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使用fMRI进行全脑因果发现

Fahimeh Arab¹, AmirEmad Ghassami², Hamidreza Jamalabadi³

¹Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA.

Network neuroscience (Cambridge, Mass.)

|March 31, 2025

概括

从fMRI发现大脑连接是困难的. 一种名为CaLLTiF (Causal discovery for Large-scale Low-resolution Time-series with Feedback) 的新方法,准确地绘制了大脑网络,克服了旧技术的局限性.

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:

在功能磁共振成像 (fMRI) 数据中发现因果关系是一个重大挑战.
像格兰杰因果关系和动态因果模型这样的现有方法与同时效应和潜在的共同原因作斗争.
因果结构学习方法面临可扩展性问题,通常需要非循环假设,限制它们的应用到大规模的大脑网络.

研究的目的:

解决当前fMRI因果发现方法的局限性.
开发一种可扩展和准确的方法,从大规模的低分辨率时间序列fMRI数据中推断因果关系,并结合反.
在全脑fMRI分析中建立因果发现的新标准.

主要方法:

在模拟数据上对现有的fMRI因果发现方法进行了分类和比较分析.
开发了一种基于约束的新方法,即大规模低分辨率时间序列与反 (CaLLTiF) 的因果发现.
为了确定因果关系,CaLLTiF使用对同时变量和滞后变量的条件独立性测试.

主要成果:

与现有的方法相比,CaLLTiF在模拟的fMRI数据上表现出卓越的准确性和可扩展性.
对人类休息状态fMRI的分析揭示了使用CaLLTiF的个体中高度一致的因果连接体.
学习的因果连接体表现出从注意力和默认模式网络向感觉运动网络的上下因果流,其影响取决于欧几里德距离,并由同时相互作用主导.

结论:

CaLLTiF代表了从全脑fMRI数据的因果发现的重大进步.
该方法克服了以前方法的关键局限性,提供了更好的准确性和可扩展性.
这项工作为未来的研究制定了一个新的基准,通过因果推理来理解大脑连接和功能.

关键词:

大脑网络大脑网络因果发现因果发现.认知神经科学是一种认知神经科学.统计算法统计算法功能磁力共振成像 (fMRI) 是一种

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