通过信号传输的马尔科夫模型,结构连接对大脑功能的作用
Rostam M Razban1, Anupam Banerjee1, Lilianne R Mujica-Parodi1,2
1Laufer Center for Physical and Quantitative Biology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794.
bioRxiv : the preprint server for biology
|February 24, 2025
概括
大脑的结构和功能通过信号传播联系在一起. 通勤时间,是网络穿越的度量,在人类大脑成像数据中显示出与功能连接的弱但强大的相关性.
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:
- 大脑结构和功能之间的关系是神经科学的一个基本概念.
- 在人类大脑神经成像数据中观察这种结构功能联系已经证明具有挑战性.
- 以前的方法还没有完全捕捉到间接神经元通路的作用.
研究的目的:
- 通过一种新的网络度量来研究大脑结构和功能之间的联系.
- 为了测试大脑信号在结构连接上传播为马科夫过程的假设.
- 量化结构上下班时间和功能连接之间的关系.
主要方法:
- 利用扩散MRI绘制白质道图并推导结构连接性.
- 计算通勤时间,测量网络路径长度,大脑区域之间.
- 相关的通勤时间与功能性MRI连接数据来自英国生物库和HCP数据集.
- 采用自身值分解来比较主要连接方式.
主要成果:
- 通勤时间显示了与功能连接的显著负的斯皮尔曼相关性 (平均r ≈ -0.25).
- 当比较通勤时间和功能连接的主要贡献时,相关性得到改善 (平均r ≈ -0.34).
- 通勤时间比搜索信息和可传达性等其他沟通措施的表现要好33%,高达5倍.
结论:
- 结构性神经元连接和大脑功能之间存在着弱但强大的关系.
- 通勤时间有效地捕捉了多突触通路对大脑功能的影响.
- 这项研究强调了通勤时间作为理解大脑网络组织和功能的指标的实用性.
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