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The neurobiological basis of these risks remains poorly understood. We aimed to identify biomarkers underlying risk by characterizing abnormalities in the brain connectome of symptomatic youth at familial risk for BD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Depressed and\/or anxious youth (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009119, age\u2009=\u200914.9\u2009\u00b1\u20091.6\u2009years) with a family history of BD but no prior antidepressant exposure and typically developing controls (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200957, age\u2009=\u200914.8\u2009\u00b1\u20091.7\u2009years) received functional magnetic resonance imaging (fMRI) during an emotional continuous performance task. A generalized psychophysiological interaction (gPPI) analysis was performed to compare their brain connectome patterns, followed by machine learning of topological metrics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      High\u2010risk youth showed weaker connectivity patterns that were mainly located in the default mode network (DMN) (network weight\u2009=\u200950.1%) relative to controls, and connectivity patterns derived from the visual network (VN) constituted the largest proportion of aberrant stronger pairs (network weight\u2009=\u200954.9%). Global local efficiency (\n                      <jats:italic>E<\/jats:italic>\n                      <jats:sub>local<\/jats:sub>\n                      ,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.022) and clustering coefficient (\n                      <jats:italic>C<\/jats:italic>\n                      <jats:sub>\n                        <jats:italic>p<\/jats:italic>\n                      <\/jats:sub>\n                      ,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.029) and nodal metrics of the right superior frontal gyrus (SFG) (\n                      <jats:italic>E<\/jats:italic>\n                      <jats:sub>local<\/jats:sub>\n                      :\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u2009.001;\n                      <jats:italic>C<\/jats:italic>\n                      <jats:sub>\n                        <jats:italic>p<\/jats:italic>\n                      <\/jats:sub>\n                      :\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.001) in the high\u2010risk group were significantly higher than those in healthy subjects, and similar patterns were also found in the left insula (degree:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.004; betweenness:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.005; age\u2010by\u2010group interaction,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.038) and right hippocampus (degree:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.003; betweenness:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.003). The case\u2013control classifier achieved a cross\u2010validation accuracy of 78.4%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our findings of abnormal connectome organization in the DMN and VN may advance mechanistic understanding of risk for BD. Neuroimaging biomarkers of increased network segregation in the SFG and altered topological centrality in the insula and hippocampus in broader limbic systems may be used to target interventions tailored to mitigate the underlying risk of brain abnormalities in these at\u2010risk youth.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jcpp.13946","type":"journal-article","created":{"date-parts":[[2024,1,14]],"date-time":"2024-01-14T21:31:25Z","timestamp":1705267885000},"page":"1072-1086","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task\u2010based\n                    <scp>fMRI<\/scp>\n                    connectome 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