Decreased Functional Brain Connectivity in Adolescents with Internet Addiction
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{"title"=>"Decreased Functional Brain Connectivity in Adolescents with Internet Addiction", "type"=>"journal", "authors"=>[{"first_name"=>"Soon Beom", "last_name"=>"Hong", "scopus_author_id"=>"13104391700"}, {"first_name"=>"Andrew", "last_name"=>"Zalesky", "scopus_author_id"=>"6602717574"}, {"first_name"=>"Luca", "last_name"=>"Cocchi", "scopus_author_id"=>"18433437300"}, {"first_name"=>"Alex", "last_name"=>"Fornito", "scopus_author_id"=>"15769060400"}, {"first_name"=>"Eun Jung", "last_name"=>"Choi", "scopus_author_id"=>"55441265100"}, {"first_name"=>"Ho Hyun", "last_name"=>"Kim", "scopus_author_id"=>"55607695100"}, {"first_name"=>"Jeong Eun", "last_name"=>"Suh", "scopus_author_id"=>"55607309300"}, {"first_name"=>"Chang Dai", "last_name"=>"Kim", "scopus_author_id"=>"26424707700"}, {"first_name"=>"Jae Won", "last_name"=>"Kim", "scopus_author_id"=>"56423490100"}, {"first_name"=>"Soon Hyung", "last_name"=>"Yi", "scopus_author_id"=>"55607143400"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84874350024", "doi"=>"10.1371/journal.pone.0057831", "sgr"=>"84874350024", "isbn"=>"1932-6203", "pmid"=>"23451272", "issn"=>"19326203", "pui"=>"368418086"}, "id"=>"f89a1058-87eb-3d18-bf93-e899728cf540", "abstract"=>"Background: Internet addiction has become increasingly recognized as a mental disorder, though its neurobiological basis is unknown. This study used functional neuroimaging to investigate whole-brain functional connectivity in adolescents diagnosed with internet addiction. Based on neurobiological changes seen in other addiction related disorders, it was predicted that connectivity disruptions in adolescents with internet addiction would be most prominent in cortico-striatal circuitry. Methods: Participants were 12 adolescents diagnosed with internet addiction and 11 healthy comparison subjects. Resting-state functional magnetic resonance images were acquired, and group differences in brain functional connectivity were analyzed using the network-based statistic. We also analyzed network topology, testing for between-group differences in key graph-based network measures. Results: Adolescents with internet addiction showed reduced functional connectivity spanning a distributed network. The majority of impaired connections involved cortico-subcortical circuits (,24% with prefrontal and ,27% with parietal cortex). Bilateral putamen was the most extensively involved subcortical brain region. No between-group difference was observed in network topological measures, including the clustering coefficient, characteristic path length, or the small-worldness ratio. Conclusions: Internet addiction is associated with a widespread and significant decrease of functional connectivity in cortico-striatal circuits, in the absence of global changes in brain functional network topology. Citation: Hong S-B, Zalesky A, Cocchi L, Fornito A, Choi E-J, et al. (2013) Decreased Functional Brain Connectivity in Adolescents with Internet Addiction. PLoS ONE 8(2): e57831.", "link"=>"http://www.mendeley.com/research/decreased-functional-brain-connectivity-adolescents-internet-addiction", "reader_count"=>153, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>8, "Librarian"=>2, "Researcher"=>19, "Student > Doctoral Student"=>10, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/970368"], "description"=>"<p>Red dots represent stereotactic centroids of brain regions (nodes) defined by Automated Anatomical Labeling (AAL) atlas, and blue lines represent suprathreshold links (t = 3.0) comprising the affected network identified with the network-based statistic (NBS) (<i>p</i><0.05, component-wise corrected).The axial view illustrates the involvement of interhemispheric connections (i.e., connections crossing between the right and left hemisphere). The sagittal view illustrates the involvement of frontal, temporal, and parietal lobes in the affected network.</p>", "links"=>[], "tags"=>["connectivity", "adolescents", "internet"], "article_id"=>639645, "categories"=>["Mathematics", "Neuroscience", "Mental Health"], "users"=>["Soon-Beom Hong", "Andrew Zalesky", "Luca Cocchi", "Alex Fornito", "Eun-Jung Choi", "Ho-Hyun Kim", "Jeong-Eun Suh", "Chang-Dai Kim", "Jae-Won Kim", "Soon-Hyung Yi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0057831.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_of_decreased_brain_functional_connectivity_in_adolescents_with_internet_addiction_/639645", "title"=>"Network of decreased brain functional connectivity in adolescents with internet addiction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-26 10:51:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/970372"], "description"=>"<p>The number of links involving each pair of divisions is normalized by total number of pair-wise links.Note that the hippocampus, globus pallidus, and putamen were assigned to the subcortical category, and the anterior cingulate gyrus was assigned to the frontal category. The amygdala and caudate nucleus were not included in the disrupted network and thus there was no need to assign these regions to a lobe. The putamen, bilaterally, was the most extensively involved subcortical region, showing decreased connections with all three major cerebral lobes involved.</p>", "links"=>[], "tags"=>["connections", "affected", "internet", "addiction", "linking", "pairs", "cerebral"], "article_id"=>639649, "categories"=>["Mathematics", "Neuroscience", "Mental Health"], "users"=>["Soon-Beom Hong", "Andrew Zalesky", "Luca Cocchi", "Alex Fornito", "Eun-Jung Choi", "Ho-Hyun Kim", "Jeong-Eun Suh", "Chang-Dai Kim", "Jae-Won Kim", "Soon-Hyung Yi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0057831.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proportion_of_connections_affected_in_internet_addiction_linking_distinct_pairs_of_broad_cerebral_divisions_/639649", "title"=>"Proportion of connections affected in internet addiction linking distinct pairs of broad cerebral divisions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-26 10:52:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/970378"], "description"=>"<p>Small-world parameters of brain functional connectivity in adolescents with internet addiction.</p>", "links"=>[], "tags"=>["connectivity", "adolescents", "internet"], "article_id"=>639652, "categories"=>["Mathematics", "Neuroscience", "Mental Health"], "users"=>["Soon-Beom Hong", "Andrew Zalesky", "Luca Cocchi", "Alex Fornito", "Eun-Jung Choi", "Ho-Hyun Kim", "Jeong-Eun Suh", "Chang-Dai Kim", "Jae-Won Kim", "Soon-Hyung Yi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0057831.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Small_world_parameters_of_brain_functional_connectivity_in_adolescents_with_internet_addiction_/639652", "title"=>"Small-world parameters of brain functional connectivity in adolescents with internet addiction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-26 10:53:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/970383", "https://ndownloader.figshare.com/files/970387", "https://ndownloader.figshare.com/files/970389", "https://ndownloader.figshare.com/files/970391"], "description"=>"<div><p>Background</p><p>Internet addiction has become increasingly recognized as a mental disorder, though its neurobiological basis is unknown. This study used functional neuroimaging to investigate whole-brain functional connectivity in adolescents diagnosed with internet addiction. Based on neurobiological changes seen in other addiction related disorders, it was predicted that connectivity disruptions in adolescents with internet addiction would be most prominent in cortico-striatal circuitry.</p> <p>Methods</p><p>Participants were 12 adolescents diagnosed with internet addiction and 11 healthy comparison subjects. Resting-state functional magnetic resonance images were acquired, and group differences in brain functional connectivity were analyzed using the network-based statistic. We also analyzed network topology, testing for between-group differences in key graph-based network measures.</p> <p>Results</p><p>Adolescents with internet addiction showed reduced functional connectivity spanning a distributed network. The majority of impaired connections involved cortico-subcortical circuits (∼24% with prefrontal and ∼27% with parietal cortex). Bilateral putamen was the most extensively involved subcortical brain region. No between-group difference was observed in network topological measures, including the clustering coefficient, characteristic path length, or the small-worldness ratio.</p> <p>Conclusions</p><p>Internet addiction is associated with a widespread and significant decrease of functional connectivity in cortico-striatal circuits, in the absence of global changes in brain functional network topology.</p> </div>", "links"=>[], "tags"=>["connectivity", "adolescents", "internet", "addiction"], "article_id"=>639656, "categories"=>["Mathematics", "Neuroscience", "Mental Health"], "users"=>["Soon-Beom Hong", "Andrew Zalesky", "Luca Cocchi", "Alex Fornito", "Eun-Jung Choi", "Ho-Hyun Kim", "Jeong-Eun Suh", "Chang-Dai Kim", "Jae-Won Kim", "Soon-Hyung Yi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0057831.s001", "https://dx.doi.org/10.1371/journal.pone.0057831.s002", "https://dx.doi.org/10.1371/journal.pone.0057831.s003", "https://dx.doi.org/10.1371/journal.pone.0057831.s004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Decreased_Functional_Brain_Connectivity_in_Adolescents_with_Internet_Addiction__/639656", "title"=>"Decreased Functional Brain Connectivity in Adolescents with Internet Addiction", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-02-26 10:54:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1005408"], "description"=>"<p>IQ, intelligence quotient; YIAS, Young Internet Addiction Scale</p>", "links"=>[], "tags"=>["Mental health", "neuroscience", "mathematics"], "article_id"=>666025, "categories"=>["Mathematics", "Neuroscience", "Mental Health"], "users"=>["Soon-Beom Hong", "Andrew Zalesky", "Luca Cocchi", "Alex Fornito", "Eun-Jung Choi", "Ho-Hyun Kim", "Jeong-Eun Suh", "Chang-Dai Kim", "Jae-Won Kim", "Soon-Hyung Yi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0057831.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Demographic_and_clinical_characteristics_of_the_participants_/666025", "title"=>"Demographic and clinical characteristics of the participants.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-25 01:40:25"}

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  • {"unique-ip"=>"28", "full-text"=>"24", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"42", "full-text"=>"27", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"12", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"24", "full-text"=>"22", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"25", "full-text"=>"27", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"26", "full-text"=>"32", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"21", "full-text"=>"22", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"0", "cited-by"=>"3", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"21", "full-text"=>"24", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"13", "full-text"=>"12", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[297, 488, 616, 724, 828, 939, 1038, 1127, 1223, 1311, 1393, 1479, 1556]}, {"subject_area"=>"/Computer and information sciences/Neural networks", "average_usage"=>[284, 481, 590, 705, 782, 880, 969, 1051, 1141, 1227, 1337, 1399, 1463]}, {"subject_area"=>"/Medicine and health sciences", "average_usage"=>[264, 460, 584, 692, 794, 887, 978, 1067, 1154, 1241, 1328, 1408, 1474]}, {"subject_area"=>"/Medicine and health sciences/Diagnostic medicine", "average_usage"=>[244, 425, 535, 636, 728, 814, 900, 982, 1063, 1139, 1221, 1296, 1361]}, {"subject_area"=>"/Medicine and health sciences/Radiology and imaging", "average_usage"=>[230, 403, 513, 618, 711, 806, 896, 985, 1065, 1141, 1225, 1296, 1360]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[259, 431, 541, 639, 727, 816, 898, 980, 1061, 1136, 1214, 1294, 1356]}, {"subject_area"=>"/Social sciences", "average_usage"=>[289, 475, 593, 703, 805, 902, 990, 1078, 1158, 1250, 1336, 1417, 1482]}, {"subject_area"=>"/Social sciences/Psychology", "average_usage"=>[294, 460, 580, 683, 777, 868, 957, 1044, 1124, 1202, 1276, 1356, 1422]}]}
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