BNPower:用于数据驱动网络分析的功率计算工具,用于全脑连接数据的数据.
Chuan Bi1, Thomas Nichols2, Hwiyoung Lee1
1Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States.
Imaging neuroscience (Cambridge, Mass.)
|August 13, 2025
概括
计算数据驱动网络分析的统计能力是具有挑战性的. BNPower是一个新的工具,它通过使用模拟分析来解决这个问题,以便在大脑连接组研究中进行准确的功率计算.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 网络科学 网络科学
背景情况:
- 全脑连接组分析研究大脑连接性的变化.
- 网络分析将连接数据视为结果,条件视为预测因素.
- 确定与预测器相关的子网络是关键.
研究的目的:
- 为了应对数据驱动网络分析中功率计算的挑战.
- 开发一个用户友好的工具,用于大脑连接组研究中的功率分析.
- 为了使临床和实验条件的准确网络层次推断.
主要方法:
- 开发了BNPower,一种利用模拟分析的功率计算工具.
- 实施了有效的计算策略,用于子网络提取和排列测试.
- 在分析中包含了家庭智能错误率 (FWER) 控制.
主要成果:
- 对于数据驱动的网络分析模型的复杂性,BNPower负责.
- 尽管进行复杂的分析,但该工具保持了低的计算成本.
- 有效的策略有助于分网的识别和FWER的控制.
结论:
- 在数据驱动的网络分析中,BNPower提供了用于功率计算的解决方案.
- 该工具包支持大脑连接组研究中的准确统计推断.
- BNPower是公开提供GUI和源代码的.
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