In this study, we demonstrate that Raman microscopy combined with computational analysis is a useful approach to discriminating accurately between brain tumor bio-specimens and to identifying structural changes in glioblastoma (GBM) bio-signatures after nordihydroguaiaretic acid (NDGA) administration. NDGA phenolic lignan was selected as a potential therapeutic agent because of its reported beneficial effects in alleviating and inhibiting the formation of multi-organ malignant tumors. The current analysis of NDGA’s impact on GBM human cells demonstrates a reduction in the quantity of altered protein content and of reactive oxygen species (ROS)-damaged phenylalanine; results that correlate with the ROS scavenger and anti-oxidant properties of NDGA. A novel outcome presented here is the use of phenylalanine as a biomarker for differentiating between samples and assessing drug efficacy. Treatment with a low NDGA dose shows a decline in abnormal lipid-protein metabolism, which is inferred by the formation of lipid droplets and a decrease in altered protein content. A very high dose results in cell structural and membrane damage that favors transformed protein overexpression. The information gained through this work is of substantial value for understanding NDGA’s beneficial as well as detrimental bio-effects as a potential therapeutic drug for brain cancer.

Dynamic feedback based closed-loop medical devices offer a number of advantages for treatment of heterogeneous neurological conditions. Closed-loop devices integrate a level of neurobiological feedback, which allows for real-time adjustments to be made with the overarching aim of improving treatment efficacy and minimizing risks for adverse events. One target which has not been extensively explored as a potential feedback component in closed-loop therapies is mitochondrial function. Several neurodegenerative and psychiatric disorders including Parkinson’s disease, Major Depressive disorder and Bipolar disorder have been linked to perturbations in the mitochondrial respiratory chain. This paper investigates the potential to monitor this mitochondrial function as a method of feedback for closed-loop neuromodulation treatments. A generic model of the closed-loop treatment is developed to describe the high-level functions of any system designed to control neural function based on mitochondrial response to stimulation, simplifying comparison and future meta-analysis. This model has four key functional components including: a sensor, signal manipulator, controller and effector. Each of these components are described and several potential technologies for each are investigated. While some of these candidate technologies are quite mature, there are still technological gaps remaining. The field of closed-loop medical devices is rapidly evolving, and whilst there is a lot of interest in this area, widespread adoption has not yet been achieved due to several remaining technological hurdles. However, the significant therapeutic benefits offered by this technology mean that this will be an active area for research for years to come.

Fast-scan cyclic voltammetry (FSCV) is an electrochemical technique for measuring rapid changes in the extracellular concentration of neurotransmitters within the brain. Due to its fast scan rate and large output-data size, the current analysis of the FSCV data is often conducted on a computer external to the FSCV device. Moreover, the analysis is semi-automated and requires a good understanding of the characteristics of the underlying chemistry to interpret, making it unsuitable for real-time implementation on low-resource FSCV devices. This paper presents a hardware-software co-design approach for the analysis of FSCV data. Firstly, a deep neural network (DNN) is developed to predict the concentration of a dopamine solution and identify the data recording electrode. Secondly, the DNN is pruned to decrease its computation complexity, and a custom overlay is developed to implement the pruned DNN on a low-resource FPGA-based platform. The pruned DNN attains a recognition accuracy of 97.2% with a compression ratio of 3.18. When the DNN overlay is implemented on a PYNQ-Z2 platform, it achieves the execution time of 13 ms and power consumption of 1.479 W on the entire PYNQ-Z2 board. This study demonstrates the possibility of operating the DNN for FSCV data analysis on portable FPGA-based platforms.

Objective . Stereotactic technology enables fine navigation to small structures in the human body. While current stereotactic systems facilitate accurate targeting, they are mechanically cumbersome and limited in scope. Here, we hypothesized that a stereotactic system could be developed with a reduced footprint while maintaining broad targeting capabilities in order to improve versatility in frame placement location and surgical workflow. Approach . We designed a stereotactic system around the center-of-arc principle, with mechanical properties that would enable a compact design and ample targeting and trajectory maneuverability. To examine the opportunity for a low-cost rapidly-deployable system we developed two fabrication variants, one using three dimensional (3D)-printing and the other using conventional machining. Mechanical and image-guided accuracies were tested in phantom studies using magnetic resonance imaging (MRI) and computed tomography. Using human cadaver head specimens, we assessed the system’s surgical workflow and its ability to reliably and accurately implant electrodes in deep brain stimulation (DBS) surgery. Main results . We developed a small 7.7 × 5.4 cm ² device platform that rigidly mounts to curvilinear bone and supports the attachment of surgical instrumentation. Attachment of two surgical instruments, an imaging localizer and a compact targeting device, demonstrated successful MRI-guided intervention in phantom studies with a vector error of 1.79 ± 0.41 mm. Evaluation of the 3D-printed system for DBS surgery confirmed ease of device platform attachment and instrument functionality, as well as demonstrated a surgical targeting accuracy of 1.83 ± 0.15 mm. In addition, we found the surgical time to be 78.3 ± 5.4 min for bilateral electrode implantation. Significance . We developed a light and compact stereotactic system whose accuracy is on par with those used clinically. This technology is suitable for clinical translation and its flexibility in positioning will seamlessly expand the capabilities for stereotaxy to treat a wide range of conditions, both within neurosurgery and beyond.

Fast-scan cyclic voltammetry (FSCV) is a technique for measuring phasic release of neurotransmitters with millisecond temporal resolution. The current data are captured by carbon fiber microelectrodes, and non-Faradaic current is subtracted from the background current to extract the Faradaic redox current through a background subtraction algorithm. FSCV is able to measure neurotransmitter concentrations in vivo down to the nanomolar scale, making it a very robust and useful technique for probing neurotransmitter release dynamics and communication across neural networks. In this study, we describe a technique that can further lower the limit of detection of FSCV. By taking advantage of a “waveform steering” technique and by amplifying only the oxidation peak of dopamine to reduce noise fluctuations, we demonstrate the ability to measure dopamine concentrations down to 0.17 nM. Waveform steering is a technique to dynamically alter the input waveform to ensure that the background current remains stable over time. Specifically, the region of the input waveform in the vicinity of the dopamine oxidation potential (∼0.6 V) is kept flat. Thus, amplification of the input waveform will amplify only the Faradaic current, lowering the existing limit of detection for dopamine from 5.48 to 0.17 nM, a 32-fold reduction, and for serotonin, it lowers the limit of detection from 57.3 to 1.46 nM, a 39-fold reduction compared to conventional FSCV. Finally, the applicability of steered FSCV to in vivo dopamine detection was also demonstrated in this study. In conclusion, steered FSCV might be used as a neurochemical monitoring tool for enhancing detection sensitivity.