灰色物质的基于网络的映射和神经递质架构与神经病症相关
Shu Wang1, Hu-Cheng Yang1,2, Hai-Hua Sun3
1Department of Radiology, Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, China.
Frontiers in systems neuroscience
|January 26, 2026
概括
神经病症是一种神经病症.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 遗传学 是一个遗传学.
背景情况:
- 神经病症是导致健康状况不佳的重要危险因素.
- 之前使用灰色物质体积 (GMV) 进行的神经基础神经病的研究已经产生了不一致的结果.
- 了解与神经病症相关的功能性大脑网络对于确定潜在干预措施至关重要.
研究的目的:
- 为了确定融合的功能性大脑网络背后的异质GMV神经病的相关性.
- 探索这些已识别的大脑网络的神经化学基础.
- 为了解神经病症的神经基质提供统一的框架.
主要方法:
- 系统地审查了10个基于voxel的形态测量 (VBM) 研究 (N = 1,595) 报告神经病症相关的GMV坐标.
- 应用功能连接网络映射 (FCNM) 对来自1,093名健康参与者的休息状态fMRI数据.
- 评估与正规大脑网络的重叠,并使用Juspace工具箱探索与神经递质受体分布的空间相关性.
主要成果:
- 与神经性相关的GMV变化一致地映射到三个主要的功能网络上:默认模式网络 (DMN),前端对称网络 (FPN) 和腹部注意网络 (VAN).
- 这些网络映射在不同的分析参数中是强大的.
- 隐含网络与5-基二胺受体2A (5-HT2A),大麻素受体1型 (CB1) 和转基因谷氨酸受体5 (mGluR5) 的分布显示出显著的空间相关性.
结论:
- 神经病的GMV相关物汇聚在涉及自我参考处理,认知控制和突出性的大规模大脑网络上.
- 与特定受体分布的显著空间合表明了血清激素,内分泌素和谷氨酸激素机制.
- 这种基于网络的方法统一了研究结果,协调了不一致性,并确定了神经病症预防和生物标志物开发的目标.
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