Citations

Abstract Neurochemical recording techniques have expanded our understanding of the pathophysiology of neurological disorders, as well as the mechanisms of action of treatment modalities like deep brain stimulation (DBS). DBS is used to treat diseases such as Parkinson’s disease, Tourette syndrome, and obsessive-compulsive disorder, among others. Although DBS is effective at alleviating symptoms related to these diseases and improving the quality of life of these patients, the mechanism of action of DBS is currently not fully understood. A leading hypothesis is that DBS modulates the electrical field potential by modifying neuronal firing frequencies to non-pathological rates thus providing therapeutic relief. To address this gap in knowledge, recent advances in electrochemical sensing techniques have given insight into the importance of neurotransmitters, such as dopamine, serotonin, glutamate, and adenosine, in disease pathophysiology. These studies have also highlighted their potential use in tandem with electrophysiology to serve as biomarkers in disease diagnosis and progression monitoring, as well as characterize response to treatment. Here, we provide an overview of disease-relevant neurotransmitters and their roles and implications as biomarkers, as well as innovations to the biosensors used to record these biomarkers. Furthermore, we discuss currently available neurochemical and electrophysiological recording devices, and discuss their viability to be implemented into the development of a closed-loop DBS system.

The development of closed-loop deep brain stimulation (DBS) systems represents a significant opportunity for innovation in the clinical application of neurostimulation therapies. Despite the highly dynamic nature of neurological diseases, open-loop DBS applications are incapable of modifying parameters in real time to react to fluctuations in disease states. Thus, current practice for the designation of stimulation parameters, such as duration, amplitude, and pulse frequency, is an algorithmic process. Ideal stimulation parameters are highly individualized and must reflect both the specific disease presentation and the unique pathophysiology presented by the individual. Stimulation parameters currently require a lengthy trial-and-error process to achieve the maximal therapeutic effect and can only be modified during clinical visits. The major impediment to the development of automated, adaptive closed-loop systems involves the selection of highly specific disease-related biomarkers to provide feedback for the stimulation platform. This review explores the disease relevance of neurochemical and electrophysiological biomarkers for the development of closed-loop neurostimulation technologies. Electrophysiological biomarkers, such as local field potentials, have been used to monitor disease states. Real-time measurement of neurochemical substances may be similarly useful for disease characterization. Thus, the introduction of measurable neurochemical analytes has significantly expanded biomarker options for feedback-sensitive neuromodulation systems. The potential use of biomarker monitoring to advance neurostimulation approaches for treatment of Parkinson’s disease, essential tremor, epilepsy, Tourette syndrome, obsessive-compulsive disorder, chronic pain, and depression is examined. Further, challenges and advances in the development of closed-loop neurostimulation technology are reviewed, as well as opportunities for next-generation closed-loop platforms.

Abstract Although fast-scan cyclic voltammetry (FSCV) has been widely used for in vivo neurochemical detection, the sensitivity and selectivity of the technique can be further improved. In this study, we develop fast cyclic square-wave voltammetry (FCSWV) as a novel voltammetric technique that combines large-amplitude cyclic square-wave voltammetry (CSWV) with background subtraction. A large-amplitude, square-shaped potential was applied to induce cycling through multiple redox reactions within a square pulse to increase sensitivity and selectivity when combined with a two-dimensional voltammogram. As a result, FCSWV was significantly more sensitive than FSCV (n = 5 electrodes, two-way ANOVA, p = 0.0002). In addition, FCSWV could differentiate dopamine from other catecholamines (e.g., epinephrine and norepinephrine) and serotonin better than conventional FSCV. With the confirmation that FCSWV did not influence local neuronal activity, despite the large amplitude of the square waveform, it could monitor electrically induced phasic changes in dopamine release in rat striatum before and after injecting nomifensine, a dopamine reuptake inhibitor.

A novel multi-waveform FSCV (M-FSCV) developed for characterizing adsorption/desorption kinetics of neurotransmitters.

A simple, and yet robust, high pass filter is designed to remove non-linear background drift in fast scan cyclic voltammetry.