The modulatory role of morning and afternoon basal cortisol levels on neural activation changes in healthy young males performing an n-back working memory task : an exploratory fMRI study
Bibliographic record
Abstract
Human cognitive processes, such as learning and memory, are particularly vulnerable to the effects of cortisol, the human stress hormone. In the field of magnetic resonance imaging (MRI) the different cortisol characteristics are neither controlled nor accounted for. This study will aim to investigate, by means of functional MRI, the effects of morning and afternoon cortisol levels on neural activation changes in response to a working memory task in young males. We hypothesized a significant difference between morning and afternoon subjects' cortisol levels and neural activation patterns in relation to the task. Nineteen young males were recruited, 9 for the morning group and 10 for the afternoon group. Cortisol levels, neuronal activation, and behavioural measurements (correct percentage of hits and reaction time) were assessed during the task. Six saliva samples were taken during the experiment at various time intervals. As expected, morning cortisol levels were higher than afternoon cortisol levels. Results indicate that the afternoon group had significantly slower reaction time on the frontal task compared to the morning group, whilst the percentage of correct hits did not differ. Furthermore, we observed an increased range of neural activation in the morning group compared to the afternoon group. This study demonstrates the impact of time of day of testing (i.e. different cortical levels) on neural activation in functional MRI experiments. It is important for investigators using this technique to be aware of and control for this variable.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".