As another point regarding strategic data analysis, we would like to introduce two approaches, average sample- and model-based analyses for proceeding with POTATo. In average sample-based analysis, the activation is interpreted from the significantly increased average amplitude of activation-related period compared with that of baseline period (i.e., prestimulus) without any predetermined assumption on a signal waveform. Despite its simplicity, the temporal information outside a predetermined activation-related period is neglected. On the contrary, statistical parametric mapping (SPM), a well-known spatiotemporal normalization mapping based on mass univariate analysis across voxels in positron emission tomography53–55 and fMRI,17,56,57 is used to evaluate the correlation between signal and estimated HRF. The assumption of complex HRF convolution or the dynamic cerebral autoregulation concept (e.g., myogenic, metabolic, and neurogenic activities)58–61 differentiates this SPM method with an average sample-based approach. The correlation representing the signal-model likelihood is assessed based on a linear model recognized as the general linear model (GLM).62–64 This model accommodates the voxel-wise analysis by fitting the response variables, ( observed time points) with explanatory variables, ( regressor models), weighted parameters, corresponding to each regressor, and independent error, as .65–68 Unlike the conventional time-invariant model in early SPM, the temporal adaptive (i.e., delayed and dispersed evoked response) emphasizes the advantages of focal ROIs, task variability, subject and signal (e.g., , ) characteristics in the GLM model.69–71 By evaluating the model resemblance, the irrelevant signal (e.g., physiological low-frequency oscillation) and prompt high amplitude due to motion artifacts reduce statistical robustness.72 This also emphasizes the importance of accomplishing appropriate preprocessing.