Causal-augmented Source-free Domain Adaptation with Scale-free Transformer for Schizophrenia Classification

Brain functional networks (BFNs) derived from multi-site fMRI data have been widely explored using transformer-based models to extract discriminative connectivity features for the diagnosis of psychiatric disorders, such as schizophrenia (SZ). However, existing transformer-based methods ignore physiological priors like the scale-free property, causing the attention to fail to capture the intrinsic topology of BFNs. Additionally, multisite heterogeneity makes source-free domain adaptation (DA) essential in clinical practice where data sharing is restricted. However, existing methods mainly rely on heuristic data augmentations or pseudo-labeling, without leveraging the intrinsic inter-regional dependencies, resulting in poor robustness to cross-site variability. To overcome these challenges, we proposed a source-free DA framework based on a scale-free transformer encoder for SZ classification. The transformer encoder was pre-trained on labeled source domains to capture discriminative connectivity patterns while integrating a scale-free prior to bias the attention toward hub nodes. The pretrained encoder and classifier were used to initialize the target model, where the encoder learned latent representations derived from inter-regional interactions for causal graph construction. To enhance robustness, the causal structure was perturbed via random permutation and counterfactual interventions, while entropy minimization jointly optimized the encoder and predictor to learn domain-invariant representations. Results showed that our method outperformed the other 4 transformer, 7 DA, 6 multi-site and 6 state-of-the-art methods across two SZ datasets (87.18%±0.91% and 88.39%±0.13%). Ablation results highlighted the contributions of the causal, permutation, counterfactual, and entropy minimization constraints to the performance improvement. Furthermore, the identified discriminative temporal regions provided insights into the dysfunctional neural-mechanisms in SZ.