Estimating both directed and undirected contemporaneous relations in time series data using hybrid-group iterative multiple model estimation

Like No Other? A Family-Specific Network Approach to Parenting Adolescents

Numerous theories suggest that parents and adolescents influence each other in diverse ways; however, whether these influences differ between subgroups or are unique to each family remains uncertain. Therefore, this study explored whether data-driven subgroups of families emerged that exhibited a similar daily interplay between parenting and adolescent affective well-being. To do so, Subgrouping Group Iterative Multiple Model Estimation (S-GIMME) was used to estimate family-specific dynamic network models, containing same- and next-day associations among five parenting practices (i.e., warmth, autonomy support, psychological control, strictness, monitoring) and adolescent positive and negative affect. These family-specific networks were estimated for 129 adolescents (Mage = 13.3, SDage = 1.2, 64% female, 87% Dutch), who reported each day on parenting and their affect for 100 consecutive days. The findings of S-GIMME did not identify data-driven subgroups sharing similar parenting-affect associations. Instead, each family displayed a unique pattern of temporal associations between the different practices and adolescent affect. Thus, the ways in which parenting practices were related to adolescents' affect in everyday life were family specific.

Daily Gender and Cognition: A Person-Specific Behavioral Network Analysis

Gender is person-specific, and it influences and is influenced by a breadth of multidimensional psychological factors, including cognition. Directionality is important for research on gender and cognition, as debate surrounds, for instance, whether masculine self-concepts precede spatial skills, or whether the reverse is true. In order to provide novel insights into the individualized nature of these relations, a person-specific network approach devised by Peter Molenaar and the first author - group iterative multiple model estimation for multiple solutions (GIMME-MS) - was applied to 75-day intensive longitudinal data on gender self-concept (i.e., femininity-masculinity, instrumentality, and expressivity) and cognition (i.e., mental rotations and verbal recall) from 103 young adults. GIMME-MS estimates individualized networks that contain same-day and next-day directed relations, prioritizing relations common across participants. It is ideal for analyzing behavioral time series with unclear directionality, as it generates multiple solutions from which an optimal one is selected. GIMME-MS revealed notable heterogeneity in the presence, direction, and nature of relations from gender self-concept to cognition (∼26% of participants) and vice versa (∼21% of participants). Findings are wholly novel in revealing the person-specific nature of gender and its cognitive dynamics, yet somehow, unsurprising given the revolutionary corpus of Peter Molenaar.

Blind Subgrouping of Task-based fMRI

Significant heterogeneity in network structures reflecting individuals' dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME's ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.