假设精益社区检测方法对人类连接网络的比较评估
Ayoushman Bhattacharya1, Nilanjan Chakraborty1,2, Xintian Wang3
1Department of Statistics and Data Science, Washington University in St. Louis.
bioRxiv : the preprint server for biology
|November 26, 2025
概括
我们对大脑网络社区检测方法进行了基准测试,发现使用基于概率的标准的无值加权方法可靠地确定大脑社区的数量 (K). 这种方法揭示了成人和婴儿不同的发育大脑结构.
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:
- 在功能性大脑网络中发现社区对于理解中等规模组织至关重要.
- 现有的方法通常依赖于诸如分类结构和任意值等假设,使社区计数选择 (K) 变得复杂.
- 需要基于假设的,无值的方法来进行大脑网络中强大的社区检测.
研究的目的:
- 在权重的功能性大脑连接矩阵中系统地对社区检测的假设薄,无值方法进行基准测试.
- 为了比较不同的选择社区数量的策略 (K),包括后期指数和基于概率的新标准.
- 使用这些方法来评估大脑中大尺度结构的发育轨迹.
主要方法:
- 在合成和神经成像数据集上的基准权重随机块模型,光谱聚类和K-平均值 (人类连接组项目,华盛顿大学120号,婴儿连接组项目).
- 将各种后期指数 (如轮,模块化,NMI) 与基于概率的标准进行比较,以选择K.
- 采用共识重新标记方案 (匈牙利语与哈明距离匹配) 来稳定解决方案.
主要成果:
- 在模拟中,所有方法都恢复了稳定的分区,但在弱信号和非分类混合的情况下,特此指数失败了.
- 基于概率的标准在成年人中确定了温和的K=11,与已知的大脑系统保持一致.
- 在婴儿中,同样的标准表明K≈15,显示出不同的发育中等尺度结构,包括默认模式和前端平行系统中的分类.
结论:
- 无值加权方法有效地减轻大脑网络社区检测中的分类偏差.
- 基于概率的标准为选择社区数量 (K) 提供了可重复和可靠的方法.
- 这一框架揭示了人类大脑中等尺度结构的显著发育差异.
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