使用自编码网络和二进制假设测试对家族性和非家族性ADHD进行分类
medRxiv : the preprint server for health sciences
|September 2, 2025
概括
这项研究使用深度学习和MRI扫描来区分家族性和非家族性注意力缺陷/多动症 (ADHD). 人工智能模型成功识别了ADHD亚型的不同神经生物学标记,
科学领域:
- 神经成像
- 人工智能
- 遗传学
背景情况:
- 家庭病史是注意力缺陷/多动症 (ADHD) 的重要危险因素.
- 通过神经成像来区分家族性多动症 (ADHD-F) 和非家族性多动症 (ADHD-NF) 尚未得到研究.
- 多模磁共振成像 (MRI) 可以提供详细的脑部结构和功能见解.
研究的目的:
- 研究深度学习算法与多模式MRI在区分ADHD亚型中的有效性.
- 确定特定的神经成像生物标志物,区分ADHD-F和ADHD-NF和对照.
- 为确定神经发育障碍标志物制定一个强大的,可通用的框架.
主要方法:
- 使用了438名儿童 (129名ADHD-F,159名ADHD-NF,150名对照) 的T1加权和扩散加权MRI数据.
- 使用深度学习管道,包括特征选择 (t-test,相互信息,Lasso) 和自动编码器.
- 实施了一种二元假设策略,在五倍交叉验证中进行一次遗漏测试.
主要成果:
- 该模型在对照组和ADHD- F和ADHD- NF之间的ADHD亚型分类中取得了中等准确性 (AUC范围为0.67至0.70).
- 关键的差异化指标包括骨中的平均扩散度 (MD),下额头囊中的微分异性 (FA),以及各种大脑区域的皮质厚度.
- 针对ADHD- F与对照组,ADHD- NF与对照组以及ADHD- F与ADHD- NF的不同神经成像模式.
结论:
- 一个半监督的深度学习框架可以使用多模式MRI数据可靠地区分家族性和非家族性ADHD.
- 先进的深度学习技术可以识别神经发育障碍的强大和可泛化的神经生物学标志物.
- 这种方法有望提高ADHD的诊断精度.
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