• The Korean Journal of Physiology and Pharmacology
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• 제12권6호
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• pp.307-314
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• 2008

Retinal prostheses are being developed to restore vision for the blind with retinal diseases such as retinitis pigmentosa (RP) or age-related macular degeneration (AMD). Among the many issues for prosthesis development, stimulation encoding strategy is one of the most essential electrophysiological issues. The more we understand the retinal circuitry how it encodes and processes visual information, the greater it could help decide stimulation encoding strategy for retinal prosthesis. Therefore, we examined how retinal ganglion cells (RGCs) in in-vitro retinal preparation act together to encode a visual scene with multielectrode array (MEA). Simultaneous recording of many RGCs with MEA showed that nearby neurons often fired synchronously, with spike delays mostly within 1 ms range. This synchronized firing - narrow correlation - was blocked by gap junction blocker, heptanol, but not by glutamatergic synapse blocker, kynurenic acid. By tracking down all the RGC pairs which showed narrow correlation, we could harvest 40 functional connectivity maps of RGCs which showed the cell cluster firing together. We suggest that finding functional connectivity map would be useful in stimulation encoding strategy for the retinal prosthesis since stimulating the cluster of RGCs would be more efficient than separately stimulating each individual RGC.

• 응용통계연구
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• 제29권6호
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• pp.1129-1145
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• 2016

뇌기능 연결성 문제는 뇌의 신경역학적 현상과 밀접한 관련이 있다는 의미에서 뇌과학에서 주요 연구주제이다. 본 논문에서는 기능적 자기공명영상(fMRI)자료를 뇌활동에 대한 반응 자료의 주요 형태로써 선택하였는데, 이 fMRI자료는 높은 해상도 때문에 뇌과학 연구에서 선호되는 자료 형태이다. 뇌활동에 대한 생리학적 반응을 측정해서 자료로 사용한다는 전제하에서 뇌의 기능적 연결성을 분석하는 방법들을 고찰하였다. 여기서의 전제란 상태공간 및 측정 모형을 다룬다는것을 의미하는데, 여기서 상태공간 모형은 뇌신경역학을 표현한다고 가정한다. 뇌기능 영상자료의 분석은 무엇을 측정하였느냐에 따라서 분석방법과 그 해석이 조금씩 달라진다. 실제 fMRI자료를 고차원 자기회귀모형을 적용해서 분석한 결과를 논문에 포함하였는데, 이 결과를 통해서 서로 다른 도형문제를 푸는데 서로 다른 뇌신경 역학관계가 요구된다는 것을 엿볼 수 있었다.

• Communications for Statistical Applications and Methods
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• 제12권1호
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• pp.241-252
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• 2005

In this study we suggest that the spatial correlation structure of the brain fMRI data be used to characterize the functional connectivity of the brain. For some concussion and recovery data, we examine how the correlation structure changes from one step to another in the data analyses, which will allow us to see the effect of each analysis to the spatial correlation or the functional connectivity of the brain. This will lead us to spot the processes which cause significant changes in the spatial correlation structure of the brain. We discuss whether or not we can decompose correlation matrices in terms of its causes of variations in the data.

• 한국언어정보학회지:언어와정보
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• 제10권2호
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• pp.21-45
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• 2006

Connectivity effects have been central issues in dealing with specificational pseudoclefts. While syntactic approaches motivate their analysis in order to explain connectivity effects in terms of a connected clause, these accounts have numerous problems including a wide range of anti-connectivity effects that constitute crucial counterevidence. On the other hand, semantic accounts of connectivity effects treat BV and BT connectivity by independent interpretive mechanisms providing a more fundamental explanation for connectivity effects. Yet existing semantic accounts have limitations in explaining syntactic properties and syntactic connectivity effects in SPCs, and in accounting for BV anti-connectivity effects in English. Focusing on BV connectivity, this paper explores how the relevant (anti-)connectivity facts can be accounted for by an analysis that provides both an elaborate syntactic analysis of SPCs and a semantic mechanism for bound anaphora. Based on Yoo's (2005) non-deletion based, question-answer pair analysis of SPCs, this paper shows that a functional question analysis of a specificational subject, when combined with a theory of operator scope and a non-configurational condition on bound anaphora, can explain various BV (anti-)connectivity patterns in SPCs and related constructions.

• Investigative Magnetic Resonance Imaging
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• 제23권1호
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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.

• 감성과학
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• 제20권4호
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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.

• 한국주거학회논문집
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• 제22권1호
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• pp.35-42
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• 2011

The objective of this study is to investigate the functional conformity based on the analysis of the spatial connectivity of block housing using space syntax. Three cases were selected which have important meanings in 1920s. The properties of spatial configuration were derived from an urban-context approach and without urban context analysis, and spatial connectivity and functional conformity were analyzed using references. The results of the study revealed that the arrangement of block housing were different from one another notwithstanding their similar layout characteristics. The relationships between urban streets and housing complexes were identified, and the public spaces were being arranged as semi-public spaces, whereas blocks as private spaces by separating functions. This study provides the implications for the planning of low rise-high density housings by means of analyzing the spatial connectivity of the spatial layout characteristics of European block housing, recognizing the relationships between urban communities and housing complexes.

• 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.

• Communications for Statistical Applications and Methods
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• 제27권6호
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• pp.603-624
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• 2020

There is an emerging interest in brain functional connectivity (FC) based on functional Magnetic Resonance Imaging in Alzheimer's disease (AD) studies. The complex and high-dimensional structure of FC makes it challenging to explore the association between altered connectivity and AD susceptibility. We develop a pipeline to refine FC as proper covariates in a penalized logistic regression model and classify normal and AD susceptible groups. Three different quantification methods are proposed for FC refinement. One of the methods is dimension reduction based on common component analysis (CCA), which is employed to address the limitations of the other methods. We applied the proposed pipeline to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data and deduced pathogenic FC biomarkers associated with AD susceptibility. The refined FC biomarkers were related to brain regions for cognition, stimuli processing, and sensorimotor skills. We also demonstrated that a model using CCA performed better than others in terms of classification performance and goodness-of-fit.

• 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.