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

• Science of Emotion and Sensibility
• /
• v.25 no.4
• /
• pp.63-76
• /
• 2022

Emotional labor, characterized by a dysfunctional type of emotional regulation called surface acting, has detrimental psychological consequences on employees, including depression and social anxiety. Because such disorders exhibit psychological characteristics manifested through brain activation, previous studies have succeeded in distinguishing individuals with depression and social anxiety from healthy controls using their functional connectivity characteristics. However, it has not been established whether the functional connectivity characteristics associated with emotional labor are distinguishable. Thus, we obtained resting-state fMRI data from participants in the emotion labor (EL) group and control (CTRL) group, and we subjected their whole-brain functional connectivity matrices to a linear support vector machine classifier. Our analysis revealed that the EL and CTRL groups could be successfully distinguished on the basis of individuals' connectivity patterns, and confidence in the classification was correlated with the scores on the depression and social anxiety scales. These results are expected to provide insight on the neurobiological characteristics of emotional labor and enable the sorting of employees undergoing adverse emotional labor utilizing neurobiological observations.

• Communications for Statistical Applications and Methods
• /
• v.31 no.1
• /
• pp.87-104
• /
• 2024

Most functional magnetic resonance imaging (fMRI) studies in resting state have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant. However, increased interest has recently been in quantifying possible dynamic changes in FC during fMRI experiments. FC study may provide insight into the fundamental workings of brain networks to brain activity. In this work, we focus on the specific problem of estimating the dynamic behavior of pairwise correlations between time courses extracted from two different brain regions. We compare the sliding-window techniques such as moving average (MA) and exponentially weighted moving average (EWMA), dynamic causality with vector autoregressive (VAR) model, dynamic conditional correlation (DCC) based on volatility, and the proposed alternative methods to use differencing and recursive residuals. We investigate the properties of those techniques in a series of simulation studies. We also provide an application with major depressive disorder (MDD) patient fMRI data to demonstrate studying dynamic correlations.

• Anxiety and mood
• /
• v.18 no.2
• /
• pp.39-47
• /
• 2022

Objective : This study investigated brain functional connectivity in male firefighters who showed subclinical post-traumatic stress disorder (PTSD) symptoms. Methods : We compared the data of 17 firefighters who were not diagnosed with PTSD and 18 healthy controls who had no trauma exposure. The following instruments were applied to assess psychiatric symptoms: Korean version of the Post-traumatic stress disorder Checklist for DSM-5 (PCL-5-K), Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI). For all subjects, functional magnetic resonance imaging was performed, and functional connectivity was compared between the two groups (family-wise error-corrected p<0.05). Additionally, correlations between psychiatric symptoms and functional connectivity were explored. Results : The following connectivity was higher than that of healthy controls: 1) the central opercular cortex-superior temporal gyrus, 2) planum polare-parahippocampal gyrus, 3) angular gyrus-amygdala, and 4) temporal fusiform cortex-parahippocampal gyrus. The functional connectivity of 1) the lateral occipital cortex-inferior temporal gyrus, 2) superior parietal lobule-caudate, and 3) middle temporal gyrus-thalamus were lower in firefighters. In firefighters, the connectivity of the planum polare-parahippocampal gyrus showed a negative correlation with the severity of arousal symptoms (rho=-0.586, p=0.013). The connectivity of the middle temporal gyrus-thalamus showed a positive correlation with the severity of intrusion (rho=0.552, p=0.022) and arousal symptoms (rho=0.619, p=0.008). The connectivity of the temporal fusiform cortex-parahippocampal gyrus was negatively correlated with intrusion (rho=-0.491, p=0.045) and arousal (rho=-0.579, p=0.015). Conclusion : Our results indicate that the brain functional connectivity is associated with occupational trauma exposure in firefighters without PTSD. Therefore, this study provides evidence that close monitoring and early intervention are important for firefighters with traumatic experience even at a subthreshold level.

• Sleep Medicine and Psychophysiology
• /
• v.25 no.2
• /
• pp.82-91
• /
• 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.

• Journal of Biomedical Engineering Research
• /
• v.35 no.3
• /
• pp.68-74
• /
• 2014

Recent research on mild cognitive impairment (MCI) has shown that cognitive and memory decline in this disease is accompanied by disruptive changes in the brain functional network. However, there have been no graph-theoretical studies using $^{11}C$-PIB PET data of the Alzheimer's Disease or mild cognitive impairment. In this study, we acquired $^{18}F$-FDG PET and $^{11}C$-PIB PET images of twenty-four normal aging control participants and thirty individuals with MCI from ADNI (Alzheimer's Disease Neuroimaging Initiative) database. Brain networks were constructed by thresholding binary correlation matrices using graph theoretical approaches. Both normal control and MCI group showed small-world property in $^{11}C$-PIB PET images as well as $^{18}F$-FDG PET images. $^{11}C$-PIB PET images showed significant difference between NC (normal control) and MCI over large range of sparsity values. This result will enable us to further analyze the brain using established graph-theoretical approaches for $^{11}C$-PIB PET images.

• Molecules and Cells
• /
• v.45 no.2
• /
• pp.84-92
• /
• 2022

To understand the microcircuitry of the brain, the anatomical and functional connectivity among neurons must be resolved. One of the technical hurdles to achieving this goal is that the anatomical connections, or synapses, are often smaller than the diffraction limit of light and thus are difficult to resolve by conventional microscopy, while the microcircuitry of the brain is on the scale of 1 mm or larger. To date, the gold standard method for microcircuit reconstruction has been electron microscopy (EM). However, despite its rapid development, EM has clear shortcomings as a method for microcircuit reconstruction. The greatest weakness of this method is arguably its incompatibility with functional and molecular analysis. Fluorescence microscopy, on the other hand, is readily compatible with numerous physiological and molecular analyses. We believe that recent advances in various fluorescence microscopy techniques offer a new possibility for reliable synapse detection in large volumes of neural circuits. In this minireview, we summarize recent advances in fluorescence-based microcircuit reconstruction. In the same vein as these studies, we introduce our recent efforts to analyze the long-range connectivity among brain areas and the subcellular distribution of synapses of interest in relatively large volumes of cortical tissue with array tomography and superresolution microscopy.

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

• Applied Microscopy
• /
• v.48 no.4
• /
• pp.102-109
• /
• 2018

Multiple synaptic boutons (MSBs) have been reported to be synapse with two or more postsynaptic terminals in one presynaptic terminal. These MSBs are known to be increased by various brain stimuli. In the motor cortex, increased number of MSB was observed in both acrobat training (AC) model and traumatic brain injury (TBI) model. Interestingly one is a physiological stimuli and the other is pathological insult. The purpose of this study is to compare the connectivity of MSBs between AC model and TBI model in the cerebral motor cortex, based on the hypothesis that the connectivity of MSBs might be different according to the models. The motor cortex was dissected from perfused brain of each experimental animal, the samples were prepared for routine transmission electron microscopy. The 60~70 serial sections were mounted on the one-hole grid and MSB was analyzed. The 3-dimensional analysis revealed that 94% of MSBs found in AC model synapse two postsynaptic spines from same dendrite. But, 28% MSBs from TBI models synapse two postsynaptic spines from different dendrite. This imply that the MSBs observed in motor cortex of AC model and TBI model might have different circuits for the processing the information.

• Korean Journal of Radiology
• /
• v.24 no.3
• /
• pp.247-258
• /
• 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.