功能连接分析的voxel智能或区域智能干扰回归:是否重要?
Tobias Muganga1,2, Leonard Sasse1,2,3, Daouia I Larabi4
1Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
Human brain mapping
|August 21, 2025
概括
区域级别的静止状态功能连接 (rs-FC) 无声化与声量级别的无声化同样有效,特别是在平均总和中. 这种高效的方法保持了大脑行为研究的个体特异性和预测准确性.
科学领域:
- 神经成像
- 计算神经科学
- 功能神经成像
背景情况:
- 休息状态功能连接 (rs-FC) 分析需要从BOLD时间序列中删除干扰信号.
- 在Voxel级别进行无声化是标准的,但计算密集.
- 区域级无声化提供了潜在的计算效率,但其对 rs-FC 分析的影响尚不清楚.
研究的目的:
- 系统地比较 voxel 层面与区域层面的无声化对 rs-FC 的影响.
- 调查聚合方法 (平均值,EV) 和分片颗粒度对消毒结果的影响.
- 评估无声化解决方案对个体特异性和年龄和认知分数的预测的影响.
主要方法:
- 分析了来自HCP-YA数据集的370名非相关受试者.
- 比较voxel级别和区域级别的消噪策略.
- 通过100,400和1000个区域的平均和第一个特异变量 (EV) 聚合方法进行评估.
- 评估个体特异性 (指纹) 和预测年龄和认知分数.
主要成果:
- 在各种指标上,区域级的无声化表现与声量级的无声化表现相等或更好.
- 平均汇总结果为两种消音分辨率的可比个体特异性和预测性能.
- 相比于区域级别的消声,EV聚合显示出较低的个人特异性.
- 土地分片的细分度增加通常会提高个体特异性.
结论:
- 在 rs-FC 分析中,区域级无声化是一种可行且计算效率高的替代方案.
- 选择的聚合方法显著地影响了voxel级和区域级排噪的比较.
- 由于其效率和可比性性能,建议在脑行为研究中使用平均聚合的区域级消极化.
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