Zhang Wei, Hou Yingling, Wang Xinyi, Sun Yurong, Shao Junneng, Yan Rui, Kang Xuejun, Yao Zhijian, Lu Qing
School of Biological Sciences & Medical Engineering, Southeast University, Jiangsu Province, No. 2 Sipailou, Nanjing, 210096, China.
Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China.
Brain Imaging Behav. 2025 Sep 29. doi: 10.1007/s11682-025-01049-y.
Due to the lack of consistent findings across different modalities, the neurobiological underpinning of bipolar disorder (BD) remains elusive. This study aims to employ a multimodal fusion algorithm, integrating multimodal imaging data, to unravel the neurobiological underpinning of BD. A data-driven multimodal fusion algorithm was utilized to analyze covariant patterns across modalities in a cohort of 125 BD patients and 113 healthy controls (HCs). The study focused on fusing regional homogeneity (ReHo), gray matter volume (GMV), and fractional anisotropy (FA) derived from MRI scans to generate group-discriminative joint independent components (jIC). That differentiated BD patients from HCs across three modalities. An inverse functional pattern was observed in the default mode network (DMN) and sensorimotor network (SMN) in BD patients, characterized by increased ReHo in the DMN and decreased ReHo in the SMN compared to healthy individuals. This inverse pattern was also mirrored in GMV, showing increase in the DMN and decreases in the SMN. Meanwhile, significant functional hyperactivation coupled with decreased structural volume in the precuneus underscores its role in cognitive function in BD. Multimodal neuroimaging fusion provides a comprehensive understanding in pathophysiology of BD, offering valuable insights that could be pivotal in advancing the diagnosis and treatment of BD.
由于在不同模态下缺乏一致的研究结果，双相情感障碍（BD）的神经生物学基础仍然难以捉摸。本研究旨在采用一种多模态融合算法，整合多模态成像数据，以揭示BD的神经生物学基础。利用数据驱动的多模态融合算法分析了125例BD患者和113名健康对照（HCs）队列中各模态之间的协变模式。该研究重点是融合从MRI扫描中获得的局部一致性（ReHo）、灰质体积（GMV）和分数各向异性（FA），以生成区分组别的联合独立成分（jIC），从而在三种模态下区分BD患者和HCs。在BD患者的默认模式网络（DMN）和感觉运动网络（SMN）中观察到一种反向功能模式，其特征是与健康个体相比，DMN中的ReHo增加，而SMN中的ReHo减少。这种反向模式在GMV中也有体现，表现为DMN增加而SMN减少。同时，楔前叶显著的功能亢进与结构体积减小，突出了其在BD认知功能中的作用。多模态神经影像融合为BD的病理生理学提供了全面的理解，提供了有价值的见解，这可能对推进BD的诊断和治疗至关重要。
