https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/59 Sat, 04 Jul 2026 03:53:04 GMT 2026-07-04T03:53:04Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/173098 Title: Software for near-real-time voltammetric tracking of tonic neurotransmitter levels in vivo Authors: Goyal, Abhinav; Hwang, Sangmun; Rusheen, Aaron E.; Blaha, Charles D.; Bennet, Kevin E.; Lee, Kendall H.; Jang, Dong Pyo; Oh, Yoonbae; Shin, Hojin Abstract: Tonic extracellular neurotransmitter concentrations are important modulators of central network homeostasis. Disruptions in these tonic levels are thought to play a role in neurologic and psychiatric disease. Therefore, ways to improve their quantification are actively being investigated. Previously published voltammetric software packages have implemented FSCV, which is not capable of measuring tonic concentrations of neurotransmitters in vivo. In this paper, custom software was developed for near-real-time tracking (scans every 10 s) of neurotransmitters’ tonic concentrations with high sensitivity and spatiotemporal resolution both in vitro and in vivo using cyclic voltammetry combined with dynamic background subtraction (M-CSWV and FSCAV). This software was designed with flexibility, speed, and user-friendliness in mind. This software enables near-real-time measurement by reducing data analysis time through an optimized modeling algorithm, and efficient memory handling makes long-term measurement possible. The software permits customization of the cyclic voltammetric waveform shape, enabling experiments to detect a specific analyte of interest. Finally, flexibility considerations allow the user to alter the fitting parameters, filtering characteristics, and size and shape of the analyte kernel, based on data obtained live during the experiment to obtain accurate measurements as experimental conditions change. Herein, the design and advantages of this near-real-time voltammetric software are described, and its use is demonstrated in in vivo experiments. Thu, 01 Sep 2022 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/173098 2022-09-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140145 Title: Enhanced Dopamine Sensitivity Using Steered Fast-Scan Cyclic Voltammetry Authors: Kang, Yumin; Goyal, Abhinav; Hwang, Sangmun; Park, Cheonho; Cho, Hyun U.; Shin, Hojin; Park, Jinsick; Bennet, Kevin E.; Lee, Kendall H.; Oh, Yoonbae; Jang, Dong Pyo Abstract: 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 steeringtechnique 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. Wed, 01 Dec 2021 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140145 2021-12-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140147 Title: Non-human primate epidural ECoG analysis using explainable deep learning technology Authors: Choi, Hoseok; Lim, Seokbeen; Min, Kyeongran; Ahn, Kyoung-ha; Lee, Kyoung-Min; Jang, Dong Pyo Abstract: Objective. With the development in the field of neural networks, explainable AI (XAI), is being studied to ensure that artificial intelligence models can be explained. There are some attempts to apply neural networks to neuroscientific studies to explain neurophysiological information with high machine learning performances. However, most of those studies have simply visualized features extracted from XAI and seem to lack an active neuroscientific interpretation of those features. In this study, we have tried to actively explain the high-dimensional learning features contained in the neurophysiological information extracted from XAI, compared with the previously reported neuroscientific results. Approach. We designed a deep neural network classifier using 3D information (3D DNN) and a 3D class activation map (3D CAM) to visualize high-dimensional classification features. We used those tools to classify monkey electrocorticogram (ECoG) data obtained from the unimanual and bimanual movement experiment. Main results. The 3D DNN showed better classification accuracy than other machine learning techniques, such as 2D DNN. Unexpectedly, the activation weight in the 3D CAM analysis was high in the ipsilateral motor and somatosensory cortex regions, whereas the gamma-band power was activated in the contralateral areas during unimanual movement, which suggests that the brain signal acquired from the motor cortex contains information about both contralateral movement and ipsilateral movement. Moreover, the hand-movement classification system used critical temporal information at movement onset and offset when classifying bimanual movements. Significance. As far as we know, this is the first study to use high-dimensional neurophysiological information (spatial, spectral, and temporal) with the deep learning method, reconstruct those features, and explain how the neural network works. We expect that our methods can be widely applied and used in neuroscience and electrophysiology research from the point of view of the explainability of XAI as well as its performance. Wed, 01 Dec 2021 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140147 2021-12-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140845 Title: The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance Authors: Jung, Wongyu; Lim, Seokbeen; Kwak, Youngjong; Sim, Jeongeun; Park, Jinsick; Jang, Dongpyo Abstract: Numerous brain-machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural feature, various areas of the brain, such as the motor cortex or parietal cortex, have been selected as BMI target brain regions. However, although the selection of target frequency and brain region appears to be crucial in obtaining optimal BMI performance, the direct comparison of BMI learning performance as it relates to various brain regions and frequency bands has not been examined in detail. In this study, ECoG-based BMI learning performances were compared using alpha, beta, and gamma bands, respectively, in a single rodent model. Brain area dependence of learning performance was also evaluated in the frontal cortex, the motor cortex, and the parietal cortex. The findings indicated that BMI learning performance was best in the case of the gamma frequency band and worst in the alpha band (one-way ANOVA, F = 4.41, p < 0.05). In brain area dependence experiments, better BMI learning performance appears to be shown in the primary motor cortex (one-way ANOVA, F = 4.36, p < 0.05). In the frontal cortex, two out of four animals failed to learn the feeding tube control even after a maximum of 10 sessions. In conclusion, the findings reported in this study suggest that the selection of target frequency and brain region should be carefully considered when planning BMI protocols and for performing optimized BMI. Fri, 01 Oct 2021 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140845 2021-10-01T00:00:00Z