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Updated: Jun 21, 2025

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

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Published on: November 27, 2019

基于坐标的元分析数据的神经成像元回归与空间模型.

Yifan Yu¹, Rosario Pintos Lobo², Michael Cody Riedel³

¹Oxford Big Data Institute, University of Oxford, Old road campus, Oxford, OX3 7LF, United Kingdom.

Biostatistics (Oxford, England)

|July 13, 2024

概括

此摘要是机器生成的。

我们介绍了一个新的基于坐标的生成元回归 (CBMR) 框架用于大脑激活分析. 这种方法提供了神经成像数据和共变效应的高效统计建模.

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Last Updated: Jun 21, 2025

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

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科学领域:

神经成像是一种神经成像.
统计建模统计建模
空间统计的空间统计.

背景情况:

基于坐标的元分析合成神经成像研究证据,绘制大脑激活的地图.
一个重大挑战是为激活焦点位置开发计算效率高且可统计解释的模型.
现有的方法在建模空间激活模式和共变影响方面可能缺乏灵活性.

研究的目的:

提出一种新的基于坐标的生成元回归 (CBMR) 框架.
为了近似一个平滑的激活强度函数和模型空间大脑激活.
调查研究水平共变量对激活模式的影响.

主要方法:

使用线参数化来捕捉大脑激活的空间结构.
使用四种不同的随机模型来解释激活焦点的随机变化.
开发一个基于坐标的元回归 (CBMR) 的生成框架.

主要成果:

该CBMR框架应用于20个元分析数据集.
在voxel级别进行空间均性测试以验证模型.
性能与现有的基于内核和基于模型的元分析方法进行了比较.

结论:

拟议的CBMR框架为基于坐标的元分析提供了一个可统计解释和计算效率高的方法.
线条参数化有效地模拟了大脑激活的空间分布.
该框架成功地结合了研究水平的共变量,增强了元分析洞察力.