可解释性障碍特征:探测精神分裂症,阿尔茨海默氏症和自闭症神经潜伏空间分层
Zafar Iqbal1,2, Md Mahfuzur Rahman1, Qasim Zia1
1Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA.
Brain sciences
|September 27, 2025
概括
时间逆转 (TR) 预训练改善了对大脑疾病分类的深度学习,在数据集中识别了一致的生物标志物. 这种人工智能方法提高了神经成像中的模型解释性和临床相关性.
科学领域:
- 神经成像和人工智能的人工智能
- 计算神经科学是一种神经科学.
- 临床应用中的机器学习.
背景情况:
- 确定神经和精神疾病的可靠生物标志物是一项挑战.
- 深度学习模型需要强大的特征提取来进行有效的分类.
- 自主监督学习为在未标记的神经成像数据上预训练模型提供了潜力.
研究的目的:
- 通过自主监督的时间逆转 (TR) 预训练开发和验证可解释的深度学习框架.
- 在多种大脑疾病中识别一致的,生物学上可信的功能网络生物标志物.
- 提高功能性MRI (fMRI) 数据分类模型的性能和可解释性.
主要方法:
- 在人类结合体项目 (HCP) 数据集上使用TR借口任务预训练了一个层次化的LSTM模型.
- 预训练的体重在五个临床fMRI数据集 (精神分裂症,阿尔茨海默氏症,自闭症谱系障碍) 上进行了微调.
- 使用后勤回归探测分析来解码功能网络贡献;从头开始训练的模型作为基线.
主要成果:
- 在4/5的数据集中,TR预训练提高了分类性能,AUC增长高达5.3%,特别是在数据稀缺的环境中.
- 探测发现了一致的,生物学上有意义的模式:精神分裂症的听觉活动减少,阿尔茨海默病的默认模式/小脑网络中断,自闭症的感觉运动异常.
- 与未预训练模型相比,TR预训练模型在数据集中产生了更具统计意义和更一致的潜在特征.
结论:
- 时间逆转预训练提高了fMRI分类深度学习模型的性能和可解释性.
- TR预训练能够实现稳定,生物可信的表示,为疾病特异性网络中断提供临床相关的见解.
- 可解释的自我监督模型显示出在精神病学和神经成像中透明和可靠的AI应用的前景.
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