Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, United States of America.
Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States of America.
PLoS One. 2024 Apr 4;19(4):e0289401. doi: 10.1371/journal.pone.0289401. eCollection 2024.
Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.
确定生物标志物对于在治疗老年期抑郁症（LLD）患者时获得最佳治疗效果至关重要。我们使用静息态功能磁共振和弥散张量成像数据比较了 LLD 患者和健康对照组（HC），以确定可能与 LLD 潜在病理生理学相关的神经影像学生物标志物。我们使用贝叶斯多模态局部假发现率方法来比较 LLD 患者与 HC 之间的功能连接，从结构连接中获取优势，以识别 LLD 患者的功能连接中断。在贝叶斯框架下，我们开发了一种算法来控制我们发现结果的总体假发现率。我们将我们的发现与文献进行比较，并表明我们的方法可以更好地检测到一些以前从未发现过的 LLD 患者的区域。我们的发现与各种神经行为障碍的中心可以用于开发行为干预措施，以治疗对抗抑郁药无反应的 LLD 患者。
