Department of Human Development & Family Studies, Colorado State University, Fort Collins, CO, United States.
Department of Molecular, Cellular & Integrative Neurosciences, Colorado State University, Fort Collins, CO, United States.
Neuropsychologia. 2019 Sep;132:107128. doi: 10.1016/j.neuropsychologia.2019.107128. Epub 2019 Jun 20.
This study demonstrates the utility of combining principles of connectionist theory with a sophisticated statistical approach, structural equation modeling (SEM), to better understand brain-behavior relationships in studies using event-related potentials (ERPs). The models show how sequential phases of neural processing measured by averaged ERP waveform components can successfully predict task behavior (response time; RT) while accounting for individual differences in maturation and sex. The models assume that all ERP measures are affected by individual differences in physical and mental state that inflate measurement error. ERP data were collected from 154 neurotypical children (7-13 years, M = 10.22, SD = 1.48; 74 males) performing a cued Go/No-Go task during two separate sessions. Using SEM, we show a latent variable path model with good fit (e.g., χ(51) = 56.20, p = .25; RMSEA = .03; CFI = .99; SRMR = .06) yielding moderate-to-large predictive coefficients from N1 through the E-wave latent variables (N1 β = -.29 → P2 β = -.44 → N2 β = .28 → P3 β =.64→ E-wave), which in turn significantly predicted RT (β =.34, p = .02). Age significantly related to N1 and P3 latent variables as well as RT (β =.31, -.58, & -.40 respectively), and Sex significantly related to the E-wave latent variable and RT (β =.36 & 0.21 respectively). Additionally, the final model suggested that individual differences in emotional and physical state accounted for a significant proportion of variance in ERP measurements, and that individual states systematically varied across sessions (i.e., the variance was not just random noise). These findings suggest that modeling ERPs as a system of inter-related processes may be a more informative approach to examining brain-behavior relationships in neurotypical and clinical groups than traditional analysis techniques.
这项研究展示了将连接主义理论的原理与复杂的统计方法——结构方程建模（SEM）相结合，以更好地理解使用事件相关电位（ERP）的研究中的大脑-行为关系的实用性。这些模型表明，通过平均 ERP 波形成分测量的神经处理的连续阶段可以成功地预测任务行为（反应时间；RT），同时考虑到成熟度和性别的个体差异。这些模型假设，所有的 ERP 测量都受到个体在身体和精神状态上的差异的影响，这些差异会增加测量误差。这项研究从 154 名神经典型儿童（7-13 岁，M=10.22，SD=1.48；74 名男性）中收集了数据，他们在两次不同的会议期间执行了一个提示的 Go/No-Go 任务。使用 SEM，我们展示了一个具有良好拟合的潜在变量路径模型（例如，χ(51) = 56.20，p =.25；RMSEA =.03；CFI =.99；SRMR =.06），从 N1 到 E 波的潜在变量都产生了从中等到较大的预测系数（N1 β= -.29→P2 β= -.44→N2 β=.28→P3 β =.64→E 波），这反过来又显著预测了 RT（β =.34，p =.02）。年龄与 N1 和 P3 潜在变量以及 RT 显著相关（β =.31，-.58 和-.40 分别），性别与 E 波潜在变量和 RT 显著相关（β =.36 和.21 分别）。此外，最终模型表明，个体在情绪和身体状态上的差异占 ERP 测量中很大一部分的差异，并且个体状态在两次会议之间系统地变化（即，方差不仅仅是随机噪声）。这些发现表明，将 ERP 建模为一个相互关联的过程系统可能是一种比传统分析技术更具信息性的方法，可以检查神经典型和临床群体中的大脑-行为关系。
