Conducting decoded neurofeedback studies

Abstract Closed-loop neurofeedback has sparked great interest since its inception in the late 1960s. However, the field has historically faced various methodological challenges. Decoded fMRI neurofeedback may provide solutions to some of these problems. Notably, thanks to the recent advancements of...

Full description

Saved in:
Bibliographic Details
Published in:Social cognitive and affective neuroscience 2021-08, Vol.16 (8), p.838-848
Main Authors: Taschereau-Dumouchel, Vincent, Cortese, Aurelio, Lau, Hakwan, Kawato, Mitsuo
Format: Article
Language:English
Subjects:
Biofeedback training
Humans
Machine Learning
Magnetic Resonance Imaging
Neurofeedback
Original Manuscript
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c583t-2ed104ecf7e51268dd0de56483dfd28f35c9a949ff99b47429ba467c4e606513
cites cdi_FETCH-LOGICAL-c583t-2ed104ecf7e51268dd0de56483dfd28f35c9a949ff99b47429ba467c4e606513
container_end_page 848
container_issue 8
container_start_page 838
container_title Social cognitive and affective neuroscience
container_volume 16
creator Taschereau-Dumouchel, Vincent
Cortese, Aurelio
Lau, Hakwan
Kawato, Mitsuo
description Abstract Closed-loop neurofeedback has sparked great interest since its inception in the late 1960s. However, the field has historically faced various methodological challenges. Decoded fMRI neurofeedback may provide solutions to some of these problems. Notably, thanks to the recent advancements of machine learning approaches, it is now possible to target unconscious occurrences of specific multivoxel representations. In this tools of the trade paper, we discuss how to implement these interventions in rigorous double-blind placebo-controlled experiments. We aim to provide a step-by-step guide to address some of the most common methodological and analytical considerations. We also discuss tools that can be used to facilitate the implementation of new experiments. We hope that this will encourage more researchers to try out this powerful new intervention method.
doi_str_mv 10.1093/scan/nsaa063
format article
fullrecord <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8343564</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A775957577</galeid><oup_id>10.1093/scan/nsaa063</oup_id><sourcerecordid>A775957577</sourcerecordid><originalsourceid>FETCH-LOGICAL-c583t-2ed104ecf7e51268dd0de56483dfd28f35c9a949ff99b47429ba467c4e606513</originalsourceid><addsrcrecordid>eNp90d1LWzEYB-AgEz93t-tR2IUOrOY7JzeClG0KgqDehzR502U7TbqTc0T_e1PaFQsiuUhInvcXkhehLwSfE6zZRXE2XaRiLZZsBx0QxfVYYMo_bdZE7qPDUv5gLDTHbA_tM8qkIqw5QKeTnPzg-phmIw8ue_CjBEOXA4CfWvd3VPrBRyjHaDfYtsDn9XyEHn_-eJxcj2_vft1Mrm7HTjSsH1PwBHNwQYEgVDbeYw9C8ob54GkTmHDaaq5D0HrKFad6arlUjoPEUhB2hC5XsYthOgfvIPWdbc2ii3PbvZhso9k-SfG3meUn0zDO6j014HQd0OV_A5TezGNx0LY2QR6KoUw3klKOm0q_rejMtmBiCrkmuiU3V0oJLZRQqqrzd1QdHubR5QQh1v2tgu9bBdX08NzP7FCKuXm437ZnK-u6XEoHYfNSgs2yvWbZXrNub-Vf3_7OBv_vZwUnK5CHxcdRr5UXraQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2398622408</pqid></control><display><type>article</type><title>Conducting decoded neurofeedback studies</title><source>Open Access: PubMed Central</source><source>Open Access: Oxford University Press Open Journals</source><creator>Taschereau-Dumouchel, Vincent ; Cortese, Aurelio ; Lau, Hakwan ; Kawato, Mitsuo</creator><creatorcontrib>Taschereau-Dumouchel, Vincent ; Cortese, Aurelio ; Lau, Hakwan ; Kawato, Mitsuo</creatorcontrib><description>Abstract Closed-loop neurofeedback has sparked great interest since its inception in the late 1960s. However, the field has historically faced various methodological challenges. Decoded fMRI neurofeedback may provide solutions to some of these problems. Notably, thanks to the recent advancements of machine learning approaches, it is now possible to target unconscious occurrences of specific multivoxel representations. In this tools of the trade paper, we discuss how to implement these interventions in rigorous double-blind placebo-controlled experiments. We aim to provide a step-by-step guide to address some of the most common methodological and analytical considerations. We also discuss tools that can be used to facilitate the implementation of new experiments. We hope that this will encourage more researchers to try out this powerful new intervention method.</description><identifier>ISSN: 1749-5016</identifier><identifier>ISSN: 1749-5024</identifier><identifier>EISSN: 1749-5024</identifier><identifier>DOI: 10.1093/scan/nsaa063</identifier><identifier>PMID: 32367138</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Biofeedback training ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; Neurofeedback ; Original Manuscript</subject><ispartof>Social cognitive and affective neuroscience, 2021-08, Vol.16 (8), p.838-848</ispartof><rights>The Author(s) 2020. Published by Oxford University Press. 2020</rights><rights>The Author(s) 2020. Published by Oxford University Press.</rights><rights>COPYRIGHT 2021 Oxford University Press</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c583t-2ed104ecf7e51268dd0de56483dfd28f35c9a949ff99b47429ba467c4e606513</citedby><cites>FETCH-LOGICAL-c583t-2ed104ecf7e51268dd0de56483dfd28f35c9a949ff99b47429ba467c4e606513</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8343564/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8343564/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,1604,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32367138$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Taschereau-Dumouchel, Vincent</creatorcontrib><creatorcontrib>Cortese, Aurelio</creatorcontrib><creatorcontrib>Lau, Hakwan</creatorcontrib><creatorcontrib>Kawato, Mitsuo</creatorcontrib><title>Conducting decoded neurofeedback studies</title><title>Social cognitive and affective neuroscience</title><addtitle>Soc Cogn Affect Neurosci</addtitle><description>Abstract Closed-loop neurofeedback has sparked great interest since its inception in the late 1960s. However, the field has historically faced various methodological challenges. Decoded fMRI neurofeedback may provide solutions to some of these problems. Notably, thanks to the recent advancements of machine learning approaches, it is now possible to target unconscious occurrences of specific multivoxel representations. In this tools of the trade paper, we discuss how to implement these interventions in rigorous double-blind placebo-controlled experiments. We aim to provide a step-by-step guide to address some of the most common methodological and analytical considerations. We also discuss tools that can be used to facilitate the implementation of new experiments. We hope that this will encourage more researchers to try out this powerful new intervention method.</description><subject>Biofeedback training</subject><subject>Humans</subject><subject>Machine Learning</subject><subject>Magnetic Resonance Imaging</subject><subject>Neurofeedback</subject><subject>Original Manuscript</subject><issn>1749-5016</issn><issn>1749-5024</issn><issn>1749-5024</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNp90d1LWzEYB-AgEz93t-tR2IUOrOY7JzeClG0KgqDehzR502U7TbqTc0T_e1PaFQsiuUhInvcXkhehLwSfE6zZRXE2XaRiLZZsBx0QxfVYYMo_bdZE7qPDUv5gLDTHbA_tM8qkIqw5QKeTnPzg-phmIw8ue_CjBEOXA4CfWvd3VPrBRyjHaDfYtsDn9XyEHn_-eJxcj2_vft1Mrm7HTjSsH1PwBHNwQYEgVDbeYw9C8ob54GkTmHDaaq5D0HrKFad6arlUjoPEUhB2hC5XsYthOgfvIPWdbc2ii3PbvZhso9k-SfG3meUn0zDO6j014HQd0OV_A5TezGNx0LY2QR6KoUw3klKOm0q_rejMtmBiCrkmuiU3V0oJLZRQqqrzd1QdHubR5QQh1v2tgu9bBdX08NzP7FCKuXm437ZnK-u6XEoHYfNSgs2yvWbZXrNub-Vf3_7OBv_vZwUnK5CHxcdRr5UXraQ</recordid><startdate>20210801</startdate><enddate>20210801</enddate><creator>Taschereau-Dumouchel, Vincent</creator><creator>Cortese, Aurelio</creator><creator>Lau, Hakwan</creator><creator>Kawato, Mitsuo</creator><general>Oxford University Press</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20210801</creationdate><title>Conducting decoded neurofeedback studies</title><author>Taschereau-Dumouchel, Vincent ; Cortese, Aurelio ; Lau, Hakwan ; Kawato, Mitsuo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c583t-2ed104ecf7e51268dd0de56483dfd28f35c9a949ff99b47429ba467c4e606513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Biofeedback training</topic><topic>Humans</topic><topic>Machine Learning</topic><topic>Magnetic Resonance Imaging</topic><topic>Neurofeedback</topic><topic>Original Manuscript</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Taschereau-Dumouchel, Vincent</creatorcontrib><creatorcontrib>Cortese, Aurelio</creatorcontrib><creatorcontrib>Lau, Hakwan</creatorcontrib><creatorcontrib>Kawato, Mitsuo</creatorcontrib><collection>Open Access: Oxford University Press Open Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Social cognitive and affective neuroscience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Taschereau-Dumouchel, Vincent</au><au>Cortese, Aurelio</au><au>Lau, Hakwan</au><au>Kawato, Mitsuo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Conducting decoded neurofeedback studies</atitle><jtitle>Social cognitive and affective neuroscience</jtitle><addtitle>Soc Cogn Affect Neurosci</addtitle><date>2021-08-01</date><risdate>2021</risdate><volume>16</volume><issue>8</issue><spage>838</spage><epage>848</epage><pages>838-848</pages><issn>1749-5016</issn><issn>1749-5024</issn><eissn>1749-5024</eissn><abstract>Abstract Closed-loop neurofeedback has sparked great interest since its inception in the late 1960s. However, the field has historically faced various methodological challenges. Decoded fMRI neurofeedback may provide solutions to some of these problems. Notably, thanks to the recent advancements of machine learning approaches, it is now possible to target unconscious occurrences of specific multivoxel representations. In this tools of the trade paper, we discuss how to implement these interventions in rigorous double-blind placebo-controlled experiments. We aim to provide a step-by-step guide to address some of the most common methodological and analytical considerations. We also discuss tools that can be used to facilitate the implementation of new experiments. We hope that this will encourage more researchers to try out this powerful new intervention method.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>32367138</pmid><doi>10.1093/scan/nsaa063</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1749-5016
ispartof Social cognitive and affective neuroscience, 2021-08, Vol.16 (8), p.838-848
issn 1749-5016
1749-5024
1749-5024
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8343564
source Open Access: PubMed Central; Open Access: Oxford University Press Open Journals
subjects Biofeedback training
Humans
Machine Learning
Magnetic Resonance Imaging
Neurofeedback
Original Manuscript
title Conducting decoded neurofeedback studies
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T03%3A19%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Conducting%20decoded%20neurofeedback%20studies&rft.jtitle=Social%20cognitive%20and%20affective%20neuroscience&rft.au=Taschereau-Dumouchel,%20Vincent&rft.date=2021-08-01&rft.volume=16&rft.issue=8&rft.spage=838&rft.epage=848&rft.pages=838-848&rft.issn=1749-5016&rft.eissn=1749-5024&rft_id=info:doi/10.1093/scan/nsaa063&rft_dat=%3Cgale_pubme%3EA775957577%3C/gale_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c583t-2ed104ecf7e51268dd0de56483dfd28f35c9a949ff99b47429ba467c4e606513%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2398622408&rft_id=info:pmid/32367138&rft_galeid=A775957577&rft_oup_id=10.1093/scan/nsaa063&rfr_iscdi=true