Dual-Transformer Cross-Attention Framework for Alzheimer’s disease detection via dPTE-Guided EEG channel selection and multi-modal integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate and efficient detection of Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) poses significant challenges in neuroscience and healthcare. Our proposed AI framework, Dual-Transformer Cross-Attention Network (DTCA-Net), combines deep learning and directed Phase Transfer Entropy (dPTE) to optimize channel selection crucial for the detection of AD and FTD. Our method identified six critical EEG channels (F7, F8, T3, T4, O1, and O2) through dPTE analysis. We propose the DTCA-Net architecture, which fuses dPTE and differential entropy (DE) features via a multi-head cross-attention layer, projecting dPTE into queries and DE into keys/values over the temporal dimension, so that connectivity (dPTE) shifts are explicitly aligned with spectral complexity (DE), yielding richer spatiotemporal representations. By leveraging a reduced set of EEG channels identified via dPTE, DTCA-Net performance is comparable to previous state-of-the-art models. Additionally, we introduce an adaptive post-processing voting mechanism to enhance subject-level predictions. This approach achieves an F1 score of 84.9% for AD vs. control (CN) detection and 66.5% for FTD vs. CN detection. Overall, compared to traditional full-channel utilization, our model demonstrates the practicality of a reduced-channel solution for clinical applications in AD and FTD detection, enhancing the accessibility and cost-effectiveness of EEG-based diagnostics. The code has been released on GitHub .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it