Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional Connectivity.

At the model level (Figure 7a ), nominally significant group differences were observed for the repeat data and BrainSurfGCN, with above‐average performers showing higher correlation differences than below‐average performers; however, these effects did not survive FDR correction, potentially reflecting limited statistical power.

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At the task level, only two contrasts, Working Memory: 2‐Back Body and Working Memory: 2‐Back Place, showed borderline significance after correction, with higher correlation differences in the above‐average group.

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