A simulation-based approach to improve decoded neurofeedback performance
The neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback based on these distributed patterns of brain activity presents a unique ability for precise neuromo...
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description | The neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback based on these distributed patterns of brain activity presents a unique ability for precise neuromodulation. Recent applications of this technique, known as decoded neurofeedback, have manipulated fear conditioning, visual perception, confidence judgements and facial preference. However, there has yet to be an empirical justification of the timing and data processing parameters of these experiments. Suboptimal parameter settings could impact the efficacy of neurofeedback learning and contribute to the ‘non-responder’ effect. The goal of this study was to investigate how design parameters of decoded neurofeedback experiments affect decoding accuracy and neurofeedback performance. Subjects participated in three fMRI sessions: two ‘finger localizer’ sessions to identify the fMRI patterns associated with each of the four fingers of the right hand, and one ‘finger finding’ neurofeedback session to assess neurofeedback performance. Using only the localizer data, we show that real-time decoding can be degraded by poor experiment timing or ROI selection. To set key parameters for the neurofeedback session, we used offline simulations of decoded neurofeedback using data from the localizer sessions to predict neurofeedback performance. We show that these predictions align with real neurofeedback performance at the group level and can also explain individual differences in neurofeedback success. Overall, this work demonstrates the usefulness of offline simulation to improve the success of real-time decoded neurofeedback experiments.
•Real-time fMRI decoding accuracy relies most on experiment timing and ROI selection.•Parameters of decoded neurofeedback experiments can be designed using simulations.•Offline simulations of decoded neurofeedback can predict neurofeedback performance. |
doi_str_mv | 10.1016/j.neuroimage.2019.03.062 |
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•Real-time fMRI decoding accuracy relies most on experiment timing and ROI selection.•Parameters of decoded neurofeedback experiments can be designed using simulations.•Offline simulations of decoded neurofeedback can predict neurofeedback performance.</description><identifier>ISSN: 1053-8119</identifier><identifier>EISSN: 1095-9572</identifier><identifier>DOI: 10.1016/j.neuroimage.2019.03.062</identifier><identifier>PMID: 30954707</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Accuracy ; Adult ; Biofeedback ; Brain mapping ; Brain Mapping - methods ; Brain research ; Electroencephalography ; Experiments ; Explicit knowledge ; Fear conditioning ; Feedback ; Female ; Finger ; fMRI ; Functional magnetic resonance imaging ; Human performance ; Humans ; Image Processing, Computer-Assisted ; Information processing ; Machine Learning ; Magnetic Resonance Imaging ; Male ; Medical imaging ; Medical research ; Multi-voxel pattern analysis ; Neurofeedback ; Neurofeedback - methods ; Neuromodulation ; Neurosciences ; Orientation behavior ; Research Design ; Sensorimotor cortex ; Sensorimotor Cortex - physiology ; Simulation ; Visual perception</subject><ispartof>NeuroImage (Orlando, Fla.), 2019-07, Vol.195, p.300-310</ispartof><rights>2019 Elsevier Inc.</rights><rights>Copyright © 2019 Elsevier Inc. All rights reserved.</rights><rights>2019. Elsevier Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c518t-ee83d6bf1539b72b704385dacd179ee18d6f1509ae0cfaf63d336aa5911f13063</citedby><cites>FETCH-LOGICAL-c518t-ee83d6bf1539b72b704385dacd179ee18d6f1509ae0cfaf63d336aa5911f13063</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1053811919302629$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30954707$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Oblak, Ethan F.</creatorcontrib><creatorcontrib>Sulzer, James S.</creatorcontrib><creatorcontrib>Lewis-Peacock, Jarrod A.</creatorcontrib><title>A simulation-based approach to improve decoded neurofeedback performance</title><title>NeuroImage (Orlando, Fla.)</title><addtitle>Neuroimage</addtitle><description>The neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback based on these distributed patterns of brain activity presents a unique ability for precise neuromodulation. Recent applications of this technique, known as decoded neurofeedback, have manipulated fear conditioning, visual perception, confidence judgements and facial preference. However, there has yet to be an empirical justification of the timing and data processing parameters of these experiments. Suboptimal parameter settings could impact the efficacy of neurofeedback learning and contribute to the ‘non-responder’ effect. The goal of this study was to investigate how design parameters of decoded neurofeedback experiments affect decoding accuracy and neurofeedback performance. Subjects participated in three fMRI sessions: two ‘finger localizer’ sessions to identify the fMRI patterns associated with each of the four fingers of the right hand, and one ‘finger finding’ neurofeedback session to assess neurofeedback performance. Using only the localizer data, we show that real-time decoding can be degraded by poor experiment timing or ROI selection. To set key parameters for the neurofeedback session, we used offline simulations of decoded neurofeedback using data from the localizer sessions to predict neurofeedback performance. We show that these predictions align with real neurofeedback performance at the group level and can also explain individual differences in neurofeedback success. Overall, this work demonstrates the usefulness of offline simulation to improve the success of real-time decoded neurofeedback experiments.
•Real-time fMRI decoding accuracy relies most on experiment timing and ROI selection.•Parameters of decoded neurofeedback experiments can be designed using simulations.•Offline simulations of decoded neurofeedback can predict neurofeedback performance.</description><subject>Accuracy</subject><subject>Adult</subject><subject>Biofeedback</subject><subject>Brain mapping</subject><subject>Brain Mapping - methods</subject><subject>Brain research</subject><subject>Electroencephalography</subject><subject>Experiments</subject><subject>Explicit knowledge</subject><subject>Fear conditioning</subject><subject>Feedback</subject><subject>Female</subject><subject>Finger</subject><subject>fMRI</subject><subject>Functional magnetic resonance imaging</subject><subject>Human performance</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Information processing</subject><subject>Machine Learning</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Medical research</subject><subject>Multi-voxel pattern analysis</subject><subject>Neurofeedback</subject><subject>Neurofeedback - methods</subject><subject>Neuromodulation</subject><subject>Neurosciences</subject><subject>Orientation behavior</subject><subject>Research Design</subject><subject>Sensorimotor cortex</subject><subject>Sensorimotor Cortex - physiology</subject><subject>Simulation</subject><subject>Visual perception</subject><issn>1053-8119</issn><issn>1095-9572</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqFkE1LxDAQhoMofv8FKXjx0jrTbNrmqOIXCF70HNJkqlm3zZq0gv_erOsHePE0A_PMvMPDWIZQIGB1Oi8GmoJ3vX6iogSUBfACqnKD7SJIkUtRl5urXvC8QZQ7bC_GOQBInDXbbIcnaFZDvctuzrLo-mmhR-eHvNWRbKaXy-C1ec5Gn7k-9W-UWTLeptlnbkdkW21esiWFzodeD4YO2FanF5EOv-o-e7y6fLi4ye_ur28vzu5yI7AZc6KG26rtUHDZ1mVbw4w3wmpjsZZE2NgqzUBqAtPpruKW80prIRE75FDxfXayvpv-ep0ojqp30dBioQfyU1RlCWIGMjlJ6PEfdO6nMKTvElVKISuOZaKaNWWCjzFQp5YhiQ3vCkGtbKu5-rWtVrYVcJVsp9Wjr4Cp7cn-LH7rTcD5GqBk5M1RUNE4SrasC2RGZb37P-UDm4GVlw</recordid><startdate>20190715</startdate><enddate>20190715</enddate><creator>Oblak, Ethan F.</creator><creator>Sulzer, James S.</creator><creator>Lewis-Peacock, Jarrod A.</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><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>3V.</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>M7P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20190715</creationdate><title>A simulation-based approach to improve decoded neurofeedback performance</title><author>Oblak, Ethan F. ; Sulzer, James S. ; Lewis-Peacock, Jarrod A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c518t-ee83d6bf1539b72b704385dacd179ee18d6f1509ae0cfaf63d336aa5911f13063</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Accuracy</topic><topic>Adult</topic><topic>Biofeedback</topic><topic>Brain mapping</topic><topic>Brain Mapping - methods</topic><topic>Brain research</topic><topic>Electroencephalography</topic><topic>Experiments</topic><topic>Explicit knowledge</topic><topic>Fear conditioning</topic><topic>Feedback</topic><topic>Female</topic><topic>Finger</topic><topic>fMRI</topic><topic>Functional magnetic resonance imaging</topic><topic>Human performance</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted</topic><topic>Information processing</topic><topic>Machine Learning</topic><topic>Magnetic Resonance Imaging</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Medical research</topic><topic>Multi-voxel pattern analysis</topic><topic>Neurofeedback</topic><topic>Neurofeedback - methods</topic><topic>Neuromodulation</topic><topic>Neurosciences</topic><topic>Orientation behavior</topic><topic>Research Design</topic><topic>Sensorimotor cortex</topic><topic>Sensorimotor Cortex - physiology</topic><topic>Simulation</topic><topic>Visual perception</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oblak, Ethan F.</creatorcontrib><creatorcontrib>Sulzer, James S.</creatorcontrib><creatorcontrib>Lewis-Peacock, Jarrod A.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest Psychology</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>NeuroImage (Orlando, Fla.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Oblak, Ethan F.</au><au>Sulzer, James S.</au><au>Lewis-Peacock, Jarrod A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A simulation-based approach to improve decoded neurofeedback performance</atitle><jtitle>NeuroImage (Orlando, Fla.)</jtitle><addtitle>Neuroimage</addtitle><date>2019-07-15</date><risdate>2019</risdate><volume>195</volume><spage>300</spage><epage>310</epage><pages>300-310</pages><issn>1053-8119</issn><eissn>1095-9572</eissn><abstract>The neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback based on these distributed patterns of brain activity presents a unique ability for precise neuromodulation. Recent applications of this technique, known as decoded neurofeedback, have manipulated fear conditioning, visual perception, confidence judgements and facial preference. However, there has yet to be an empirical justification of the timing and data processing parameters of these experiments. Suboptimal parameter settings could impact the efficacy of neurofeedback learning and contribute to the ‘non-responder’ effect. The goal of this study was to investigate how design parameters of decoded neurofeedback experiments affect decoding accuracy and neurofeedback performance. Subjects participated in three fMRI sessions: two ‘finger localizer’ sessions to identify the fMRI patterns associated with each of the four fingers of the right hand, and one ‘finger finding’ neurofeedback session to assess neurofeedback performance. Using only the localizer data, we show that real-time decoding can be degraded by poor experiment timing or ROI selection. To set key parameters for the neurofeedback session, we used offline simulations of decoded neurofeedback using data from the localizer sessions to predict neurofeedback performance. We show that these predictions align with real neurofeedback performance at the group level and can also explain individual differences in neurofeedback success. Overall, this work demonstrates the usefulness of offline simulation to improve the success of real-time decoded neurofeedback experiments.
•Real-time fMRI decoding accuracy relies most on experiment timing and ROI selection.•Parameters of decoded neurofeedback experiments can be designed using simulations.•Offline simulations of decoded neurofeedback can predict neurofeedback performance.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>30954707</pmid><doi>10.1016/j.neuroimage.2019.03.062</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Adult Biofeedback Brain mapping Brain Mapping - methods Brain research Electroencephalography Experiments Explicit knowledge Fear conditioning Feedback Female Finger fMRI Functional magnetic resonance imaging Human performance Humans Image Processing, Computer-Assisted Information processing Machine Learning Magnetic Resonance Imaging Male Medical imaging Medical research Multi-voxel pattern analysis Neurofeedback Neurofeedback - methods Neuromodulation Neurosciences Orientation behavior Research Design Sensorimotor cortex Sensorimotor Cortex - physiology Simulation Visual perception |
title | A simulation-based approach to improve decoded neurofeedback performance |
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