大脑功能网络连接的有条件图形重建揭示了性和流体智能的可解释的潜在轴
bioRxiv : the preprint server for biology
|February 27, 2026
概括
我们开发了一个新的AI框架来模拟大脑连接,揭示了性别和智力等因素如何影响大脑网络. 这种方法有助于理解大脑功能的个体差异,以获得潜在的心理健康应用.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 网络科学 网络科学
背景情况:
- 了解大脑功能连接对于将大脑网络属性与临床评估联系起来至关重要.
- 现有的模型往往难以捕捉大脑连接中的特定变量模式.
研究的目的:
- 提出一个生成框架来建模人类大脑的功能连接.
- 编码和重建静态功能网络连接 (sFNC) 功能,同时保留与人口统计和认知变量相关的网络指标和差异.
主要方法:
- 使用条件图变化自编码器 (c-GVAE) 来编码sFNC特征到隐藏的表示.
- 使用潜伏表示来重建sFNC数据的变量条件 (例如,生物性别,流体智能).
- 识别了与潜空间中的条件变量相关的歧视性连接特征.
主要成果:
- 在超过20,000名英国生物库受试者中,展示了sFNC数据的高保真重建,保存了特定条件的网络模式.
- 展示了潜伏空间捕捉了与人口和认知变量相关的可解释模式.
- 通过探测和向前映射到连接模式,突出显示潜空间中的群体差异.
结论:
- 拟议的框架为研究大脑功能连接和个体差异提供了一个可扩展的,基于网络的方法.
- 这种方法可以通过将其与临床观察到的评估变量联系起来,从而潜在地描述心理健康状况的功能特征.
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