Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis

Autor: 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 C. 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, Fukuda Megumi, Bettina Sorger, Tabea Kamp, Sook-Lei Liew, Ralf Veit, Maartje Spetter, Nikolaus Weiskopf, Frank Scharnowski, David Steyrl

Publikováno v: NeuroImage, Vol 237, Iss , Pp 118207- (2021)

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of

Externí odkaz: https://doaj.org/article/a02d62b9cbe24b19b6aba414fbe6d977

Predictors of real-time fMRI neurofeedback performance and improvement: A machine learning mega-analysis

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

Publikováno v: NeuroImage
Neuroimage, 237:118207. Elsevier Science
NeuroImage, Vol 237, Iss, Pp 118207-(2021)
Neuroimage 237:118207 (2021)
NeuroImage 237, 118207-(2021). doi:10.1016/j.neuroimage.2021.118207

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e78400d6f2b08796d6e691d317a0aab0
https://hdl.handle.net/21.11116/0000-0008-9C56-8

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

Autor: Gunther Meinlschmidt, Dong Youl Kim, Jong-Hwan Lee, Seung-Schik Yoo, Marion Tegethoff

Publikováno v: Neuroreport. 32(9)

BACKGROUND Modulation of cigarette craving and neuronal activations from nicotine-dependent cigarette smokers using real-time functional MRI (rtfMRI)-based neurofeedback (rtfMRI-NF) has been previously reported. OBJECTIVES The aim of this study was t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4cb90858675a4f76eb020d54b8e56fc4
https://pubmed.ncbi.nlm.nih.gov/33901056

Practical design of a diffusion-type time-temperature indicator with intrinsic low temperature dependency

Autor: Panuwat Suppakul, Seung Ju Lee, Jung Hwa Yang, Sang Bong Lee, Dong Youl Kim

Publikováno v: Journal of Food Engineering. 223:22-31

A diffusion-type time-temperature indicator (TTI) is known to have relatively low temperature dependency, which is an inherent weakness that causes an inevitable prediction error of food shelf-life: under- or over-prediction. In this study, a TTI des

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a56b0fac71d5edec11bb36d306aba632
https://doi.org/10.1016/j.jfoodeng.2017.11.026

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

Autor: Megumi Fukuda, Jong-Hwan Lee, Talma Hendler, Amelie Haugg, Marcus Herdener, Marina Papoutsi, Matthias Kirschner, Gustavo S. P. Pamplona, Kymberly D. Young, Nikolaus Weiskopf, R. Alison Adcock, Benjamin Becker, Tabea Kamp, Jeff MacInnes, Kathrin Cohen Kadosh, Amalia McDonald, Catharina Zich, Dong Youl Kim, Yury Koush, Frank Scharnowski, Bettina Sorger, Ronald Sladky, Kirsten Emmert, Maria Laura Blefari, Maartje S. Spetter, Sven Haller, R. Cameron Craddock, Tibor Auer, Stavros Skouras, Jackob N. Keynan, Renate Schweizer, Theo Marins, Ralf Veit, Shuxia Yao, Nan-kuei Chen, Sook-Lei Liew, Kathryn C. Dickerson, Jerzy Bodurka, Dimitri Van De Ville

Publikováno v: Hum. Brain Mapp. 41, 3839-3854 (2020)
Human Brain Mapping, Vol. 41, No 14 (2020) pp. 3839-3854
Human Brain Mapping
Human Brain Mapping, 41(14), 3839-3854. Wiley

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real‐time fMRI neurofeedback studies re

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bbf1c3749531263260d38a209813c04
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417506