| Autor: |
Pan, Nanfang, Qin, Kun, Patino, Luis R., Tallman, Maxwell J., Lei, Du, Lu, Lu, Li, Wenbin, Blom, Thomas J., Bruns, Kaitlyn M., Welge, Jeffrey A., Strawn, Jeffrey R., Gong, Qiyong, Sweeney, John A., Singh, Manpreet K., DelBello, Melissa P. |
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| Zdroj: |
Journal of Child Psychology; Aug2024, Vol. 65 Issue 8, p1072-1086, 15p |
| Abstrakt: |
Background: Youth with a family history of bipolar disorder (BD) may be at increased risk for mood disorders and for developing side effects after antidepressant exposure. 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. Methods: Depressed and/or anxious youth (n = 119, age = 14.9 ± 1.6 years) with a family history of BD but no prior antidepressant exposure and typically developing controls (n = 57, age = 14.8 ± 1.7 years) 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. Results: High‐risk youth showed weaker connectivity patterns that were mainly located in the default mode network (DMN) (network weight = 50.1%) relative to controls, and connectivity patterns derived from the visual network (VN) constituted the largest proportion of aberrant stronger pairs (network weight = 54.9%). Global local efficiency (Elocal, p =.022) and clustering coefficient (Cp, p =.029) and nodal metrics of the right superior frontal gyrus (SFG) (Elocal: p <.001; Cp: p =.001) in the high‐risk group were significantly higher than those in healthy subjects, and similar patterns were also found in the left insula (degree: p =.004; betweenness: p =.005; age‐by‐group interaction, p =.038) and right hippocampus (degree: p =.003; betweenness: p =.003). The case–control classifier achieved a cross‐validation accuracy of 78.4%. Conclusions: 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‐risk youth. [ABSTRACT FROM AUTHOR] |
| Databáze: |
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