Real‐time prediction of working memory performance: A machine learning‐based approach towards dementia prevention
Bibliographic record
Abstract
Abstract Background MCI is a clinical state that typically precedes Alzheimer’s dementia (AD). Working memory deficits are common in MCI and affect routine activities. Thus, developing an intervention that enhances working memory could enhance day‐to‐day function in these patients and, in turn, prevention progression to dementia. Towards this goal, a model that predicts individual‐specific working memory performance in this population could be instrumental for personalized interventions. Electroencephalography (EEG) captures the time dimension of cognitive events that happen during working memory performance and could predict individual‐specific performance. EEG predictive markers can then be targeted by interventions to enhance performance. Method We propose a single‐trial classification process that predicts individuals’ responses i.e. target correct (TC) vs. target noncorrect (TNC) responses during a working memory task, N‐back. We applied this process to EEG data of 15 healthy participants (mean age (SD) = 29.8 (7.6)) while performing the 3‐back task. We used event related (de‐)synchronization (ERD/ERS) from EEG signals 600 milliseconds prior to stimulus presentation as input features to a support vector machine classifier. To avoid overfitting of the model, we applied recursive feature elimination and cross validation to the first two‐thirds of the task. A trained classifier was then tested on the last third of the session. Non‐parametric permutation testing was used to ensure that the extracted pattern is associated with the original data rather than a random pattern. Result Our model identified the brain regions where ERD/ERS predicted each individual’s working memory performance. Mean (SD) prediction accuracy across 15 participants was 70.1% (5.9). Accuracy was significantly above chance in 12 out of the 15 participants. The total number of the predictive EEG features across all participants ranged between 4 and 9. The mode was 6. As an example, in one participant, we achieved 69.2% accuracy based on 6 features: decreased parietal theta ERS; increased prefrontal theta ERS; decreased right temporal and occipital gamma ERS; and increased frontal and right temporal alpha ERD. Conclusion This pilot study could lead to a machine‐learning based approach to increase the efficacy of personalized AD preventative interventions by individualizing the targets for these interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".