• Investigative Magnetic Resonance Imaging
• /
• v.1 no.1
• /
• pp.42-50
• /
• 1997

• Proceedings of the KSMRM Conference
• /
• 1996.10a
• /
• pp.79-84
• /
• 1996

• Korean Journal of Radiology
• /
• v.1 no.1
• /
• pp.19-24
• /
• 2000

Objective: In order to investigate the functional brain anatomy associated with verbal and visual working memory, functional magnetic resonance imaging was performed. Materials and Methods: In ten normal right handed subjects, functional MR images were obtained using a 1.5-T MR scanner and the EPI BOLD technique. An item recognition task was used for stimulation, and during the activation period of the verbal working memory task, consonant letters were used. During the activation period of the visual working memory task, symbols or diagrams were employed instead of letters. For the post-processing of images, the SPM program was used, with the threshold of significance set at p < .001. We assessed activated brain areas during the two stimulation tasks and compared the activated regions between the two tasks. Results: The prefrontal cortex and secondary visual cortex were activated bilaterally by both verbal and visual working memory tasks, and the patterns of activated signals were similar in both tasks. The superior parietal cortex was also activated by both tasks, with lateralization to the left in the verbal task, and bilaterally without lateralization in the visual task. The inferior frontal cortex, inferior parietal cortex and temporal gyrus were activated exclusively by the verbal working memory task, predominantly in the left hemisphere. Conclusion: The prefrontal cortex is activated by two stimulation tasks, and this is related to the function of the central executive. The language areas activated by the verbal working memory task may be a function of the phonological loop. Bilateral prefrontal and superior parietal cortices activated by the visual working memory task may be related to the visual maintenance of objects, representing visual working memory.

• Nuclear Medicine and Molecular Imaging
• /
• v.41 no.2
• /
• pp.78-84
• /
• 2007

Quantitative analysis of dynamic brain PET data using a tracer kinetic modeling has played important roles in the investigation of functional and molecular basis of various brain diseases. Parametric imaging of the kinetic parameters (voxel-wise representation of the estimated parameters) has several advantages over the conventional approaches using region of interest (ROI). Therefore, several strategies have been suggested to generate the parametric images with a minimal bias and variability in the parameter estimation. In this paper, we will review the several approaches for parametric imaging with linearized methods which include graphical analysis and mulilinear regression analysis.

• Science of Emotion and Sensibility
• /
• v.8 no.4
• /
• pp.365-374
• /
• 2005

Pain has at least two dimensions such as somatosensory qualities and affect and patients are frequently asked to score the intensity of their pain on a numerical pain rating scale. However, the use of a undimensional scale is questionable in view of the belief, overwhelmingly supported by clinical experience as well as by empirical evidence from multidimensional scaling and other sources, that pain has multidimensions such as sensory-discrimitive, motivational-affective and cognitive-evaluative The study of pain has recently received much attention, especially in understanding its neurophysiology by using new brain imaging techniques, such as positron emission tomography(PET) and functional magnetic resonance imaging (fMRI), both of which allow us to visualize brain function in vivo. Also the new brainimaging devices allow us to evaluate the patients pain status and plan To treat patients objectively. Base4 on our findings we presented what are the new brain imaging devices and the results of study by using brain imaging devices.

• International Journal of Contents
• /
• v.8 no.4
• /
• pp.56-63
• /
• 2012

In recent decades, analyzing the activities of human brain achieved some accomplishments by using the functional Magnetic Resonance Imaging (fMRI) technique. fMRI data provide a sequence of three-dimensional images related to human brain's activity which can be used to detect instantaneous cognitive states by applying machine learning methods. In this paper, we propose a new approach for distinguishing human's cognitive states such as "observing a picture" versus "reading a sentence" and "reading an affirmative sentence" versus "reading a negative sentence". Since fMRI data are high dimensional (about 100,000 features in each sample), extremely sparse and noisy, feature selection is a very important step for increasing classification accuracy and reducing processing time. We used the Fisher Discriminant Ratio to select the most powerful discriminative features from some Regions of Interest (ROIs). The experimental results showed that our approach achieved the best performance compared to other feature extraction methods with the average accuracy approximately 95.83% for the first study and 99.5% for the second study.

• Communications for Statistical Applications and Methods
• /
• v.27 no.6
• /
• pp.603-624
• /
• 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.

• Proceedings of the KSMRM Conference
• /
• 1996.03a
• /
• pp.6-12
• /
• 1996

• 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.

• Molecules and Cells
• /
• v.40 no.7
• /
• pp.495-502
• /
• 2017

The $5-HT_6R$ has been considered as an attractive therapeutic target in the brain due to its exclusive expression in the brain. However, the mechanistic linkage between $5-HT_6Rs$ and brain functions remains poorly understood. Here, we examined the effects of $5-HT_6R$-mediated cell morphological changes using immunocytochemistry, Western blot, and live-cell imaging assays. Our results showed that the activation of $5-HT_6Rs$ caused morphological changes and increased cell surface area in HEK293 cells expressing $5-HT_6Rs$. Treatment with 5-HT specifically increased RhoA-GTP activity without affecting other Rho family proteins, such as Rac1 and Cdc42. Furthermore, live-cell imaging in hippocampal neurons revealed that activation of $5-HT_6Rs$ using a selective agonist, ST1936, increased the density and size of dendritic protrusions along with the activation of RhoA-GTP activity and that both effects were blocked by pretreatment with a selective $5-HT_6R$ antagonist, SB258585. Taken together, our results show that $5-HT_6R$ plays an important role in the regulation of cell morphology via a RhoA-dependent pathway in mammalian cell lines and primary neurons.