Mediation analysis has been widely adopted to elucidate the role of intermediary variables derived from neuroimaging data. Structural equation models (SEMs) are typically employed to investigate the influences of exposures on outcomes, with model coefficients being interpreted as causal effects. While existing SEMs are effective tools, limited research has considered shape mediators. In addition, the linear assumption may lead to efficiency losses and decreased predictive accuracy in real-world applications. To address these challenges, we introduce a novel framework for shape mediation analysis, designed to explore the causal relationships between genetic exposures and clinical outcomes, whether mediated or unmediated by shape-related factors while accounting for potential confounding variables. We propose a two-layer shape regression model to characterize the relationships among neurocognitive outcomes, elastic shape mediators, genetic exposures, and clinical confounders. Both simulated studies and real-data analyses demonstrate the superior performance of our proposed method in terms of estimation accuracy and robustness when compared to existing approaches.