• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.359-366
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• 2001

This paper overviews the design of SANique$^{TM}$ -a shred file system for Linux cluster based on SAN environment. SANique$^{TM}$ has the capability of transferring user data from network-attached SAN disks to client applcations directly without the control of centralized file server system. The paper also presents the characteristics of each SANique$^{TM}$ subsystem: CFM(Cluster File Manager), CVM(Cluster Volume Manager), CLM(Cluster Lock Manager), CBM(Cluster Buffer Manager) and CRM(Cluster Recovery Manager). Under the SANique$^{TM}$ design layout, then, the syndrome of '||'&'||'quot;split-brain'||'&'||'quot; in shared file system environments is described and defined. The work first generalizes and illustrates possible situations in each of which a shared file system environment may split into two or more pieces of separate brain. Finally, the work describes the SANique$^{TM}$ approach to the given "split-brain"problem using SAN disk named "split-brain" and develops the overall recovery procedure of shared file systems.

• Korean Journal of Biological Psychiatry
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• v.22 no.3
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• pp.101-108
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• 2015

Recent breakthroughs in functional neuroimaging techniques have launched the quest of mapping the connections of the human brain, otherwise known as the human connectome. Imaging connectomics is an umbrella term that refers to the neuroimaging techniques used to generate these maps, which recently has enabled comprehensive brain mapping of network connectivity combined with graph theoretic methods. In this review, we present an overview of the key concepts in functional connectomics. Furthermore, we discuss articles that applied task-based and/or resting-state functional magnetic resonance imaging to examine network deficits in post-traumatic stress disorder (PTSD). These studies have provided important insights regarding the etiology of PTSD, as well as the overall organization of the brain network. Advances in functional connectomics are expected to provide insight into the pathophysiology and the development of biomarkers for diagnosis and treatment of PTSD.

• Journal of Korean Neurosurgical Society
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• v.62 no.1
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• pp.10-26
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• 2019

Magnetic resonance-guided focused ultrasound (MRgFUS) is an emerging new technology with considerable potential to treat various neurological diseases. With refinement of ultrasound transducer technology and integration with magnetic resonance imaging guidance, transcranial sonication of precise cerebral targets has become a therapeutic option. Intensity is a key determinant of ultrasound effects. High-intensity focused ultrasound can produce targeted lesions via thermal ablation of tissue. MRgFUS-mediated stereotactic ablation is non-invasive, incision-free, and confers immediate therapeutic effects. Since the US Food and Drug Administration approval of MRgFUS in 2016 for unilateral thalamotomy in medication-refractory essential tremor, studies on novel indications such as Parkinson's disease, psychiatric disease, and brain tumors are underway. MRgFUS is also used in the context of blood-brain barrier (BBB) opening at low intensities, in combination with intravenously-administered microbubbles. Preclinical studies show that MRgFUS-mediated BBB opening safely enhances the delivery of targeted chemotherapeutic agents to the brain and improves tumor control as well as survival. In addition, BBB opening has been shown to activate the innate immune system in animal models of Alzheimer's disease. Amyloid plaque clearance and promotion of neurogenesis in these studies suggest that MRgFUS-mediated BBB opening may be a new paradigm for neurodegenerative disease treatment in the future. Here, we review the current status of preclinical and clinical trials of MRgFUS-mediated thermal ablation and BBB opening, described their mechanisms of action, and discuss future prospects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.962-975
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• 2020

The accurate segmentation of infant brain MR image into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is very important for early studying of brain growing patterns and morphological changes in neurodevelopmental disorders. Because of inherent myelination and maturation process, the WM and GM of babies (between 6 and 9 months of age) exhibit similar intensity levels in both T1-weighted (T1w) and T2-weighted (T2w) MR images in the isointense phase, which makes brain tissue segmentation very difficult. We propose a deep network architecture based on U-Net, called Triple Residual Multiscale Fully Convolutional Network (TRMFCN), whose structure exists three gates of input and inserts two blocks: residual multiscale block and concatenate block. We solved some difficulties and completed the segmentation task with the model. Our model outperforms the U-Net and some cutting-edge deep networks based on U-Net in evaluation of WM, GM and CSF. The data set we used for training and testing comes from iSeg-2017 challenge (http://iseg2017.web.unc.edu).

• Molecules and Cells
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• v.41 no.10
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• pp.881-888
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• 2018

During the cortical development, cells in the brain acquire somatic mutations that can be implicated in various neurodevelopmental disorders. There is increasing evidence that brain somatic mutations lead to sporadic form of epileptic disorders with previously unknown etiology. In particular, malformation of cortical developments (MCD), ganglioglioma (GG) associated with intractable epilepsy and non-lesional focal epilepsy (NLFE) are known to be attributable to brain somatic mutations in mTOR pathway genes and others. In order to identify such somatic mutations presenting as low-level in epileptic brain tissues, the mutated cells should be enriched and sequenced with high-depth coverage. Nevertheless, there are a lot of technical limitations to accurately detect low-level of somatic mutations. Also, it is important to validate whether identified somatic mutations are truly causative for epileptic seizures or not. Furthermore, it will be necessary to understand the molecular mechanism of how brain somatic mutations disturb neuronal circuitry since epilepsy is a typical example of neural network disorder. In this review, we overview current genetic techniques and experimental tools in neuroscience that can address the existence and significance of brain somatic mutations in epileptic disorders as well as their effect on neuronal circuitry.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.161-175
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• 2022

Oscillatory magnetic fields produced in the brain due to neuronal activity can be measured by the sensor. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution, which gives information about the brain's functional activity. Potential utilization of high spatial resolution in MEG is likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in some diseases under resting state or task state. This review is a comprehensive report to introduce statistical models from MEG data including graphical network modelling. It is also meaningful to note that statisticians should play an important role in the brain science field.

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

• 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.

• Applied Microscopy
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• v.46 no.2
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• pp.100-104
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• 2016

Electron microscopy is currently the only available technique with a spatial resolution sufficient to identify fine neuronal processes and synaptic structures in densely packed neuropil. For large-scale volume reconstruction of neuronal connectivity, serial block-face scanning electron microscopy allows us to acquire thousands of serial images in an automated fashion and reconstruct neural circuits faster by reducing the alignment task. Here we introduce the whole reconstruction procedure of synaptic network in the rat hippocampal CA1 area and discuss technical issues to be resolved for improving image quality and segmentation. Compared to the serial section transmission electron microscopy, serial block-face scanning electron microscopy produced much reliable three-dimensional data sets and accelerated reconstruction by reducing the need of alignment and distortion adjustment. This approach will generate invaluable information on organizational features of our connectomes as well as diverse neurological disorders caused by synaptic impairments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.59-63
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• 2016

Secondary damage to the user is a problem in biometrics. A brain-wave has no shape and a malicious user may not cause secondary damage to a user. However, if user sends brain-wave signals to an authentication system using a network, a malicious user could easily capture the brain-wave signals. Then, the malicious user could access the authentication system using the captured brain-wave signals. In addition, the dataset containing the brain-wave signals is large and the transfer time is long. However, user authentication requires a real-time processing, and an encryption scheme on brain-wave signals is necessary. In this paper, we propose an efficient encryption scheme using a chaos map and adaptive junk data on the brain-wave signals for user authentication. As a result, the encrypted brain-wave signals are produced and the processing time for authentication is reasonable in real-time.