Exploration of the dynamics between brain regions associated with the default‐mode network and frontostriatal pathway with regards to task familiarity
Bibliographic record
Abstract
Specific brain regions have consistently been reported to be activated during resting state period, and they were described as being part of a particular network called the default-mode network (DMN). It has been shown that the DMN would deactivate during goal-directed tasks, but the actual relationship between them is still a matter of debate. In a previous study, we reported a specific pattern of activation of the frontostriatal regions during a set-shifting task in which these regions were increasing their activity as set-shifts were performed continuously and decreasing when the same rule was executed repeatedly. The present study aimed at assessing the relationship between the frontostriatal regions and the DMN. We hypothesized that the DMN would be anticorrelated with the frontostriatal regions so the DMN would be more deactivated as set-shifts are executed for a long period, but would start increasing when the same rule is being executed for a long period. Here, 15 participants underwent functional magnetic resonance imaging while performing a card-sorting task. We observed increased activity in the frontostriatal regions as more set-shifts are being performed while the DMN gets more deactivated. Interestingly, as decreased activity was observed in the frontostriatal regions during the execution of the same rule for a long period, the DMN showed increasing activity. We argue that there is an anticorrelation between the frontostriatal regions and the DMN, but also that the DMN could show positive activation during performance of a familiar goal-directed task.
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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 | 0.001 |
| 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 | 0.001 |
| 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".