Brain-Atlas-Guided Generative Counterfactual Attention for Explainable Cognitive Decline Diagnosis Using Multimodal Connectomes

Researchers propose GCAN, a novel deep learning framework that uses counterfactual generation and brain atlas constraints to improve the explainability of cognitive decline diagnosis from brain imaging data. The method achieves competitive classification performance on mild cognitive impairment and subjective cognitive decline detection while providing interpretable insights into disease-related connectivity changes.

This research addresses a critical gap in medical AI: the tension between predictive accuracy and clinical interpretability in neuroimaging analysis. While deep learning models excel at classification tasks, their black-box nature limits clinical adoption for conditions like Alzheimer's disease, where understanding the biological mechanisms driving diagnosis is essential for patient trust and treatment planning.

The GCAN framework innovates by formulating diagnosis as a counterfactual problem—essentially asking what brain connectivity patterns would need to change for a healthy person to appear cognitively impaired. By anchoring this analysis to established brain atlases, the model maintains anatomical plausibility while generating attention maps that highlight clinically relevant regions. The dual-modality approach integrating functional and structural connectivity captures complementary biological information, providing a more holistic view of neurodegeneration.

The clinical implications are substantial. Early detection of mild cognitive impairment and subjective cognitive decline represents a critical intervention window for Alzheimer's prevention. An explainable diagnostic tool could enable clinicians to identify disease-specific connectivity biomarkers, potentially guiding patient stratification and personalized treatment strategies. The validation across multiple datasets including ADNI, a standard benchmark in neuroimaging research, strengthens confidence in generalizability.

Moving forward, the adoption of explainability-first AI in medical imaging could reshape how institutions approach diagnostic AI deployment. Regulatory bodies increasingly demand interpretable models for clinical use, making frameworks like GCAN valuable not just scientifically but commercially. Integration into clinical workflows requires further validation in prospective studies and evaluation against traditional biomarkers.

• →GCAN uses counterfactual generation to explain cognitive decline diagnosis while maintaining brain atlas topology constraints.
• →The framework integrates functional and structural connectivity analysis for comprehensive neurodegeneration assessment.
• →Explainability mechanisms including attention maps and circular connectome visualization support clinical interpretability.
• →Competitive performance across HC vs. SCD, HC vs. MCI, and SCD vs. MCI classification tasks demonstrated on multiple datasets.
• →The architecture separates pre-trained modality-specific classifiers from downstream diagnosis to prevent data leakage.