向颗粒大脑内在连接网络和对精神分裂症的洞察力
Shiva Mirzaeian1,2, Kyle M Jensen1,3, Ram Ballem1,4
1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.
bioRxiv : the preprint server for biology
|July 15, 2025
概括
500个组件的高阶空间组独立组件分析 (sgr-ICA) 在静止状态fMRI (rsfMRI) 数据中揭示了细粒度的内在连接网络 (ICNs). 这种方法增强了精神分裂症连接模式的表征,检测了低级分析中错过的差异.
科学领域:
- 神经成像是一种神经成像.
- 大脑的连接性 大脑的连接性
- 精神疾病 精神疾病
背景情况:
- 空间组独立组分分析 (sgr-ICA) 对于分析功能磁共振成像 (fMRI) 数据,特别是静止状态fMRI (rsfMRI) 数据至关重要.
- 在sgr-ICA中更高的模型订单显示了更精细的内在连接网络 (ICNs),提供了详细的功能性大脑架构.
- 之前的研究在探索ICN的更高模型订单时受到小数据集的限制.
研究的目的:
- 使用大型rsfMRI数据集评估一个500个组件的高阶sgr-ICA模型.
- 使用细粒度ICNs来描述精神分裂症中的功能网络连接 (FNC) 差异.
- 将结果与低级ICN进行比较,并评估高级ICA的临床实用性.
主要方法:
- 将500个组件的sgr-ICA应用于一个大型的rsfMRI数据集 (>100,000名受试者).
- 在502名精神分裂症患者和640名对照人群中,使用衍生ICN分析了全脑FNC.
- 从一个低级别的多空间尺度模板中与ICN比较结果.
主要成果:
- 创建了一套全面的可靠,细粒度的ICNs,改善了精神分裂症失联性特征.
- 鉴定了小脑和皮区域的众多ICN,大脑和皮下区域之间存在显著的低连接性.
- 观察到大脑小脑和较高的认知/感官领域之间的超连接性,以及感觉运动和皮质下区域之间的超连接性,突出显示颗粒ICNs的敏感性.
结论:
- 高级ICA有效地捕获了独特的,细粒度的ICN,丰富了FNC的理解.
- 颗粒型ICN检测出较大规模ICN错过的显著队列差异.
- 衍生ICN为未来的研究提供了有价值的参考资料,有可能提高rsfMRI在精神疾病中的临床实用性.
关键词:
功能网络连接 (FNC) 功能网络连接颗粒式内在连接网络 (ICN) 是一种内在连接网络.独立组成部分分析 (ICA)休息状态的fMRI (rsfMRI) 是指静止状态的fMRI.精神分裂症是一种精神分裂症.更多相关视频
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