• Investigative Magnetic Resonance Imaging
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• v.23 no.1
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• pp.55-64
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• 2019

Purpose: One of the suggested potential mechanisms of tinnitus is an alteration in perception in the neural auditory pathway. The aim of this study was to investigate the difference in laterality in functional connectivity between tinnitus patients and healthy controls using resting state functional MRI (rs-fMRI). Materials and Methods: Thirty-eight chronic tinnitus subjects and 45 age-matched healthy controls were enrolled in this study. Connectivity was investigated using independent component analysis, and the laterality index map was calculated based on auditory (AN) and dorsal attention (DAN), default mode (DMN), sensorimotor, salience (SalN), and visual networks (VNs). The laterality index (LI) of tinnitus subjects was compared with that of normal controls using region-of-interest (ROI) and voxel-based methods and a two-sample unpaired t-test. Pearson correlation was conducted to assess the associations between the LI in each network and clinical variables. Results: The AN and VN showed significant differences in LI between the two groups in ROI analysis (P < 0.05), and the tinnitus group had clusters with significantly decreased laterality of AN, SalN, and VN in voxel-based comparisons. The AN was positively correlated with tinnitus distress (tinnitus handicap inventory), and the SalN was negatively correlated with symptom duration (P < 0.05). Conclusion: The results of this study suggest that various functional networks related to psychological distress can be modified by tinnitus, and that this interrelation can present differently on the right and left sides, according to the dominance of the network.

• Anxiety and mood
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• v.14 no.2
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• pp.63-70
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• 2018

It has been suggested that aberrant self-referential processing (SRP) is one of the important components of the explanatory models of social anxiety disorder (SAD). The default mode network (DMN), which reflects intrinsic brain functions, is known to play a critical role in SRP. Recently, resting state functional magnetic resonance imaging (fMRI) research on the functional connectivity in the brain network has gained greater attention as a tool to elucidate the neurobiological basis of various psychiatric disorders. We reviewed resting state fMRI studies that investigated the resting state functional connectivity (RSFC) of the DMN in SAD. Despite of the heterogeneity of the analytic methods and occasional negative findings, most studies consistently reported abnormalities of RSFC within the DMN, suggesting that the DMN may be significant neural correlates of aberrant SRP in SAD. Also, changes in RSFC of the DMN are associated with clinical improvements of therapeutic interventions. Moreover, emerging findings provide the basis for potential use of RSFC as a complementary method in diagnosis of SAD. Ongoing and future research to investigate RSFC of the DMN could broaden our understanding regarding the neurobiological basis of SAD, and contribute to the development of novel treatments for SAD.

• 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.

• Science of Emotion and Sensibility
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• v.20 no.4
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• pp.101-112
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• 2017

We investigated the neural representation of reward probability recognition and its neural connectivity with other regions of the brain. Using functional magnetic resonance imaging (fMRI), we used a simple guessing task with different probabilities of obtaining rewards across trials to assay local and global regions processing reward probability. The results of whole brain analysis demonstrated that lateral prefrontal cortex, inferior parietal lobe, and postcentral gyrus were activated during probability-based decision making. Specifically, the higher the expected value was, the more these regions were activated. Fronto-parietal connectivity, comprising inferior parietal regions and right lateral prefrontal cortex, conjointly engaged during high reward probability recognition compared to low reward condition, regardless of whether the reward information was extrinsically presented. Finally, the result of a regression analysis identified that cortico-subcortical connectivity was strengthened during the high reward anticipation for the subjects with higher cognitive impulsivity. Our findings demonstrate that interregional functional involvement is involved in valuation based on reward probability and that personality trait such as cognitive impulsivity plays a role in modulating the connectivity among different brain regions.

• Korean Journal of Biological Psychiatry
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• v.24 no.2
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• pp.68-74
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• 2017

Objectives It is well known that problem drinking is associated with alterations of brain structures and functions. Brain functions related to alcohol consumption can be determined by the resting state functional connectivity in various resting state networks (RSNs). This study aims to ascertain the alcohol effect on the structures forming predetermined RSNs by assessing their cortical thickness. Methods Twenty-six abstinent male patients with alcohol dependence and the same number of age-matched healthy control were recruited from an inpatient mental hospital and community. All participants underwent a 3T MRI scan. Averaged cortical thickness of areas constituting 7 RSNs were determined by using FreeSurfer with Yeo atlas derived from cortical parcellation estimated by intrinsic functional connectivity. Results There were significant group differences of mean cortical thicknesses (Cohen's d, corrected p) in ventral attention (1.01, < 0.01), dorsal attention (0.93, 0.01), somatomotor (0.90, 0.01), and visual (0.88, 0.02) networks. We could not find significant group differences in the default mode network. There were also significant group differences of gray matter volumes corrected by head size across the all networks. However, there were no group differences of surface area in each network. Conclusions There are differences in degree and pattern of structural recovery after abstinence across areas forming RSNs. Considering the previous observation that group differences of functional connectivity were significant only in networks related to task-positive networks such as dorsal attention and cognitive control networks, we can explain recovery pattern of cognition and emotion related to the default mode network and the mechanisms for craving and relapse associated with task-positive networks.

• Molecules and Cells
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• v.42 no.8
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• pp.579-588
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• 2019

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

• 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 The Korean Association For Science Education
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• v.32 no.3
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• pp.524-531
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• 2012

Categorization is an important human function used to process different stimuli. It is also one of the most important factors affecting measurement of a person's classification ability. Explicit categorization, the representative system by which categorization ability is measured, can verbally describe the categorization rule. The purpose of this study was to develop a prediction model for categorization ability as it relates to the classification process of living organisms using fMRI. Fifty-five participants were divided into two groups: a model generation group, comprised of twenty-seven subjects, and a model verification group, made up of twenty-eight subjects. During prediction model generation, functional connectivity was used to analyze temporal correlations between brain activation regions. A classification ability quotient (CQ) was calculated to identify the verbal categorization ability distribution of each subject. Additionally, the connectivity coefficient (CC) was calculated to quantify the functional connectivity for each subject. Hence, it was possible to generate a prediction model through regression analysis based on participants' CQ and CC values. The resultant categorization ability regression model predictor was statistically significant; however, researchers proceeded to verify its predictive ability power. In order to verify the predictive power of the developed regression model, researchers used the regression model and subjects' CC values to predict CQ values for twenty-eight subjects. Correlation between the predicted CQ values and the observed CQ values was confirmed. Results of this study suggested that explicit categorization ability differs at the brain network level of individuals. Also, the finding suggested that differences in functional connectivity between individuals reflect differences in categorization ability. Last, researchers have provided a new method for predicting an individual's categorization ability by measuring brain activation.

• Annals of Clinical Neurophysiology
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• v.23 no.2
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• pp.69-81
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• 2021

Cortico-cortical evoked potential (CCEP) mapping is a rapidly developing method for visualizing the brain network and estimating cortical excitability. The CCEP comprises the early N1 component the occurs at 10-30 ms poststimulation, indicating anatomic connectivity, and the late N2 component that appears at < 200 ms poststimulation, suggesting long-lasting effective connectivity. A later component at 200-1,000 ms poststimulation can also appear as a delayed response in some studied areas. Such delayed responses occur in areas with changed excitability, such as an epileptogenic zone. CCEP mapping has been used to examine the brain connections causally in functional systems such as the language, auditory, and visual systems as well as in anatomic regions including the frontoparietal neocortices and hippocampal limbic areas. Task-based CCEPs can be used to measure behavior. In addition to evaluations of the brain connectome, single-pulse electrical stimulation (SPES) can reflect cortical excitability, and so it could be used to predict a seizure onset zone. CCEP brain mapping and SPES investigations could be applied both extraoperatively and intraoperatively. These underused electrophysiologic tools in basic and clinical neuroscience might be powerful methods for providing insight into measures of brain connectivity and dynamics. Analyses of CCEPs might enable us to identify causal relationships between brain areas during cortical processing, and to develop a new paradigm of effective therapeutic neuromodulation in the future.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.377-378
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• 2011

본 논문은 뇌기능 연구에 크게 기여하는 기능적 자기공명영상을 효과적으로 분석하기 위한 유효 연결성(Effective Connectivity, EC)을 이용한 대규모 네트워크(Large-Scale Network, LSN) 분석(LSN-EC)을 제안한다. 유효 연결성은 뇌영역간의 시공간적 인과관계를 표현한 연결성이며, 뇌의 기능적 연결성 및 구조탐색 사용된다. LSN-EC는 뇌영역간의 EC를 표현하고 그룹간의 차이분석을 통하여 뇌질환 분석 및 진단 연구로의 응용이 가능하다. 실험결과에서 알츠하이머병과 관련이 높다고 알려진 후대상피질(Posterior Cingulate Cortex)과 해마(Hippocampus)가 포함된 변연엽(Limbic Lobe), 기저핵 및 시상(Basal Ganglion and Thalamus) 주변 영역에서 감소된 EC를 확인하였다.