4.2. ROI-level
CORTEX, MRS
At the ROI-level, we applied Brainnetome atlas parcellation to both individualized head models and functional maps in order to extract averaged EF intensity and beta values. While we observed strong correlations in specific brain regions, such as the orbital gyrus and middle temporal gyrus, none of these regions survived the false discovery rate (FDR) correction, as we considered all brain subregions in the Brainnetome atlas for our analyses. In this regard, previous studies have reported significant correlations within predefined regions of interest; however, there is a lack of consensus regarding rules or hypotheses for ROI selection. For instance, among studies that examined dose-response relationships at the ROI-level, some studies defined spherical seed regions with varying sizes, including 2.5 mm, 6 mm, 8 mm around specific coordinates such as the peak activation voxel in fMRI analysis results, mean center coordinates obtained from MRS analyses, or around the hand knob region [Despite the methodological differences, the ability to detect significant correlations at the ROI-level is highly dependent on how ROIs are defined. These definitions can vary widely, from using standard anatomical or functional atlases for parcellation to placing small spheres around specific brain coordinates. The chosen method for defining ROIs plays a critical role in successfully identifying meaningful relationships between dose and neural response. For example, Esmaielpour et al. employed two different approaches for ROI definition: anatomical atlas parcellation of the prefrontal cortex and parcellation based on EF strength [In dose-response relationships using regional-based approaches (including both ROIs or clusters), it is essential to extract features from each region. This involves averaging the estimated parameters, such as beta activation maps and EF intensities, across the voxels within the predefined ROIs to calculate the relationships between fMRI-derived parameters and transcranial electrical stimulation-induced EFs. The ROI-based approach overcomes the low SNR observed in voxel-wise analysis by aggregating data from multiple voxels. Moreover, performing statistical testing on ROIs rather than voxels reduces the number of tests conducted. However, this approach comes with certain limitations. Results in brain regions outside of the pre-defined and hypothesized areas may be overlooked, and the selection of appropriate brain regions can be challenging, requiring specific knowledge about the research problem for defining relevant areas.Taken together, our ROI-level analysis demonstrated the absence of significant correlations between EFs and BOLD signal changes when considering multiple comparison corrections. This suggests that the effects of EFs may be region-specific and not uniformly distributed throughout the brain. The main challenge in finding significant correlations at the ROI-level corresponds to the absence of a hypothesis to appropriately define ROIs. Further investigations are needed to better understand the underlying mechanisms and functional implications of these region-specific effects. Additionally, future studies should carefully consider the methodology for defining ROIs and the potential trade-off between hypothesis-driven analysis and the risk of missing significant associations in unexpected brain regions.