Identifying Individualized Neurophysiological Causal Features for Working Memory Performance: Implications for Non‐Invasive Brain Stimulation
Bibliographic record
Abstract
Abstract Background Non‐invasive brain stimulation (NIBS) has been extensively used to target specific neural oscillations with the aim of improving working memory performance. However, current NIBS paradigms have two main limitations: first, lack of personalization and second, a need for a randomized controlled trial (RCT) to infer causality between targets of NIBS and subsequent performance. RCT is a powerful measure to infer causality, but its implementation is expensive, time consuming, and sometimes simply not possible. Therefore, our aim was to use computational models and observational data to discover causal relations between neural oscillations and working memory performance to identify potential targets and stimulation parameters for personalized NIBS paradigms. Method We used electroencephalography (EEG) data of 66 young healthy participants collected while performing a 3‐back working memory task. Using graphical causal modeling, we extracted individualized causal brain oscillations of 3‐back performance and compared the causal features between two groups: high and low performers. Result Total number of causal features in high performers was higher than low performers. Among the causal features, right temporal gamma oscillation was ∼5 times (z‐score = 3.87, p = 0.003) more frequently a causal feature among high performers than low performers. However, the power of causal temporal gamma oscillation was not different between the two groups. Conclusion Our findings suggest that a potential approach to improve working memory performance using NIBS is to induce more causal gamma oscillations by, for example, generating more local gamma entrainment over the right temporal cortex and not necessarily by increasing gamma power.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".