Literature DB >> 25743268

Resting-state sensorimotor rhythm (SMR) power predicts the ability to up-regulate SMR in an EEG-instrumental conditioning paradigm.

Johanna Louise Reichert1, Silvia Erika Kober2, Christa Neuper3, Guilherme Wood2.   

Abstract

OBJECTIVE: Instrumental conditioning of EEG activity (EEG-IC) is a promising method for improvement and rehabilitation of cognitive functions. However, it has been found that even healthy adults are not always able to learn how to regulate their brain activity during EEG-IC. In the present study, the role of a neurophysiological predictor of EEG-IC learning performance, the resting-state power of sensorimotor rhythm (rs-SMR, 12-15Hz), was investigated.
METHODS: Eyes-open and eyes-closed rs-SMR power was assessed before N=28 healthy adults underwent 10 training sessions of instrumental SMR conditioning (ISC), in which participants should learn to voluntarily increase their SMR power by means of audio-visual feedback. A control group of N=19 participants received gamma (40-43Hz) or sham EEG-IC.
RESULTS: N=19 of the ISC participants could be classified as "responders" as they were able to increase SMR power during training sessions, while N=9 participants ("non-responders") were not able to increase SMR power. Rs-SMR power in responders before start of ISC was higher in widespread parieto-occipital areas than in non-responders. A discriminant analysis indicated that eyes-open rs-SMR power in a central brain region specifically predicted later ISC performance, but not an increase of SMR in the control group.
CONCLUSIONS: Together, these findings indicate that rs-SMR power is a specific and easy-to-measure predictor of later ISC learning performance. SIGNIFICANCE: The assessment of factors that influence the ability to regulate brain activity is of high relevance, as it could be used to avoid potentially frustrating and expensive EEG-IC training sessions for participants who have a low chance of success.
Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  EEG instrumental conditioning; Neurofeedback; Regulation of brain activity; Responders; Resting-state EEG activity; Sensorimotor rhythm

Mesh:

Year:  2015        PMID: 25743268     DOI: 10.1016/j.clinph.2014.09.032

Source DB:  PubMed          Journal:  Clin Neurophysiol        ISSN: 1388-2457            Impact factor:   3.708


  22 in total

1.  Effects of SMR Neurofeedback on Cognitive Functions in an Adult Population with Sleep Problems: A Tele-neurofeedback Study.

Authors:  Ylka Kolken; Pierre Bouny; Martijn Arns
Journal:  Appl Psychophysiol Biofeedback       Date:  2022-09-17

2.  Sensorimotor rhythm neurofeedback training relieves anxiety in healthy people.

Authors:  Shuang Liu; Xinyu Hao; Xiaoya Liu; Yuchen He; Ludan Zhang; Xingwei An; Xizi Song; Dong Ming
Journal:  Cogn Neurodyn       Date:  2021-12-02       Impact factor: 3.473

3.  A Single Session of SMR-Neurofeedback Training Improves Selective Attention Emerging from a Dynamic Structuring of Brain-Heart Interplay.

Authors:  Pierre Bouny; Laurent M Arsac; Yvan Pratviel; Alexis Boffet; Emma Touré Cuq; Veronique Deschodt-Arsac
Journal:  Brain Sci       Date:  2022-06-17

4.  Shutting Down Sensorimotor Interferences after Stroke: A Proof-of-Principle SMR Neurofeedback Study.

Authors:  Johanna L Reichert; Silvia E Kober; Daniela Schweiger; Peter Grieshofer; Christa Neuper; Guilherme Wood
Journal:  Front Hum Neurosci       Date:  2016-07-15       Impact factor: 3.169

5.  Ability to Gain Control Over One's Own Brain Activity and its Relation to Spiritual Practice: A Multimodal Imaging Study.

Authors:  Silvia E Kober; Matthias Witte; Manuel Ninaus; Karl Koschutnig; Daniel Wiesen; Gabriela Zaiser; Christa Neuper; Guilherme Wood
Journal:  Front Hum Neurosci       Date:  2017-05-24       Impact factor: 3.169

6.  Time estimation and beta segregation: An EEG study and graph theoretical approach.

Authors:  Amir Hossein Ghaderi; Shadi Moradkhani; Arvin Haghighatfard; Fatemeh Akrami; Zahra Khayyer; Fuat Balcı
Journal:  PLoS One       Date:  2018-04-06       Impact factor: 3.240

7.  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

8.  Resting and Initial Beta Amplitudes Predict Learning Ability in Beta/Theta Ratio Neurofeedback Training in Healthy Young Adults.

Authors:  Wenya Nan; Feng Wan; Mang I Vai; Agostinho C Da Rosa
Journal:  Front Hum Neurosci       Date:  2015-12-21       Impact factor: 3.169

Review 9.  The Do's and Don'ts of Neurofeedback Training: A Review of the Controlled Studies Using Healthy Adults.

Authors:  Jacek Rogala; Katarzyna Jurewicz; Katarzyna Paluch; Ewa Kublik; Ryszard Cetnarski; Andrzej Wróbel
Journal:  Front Hum Neurosci       Date:  2016-06-17       Impact factor: 3.169

10.  Reply: On assessing neurofeedback effects: should double-blind replace neurophysiological mechanisms?

Authors:  Manuel Schabus
Journal:  Brain       Date:  2017-10-01       Impact factor: 13.501

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