• Applied Microscopy
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• v.46 no.4
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• pp.179-183
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• 2016

Mapping brain structural and functional connections through the whole brain is essential for understanding brain mechanisms and the physiological bases of brain diseases. Although region specific structural or functional deficits cause brain diseases, the changes of interregional connections could also be important factors of brain diseases. This review will introduce common neuroimaging modalities, including structural magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging, and other recent neuroimaging analyses methods, such as voxel-based morphometry, cortical thickness analysis, local gyrification index, and shape analysis for structural imaging. Tract-Based Spatial Statistics, TRActs Constrained by UnderLying Anatomy for diffusion MRI, and independent component analysis for fMRI also will also be introduced.

• Journal of Korean Medical Science
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• v.33 no.51
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• pp.329.1-329.9
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• 2018

Background: Brain magnetic resonance imaging (MRI) is routinely performed to identify brain lesions in girls with central precocious puberty (CPP). We aimed to investigate the prevalence and type of brain lesions among Korean girls with CPP and evaluate the need for routine brain MRI examinations. Methods: This retrospective cross-sectional study evaluated data on 3,528 girls diagnosed with CPP from April 2003 to December 2016, and identified 317 girls who underwent sellar MRI. Exclusion criteria were patients with a known brain tumor or who did not undergo brain MRI due to refusal or the decision of the pediatric endocrinologist. Results: Normal sellar MRI findings were observed in 291 of the 317 girls (91.8%). Incidental findings were observed in 26 girls (8.2%). None of the patients had pathological brain lesions. Conclusion: The prevalence of intracranial lesions among girls who were generally healthy and without neurological symptoms but diagnosed with CPP was lower than that previously reported. Furthermore, none of the identified lesions required treatment. It may be prudent to reconsider the routine use of brain MRI to screen all patients with CPP, especially if they are healthy and neurologically asymptomatic, and are girls aged 6-8 years.

• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.25-28
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• 2020

Automatic segmentation of brain tissues such as WM, GM, and CSF from brain MRI scans is helpful for the diagnosis of many neurological disorders. Accurate segmentation of these brain structures is a very challenging task due to low tissue contrast, bias filed, and partial volume effects. With the aim to improve brain MRI segmentation accuracy, we propose an end-to-end convolutional based U-SegNet architecture designed with multi-scale kernels, which includes cascaded dilated convolutions for the task of brain MRI segmentation. The multi-scale convolution kernels are designed to extract abundant semantic features and capture context information at different scales. Further, the cascaded dilated convolution scheme helps to alleviate the vanishing gradient problem in the proposed model. Experimental outcomes indicate that the proposed architecture is superior to the traditional deep-learning methods such as Segnet, U-net, and U-Segnet and achieves high performance with an average DSC of 93% and 86% of JI value for brain MRI segmentation.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.832-837
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• 2007

Many researchers are studying brain activity to using functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG), and etc. They are used detection of seizures or epilepsy and deception detection in the main. In this paper, we focus on emotion recognition by recording brain waves. We specially use fMRI, TRS, and EEG for measuring brain activity Researchers are experimenting brain waves to get only a measuring apparatus or to use both fMRI and EEG. This paper is measured that we take images of fMRI and TRS about brain activity as human emotions and then we take data of EEG signals. Especially, we focus on EEG signals analysis. We analyze not only original features in brain waves but also transferred features to classify into five sections as frequency. And we eliminate low frequency from 0.2 to 4Hz for EEG artifacts elimination.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.623-632
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• 2016

The purpose of the present study was to evaluate the effect of an offered blanket on the anxiety of patients undergoing brain MRI. The participants were 52 patients who underwent brain MRI in C university hospital of G city. Blanket was applied to the experimental group (n=26) before MRI. Data were collected from May to December 2015, and analysed using the chi-squared test, the independent t-test, and repeated measures ANOVA. The results showed that there was no significant difference in the anxiety measured by STAI of the two groups. But, the level of anxiety measured by the visual analog scales was reduced in the experimental group (t=-2.75, p=0.008). There were no difference in the blood pressure and pulse rate in the experimental and control groups. Therefore, further study is needed to decrease the level of anxiety of patients undergoing brain MRI.

• Journal of Korean Neurosurgical Society
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• v.54 no.2
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• pp.100-106
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• 2013

Objective : To investigate the cases of intracranial abnormal brain MRI findings even in the negative brain CT scan after mild head injury. Methods : During a 2-year period (January 2009-December 2010), we prospectively evaluated both brain CT and brain MRI of 180 patients with mild head injury. Patients were classified into two groups according to presence or absence of abnormal brain MRI finding even in the negative brain CT scan after mild head injury. Two neurosurgeons and one neuroradiologist validated the images from both brain CT scan and brain MRI double blindly. Results : Intracranial injury with negative brain CT scan after mild head injury occurred in 18 patients (10.0%). Headache (51.7%) without neurologic signs was the most common symptom. Locations of intracranial lesions showing abnormal brain MRI were as follows; temporal base (n=8), frontal pole (n=5), falx cerebri (n=2), basal ganglia (n=1), tentorium (n=1), and sylvian fissure (n=1). Intracranial injury was common in patients with a loss of consciousness, symptom duration >2 weeks, or in cases of patients with linear skull fracture (p=0.00013), and also more frequent in multiple associated injury than simple one (35.7%>8.6%) (p=0.105). Conclusion : Our investigation showed that patients with mild head injury even in the negative brain CT scan had a few cases of intracranial injury. These findings indicate that even though the brain CT does not show abnormal findings, they should be thoroughly watched in further study including brain MRI in cases of multiple injuries and when their complaints are sustained.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.343-351
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• 2022

A brain tumor forms when some tissue becomes old or damaged but does not die when it must, preventing new tissue from being born. Manually finding such masses in the brain by analyzing MRI images is challenging and time-consuming for experts. In this study, our main objective is to detect the brain's tumorous part, allowing rapid diagnosis to treat the primary disease instantly. With image processing techniques and deep learning prediction algorithms, our research makes a system capable of finding a tumor in MRI images of a brain automatically and accurately. Our tumor segmentation adopts the U-Net deep learning segmentation on the standard MICCAI BRATS 2018 dataset, which has MRI images with different modalities. The proposed approach was evaluated and achieved Dice Coefficients of 0.9795, 0.9855, 0.9793, and 0.9950 across several test datasets. These results show that the proposed system achieves excellent segmentation of tumors in MRIs using deep learning techniques such as the U-Net algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.393-396
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• 2011

In this paper, we propose a constrained alternating least squares nonnegative matrix factorization algorithm (cALSNMF) to detect active brain regions from single subject's task-related fMRI data. In cALSNMF, we define a new cost function which considers the uncorrelation and noisy problems of fMRI data by adding decorrelation and smoothing constraints in original Euclidean distance cost function. We also generate a novel training procedure by modifying the update rules and combining with optimal brain surgeon (OBS) algorithm. The experimental results on visuomotor task fMRI data show that our cALSNMF fits fMRI data better than original ALSNMF in detecting task-related brain activation from single subject's fMRI data.

• Journal of Korean Elementary Science Education
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• v.26 no.1
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• pp.49-62
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• 2007

The higher cognitive functions of the human brain including teaming are hypothesized to be selectively distributed across large-scale neural networks interconnected to the cortical and subcortical areas. Recently, advances in functional imaging have made it possible to visualize the brain areas activated by certain cognitive activities in vivo. Neural substrates for teaming and motivation have also begun to be revealed. Functional magnetic resonance imaging (fMRI) provides a non-invasive indirect mapping of cerebral activity, based on the blood- oxygen level dependent (BOLD) contrast which is based on the localized hemodynamic changes following neural activities in certain areas of the brain. The fMRI method is now becoming an essential tool used to define the neuro-functional mechanisms of higher brain functions such as memory, language, attention, learning, plasticity and emotion. Further research in the field of education will accelerate the verification of the effects on loaming or help in the selection of model teaching strategies. Thus, the purpose of this study was to review brain study methods using fMRI in science education. In conclusion, a number of possible strategies using fMRI for the study of elementary science education were suggested.