Quantifying neural efficiency and neural capacity across the brain during a working memory task using a quadratic model of neural–cognitive relationships
Bibliographic record
Abstract
Task-based functional magnetic resonance imaging (MRI) neuroimaging for the study of cognitive neuroscience provides insight into how the brain responds to increasing cognitive demands. Theoretical models of neural-cognitive relationships define neural efficiency and neural capacity as two parameters to describe the rate of gain of brain activity and the upper limits of the increases, respectively. Although qualitatively well described, there are limited discussions and demonstrations of estimating these parameters from data. The current work presents a method using a quadratic equation model of cognitive demands. The methods were applied to 21 healthy young adults while engaging in four levels of the verbal n-back working memory task (0, 1, 2 and 3-back). Measures of the area under the curve captured both linear and curvilinear responses to quantify the locations and amounts of task-related increases in brain activity. In addition, we calculated voxel-wise maps of neural efficiency and neural capacity. Results showed increases in brain activation in regions typically involved in working memory. The relationships between increasing cognitive demands and brain activity also differed across the brain within and between individuals, as indicated by differential neural efficiency and neural capacity values. This approach provides tools for evaluating current theories about the relationships between brain activity and cognition. Neural efficiency and neural capacity provide physiological measures of brain activity, which will help evaluate how age, disease and intervention processes affect brain activity. Finally, all presented methods are readily applicable using standard current software packages for neuroimaging.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".