Author

Jong‐Hwan Lee

Korea UniversityORCID 0000-0002-8902-6009Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Organism:human· 2 Assay:imaging· 2 Approach:statistical model· 2 Research Focus:clustering· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Amalia McDonald· 2 shared Amelie HauggiD· 2 shared Benjamin BeckeriD· 2 shared Bettina SorgeriD· 2 shared Catharina ZichiD· 2 shared Dimitri Van De VilleiD· 2 shared Dong‐Youl KimiD· 2 shared Frank ScharnowskiiD· 2 shared

Activity by Year

2020-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersNeuroscience

Amalia McDonald

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Author profile →

3 papers

Recent·Most cited

A naturalistic reinforcement learning task uncovers historical neural representations

Sangsoo Jin, Juhyeon Lee, Satrajit Ghosh, Jong‐Hwan Lee

This study introduces a naturalistic reinforcement learning task to investigate neural representations during exploration, finding robust feedback history encoding in specific brain regions across two participant groups.

bioRxiv · Jul 17, 2025 · PreprintNeuroscience

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis

Amelie Haugg, Fabian M. Renz, Andrew A. Nicholson, Cindy Sumaly Lor, Sebastian J. Götzendorfer, Ronald Sladky, Stavros Skouras, Amalia McDonald, Richard Cameron Craddock, Lydia Hellrung, Matthias Kirschner, Marcus Herdener, Yury Koush, Marina Papoutsi, Jackob Nimrod Keynan, , David Steyrl

This study uses machine learning to analyze 608 participants and identifies two factors improving neurofeedback performance: pre-training no-feedback runs and training patients over healthy participants.

bioRxiv · Oct 21, 2020 · PreprintNeuroscience

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

Amelie Haugg, Ronald Sladky, Stavros Skouras, Amalia McDonald, Cameron Craddock, Matthias Kirschner, Marcus Herdener, Yury Koush, Marina Papoutsi, Jackob Nimrod Keynan, Talma Hendler, Kathrin Cohen Kadosh, Catharina Zich, Jeff MacInnes, R. Alison Adcock, , Frank Scharnowski

This study investigates whether pre-training brain activity can predict success in real-time fMRI neurofeedback learning across 24 studies and 401 participants, finding a weak correlation but no consistent predictors.

Human Brain Mapping · Jul 30, 2020Peer-reviewedNeuroscience