基于LLM的特征选择和反事实解释应用于精神分裂症功能连接分析
Xinyan Yuan1, Tiantian Chen1, Yanyan He1
1School of Artificial Intelligence, Jiangsu Vocational College of Business, Nantong, China.
Frontiers in neuroscience
|January 28, 2026
概括
本研究引入了一种使用大型语言模型 (LLM) 分析精神分裂症 (SZ) 功能连接 (FC) 的新框架. 该方法增强了特征选择,并提供了对大脑机制的可解释的见解,改善了SZ分析.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 精神疾病 精神疾病
背景情况:
- 由于神经机制不清楚,精神分裂症 (SZ) 带来了挑战.
- 高维功能连接 (FC) 数据使功能选择和解释变得复杂.
- 传统方法缺乏整合神经科学知识,限制了临床相关性.
研究的目的:
- 使用LLM指导的FC特征选择开发SZ分析的创新框架.
- 将LLM编码的大脑疾病知识整合到生物学上可信的特征选择中.
- 通过反事实解释来增强模型的可解释性和临床相关性.
主要方法:
- 一个新的框架,结合了LLM指导的特征选择和FC数据的反事实解释.
- 利用LLM知识来减少高维FC数据的维度.
- 生成因果干预示例并将其转化为自然语言解释.
主要成果:
- 在5个现实世界SZ数据集上进行验证.
- 在SZ的模型分类性能中证明了改进.
- 通过增强的特征选择和可解释性,为SZ分析提供了新的见解.
结论:
- 基于LLM的FC分析方法有效地选了SZ的大脑区域的关键FC特征.
- 该方法为SZ数据集提供了更好的特征选择和可解释性.
- 局限性包括由于数据异质性和非优化的超参数而导致的临床应用挑战.
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