• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.72-74
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• 1997

We have developed a 3-D image processing and display technique that include image resampling, modification of MIP, and fusion of MIP image and volumetric rendered image. This technique facilitates the visualization of the three-dimensional spatial relationship between vasculature and surrounding organs by overlapping the MIP image on the volumetric rendered image of the organ. We applied this technique to a MR brain image data to produce an MRI angiogram that is overlapped with 3-D volume rendered image of brain. MIP technique was used to visualize the vasculature of brain, and volume rendering was used to visualize the other structures of brain. The two images are fused after adjustment of contrast and brightness levels of each image in such a way that both the vasculature and brain structure are well visualized either by selecting the maximum value of each image or by assigning different color table to each image. The resultant image with this technique visualizes both the brain structure and vasculature simultaneously, allowing the physicians to inspect their relationship more easily. The presented technique will be useful for surgical planning for neurosurgery.

• Journal of Ginseng Research
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• 제48권3호
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• pp.286-297
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• 2024

Brain plasticity refers to the brain's ability to modify its structure, accompanied by its functional changes. It is influenced by learning, experiences, and dietary factors, even in later life. Accumulated researches have indicated that ginseng may protect the brain and enhance its function in pathological conditions. There is a compelling need for a more comprehensive understanding of ginseng's role in the physiological condition because many individuals without specific diseases seek to improve their health by incorporating ginseng into their routines. This review aims to deepen our understanding of how ginseng affects brain plasticity of people undergoing normal aging process. We provided a summary of studies that reported the impact of ginseng on brain plasticity and related factors in human clinical studies. Furthermore, we explored researches focused on the molecular mechanisms underpinning the influence of ginseng on brain plasticity and factors contributing to brain plasticity. Evidences indicate that ginseng has the potential to enhance brain plasticity in the context of normal aging by mediating both central and peripheral systems, thereby expecting to improve age-related declines in brain function. Moreover, given modern western diet can damage neuroplasticity in the long term, ginseng can be a beneficial supplement for better brain health.

• Communications for Statistical Applications and Methods
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• 제30권6호
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• pp.561-575
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• 2023

Brain network analysis has attracted the interest of neuroscience researchers in studying brain diseases. Magnetoencephalography (MEG) is especially proper for analyzing functional connectivity due to high temporal and spatial resolution. The application of graph theory for functional connectivity analysis has been studied widely, but research on network modeling for MEG still needs more. Temporal exponential random graph model (TERGM) considers temporal dependencies of networks. We performed the brain network analysis, including static/temporal network statistics, on two groups of epilepsy patients who removed the left (LT) or right (RT) part of the brain and healthy controls. We investigate network differences using Multiset canonical correlation analysis (MCCA) and TERGM between epilepsy patients and healthy controls (HC). The brain network of healthy controls had fewer temporal changes than patient groups. As a result of TERGM, on the simulation networks, LT and RT had less stable state than HC in the network connectivity structure. HC had a stable state of the brain network.

• 한국초등과학교육학회지:초등과학교육
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• 제33권1호
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• pp.140-161
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• 2014

The purpose of this study was to analyze 181 papers about brain-based learning appeared in domestic scientific journals from 1989 to May of 2012 and suggest application conditions in elementary science education. The results of this study summarizes as follows; First, learning activity suggested by brain-based learning study is mainly explained by working of brain function. Learning activity explained by brain-based learning study are divided into 'learning according to specialized brain function, learning according to brain function integration and learning beyond specialization and integration of hemispheres'. Second, it searched how increased knowledge of brain structure and function affects learning. Analysis from this point of view suggests that brain-based learning study affects learning in many ways especially emotion, creativity and learning motivation. Third, brain-based learning study suggests various possibilities of learning activity reflecting brain plasticity. Plasticity which is one of most important characteristics of brain supports the validity of learning activity as learning disorder treatment and explains the possibility of selective increment of brain function by leaning activity and the need of whole-brain approach to learning activity. Fourth, brain-based learning brought paradigm shifts in education field. It supports learning sophistication on the understanding of student's learning activity, guides learning method that reflects the characteristics of subject and demands reconstruction of curriculum. Fifth, there are many conditions to apply brain-based learning in elementary science education field, learning environment that fits brain-based learning, change of perspectives on teaching and learning of science educators and development of brain-based learning curriculum are needed.

• 대한의용생체공학회:의공학회지
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• 제26권3호
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• pp.129-132
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• 2005

It is one of the most important issues to determine a target brain image that gives a common coordinate system for a constructing population-based brain atlas. The purpose of this study is to provide a simple and reliable procedure that determines the target brain image among the group based on the inherent structural information of three-dimensional magnetic resonance (MR) images. It uses only 11 lines defined automatically as a feature vector representing structural variations based on the Talairach coordinate system. Average characteristic vector of the group and the difference vectors of each one from the average vector were obtained. Finally, the individual data that had the minimum difference vector was determined as the target. We determined the target brain image by both our algorithm and conventional visual inspection for 20 healthy young volunteers. Eighteen fiducial points were marked independently for each data to evaluate the similarity. Target brain image obtained by our algorithm showed the best result, and the visual inspection determined the second one. We concluded that our method could be used to determine an appropriate target brain image in constructing brain atlases such as disease-specific ones.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1233-1241
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• 2022

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.

• Journal of the Korean Statistical Society
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• 제30권2호
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• pp.179-192
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• 2001

When a statistical model has a hierarchical structure such as multilayer perceptrons in neural networks or Gaussian mixture density representation, the model includes distribution with unidentifiable parameters when the structure becomes redundant. Since the exact structure is unknown, we need to carry out statistical estimation or learning of parameters in such a model. From the geometrical point of view, distributions specified by unidentifiable parameters become a singular point in the parameter space. The problem has been remarked in many statistical models, and strange behaviors of the likelihood ratio statistics, when the null hypothesis is at a singular point, have been analyzed so far. The present paper studies asymptotic behaviors of the maximum likelihood estimator and the Bayesian predictive estimator, by using a simple cone model, and show that they are completely different from regular statistical models where the Cramer-Rao paradigm holds. At singularities, the Fisher information metric degenerates, implying that the cramer-Rao paradigm does no more hold, and that he classical model selection theory such as AIC and MDL cannot be applied. This paper is a first step to establish a new theory for analyzing the accuracy of estimation or learning at around singularities.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.224-229
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• 1993

The robot has spread remarkably, is used not only in manufacturing but also in various other fields, and is becoming more popular in everyday life. At the same time, the functional demands for all manner of robots have been diversified. Education regarding robots has been developing in the computer, mechanism, sensor and artificial intelligence fields. Technical education which integrates all of the above is necessary and in great demand. We have developed an educational robot so that it can be used in education in fields including structure, sensory and brain function and can also organically integrate those.

• 만화애니메이션 연구
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• 통권38호
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• pp.71-91
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• 2015

본 연구는 애니메이션의 전문적인 특성을 고려한 라이프 드로잉 교수법을 연구하는 과정으로 두뇌의 창작 기제를 고려한 전략을 적용한 모형과 교수방법 설계를 목적으로 한다. 최근 들어 창의성을 기반으로 하는 각 전문분야의 교육방법에 대한 대안적인 논의로 뇌 기반 학습원리를 적용한 융합적 교수법에 대한 연구결과들이 발표되고 있다. 즉, 뇌의 창의기제를 기반으로 한 융합적 교육은 미술과 드로잉 교육뿐만 아니라, 예술전반에서 적용되고 있는 것이다. 라이프 드로잉은 인체에 대한 구조적 지식을 넘어서 인지적 감각, 창의성, 그리고 동작을 통한 대상과의 소통방식을 이해한 생동감 표현법 등을 숙련할 수 있는 종합적인 교수법을 요하는 분야이다. 이에 본 연구에서는 연구의 앞선 단계에서 분석된 창의, 학습기제와 내용요소를 바탕으로 하여 라이프 드로잉 숙련을 위한 전략과 방법 그것을 정리한 교육모형 구조도를 설계하여 본다. 그 결과 이전 연구의 결과물인 뇌의 창의, 학습 기제를 기반으로 한 라이프드로잉의 능력요소와 두뇌기반 촉진요소가 유기적으로 결합되기 위해서는 5단계 인지전략단계인 뇌 활성화 준비단계, 대뇌피질 기능 활성화, 고등사고촉진단계, 고등사고단계, 통합단계를 거쳤을 때 가능하다는 결론에 도달하였다. 또한 이를 실행하기위한 전략적 방법으로는 브레인짐(brain gym), 우뇌활성화드로잉, HSP(고차인지)트레이닝으로 설계되었다. 이를 토대로 하여 설계된 교수학습모형 구조도는 이후의 연구에서 해당 회기 동안의 교수학습지도안 설계로 이어진다.

• Asian Pacific Journal of Cancer Prevention
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• 제16권3호
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• pp.971-977
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• 2015

Malignant glioblastoma multiforme (GBM) is the most malignant brain tumor and despite recent advances in diagnostics and treatment prognosis remains poor. In this retrospective study, we assessed the clinical and radiological parameters, as well as fluorescence in situ hybridization (FISH) of 1p19q deletion, in a series of cases. A total of 816 patients with GBM who received surgery and radiation between January 2010 and May 2014 were included in this study. Kaplan-Meier survival analysis and Cox regression analysis were used to find the factors independently influencing patient progression free survival (PFS) and overall survival (OS). Age at diagnosis, preoperative Karnofsky Performance Scale (KPS) score, KPS score change at 2 weeks after operation, neurological deficit symptoms, tumor resection extent, maximal tumor diameter, involvement of eloquent cortex or deep structure, involvement of brain lobe, Ki-67 and MMP9 expression level and adjuvant chemotherapy were statistically significant factors (p<0.05) for both PFS and OS in the univariate analysis. Cox proportional hazards modeling revealed that age ${\leq}50$ years, preoperative KPS score ${\geq}80$, KPS score change after operation ${\geq}0$, involvement of single frontal lobe, deep structure involvement, low Ki-67 and MMP9 expression and adjuvant chemotherapy were independent favorable factors (p<0.05) for patient clinical outcomes.