The Association between Resting Functional Connectivity and Creativity
Bibliographic record
Abstract
The analysis of functional connectivity at rest (rFC) enables us to know how brain regions within and between networks interact. In this study, we used resting-state functional magnetic resonance imaging and a creativity test of divergent thinking (DT) to investigate the relationship between creativity measured by DT and rFC. We took the medial prefrontal cortex (mPFC) to be the seed region and investigated correlations across subjects between the score of the DT test and the strength of rFC between the mPFC and other brain regions. Our results showed that the strength of rFC with the mPFC significantly and positively correlated with creativity as measured by the DT test in the posterior cingulate cortex (PCC). These results showed that higher creativity measured by DT is associated with rFC between the mPFC and the PCC, the key nodes of the default mode network (DMN). Increased rFC between these regions is completely opposite from that is generally expected from the association between higher creativity and reduced deactivation in DMN during an externally directed attention-demanding task shown in our previous study but is similar to the pattern seen in relatives of schizophrenia. These findings are comparable to the previously reported psychological associations between schizotypy and creativity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".