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

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

Haugg, A., Renz, F., Nicholson, A., Lor, C., Goetzendorfer, S., Sladky, R., et al. (submitted). Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis.

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Date of Discard: 2022-10-10

Comment: dublette

Creators:
Haugg, A
Renz, FM
Nicholson, AA
Lor, C
Goetzendorfer, SJ
Sladky, R
Skouras, S
McDonald, A
Craddock, C
Hellrung, L
Kirschner, M
Herdener, M¹
Koush, Y
Papoutsi, M
Keynan, JN
Hendler, T
Kadosh, KC
Zich, C
Kohl, SH
Hallschmid, M
MacInnes, JAdcock, RADickerson, KChen, N-KYoung, KBodurka, JMarxen, MYao, SBecker, BAuer, TSchweizer, RPamplona, GLanius, REmmert, KHaller, SVan De Ville, DKim, D-YLee, J-HMarins, TMegumi, FSorger, BKamp, TLiew, S-LVeit, R^{1, 2} Spetter, MWeiskopf, NScharnowski, FSteyrl, D more..

Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794
2Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796

Dates: Submitted: 2020-10

Files: 0 Files

Locators: 1 Locator

version ID: item_3260572_2

Item State: Discarded

Name of Context: Import Context of the MPI for Biological Cybernetics, Affiliated to: Max Planck Institute for Biological Cybernetics