No strong evidence that social network index is associated with gray matter volume from a data-driven investigation.
No strong evidence that social network index is associated with gray matter volume from a data-driven investigation.
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DOI：
10.1016/j.cortex.2020.01.021
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发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Adolphs R
中科院分区：
文献类型：
--
作者：
Lin C;Keles U;Tyszka JM;Gallo M;Paul L;Adolphs R
最近对成年人的研究报告了人的社交网络指数（SNI）和大脑多个区域的灰质体积（GMV）的个体差异之间的相关性。然而，不同研究中确定的皮质和皮质下位点不一致。这些差异可能是因为在不同的研究中假设和检验了不同的感兴趣区域，而没有控制多重比较，和/或样本量不够大，无法完全避免统计学上不可靠的结果。在这里，我们在一项预先注册的研究中采用了数据驱动的方法，使用三种预测建模框架，全面研究了每个皮层和皮层下区域的SNI和GMV之间的关系。我们还包括心理预测因素，如认知和情绪智力，个性和情绪。在健康成年人样本（n = 92）中，多变量框架（例如，具有交叉验证的岭回归）或单变量框架（例如，交叉验证的单变量线性回归）显示SNI与多重比较校正后的任何GMV或心理特征之间存在显著关联（所有R平方值≤ 0.1）。这些结果强调了大样本量和假设驱动的研究的重要性，以得出统计上可靠的结论，并建议未来的荟萃分析将需要更准确地估计在这一领域的真实效果大小。
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 ≤ 0.1). 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.
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影响因子：
5
作者：
COHEN, S;KAMARCK, T;MERMELSTEIN, R
通讯作者：
MERMELSTEIN, R
DOI：
10.1098/rstb.2016.0244
发表时间：
2017-08-19
期刊：
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
影响因子：
--
作者：
Dunbar RIM;Shultz S
通讯作者：
Shultz S
DOI：
10.1098/rstb.2006.2001
发表时间：
2007-04-29
影响因子：
6.3
作者：
Dunbar, R. I. M.;Shultz, Susanne
通讯作者：
Shultz, Susanne
影响因子：
5.7
作者：
Lewis PA;Rezaie R;Brown R;Roberts N;Dunbar RI
通讯作者：
Dunbar RI
影响因子：
5.7
作者：
Fischl, Bruce
通讯作者：
Fischl, Bruce