解码功能连接的皮质梯度的方法
Julio A Peraza1, Taylor Salo2, Michael C Riedel3
1Department of Physics, Florida International University, Miami, FL, USA.
bioRxiv : the preprint server for biology
|August 14, 2023
概括
这项研究优化了使用元分析解码大脑连接梯度的方法. 最好的方法是将k-means细分与LDA元分析和NeuroQuery数据库相结合,以获得功能性见解.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
背景情况:
- 宏观梯度是理解大脑组织的关键,将传感运动映射到抽象的功能网络.
- 使用元分析功能解码来解释这些渐变,需要进行方法上的改进.
结论:
- 提供最佳实践建议和灵活的方法,用于基于梯度的功能解码fMRI数据.
- 提高了对神经成像中元分析技术的定量评估和应用.
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