• 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.

• Korean Journal of Radiology
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• 제24권3호
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• pp.247-258
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• 2023

Objective: To localize the neuroanatomical substrate of rapid eye movement sleep behavior disorder (RBD) and to investigate the neuroanatomical locational relationship between RBD and α-synucleinopathy neurodegenerative diseases. Materials and Methods: Using a systematic PubMed search, we identified 19 patients with lesions in different brain regions that caused RBD. First, lesion network mapping was applied to confirm whether the lesion locations causing RBD corresponded to a common brain network. Second, the literature-based RBD lesion network map was validated using neuroimaging findings and locations of brain pathologies at post-mortem in patients with idiopathic RBD (iRBD) who were identified by independent systematic literature search using PubMed. Finally, we assessed the locational relationship between the sites of pathological alterations at the preclinical stage in α-synucleinopathy neurodegenerative diseases and the brain network for RBD. Results: The lesion network mapping showed lesions causing RBD to be localized to a common brain network defined by connectivity to the pons (including the locus coeruleus, dorsal raphe nucleus, central superior nucleus, and ventrolateral periaqueductal gray), regardless of the lesion location. The positive regions in the pons were replicated by the neuroimaging findings in an independent group of patients with iRBD and it coincided with the reported pathological alterations at post-mortem in patients with iRBD. Furthermore, all brain pathological sites at preclinical stages (Braak stages 1-2) in Parkinson's disease (PD) and at brainstem Lewy body disease in dementia with Lewy bodies (DLB) were involved in the brain network identified for RBD. Conclusion: The brain network defined by connectivity to positive pons regions might be the regulatory network loop inducing RBD in humans. In addition, our results suggested that the underlying cause of high phenoconversion rate from iRBD to neurodegenerative α-synucleinopathy might be pathological changes in the preclinical stage of α-synucleinopathy located at the regulatory network loop of RBD.

• 한국전자통신학회논문지
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• 제11권9호
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• pp.917-922
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• 2016

사람들의 건강상태를 모니터링하기 위해 뇌파신호에 관련된 많은 연구가 진행되고 있다. 특히, 병원에 상주하는 환자들은 뇌경색, 간질 등과 같은 위급상황에 대비하여 모니터링할 필요가 있다. 뇌파 네트워크 서비스의 안정성을 요구하는 효율적인 네트워크 토폴로지가 필요하며 본 실험을 위해 OPNet 시뮬레이터를 활용하였다. 따라서, 환자들의 뇌파는 네트워크에 있는 센서장치로부터 읽어들인다. 네트워크의 성능을 비교하기위해 두 가지의 센서 네트워크 토폴로지를 제안하고 시뮬레이션하였다. 하나는 지역화된 네트워크이고 다른 하나는 비지역화된 네트워크이다. 옵넷시뮬레이터를 이용하여 시뮬레이션을 수행하였다.

• Applied Microscopy
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• 제46권4호
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• pp.171-175
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• 2016

A distinctive neuronal network in the brain is believed to make us unique individuals. Electron microscopy is a valuable tool for examining ultrastructural characteristics of neurons, synapses, and subcellular organelles. A recent technological breakthrough in volume electron microscopy allows large-scale circuit reconstruction of the nervous system with unprecedented detail. Serial-section electron microscopy-previously the domain of specialists-became automated with the advent of innovative systems such as the focused ion beam and serial block-face scanning electron microscopes and the automated tape-collecting ultramicrotome. Further advances in microscopic design and instrumentation are also available, which allow the reconstruction of unprecedentedly large volumes of brain tissue at high speed. The recent introduction of correlative light and electron microscopy will help to identify specific neural circuits associated with behavioral characteristics and revolutionize our understanding of how the brain works.

• Annals of Clinical Neurophysiology
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• 제22권2호
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• pp.82-91
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• 2020

Electroencephalography (EEG) produces time-series data of neural oscillations in the brain, and is one of the most commonly used methods for investigating both normal brain functions and brain disorders. Quantitative EEG analysis enables identification of frequencies and brain activity that are activated or impaired. With studies on the structural and functional networks of the brain, the concept of the brain as a complex network has been fundamental to understand normal brain functions and the pathophysiology of various neurological disorders. Functional connectivity is a measure of neural synchrony in the brain network that refers to the statistical interdependency between neural oscillations over time. In this review, we first discuss the basic methods of EEG analysis, including preprocessing, spectral analysis, and functional-connectivity and graph-theory measures. We then review previous EEG studies of brain network characterization in several neurological disorders, including epilepsy, Alzheimer's disease, dementia with Lewy bodies, and idiopathic rapid eye movement sleep behavior disorder. Identifying the EEG-based network characteristics might improve the understanding of disease processes and aid the development of novel therapeutic approaches for various neurological disorders.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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• pp.139-142
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• 2002

The brain of the human is the best model for the artificial intelligence and is studied by many natural, medical scientists and engineers. In the engineering department, the brain model becomes a main subject in the area of development of a system that can represent and think like human. In this paper, we approach and define the function of the brain biologically and especially, make a model for the function of cerebral cortex, known as a part that performs behavior inference and decision for sensitive information from the thalamus. Therefore, we try to make a model for the transfer process of the brain. The brain takes the sensory information from sensory organ, proceeds behavior inference and decision and finally, commands behavior to the motor nerves. We use the modular neural network in this model. finally, we would like to design the intelligent system that can sense, recognize, think and decide like the brain by learning the information process in the brain with the modular neural network.

• 한국과학교육학회지
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• 제29권7호
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• pp.751-758
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• 2009

The purpose of this study was to investigate brain activation pattern and functional connectivity network during classification on the biological phenomena. Twenty six right-handed healthy science teachers volunteered to be in the present study. To investigate participants' brain activities during the tasks, 3.0T fMRI system with the block experimental-design was used to measure BOLD signals of their brain. According to the analyzed data, superior, middle and inferior frontal gyrus, superior and inferior parietal lobule, fusiform gyrus, lingual gyrus, and bilateral cerebellum were significantly activated during participants' carrying-out classification. The network model was consisting of six nodes (ROIs) and its fourteen connections. These results suggested the notion that the activation and connections of these regions mean that classification is consist of two sub-network systems (top-down and bottom-up related) and it functioning reciprocally. These results enable the examination of the scientific classification process from the cognitive neuroscience perspective, and may be used as basic materials for developing a teaching-learning program for scientific classification such as brain-based science education curriculum in the science classrooms.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.198-204
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• 2021

Glioma is one of the common types of brain tumors starting in the brain's glial cell. These tumors are classified into low-grade or high-grade tumors. Physicians analyze the stages of brain tumors and suggest treatment to the patient. The status of the tumor has an importance in the treatment. Nowadays, computerized systems are used to analyze and classify brain tumors. The accurate grading of the tumor makes sense in the treatment of brain tumors. This paper aims to develop a classification of low-grade glioma and high-grade glioma using a deep learning algorithm. This system utilizes four transfer learning algorithms, i.e., AlexNet, GoogLeNet, ResNet18, and ResNet50, for classification purposes. Among these algorithms, ResNet18 shows the highest classification accuracy of 97.19%.

• 한국과학교육학회지
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• 제29권3호
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• pp.348-358
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• 2009

The purpose of this study was to investigate brain activation pattern and functional connectivity network during experimental design on the biological phenomena. Twenty six right-handed healthy science teachers volunteered to be in the present study. To investigate participants' brain activities during the tasks, 3.0T fMRI system with the block experimental-design was used to measure BOLD signals of their brain and SPM2 software package was applied to analyze the acquired initial image data from the fMRI system. According to the analyzed data, superior, middle and inferior frontal gyrus, superior and inferior parietal lobule, fusiform gyrus, lingual gyrus, and bilateral cerebellum were significantly activated during participants' carrying-out experimental design. The network model was consisting of six nodes (ROIs) and its six connections. These results suggested the notion that the activation and connections of these regions mean that experimental design process couldn't succeed just a memory retrieval process. These results enable the scientific experimental design process to be examined from the cognitive neuroscience perspective, and may be used as a basis for developing a teaching-learning program for scientific experimental design such as brain-based science education curriculum.

• 감성과학
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• 제21권3호
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• pp.83-102
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• 2018

The process model of emotion regulation suggests that cognitive reappraisal and expressive suppression engage at different time points in the regulation process. Although multiple brain regions and networks have been identified for each strategy, no articles have explored changes in network characteristics or network connectivity over time. The present study examined (a) the whole-brain network and six other resting-state networks, (b) their modularity and global efficiency, which is an index of the efficiency of information exchange across the network, (c) the degree and betweenness centrality for 160 brain regions to identify the hub nodes with the most control over the entire network, and (d) the intra-network and inter-network functional connectivity (FC). Such investigations were performed using a traditional large-scale FC analysis and a relatively recent sliding window correlation analysis. The results showed that the right inferior orbitofrontal cortex was the hub region of the whole-brain network for both strategies. The present findings of temporally altering functional activity of the networks revealed that the default mode network (DMN) activated at the early stage of reappraisal, followed by the task-positive networks (cingulo-opercular network and fronto-parietal network), emotion-processing networks (the cerebellar network and DMN), and sensorimotor network (SMN) that activated at the early stage of suppression, followed by the greater recruitment of task-positive networks and their functional connection with the emotional response-related networks (SMN and occipital network). This is the first study that provides neuroimaging evidence supporting the process model of emotion regulation by revealing the temporally varying network efficiency and intra- and inter-network functional connections of reappraisal and suppression.