Diagnostic identification of chronic insomnia using ALFF and FC features of resting-state functional MRI and logistic regression approach

作者：Ning Yang, Shuyi Yuan, Chunlong Li, Wenqing Xiao, Shuangcong Xie, Liming Li, Guihua Jiang, Xiaofen Ma · 发表于：Scientific Reports · 年份：2023 · DOI：10.1038/s41598-022-24837-8 · 被引用次数：30 · 研究领域：Functional Brain Connectivity Studies、Advanced Neuroimaging Techniques and Applications、Advanced MRI Techniques and Applications

This study investigated whether the amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) features could be used as potentially neurological markers to identify chronic insomnia (CI) using resting-state functional MRI and machine learning method logistic regression (LR). This study included 49 CI patients and 47 healthy controls (HC). Voxel-wise features, including the amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC), were extracted from resting-state functional magnetic resonance brain images. Then, we divided the data into two independent cohorts for training (44 CI patients and 42 HC patients), and independent validation (5 CI patients and 5 HC patients) by using logistic regression. The model was evaluated using 20 rounds of fivefold cross‑validation for training. In particular, a two-sample t-test (GRF corrected, p-voxel < 0.001, p-cluster < 0.05) was used for feature selection during the model training. Finally, single‑shot testing of the final model was performed on the independent validation cohort. A correlation analysis (Bonferroni correction, p < 0.05/4) was also conducted to determine whether the features contributing to the prediction were correlated with clinical characteristics, including the Insomnia Severity Index (ISI), Pittsburgh sleep quality index (PSQI), self-rating anxiety scale (SAS), and self-rating depression scale (SDS). Results showed that resting-state features had a discrimination accuracy o...