Combining Classification with fMRI-Derived Complex Network Measures for Potential Neurodiagnostics
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{"title"=>"Combining Classification with fMRI-Derived Complex Network Measures for Potential Neurodiagnostics", "type"=>"journal", "authors"=>[{"first_name"=>"Tomer", "last_name"=>"Fekete", "scopus_author_id"=>"37004346900"}, {"first_name"=>"Meytal", "last_name"=>"Wilf", "scopus_author_id"=>"36515832500"}, {"first_name"=>"Denis", "last_name"=>"Rubin", "scopus_author_id"=>"7202307226"}, {"first_name"=>"Shimon", "last_name"=>"Edelman", "scopus_author_id"=>"7101628395"}, {"first_name"=>"Rafael", "last_name"=>"Malach", "scopus_author_id"=>"7005863798"}, {"first_name"=>"Lilianne R.", "last_name"=>"Mujica-Parodi", "scopus_author_id"=>"8568129700"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84877092317", "sgr"=>"84877092317", "pui"=>"368853039", "isbn"=>"1932-6203", "pmid"=>"23671641", "doi"=>"10.1371/journal.pone.0062867"}, "id"=>"9e8c1e2d-a220-3b7d-8382-c115009168b2", "abstract"=>"Complex network analysis (CNA), a subset of graph theory, is an emerging approach to the analysis of functional connectivity in the brain, allowing quantitative assessment of network properties such as functional segregation, integration, resilience, and centrality. Here, we show how a classification framework complements complex network analysis by providing an efficient and objective means of selecting the best network model characterizing given functional connectivity data. We describe a novel kernel-sum learning approach, block diagonal optimization (BDopt), which can be applied to CNA features to single out graph-theoretic characteristics and/or anatomical regions of interest underlying discrimination, while mitigating problems of multiple comparisons. As a proof of concept for the method's applicability to future neurodiagnostics, we apply BDopt classification to two resting state fMRI data sets: a trait (between-subjects) classification of patients with schizophrenia vs. controls, and a state (within-subjects) classification of wake vs. sleep, demonstrating powerful discriminant accuracy for the proposed framework.", "link"=>"http://www.mendeley.com/research/combining-classification-fmriderived-complex-network-measures-potential-neurodiagnostics", "reader_count"=>58, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>7, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>4, "Student > Master"=>11, "Student > Bachelor"=>5, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>7, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>4, "Student > Master"=>11, "Student > Bachelor"=>5, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>11, "Mathematics"=>1, "Agricultural and Biological Sciences"=>6, "Medicine and Dentistry"=>8, "Neuroscience"=>5, "Physics and Astronomy"=>3, "Psychology"=>16, "Computer Science"=>3, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>11}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>8}, "Neuroscience"=>{"Neuroscience"=>5}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Psychology"=>{"Psychology"=>16}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Computer Science"=>{"Computer Science"=>3}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"Austria"=>1, "United States"=>1, "Finland"=>1, "Malaysia"=>2, "Germany"=>1, "India"=>2}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1053598"], "description"=>"<p>Top: An illustration of a block diagonal binary matrix; white = 1, in which <i>n<sub>1</sub></i> and <i>n<sub>2</sub></i> denote the sizes of groups 1 and 2 respectively. In case of an affinity matrix, structure such as in the above depiction represents an ideal scenario in which the ratio between inner group similarity to inter group similarity is maximal. Note that block diagonal binary matrices can represent any number of groups, and of course are not contingent on row/column ordering when used in our multi-kernel optimization routine. <i>Bottom:</i> We show an actual example derived from the <i>Wake vs. Sleep</i> data-set<b>,</b> during cross validation. Matrix entries are ordered by condition (awake, then sleep). <i>Left:</i> a direct kernel sum (all kernels normalized to unit diagonal). <i>Right:</i> a BDopt sum optimized according to the training sample labels. As can be seen, BDopt enhances block contrast (i.e. homogeneity within each of the four blocks).</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "diagonal", "optimization"], "article_id"=>697803, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Kernel_sum_block_diagonal_optimization_BDopt_/697803", "title"=>"Kernel sum block diagonal optimization (BDopt).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-06 02:10:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053599"], "description"=>"<p>Application of BDopt to these data resulted in a ranked list of 12 graphs. The global complex network measures in each of these 12 graphs were concatenated. A two sample <i>t-</i>test was applied after which only the top 25% of features were retained. Next, principal component analysis was carried out, and the loads of the data on the first three principal components were used to embed data in 3D.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "hoc", "schizophrenia"], "article_id"=>697804, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Post_hoc_analysis_of_Patients_with_Schizophrenia_vs_Controls_/697804", "title"=>"Post hoc analysis of <i>Patients with Schizophrenia vs. Controls</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-06 02:10:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053601"], "description"=>"<p>Binary small worldness across the 5 levels of connectivity fraction threshold used in this study. The ratio was computed from the connectivity pattern from which the top ranked graph out of the 12 graphs selected by BDopt classification based on global CNA measures was derived. As reported previously by several studies, patients are characterized by a reduced small worldness indicating compromised network efficiency. * <i>p</i><0.05 ** <i>p</i><0.01 (uncorrected for number of graphs).</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "binary", "worldness", "schizophrenia"], "article_id"=>697806, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Difference_in_binary_small_worldness_Patients_with_Schizophrenia_vs_Controls_/697806", "title"=>"Difference in binary small worldness (<i>Patients with Schizophrenia vs. Controls</i>).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-06 02:10:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053604"], "description"=>"<p>Application of BDopt to local complex network features resulted in a ranked subset of regions that led to maximal classification accuracy. A two sample <i>t-</i>test was carried out on the CNA features within these ROIs. The top 25% of features were retained and used for principal component analysis. These data were projected upon the first two principal components and color coded by condition.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "hoc"], "article_id"=>697809, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Post_hoc_analysis_of_Wake_vs_Sleep_/697809", "title"=>"Post hoc analysis of <i>Wake vs. Sleep</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-06 02:10:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053607"], "description"=>"<p>Gray matter ROI participation (across training data folds) resulting from BDopt applied to local CNA measures. Only ROIs that contributed to maximal classification in a given training set are included.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "bdopt"], "article_id"=>697812, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROIs_selected_by_BDopt_for_Wake_vs_Sleep_/697812", "title"=>"ROIs selected by BDopt for <i>Wake vs. Sleep</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-06 02:10:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053609"], "description"=>"<p>Four different classifiers were applied to global CNA features, derived from inter area functional connectivity. Classifiers are ordered according to accuracy. Significance was assessed using Fisher's exact test. CV denotes accuracy across training folds.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "cna"], "article_id"=>697814, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.t008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classifier_accuracy_for_Wake_vs_Sleep_global_CNA_features_/697814", "title"=>"Classifier accuracy for <i>Wake vs. Sleep-</i> global CNA features.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-06 02:10:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053610"], "description"=>"<p>Local CNA features were used as the basis of classification. ROIs are listed according to their rank, achieved through recursive kernel elimination. <i>Participation</i> denotes the fraction of training folds in which a given ROI was selected during the classification.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "rois", "bdopt", "classification"], "article_id"=>697815, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.t007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Anatomical_ROIs_selected_through_BDopt_classification_Wake_vs_Sleep_/697815", "title"=>"Anatomical ROIs selected through BDopt classification <i>Wake vs. Sleep.</i>", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-06 02:10:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053611"], "description"=>"<p>Five different classifiers were applied to local CNA features, derived from inter area partial correlations in the 0.01–01 Hz band. Classifiers are ordered according to accuracy. Significance was assessed using Fisher's exact test. CV denotes accuracy across training folds. FuncCon denotes BDopt classification using the raw functional connectivity.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "sleep-"], "article_id"=>697816, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.t006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classifier_accuracy_for_Wake_vs_Sleep_local_CNA_features_/697816", "title"=>"Classifier accuracy for <i>Wake vs. Sleep- local CNA</i> features.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-06 02:10:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053612"], "description"=>"<p>Local complex network measures were computed for the top graph implicated by global CNA classification using BDopt (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062867#pone-0062867-t003\" target=\"_blank\">Table 3</a>) - lag 1 (1 TR) correlation between the 116 ROIs spanning brain gray matter employed in this study. Significance was assessed using Fisher's exact test. FuncCon denotes BDopt classification using the raw functional connectivity. BalAcc denotes balanced accuracy. CV denotes accuracy across training folds.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "schizophrenia"], "article_id"=>697817, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.t005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_using_local_features_Patients_with_Schizophrenia_vs_Controls_/697817", "title"=>"Classification using local features (<i>Patients with Schizophrenia vs. Controls</i>).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-06 02:10:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053613"], "description"=>"<p>Out of the 12 graphs selected by BDopt classification based on global CNA measures, six showed significant differences in the binary small world ratio between patients and controls, and in small worldness (as the difference is only in a scaling factor - shown in parenthesis). The numbers denoting the graphs correspond with <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062867#pone-0062867-t003\" target=\"_blank\">Table 3</a>. As reported previously by several studies, patients are characterized by a reduced small worldness index (and ratio), indicating compromised network efficiency.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "binary", "schizophrenia"], "article_id"=>697818, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Difference_in_binary_small_world_properties_Patients_with_Schizophrenia_vs_Controls_/697818", "title"=>"Difference in binary small world properties (<i>Patients with Schizophrenia vs. Controls</i>).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-06 02:10:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053614"], "description"=>"<p>We applied BDopt in conjunction with recursive kernel selection to global complex network measures. On average (across training data folds) accuracy peaked at 12 graphs. Graphs are listed according to their rank by the BDopt classifier.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "ranked", "diagonal", "optimization", "schizophrenia"], "article_id"=>697819, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graphs_ranked_by_block_diagonal_optimization_Patients_with_Schizophrenia_vs_Controls_/697819", "title"=>"Graphs ranked by block diagonal optimization (<i>Patients with Schizophrenia vs. Controls</i>).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-06 02:10:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053615"], "description"=>"<p>Global complex network analysis (CNA) measures were derived for resting state data, and used as features for several support vector based classifiers. Significance was derived using Fisher's exact test. BalAcc denotes balanced accuracy. CV denotes accuracy across training folds.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "patients"], "article_id"=>697820, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_accuracy_of_data_set_1_Schizophrenic_Patients_vs_Controls_/697820", "title"=>"Classification accuracy of data set 1 (Schizophrenic Patients vs. Controls).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-06 02:10:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053616"], "description"=>"<p>Significance was assessed using two sided two sample <i>t</i>-tests.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "patients"], "article_id"=>697821, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Demographics_of_participants_in_study_1_Schizophrenic_Patients_vs_Controls_/697821", "title"=>"Demographics of participants in study 1 (Schizophrenic Patients vs. Controls).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-06 02:10:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/1053619", "https://ndownloader.figshare.com/files/1053620", "https://ndownloader.figshare.com/files/1053623", "https://ndownloader.figshare.com/files/1053625"], "description"=>"<div><p>Complex network analysis (CNA), a subset of graph theory, is an emerging approach to the analysis of functional connectivity in the brain, allowing quantitative assessment of network properties such as functional segregation, integration, resilience, and centrality. Here, we show how a classification framework complements complex network analysis by providing an efficient and objective means of selecting the best network model characterizing given functional connectivity data. We describe a novel kernel-sum learning approach, block diagonal optimization (BDopt), which can be applied to CNA features to single out graph-theoretic characteristics and/or anatomical regions of interest underlying discrimination, while mitigating problems of multiple comparisons. As a proof of concept for the method’s applicability to future neurodiagnostics, we apply BDopt classification to two resting state fMRI data sets: a trait (between-subjects) classification of patients with schizophrenia vs. controls, and a state (within-subjects) classification of wake vs. sleep, demonstrating powerful discriminant accuracy for the proposed framework.</p></div>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "fmri", "Neurobiology of disease and regeneration", "Diagnostic medicine", "pathology", "anatomical pathology", "neuropathology", "Clinical neurophysiology", "Mental health", "psychiatry", "Neuropsychiatric disorders", "classification", "fmri-derived", "measures"], "article_id"=>697822, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Tomer Fekete", "Meytal Wilf", "Denis Rubin", "Shimon Edelman", "Rafael Malach", "Lilianne R. Mujica-Parodi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062867.s001", "https://dx.doi.org/10.1371/journal.pone.0062867.s002", "https://dx.doi.org/10.1371/journal.pone.0062867.s003", "https://dx.doi.org/10.1371/journal.pone.0062867.s004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Combining_Classification_with_fMRI_Derived_Complex_Network_Measures_for_Potential_Neurodiagnostics_/697822", "title"=>"Combining Classification with fMRI-Derived Complex Network Measures for Potential Neurodiagnostics", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-05-06 02:10:22"}

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Relative Metric

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