Browsing by Subject "Evoked compound action potential"

Deep brain stimulation (DBS) is an established surgical therapy for movement disorders. The mechanisms of action of DBS remain unclear, and selection of stimulation parameters is a clinical challenge and can result in sub-optimal outcomes. Closed-loop DBS systems would use a feedback control signal for automatic adjustment of DBS parameters and improved therapeutic effectiveness. We hypothesized that evoked compound action potentials (ECAPs), generated by activated neurons in the vicinity of the stimulating electrode, would reveal the type and spatial extent of neural activation, as well as provide signatures of clinical effectiveness. The objective of this dissertation was to record and characterize the ECAP during DBS to determine its suitability as a feedback signal in closed-loop systems. The ECAP was investigated using computer simulation and in vivo experiments, including the first preclinical and clinical ECAP recordings made from the same DBS electrode implanted for stimulation.

First, we developed DBS-ECAP recording instrumentation to reduce the stimulus artifact and enable high fidelity measurements of the ECAP at short latency. In vitro and in vivo validation experiments demonstrated the capability of the instrumentation to suppress the stimulus artifact, increase amplifier gain, and reduce distortion of short latency ECAP signals.

Second, we characterized ECAPs measured during thalamic DBS across stimulation parameters in anesthetized cats, and determined the neural origin of the ECAP using pharmacological interventions and a computer-based biophysical model of a thalamic network. This model simulated the ECAP response generated by a population of thalamic neurons, calculated ECAPs similar to experimental recordings, and indicated the relative contribution from different types of neural elements to the composite ECAP. Signal energy of the ECAP increased with DBS amplitude or pulse width, reflecting an increased extent of activation. Shorter latency, primary ECAP phases were generated by direct excitation of neural elements, whereas longer latency, secondary phases were generated by post-synaptic activation.

Third, intraoperative studies were conducted in human subjects with thalamic DBS for tremor, and the ECAP and tremor responses were measured across stimulation parameters. ECAP recording was technically challenging due to the presence of a wide range of stimulus artifact magnitudes across subjects, and an electrical circuit equivalent model and finite element method model both suggested that glial encapsulation around the DBS electrode increased the artifact size. Nevertheless, high fidelity ECAPs were recorded from acutely and chronically implanted DBS electrodes, and the energy of ECAP phases was correlated with changes in tremor.

Fourth, we used a computational model to understand how electrode design parameters influenced neural recording. Reducing the diameter or length of recording contacts increased the magnitude of single-unit responses, led to greater spatial sensitivity, and changed the relative contribution from local cells or passing axons. The effect of diameter or contact length varied across phases of population ECAPs, but ECAP signal energy increased with greater contact spacing, due to changes in the spatial sensitivity of the contacts. In addition, the signal increased with glial encapsulation in the peri-electrode space, decreased with local edema, and was unaffected by the physical presence of the highly conductive recording contacts.

It is feasible to record ECAP signals during DBS, and the correlation between ECAP characteristics and tremor suggests that this signal could be used in closed-loop DBS. This was demonstrated by implementation in simulation of a closed-loop system, in which a proportional-integral-derivative (PID) controller automatically adjusted DBS parameters to obtain a target ECAP energy value, and modified parameters in response to disturbances. The ECAP also provided insight into neural activation during DBS, with the dominant contribution to clinical ECAPs derived from excited cerebellothalamic fibers, suggesting that activation of these fibers is critical for DBS therapy.

Deep brain stimulation (DBS) is a highly successful neuromodulation therapy for treating the motor symptoms of Parkinson’s disease (PD). However, DBS has been used for over 30 years with little change in clinically used stimulation parameters and technology, and consequently, there have been few improvements in therapeutic efficacy during that period. Fortunately, recent advances in DBS devices and techniques, including automated stimulation parameter selection, directional leads, closed-loop stimulation, and model-optimized temporal patterns of stimulation, have the potential to improve symptom reduction, decrease side effects, and extend device battery life. However, making use of many of these techniques requires a recordable electrophysiological signal, or biomarker, that correlates strongly with clinical outcomes. The goals of this dissertation were to develop tools that assist in the recording and application of biomarkers, to characterize a new potential biomarker, the cortical evoked potential (cEP), and correlate it with symptom reduction, and to understand mechanistically how the cEP relates to symptom reduction during DBS. First, we quantified the effects of a novel electrodeposited platinum-iridium coating (EPIC) on single unit recording performance. We implanted electrodes in rats and used electrophysiological and histological measurements to compare quantitatively the single unit recording performance of coated vs. uncoated electrodes over a 12-week period. The coated electrodes had lower impedance, reduced noise, increased signal-to-noise ratio, and an increased number of discernible units per electrode as compared to the uncoated electrodes. These results demonstrated that EPIC electrodes provided recording performance and longevity superior to uncoated electrodes, thus improving our ability to quantify potential biomarkers from single unit recordings. Second, we developed a modified genetic algorithm (GA) designed to optimize temporal patterns of stimulation. We developed five modifications to the standard GA repopulation step that adapted the GA to design patterns for neuromodulation applications. We evaluated each modification individually and all modifications collectively by comparing performance to a standard GA across three test functions and two biophysically-based models of neural stimulation. The modifications improved performance across the test functions and performed best when all were used collectively. Thus, we developed a powerful tool for optimizing temporal patterns of stimulation using model-based proxies of DBS biomarkers. Third, we characterized a new candidate biomarker for DBS, the cEP, and quantified its correlation with symptom reduction during DBS. We used the unilateral 6-hydroxydopamine (6-OHDA) lesioned rat model or parkinsonism, with stimulating electrodes implanted in the subthalamic nucleus (STN) and the electrocorticography (ECoG) recorded above motor cortex (M1). We recorded the cEP during a range of stimulation conditions and while performing behavioral assessments of hypokinetic symptoms. The cEP was strongly affected by stimulation condition, and the cEP magnitude declined and the cEP latency increased with higher stimulation frequencies. These effects occurred over multiple minutes and with multiple time-scales. Additionally, the cEP magnitude and latency were each strongly correlated with symptom reduction during DBS, with correlations that were stronger and more consistent than those of conventional spectral-based biomarkers. This study demonstrated the potential utility of the cEP as a biomarker for symptom reduction from DBS. Fourth, to understand better how the cEP may relate mechanistically to symptom reduction from DBS, we developed a computational model of antidromic cortical activation during STN DBS and assessed the ability of DBS to desynchronize pathological cortical beta band oscillations. We tuned and validated the model using experimental data from the 6-OHDA lesioned rat model, and we implemented a stochastic model of antidromic spike failure, which is the presumed cause of the observed changes in the cEP magnitude, to determine how changes in the cEP relate to cortical desynchronization. STN DBS desynchronized pathological oscillations at high stimulation frequencies via a mechanism analogous to the informational lesion theory. Specifically, the DBS-evoked spikes masked the intrinsic pathological spiking via a combination of refractoriness, spike collision, and synaptic depletion. Further, the model revealed that antidromic spike failure played a critical role in shaping the therapeutic frequency profile of this masking effect, enforcing a parabolic shape with maximum desynchronization at ~130 Hz. The results in this dissertation advance our understanding of the therapeutic mechanism of STN DBS, provide important tools for application of electrophysiological biomarkers in DBS, and characterize the utility of the cEP as a potential biomarker to improve therapeutic outcomes.