Evaluating EEG complexity and spectral signatures in Alzheimer’s disease and frontotemporal dementia: evidence for rostrocaudal asymmetry
Bibliographic record
Abstract
Accurate classification of neurodegenerative disorders remains a challenge in neuroscience. Using open-source electroencephalography (EEG) data, we investigated electrophysiological signatures to differentiate frontotemporal dementia (FTD) from Alzheimer's disease (AD) via complexity measures. Traditional relative band power analysis showed consistent increases in lower-frequency activity but did not distinguish the two disorders after correction. In contrast, fractal dimension and long-range temporal correlations (LRTCs) revealed distinct topographical differences: AD exhibited rostral dominance in fractal dimension, whereas FTD showed caudal dominance. Both disorders demonstrated reduced LRTCs, particularly in caudal regions, indicating disrupted large-scale neural dynamics. These findings suggest that complexity-based EEG features may offer a reliable, cost-effective tool for distinguishing neurodegenerative conditions, complementing traditional neuroimaging approaches.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".