Intelligent classification of major depressive disorder using rs-fMRI of the posterior cingulate cortex

作者：Shihao Huang, Hao Shisheng, Yue Si, Dan Shen, Lan Cui, Yuandong Zhang, Hang Lin, Sanwang Wang, Yujun Gao, Xin Guo · 发表于：Journal of Affective Disorders · 年份：2024 · DOI：10.1016/j.jad.2024.03.166 · 被引用次数：8 · 研究领域：Functional Brain Connectivity Studies、Advanced MRI Techniques and Applications、Advanced Neuroimaging Techniques and Applications

Major Depressive Disorder (MDD) is a widespread psychiatric condition that affects a significant portion of the global population. The classification and diagnosis of MDD is crucial for effective treatment. Traditional methods, based on clinical assessment, are subjective and rely on healthcare professionals' expertise. Recently, there's growing interest in using Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to objectively understand MDD's neurobiology, complementing traditional diagnostics. The posterior cingulate cortex (PCC) is a pivotal brain region implicated in MDD which could be used to identify MDD from healthy controls. Thus, this study presents an intelligent approach based on rs-fMRI data to enhance the classification of MDD. Original rs-fMRI data were collected from a cohort of 430 participants, comprising 197 patients and 233 healthy controls. Subsequently, the data underwent preprocessing using DPARSF, and the amplitudes of low-frequency fluctuation values were computed to reduce data dimensionality and feature count. Then data associated with the PCC were extracted. After eliminating redundant features, various types of Support Vector Machines (SVMs) were employed as classifiers for intelligent categorization. Ultimately, we compared the performance of each algorithm, along with its respective optimal classifier, based on classification accuracy, true positive rate, and the area under the receiver operating characteristic curve (AUC-ROC). Upon a...