Magnetoencephalography brain biomarkers of Alzheimer's disease: an ongoing study based on the BioFIND dataset
Bibliographic record
Abstract
Magnetoencephalography (MEG) is widely used to study neurodegenerative disorders, particularly Alzheimer's disease (AD). AD is linked to amyloid-beta and tau protein formation, which disrupts brain anatomical and functional networks, leading to memory and cognitive impairments. Due to its noninvasive nature and excellent temporal resolution, MEG is valuable for examining functional changes in the AD brain. We evaluated a pipeline, based on nested cross-validation with Monte-Carlo replications, with MEG-derived sensors and source-based spectral features to discriminate healthy controls (HC) versus mild cognitive impairment (MCI) brain activity features. We also compared the effectiveness of combining MEG and MRI features extracted for 324 participants (158 MCI, 166 HC) in the BioFIND dataset. A robust selection of brain source activity biomarkers was implemented through five independently tested inverse solutions, including a linearly constrained minimum variance (LCMV) beamformer and exact low-resolution electromagnetic tomography (eLORETA). Several machine learning classifiers were also evaluated, including Support Vector Machine (SVM) and Logistic Regression with L1 penalty (GLMNET). Initial results showed that combining MRI features with source-based MEG features yielded the best performance based exclusively on spectral features (Acc=76.31±1.47%) using the GLMNET classifier. MEG features alone, particularly those extracted from LCMV and eLORETA analyses, demonstrated good performance (Acc=74.77±1.57%), surpassing MRI- and sensor-based analyses using an SVM classifier (Acc=72.74±1.34% and Acc=69.29±1.68% respectively). However, ongoing more advanced analyses relying on features extracted from LCMV and eLORETA and derived functional connectivity solutions (coherence - COH, imaginary COH - iCOH, wPLI, AEC, EIC) have surpassed these and previous benchmarks for the BioFIND dataset (Acc=94.91±0.01%, F1=94.63±0.01%, MCC=94.96±0.01% for MEGMAG-LCMV-wPLI; Acc=93.29±0.01%, F1=93.14±0.01%, MCC=93.30±0.01% for MEGMAG-LCMV-EIC; Acc=92.16±0.01%, F1=92.16±0.01%, MCC=93.30±0.01% for MEGGRAD-LCMV-EIC), while using a similar pipeline. Our findings highlight the potential of using MEG signals to find MCI biomarkers, supporting critical early intervention. They also provide evidence on potential "best" approaches to analysing magneto-electroencephalography-based brain activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".