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Updated: Jan 8, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

可扩展和适应的时空建模用于基于任务的fMRI分析.

Jungin Choi¹, Abhirup Datta¹, Martin A Lindquist¹

¹Department of Biostatistics, Johns Hopkins University.

bioRxiv : the preprint server for biology

|December 22, 2025

概括

此摘要是机器生成的。

我们开发了SPLASH,这是一个用于分析fMRI数据中的大脑活动的新框架. SPLASH提高了空间精度和计算效率,为大脑功能提供了更好的洞察力.

关键词:

适应空间建模适应空间建模功能性核磁共振成像 (MRI) 是一种功能性核磁共振成像.血液动力学反应建模模型.层次错误发现率的错误发现率.时间空间线回归回归.

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Last Updated: Jan 8, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

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Published on: October 20, 2023

科学领域:

神经成像是一种神经成像.
功能磁共振成像 (fMRI) 是一种功能性磁共振成像技术.
统计建模统计建模

背景情况:

基于Voxel的一般线性模型 (GLM) 是基于任务的fMRI的标准,但产生了碎片化的激活图.
现有的空间方法,如内核平滑模糊边界,而贝叶斯模型是计算密集的.

研究的目的:

介绍SPLASH (Spline-Based Processing for Localized Adaptive Spatial Hemodynamics),这是一个用于可扩展和空间适应的fMRI分析的新型框架.
通过提高空间分辨率和计算效率来解决当前方法的局限性.

主要方法:

在大脑小片内利用局部薄板脊柱回归来进行空间适应.
实施了具有两阶段选择性推断程序的等级结构,用于控制错误发现率.
通过模拟和应用到人类连接器项目数据进行验证.

主要成果:

在模拟中,SPLASH表现出卓越的性能,平均平方误差 (MSE) 比之前的空间模型低20-40%.
在模拟中保持控制的假阳性率 (FPR) 和假阴性率 (FNR).
仅需要贝叶斯空间方法的2%的计算时间,并且在真实数据上显示出更高的可重现性.

结论:

SPLASH为大规模的fMRI研究提供了一个可泛化,空间适应和可扩展的框架.
通过产生更清晰的激活模式,提高了统计推断和神经科学解释性.
为现有的空间分析方法提供了计算效率高的替代方案.