Halko Marja-Liisa, Lahti Tom, Hytönen Kaisa, Wincent Joakim, Jääskeläinen Iiro P, Schürmann Martin
Faculty of Social Sciences / Economics, University of Helsinki, Helsinki, Finland.
Advanced Magnetic Imaging Centre, Aalto University School of Science, Espoo, Finland.
Sci Rep. 2025 Sep 1;15(1):32203. doi: 10.1038/s41598-025-14564-1.
In what way do entrepreneurs differ from non-entrepreneurs? We conducted a comprehensive analysis of behavioral responses, brain activation patterns, and gray matter volume (GMV) to compare entrepreneurs with a control group of employees who lack entrepreneurial backgrounds but often hold managerial responsibilities. Using behavioral, fMRI and GMV data, we applied a machine learning approach to predict participants' likelihood of being classified as entrepreneurs. Our findings suggest brain activation in valuation-related areas varies with risk attitudes. Structural analysis reveals a significant negative association between dorsomedial prefrontal cortex GMV and participants' risk attitudes, while risk-taking propensity, especially among entrepreneurs, exhibits a positive relationship with GMV in the right and left anterior insula. Notably, the predictive model integrating valuation decisions with fMRI data from risky trials demonstrates the highest accuracy in classifying participants as entrepreneurs. These results highlight the significance of immediate brain responses to risk as a robust predictor of entrepreneurial propensity, offering new insights into the neural basis of high-risk tolerance in entrepreneurship.
企业家与非企业家在哪些方面存在差异？我们对行为反应、大脑激活模式和灰质体积（GMV）进行了全面分析，以将企业家与一组缺乏创业背景但通常担任管理职责的员工对照组进行比较。利用行为、功能磁共振成像（fMRI）和GMV数据，我们采用机器学习方法来预测参与者被归类为企业家的可能性。我们的研究结果表明，与估值相关区域的大脑激活随风险态度而变化。结构分析显示，背内侧前额叶皮质GMV与参与者的风险态度之间存在显著负相关，而冒险倾向，尤其是在企业家中，与左右前脑岛的GMV呈正相关。值得注意的是，将估值决策与来自风险试验的fMRI数据相结合的预测模型在将参与者归类为企业家方面表现出最高的准确性。这些结果突出了大脑对风险的即时反应作为创业倾向的有力预测指标的重要性，为创业中高风险容忍度的神经基础提供了新的见解。
