Sungkyunkwan University · Neuroscience
Professor Jae-Joong Lee's research lab specializes in neuroscience and neuroimaging, focusing on the neural mechanisms underlying pain and emotion. The lab investigates how the brain constructs affective experiences—particularly pain and pleasure—through dynamic functional brain networks and personalized brain decoding. Using advanced fMRI techniques and computational modeling, the lab explores the representation of affective valence and intensity in key brain regions such as the prefrontal cortex, insula, and cingulate cortex. A central theme is the development of individualized brain decoding methods for chronic pain, aiming to improve diagnosis and treatment through neuroscientific insights.
Figures are computed from collected data and may differ slightly.
Pain is constructed through complex interactions among multiple brain systems, but it remains unclear how functional brain networks are reconfigured over time while experiencing pain. Here, we investigated the time-varying changes in the functional brain networks during 20 min capsaicin-induced sustained orofacial pain. In the early stage, the orofacial areas of the primary somatomotor cortex were separated from other areas of the somatosensory cortex and integrated with subcortical and frontopa
Pleasure and pain are two fundamental, intertwined aspects of human emotions. Pleasurable sensations can reduce subjective feelings of pain and vice versa, and we often perceive the termination of pain as pleasant and the absence of pleasure as unpleasant. This implies the existence of brain systems that integrate them into modality-general representations of affective experiences. Here, we examined representations of affective valence and intensity in an functional MRI (fMRI) study (<i>n</i> =
In order to present that the electromagnetic compatibility standards following the frequency goes up which is based automotive electronics, in this paper, a hybrid/electric vehicle battery which reflects the frequency of the equivalent circuit model is introduced. By using this circuit modeling, the impedance characteristics can be analysed and an analyze of battery one cell is finished. Using this model, each different from the discharging situation, the discharge characteristic curve could be
This dataset includes behavioral data and analysis-derived outputs from fMRI data associated with the following study:Jae-Joong Lee, Seongwoo Jo, Sungkun Cho, Choong-Wan Woo, Personalized Brain Decoding of Spontaneous Pain in Individuals With Chronic Pain, 2026, <i>Nature Neuroscience</i>For more information about preprocessing and analysis code accompanying the data, please visit the following repository: https://github.com/cocoanlab/DEIPP
ABSTRACT Pleasure and pain are two opposites that compete and influence each other, implying the existence of brain systems that integrate them to generate modality-general affective experiences. Here, we examined the brain’s general affective codes (i.e., affective valence and intensity) across sustained pleasure and pain through an fMRI experiment ( n = 58). We found that the distinct sub-populations of voxels within the ventromedial and lateral prefrontal cortices, the orbitofrontal cortex, t
A bstract Pain is constructed through complex interactions among multiple brain systems, but it remains unclear how functional brain network representations are dynamically reconfigured over time while experiencing pain. Here, we investigated the dynamic changes in the functional brain networks during 20-min capsaicin-induced sustained orofacial pain. In the early stage, the orofacial areas of the primary somatomotor cortex were separated from the other primary somatomotor cortices and integrate
This dataset includes behavioral data and analysis-derived outputs from fMRI data associated with the following study:Jae-Joong Lee, Seongwoo Jo, Sungkun Cho, Choong-Wan Woo, Personalized Brain Decoding of Spontaneous Pain in Individuals With Chronic Pain, 2026, <i>Nature Neuroscience</i>For more information about preprocessing and analysis code accompanying the data, please visit the following repository: https://github.com/cocoanlab/DEIPP
This dataset includes behavioral data and analysis-derived outputs from fMRI data associated with the following study:Jae-Joong Lee, Seongwoo Jo, Sungkun Cho, Choong-Wan Woo, Personalized Brain Decoding of Spontaneous Pain in Individuals With Chronic Pain, 2026, <i>Nature Neuroscience</i>For more information about preprocessing and analysis code accompanying the data, please visit the following repository: https://github.com/cocoanlab/DEIPP
Detecting depression from conversational text using large language models (LLMs) has garnered significant interest. However, the limited interpretability of existing methods presents a major challenge for clinical application. To address this, we propose a novel framework for automatic depression assessment, which employs LLM prompting to extract interpretable factors linked to depression from text and uses linear regression to predict severity scores. We evaluated our approach using a benchmark
This dataset contains de-identified functional magnetic resonance imaging (fMRI) and dynamic functional connectivity data, and the self-reported pain ratings.<br>For more information, please visit out GitHub repository (https://github.com/cocoanlab/tops)!<br>Paper title: "A neuroimaging biomarker for sustained experimental and clinical pain"
This dataset includes behavioral data and analysis-derived outputs from fMRI data associated with the following study:Jae-Joong Lee, Seongwoo Jo, Sungkun Cho, Choong-Wan Woo, Personalized Brain Decoding of Spontaneous Pain in Individuals With Chronic Pain, 2026, <i>Nature Neuroscience</i>For more information about preprocessing and analysis code accompanying the data, please visit the following repository: https://github.com/cocoanlab/DEIPP
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