基于深度聚类的功能成像衍生ADHD生物型:对个性化药物治疗指导的研究
Aichen Feng1,2, Dongmei Zhi3, Yuan Feng4
1Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
EClinicalMedicine
|January 7, 2025
概括
研究人员使用神经成像和临床数据确定了注意力缺陷多动障碍 (ADHD) 的两种不同的亚型. 一个亚型与阿托莫克赛丁相比,甲基酸的恢复更好,这表明个性化的ADHD治疗方法.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 精神病学是一个精神病学.
背景情况:
- 注意缺陷多动性障碍 (ADHD) 是一种常见的神经发育障碍,亚型和药物有效性之间没有明确的联系.
- 需要客观的神经成像标记来分类ADHD生物型,以实现个性化的治疗策略.
研究的目的:
- 开发和验证一个深度学习模型来识别ADHD生物型,使用功能网络连接和表型数据.
- 探索已识别的ADHD生物型之间治疗反应的潜在差异.
主要方法:
- 提出了一个用于生物亚型检测的图形卷积网络 (GCN-BSD),集成功能网络连接 (FNC) 和非成像类型数据.
- 该GCN-BSD模型应用于来自青少年大脑和认知发育 (ABCD) 研究的大量ADHD队列,并与纵向药物数据在独立数据集上进行验证.
主要成果:
- 在ABCD研究中,在1069名ADHD患者中确定了两个ADHD生物型,并在130名ADHD青少年中得到验证.
- 在对基线得分进行调整后,生物型1在接受甲基酸治疗时,在心理症状 (p < 0.05) 中显著更好地恢复 (p < 0.05).
- 生物型之间也观察到认知表现和过度活跃/冲动症状的差异.
结论:
- 以图像驱动,生物型为导向的方法显示出个性化多动症治疗的前景.
- 深度学习算法可以帮助界定多动症生物型,潜在地提高药物有效性和指导治疗选择.
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