https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/60 Fri, 24 Jul 2026 08:07:49 GMT 2026-07-24T08:07:49Z 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 https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/141467 Title: Cocaine-Induced Changes in Tonic Dopamine Concentrations Measured Using Multiple-Cyclic Square Wave Voltammetry in vivo Authors: Yuen, Jason; Goyal, Abhinav; Rusheen, Aaron E.; Kouzani, Abbas Z.; Berk, Michael; Kim, Jee Hyun; Tye, Susannah J.; Blaha, Charles D.; Bennet, Kevin E.; Jang, Dong-Pyo; Lee, Kendall H.; Shin, Hojin; Oh, Yoonbae Abstract: For over 40 years, in vivo microdialysis techniques have been at the forefront in measuring the effects of illicit substances on brain tonic extracellular levels of dopamine that underlie many aspects of drug addiction. However, the size of microdialysis probes and sampling rate may limit this technique's ability to provide an accurate assessment of drug effects in microneural environments. A novel electrochemical method known as multiple-cyclic square wave voltammetry (M-CSWV), was recently developed to measure second-to-second changes in tonic dopamine levels at microelectrodes, providing spatiotemporal resolution superior to microdialysis. Here, we utilized M-CSWV and fast-scan cyclic voltammetry (FSCV) to measure changes in tonic or phasic dopamine release in the nucleus accumbens core (NAcc) after acute cocaine administration. Carbon-fiber microelectrodes (CFM) and stimulating electrodes were implanted into the NAcc and medial forebrain bundle (MFB) of urethane anesthetized (1.5 g/kg i.p.) Sprague-Dawley rats, respectively. Using FSCV, depths of each electrode were optimized by determining maximal MFB electrical stimulation-evoked phasic dopamine release. Changes in phasic responses were measured after a single dose of intravenous saline or cocaine hydrochloride (3 mg/kg; n = 4). In a separate group, changes in tonic dopamine levels were measured using M-CSWV after intravenous saline and after cocaine hydrochloride (3 mg/kg; n = 5). Both the phasic and tonic dopamine responses in the NAcc were augmented by the injection of cocaine compared to saline control. The phasic and tonic levels changed by approximately x2.4 and x1.9, respectively. These increases were largely consistent with previous studies using FSCV and microdialysis. However, the minimal disruption/disturbance of neuronal tissue by the CFM may explain why the baseline tonic dopamine values (134 +/- 32 nM) measured by M-CSWV were found to be 10-fold higher when compared to conventional microdialysis. In this study, we demonstrated phasic dopamine dynamics in the NAcc with acute cocaine administration. M-CSWV was able to record rapid changes in tonic levels of dopamine, which cannot be achieved with other current voltammetric techniques. Taken together, M-CSWV has the potential to provide an unprecedented level of physiologic insight into dopamine signaling, both in vitro and in vivo, which will significantly enhance our understanding of neurochemical mechanisms underlying psychiatric conditions. Thu, 01 Jul 2021 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/141467 2021-07-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/141466 Title: Redefining differential roles of MAO-A in dopamine degradation and MAO-B in tonic GABA synthesis Authors: Cho, Hyun-U; Kim, Sunpil; Sim, Jeongeun; Yang, Seulkee; An, Heeyoung; Nam, Min-Ho; Jang, Dong-Pyo; Lee, C. Justin Abstract: Monoamine oxidase (MAO) is believed to mediate the degradation of monoamine neurotransmitters, including dopamine, in the brain. Between the two types of MAO, MAO-B has been believed to be involved in dopamine degradation, which supports the idea that the therapeutic efficacy of MAO-B inhibitors in Parkinson's disease can be attributed to an increase in extracellular dopamine concentration. However, this belief has been controversial. Here, by utilizing in vivo phasic and basal electrochemical monitoring of extracellular dopamine with fast-scan cyclic voltammetry and multiple-cyclic square wave voltammetry and ex vivo fluorescence imaging of dopamine with GRAB(DA2m), we demonstrate that MAO-A, but not MAO-B, mainly contributes to striatal dopamine degradation. In contrast, our whole-cell patch-clamp results demonstrated that MAO-B, but not MAO-A, was responsible for astrocytic GABA-mediated tonic inhibitory currents in the rat striatum. We conclude that, in contrast to the traditional belief, MAO-A and MAO-B have profoundly different roles: MAO-A regulates dopamine levels, whereas MAO-B controls tonic GABA levels. Parkinson's disease: rewriting the roles of a critical enzyme The inhibition of two forms of an enzyme that modulate key processes in the brain has different benefits for patients with Parkinson's disease than previously thought. Monoamine oxidase (MAO) is present in the brain as MAO-A and MAO-B, both of which were thought to be involved in dopamine degradation. MAO inhibitors are used to limit dopamine degradation in Parkinson's disease and depression, improving symptoms by increasing levels of usable dopamine. In experiments on rats, Hyun-U Cho at Hanyang University, Seoul, South Korea, and coworkers have shown that MAO-A, but not MAO-B, affects dopamine degradation. The team found that MAO-B instead mediates the synthesis of a key neurotransmitter, GABA, the upregulation of which is linked to Parkinson's motor symptoms. Taking MAO-B inhibitors may be addressing these symptoms, explaining why patients show improvement. Thu, 01 Jul 2021 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/141466 2021-07-01T00:00:00Z