An Attention-Based Framework for Alzheimers Disease Classification Using Resting-State fMRI
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 g...
| Authors | Harshiddhi Pathak, Gowtham Reddy N, Mrinal Acharya et al. |
|---|---|
| Primary category | eess.IV |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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