人类连接体中的社区检测:方法类型,差异及其对推理的影响
Skylar J Brooks1,2, Victoria O Jones3, Haotian Wang4
1Boston Children's Hospital, Department of Pediatrics, Boston, Massachusetts, USA.
Human brain mapping
|March 30, 2024
概括
选择正确的大脑网络社区检测方法至关重要. 在随机区块建模 (SBM) 中,像贝叶斯推理这样的概率论方法在各种大脑网络拓学中提供可靠的社区结构估计.
科学领域:
- 神经科学是一个神经科学.
- 网络科学 网络科学
- 计算生物学 计算生物学
背景情况:
- 大脑网络社区结构是组织的关键,但没有标准的方法来检测它.
- 社区检测方法选择对准确性,稳定性和生物相关性的影响尚不清楚.
研究的目的:
- 为了比较三个类型的社区检测方法:模块化最大化,概率 (贝叶斯推理在SBM),和几何.
- 研究大脑网络拓如何影响方法的准确性,可靠性和一致性.
- 评估网络社区和个别措施之间的依赖方法的关联.
主要方法:
- 从青少年大脑认知发展 (ABCD) 研究中大规模休息状态fMRI数据的分析 ( =\u20095251).
- 分析各种拓的合成网络 ( =\u20095338).
- 在SBM中对纽曼,卢温,贝叶斯推理和图里奇流量方法进行比较.
主要成果:
- 方法的准确性和可靠性在很大程度上取决于大脑网络拓.
- 在具有明确社区结构的网络中,大多数方法的性能都类似.
- 在复杂网络中,贝叶斯推理在SBM中显示出更高的准确性.
- 在网络社区和个别措施之间发现了一些依赖于方法的关联.
结论:
- 概率学方法,特别是SBM中的贝叶斯推理,在网络拓学中提供可靠的社区结构估计.
- 结果依赖于方法强调了可靠性和可重复性方面的潜在问题.
- 建议通过多种检测方法确认网络社区及其相关物,以获得强大的生物推理.
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