Fig. 1. Analysis overview and prediction results.

a For the model development, we first predefined 20 seed regions within the DMN based on ref. ³⁵. We then calculated the Dynamic Conditional Correlation (DCC) between each seed region and 280 Brainnetome-based parcels using rsfMRI data from 84 participants. Using the variance of DCC time-series data as input features, we trained predictive models of the Ruminative Response Scale (RRS) subscales. The “B” stands for the brooding subscale, “D” for the depressive rumination subscale, and “R” for the reflective pondering subscale. We used Lasso regression with leave-one-participant-out cross-validation. We then selected and tested only good-performing models on the next independent test datasets. b Among the initial 60, we selected seven predictive models that showed significant cross-validated prediction performance (q < 0.05, false discovery rate) in the training dataset (n = 84). Among the seven predictive models, we again selected one predictive model that showed significant independent prediction performance at p < 0.05 (one-sided permutation test) with the validation dataset (n = 61). The selected model was the dmPFC-based predictive model of depressive rumination. We finally tested the model on the last independent test dataset (n = 48) to evaluate the model’s generalizability. A red-dashed circle indicates the data point that was identified as an outlier (i.e., greater than three standard deviations away from the mean), which did not affect the significance after its removal (r = 0.276, p = 0.028, one-sided permutation test, 95% CI [−0.012, 0.579]).