Literature DB >> 26419389

Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?

Kirsten Emmert1, Rotem Kopel2, James Sulzer3, Annette B Brühl4, Brian D Berman5, David E J Linden6, Silvina G Horovitz7, Markus Breimhorst8, Andrea Caria9, Sabine Frank9, Stephen Johnston10, Zhiying Long11, Christian Paret12, Fabien Robineau13, Ralf Veit14, Andreas Bartsch15, Christian F Beckmann16, Dimitri Van De Ville2, Sven Haller17.   

Abstract

An increasing number of studies using real-time fMRI neurofeedback have demonstrated that successful regulation of neural activity is possible in various brain regions. Since these studies focused on the regulated region(s), little is known about the target-independent mechanisms associated with neurofeedback-guided control of brain activation, i.e. the regulating network. While the specificity of the activation during self-regulation is an important factor, no study has effectively determined the network involved in self-regulation in general. In an effort to detect regions that are responsible for the act of brain regulation, we performed a post-hoc analysis of data involving different target regions based on studies from different research groups. We included twelve suitable studies that examined nine different target regions amounting to a total of 175 subjects and 899 neurofeedback runs. Data analysis included a standard first- (single subject, extracting main paradigm) and second-level (single subject, all runs) general linear model (GLM) analysis of all participants taking into account the individual timing. Subsequently, at the third level, a random effects model GLM included all subjects of all studies, resulting in an overall mixed effects model. Since four of the twelve studies had a reduced field of view (FoV), we repeated the same analysis in a subsample of eight studies that had a well-overlapping FoV to obtain a more global picture of self-regulation. The GLM analysis revealed that the anterior insula as well as the basal ganglia, notably the striatum, were consistently active during the regulation of brain activation across the studies. The anterior insula has been implicated in interoceptive awareness of the body and cognitive control. Basal ganglia are involved in procedural learning, visuomotor integration and other higher cognitive processes including motivation. The larger FoV analysis yielded additional activations in the anterior cingulate cortex, the dorsolateral and ventrolateral prefrontal cortex, the temporo-parietal area and the visual association areas including the temporo-occipital junction. In conclusion, we demonstrate that several key regions, such as the anterior insula and the basal ganglia, are consistently activated during self-regulation in real-time fMRI neurofeedback independent of the targeted region-of-interest. Our results imply that if the real-time fMRI neurofeedback studies target regions of this regulation network, such as the anterior insula, care should be given whether activation changes are related to successful regulation, or related to the regulation process per se. Furthermore, future research is needed to determine how activation within this regulation network is related to neurofeedback success.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Brain regulation; Neurofeedback; Real-time fMRI

Mesh:

Year:  2015        PMID: 26419389     DOI: 10.1016/j.neuroimage.2015.09.042

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


  60 in total

1.  Resting state differences between successful and unsuccessful restrained eaters.

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Review 2.  Closed-loop brain training: the science of neurofeedback.

Authors:  Ranganatha Sitaram; Tomas Ros; Luke Stoeckel; Sven Haller; Frank Scharnowski; Jarrod Lewis-Peacock; Nikolaus Weiskopf; Maria Laura Blefari; Mohit Rana; Ethan Oblak; Niels Birbaumer; James Sulzer
Journal:  Nat Rev Neurosci       Date:  2016-12-22       Impact factor: 34.870

3.  How feedback, motor imagery, and reward influence brain self-regulation using real-time fMRI.

Authors:  Pradyumna Sepulveda; Ranganatha Sitaram; Mohit Rana; Cristian Montalba; Cristian Tejos; Sergio Ruiz
Journal:  Hum Brain Mapp       Date:  2016-06-06       Impact factor: 5.038

4.  The effects of psychiatric history and age on self-regulation of the default mode network.

Authors:  Stavros Skouras; Frank Scharnowski
Journal:  Neuroimage       Date:  2019-05-16       Impact factor: 6.556

5.  Monitoring and control of amygdala neurofeedback involves distributed information processing in the human brain.

Authors:  Christian Paret; Jenny Zähringer; Matthias Ruf; Martin Fungisai Gerchen; Stephanie Mall; Talma Hendler; Christian Schmahl; Gabriele Ende
Journal:  Hum Brain Mapp       Date:  2018-03-30       Impact factor: 5.038

6.  Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma.

Authors:  D Rangaprakash; Michael N Dretsch; Archana Venkataraman; Jeffrey S Katz; Thomas S Denney; Gopikrishna Deshpande
Journal:  Hum Brain Mapp       Date:  2017-10-23       Impact factor: 5.038

Review 7.  Amygdala real-time functional magnetic resonance imaging neurofeedback for major depressive disorder: A review.

Authors:  Kymberly D Young; Vadim Zotev; Raquel Phillips; Masaya Misaki; Wayne C Drevets; Jerzy Bodurka
Journal:  Psychiatry Clin Neurosci       Date:  2018-05-21       Impact factor: 5.188

8.  Degrading traumatic memories with eye movements: a pilot functional MRI study in PTSD.

Authors:  Kathleen Thomaes; Iris M Engelhard; Marit Sijbrandij; Danielle C Cath; Odile A Van den Heuvel
Journal:  Eur J Psychotraumatol       Date:  2016-11-29

Review 9.  Process-based framework for precise neuromodulation.

Authors:  Nitzan Lubianiker; Noam Goldway; Tom Fruchtman-Steinbok; Christian Paret; Jacob N Keynan; Neomi Singer; Avihay Cohen; Kathrin Cohen Kadosh; David E J Linden; Talma Hendler
Journal:  Nat Hum Behav       Date:  2019-04-15

10.  Neural Correlates of Success and Failure Signals During Neurofeedback Learning.

Authors:  Joaquim Radua; Teodora Stoica; Dustin Scheinost; Christopher Pittenger; Michelle Hampson
Journal:  Neuroscience       Date:  2016-04-05       Impact factor: 3.590

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