Advancing (Neuro)Entrepreneurship Cognition Research Through Resting-State fMRI: A Methodological Brief
Bibliographic record
Abstract
Despite many calls, functional brain magnetic resonance imaging (fMRI) studies are relatively rare in the domain of entrepreneurship research. This methodological brief presents the brain-imaging method of resting-state fMRI (rs-fMRI) and illustrates its application in neuroentrepreneurship for the first time. In contrast to the traditional task-based fMRI approach, rs-fMRI observes the brain in the absence of cognitive tasks or presentation of stimuli, which offers benefits for improving our understanding of the entrepreneurial mind. Here, we describe the method and provide methodological motivations for performing brain resting-state functional neuroimaging studies on entrepreneurs. In addition, we illustrate the use of seed-based correlation analysis, one of the most common analytical approaches for analyzing rs-fMRI data. In this illustration, we show that habitual entrepreneurs have increased functional connectivity between the insula (a region associated with cognitive flexibility) and the anterior prefrontal cortex (a key region for explorative choice) as compared to managers. This increased connectivity could help promote flexible behavior. Thus in brief, we provide an exemplar of a novel way to expand our understanding of the brain in the domain of entrepreneurship. We discuss possible directions for future research and challenges to be addressed to facilitate the inclusion of re-fMRI studies into neuroentrepreneurship.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".