Ou Wanmei, Nummenmaa Aapo, Golland Polina, Hamalainen Matti S
Computer Science and Artificial Intelligence Laboratory, MIT, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:1926-9. doi: 10.1109/IEMBS.2009.5333926.
We propose a novel method, fMRI-Informed Regional Estimation (FIRE), which utilizes information from fMRI in E/MEG source reconstruction. FIRE takes advantage of the spatial alignment between the neural and the vascular activities, while allowing for substantial differences in their dynamics. Furthermore, with the regional approach, FIRE can be efficiently applied to a dense grid of sources. Inspection of our optimization procedure reveals that FIRE is related to the re-weighted minimum-norm algorithms, the difference being that the weights in the proposed approach are computed from both the current estimates and fMRI data. Analysis of both simulated and human fMRI-MEG data shows that FIRE reduces the ambiguities in source localization present in the minimum-norm estimates. Comparisons with several joint fMRI-E/MEG algorithms demonstrate robustness of FIRE in the presence of sources silent to either fMRI or E/MEG measurements.
我们提出了一种新颖的方法，即功能磁共振成像引导的区域估计（FIRE），该方法在脑电/脑磁图（E/MEG）源重建中利用功能磁共振成像（fMRI）的信息。FIRE利用神经活动和血管活动之间的空间对齐，同时允许它们在动态上存在显著差异。此外，通过区域方法，FIRE可以有效地应用于密集的源网格。对我们的优化过程进行检查发现，FIRE与重新加权的最小范数算法相关，不同之处在于所提出方法中的权重是根据当前估计值和fMRI数据计算得出的。对模拟和人类fMRI-MEG数据的分析表明，FIRE减少了最小范数估计中存在的源定位模糊性。与几种联合fMRI-E/MEG算法的比较表明，在存在对fMRI或E/MEG测量无反应的源的情况下，FIRE具有稳健性。
