Articles

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

Sungman Jo; Hyun-Chul Kim; Niv Lustig; Gang Chen; Jong-Hwan Lee

Human Brain Mapping, 2021 Aug 20. doi: 10.1002/hbm.25627.

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Acoustic simulation for transcranial focused ultrasound using GAN-based synthetic CT

Heekyung Koh; Tae Young Park; Yong An Chung; Jong-Hwan Lee; Hyungmin Kim

IEEE Journal of Biomedical and Health Informatics (J-BHI), 2021 Aug 13; doi: 10.1109/JBHI.2021.3103387.

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Predictors of Real-Time fMRI Neurofeedback Performance and Improvement – a Machine Learning Mega-Analysis

Amelie Haugg, Fabian M. Renz; Andrew A. Nicholson; Cindy Lor; Sebastian J. Götzendorfer; Ronald Sladky; Stavros Skouras; Amalia McDonald; Cameron Craddock; Lydia Hellrung; Matthias Kirschner; Marcus Herdener; Yury Koush; Marina Papoutsi; Jackob Keynan; Talma Hendler; Kathrin Cohen Kadosh; Catharina Zich; Simon H. Kohl; Manfred Hallschmid; Jeff MacInnes; R. Alison Adcock; Kathryn Dickerson; Nan-Kuei Chen; Kymberly Young; Jerzy Bodurka; Michael Marxen; Shuxia Yao; Benjamin Becker; Tibor Auer; Renate Schweizer; Gustavo Pamplona; Ruth A. Lanius; Kirsten Emmert; Sven Haller; Dimitri Van De Ville; Dong-Youl Kim; Jong-Hwan Lee; Theo Marins; Megumi Fukuda; Bettina Sorger; Tabea Kamp; Sook-Lei Liew; Ralf Veit; Maartje Spetter; Nikolaus Weiskopf; Frank Scharnowski; David Steyrl

NeuroImage, 237, 15 August 2021, 118207. doi: 10.1016/j.neuroimage.2021.118207

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Cigarette craving modulation is more feasible than resistance modulation for heavy cigarette smokers: Empirical evidence from functional MRI data

DY Kim, M Tegethoff, G Meinlschmidt, SS Yoo, JH Lee

Neuroreport, 32 (9), June 9, 2021 doi: 10.1097/WNR.0000000000001653

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Deep learning methods and applications in neuroimaging

Jing Sui, MingXia Liu, Jong-Hwan Lee, Jun Zhang, Vince Calhoun

J Neurosci Methods. 2020 Jun 1;339:108718. doi: 10.1016/j.jneumeth.2020.108718. Epub 2020 Apr 6.

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

Vu H, Kim HC, Jung M, Lee JH

NeuroImage, Vol. 223, Dec. 2020, doi: 10.1016/j.neuroimage.2020.117328

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

Haugg A., Sladky R., Skouras S., McDonald A., Craddock C., Kirschner M., Herdener M., Koush Y., Papoutsi M., Keynan J.N., Hendler T., Cohen Kadosh K., Zich C., MacInnes J., Adcock A., Dickerson K., Chen N-K., Young K., Bodurka J., Yao S., Becker B., Auer T., Schweizer R., Pamplona G., Emmert K., Haller S., Van De Ville D., Blefari M.L., Kim D-Y., Lee J-H., Marins T.F., Megumi F., Sorger B., Kamp T., Liew S-L., Veit R., Spetter M., Weiskopf N., Scharnowski F

Human Brain Mapping, 30 July 2020, doi: 10.1002/hbm.25089

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

Kim HC, Jin S, Jo S, Lee JH

NeuroImage, Vol. 216, 2020 Aug 1;216:116617. doi: 10.1016/j.neuroimage.2020.116617.

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Does fMRI neurofeedback in the context of stress influence mood and arousal? A randomised controlled trial with parallel group design

Angelo Belardi, Jong-Hwan Lee, Hyun-Chul Kim, Esther Stalujanis, Eun Kyung Jung, Minkyung Oh, Seung-Schik Yoo, Jens C. Pruessner, Marion Tegethoff, Gunther Meinlschmidt

F1000 Research, In Press

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