Jong-Hwan Lee

Assistant, Associate, and Full Professor

Korea University

South Korea

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About

Jong-Hwan Lee is a Full Professor at Korea University in South Korea. His research primarily focuses on neuroimaging, psychopathology prediction, and the neural correlates of cognitive processes. Recent publications include studies on deep learning applications in functional MRI, neural activity associated with impulsivity in adolescents, and mindfulness in relation to cognitive performance.

Jong-Hwan Lee

Research Interests

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Articles (17)

Transfer Learning of Deep Neural Networks Pretrained Using the ABCD Dataset for General Psychopathology Prediction in Korean Adolescents

Abnormal Association Between Neural Activity and Genetic Expressions of Impulsivity in Attention-Deficit/Hyperactivity Disorder: An Adolescent Brain Cognitive Development Study

Discovering individual fingerprints in resting‐state functional connectivity using deep neural networks

Autoencoder and restricted Boltzmann machine for transfer learning in functional magnetic resonance imaging task classification

Reliability of self-reported dispositional mindfulness scales and their association with working memory performance and functional connectivity

Neural representations of the perception of handwritten digits and visual objects from a convolutional neural network compared to humans

General psychopathology factor (p-factor) prediction using resting-state functional connectivity and a scanner-generalization neural network

Acoustic Simulation for Transcranial Focused Ultrasound Using GAN-Based Synthetic CT

Electronic Cigarette Vaping Did Not Enhance the Neural Process of Working Memory for Regular Cigarette Smokers

Spectral dynamic causal modeling of mindfulness, mind-wandering, and resting-state in the triple network using fMRI

Systematic evaluation of recursive approach of EEG-segment-based PCA for removal of helium-pump artefact from MRI

Mixed‐effects multilevel analysis followed by canonical correlation analysis is an effective fMRI tool for the investigation of idiosyncrasies

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?

fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations

Functional magnetic resonance imaging multivoxel pattern analysis reveals neuronal substrates for collaboration and competition with myopic and predictive strategic reasoning

A naturalistic viewing paradigm using 360° panoramic video clips and real-time field-of-view changes with eye-gaze tracking

Deep learning methods and applications in neuroimaging

Collaborators (3)

Marina Papoutsi

Hyun-Chul Kim

Nan-kuei Chen

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