Yonsei University · Neuroscience
Professor Hae-Jeong Park's research lab specializes in computational and systems neuroscience, focusing on the neural mechanisms underlying brain network dynamics, functional integration, and their disruptions in neurological and psychiatric disorders. The lab employs advanced neuroimaging techniques—such as fMRI, DT-MRI, EEG, and LORETA—combined with computational modeling (e.g., dynamic causal modeling and spectral DCM) to investigate structural-functional relationships, dynamic connectivity, and electrophysiological biomarkers. A key focus is on translating these findings into clinical applications, particularly through deep learning for early diagnosis of neurological and ear diseases. The lab also develops innovative signal processing methods for artifact removal in EEG, enhancing data quality for brain-computer interface and clinical diagnostic applications.
Figures are computed from collected data and may differ slightly.
How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience. Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local (segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrai
Population maps of the corpus callosum (CC) and cortical lobe connections were generated by combining cortical gray matter parcellation with the diffusion tensor fiber tractography of individual subjects. This method is based on the fact that the cortical lobes of both hemispheres are interconnected by the corpus callosal fibers. T1-weighted structural MRIs and diffusion tensor MRIs (DT-MRI) of 22 right-handed, healthy subjects were used. Forty-seven cortical parcellations in the dorsal prefront
Context-sensitive and activity-dependent fluctuations in connectivity underlie functional integration in the brain and have been studied widely in terms of synaptic plasticity, learning and condition-specific (e.g., attentional) modulations of synaptic efficacy. This dynamic aspect of brain connectivity has recently attracted a lot of attention in the resting state fMRI community. To explain dynamic functional connectivity in terms of directed effective connectivity among brain regions, we intro
The current study is unprecedented in terms of both disease diversity and diagnostic accuracy, which is compatible or even better than an average otolaryngologist. The classifier was trained with data in a various acquisition condition, which is suitable for the practical environment. This study shows the usefulness of utilizing a deep learning model in the early detection and treatment of ear disease in the clinical situation. FUND: This research was supported by Brain Research Program through
We describe a method for the statistical parametric mapping of low resolution electromagnetic tomography (LORETA) using high-density electroencephalography (EEG) and individual magnetic resonance images (MRI) to investigate the characteristics of the mismatch negativity (MMN) generators in schizophrenia. LORETA, using a realistic head model of the boundary element method derived from the individual anatomy, estimated the current density maps from the scalp topography of the 128-channel EEG. From
An automated method for electrocardiogram (ECG)-artifact detection and elimination is proposed for application to a single-channel electroencephalogram (EEG) without a separate ECG channel for reference. The method is based on three characteristics of ECG artifacts: the spike-like property, the periodicity and the lack of correlation with the EEG. The method involves a two-step process: ECG artifact detection using the energy interval histogram (EIH) method and ECG artifact elimination using a m
Common representations of functional networks of resting state fMRI time series, including covariance, precision, and cross-correlation matrices, belong to the family of symmetric positive definite (SPD) matrices forming a special mathematical structure called Riemannian manifold. Due to its geometric properties, the analysis and operation of functional connectivity matrices may well be performed on the Riemannian manifold of the SPD space. Analysis of functional networks on the SPD space takes
To explore the effects of white matter in the absence of auditory input in the early deaf, we conducted a tract-based statistical analysis of the diffusion tensor anisotropy and the voxel-based morphometry in the white matter of 13 early deaf and 29 hearing individuals. Deaf individuals showed significant decreases in diffusion anisotropy and in regional volume reductions within the temporal white matter. Decreased anisotropy was also found at the internal capsule, superior longitudinal fascicul
Open papers in the app to read, cite, and organize with AI.
