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大脑网络景观的空间:一种新的方法来评估大脑网络中的空间障碍

Clayton C McIntyre¹, Shannon M O'Donnell¹, Mohammadreza Khodaei²

¹Neuroscience Graduate Program, Wake Forest Graduate School of Arts and Sciences.

bioRxiv : the preprint server for biology

|November 24, 2025

概括

我们开发了一种绘制大脑网络的方法,揭示了大脑区域的有序或无序程度. 在认知任务期间,在特定区域下降,这表明神经自我组织.

科学领域:

神经科学是一个神经科学.
复杂系统分析复杂系统分析
网络科学网络科学

背景情况:

功能性大脑网络表现出社区结构.
了解这些社区的空间组织对于认知神经科学至关重要.
在大脑网络中量化局部秩序/混乱是一个新兴的研究领域.

研究的目的:

引入一种用于绘制功能大脑网络社区结构的空间的新方法.
根据邻近社区的成员关系量化voxel-wise大脑网络.
为了生成大脑网络的可解释地图.

主要方法:

开发了一种基于voxel的方法,使用功能大脑网络社区数据计算空间.
将每个voxel的值与周围voxel的社区结构关联起来.
产生了大脑的空间图.

主要成果:

成功创建了大脑网络空间的可解释地图.
在工作记忆和听音乐任务期间,在可预测的大脑区域观察到局部的显著下降.
证明区域缩与特定的认知功能相关.

结论:

引入的方法为分析大脑网络中的空间提供了一个框架.
在认知任务期间区域的减少表明神经自我组织.
这种方法可以扩展到其他复杂的网络映射到欧几里德空间.

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