Building an auxiliary diagnostic and treatment efficacy prediction model for adolescent depression using machine learning based on electroencephalography technology.

Conversely, the beta band SMN-DAN (importance score 1.8402) exhibits a decreasing trend within the 0.2–0.6 range, suggesting that stronger SMN-DAN connectivity may suppress the model’s positive prediction bias.

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Further analysis using Bonferroni correction for multiple comparisons is shown in Figure 7b that β -band SMN-DAN ( r = −0.203, p = 0.0022), β -band SN-DMN ( r = −0.207, p = 0.0017), and α -band SMN-DAN ( r = −0.333, p < 0.001) exhibited significant negative correlations, indicating that lower connectivity strength increases the model’s predictive probability for depression; whereas the β -band SMN-SN ( r = 0.136, p = 0.106) did not reach statistical significance after adjustment.

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The alpha band SN (importance score 1.1043) exhibited an increasing trend within the 0.1–0.5 range, turning positive when connection strength exceeded 0.3.

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