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This study introduces an attention-based deep learning framework for classifying Alzheimer's disease (AD) using resting-state fMRI, addressing challenges posed by high dimensionality and noise in traditional methods. By treating brain regions as tokens and leveraging a Transformer-inspired self-attention mechanism, the model effectively captures long-range functional dependencies without manual feature engineering. The framework achieves an accuracy of 88.95% and a ROC-AUC of 0.90 in distinguishing between cognitively normal subjects and those with AD, demonstrating its robustness and interpretability in clinical applications.
An attention-based model for Alzheimer's classification achieves 88.95% accuracy by directly analyzing resting-state fMRI data, bypassing traditional feature engineering pitfalls.
Accurate identification of Alzheimers disease (AD) using resting-state functional magnetic resonance imaging (rs-fMRI) remains challenging due to the high dimensionality, noise, and complex inter-regional dependencies inherent in functional brain connectivity, which limit the effectiveness of traditional approaches based on handcrafted connectivity features or conventional machine learning models. In this work, we present an attention-based deep learning framework for Alzheimers disease classification that operates directly on rs-fMRI functional connectivity matrices by treating brain regions as tokens and employing a Transformer-inspired self-attention mechanism to model long-range and global functional dependencies across distributed brain networks. The proposed framework learns discriminative functional representations without reliance on manual feature engineering and is evaluated on a longitudinal cohort from the Alzheimers Disease Neuroimaging Initiative (ADNI) comprising cognitively normal and Alzheimers disease subjects with multiple visits. A subject-wise evaluation protocol is adopted to prevent information leakage across visits, and class-weighted optimization is incorporated to address mild class imbalance. Experimental results for binary AD versus cognitively normal classification demonstrate that the proposed attention- based rs-fMRI model achieves an accuracy of 88.95% and a ROC-AUC of 0.90, along with a favorable precision-recall balance, highlighting the effectiveness of self-attention-driven functional connectivity modeling as a robust and interpretable approach for Alzheimers disease detection using resting-state fMRI.
