https://scholar.dgist.ac.kr/handle/20.500.11750/757 Wed, 11 Nov 2026 11:12:53 GMT 2026-11-11T11:12:53Z https://scholar.dgist.ac.kr/handle/20.500.11750/61169 Title: Graded prefrontal–sensory connectivity underlies cross-modal hierarchical control Author(s): Yoo, Taehyun; Jeon, Hyeon-Ae Abstract: Does the rostro–caudal architecture of the prefrontal cortex (PFC) generalize to support hierarchical control across sensory modalities? To address this question, we used functional magnetic resonance imaging while participants performed three cue-based tasks integrating auditory and visual modalities, requiring policy abstraction from auditory cues to motor responses, visual features, or visual dimensions. Behaviorally, increasing abstraction and complexity produced robust costs in accuracy and reaction time, underscoring the computational demands of cross-modal control. Univariate analyses revealed complexity-modulated effects within each level of abstraction and a corresponding anterior shift in PFC activation paralleling the hierarchical gradient. Multivoxel pattern analysis further demonstrated a representational shift, where caudal PFC encoded concrete motor responses, whereas rostral PFC represented abstract dimensions. Functional connectivity analyses revealed a complementary dissociation: caudal subregions showed selective, complexity-modulated coupling with primary sensory cortices during lower-level control, whereas rostral PFC exhibited functional independence from direct sensory coupling at the highest level of abstraction. Instead, increasing abstraction was associated with enhanced coupling among higher-order association areas, particularly between the superior temporal and intraparietal regions. Together, these findings extend prior unimodal research by demonstrating that hierarchical control is instantiated along a rostro–caudal gradient that flexibly reconfigures prefrontal–sensory interactions to meet multiple sensory demands. This cross-modal hierarchical architecture enables a transition from direct sensorimotor mappings to abstract regulation, providing a neural basis for adaptive cognition in the complex, multisensory environments of everyday life. © 2026 The Author(s). Tue, 30 Jun 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/61169 2026-06-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/17340 Title: A Memorial Tribute to Kyoung-Min Lee: An Outstanding Behavioral Neurologist and Cognitive Neuroscientist Author(s): Woo, Sung-Ho; Jeon, Hyeon-Ae; Kang, Soyoung; Joo, Hyeyeon; Seo, Min-Hee; Lee, Eunbeen; Heo, Jae-Hyeok; Cha, Jeong-In; Ryu, Jeh-Kwang; Kim, Min-Jeong Abstract: [No abstract available] Mon, 31 Oct 2022 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/17340 2022-10-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/16949 Title: Reduced functional connectivity supports statistical learning of temporally distributed regularities Author(s): Park, Jungtak; Janacsek, Karolina; Nemeth, Dezso; Jeon, Hyeon-Ae Abstract: Statistical learning is a powerful ability that extracts regularities from our environment and makes predictions about future events. Using functional magnetic resonance imaging, we aimed to probe how a wide range of brain areas are intertwined to support statistical learning, characterising its architecture in the whole-brain functional connectivity (FC). Participants performed a statistical learning task of temporally distributed regularities. We used refined behavioural learning scores to associate individuals’ learning performances with the FC changed by statistical learning. As a result, the learning performance was mediated by the activation strength in the lateral occipital cortex, angular gyrus, precuneus, anterior cingulate cortex, and superior frontal gyrus. Through a group independent component analysis, activations of the superior frontal network showed the largest correlation with the statistical learning performances. Seed-to-voxel whole-brain and seed-to-ROI FC analyses revealed that the FC between the superior frontal gyrus and the salience, language, and dorsal attention networks were reduced during statistical learning. We suggest that the weakened functional connections between the superior frontal gyrus and brain regions involved in top-down control processes serve a pivotal role in statistical learning, supporting better processing of novel information such as the extraction of new patterns from the environment. © 2022 The Author(s) Fri, 30 Sep 2022 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/16949 2022-09-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/15892 Title: Refined prefrontal working memory network as a neuromarker for Alzheimer’s disease Author(s): Kim, Eunho; Yu, Jin-Woo; Kim, Bomin; Lim, Sung‐Ho; Lee, Sang-Ho; Kim, Kwangsu; Son, Gowoon; Jeon, Hyeon-Ae; Moon, Cheil; Sakong, Joon; Choi, Ji-Woong Abstract: Detecting Alzheimer’s disease (AD) is an important step in preventing pathological brain damage. Working memory (WM)-related network modulation can be a pathological feature of AD, but is usually modulated by untargeted cognitive processes and individual variance, resulting in the concealment of this key information. Therefore, in this study, we comprehensively investigated a new neuromarker, named “refined network,” in a prefrontal cortex (PFC) that revealed the pathological features of AD. A refined network was acquired by removing unnecessary variance from the WM-related network. By using a functional near-infrared spectroscopy (fNIRS) device, we evaluated the reliability of the refined network, which was identified from the three groups classified by AD progression: healthy people (N=31), mild cognitive impairment (N=11), and patients with AD (N=18). As a result, we identified edges with significant correlations between cognitive functions and groups in the dorsolateral PFC. Moreover, the refined network achieved a significantly correlating metric with neuropsychological test scores, and a remarkable three-class classification accuracy (95.0%). These results implicate the refined PFC WM-related network as a powerful neuromarker for AD screening. © 2021 Optical Society of America Sun, 31 Oct 2021 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/15892 2021-10-31T15:00:00Z