Contributors |
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ix | |
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Chapter 1 Autism Spectrum Disorders: Unbiased Functional Connectomics Provide New Insights into a Multifaceted Neurodevelopmental Disorder |
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1 | (20) |
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1 | (2) |
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Functional Connectomics as a Window Into ASD |
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3 | (1) |
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An Unbiased Bayesian Framework for Functional Connectomics |
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4 | (3) |
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Multisite Network Analysis of Autism |
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7 | (6) |
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7 | (2) |
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Network-Based Differences in ASD |
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9 | (4) |
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Toward Characterizing Patient Heterogeneity |
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13 | (4) |
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14 | (1) |
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Network Dysfunction Linked to ASD Severity |
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15 | (2) |
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17 | (2) |
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19 | (2) |
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Chapter 2 Insights Into Cognition from Network Science Analyses of Human Brain Functional Connectivity: Working Memory as a Test Case |
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21 | (22) |
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27 | (10) |
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28 | (2) |
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Tasks for Studying Working Memory |
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30 | (1) |
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Insights From Network Science Analyses of Human Brain Functional Connectivity Resting State Data |
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31 | (1) |
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Resting State Functional Connectivity and Working Memory |
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32 | (1) |
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Functional Connectivity During Working Memory Task Performance |
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33 | (4) |
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37 | (2) |
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39 | (2) |
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41 | (2) |
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Chapter 3 Overlapping and Dynamic Networks of the Emotional Brain |
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43 | (20) |
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Brain Networks are Overlapping |
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44 | (8) |
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47 | (1) |
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Node Taxonomy: Hubs and Bridges |
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48 | (4) |
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Brain Networks are Dynamic |
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52 | (6) |
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Evolution of Network Organization Across Time |
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52 | (4) |
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Fluid Network Identity Across Time |
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56 | (2) |
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58 | (1) |
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59 | (2) |
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61 | (2) |
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Chapter 4 The Uniqueness of the Individual Functional Connectome |
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63 | (20) |
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63 | (11) |
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64 | (1) |
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FC Identification: Early Work and Results in Adult Subjects |
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65 | (1) |
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Results in Adolescents and Applications to Disease |
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66 | (2) |
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68 | (1) |
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Factors Affecting the Detection of Individual Differences |
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69 | (2) |
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Identification in Rest, Task, and Naturalistic Scans |
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71 | (2) |
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Identification Relevance to Behavior |
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73 | (1) |
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74 | (1) |
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75 | (8) |
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Chapter 5 Dysfunctional Brain Network Organization in Neurodevelopmental Disorders |
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83 | (18) |
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Attention Deficit Hyperactivity Disorder |
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87 | (3) |
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90 | (4) |
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Integration and Segregation as a Framework for Understanding Neurodevelopmental Disorders: Next Steps |
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94 | (2) |
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96 | (5) |
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Chapter 6 Addiction: Informing Drug Abuse Interventions with Brain Networks |
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101 | (22) |
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Seed-Based Functional Connectivity |
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105 | (1) |
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Independent Component Analysis |
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106 | (3) |
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109 | (3) |
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Discussion and Future Directions |
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112 | (3) |
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115 | (1) |
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115 | (8) |
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Chapter 7 Connectivity and Dysconnectivity: A Brief History of Functional Connectivity Research in Schizophrenia and Future Directions |
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123 | (32) |
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123 | (1) |
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124 | (2) |
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126 | (1) |
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126 | (1) |
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127 | (15) |
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129 | (3) |
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ROI-/Seed-Based Functional Connectivity |
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132 | (2) |
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Independent Component Analysis |
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134 | (1) |
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Static Functional Network Connectivity |
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135 | (7) |
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142 | (2) |
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144 | (1) |
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145 | (10) |
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Chapter 8 Genetics of Brain Networks and Connectivity |
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155 | (26) |
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Motivation for Genetic Studies of the Brain's Structural and Functional Connectivity |
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155 | (1) |
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To What Extent are Brain Variations Influenced by Genetics? A History of Heritability With Twin and Family Studies |
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156 | (2) |
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Altered Brain Connectivity in Neurogenetic Disorders and Genetic Deletions and Duplications |
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158 | (3) |
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158 | (1) |
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159 | (1) |
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22q11.2 Deletion Syndrome |
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159 | (1) |
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160 | (1) |
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160 | (1) |
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161 | (1) |
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161 | (1) |
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Digging Deeper---Searching for the Effect of Single Nucleotide Polymorphisms |
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161 | (1) |
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Genome-Wide Searches and Boosting Statistical Power to Address Small Effect Sizes |
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162 | (2) |
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164 | (1) |
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Translating Findings to the Clinic |
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164 | (3) |
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Altered Connectivity in Traumatic Brain Injury |
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165 | (1) |
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The Interaction Between Genetics and TBI |
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166 | (1) |
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167 | (1) |
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168 | (1) |
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168 | (13) |
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Chapter 9 Characterizing Dynamic Functional Connectivity Using Data-Driven Approaches and its Application in the Diagnosis of Alzheimer's Disease igi |
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181 | (18) |
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182 | (4) |
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Dynamic Functional Connectivity |
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182 | (3) |
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185 | (1) |
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Constructing Robust Static Functional Connectivity |
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186 | (9) |
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Data-Driven Approach to Measure Functional Connectivity |
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186 | (1) |
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Data-Driven Approach to Characterize Dynamic Functional Connectivities |
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187 | (1) |
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Statistic Model to Capture Functional Connectivity Variations |
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188 | (1) |
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Optimization of Tensor Statistic Model |
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189 | (1) |
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Obtain Compact Representation by the Learned Tensor Statistic Model |
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190 | (1) |
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Application of Tensor Statistic Model in AD Diagnosis |
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191 | (4) |
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195 | (1) |
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195 | (4) |
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Chapter 10 Toward a more Integrative Cognitive Neuroscience of Episodic Memory |
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199 | (20) |
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199 | (4) |
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Univariate Activation Analyses |
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203 | (2) |
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Bivariate and Seed-Based Functional Connectivity Analyses |
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205 | (6) |
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Multivariate Network Analyses |
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211 | (3) |
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Conclusions and Future Directions |
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214 | (1) |
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215 | (3) |
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218 | (1) |
Index |
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219 | |