Recent studies in adult humans have reported correlations between individual differences in people's Social Network Index (SNI) and gray matter volume (GMV) across multiple regions of the brain. However, the cortical and subcortical loci identified are inconsistent across studies. These discrepancies might arise because different regions of interest were hypothesized and tested in different studies without controlling for multiple comparisons, and/or from insufficiently large sample sizes to fully protect against statistically unreliable findings. Here we took a data-driven approach in a pre-registered study to comprehensively investigate the relationship between SNI and GMV in every cortical and subcortical region, using three predictive modeling frameworks. We also included psychological predictors such as cognitive and emotional intelligence, personality, and mood. In a sample of healthy adults (n = 92), neither multivariate frameworks (e.g., ridge regression with cross-validation) nor univariate frameworks (e.g., univariate linear regression with cross-validation) showed a significant association between SNI and any GMV or psychological feature after multiple comparison corrections (all R-squared values ≤ .1). These results emphasize the importance of large sample sizes and hypothesis-driven studies to derive statistically reliable conclusions, and suggest that future meta-analyses will be needed to more accurately estimate the true effect sizes in this field.

中文翻译：

---

从数据驱动的调查中，没有强有力的证据表明社交网络指数与灰质量相关。

成年人的最新研究报告了人们的社交网络指数（SNI）的个体差异与大脑多个区域的灰质体积（GMV）之间的相关性。但是，在研究之间确定的皮质和皮质下基因座是不一致的。之所以会出现这些差异，是因为在不同的研究中假设并在不同的研究中测试了不同的目标区域，而没有控制多重比较，并且/或者样本量不足，无法完全抵御统计上不可靠的发现。在这里，我们采用一种数据驱动的方法进行了预先注册的研究，使用三个预测性建模框架全面研究了每个皮质和皮质下区域中SNI和GMV之间的关系。我们还包括心理预测因素，例如认知和情绪智力，性格和情绪。在健康成年人（n = 92）的样本中，多元框架（例如，具有交叉验证的岭回归）和单变量框架（例如，具有交叉验证的单变量线性回归）均未显示SNI与任何GMV或心理疾病之间存在显着关联经过多次比较校正后的特征（所有R平方值≤.1）。这些结果强调了大样本量和假设驱动的研究对得出统计上可靠的结论的重要性，并建议将来需要进行荟萃分析，以更准确地估计该领域的真实效应量。进行多重比较校正（所有R平方值≤0.1）后，采用交叉验证的岭回归）或单变量框架（例如，使用交叉验证的单变量线性回归）均显示SNI与任何GMV或心理特征之间存在显着关联。这些结果强调了大样本量和假设驱动的研究对得出统计上可靠的结论的重要性，并建议将来需要进行荟萃分析，以更准确地估计该领域的真实效应量。进行多重比较校正（所有R平方值≤0.1）后，采用交叉验证的岭回归）或单变量框架（例如，使用交叉验证的单变量线性回归）均显示SNI与任何GMV或心理特征之间存在显着关联。这些结果强调了大样本量和假设驱动的研究对得出统计上可靠的结论的重要性，并建议将来需要进行荟萃分析，以更准确地估计该领域的真实效应量。