Spatial vs. Temporal Features in ICA of Resting-State fMRI – A Quantitative and Qualitative Investigation in the Context of Response Inhibition
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{"title"=>"Spatial vs. Temporal Features in ICA of Resting-State fMRI - A Quantitative and Qualitative Investigation in the Context of Response Inhibition", "type"=>"journal", "authors"=>[{"first_name"=>"Lixia", "last_name"=>"Tian", "scopus_author_id"=>"11439368100"}, {"first_name"=>"Yazhuo", "last_name"=>"Kong", "scopus_author_id"=>"55004151200"}, {"first_name"=>"Juejing", "last_name"=>"Ren", "scopus_author_id"=>"41662198100"}, {"first_name"=>"Gaël", "last_name"=>"Varoquaux", "scopus_author_id"=>"12808763400"}, {"first_name"=>"Yufeng", "last_name"=>"Zang", "scopus_author_id"=>"7102641968"}, {"first_name"=>"Stephen M.", "last_name"=>"Smith", "scopus_author_id"=>"7406656097"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84879183305", "pmid"=>"23825545", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-84879183305", "issn"=>"19326203", "pui"=>"369142407", "doi"=>"10.1371/journal.pone.0066572"}, "id"=>"8bb298b3-a6c6-3d29-9b78-36236aed47a9", "abstract"=>"Independent component analysis (ICA) can identify covarying functional networks in the resting brain. Despite its relatively widespread use, the potential of the temporal information (unlike spatial information) obtained by ICA from resting state fMRI (RS-fMRI) data is not always fully utilized. In this study, we systematically investigated which features in ICA of resting-state fMRI relate to behaviour, with stop signal reaction time (SSRT) in a stop-signal task taken as a test case. We did this by correlating SSRT with the following three kinds of measure obtained from RS-fMRI data: (1) the amplitude of each resting state network (RSN) (evaluated by the standard deviation of the RSN timeseries), (2) the temporal correlation between every pair of RSN timeseries, and (3) the spatial map of each RSN. For multiple networks, we found significant correlations not only between SSRT and spatial maps, but also between SSRT and network activity amplitude. Most of these correlations are of functional interpretability. The temporal correlations between RSN pairs were of functional significance, but these correlations did not appear to be very sensitive to finding SSRT correlations. In addition, we also investigated the effects of the decomposition dimension, spatial smoothing and Z-transformation of the spatial maps, as well as the techniques for evaluating the temporal correlation between RSN timeseries. Overall, the temporal information acquired by ICA enabled us to investigate brain function from a complementary perspective to the information provided by spatial maps.", "link"=>"http://www.mendeley.com/research/spatial-vs-temporal-features-ica-restingstate-fmri-quantitative-qualitative-investigation-context-re", "reader_count"=>70, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Student > Doctoral Student"=>7, "Researcher"=>13, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>1, "Student > Master"=>11, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Student > Doctoral Student"=>7, "Researcher"=>13, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>1, "Student > Master"=>11, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>5}, "reader_count_by_subject_area"=>{"Engineering"=>14, "Unspecified"=>5, "Mathematics"=>1, "Agricultural and Biological Sciences"=>9, "Medicine and Dentistry"=>5, "Neuroscience"=>8, "Physics and Astronomy"=>2, "Psychology"=>20, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>14}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Neuroscience"=>{"Neuroscience"=>8}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>20}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>9}, "Computer Science"=>{"Computer Science"=>6}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>5}}, "reader_count_by_country"=>{"Cuba"=>1, "Iran"=>1, "Japan"=>1, "United Kingdom"=>1, "Chile"=>1, "Germany"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1091512"], "description"=>"<p>The results obtained at two decomposing dimensions, arranged according to their correspondence in the function of components. The threshold (determining inclusion in this table) was <i>p</i><0.05 (FDR corrected), which corresponds to uncorrected for the 27-component analysis and uncorrected for the 70-component analysis. For components exhibiting significant SSRT correlations, their counterparts obtained at the other decomposing dimension were also listed here even if they did not survive the threshold, and are indicated by <sup>a</sup>.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Neural pathways", "neuroscience", "neuroanatomy", "Connectomics", "neuroimaging", "fmri", "Behavioral neuroscience", "Cognitive neuroscience", "computational neuroscience", "neural networks", "Bioengineering", "Biomedical Engineering", "signal processing", "rsn", "amplitude"], "article_id"=>723968, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Lixia Tian", "Yazhuo Kong", "Juejing Ren", "Gaël Varoquaux", "Yufeng Zang", "Stephen M. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066572.t001", "stats"=>{"downloads"=>11, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_negative_correlation_of_RSN_amplitude_timeseries_standard_deviation_vs_SSRT_/723968", "title"=>"Significant negative correlation of RSN amplitude (timeseries standard deviation) vs. SSRT.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-18 01:06:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1091515", "https://ndownloader.figshare.com/files/1091516", "https://ndownloader.figshare.com/files/1091517", "https://ndownloader.figshare.com/files/1091518", "https://ndownloader.figshare.com/files/1091520", "https://ndownloader.figshare.com/files/1091521", "https://ndownloader.figshare.com/files/1091522", "https://ndownloader.figshare.com/files/1091523", "https://ndownloader.figshare.com/files/1091525", "https://ndownloader.figshare.com/files/1091526", "https://ndownloader.figshare.com/files/1091527", "https://ndownloader.figshare.com/files/1091528", "https://ndownloader.figshare.com/files/1091530", "https://ndownloader.figshare.com/files/1091531", "https://ndownloader.figshare.com/files/1091532", "https://ndownloader.figshare.com/files/1091535"], "description"=>"<div><p>Independent component analysis (ICA) can identify covarying functional networks in the resting brain. Despite its relatively widespread use, the potential of the temporal information (unlike spatial information) obtained by ICA from resting state fMRI (RS-fMRI) data is not always fully utilized. In this study, we systematically investigated which features in ICA of resting-state fMRI relate to behaviour, with stop signal reaction time (SSRT) in a stop-signal task taken as a test case. We did this by correlating SSRT with the following three kinds of measure obtained from RS-fMRI data: (1) the amplitude of each resting state network (RSN) (evaluated by the standard deviation of the RSN timeseries), (2) the temporal correlation between every pair of RSN timeseries, and (3) the spatial map of each RSN. For multiple networks, we found significant correlations not only between SSRT and spatial maps, but also between SSRT and network activity amplitude. Most of these correlations are of functional interpretability. The temporal correlations between RSN pairs were of functional significance, but these correlations did not appear to be very sensitive to finding SSRT correlations. In addition, we also investigated the effects of the decomposition dimension, spatial smoothing and Z-transformation of the spatial maps, as well as the techniques for evaluating the temporal correlation between RSN timeseries. Overall, the temporal information acquired by ICA enabled us to investigate brain function from a complementary perspective to the information provided by spatial maps.</p></div>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Neural pathways", "neuroscience", "neuroanatomy", "Connectomics", "neuroimaging", "fmri", "Behavioral neuroscience", "Cognitive neuroscience", "computational neuroscience", "neural networks", "Bioengineering", "Biomedical Engineering", "signal processing", "temporal", "ica", "resting-state", "quantitative", "qualitative"], "article_id"=>723971, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Lixia Tian", "Yazhuo Kong", "Juejing Ren", "Gaël Varoquaux", "Yufeng Zang", "Stephen M. Smith"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0066572.s001", "https://dx.doi.org/10.1371/journal.pone.0066572.s002", "https://dx.doi.org/10.1371/journal.pone.0066572.s003", "https://dx.doi.org/10.1371/journal.pone.0066572.s004", "https://dx.doi.org/10.1371/journal.pone.0066572.s005", "https://dx.doi.org/10.1371/journal.pone.0066572.s006", "https://dx.doi.org/10.1371/journal.pone.0066572.s007", "https://dx.doi.org/10.1371/journal.pone.0066572.s008", "https://dx.doi.org/10.1371/journal.pone.0066572.s009", "https://dx.doi.org/10.1371/journal.pone.0066572.s010", "https://dx.doi.org/10.1371/journal.pone.0066572.s011", "https://dx.doi.org/10.1371/journal.pone.0066572.s012", "https://dx.doi.org/10.1371/journal.pone.0066572.s013", "https://dx.doi.org/10.1371/journal.pone.0066572.s014", "https://dx.doi.org/10.1371/journal.pone.0066572.s015", "https://dx.doi.org/10.1371/journal.pone.0066572.s016"], "stats"=>{"downloads"=>43, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_vs_Temporal_Features_in_ICA_of_Resting_State_fMRI_8211_A_Quantitative_and_Qualitative_Investigation_in_the_Context_of_Response_Inhibition_/723971", "title"=>"Spatial vs. Temporal Features in ICA of Resting-State fMRI – A Quantitative and Qualitative Investigation in the Context of Response Inhibition", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-06-18 01:06:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1091501"], "description"=>"<p>All sixteen non-artefactual components from the 27-component analysis (A) and 12 non-artefactual components exhibiting significant or marginal significant amplitude-vs-SSRT correlations from the 70-component analysis (B) were shown. The number of each component was based on the ranking of variance explained by the component. A summary of the functions of the components shown in subfigure (A) can be found in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066572#pone.0066572.s013\" target=\"_blank\">Table S1</a>.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Neural pathways", "neuroscience", "neuroanatomy", "Connectomics", "neuroimaging", "fmri", "Behavioral neuroscience", "Cognitive neuroscience", "computational neuroscience", "neural networks", "Bioengineering", "Biomedical Engineering", "signal processing", "rsns", "27-component", "70-component"], "article_id"=>723957, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Lixia Tian", "Yazhuo Kong", "Juejing Ren", "Gaël Varoquaux", "Yufeng Zang", "Stephen M. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066572.g001", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Group_ICA_estimated_RSNs_based_on_27_component_analysis_A_and_70_component_analysis_B_/723957", "title"=>"Group-ICA estimated RSNs based on 27-component analysis (A) and 70-component analysis (B).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-18 01:05:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1091503"], "description"=>"<p>The coordinates in each subfigure indicate the number of the component within the set of non-artefactual components. The correspondence between the component numbers and their coordinates in the subfigures can be found in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066572#pone.0066572.s014\" target=\"_blank\">Table S2</a>.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Neural pathways", "neuroscience", "neuroanatomy", "Connectomics", "neuroimaging", "fmri", "Behavioral neuroscience", "Cognitive neuroscience", "computational neuroscience", "neural networks", "Bioengineering", "Biomedical Engineering", "signal processing"], "article_id"=>723959, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Lixia Tian", "Yazhuo Kong", "Juejing Ren", "Gaël Varoquaux", "Yufeng Zang", "Stephen M. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066572.g002", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Maps_of_the_mean_across_subjects_full_partial_correlation_matrices_/723959", "title"=>"Maps of the mean (across subjects) full/partial correlation matrices.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-18 01:05:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1091504"], "description"=>"<p>Correlation of SSRT vs. the full/partial (within-subject) RSN timeseries correlation matrices across subjects.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Neural pathways", "neuroscience", "neuroanatomy", "Connectomics", "neuroimaging", "fmri", "Behavioral neuroscience", "Cognitive neuroscience", "computational neuroscience", "neural networks", "Bioengineering", "Biomedical Engineering", "signal processing", "ssrt", "rsn", "timeseries", "matrices"], "article_id"=>723960, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Lixia Tian", "Yazhuo Kong", "Juejing Ren", "Gaël Varoquaux", "Yufeng Zang", "Stephen M. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066572.g003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_of_SSRT_vs_the_full_partial_within_subject_RSN_timeseries_correlation_matrices_across_subjects_/723960", "title"=>"Correlation of SSRT vs. the full/partial (within-subject) RSN timeseries correlation matrices across subjects.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-18 01:06:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1091507"], "description"=>"<p>The <i>negative</i> spatial-map-vs-SSRT correlation maps were thresholded at <i>p</i><0.05 (TFCE corrected for multiple comparisons across space, and for two-sided tests, but not corrected across multiple RSNs). <i>Non-Z-Map</i> indicates SSRT correlations on the spatial maps produced directly by dual-regression, and <i>Z-Map</i> indicates the correlations produced by the z-transformed version of these spatial maps. <i>No Smooth</i> indicates SSRT correlations based on the unsmoothed (i.e., only the 5-mm FWHM smoothing at the preprocessing stage) spatial maps, and <i>10-mm smooth</i> indicates the SSRT correlations based on the spatial maps additionally smoothed with a Gaussian kernel of FWHM 10 mm. The correlation maps were superimposed on their respective <i>group-mean</i> spatial maps obtained by Group-ICA and then on the MNI152 template. The <i>group-mean</i> spatial maps were provided here to show whether the significant regions lie within or outside the group-level RSNs. Red-yellow indicates significant regions in the <i>group-mean</i> spatial map, blue indicates significant spatial-map-vs-SSRT correlations that do not overlap with the group-mean map, and green indicates regions of overlap. The results were based on components corresponding to primary-medial (high eccentricity) visual networks, namely, component No. 14 from the 27-component analysis and component No. 54 from the 70-component analysis. The spatial-map-vs-SSRT correlations based on the z-transformed and 10-mm smoothed components were also shown though no significant voxel was observed. It can be seen that a greater number of significant voxels, if any, could be detected based on non-z-transformed and 10-mm smoothed spatial maps.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Neural pathways", "neuroscience", "neuroanatomy", "Connectomics", "neuroimaging", "fmri", "Behavioral neuroscience", "Cognitive neuroscience", "computational neuroscience", "neural networks", "Bioengineering", "Biomedical Engineering", "signal processing", "spatial", "smoothing", "z-transformation", "spatial-map-vs-ssrt"], "article_id"=>723963, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Lixia Tian", "Yazhuo Kong", "Juejing Ren", "Gaël Varoquaux", "Yufeng Zang", "Stephen M. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066572.g004", "stats"=>{"downloads"=>1, "page_views"=>36, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effects_of_spatial_smoothing_and_z_transformation_upon_spatial_map_vs_SSRT_correlations_/723963", "title"=>"The effects of spatial smoothing and z-transformation upon spatial-map-vs-SSRT correlations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-18 01:06:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/1091510"], "description"=>"*<p>indicated that the SSRT correlation survived a threshold of <i>p</i><0.05 (FDR corrected). R-values in bold indicate that the SSRT correlation survived the threshold of uncorrected <i>p</i><0.01.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Neural pathways", "neuroscience", "neuroanatomy", "Connectomics", "neuroimaging", "fmri", "Behavioral neuroscience", "Cognitive neuroscience", "computational neuroscience", "neural networks", "Bioengineering", "Biomedical Engineering", "signal processing", "matrix", "ssrt", "correlations", "27-component", "survived", "uncorrected"], "article_id"=>723966, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Lixia Tian", "Yazhuo Kong", "Juejing Ren", "Gaël Varoquaux", "Yufeng Zang", "Stephen M. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066572.t002", "stats"=>{"downloads"=>7, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_matrix_vs_SSRT_correlations_from_the_27_component_analysis_that_survived_a_threshold_of_uncorrected_p_/723966", "title"=>"Network matrix vs. SSRT correlations from the 27-component analysis that survived a threshold of uncorrected <i>p</i><0.01.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-18 01:06:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1091511"], "description"=>"<p> is the number of significant voxels based on z-transformed spatial maps; is the number of significant voxels based on original spatial maps; is the number of significant voxels in common for the results based on z-transformed spatial maps and those based on original spatial maps; describes the consistency of the results based on z-transformed spatial maps and those based on original spatial maps. The threshold was p<0.05 (TFCE enhanced, FWE corrected across space, two-sided, but not corrected across RSNs). is the TFCE corrected p-value (across space, two-sided, but not across RSNs) of the strongest spatial-map-vs-SSRT correlation based on z-transformed spatial maps, and is that based on original spatial maps. “Y” indicates the results were based on 10-mm smoothed spatial maps and “N” indicates no spatial smoothing. * indicates that the SSRT correlation survived a fully-corrected threshold of p<0.05 (TFCE enhanced, FWE corrected for multiple comparisons across space, and for two-sided tests, and further corrected across multiple RSNs. To note, these RSNs also showed significant amplitude-vs-SSRT correlations). a indicates that significant clusters were detected only on the correlation maps based on unsmoothed spatial-maps.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Neural pathways", "neuroscience", "neuroanatomy", "Connectomics", "neuroimaging", "fmri", "Behavioral neuroscience", "Cognitive neuroscience", "computational neuroscience", "neural networks", "Bioengineering", "Biomedical Engineering", "signal processing", "z-transformation", "spatial", "smoothing", "spatial-map-vs-ssrt"], "article_id"=>723967, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Lixia Tian", "Yazhuo Kong", "Juejing Ren", "Gaël Varoquaux", "Yufeng Zang", "Stephen M. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066572.t003", "stats"=>{"downloads"=>4, "page_views"=>41, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effects_of_z_transformation_and_spatial_smoothing_on_spatial_map_vs_SSRT_correlations_/723967", "title"=>"The effects of z-transformation and spatial smoothing on spatial-map-vs-SSRT correlations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-18 01:06:07"}

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Relative Metric

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