Literature DB >> 30315905

A systematic review of the psychological factors that influence neurofeedback learning outcomes.

Kathrin Cohen Kadosh1, Graham Staunton2.   

Abstract

Real-time functional magnetic resonance imaging (fMRI)-based neurofeedback represents the latest applied behavioural neuroscience methodology developed to train participants in the self-regulation of brain regions or networks. However, as with previous biofeedback approaches which rely on electroencephalography (EEG) or related approaches such as brain-machine interface technology (BCI), individual success rates vary significantly, and some participants never learn to control their brain responses at all. Given that these approaches are often being developed for eventual use in a clinical setting (albeit there is also significant interest in using NF for neuro-enhancement in typical populations), this represents a significant hurdle which requires more research. Here we present the findings of a systematic review which focused on how psychological variables contribute to learning outcomes in fMRI-based neurofeedback. However, as this is a relatively new methodology, we also considered findings from EEG-based neurofeedback and BCI. 271 papers were found and screened through PsycINFO, psycARTICLES, Psychological and Behavioural Sciences Collection, ISI Web of Science and Medline and 21 were found to contribute towards the aim of this survey. Several main categories emerged: Attentional variables appear to be of importance to both performance and learning, motivational factors and mood have been implicated as moderate predictors of success, while personality factors have mixed findings. We conclude that future research will need to systematically manipulate psychological variables such as motivation or mood, and to define clear thresholds for a successful neurofeedback effect. Non-responders need to be targeted for interventions and tested with different neurofeedback setups to understand whether their non-response is specific or general. Also, there is a need for qualitative evidence to understand how psychological variables influence participants throughout their training. This will help us to understand the subtleties of psychological effects over time. This research will allow interventions to be developed for non-responders and better selection procedures in future to improve the efficacy of neurofeedback.
Copyright © 2018 Elsevier Inc. All rights reserved.

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Year:  2018        PMID: 30315905     DOI: 10.1016/j.neuroimage.2018.10.021

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  20 in total

Review 1.  The Effects of Neurofeedback on Aging-Associated Cognitive Decline: A Systematic Review.

Authors:  Fátima Laborda-Sánchez; Selene Cansino
Journal:  Appl Psychophysiol Biofeedback       Date:  2021-01-02

2.  The importance of self-efficacy and negative affect for neurofeedback success for central neuropathic pain after a spinal cord injury.

Authors:  Krithika Anil; Sara Demain; Jane Burridge; David Simpson; Julian Taylor; Imogen Cotter; Aleksandra Vuckovic
Journal:  Sci Rep       Date:  2022-06-29       Impact factor: 4.996

Review 3.  Effects of Transcranial Alternating Current Stimulation and Neurofeedback on Alpha (EEG) Dynamics: A Review.

Authors:  Mária Orendáčová; Eugen Kvašňák
Journal:  Front Hum Neurosci       Date:  2021-07-08       Impact factor: 3.169

4.  Neurofeedback Training of Alpha Relative Power Improves the Performance of Motor Imagery Brain-Computer Interface.

Authors:  Qing Zhou; Ruidong Cheng; Lin Yao; Xiangming Ye; Kedi Xu
Journal:  Front Hum Neurosci       Date:  2022-04-08       Impact factor: 3.473

5.  Neurofunctional and behavioural measures associated with fMRI-neurofeedback learning in adolescents with Attention-Deficit/Hyperactivity Disorder.

Authors:  Sheut-Ling Lam; Marion Criaud; Analucia Alegria; Gareth J Barker; Vincent Giampietro; Katya Rubia
Journal:  Neuroimage Clin       Date:  2020-05-26       Impact factor: 4.881

6.  Reinforcement and Punishment Shape the Learning Dynamics in fMRI Neurofeedback.

Authors:  Manfred Klöbl; Paul Michenthaler; Godber Mathis Godbersen; Simon Robinson; Andreas Hahn; Rupert Lanzenberger
Journal:  Front Hum Neurosci       Date:  2020-07-24       Impact factor: 3.169

Review 7.  Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review.

Authors:  Stephan Heunis; Rolf Lamerichs; Svitlana Zinger; Cesar Caballero-Gaudes; Jacobus F A Jansen; Bert Aldenkamp; Marcel Breeuwer
Journal:  Hum Brain Mapp       Date:  2020-04-25       Impact factor: 5.038

8.  Home used, patient self-managed, brain-computer interface for the management of central neuropathic pain post spinal cord injury: usability study.

Authors:  M K H Al-Taleb; M Purcell; M Fraser; N Petric-Gray; A Vuckovic
Journal:  J Neuroeng Rehabil       Date:  2019-10-30       Impact factor: 4.262

9.  Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback.

Authors:  Mareike Daeglau; Frank Wallhoff; Stefan Debener; Ignatius Sapto Condro; Cornelia Kranczioch; Catharina Zich
Journal:  Sensors (Basel)       Date:  2020-03-14       Impact factor: 3.576

Review 10.  Imaging the socially-anxious brain: recent advances and future prospects.

Authors:  Janna Marie Bas-Hoogendam; P Michiel Westenberg
Journal:  F1000Res       Date:  2020-04-02
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