Piantoni Giovanni, Hermes Dora, Ramsey Nick, Petridou Natalia
Dept Neurology & Neurosurgery, UMC Utrecht, Heidelberglaan 100, Utrecht 3584 CX, the Netherlands.
Dept Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, United States; Dept Neurology, Mayo Clinic, Rochester, MN, United States; Dept Radiology, Mayo Clinic, Rochester, MN, United States.
Neuroimage. 2021 Nov 15;242:118459. doi: 10.1016/j.neuroimage.2021.118459. Epub 2021 Aug 6.
Electrocorticography (ECoG) is typically employed to accurately identify the seizure focus as well as the location of brain functions to be spared during surgical resection in participants with drug-resistant epilepsy. Increasingly, this technique has become a powerful tool to map cognitive functions onto brain regions. Cortical mapping is more commonly investigated with functional MRI (fMRI), which measures blood-oxygen level dependent (BOLD) changes induced by neuronal activity. The multimodal integration between typical 3T fMRI activity maps and ECoG measurements can provide unique insight into the spatiotemporal aspects of cognition. However, the optimal integration of fMRI and ECoG requires fundamental insight into the spatial smoothness of the BOLD signal under each electrode. Here we use ECoG as ground truth for the extent of activity, as each electrode is thought to record from the cortical tissue directly underneath the contact, to estimate the spatial smoothness of the associated BOLD response at 3T fMRI. We compared the high-frequency broadband (HFB) activity recorded with ECoG while participants performed a motor task. Activity maps were obtained with fMRI at 3T for the same task in the same participant prior to surgery. We then correlated HFB power with the fMRI BOLD signal change in the area around each electrode. This latter measure was quantified by applying a 3D Gaussian kernel of varying width (sigma between 1 mm and 20 mm) to the fMRI maps including only gray-matter. We found that the correlation between HFB and BOLD activity increased sharply up to the point when the kernel width was set to 4 mm, which we defined as the kernel width of maximal spatial specificity. After this point, as the kernel width increased, the highest level of explained variance was reached at a kernel width of 9 mm for most participants. Intriguingly, maximal specificity was also limited to 4 mm for low-frequency bands, such as alpha and beta, but the kernel width with the highest explained variance was less spatially limited than the HFB. In summary, spatial specificity is limited to a kernel width of 4 mm but explained variance keeps on increasing as you average over more and more voxels containing the relatively noisy BOLD signal. Future multimodal studies should choose the kernel width based on their research goal. For maximal spatial specificity, ECoG electrodes are best compared to 3T fMRI with a kernel width of 4 mm. When optimizing the correlation between modalities, highest explained variance can be obtained at larger kernel widths of 9 mm, at the expense of spatial specificity. Finally, we release the complete pipeline so that researchers can estimate the most appropriate kernel width from their multimodal datasets.
脑皮层电图（ECoG）通常用于准确识别耐药性癫痫患者手术切除过程中的癫痫发作病灶以及需保留的脑功能位置。越来越多的是，这项技术已成为将认知功能映射到脑区的有力工具。皮层映射更常通过功能磁共振成像（fMRI）进行研究，fMRI测量神经元活动引起的血氧水平依赖（BOLD）变化。典型的3T fMRI活动图与ECoG测量之间的多模态整合可以为认知的时空方面提供独特的见解。然而，fMRI和ECoG的最佳整合需要深入了解每个电极下BOLD信号的空间平滑度。在这里，我们将ECoG作为活动范围的基本事实，因为每个电极被认为是直接从触点下方的皮质组织记录信号，以估计3T fMRI时相关BOLD反应的空间平滑度。我们比较了参与者执行运动任务时用ECoG记录的高频宽带（HFB）活动。在手术前，对同一参与者在3T下针对相同任务用fMRI获得活动图。然后，我们将HFB功率与每个电极周围区域的fMRI BOLD信号变化进行关联。后一项测量是通过将宽度可变（标准差在1毫米至20毫米之间）的3D高斯核应用于仅包括灰质的fMRI图来量化的。我们发现，HFB与BOLD活动之间的相关性在将核宽度设置为4毫米时急剧增加，我们将其定义为最大空间特异性的核宽度。在此之后，随着核宽度增加，大多数参与者在核宽度为9毫米时达到最高解释方差水平。有趣的是，对于低频波段，如α和β，最大特异性也限于4毫米，但具有最高解释方差的核宽度在空间上的限制比HFB小。总之，空间特异性限于4毫米的核宽度，但随着你对包含相对嘈杂的BOLD信号的越来越多体素进行平均，解释方差会持续增加。未来的多模态研究应根据其研究目标选择核宽度。为了获得最大空间特异性，将ECoG电极与核宽度为4毫米的3T fMRI进行比较最佳。在优化模态之间的相关性时，在9毫米的较大核宽度下可以获得最高解释方差，但以牺牲空间特异性为代价。最后，我们发布了完整的流程，以便研究人员可以从他们的多模态数据集中估计最合适的核宽度。
