The Hierarchical Brain Network for Face Recognition
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{"title"=>"The Hierarchical Brain Network for Face Recognition", "type"=>"journal", "authors"=>[{"first_name"=>"Zonglei", "last_name"=>"Zhen", "scopus_author_id"=>"35212204800"}, {"first_name"=>"Huizhen", "last_name"=>"Fang", "scopus_author_id"=>"55470728700"}, {"first_name"=>"Jia", "last_name"=>"Liu", "scopus_author_id"=>"55705831100"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "arxiv"=>"NIHMS150003", "scopus"=>"2-s2.0-84875183297", "pui"=>"368562421", "doi"=>"10.1371/journal.pone.0059886", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "sgr"=>"84875183297", "pmid"=>"23527282"}, "id"=>"a4c444ce-d7a8-301e-b292-a929b49ddb14", "abstract"=>"Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.", "link"=>"http://www.mendeley.com/research/hierarchical-brain-network-face-recognition-1", "reader_count"=>129, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>3, "Researcher"=>31, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>36, "Student > Postgraduate"=>4, "Student > Master"=>11, "Other"=>7, "Student > Bachelor"=>16, "Lecturer"=>1, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>3, "Researcher"=>31, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>36, "Student > Postgraduate"=>4, "Student > Master"=>11, "Other"=>7, "Student > Bachelor"=>16, "Lecturer"=>1, "Professor"=>9}, "reader_count_by_subject_area"=>{"Unspecified"=>10, "Agricultural and Biological Sciences"=>12, "Philosophy"=>1, "Computer Science"=>9, "Engineering"=>7, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>12, "Neuroscience"=>17, "Physics and Astronomy"=>1, "Psychology"=>56, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>12}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>56}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>10}, "Environmental Science"=>{"Environmental Science"=>1}, "Engineering"=>{"Engineering"=>7}, "Neuroscience"=>{"Neuroscience"=>17}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>12}, "Computer Science"=>{"Computer Science"=>9}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Cuba"=>1, "Canada"=>1, "Sweden"=>1, "Belgium"=>1, "Iran"=>1, "United States"=>6, "Uruguay"=>1, "Japan"=>1, "China"=>1, "United Kingdom"=>3, "France"=>2, "Switzerland"=>1}, "group_count"=>5}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/992466"], "description"=>"<p>The probabilistic map was created by overlaying participants’ binarized activation maps with the contrast of faces versus objects. Color bar indicates the number of participants who showed face-selective activation. Spheres indicate the peak locations of thirty-seven group-level ROIs. ROIs that failed to meet the criteria of cross-subject reliability, cross-session reliability and face selectivity were colored with magenta, blue and cyan, respectively. ROIs that met all criteria were colored with green. L: left hemisphere; R: right hemisphere.</p>", "links"=>[], "tags"=>["locations", "group-level", "rois", "overlaid", "mni152"], "article_id"=>656149, "categories"=>["Physiology", "Neuroscience"], "users"=>["Zonglei Zhen", "Huizhen Fang", "Jia Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059886.g001", "stats"=>{"downloads"=>2, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probabilistic_map_and_peak_locations_of_group_level_ROIs_overlaid_on_mean_MNI152_brain_surface_/656149", "title"=>"Probabilistic map and peak locations of group-level ROIs overlaid on mean MNI152 brain surface.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-21 01:37:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/992467"], "description"=>"<p>(A) Dendrogram from the hierarchy clustering analysis based on the strength of functional connectivity among the face-selective ROIs when the participants performed a face recognition task. The face-processing network consists of three relatively independent sub-networks that correspond to the recognition of individual identity (Identification), retrieval of personal knowledge (Semantic) and analysis of facial expression (Expression). (B) Sub-networks are displayed on the mean MNI152 brain surface with nodes and edges. The sub-network for Identification, Semantic, and Expression is colored with blue, red and green, respectively. Note that for display purposes, edges between the sub-networks are not shown.</p>", "links"=>[], "tags"=>["structured", "face-processing"], "article_id"=>656150, "categories"=>["Physiology", "Neuroscience"], "users"=>["Zonglei Zhen", "Huizhen Fang", "Jia Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059886.g002", "stats"=>{"downloads"=>8, "page_views"=>158, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hierarchically_structured_face_processing_network_/656150", "title"=>"Hierarchically structured face-processing network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-21 01:38:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/1007632"], "description"=>"<p>FG, Fusiform Gyrus; LING, Lingual Gyrus; STS, Superior Temporal Sulcus; IOG, Inferior Occipital Gyrus; LOC, Lateral Occipital Cortex; IPS, Intraparietal Sulcus; SMG, Supramarginal Gyrus; IFG, Inferior Frontal Gyrus; CING, Paracingulate.</p><p>Gyrus; OFC, Orbital Frontal Cortex; FP, Frontal Pole; MFG, Middle Frontal Gyrus; PCG, Precentral Gyrus; INS, Insular.</p><p>Cortex; THA, Thalamus; a, anterior; p, posterior; s, superior; i, inferior; L, left; R, right.</p>", "links"=>[], "tags"=>["group-level", "individual-level", "face-selective"], "article_id"=>668254, "categories"=>["Physiology", "Neuroscience"], "users"=>["Zonglei Zhen", "Huizhen Fang", "Jia Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059886.t001", "stats"=>{"downloads"=>6, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_group_level_and_individual_level_face_selective_ROIs_reliability_selectivity_and_coordinates_/668254", "title"=>"The group-level and individual-level face-selective ROIs: reliability, selectivity, and coordinates.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-20 02:17:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/992468"], "description"=>"<p>The matrix indicates changes in functional connectivity among the face-selective regions when the participants switched from the face-recognition task to the object-recognition task. Black cells indicate that the change in functional connectivity is not significant, whereas colored cells show that the functional connectivity between the face-selectivity regions is significantly reduced (<i>p</i><0.05, FDR corrected). Note that the decrease in functional connectivity is mainly observed between the IOG (i.e., OFA) and the rest of the face-selective regions.</p>", "links"=>[], "tags"=>["iog", "pivotal", "face-processing"], "article_id"=>656151, "categories"=>["Physiology", "Neuroscience"], "users"=>["Zonglei Zhen", "Huizhen Fang", "Jia Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059886.g003", "stats"=>{"downloads"=>10, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_IOG_plays_a_pivotal_role_in_the_face_processing_network_/656151", "title"=>"The IOG plays a pivotal role in the face-processing network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-21 01:38:51"}

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