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
Přispěvatelé: Vision, RS: FPN CN 1
Jazyk: angličtina
Rok vydání: 2020
Předmět:
DOWN-REGULATION
Brain activity and meditation
real-time fMRI
ACTIVATION
0302 clinical medicine
CONNECTIVITY
Research Articles
Brain Mapping
learning
Radiological and Ultrasound Technology
05 social sciences
fMRI
PAIN
Brain
neurofeedback
Prognosis
Magnetic Resonance Imaging
NETWORKS
medicine.anatomical_structure
Neurology
Meta-analysis
Anatomy
Psychology
Research Article
Radiology
Nuclear Medicine and Medical Imaging
Adult
medicine.medical_specialty
functional neuroimaging
Positive correlation
ddc:616.0757
050105 experimental psychology
03 medical and health sciences
Physical medicine and rehabilitation
Functional neuroimaging
Fmri
Functional Neuroimaging
Learning
Neurofeedback
Real-time Fmri
real‐time fMRI
medicine
Humans
0501 psychology and cognitive sciences
Radiology
Nuclear Medicine and imaging
MODULATION
Anterior cingulate cortex
meta-analysis
Institutional repository
REDUCTION
SELF-REGULATION
ANTERIOR CINGULATE CORTEX
meta‐analysis
Practice
Psychological
Neurology (clinical)
Radiologi och bildbehandling
030217 neurology & neurosurgery
TIME FMRI NEUROFEEDBACK
Zdroj: 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
ISSN: 1065-9471
Popis: 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 report large inter‐individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs (i.e., self‐regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain‐based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.
Many real‐time fMRI neurofeedback studies report large inter‐individual differences in learning success, but the factors that cause this vast variability between participants remain unknown. Here, we used a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs could predict neurofeedback learning success. We were not able to identify common brain‐based success predictors across our diverse cohort of studies.
Databáze: OpenAIRE
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