Mattoni Matthew, Smith David V, Olino Thomas M
Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA.
Netw Neurosci. 2023 Jun 30;7(2):787-810. doi: 10.1162/netn_a_00306. eCollection 2023.
Associations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average networks between known groups. However, neural heterogeneity within groups may limit the ability to make inferences at the individual level as qualitatively distinct processes across individuals may be obscured in group averages. This study characterizes the heterogeneity of effective connectivity reward networks among 103 early adolescents and examines associations between individualized features and multiple behavioral and clinical outcomes. To characterize network heterogeneity, we used extended unified structural equation modeling to identify effective connectivity networks for each individual and an aggregate network. We found that an aggregate reward network was a poor representation of individuals, with most individual-level networks sharing less than 50% of the group-level network paths. We then used Group Iterative Multiple Model Estimation to identify a group-level network, subgroups of individuals with similar networks, and individual-level networks. We identified three subgroups that appear to reflect differences in network maturity, but this solution had modest validity. Finally, we found numerous associations between individual-specific connectivity features and behavioral reward functioning and risk for substance use disorders. We suggest that accounting for heterogeneity is necessary to use connectivity networks for inferences precise to the individual.
连接网络与诸如抑郁等行为结果之间的关联通常是通过比较已知群体之间的平均网络来进行研究的。然而，群体内部的神经异质性可能会限制在个体层面进行推断的能力，因为个体之间质的不同过程可能会在群体平均值中被掩盖。本研究刻画了103名青少年早期有效连接奖励网络的异质性，并研究了个体特征与多种行为和临床结果之间的关联。为了刻画网络异质性，我们使用扩展统一结构方程模型来识别每个个体和一个总体网络的有效连接网络。我们发现，总体奖励网络并不能很好地代表个体，大多数个体层面的网络与群体层面的网络路径共享比例不到50%。然后，我们使用群体迭代多模型估计来识别一个群体层面的网络、具有相似网络的个体亚组以及个体层面的网络。我们确定了三个亚组，它们似乎反映了网络成熟度的差异，但这个解决方案的有效性一般。最后，我们发现了个体特异性连接特征与行为奖励功能以及物质使用障碍风险之间的许多关联。我们认为，考虑异质性对于使用连接网络进行精确到个体的推断是必要的。
