Schizophrenia is challenging to identify from resting-state functional MRI (rs-fMRI) due to subtle, distributed changes and the clinical need for transparent models. We build on the Swin 4D fMRI Transformer (SwiFT) to classify schizophrenia vs. controls and explain predictions with Transformer Layer-wise Relevance Propagation (TransLRP). We further introduce Swarm-LRP, a particle swarm optimization (PSO) scheme that tunes Layer-wise Relevance Propagation (LRP) rules against model-agnostic explainability (XAI) metrics from Quantus. On the COBRE dataset, TransLRP yields higher faithfulness and lower sensitivity/complexity than Integrated Gradients, and highlights physiologically plausible regions. Swarm-LRP improves single-subject explanation quality over baseline LRP by optimizing (α,γ,ϵ) values and discrete layer-rule assignments. These results suggest that architecture-aware explanations can recover spatiotemporal patterns of rs-fMRI relevant to schizophrenia while improving attribution robustness. This feasibility study indicates a path toward clinically interpretable neuroimaging models.