Figure 1. Predictive modeling analysis pipeline (IMAGE)
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First, the data was independently segmented into quintiles (5 levels) for self-relevance and valence based on participant’s ratings. Next, time points (TRs) were assigned according to the levels of these two dimensions, resulting in a total of 5×5 quantized TR indices. Utilizing these indices, exemplified by level 2 for self-relevance and level 5 for valence highlighted as red-shaded TRs in the figure, each index's fMRI and rating data were averaged, thereby generating 25 fMRI images and corresponding rating data for each participant. Subsequently, employing these orthogonalized data, whole-brain pattern-based predictive models were developed using principal component regression (PCR) along with leave-one-subject-out cross-validation (LOSO-CV) and random-split cross-validation (RS-CV).
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