• Molecules and Cells
• /
• v.43 no.9
• /
• pp.821-830
• /
• 2020

Altered dendritic morphology is frequently observed in various neurological disorders including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), but the cellular and molecular basis underlying these pathogenic dendritic abnormalities remains largely unclear. In this study, we investigated dendritic morphological defects caused by dipeptide repeat protein (DPR) toxicity associated with G4C2 expansion mutation of C9orf72 (the leading genetic cause of ALS and FTD) in Drosophila neurons and characterized the underlying pathogenic mechanisms. Among the five DPRs produced by repeat-associated non-ATG translation of G4C2 repeats, we found that arginine-rich DPRs (PR and GR) led to the most significant reduction in dendritic branches and plasma membrane (PM) supply in Class IV dendritic arborization (C4 da) neurons. Furthermore, expression of PR and GR reduced the number of Golgi outposts (GOPs) in dendrites. In Drosophila brains, expression of PR, but not GR, led to a significant reduction in the mRNA level of CrebA, a transcription factor regulating the formation of GOPs. Overexpressing CrebA in PR-expressing C4 da neurons mitigated PM supply defects and restored the number of GOPs, but the number of dendritic branches remained unchanged, suggesting that other molecules besides CrebA may be involved in dendritic branching. Taken together, our results provide valuable insight into the understanding of dendritic pathology associated with C9-ALS/FTD.

• Journal of Internet of Things and Convergence
• /
• v.9 no.6
• /
• pp.17-22
• /
• 2023

Brain-Machine Interfaces(BMI) are interfaces that control machines by decoding brainwaves, which are electrical signals generated from neural activities. Although BMIs can be applied in various fields, their widespread usage is hindered by the low portability of the hardware required for brainwave measurement and decoding. To address this issue, previous research proposed a brain-machine interface system based on the Internet of Things (IoT) using cloud computing. In this study, we developed and tested an application that uses brainwaves to control the Pong game, demonstrating the real-time usability of the system. The results showed that users of the proposed BMI achieved scores comparable to optimal control artificial intelligence in real-time Pong game matches. Thus, this research suggests that IoT-based brain-machine interfaces can be utilized in a variety of real-time applications in everyday life.

• Journal of the Korea Safety Management & Science
• /
• v.14 no.1
• /
• pp.1-6
• /
• 2012

A number of Brain-Computer Interface (BCI) studies have been performed to assess the cognitive status through EEG signal. However, there are a few studies trying to prevent user from unexpected safety-accident in BCI study. The EEGs were collected from 19 subjects who participated in two experiments (rest & event-related potential measurement). There was significant difference in EEG changes of both spontaneous and event-related potential. Beta power and P300 latency may be useful as a biomarker for prevention of response to safety-accident.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.1
• /
• pp.137-142
• /
• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.12
• /
• pp.2575-2581
• /
• 2011

Recently, systematic research on the brain has been conducted and BCI(Brain -Computer Interface) technology applying electroencephalogram has been actively researched. Especially, serious game technology using BCI device has been the subject of interest. This paper develops a "twosome collaboration dance game," which is a serious game that takes advantage of NeuroSky's SDK(System Development Kit) and helps developing the spirit of team work and sociality based on attention and meditation, unlike existing single player games. We expect that this game will help to visualize brain functions of people and to cure ADHD children and the elderly people with MCI(Mild Cognitive Disorder). It is also expected to play a role of social catalyst to the game culture of the adolescent.

• Research in Community and Public Health Nursing
• /
• v.22 no.4
• /
• pp.389-398
• /
• 2011

Purpose: This study of this study was to identify factors influencing the burden of main family caregivers who take care of elderly patients with brain and spinal diseases. Methods: This was conducted as descriptive research and data were collected from 255 main family caregivers who were taking care of elderly patients with brain and spinal diseases from 4 hospitals in Daegu and Gyeongbuk Province. Stepwise-multiple regression was used to identify the influencing factors of burden felt. Results: As the score of burden felt by the main family, economic, social, physical, interdependent and emotional burdens were high in order. Factors influencing burden felt by main family care givers taking care of elderly patients with brain and spinal diseases were changed relation with patient after hospitalization, daily life ability, marital status, education and family caregiver's personality (explanatory power of 24.6%). Family caregivers felt a heavier burden when their relation with the patient was changed negatively or when the patient's activity of daily living was low. Conclusion: Based on these results, we need to develop coping measures and interventional programs for reducing the burden felt by the main family caregivers of elderly patients with brain and spinal diseases.

• Electronics and Telecommunications Trends
• /
• v.36 no.3
• /
• pp.106-118
• /
• 2021

The field of brain science (or neuroscience in a broader sense) has inspired researchers in artificial intelligence (AI) for a long time. The outcomes of neuroscience such as Hebb's rule had profound effects on the early AI models, and the models have developed to become the current state-of-the-art artificial neural networks. However, the recent progress in AI led by deep learning architectures is mainly due to elaborate mathematical methods and the rapid growth of computing power rather than neuroscientific inspiration. Meanwhile, major limitations such as opacity, lack of common sense, narrowness, and brittleness have not been thoroughly resolved. To address those problems, many AI researchers turn their attention to neuroscience to get insights and inspirations again. Biologically plausible neural networks, spiking neural networks, and connectome-based networks exemplify such neuroscience-inspired approaches. In addition, the more recent field of brain network analysis is unveiling complex brain mechanisms by handling the brain as dynamic graph models. We argue that the progress toward the human-level AI, which is the goal of AI, can be accelerated by leveraging the novel findings of the human brain network.

• Journal of Acupuncture Research
• /
• v.18 no.4
• /
• pp.188-200
• /
• 2001

Background and Objetive : Acupuncture is a valuable method of oriental medicine with broad application in many disease. It is based on the experiences of traditional oriental medicine as well as on experimentally proven biological (biochemical and neurophysiological) effects. Acupuncture theory has been explained by the meridian system that is thought to be linked with particular organs. However, in western medicine it is held that many disorders are either controlled or affected by the brain. Material and Method : In order to review the studies concerned with the mechanism related brain, we have referred to the Pubmed site and the Journal of Korean acupuncture and moxibustion Result and Conculsion : Among the 12 studies in the Journal of Korean acupuncture and moxibustion, 8 papers related neurotransmitters were done by experimental study, 4 papers related brain mapping were done by clinical study. Among the 8 studies related brain mapping in the Pubmed site, 6 clinical studies using functional magnetic resonance imaging(fMRI) were done and I clinical study using single-photon emission computed tomography(SPECT) was done, I paper was review article. By the above result, it would be needed further research on the acupuncture mechanism related brain using SPECT, fMRI, positron emission tomography(PET) etc.

• Journal of Biomedical Engineering Research
• /
• v.26 no.1
• /
• pp.9-16
• /
• 2005

One of the most important roles of a brain atlas is providing a spatial reference system in which multiple images can be interpreted in a consistent way. The brain atlase based on Western populations such as the International Consortium for Brain Mapping's 452 T-1 Weighted Average Atlas was widely used; however, they may not be the optimal choice for use with brain images from other ethnic groups, because structural differences between occidental and oriental brains have been reported. Therefore, in this study, we created an average brain atlas from 100 healthy Koreans (100 cases (M/F=53/47), 39.0±17.0 years). The purpose of this study was to make a Korean average-brain atlas and to measure its differences from a widely accepted average brain atlas built on an occidental population. The average brain atlas for Koreans was developed using widely accepted tools and procedures. The comparison between the Korean and occidental averages was performed using tissue probability maps and a registration tool, and it was shown that the global pattern of differences between the two average brains found in this work agreed with previously reported differences: Korean brains are wider and shorter in size, and smaller in volume, yet no hemispheric volume asymmetry was found.

• Journal of Biomedical Engineering Research
• /
• v.42 no.6
• /
• pp.268-276
• /
• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.