ArticleDepression and anxiety2024
Brain Connectomics Improve the Prediction of High-Risk Depression Profiles in the First Year following Breast Cancer Diagnosis.
Article in Depression and anxiety, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Longitudinal Correlational Study of Psychological Resilience, Depression Disorder, and Brain Functional-Structural Hybrid Connectome in Breast Cancer.Depression and anxiety · 2024Trial
- The interplay between sleep and cancer-related fatigue in breast cancer: A casual and computer-simulated network analysis.Asia-Pacific journal of oncology nursing · 2025Article
- Associations Between Brain Structural Connectivity and 1-Year Demoralization in Breast Cancer: A Longitudinal Diffusion Tensor Imaging Study.Depression and anxiety · 2024Article
- A novel brain functional-structural hybrid analysis to explain the effect of a 6-month psychosocial intervention on resilience in breast cancer.International journal of clinical and health psychology : IJCHPArticle
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11 authors.
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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.
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