Early Detection of Alzheimer’s Disease From Cortical and Hippocampal Local Field Potentials Using an Ensembled Machine Learning Model
Bibliographic record
Abstract
Early diagnosis of Alzheimer's disease (AD) is a very challenging problem and has been attempted through data-driven methods in recent years. However, considering the inherent complexity in decoding higher cognitive functions from spontaneous neuronal signals, these data-driven methods benefit from the incorporation of multimodal data. This work proposes an ensembled machine learning model with explainability (EXML) to detect subtle patterns in cortical and hippocampal local field potential signals (LFPs) that can be considered as a potential marker for AD in the early stage of the disease. The LFPs acquired from healthy and two types of AD animal models (n = 10 each) using linear multielectrode probes were endorsed by electrocardiogram and respiration signals for their veracity. Feature sets were generated from LFPs in temporal, spatial and spectral domains and were fed into selected machine-learning models for each domain. Using late fusion, the EXML model achieved an overall accuracy of 99.4%. This provided insights into the amyloid plaque deposition process as early as 3 months of the disease onset by identifying the subtle patterns in the network activities. Lastly, the individual and ensemble models were found to be robust when evaluated by randomly masking channels to mimic the presence of artefacts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".