预测青春期早期的新发性思维障碍与优化深度学习意味着环境-putamen相互作用
Nina de Lacy1,2, Michael J Ramshaw1,2
1Huntsman Mental Health Institute, Salt Lake City, UT 84103.
medRxiv : the preprint server for health sciences
|November 14, 2023
概括
人工智能准确地预测了青少年使用多式联络数据的思维障碍 (TD) 发作. 结构性大脑差异和心理社会因素确定了有风险的年轻人,为潜在的干预提供了信息.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 人工智能的人工智能
背景情况:
- 思想障碍 (TD) 是精神分裂症风险的一个关键指标.
- 早期识别有风险的年轻人至关重要.
- 很少有研究预测TD在一般年轻人群中的发病情况.
研究的目的:
- 在青春期早期前性地预测新发性思维障碍 (TD).
- 为了确定TD发病的预测生物标志物和风险因素.
- 利用人工智能 (AI) 进行高精度预测.
主要方法:
- 深度学习人工智能分析了ABCD研究中的5777个多式联络特征 (9-10岁).
- 包括5,014个神经指标来预测TD在11-12岁开始.
- 复制设计用于现有的TD案例.
主要成果:
- 在预测TD发病时,获得了92%的准确性,F1得分和0.96 AUROC.
- 关键预测因素包括左膜结构,睡眠障碍和父母的外部化行为.
- 与青少年亲社会性,家长问题和先前临床关注的相互作用是显著的.
结论:
- 确定了早期青少年TD的强大,个人级别的签名.
- 左门的结构差异是与心理社会压力因素相互作用的候选生物标志物.
- 针对睡眠和心理社会压力因素的干预措施可能会调节TD风险.
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