新生儿功能连接的个体模式,由基于表面的贝叶斯模型揭示.
Diego Derman1, Damon D Pham2, Amanda F Mejia2
1Department of Intelligent Systems Engineering, Indiana University, USA.
bioRxiv : the preprint server for biology
|August 16, 2024
概括
研究人员开发了一种新的贝叶斯框架,利用功能磁共振成像 (fMRI) 对婴儿的个体大脑网络进行映射. 这种方法成功地在289名婴儿中发现了独特的大脑连接模式,揭示了与年龄相关的网络强度变化.
科学领域:
- 神经科学是一个神经科学.
- 发育神经科学的发展神经科学.
- 脑部成像 脑部成像
背景情况:
- 休息状态功能连接 (rsFC) 对于理解大脑发育至关重要.
- 由于运动工件和数据限制,估计婴儿的rsFC具有挑战性.
- 以前的研究往往在婴儿大脑网络分析中缺乏个体级别的精度.
研究的目的:
- 描述大脑功能网络组织的个体变异性,在一个大队伍的出生婴儿.
- 开发和验证一个新的数据驱动的贝叶斯框架,用于婴儿RSFC分析.
- 调查不同大脑网络中年龄和功能连接强度之间的关系.
主要方法:
- 利用了来自正在发展的人类结合体项目 (dHCP) 队列的休息状态功能磁共振成像 (fMRI) 数据 (N=289).
- 采用了新的数据驱动的贝叶斯框架来进行网络估计.
- 仅在皮层表面进行分析,使用基于表面的注册,以新生儿图谱为指导,以提高跨主题对齐.
主要成果:
- 成功估计了14个大脑网络/子网络的学科级功能连接地图.
- 生成了个别的功能分片地图,突出显著的学科间差异.
- 确定了婴儿年龄和所有大脑区域的平均连接强度之间的显著积极关系,包括更高阶网络.
结论:
- 新的贝叶斯框架和基于表面的分析有效地捕捉了婴儿大脑网络中的个体变异性.
- 这些发现为早期生命中功能性大脑组织的发展轨迹提供了宝贵的见解.
- 这种方法为使用fMRI数据研究年轻人群中的大脑发育提供了更高的精度.
关键词:
贝叶斯模型是贝叶斯模型.在RSFC中使用RSFC.大脑发育大脑的发育大脑的发展功能磁力共振成像 (fMRI) 是一种功能共振成像.功能连接性的功能连接性.新生儿科学 新生儿科学静止状态网络是一个静止状态网络.统计方法 统计方法更多相关视频
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