Hyeon-Ae Jeon
Seoul National University · 神経科学
研究室紹介
Professor Hyeon-Ae Jeon's research lab specializes in cognitive neuroscience, focusing on the neural mechanisms underlying hierarchical information processing in the human brain. The lab investigates how prefrontal and parietal cortical regions, particularly Brodmann area 44, support complex cognitive functions across domains such as language, music, visuo-spatial reasoning, and mathematical expertise. Using advanced neuroimaging techniques—including high-field fMRI, dMRI, and PET—the lab explores the functional and structural brain networks that support statistical learning, cognitive control, and the impact of expertise and neurodegeneration (e.g., in Alzheimer’s disease). A central theme is understanding the role of cortico-striatal-thalamo-cortical loops and cholinergic systems in cognitive hierarchy and their disruption in neurodegenerative conditions.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15This review scrutinizes several findings on human hierarchical processing within the prefrontal cortex (PFC) in diverse cognitive domains. Converging evidence from previous studies has shown that the PFC, specifically, BA44, may function as the essential region for hierarchical processing across the domains. In language fMRI studies, BA 44 was significantly activated for the hierarchical processing of center-embedded sentences and this pattern of activations was also observed in artificial gramm
Despite myriads of studies on a parallel organization of cortico-striatal-thalamo-cortical loops, direct evidence of this has been lacking for the healthy human brain. Here, we scrutinize the functional specificity of the cortico-subcortical loops depending on varying levels of cognitive hierarchy as well as their structural connectivity with high-resolution fMRI and diffusion-weighted MRI (dMRI) at 7 tesla. Three levels of cognitive hierarchy were implemented in two domains: second language and
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 ass
Cholinergic dysfunction is well known to significantly contribute to the cognitive decline in Alzheimer's disease (AD). However, it has not been clarified whether the cholinergic dysfunction is a primary event or a retrograde event secondary to neuronal loss of the cholinergic targets. Analysis of the in vivo neuronal activity of the basal forebrain in the early stages of AD could yield more information about this issue. In the present study, uptake of [18F]-fluorodeoxyglucose (FDG) in the basal
To what extent is varying cognitive expertise reflected in the brain's functional specificity and connectivity? We addressed this question by examining expertise in mathematics based on the fact that mathematical skills are one of the most critical cognitive abilities known to be a good predictor of academic achievement. We investigated processing of hierarchical structures, which is a fundamental process for building complex cognitive architecture. Experts and nonexperts in mathematics particip
To what extent are levels of cognitive expertise reflected in differential structural connectivity of the brain? We addressed this question by analyzing the white matter brain structure of experts (mathematicians) versus non-experts (non-mathematicians) using probabilistic tractography. Having mathematicians and non-mathematicians as participant groups enabled us to directly compare profiles of structural connectivity arising from individual levels of expertise in mathematics. Tracking from func
Statistical learning (SL) is essential in enabling humans to extract probabilistic regularities from the world. The ability to accomplish ultimate learning performance with training (i.e., the potential of learning) has been known to be dissociated with performance improvement per amount of learning time (i.e., the efficiency of learning). Here, we quantified the potential and efficiency of SL separately through mathematical modeling and scrutinized how they were affected by various executive fu
Recent advances in neuroimaging have augmented numerous findings in the human reasoning process but have yielded varying results. One possibility for this inconsistency is that reasoning is such an intricate cognitive process, involving attention, memory, executive functions, symbolic processing, and fluid intelligence, whereby various brain regions are inevitably implicated in orchestrating the process. Therefore, researchers have used meta-analyses for a better understanding of neural mechanis
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 r
We address the question of, among several executive functions, which one has a strong influence on metaphor comprehension. To this end, participants took part in a metaphor comprehension task where metaphors had varying levels of familiarity (familiar vs. novel metaphors) with different conditions of context (supporting vs. opposing contexts). We scrutinized each participant's detailed executive functions using seven neuropsychological tests. More interestingly, we modelled their responses in me