Brain Connectomics Improve the Prediction of High-Risk Depression Profiles in the First Year following Breast Cancer Diagnosis

作者全名：Liang, Mu Zi; Chen, Peng; Tang, Ying; Tang, Xiao Na; Molassiotis, Alex; Knobf, M. Tish; Liu, Mei Ling; Hu, Guang Yun; Sun, Zhe; Yu, Yuan Liang; Ye, Zeng Jie

作者地址：[Liang, Mu Zi] Guangdong Acad Populat Dev, Guangzhou, Peoples R China; [Chen, Peng] Guizhou Univ Tradit Chinese Med, Basic Med Sch, Guiyang, Peoples R China; [Tang, Ying] Guangzhou Univ Chinese Med, Inst Tumor, Guangzhou, Peoples R China; [Tang, Xiao Na] Guangzhou Univ Chinese Med, Shenzhen Baoan Tradit Chinese Med Hosp, Shenzhen, Peoples R China; [Molassiotis, Alex] Univ Derby, Coll Arts Humanities & Educ, Derby, England; [Knobf, M. Tish] Yale Univ, Sch Nursing, Orange, CT USA; [Liu, Mei Ling] Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, State Key Lab Oncol South China, Canc Ctr, Guangzhou, Peoples R China; [Hu, Guang Yun] Army Med Univ, Chongqing, Peoples R China; [Sun, Zhe] Guangzhou Univ Chinese Med, Affiliated Hosp 1, Guangzhou, Peoples R China; [Yu, Yuan Liang] South China Univ Technol, Guangzhou, Peoples R China; [Ye, Zeng Jie] Guangzhou Med Univ, Sch Nursing, Guangzhou, Guangdong, Peoples R China

通信作者：Ye, ZJ (通讯作者)，Guangzhou Med Univ, Sch Nursing, Guangzhou, Guangdong, Peoples R China.

来源：DEPRESSION AND ANXIETY

ESI学科分类：PSYCHIATRY/PSYCHOLOGY

WOS号：WOS:001230954700001

JCR分区：Q1

影响因子：4.7

年份：2024

卷号：2024

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：　

摘要：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.

基金机构：Sanming Project of Medicine in Shenzhen; National Natural Science Foundation of China [72274043, 71904033]; Young Elite Scientists Sponsorship Program by CACM [2021-QNRC2-B08]; [SZZYSM202206014]

基金资助正文：This research was funded by grants from the National Natural Science Foundation of China (Nos. 72274043 and 71904033), Young Elite Scientists Sponsorship Program by CACM (No. 2021-QNRC2-B08), and Sanming Project of Medicine in Shenzhen (No. SZZYSM202206014).