Biology subjects

Choi, U.-S.

Publications and source records attributed to Choi, U.-S..

4 recordsLinked to original sources

bioRxiv2025

Enhancing Functional Connectivity Analysis in Task-Based fMRI Using the BOLD-Filter Method: Greater Network and Activation Voxel Sensitivities

Task-based functional MRI (tb-fMRI) has gained prominence for investigating brain connectivity by engaging specific functional networks during cognitive or behavioral tasks. Compared to resting-state fMRI (rs-fMRI), tb-fMRI provides greater specificity and interpretability, making it a valuable tool for examining task-relevant networks and individual differences in brain function. In this study, we evaluated the utility of the BOLD-filter--a method originally developed to extract reliable BOLD (blood oxygenation level-dependent) components from rs-fMRI--by applying it to tb-fMRI data as a preprocessing step for functional connectivity (FC) analysis. The goal was to enhance the sensitivity and specificity of detecting task-induced functional activity. Compared to the conventional preprocessing method, the BOLD-filter substantially improved the isolation of task-evoked BOLD signals. It identified over eleven times more activation voxels at a high statistical threshold and more than twice as many at a lower threshold. Moreover, FC networks derived from BOLD-filtered signals revealed clearer task-related patterns, including gender-specific differences in brain regions linked to everyday behaviors. These patterns were not detectable using standard preprocessing approaches. Our findings demonstrate that the BOLD-filter enhances the robustness and interpretability of FC analysis in tb-fMRI. By effectively isolating meaningful functional networks, this approach offers significant advantages over conventional preprocessing methods. The BOLD-filter holds promise for advancing both basic neuroscience research and clinical applications by enabling more precise characterization of task-induced brain activity.

neuroscience↗

bioRxiv2022

DeepParcellation: a novel deep learning method for robust brain magnetic resonance imaging parcellation in older East Asians

Accurate parcellation of cortical regions is crucial for distinguishing morphometric changes in aged brains, particularly in degenerative brain diseases. Normal aging and neurodegeneration precipitate brain structural changes, leading to distinct tissue contrast and shape in people aged > 60 years. Manual parcellation by trained radiologists can yield a highly accurate outline of the brain; however, analyzing large datasets is laborious and expensive. Alternatively, newly-developed computational models can quickly and accurately conduct brain parcellation, although thus far only for the brains of Caucasian individuals. DeepParcellation, our novel deep learning model for 3D magnetic resonance imaging (MRI) parcellation, was trained on 5,035 brains of older East Asians (Gwangju Alzheimers & Related Dementia) and 2,535 brains of Caucasians. We trained full 3D models for N-way individual regions of interest using memory reduction techniques. Our method showed the highest similarity and robust reliability among age-ethnicity groups, especially when parcellating the brains of older East Asians.

neuroscience↗

bioRxiv2019

Cerebral artery segmentation based on magnetization-prepared two rapid acquisition gradient echo multi-contrast images in 7 Tesla magnetic resonance imaging

Cerebral artery segmentation plays an important role in the direct visualization of the human brain to obtain vascular system information. At ultra-high field magnetic resonance imaging, hyperintensity of the cerebral arteries in T1 weighted (T1w) images could be segmented from brain tissues such as gray and white matter. In this study, we propose an automated method to segment the cerebral arteries using multi-contrast images including T1w images of a magnetization-prepared two rapid acquisition gradient echoes (MP2RAGE) sequence at 7 T. The proposed method employed a seed-based region-growing strategy with the following procedures. (1) Two seed regions were defined by Frangi filtering applied to T1w images and by a simple calculation from multi-contrast images, (2) the two seed regions were combined, (3) the combined seed regions were expanded using a region growing algorithm to acquire the cerebral arteries. Time-of-flight (TOF) images were obtained as a reference to evaluate the proposed method. We successfully performed vessel segmentations from T1w MP2RAGE images, which mostly overlapped with the segmentations from the TOF images. As large arteries can affect the normalization of anatomical images to the standard coordinate space in functional and structural studies, we also investigated the effect of the cerebral arteries on spatial transformation using vessel segmentation by the proposed method. As a result, the T1w image removing the cerebral arteries showed better agreement with the standard atlas compared with the T1w image containing the arteries. Thus, because the proposed method using MP2RAGE images can obtain brain tissue anatomical information as well as cerebral artery information without need for additional acquisitions such as of the TOF sequence, it is useful and time saving for medical diagnosis and functional and structural studies.

neuroscience↗

bioRxiv2018

Brain tissue segmentation based on MP2RAGE multi-contrast images in 7 T MRI

We proposed a method for segmentation of brain tissues--gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF)--using multi-contrast images, including a T1 map and a uniform T1-weighted image, from a magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) sequence at 7 Tesla. The proposed method was evaluated with respect to the processing time and the similarity of the segmented masks of brain tissues with those obtained using FreeSurfer, FSL, and SPM12. The processing time of the proposed method (28 {+/-} 0 s) was significantly shorter than those of FSL and SPM12 (444 {+/-} 4 s and 159 {+/-} 2 s for FSL and SPM12, respectively). In the similarity assessment, the tissue mask of the brain obtained by the proposed method showed higher consistency with those obtained by FSL than with those obtained by SPM12. The proposed method misclassified the subcortical structures and large vessels since it is based on the intensities of multi-contrast images obtained using MP2RAGE, which uses a similar segmentation approach as FSL but is not based on a template image or a parcellated brain atlas, which are used for FreeSurfer and SPM12, respectively. However, the proposed method showed good segmentation in the cerebellum and WM in the medial part of the brain in comparison with the other methods. Thus, because the proposed method using different contrast images of MP2RAGE sequence showed the shortest processing time and similar segmentation ability as the other methods, it may be useful for both neuroimaging research and clinical diagnosis.

neuroscience↗