对功能连接体和心理过程的研究中相关系数的限制
Haojie Fu1,2, Shuang Tang3, Xudong Zhao4,5
1Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China.
Human brain mapping
|July 10, 2025
概括
皮尔森相关性在神经科学中对评估大脑行为模型有局限性. 使用MAE和MSE等多个指标,以及基线比较,可以对功能连接组进行更强大的分析.
科学领域:
- 神经科学是一个神经科学.
- 心理学 心理学 心理学
- 计算生物学 计算生物学
背景情况:
- 皮尔森相关性在神经科学和心理学中常见于特征选择和模型评估.
- 它用于检查大脑活动和心理行为之间的关系.
研究的目的:
- 确定皮尔森相关性在基于连接体的心理过程预测中的局限性.
- 提出一种更强大的方法来评估神经科学中的模型性能.
主要方法:
- 皮尔森相关性的批判性分析局限性:复杂性捕获,错误反射和数据可比性.
- 建议使用多个评估指标 (例如,MAE,MSE) 和基线比较 (例如,平均值,线性回归).
主要成果:
- 皮尔森相关性不充分地捕捉到大脑网络的复杂性和模型错误 (偏差,非线性).
- 它对数据变化和异常值敏感,影响跨数据集的可比性.
- 综合指标和基线提供了一个更全面的模型评估.
结论:
- 仅靠皮尔森相关性就不足以对预测心理过程的连接组模型进行可靠的评估.
- 具有基线比较的多度指标方法提高了模型性能分析的可靠性和可解释性.
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