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核心技术专利：CN118964589B侵权必究
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用于 EEG/MEG 逆问题的参数经验贝叶斯框架：多主体和多模态集成的生成模型。

A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration.

机构信息

Cognition and Brain Sciences Unit, Medical Research Council Cambridge, UK.

出版信息

Front Hum Neurosci. 2011 Aug 24;5:76. doi: 10.3389/fnhum.2011.00076. eCollection 2011.

DOI:10.3389/fnhum.2011.00076
PMID:21904527
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3160752/
Abstract

We review recent methodological developments within a parametric empirical Bayesian (PEB) framework for reconstructing intracranial sources of extracranial electroencephalographic (EEG) and magnetoencephalographic (MEG) data under linear Gaussian assumptions. The PEB framework offers a natural way to integrate multiple constraints (spatial priors) on this inverse problem, such as those derived from different modalities (e.g., from functional magnetic resonance imaging, fMRI) or from multiple replications (e.g., subjects). Using variations of the same basic generative model, we illustrate the application of PEB to three cases: (1) symmetric integration (fusion) of MEG and EEG; (2) asymmetric integration of MEG or EEG with fMRI, and (3) group-optimization of spatial priors across subjects. We evaluate these applications on multi-modal data acquired from 18 subjects, focusing on energy induced by face perception within a time-frequency window of 100-220 ms, 8-18 Hz. We show the benefits of multi-modal, multi-subject integration in terms of the model evidence and the reproducibility (over subjects) of cortical responses to faces.

摘要

我们回顾了在参数经验贝叶斯（PEB）框架内最近的方法学发展，该框架用于在线性高斯假设下重建颅外脑电图（EEG）和脑磁图（MEG）数据的颅内源。PEB 框架为整合这个逆问题的多个约束（空间先验）提供了一种自然的方法，例如来自不同模态（例如，来自功能磁共振成像 fMRI）或多个重复（例如，受试者）的约束。我们使用相同基本生成模型的变体来说明 PEB 在三种情况下的应用：（1）MEG 和 EEG 的对称整合（融合）；（2）MEG 或 EEG 与 fMRI 的不对称整合；（3）跨受试者的空间先验的组优化。我们在来自 18 个受试者的多模态数据上评估这些应用，重点关注 100-220 ms、8-18 Hz 的时间频率窗口内面部感知引起的能量。我们展示了多模态、多受试者整合在模型证据和面部皮质反应的可重复性（受试者间）方面的优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a75d/3160752/c70dc1da7607/fnhum-05-00076-g011.jpg
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本文引用的文献

[1]
Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach.

J Cogn Neurosci. 1993

[2]
Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors.

Neuroimage. 2010-12-2

[3]
Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation.

Neuroimage. 2010-3-6

[4]
A parametric empirical Bayesian framework for fMRI-constrained MEG/EEG source reconstruction.

Hum Brain Mapp. 2010-10

[5]
Approximate average head models for EEG source imaging.

J Neurosci Methods. 2009-9-10

[6]
Selecting forward models for MEG source-reconstruction using model-evidence.

Neuroimage. 2009-5-15

[7]
MEG and EEG data fusion: simultaneous localisation of face-evoked responses.

Neuroimage. 2009-8-15

[8]
Electromagnetic source reconstruction for group studies.

Neuroimage. 2008-10-1

[9]
Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation.

Neuroimage. 2008-9-1

[10]
A unified Bayesian framework for MEG/EEG source imaging.

Neuroimage. 2009-2-1

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