• Sleep Medicine and Psychophysiology
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• v.25 no.2
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• pp.82-91
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• 2018

Objectives: Synchronous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been used to explore sleep stage dependent functional brain networks. Despite a growing number of sleep studies using EEG-fMRI, few studies have conducted network analysis on whole night sleep due to difficulty in data acquisition, artifacts, and sleep management within the MRI scanner. Methods: In order to perform network analysis for whole night sleep, we proposed experimental procedures and data processing techniques for EEG-fMRI. We acquired 6-7 hours of EEG-fMRI data per participant and conducted signal processing to reduce artifacts in both EEG and fMRI. We then generated a functional brain atlas with 68 brain regions using independent component analysis of sleep fMRI data. Using this functional atlas, we constructed sleep level dependent functional brain networks. Results: When we evaluated functional connectivity distribution, sleep showed significantly reduced functional connectivity for the whole brain compared to that during wakefulness. REM sleep showed statistically different connectivity patterns compared to non-REM sleep in sleep-related subcortical brain circuits. Conclusion: This study suggests the feasibility of exploring functional brain networks using sleep EEG-fMRI for whole night sleep via appropriate experimental procedures and signal processing techniques for fMRI and EEG.

• Investigative Magnetic Resonance Imaging
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• v.15 no.2
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• pp.110-122
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• 2011

Purpose : The purpose of this study is to establish the method generating human brain anatomical connectivity from Korean children and evaluating the network topological properties using small-world network analysis. Materials and Methods : Using diffusion tensor images (DTI) and parcellation maps of structural MRIs acquired from twelve healthy Korean children, we generated a brain structural connectivity matrix for individual. We applied one sample t-test to the connectivity maps to derive a representative anatomical connectivity for the group. By spatially normalizing the white matter bundles of participants into a template standard space, we obtained the anatomical brain network model. Network properties including clustering coefficient, characteristic path length, and global/local efficiency were also calculated. Results : We found that the structural connectivity of Korean children group preserves the small-world properties. The anatomical connectivity map obtained in this study showed that children group had higher intra-hemispheric connectivity than inter-hemispheric connectivity. We also observed that the neural connectivity of the group is high between brain stem and motorsensory areas. Conclusion : We suggested a method to examine the anatomical brain network of Korean children group. The proposed method can be used to evaluate the efficiency of anatomical brain networks in people with disease.

• 건강소식
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• v.30 no.3 s.328
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• pp.32-33
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• 2006

아기는 약 200억 개의 뇌세포를 가지고 바깥 세상에 태어난다. 게다가 더 놀라운 사실은 이 200억 개의 뇌세포가 또다시 각각 2만 개 이상의 다른 가지들로 연결되면서 신경망을 형성해 나간다는 것이다. 이 신경 전달망이 바로 아이 인생의 절대적인 영향을 끼치는 잠재력의 근원이다. 다시 말해 똑똑한 아이일수록 이 신경망들이 숫자도 훨씬 많고 복잡하게 얽혀있다는 것이다. 그러나 이렇게 형성된 뇌세포들은 적절한 연락이 오지 않으면 스스로 쓸모없다고 판단하여 임신 8개월 전에 40~75% 가량이 죽어버린다.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.37-44
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• 2021

Automatic classification of brain MRI images play an important role in early diagnosis of brain tumors. In this work, we present a deep learning-based brain tumor classification model in MRI images using ensemble of deep features. In our proposed framework, three different deep features from brain MR image are extracted using three different pre-trained models. After that, the extracted deep features are fed to the classification module. In the classification module, the three different deep features are first fed into the fully-connected layers individually to reduce the dimension of the features. After that, the output features from the fully-connected layers are concatenated and fed into the fully-connected layer to predict the final output. To evaluate our proposed model, we use openly accessible brain MRI dataset from web. Experimental results show that our proposed model outperforms other machine learning-based models.

• Computational Structural Engineering
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• v.9 no.2
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• pp.93-101
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• 1996

An artificial neural network is a computational model that mimics the biological system of the brain and it consists of a number of interconnected processing units where it can reasonably infer by them. Because the neural network is particularly useful for evaluating systems with a multitude of nonlinear variables, it can be used in experimental results predictions, in structural planning and in optimum design of structures. This paper describes the basic theory related to the neural networks and discusses the applicability of neural networks to predict the ultimate shear capacity of the precast concrete vertical joints by comparing the neural networks with a conventional method such as regression.

• Therapeutic Science for Rehabilitation
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• v.6 no.1
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• pp.11-23
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• 2017

Spatial neglect is a neurological disorder following stroke, a lesion that usually affects the right hemisphere, fail to process or attention on the contralateral side of body and space. Functional neuroimaging studies report that spatial neglect is associated with lesions of large middle cerebral artery, perisylvian network and attention network. Spatial neglect is associated with a poor outcome. For optimal diagnosis and intervention, Types and theories of spatial neglect should be considered, in addition to clinical assessment with the conventional test and functional test. The treatment for spatial neglect could be consist of top-down approaches and bottom-up approaches. Recent trends in rehabilitation intervention for spatial neglect have reported prism adaptation.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2003.09a
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• pp.81-81
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• 2003

목적 : 현재, 많은 병원이 방사선과 의료영상정보를 기존의 필름형태로 판독하고, 진료하는 방식에서 PACS 를 도입하여 디지털 형태로 영상을 전송, 저장, 검색, 판독하는 환경으로 변화하고 있다. 한편, PACS 가 가지는 가장 큰 제한점은 휴대성의 결핍이다. 본 연구는 이동형 장치가 가지는 호스트의 이동성 및 휴대성의 장점들을 살리면서, 무선 채널 용량의 한계, 무선 링크 사용이라는 제약점들을 감안하여 의료영상을 JPEG2000 영상압축 방식으로 부호화한 후 무선 환경을 고려한 전송 패킷의 크기를 결정하고자 하였으며, 무선 통신 중 발생되는 패킷 손실에 대응하기 위한 자동 오류 수정 기능도 함께 구현하고자하였다. 방법 : Window 2000 운영체계에서 의료영상을 로드하고, 데이터베이스화하며, 저장하고, 다른 네트워크와 접속, 제어가 가능한 PC급 서버를 구축하였다. 영상데이터는 무선망을 통해 전송하기 때문에 가장 높은 압축비율을 지원하면서 에너지 밀도가 높은 JPEG2000 알고리즘을 사용하여 영상을 압축하였다. 또한, 무선망 사용으로 인한 패킷 손실에 대비하여, 영상을 JPEG2000 방식으로 부호화한 후 각 블록단위로 전송하였다. 결과 : PDA에서 JPEG2000 영상을 복호화 하는데 걸리는 시간은 256$\times$256 크기의 MR 뇌영상의 경우 바로 확인할 수 있었지만, 800$\times$790 크기의 CR 흉부 영상의 경우 약 5 초 정도의 시간이 걸렸다. CDMA 1X(Code Division Multiple Access 1st Generation) 모듈을 사용하여 영상을 전송하는 경우, 256 byte/see 정도에서는 안정된 전송 결과를 보여주었고, 1 Kbyte/see 정도의 전송의 경우 중간 중간에 패킷이 손실되는 결과를 관찰할 수 있었다. 반면 무선 랜의 경우 이보다 더 큰 패킷을 전송하더라도 문제점은 발견되지 않았다. 결론 : 현재의 PACS는 유선과 무선사이의 인터페이스의 부재로 인해 유무선 연동이 되지 못하고 있다. 따라서 이동형 JPEG2000 영상 뷰어는 PACS가 가지는 문제점인 휴대성을 보완하기 위하여 개발되었다. 또한 무선망이 가지는 데이터 손실에 대하여서도 허용할 수 있는 범위에서 재전송을 가능하게 함으로서 약한 연결성을 보완하였다. 본 JPEG2000 영상 뷰어 시스템은 기존 유선상의 PACS와 이동형 장치간에 유기적인 인터페이스 역할을 하리라 기대된다.

• Journal of Biomedical Engineering Research
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• v.35 no.3
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• pp.68-74
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• 2014

Recent research on mild cognitive impairment (MCI) has shown that cognitive and memory decline in this disease is accompanied by disruptive changes in the brain functional network. However, there have been no graph-theoretical studies using $^{11}C$-PIB PET data of the Alzheimer's Disease or mild cognitive impairment. In this study, we acquired $^{18}F$-FDG PET and $^{11}C$-PIB PET images of twenty-four normal aging control participants and thirty individuals with MCI from ADNI (Alzheimer's Disease Neuroimaging Initiative) database. Brain networks were constructed by thresholding binary correlation matrices using graph theoretical approaches. Both normal control and MCI group showed small-world property in $^{11}C$-PIB PET images as well as $^{18}F$-FDG PET images. $^{11}C$-PIB PET images showed significant difference between NC (normal control) and MCI over large range of sparsity values. This result will enable us to further analyze the brain using established graph-theoretical approaches for $^{11}C$-PIB PET images.

• Korean Journal of Cognitive Science
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• v.29 no.3
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• pp.161-192
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• 2018

Retrieving temporal information of encoded events is one of the core control processes in episodic memory. Despite much prior neuroimaging research on episodic retrieval, little is known about how large-scale connectivity patterns are involved in the retrieval of sequentially organized episodes. Task-related functional connectivity multivariate pattern analysis was used to distinguish the different sequential retrieval. In this study, participants performed temporal episodic memory tasks in which they were required to retrieve the encoded items in either the forward or backward direction. While separately parsed local networks did not yield substantial efficiency in classification performance, the large-scale patterns of interactivity across the cortical and sub-cortical brain regions implicated in both the cognitive control of memory and goal-directed cognitive processes encompassing lateral and medial prefrontal regions, inferior parietal lobules, middle temporal gyrus, and caudate yielded high discriminative power in classification of temporal retrieval processes. These findings demonstrate that mnemonic control processes across cortical and subcortical regions are recruited to re-experience temporally-linked series of memoranda in episodic memory and are mirrored in the qualitatively distinct global network patterns of functional connectivity.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.359-373
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• 2015

Cognitive reserve(CR) is the ability to optimize or maximize performance through complementary brain networks. CR is relevant to normal aging in cognitive-linguistic abilities. There are few domestic systematic reviews or meta-analyses that analyze the relationships between multiple CR and cognitive-linguistic domains in healthy older people. This meta-analysis included 32 studies published since 2000. In result, education level topped the list, followed by the occupation, cognitively stimulating activities, and the multilingualism. Most studies were related to memory, global cognition, and language. CR had a modest positive association with cognitive-linguistic performance. Multiple domains including memory and language also showed the significant correlations across most measures of CR. This study provides evidence-based information to support cognitive-linguistic ability in normal aging.