Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex
Orientation-selective responses can be decoded from fMRI activity patterns in the human visual cortex, using multivariate pattern analysis (MVPA). To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these a...
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description | Orientation-selective responses can be decoded from fMRI activity patterns in the human visual cortex, using multivariate pattern analysis (MVPA). To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these activity patterns be predicted by the gross amplitude of the stimulus-driven BOLD response? Observers viewed oriented gratings that varied in luminance contrast (4, 20 or 100%) or spatial frequency (0.25, 1.0 or 4.0cpd). As predicted, activity patterns in early visual areas led to better discrimination of orientations presented at high than low contrast, with greater effects of contrast found in area V1 than in V3. A second experiment revealed generally better decoding of orientations at low or moderate as compared to high spatial frequencies. Interestingly however, V1 exhibited a relative advantage at discriminating high spatial frequency orientations, consistent with the finer scale of representation in the primary visual cortex. In both experiments, the reliability of these orientation-selective activity patterns was well predicted by the average BOLD amplitude in each region of interest, as indicated by correlation analyses, as well as decoding applied to a simple model of voxel responses to simulated orientation columns. Moreover, individual differences in decoding accuracy could be predicted by the signal-to-noise ratio of an individual's BOLD response. Our results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification.
► Decoding of orientation-selective activity patterns depends on visual input strength. ► Results largely follow the predictions of neurophysiological studies in animals. ► Areas V1–V3 are differentially affected by changes in contrast and spatial frequency. ► Amplitude of BOLD response can predict changes in orientation decoding accuracy. ► Predictions are enhanced by incorporating BOLD amplitudes into a simulation model. |
doi_str_mv | 10.1016/j.neuroimage.2012.08.005 |
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► Decoding of orientation-selective activity patterns depends on visual input strength. ► Results largely follow the predictions of neurophysiological studies in animals. ► Areas V1–V3 are differentially affected by changes in contrast and spatial frequency. ► Amplitude of BOLD response can predict changes in orientation decoding accuracy. ► Predictions are enhanced by incorporating BOLD amplitudes into a simulation model.</description><identifier>ISSN: 1053-8119</identifier><identifier>EISSN: 1095-9572</identifier><identifier>DOI: 10.1016/j.neuroimage.2012.08.005</identifier><identifier>PMID: 22917989</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Brain ; Brain - blood supply ; Brain - physiology ; Brain Mapping ; Decoding ; Female ; fMRI ; Humans ; Image Interpretation, Computer-Assisted ; Magnetic Resonance Imaging ; Male ; Multivoxel pattern analysis ; NMR ; Nuclear magnetic resonance ; Orientation - physiology ; Pattern Recognition, Visual - physiology ; Studies ; Visual Cortex - physiology ; Visual task performance ; Young Adult</subject><ispartof>NeuroImage (Orlando, Fla.), 2012-11, Vol.63 (3), p.1212-1222</ispartof><rights>2012 Elsevier Inc.</rights><rights>Copyright © 2012 Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited Nov 15, 2012</rights><rights>2012 Elsevier Inc. All rights reserved. 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c540t-ff783a8e7cba51ad9482bce9b4ae5e8ed4ae6bd05d8bd06f36e55fb1810a6ff13</citedby><cites>FETCH-LOGICAL-c540t-ff783a8e7cba51ad9482bce9b4ae5e8ed4ae6bd05d8bd06f36e55fb1810a6ff13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1506888727?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,780,784,885,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22917989$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tong, Frank</creatorcontrib><creatorcontrib>Harrison, Stephenie A.</creatorcontrib><creatorcontrib>Dewey, John A.</creatorcontrib><creatorcontrib>Kamitani, Yukiyasu</creatorcontrib><title>Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex</title><title>NeuroImage (Orlando, Fla.)</title><addtitle>Neuroimage</addtitle><description>Orientation-selective responses can be decoded from fMRI activity patterns in the human visual cortex, using multivariate pattern analysis (MVPA). To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these activity patterns be predicted by the gross amplitude of the stimulus-driven BOLD response? Observers viewed oriented gratings that varied in luminance contrast (4, 20 or 100%) or spatial frequency (0.25, 1.0 or 4.0cpd). As predicted, activity patterns in early visual areas led to better discrimination of orientations presented at high than low contrast, with greater effects of contrast found in area V1 than in V3. A second experiment revealed generally better decoding of orientations at low or moderate as compared to high spatial frequencies. Interestingly however, V1 exhibited a relative advantage at discriminating high spatial frequency orientations, consistent with the finer scale of representation in the primary visual cortex. In both experiments, the reliability of these orientation-selective activity patterns was well predicted by the average BOLD amplitude in each region of interest, as indicated by correlation analyses, as well as decoding applied to a simple model of voxel responses to simulated orientation columns. Moreover, individual differences in decoding accuracy could be predicted by the signal-to-noise ratio of an individual's BOLD response. Our results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification.
► Decoding of orientation-selective activity patterns depends on visual input strength. ► Results largely follow the predictions of neurophysiological studies in animals. ► Areas V1–V3 are differentially affected by changes in contrast and spatial frequency. ► Amplitude of BOLD response can predict changes in orientation decoding accuracy. ► Predictions are enhanced by incorporating BOLD amplitudes into a simulation model.</description><subject>Adult</subject><subject>Brain</subject><subject>Brain - blood supply</subject><subject>Brain - physiology</subject><subject>Brain Mapping</subject><subject>Decoding</subject><subject>Female</subject><subject>fMRI</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>Multivoxel pattern analysis</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Orientation - physiology</subject><subject>Pattern Recognition, Visual - physiology</subject><subject>Studies</subject><subject>Visual Cortex - physiology</subject><subject>Visual task performance</subject><subject>Young Adult</subject><issn>1053-8119</issn><issn>1095-9572</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFUk1v1DAUjBCIlsJfQJa4cEl4TuLEviDR8imtVAnB2XKc565Xib3YzkLFn8fplvJx6cVjyzPj5_emKAiFigLtXu0qh0vwdlZXWNVA6wp4BcAeFKcUBCsF6-uH6541JadUnBRPYtwBgKAtf1yc1LWgveDitPj5GSeVrHdxa_dkwPQd0ZHzy81boub9ZNMyIlFuJHuVEgZH9KRitMbqGxXxhvhg0aWbYxlxQp3sIWtWsOmaWEfSFsl2mZUjBxsXNRHtQ8IfT4tHRk0Rn93iWfH1_bsvFx_LzeWHTxdvNqVmLaTSmJ43imOvB8WoGkXL60GjGFqFDDmOGbthBDbyvHam6ZAxM1BOQXXG0OaseH303S_DjKPO1QY1yX3I_QvX0isr_71xdiuv_EE2rOlawbLBy1uD4L8tGJOcbdQ4TcqhX6KkDKDvOibo_VRK66YF0UKmvviPuvNLcLkTq2HHOe_rPrP4kaWDjzGguaubglzDIHfyTxjkGgYJXOYwZOnzv_99J_w9_Uw4PxIwd_9gMcio8yw1jjbkMcrR2_tf-QXqh8-G</recordid><startdate>20121115</startdate><enddate>20121115</enddate><creator>Tong, Frank</creator><creator>Harrison, Stephenie A.</creator><creator>Dewey, John A.</creator><creator>Kamitani, Yukiyasu</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><scope>7QO</scope><scope>5PM</scope></search><sort><creationdate>20121115</creationdate><title>Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex</title><author>Tong, Frank ; 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To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these activity patterns be predicted by the gross amplitude of the stimulus-driven BOLD response? Observers viewed oriented gratings that varied in luminance contrast (4, 20 or 100%) or spatial frequency (0.25, 1.0 or 4.0cpd). As predicted, activity patterns in early visual areas led to better discrimination of orientations presented at high than low contrast, with greater effects of contrast found in area V1 than in V3. A second experiment revealed generally better decoding of orientations at low or moderate as compared to high spatial frequencies. Interestingly however, V1 exhibited a relative advantage at discriminating high spatial frequency orientations, consistent with the finer scale of representation in the primary visual cortex. In both experiments, the reliability of these orientation-selective activity patterns was well predicted by the average BOLD amplitude in each region of interest, as indicated by correlation analyses, as well as decoding applied to a simple model of voxel responses to simulated orientation columns. Moreover, individual differences in decoding accuracy could be predicted by the signal-to-noise ratio of an individual's BOLD response. Our results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification.
► Decoding of orientation-selective activity patterns depends on visual input strength. ► Results largely follow the predictions of neurophysiological studies in animals. ► Areas V1–V3 are differentially affected by changes in contrast and spatial frequency. ► Amplitude of BOLD response can predict changes in orientation decoding accuracy. ► Predictions are enhanced by incorporating BOLD amplitudes into a simulation model.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>22917989</pmid><doi>10.1016/j.neuroimage.2012.08.005</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adult Brain Brain - blood supply Brain - physiology Brain Mapping Decoding Female fMRI Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Male Multivoxel pattern analysis NMR Nuclear magnetic resonance Orientation - physiology Pattern Recognition, Visual - physiology Studies Visual Cortex - physiology Visual task performance Young Adult |
title | Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex |
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