MEG Spectral Biomarkers of Alzheimer's Disease: Integrating MEG and MRI Features Using the BioFIND Dataset
Bibliographic record
Abstract
Background: Alzheimer's disease (AD), the leading cause of dementia, disrupts brain communication through amyloid-beta and tau protein accumulation, leading to memory and cognitive impairments. Magnetoencephalography (MEG) and magnetic resonance imaging (MRI) offer non-invasive approaches to investigate these changes. Objective: To assess the value of combining MEG power spectral density with MRI-derived brain volumetrics and to evaluate a novel classification approach using sign-constrained logistic regression with L1 regularization (GLMNET). Methods: The BioFIND dataset, including MEG from 324 participants ($\mathbf{1 5 8 ~ M C I}$,$\mathbf{1 6 6 ~ H C}$) and MRI for most subjects, was analysed. MEG source localization was performed using linearly constrained minimum variance (LCMV) beamforming and exact low-resolution electromagnetic tomography (eLORETA), which were applied separately for MEG's magnetometer (MAG) and gradiometer (GRAD) signals. MRI regional volumes were extracted with Freesurfer. Correlation-based feature preselection for different thresholds$(0: 0.05: 0.25)$was applied, and classification was conducted using Monte Carlo of a replicated 10fold nested cross-validation. Results: Highest performance was obtained by combining LCMV MAG and GRAD with MRI features at 0 -correlation threshold, with an accuracy of 77.9 % and F1 score of 75.7 %. Conclusions: This study demonstrates the effectiveness of MEG MAG GARD integrated with MRI in distinguishing healthy ageing from cognitive decline. By comparing classification performance for different combinations, selected through multiple source localization methods and varying correlation thresholds, their potential importance was assessed more robustly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".