Bennet, Kevin E.

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.

Accurate clinical evaluation of renal osteodystrophy (ROD) is currently accomplished using invasive in vivo transiliac bone biopsy, followed by in vitro histomorphometry. In this study, we demonstrate that an alternative method for ROD assessment is through a fast, label-free Raman recording of multiple biomarkers combined with computational analysis for predicting the minimally required number of spectra for sample classification at defined accuracies. Four clinically relevant biomarkers: the mineral-to-matrix ratio, the carbonate-to-matrix ratio, phenylalanine, and calcium contents were experimentally determined and simultaneously considered as input to a linear discriminant analysis (LDA). Additionally, sample evaluation was performed with a linear support vector machine (LSVM) algorithm, with a 300 variable input. The computed probabilities based on a single spectrum were only marginally different (~80% from LDA and ~87% from LSVM), both providing an unacceptable classification power for a correct sample assignment. However, the Type I and Type II assignment errors confirm that a relatively small number of independent spectra (7 spectra for Type I and 5 spectra for Type II) is necessary for a p < 0.05 error probability. This low number of spectra supports the practicality of future in vivo Raman translation for a fast and accurate ROD detection in clinical settings.

The comprehensive microscopic, spectroscopic, and in vitro voltammetric analysis presented in this work, which builds on the well-studied properties of carbon-based materials, facilitates potential ways for improvement of carbon fiber microelectrodes (CFMs) for neuroscience applications. Investigations by both, scanning electron microscopy (SEM) and confocal Raman spectroscopy, confirm a higher degree of structural ordering for the fibers exposed to carbonization temperatures. An evident correlation is also identified between the extent of structural defects observed from SEM and Raman results with the CFM electrochemical performance for dopamine detection. To improve CFM physico-chemical surface stability and increase its mechanical resistance to the induced compressive stress during anticipated in vivo tissue penetration, successful coating of the carbon fiber with boron-doped diamond (BDD) is also performed and microspectroscopically analyzed here. The absence of spectral shifts of the diamond Raman vibrational signature verifies that the growth of an unstrained BDD thin film was achieved. Although more work needs to be done to identify optimal parameter values for improved BDD deposition, this study serves as a demonstration of foundational technology for the development of more sensitive electrochemical sensors, that may have been impractical previously for clinical applications, due to limitations in either safety or performance.

With the goal of accurately detecting and quantifying the amounts of dopamine (DA) and serotonin (5-HT) in mixtures of these neurotransmitters without using any labelling, we present a detailed, comparative computational and Raman experimental study. Although discrimination between these two analytes is achievable in such mixtures for concentrations in the millimolar range, their accurate quantification remains unattainable. As shown for the first time in this work, the formation of a new composite resulting from their interactions with each other is the main reason for this lack of quantification. While this new hydrogen-bonded complex further complicates potential analyte discrimination and quantification at concentrations characteristic of physiological levels (i.e., nanomolar concentrations), it can also open new avenues for its use in drug delivery and pharmaceutical research. This remark is based not only on chemical interactions analyzed here from both theoretical and experimental approaches, but also on biological relationship, with consideration of both functional and neural proximity perspectives. Thus, this research constitutes an important contribution toward better understanding of neural processes, as well as toward possible future development of label-free biosensors.

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.

Abstract Objective . Stereotactic frame systems are the gold-standard for stereotactic surgeries, such as implantation of deep brain stimulation (DBS) devices for treatment of medically resistant neurologic and psychiatric disorders. However, frame-based systems require that the patient is awake with a stereotactic frame affixed to their head for the duration of the surgical planning and implantation of the DBS electrodes. While frameless systems are increasingly available, a reusable re-attachable frame system provides unique benefits. As such, we created a novel reusable MRI-compatible stereotactic frame system that maintains clinical accuracy through the detachment and reattachment of its stereotactic devices used for MRI-guided neuronavigation. Approach . We designed a reusable arc-centered frame system that includes MRI-compatible anchoring skull screws for detachment and re-attachment of its stereotactic devices. We validated the stability and accuracy of our system through phantom, in vivo mock-human porcine DBS-model and human cadaver testing. Main results . Phantom testing achieved a root mean square error (RMSE) of 0.94 ± 0.23 mm between the ground truth and the frame-targeted coordinates; and achieved an RMSE of 1.11 ± 0.40 mm and 1.33 ± 0.38 mm between the ground truth and the CT- and MRI-targeted coordinates, respectively. In vivo and cadaver testing achieved a combined 3D Euclidean localization error of 1.85 ± 0.36 mm ( p < 0.03) between the pre-operative MRI-guided placement and the post-operative CT-guided confirmation of the DBS electrode. Significance . Our system demonstrated consistent clinical accuracy that is comparable to conventional frame and frameless stereotactic systems. Our frame system is the first to demonstrate accurate relocation of stereotactic frame devices during in vivo MRI-guided DBS surgical procedures. As such, this reusable and re-attachable MRI-compatible system is expected to enable more complex, chronic neuromodulation experiments, and lead to a clinically available re-attachable frame that is expected to decrease patient discomfort and costs of DBS surgery.

To better understand detection and monitoring of the important neurotransmitter adenosine at physiological levels, this study combines quantum chemical density functional modeling and ultrasensitive surface-enhanced Raman spectroscopic (SERS) measurements. Combined simulation results and experimental data for an analyte concentration of about 10−11 molar indicate the presence of all known molecular forms resulting from adenosine’s complex redox-reaction. Detailed analysis presented here, besides assessing potential Raman signatures of these adenosinic forms, also sheds light on the analytic redox process and voltammetric detection. Examples of adenosine Raman fingerprints for different molecular orientations with respect to the SERS substrate are the vibrational line around 920 ± 10 cm−1 for analyte physisorption through the carbinol moiety and around 1600 ± 20 cm−1 for its fully oxidized form. However, both hydroxyl/oxygen sites and NH2/nitrogen sites contribute to molecule’s interaction with the SERS environment. Our results also reveal that contributions of partially oxidized adenosine forms and of the standard form are more likely to be detected with the first recorded voltammetric oxidation peak. The fully oxidized adenosine form contributes mostly to the second peak. Thus, this comparative theoretical–experimental investigation of adenosine’s vibrational signatures provides significant insights for advancing its detection, and for future development of opto-voltammetric biosensors.