Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models

作者全名："Huang, Yang; Zhang, Jingbo; He, Kewei; Mo, Xue; Yu, Renqiang; Min, Jing; Zhu, Tong; Ma, Yunfeng; He, Xiangqian; Lv, Fajin; Lei, Du; Liu, Mengqi"

作者地址："[Huang, Yang; Mo, Xue; Yu, Renqiang; Lv, Fajin; Liu, Mengqi] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing 400016, Peoples R China; [Zhang, Jingbo; He, Kewei; Min, Jing; Zhu, Tong; Ma, Yunfeng; He, Xiangqian; Lei, Du] Chongqing Med Univ, Coll Med Informat, Chongqing 400016, Peoples R China"

通信作者："Liu, MQ (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing 400016, Peoples R China.; Lei, D (通讯作者)，Chongqing Med Univ, Coll Med Informat, Chongqing 400016, Peoples R China."

来源：DIAGNOSTICS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001172133800001

JCR分区：Q1

影响因子：3

年份：2024

卷号：14

期号：4

开始页：　

结束页：　

文献类型：Article

关键词：bipolar disorder; major depressive disorder; gray matter; graph theory; machine learning

摘要："Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders."

基金机构：Chongqing Talents Exceptional Young Talents Project

基金资助正文：No Statement Available