MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom.
Hum Brain Mapp. 2010 Oct;31(10):1512-31. doi: 10.1002/hbm.20956.
We describe an asymmetric approach to fMRI and MEG/EEG fusion in which fMRI data are treated as empirical priors on electromagnetic sources, such that their influence depends on the MEG/EEG data, by virtue of maximizing the model evidence. This is important if the causes of the MEG/EEG signals differ from those of the fMRI signal. Furthermore, each suprathreshold fMRI cluster is treated as a separate prior, which is important if fMRI data reflect neural activity arising at different times within the EEG/MEG data. We present methodological considerations when mapping from a 3D fMRI Statistical Parametric Map to a 2D cortical surface and thence to the covariance components used within our Parametric Empirical Bayesian framework. Our previous introduction of a canonical (inverse-normalized) cortical mesh also allows deployment of fMRI priors that live in a template space; for example, from a group analysis of different individuals. We evaluate the ensuing scheme with MEG and EEG data recorded simultaneously from 12 participants, using the same face-processing paradigm under which independent fMRI data were obtained. Because the fMRI priors become part of the generative model, we use the model evidence to compare (i) multiple versus single, (ii) valid versus invalid, (iii) binary versus continuous, and (iv) variance versus covariance fMRI priors. For these data, multiple, valid, binary, and variance fMRI priors proved best for a standard Minimum Norm inversion. Interestingly, however, inversion using Multiple Sparse Priors benefited little from additional fMRI priors, suggesting that they already provide a sufficiently flexible generative model.
我们描述了一种 fMRI 和 MEG/EEG 融合的非对称方法，其中 fMRI 数据被视为电磁源的经验先验，其影响取决于 MEG/EEG 数据，通过最大化模型证据来实现。如果 MEG/EEG 信号的原因与 fMRI 信号不同，这一点很重要。此外，每个超过阈值的 fMRI 簇都被视为一个单独的先验，这对于 fMRI 数据反映 EEG/MEG 数据中不同时间出现的神经活动很重要。我们提出了从 3D fMRI 统计参数图映射到 2D 皮质表面，然后映射到我们的参数经验贝叶斯框架中使用的协方差分量的方法学考虑因素。我们之前引入的规范（反归一化）皮质网格也允许部署在模板空间中的 fMRI 先验；例如，来自不同个体的组分析。我们使用相同的面部处理范式，同时从 12 名参与者那里记录 MEG 和 EEG 数据，来评估随后的方案，该范式下获得了独立的 fMRI 数据。由于 fMRI 先验成为生成模型的一部分，我们使用模型证据来比较 (i) 多个与单个，(ii) 有效与无效，(iii) 二进制与连续，以及 (iv) 方差与协方差 fMRI 先验。对于这些数据，多个、有效、二进制和方差 fMRI 先验被证明对标准最小范数反演最有效。然而，有趣的是，使用多个稀疏先验的反演并没有从额外的 fMRI 先验中获益太多，这表明它们已经提供了一个足够灵活的生成模型。
