspinal microstimulation (ISMS). FES uses a surface electrode to delivery extremely small to the target muscles that stimulate their movement and improve signaling within the neighboring nerves. This project sought to measure the impedance of an electrode used for FES in order to characterize other neural structures involved in the electrical impulse transmission process, either through the use of components added to the electrode or through the combination of multiple impedance readings. The electrode used in the present study was composed of 15 microelectrodes, which were fully characterized through electrochemical impedance spectroscopy to analyze the impedance profile with change in frequency. The data points obtained from the microelectrodes were then averaged in order to obtain a larger picture of the impedance of the general electrode. As expected, the impedance of the microelectrodes decreased as frequency increased. The average impedance of a microelectrode at a frequency of 1 kHz was found to be 50 k, although high variance in the data requires further testing to be done to verify the validity of the values that were found.

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.

Epilepsy is a complex neurological disease that affects one in twenty-six people. Despite this prevalence, it is very difficult to diagnose. EpiFinder, Inc. has created an app to better diagnose epilepsy through the use of an epilepsy focused ontology and a heuristic algorithm. Throughout this project, efforts were made to improve the user interface and robustness of the EpiFinder app in order to ease usability and increase diagnostic accuracy. A general workflow of the app was created to aid new users with navigation of the app’s screens. Additionally, numerous diagnostic guidelines provided by the International League Against Epilepsy as well as de-identified case studies were annotated using the Knowtator plug-in in Protégé 3.3.1, where new terms not currently represented in the seizure and epilepsy syndrome ontology (ESSO) were identified for future integration into the ontology. This will help to increase the confidence level of the differential diagnosis reached. A basic evaluation of the user interface was done to provide feedback for the developers for future iterations of the app. Significant efforts were also made for better incorporation of the app into a physician’s typical workflow. For instance, an ontology of a basic review of systems of a medical history was built in Protégé 4.2 for later integration with the ESSO, which will help to increase efficiency and familiarity of the app for physician users. Finally, feedback regarding utility of the app was gathered from an epilepsy support group. These points will be taken into consideration for development of patient-based features in future versions of the EpiFinder app. It is the hope that these various improvements of the app will contribute to a more efficient, more accurate diagnosis of epilepsy patients, resulting in more appropriate treatments and an overall increased quality of life.