An implantable microelectrode array for dopamine and electrophysiological recordings in response to L-dopa therapy for Parkinson's disease

International Consensus Based Review and Recommendations for Minimum Reporting Standards in Research on Transcutaneous Vagus Nerve Stimulation (Version 2020)

Given its non-invasive nature, there is increasing interest in the use of transcutaneous vagus nerve stimulation (tVNS) across basic, translational and clinical research. Contemporaneously, tVNS can be achieved by stimulating either the auricular branch or the cervical bundle of the vagus nerve, referred to as transcutaneous auricular vagus nerve stimulation(VNS) and transcutaneous cervical VNS, respectively. In order to advance the field in a systematic manner, studies using these technologies need to adequately report sufficient methodological detail to enable comparison of results between studies, replication of studies, as well as enhancing study participant safety. We systematically reviewed the existing tVNS literature to evaluate current reporting practices. Based on this review, and consensus among participating authors, we propose a set of minimal reporting items to guide future tVNS studies. The suggested items address specific technical aspects of the device and stimulation parameters. We also cover general recommendations including inclusion and exclusion criteria for participants, outcome parameters and the detailed reporting of side effects. Furthermore, we review strategies used to identify the optimal stimulation parameters for a given research setting and summarize ongoing developments in animal research with potential implications for the application of tVNS in humans. Finally, we discuss the potential of tVNS in future research as well as the associated challenges across several disciplines in research and clinical practice.

Scalable Nanostructured Carbon Electrode Arrays for Enhanced Dopamine Detection

Dopamine is a neurotransmitter that modulates arousal and motivation in humans and animals. It plays a central role in the brain "reward" system. Its dysregulation is involved in several debilitating disorders such as addiction, depression, Parkinson's disease, and schizophrenia. Dopamine neurotransmission and its reuptake in extracellular space takes place with millisecond temporal and nanometer spatial resolution. Novel nanoscale electrodes are needed with superior sensitivity and improved spatial resolution to gain an improved understanding of dopamine dysregulation. We report on a scalable fabrication of dopamine neurochemical probes of a nanostructured glassy carbon that is smaller than any existing dopamine sensor and arrays of more than 6000 nanorod probes. We also report on the electrochemical dopamine sensing of the glassy carbon nanorod electrode. Compared with a carbon fiber, the nanostructured glassy carbon nanorods provide about 2× higher sensitivity per unit area for dopamine sensing and more than 5× higher signal per unit area at low concentration of dopamine, with comparable LOD and time response. These glassy carbon nanorods were fabricated by pyrolysis of a lithographically defined polymeric nanostructure with an industry standard semiconductor fabrication infrastructure. The scalable fabrication strategy offers the potential to integrate these nanoscale carbon rods with an integrated circuit control system and with other complementary metal oxide semiconductor (CMOS) compatible sensors.

Joint regression and classification via relational regularization for Parkinson's disease diagnosis

It is known that the symptoms of Parkinson’s disease (PD) progress successively, early and accurate diagnosis of the disease is of great importance, which slows the disease deterioration further and alleviates mental and physical suffering. In this paper, we propose a joint regression and classification scheme for PD diagnosis using baseline multi-modal neuroimaging data. Specifically, we devise a new feature selection method via relational learning in a unified multi-task feature selection model. Three kinds of relationships (e.g., relationships among features, responses, and subjects) are integrated to represent the similarities among features, responses, and subjects. Our proposed method exploits five regression variables (depression, sleep, olfaction, cognition scores and a clinical label) to jointly select the most discriminative features for clinical scores prediction and class label identification. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method on the Parkinson’s Progression Markers Initiative (PPMI) dataset. Our experimental results demonstrate that multi-modal data can effectively enhance the performance in class label identification compared with single modal data. Our proposed method can greatly improve the performance in clinical scores prediction and outperforms the state-of-art methods as well. The identified brain regions can be recognized for further medical analysis and diagnosis.