High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories
Does the “fusiform face area” (FFA) code only for faces? This question continues to elude the neuroimaging field due to at least two kinds of problems: first, the relatively low spatial resolution of fMRI in which the FFA was defined and second, the potential bias inherent in prevailing statistical...
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| description | Does the “fusiform face area” (FFA) code only for faces? This question continues to elude the neuroimaging field due to at least two kinds of problems: first, the relatively low spatial resolution of fMRI in which the FFA was defined and second, the potential bias inherent in prevailing statistical methods for analyzing the actual diagnosticity of cortical tissue. Using high-resolution (1mm×1mm×1mm) imaging data of the fusiform face area (FFA) from 4 subjects who had categorized images as ‘animal’, ‘car’, ‘face’, or ‘sculpture’, we used multivariate linear and non-linear classifiers to decode the resultant voxel patterns. Prior to identifying the appropriate classifier we performed exploratory analysis to determine the nature of the distributions over classes and the voxel intensity pattern structure between classes. The FFA was visualized using non-metric multidimensional scaling revealing “string-like” sequences of voxels, which appeared in small non-contiguous clusters of categories, intertwined with other categories. Since this analysis suggested that feature space was highly non-linear, we trained various statistical classifiers on the class-conditional distributions (labelled) and separated the four categories with 100% reliability (over replications) and generalized to out-of-sample cases with high significance (up to 50%; p |
| doi_str_mv | 10.1016/j.neuroimage.2010.08.028 |
| format | Article |
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► The FFA shows a heterogeneous and distributed selectivity code for 4 categories. ► Faces do not have an advantage according to non-linear classification analysis. ► Multivariate non-linear classifiers detect information that standard analyses do not. ► A linear bias for faces can explain past contradictions about the nature of the FFA.</description><identifier>ISSN: 1053-8119</identifier><identifier>EISSN: 1095-9572</identifier><identifier>DOI: 10.1016/j.neuroimage.2010.08.028</identifier><identifier>PMID: 20736071</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Binomial distribution ; Brain - physiology ; Brain Mapping - methods ; Classification ; Data processing ; Face ; Functional magnetic resonance imaging ; Humans ; Hypotheses ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Methods ; Models, Statistical ; Multidimensional scaling ; Neuroimaging ; Sensitivity and Specificity ; Spatial discrimination ; Statistics ; Studies ; Visual Perception - physiology</subject><ispartof>NeuroImage (Orlando, Fla.), 2011-01, Vol.54 (2), p.1715-1734</ispartof><rights>2010 Elsevier Inc.</rights><rights>Copyright © 2010 Elsevier Inc. All rights reserved.</rights><rights>2010. Elsevier Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c499t-58f460ef5b4d15ce215f263d81b6d731716e577b338ae857b5859bf777bfb2a53</citedby><cites>FETCH-LOGICAL-c499t-58f460ef5b4d15ce215f263d81b6d731716e577b338ae857b5859bf777bfb2a53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1560651956?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20736071$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hanson, Stephen José</creatorcontrib><creatorcontrib>Schmidt, Arielle</creatorcontrib><title>High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories</title><title>NeuroImage (Orlando, Fla.)</title><addtitle>Neuroimage</addtitle><description>Does the “fusiform face area” (FFA) code only for faces? This question continues to elude the neuroimaging field due to at least two kinds of problems: first, the relatively low spatial resolution of fMRI in which the FFA was defined and second, the potential bias inherent in prevailing statistical methods for analyzing the actual diagnosticity of cortical tissue. Using high-resolution (1mm×1mm×1mm) imaging data of the fusiform face area (FFA) from 4 subjects who had categorized images as ‘animal’, ‘car’, ‘face’, or ‘sculpture’, we used multivariate linear and non-linear classifiers to decode the resultant voxel patterns. Prior to identifying the appropriate classifier we performed exploratory analysis to determine the nature of the distributions over classes and the voxel intensity pattern structure between classes. The FFA was visualized using non-metric multidimensional scaling revealing “string-like” sequences of voxels, which appeared in small non-contiguous clusters of categories, intertwined with other categories. Since this analysis suggested that feature space was highly non-linear, we trained various statistical classifiers on the class-conditional distributions (labelled) and separated the four categories with 100% reliability (over replications) and generalized to out-of-sample cases with high significance (up to 50%; p<.000001, chance=25%). The increased noise inherent in high-resolution neuroimaging data relative to standard resolution resisted any further gains in category performance above ~60% (with FACE category often having the highest bias per category) even coupled with various feature extraction/selection methods. A sensitivity/diagnosticity analysis for each classifier per voxel showed: (1) reliable (with S.E.<3%) sensitivity present throughout the FFA for all 4 categories, and (2) showed multi-selectivity; that is, many voxels were selective for more than one category with some high diagnosticity but at submaximal intensity. This work is clearly consistent with the characterization of the FFA as a distributed, object-heterogeneous similarity structure and bolsters the view that the FFA response to “FACE” stimuli in standard resolution may be primarily due to a linear bias, which has resulted from an averaging artefact.
► The FFA shows a heterogeneous and distributed selectivity code for 4 categories. ► Faces do not have an advantage according to non-linear classification analysis. ► Multivariate non-linear classifiers detect information that standard analyses do not. ► A linear bias for faces can explain past contradictions about the nature of the FFA.</description><subject>Binomial distribution</subject><subject>Brain - physiology</subject><subject>Brain Mapping - methods</subject><subject>Classification</subject><subject>Data processing</subject><subject>Face</subject><subject>Functional magnetic resonance imaging</subject><subject>Humans</subject><subject>Hypotheses</subject><subject>Image Processing, Computer-Assisted</subject><subject>Magnetic Resonance Imaging</subject><subject>Methods</subject><subject>Models, Statistical</subject><subject>Multidimensional scaling</subject><subject>Neuroimaging</subject><subject>Sensitivity and Specificity</subject><subject>Spatial discrimination</subject><subject>Statistics</subject><subject>Studies</subject><subject>Visual Perception - physiology</subject><issn>1053-8119</issn><issn>1095-9572</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</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>eNqFkc1u1DAUhSMEoqXwCsgSC2CRwXbiv2WpGIpUiQ2sLce5zniU2MV2ivoQvDOeTmklNl3ZuvrOubrnNA0ieEMw4Z_2mwBrin4xE2wormMsN5jKZ80pwYq1ign6_PBnXSsJUSfNq5z3GGNFevmyOaFYdBwLctr8ufTTrk2Q47wWHwM6ePowoehQ2QFya_YupgU5YwGZBAZ92G7PP6I6r9SyzsXfmORNARRiaGcfwCRkZ5Or0EPKKO_i74xGb6YQc_HWl1tULe_wO1dbxVNMHvLr5oUzc4Y39-9Z83P75cfFZXv1_eu3i_Or1vZKlZZJ13MMjg39SJgFSpijvBslGfgoOiIIBybE0HXSgGRiYJKpwYk6cgM1rDtr3h99r1P8tUIuevHZwjybAHHNWrGeU9VT_CQpCVOcMykr-e4_ch_XFOoZmjCOOSOK8UrJI2VTzDmB09epRp5uNcH60K3e68du9aFbjaWu3Vbp2_sF67DA-CD8V2YFPh8BqNHd1Ox1th6ChdEnsEWP0T-95S8Ubrvv</recordid><startdate>20110115</startdate><enddate>20110115</enddate><creator>Hanson, Stephen José</creator><creator>Schmidt, Arielle</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></search><sort><creationdate>20110115</creationdate><title>High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories</title><author>Hanson, Stephen José ; 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This question continues to elude the neuroimaging field due to at least two kinds of problems: first, the relatively low spatial resolution of fMRI in which the FFA was defined and second, the potential bias inherent in prevailing statistical methods for analyzing the actual diagnosticity of cortical tissue. Using high-resolution (1mm×1mm×1mm) imaging data of the fusiform face area (FFA) from 4 subjects who had categorized images as ‘animal’, ‘car’, ‘face’, or ‘sculpture’, we used multivariate linear and non-linear classifiers to decode the resultant voxel patterns. Prior to identifying the appropriate classifier we performed exploratory analysis to determine the nature of the distributions over classes and the voxel intensity pattern structure between classes. The FFA was visualized using non-metric multidimensional scaling revealing “string-like” sequences of voxels, which appeared in small non-contiguous clusters of categories, intertwined with other categories. Since this analysis suggested that feature space was highly non-linear, we trained various statistical classifiers on the class-conditional distributions (labelled) and separated the four categories with 100% reliability (over replications) and generalized to out-of-sample cases with high significance (up to 50%; p<.000001, chance=25%). The increased noise inherent in high-resolution neuroimaging data relative to standard resolution resisted any further gains in category performance above ~60% (with FACE category often having the highest bias per category) even coupled with various feature extraction/selection methods. A sensitivity/diagnosticity analysis for each classifier per voxel showed: (1) reliable (with S.E.<3%) sensitivity present throughout the FFA for all 4 categories, and (2) showed multi-selectivity; that is, many voxels were selective for more than one category with some high diagnosticity but at submaximal intensity. This work is clearly consistent with the characterization of the FFA as a distributed, object-heterogeneous similarity structure and bolsters the view that the FFA response to “FACE” stimuli in standard resolution may be primarily due to a linear bias, which has resulted from an averaging artefact.
► The FFA shows a heterogeneous and distributed selectivity code for 4 categories. ► Faces do not have an advantage according to non-linear classification analysis. ► Multivariate non-linear classifiers detect information that standard analyses do not. ► A linear bias for faces can explain past contradictions about the nature of the FFA.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>20736071</pmid><doi>10.1016/j.neuroimage.2010.08.028</doi><tpages>20</tpages></addata></record> |
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| subjects | Binomial distribution Brain - physiology Brain Mapping - methods Classification Data processing Face Functional magnetic resonance imaging Humans Hypotheses Image Processing, Computer-Assisted Magnetic Resonance Imaging Methods Models, Statistical Multidimensional scaling Neuroimaging Sensitivity and Specificity Spatial discrimination Statistics Studies Visual Perception - physiology |
| title | High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories |
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