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

Kirsten Emmert, Rotem Kopel, James Sulzer, Annette B. Brühl, Brian D. Berman, David E.J. Linden, Silvina G. Horovitz, Markus Breimhorst, Andrea Caria, Sabine Frank, Stephen Johnston Orcid Logo, Zhiying Long, Christian Paret, Fabien Robineau, Ralf Veit, Andreas Bartsch, Christian F. Beckmann, Dimitri Van De Ville, Sven Haller

NeuroImage, Volume: 124, Pages: 806 - 812

Swansea University Author: Stephen Johnston Orcid Logo

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DOI (Published version): 10.1016/j.neuroimage.2015.09.042

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 neu...

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Published in: NeuroImage
ISSN: 10538119
Published: 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa24980
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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.</abstract><type>Journal Article</type><journal>NeuroImage</journal><volume>124</volume><paginationStart>806</paginationStart><paginationEnd>812</paginationEnd><publisher/><issnPrint>10538119</issnPrint><keywords>Neurofeedback, fMRI, Meta-analysis</keywords><publishedDay>31</publishedDay><publishedMonth>1</publishedMonth><publishedYear>2016</publishedYear><publishedDate>2016-01-31</publishedDate><doi>10.1016/j.neuroimage.2015.09.042</doi><url/><notes/><college>COLLEGE NANME</college><department>Psychology</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HPS</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2020-09-08T08:49:45.6553461</lastEdited><Created>2015-12-07T13:57:34.4629384</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">School of Psychology</level></path><authors><author><firstname>Kirsten</firstname><surname>Emmert</surname><order>1</order></author><author><firstname>Rotem</firstname><surname>Kopel</surname><order>2</order></author><author><firstname>James</firstname><surname>Sulzer</surname><order>3</order></author><author><firstname>Annette B.</firstname><surname>Br&#xFC;hl</surname><order>4</order></author><author><firstname>Brian D.</firstname><surname>Berman</surname><order>5</order></author><author><firstname>David E.J.</firstname><surname>Linden</surname><order>6</order></author><author><firstname>Silvina G.</firstname><surname>Horovitz</surname><order>7</order></author><author><firstname>Markus</firstname><surname>Breimhorst</surname><order>8</order></author><author><firstname>Andrea</firstname><surname>Caria</surname><order>9</order></author><author><firstname>Sabine</firstname><surname>Frank</surname><order>10</order></author><author><firstname>Stephen</firstname><surname>Johnston</surname><orcid>0000-0001-9360-8856</orcid><order>11</order></author><author><firstname>Zhiying</firstname><surname>Long</surname><order>12</order></author><author><firstname>Christian</firstname><surname>Paret</surname><order>13</order></author><author><firstname>Fabien</firstname><surname>Robineau</surname><order>14</order></author><author><firstname>Ralf</firstname><surname>Veit</surname><order>15</order></author><author><firstname>Andreas</firstname><surname>Bartsch</surname><order>16</order></author><author><firstname>Christian F.</firstname><surname>Beckmann</surname><order>17</order></author><author><firstname>Dimitri Van De</firstname><surname>Ville</surname><order>18</order></author><author><firstname>Sven</firstname><surname>Haller</surname><order>19</order></author></authors><documents><document><filename>0024980-14032018121725.pdf</filename><originalFilename>24980.pdf</originalFilename><uploaded>2018-03-14T12:17:25.4500000</uploaded><type>Output</type><contentLength>799431</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2016-04-12T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2020-09-08T08:49:45.6553461 v2 24980 2015-12-07 Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated? a5a4e9fd4ddde98a4cc3c1e3c6fa310f 0000-0001-9360-8856 Stephen Johnston Stephen Johnston true false 2015-12-07 HPS 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. Journal Article NeuroImage 124 806 812 10538119 Neurofeedback, fMRI, Meta-analysis 31 1 2016 2016-01-31 10.1016/j.neuroimage.2015.09.042 COLLEGE NANME Psychology COLLEGE CODE HPS Swansea University 2020-09-08T08:49:45.6553461 2015-12-07T13:57:34.4629384 Faculty of Medicine, Health and Life Sciences School of Psychology Kirsten Emmert 1 Rotem Kopel 2 James Sulzer 3 Annette B. Brühl 4 Brian D. Berman 5 David E.J. Linden 6 Silvina G. Horovitz 7 Markus Breimhorst 8 Andrea Caria 9 Sabine Frank 10 Stephen Johnston 0000-0001-9360-8856 11 Zhiying Long 12 Christian Paret 13 Fabien Robineau 14 Ralf Veit 15 Andreas Bartsch 16 Christian F. Beckmann 17 Dimitri Van De Ville 18 Sven Haller 19 0024980-14032018121725.pdf 24980.pdf 2018-03-14T12:17:25.4500000 Output 799431 application/pdf Accepted Manuscript true 2016-04-12T00:00:00.0000000 true eng
title Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?
spellingShingle Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?
Stephen Johnston
title_short Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?
title_full Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?
title_fullStr Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?
title_full_unstemmed Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?
title_sort Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?
author_id_str_mv a5a4e9fd4ddde98a4cc3c1e3c6fa310f
author_id_fullname_str_mv a5a4e9fd4ddde98a4cc3c1e3c6fa310f_***_Stephen Johnston
author Stephen Johnston
author2 Kirsten Emmert
Rotem Kopel
James Sulzer
Annette B. Brühl
Brian D. Berman
David E.J. Linden
Silvina G. Horovitz
Markus Breimhorst
Andrea Caria
Sabine Frank
Stephen Johnston
Zhiying Long
Christian Paret
Fabien Robineau
Ralf Veit
Andreas Bartsch
Christian F. Beckmann
Dimitri Van De Ville
Sven Haller
format Journal article
container_title NeuroImage
container_volume 124
container_start_page 806
publishDate 2016
institution Swansea University
issn 10538119
doi_str_mv 10.1016/j.neuroimage.2015.09.042
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str School of Psychology{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}School of Psychology
document_store_str 1
active_str 0
description 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.
published_date 2016-01-31T03:29:42Z
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