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

Autor(en)

Amelie Haugg , FM Renz, AA Nicholson, C Lor, SJ Götzendorfer, R Sladky, S Skouras, A McDonald, C Craddock, L Hellrung, M Kirschner, M Herdener, Y Koush, Marina Papoutsi, Jackob N. Keynan, Talma Hendler, Kathrin Cohen Kadosh, Catharina Zich, Stephan Kohl, Jeff MacInnes, R. Alison Adcock, Nan Kuei Chen, Kymberly Young, Jerzy Bodurka, M Marxen, Shuxia Yao, Benjamin Becker, T. Auer, Roberto Schweizer, Gustavo Pamplona, Ruth Lanius, Kirsten Emmert, S Haller, Dimitri Van De Ville, D Y Kim, J.H. Lee, Theo Marins, Megumi Fukuda, Bettina Sorger, Tabea Kamp, S Liew, Ralf Veit, Maartje Spetter, Nikolaus Weiskopf, Frank Scharnowski, D Steyrl

Abstrakt

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 clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments.

With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Organisation(en)

Institut für Psychologie der Kognition, Emotion und Methoden, Institut für Klinische und Gesundheitspsychologie, Institut für Geschichte

Externe Organisation(en)

Universität Zürich (UZH), Universitätsspital Zürich, Universität Wien, University College London, Tel Aviv Sourasky Medical Center, Tel Aviv University, University of Surrey, University of Oxford, Julius-Maximilians-Universität Würzburg, University of Washington, Duke University, University of Arizona, University of Pittsburgh, Laureate Institute for Brain Research, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Universität Bern, Hôpital ophtalmique Jules-Gonin, Université de Lausanne, University of Western Ontario, Universitätsklinikum Schleswig-Holstein, Universität Genf, École polytechnique fédérale de Lausanne, D’Or Institute for Research and Education (IDOR), Waseda University, Universitätsklinikum Maastricht, Helmholtz-Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt, Eberhard Karls Universität Tübingen, University of Birmingham, Max-Planck-Institut für Kognitions- und Neurowissenschaften, University of Bergen (UiB), University of Virginia, University of Texas, Austin, Yale University, Technische Universität Dresden, Uppsala University, Korea University, University of Southern California

Journal

NeuroImage

Band

237

Anzahl der Seiten

11

ISSN

1053-8119

DOI

https://doi.org/10.1016/j.neuroimage.2021.118207

Publikationsdatum

08-2021

Peer-reviewed

Ja

ÖFOS 2012

501011 Kognitionspsychologie

Schlagwörter

ASJC Scopus Sachgebiete

Neurology, Cognitive Neuroscience

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/87dae53a-243f-4bfd-acda-f8357d150046