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Atypical antidepressants, including bupropion (Wellbutrin), mirtazapine (Remeron), nefazodone (Serzone), trazodone (Desyrel), and vilazodone (Viibryd), offer unique mechanisms of action. Bupropion weakly inhibits dopamine and norepinephrine reuptake, aiding depression treatment and smoking cessation, with a low risk of sexual dysfunction. Mirtazapine enhances serotonin and norepinephrine neurotransmission, leading to sedation, increased appetite, and weight gain. As a result, it helps treat...
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MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder
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使用多模式MRI数据对主要抑郁症进行分类:个性化联合算法.

Zhipeng Fan1, Jingrui Xu1, Jianpo Su1

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.

Brain sciences
|October 29, 2025
PubMed
概括
此摘要是机器生成的。

联合学习使大脑成像模型在多个机构之间进行重大抑郁症 (MDD) 诊断的协作训练,而无需共享敏感的MRI数据. 该pF-GMCO算法实现了79.07%的准确性,提供了一个保护隐私的诊断框架.

关键词:
梯度匹配的匹配方式大型抑郁症主要是抑郁症.模型对比的优化优化多模式MRI数据个性化的联合学习.

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科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 重度抑郁症 (MDD) 的准确诊断依赖于神经成像,但多站点数据存在异质性和隐私挑战.
  • 由于所有权,安全和隐私问题,共享原始MRI数据受到限制,阻碍了强大的诊断模型开发.
  • 联合学习 (FL) 提供了一个保护隐私的方法,用于跨站点的协作模式培训,而无需共享原始数据.

研究的目的:

  • 开发一个以隐私为基础的联合学习框架,用于使用多式核磁共振 (MRI) 进行可扩展的多站点诊断主要抑郁障碍 (MDD).
  • 为了解决多站点神经成像数据中固有的领域转移问题.
  • 加强结构性MRI (sMRI) 和功能性MRI (fMRI) 的整合,以改善MDD的分类.

主要方法:

  • 提出了个性化的联邦梯度匹配和对比优化 (pF-GMCO) 算法.
  • 嵌入式梯度匹配与共弦相似性用于适应性站点贡献权重.
  • 利用对比式学习进行客户端特定模型优化和多式联网紧双线 (MCB) 聚合以实现功能集成.

主要成果:

  • 在Rest-Meta-MDD数据集上评估了pF-GMCO,包括23个地点的2293名受试者.
  • 在重大抑郁障碍 (MDD) 中,诊断准确率达到了79.07%.
  • 与现有方法相比,表现出卓越的性能和可解释性.

结论:

  • pF-GMCO提供了一种有效且对隐私有意识的框架,用于使用联合学习进行多站点MDD诊断.
  • 该方法成功地解决了域转移问题,并集成了多模式MRI数据.
  • 这种方法促进了心理健康障碍的协作研究和诊断工具的开发.