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Folly Beach Library
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Edgar Allan Poe/Sullivan's Island Library
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Wando Mount Pleasant Library
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St. Paul's/Hollywood Library
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Otranto Road Library
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Mt. Pleasant Library
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McClellanville Library
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Brain Connectomics Improve the Prediction of High-Risk Depression Profiles in the First Year following Breast Cancer Diagnosis.
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- Author(s): Mu Zi Liang1; Peng Chen2; Ying Tang3; Xiao Na Tang4; Molassiotis, Alex5; Knobf, M. Tish6; Mei Ling Liu7; Guang Yun Hu8; Zhe Sun9; Yuan Liang Yu10; Zeng Jie Ye11
- Source:
Depression & Anxiety (1091-4269). 5/17/2024, Vol. 2024, p1-11. 11p.- Subject Terms:
- Source:
- Additional Information
- Abstract: Background. Prediction of high-risk depression trajectories in the first year following breast cancer diagnosis with fMRI-related brain connectomics is unclear. Methods. The Be Resilient to Breast Cancer (BRBC) study is a multicenter trial in which 189/232 participants (81.5%) completed baseline resting-state functional magnetic resonance imaging (rs-fMRI) and four sequential assessments of depression (T0-T3). The latent growth mixture model (LGMM) was utilized to differentiate depression profiles (high vs. low risk) and was followed by multivoxel pattern analysis (MVPA) to recognize distinct brain connectivity patterns. The incremental value of brain connectomics in the prediction model was also estimated. Results. Four depression profiles were recognized and classified into high-risk (delayed and chronic, 14.8% and 12.7%) and low-risk (resilient and recovery, 50.3% and 22.2%). Frontal medial cortex and frontal pole were identified as two important brain areas against the high-risk profile outcome. The prediction model achieved 16.82-76.21% in NRI and 12.63-50.74% in IDI when brain connectomics were included. Conclusion. Brain connectomics can optimize the prediction against high-risk depression profiles in the first year since breast cancer diagnoses. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Depression & Anxiety (1091-4269) is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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