2.8.3. Cluster-level association
LME
The effects of tDCS on whole-brain functional activation were studied using a linear mixed-effect model (LME) at the voxel-level, which accounted for the non-independence of observations in repeated measure data. The model included time (pre- and post-stimulation) and group (sham and active), along with their interaction, as fixed effects. The subject was treated as a random effect. The lme function of the R-package nlme was implemented to fit the linear mixed model [For each identified cluster, the averaged BOLD signals were extracted, representing the combined response within the cluster. Cluster masks were then applied to the computational head models to extract the averaged EF intensity from each cluster. Correlations between EFs and the functional map obtained from the LME model were calculated at the cluster level using Pearson Correlation Coefficient analysis. In this study, a cluster-based analysis was employed to offer a comprehensive assessment of the functional changes induced by transcranial direct current stimulation (tDCS). This analytical approach is designed to capture both regions of increased and decreased neural activation, thereby providing a holistic view of tDCS effects on the brain. Importantly, the cluster-based method allows for the detection of distributed effects across various brain regions, encapsulating both positive and negative modulations in functional activity and connectivity. The study specifically focused on temporal changes (pre- vs. post-stimulation) and the interaction between time and experimental groups (active vs. sham) to isolate the effects attributable to the tDCS intervention. While baseline differences between the active and sham groups were possible, these were rigorously accounted for in post hoc analyses to ensure that the observed functional changes were indeed a consequence of the tDCS intervention rather than pre-existing group differences.