The influence of autoregressive relation strength and search strategy on directionality recovery in group iterative multiple model estimation

Heterogeneity in suicide risk: Evidence from personalized dynamic models

Most theories of suicide propose within-person changes in psychological states cause suicidal thoughts/behaviors; however, most studies use between-person analyses. Thus, there are little empirical data exploring current theories in the way they are hypothesized to occur. We used a form of statistical modeling called group iterative multiple model estimation (GIMME) to explore one theory of suicide: The Interpersonal Theory of Suicide (IPTS). GIMME estimates personalized statistical models for each individual and associations shared across individuals. Data were from a real-time monitoring study of individuals with a history of suicidal thoughts/behavior (adult sample: participants = 111, observations = 25,242; adolescent sample: participants = 145, observations = 26,182). Across both samples, none of theorized IPTS effects (i.e., contemporaneous effect from hopeless to suicidal thinking) were shared at the group level. There was significant heterogeneity in the personalized models, suggesting there are different pathways through which different people come to experience suicidal thoughts/behaviors. These findings highlight the complexity of suicide risk and the need for more personalized approaches to assessment and prediction.

100 days of Adolescence: Elucidating Externalizing Behaviors Through the Daily Assessment of Inhibitory Control

Inhibitory control is a transdiagnostic risk factor for externalizing behaviors, particularly during adolescence. Despite advances in understanding links between inhibitory control and externalizing behaviors across youth on average, significant questions remain about how these links play out in the day-to-day lives of individual adolescents. The goals of the current study were to: (1) validate a novel 100-occasion measure of inhibitory control; (2) assess links between day-to-day fluctuations in inhibitory control and individual differences in externalizing behaviors; and (3) illustrate the potential of intensive longitudinal studies for person-specific analyses of adolescent externalizing behaviors. Participants were 106 youth (57.5% female, Mage = 13.34 years; SDage = 1.92) who completed a virtual baseline session followed by 100 daily surveys, including an adapted Stroop Color Word task designed to assess inhibitory control. Results suggested that the novel task was generally reliable and valid, and that inhibitory control fluctuated across days in ways that were meaningfully associated with individual differences in baseline impulsive behaviors. Results of illustrative personalized analyses suggested that inhibitory control had more influence in the daily networks of adolescents who used substances during the 100 days than in a matched set of adolescents who did not. This work marks a path forward in intensive longitudinal research by validating a novel inhibitory control measure, revealing that daily fluctuations in inhibitory control may be a unique construct broadly relevant to adolescent externalizing problems, and at the same time, highlighting that links between daily inhibitory control and impulsive behaviors are adolescent-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.

Processing negative emotion in two languages of bilinguals: Accommodation and assimilation of the neural pathways based on a meta-analysis

Numerous functional magnetic resonance imaging (fMRI) studies have examined the neural mechanisms of negative emotional words, but scarce evidence is available for the interactions among related brain regions from the functional brain connectivity perspective. Moreover, few studies have addressed the neural networks for negative word processing in bilinguals. To fill this gap, the current study examined the brain networks for processing negative words in the first language (L1) and the second language (L2) with Chinese-English bilinguals. To identify objective indicators associated with negative word processing, we first conducted a coordinate-based meta-analysis on contrasts between negative and neutral words (including 32 contrasts from 1589 participants) using the activation likelihood estimation method. Results showed that the left medial prefrontal cortex (mPFC), the left inferior frontal gyrus (IFG), the left posterior cingulate cortex (PCC), the left amygdala, the left inferior temporal gyrus (ITG), and the left thalamus were involved in processing negative words. Next, these six clusters were used as regions of interest in effective connectivity analyses using extended unified structural equation modeling to pinpoint the brain networks for bilingual negative word processing. Brain network results revealed two pathways for negative word processing in L1: a dorsal pathway consisting of the left IFG, the left mPFC, and the left PCC, and a ventral pathway involving the left amygdala, the left ITG, and the left thalamus. We further investigated the similarity and difference between brain networks for negative word processing in L1 and L2. The findings revealed similarities in the dorsal pathway, as well as differences primarily in the ventral pathway, indicating both neural assimilation and accommodation across processing negative emotion in two languages of bilinguals.

Going beyond the DSM in predicting, diagnosing, and treating autism spectrum disorder with covarying alexithymia and OCD: A structural equation model and process-based predictive coding account

Background

There is much overlap among the symptomology of autistic spectrum disorders (ASDs), obsessive compulsive disorders (OCDs), and alexithymia, which all typically involve impaired social interactions, repetitive impulsive behaviors, problems with communication, and mental health.

Aim

This study aimed to identify direct and indirect associations among alexithymia, OCD, cardiac interoception, psychological inflexibility, and self-as-context, with the DV ASD and depression, while controlling for vagal related aging.

Methodology

The data involved electrocardiogram (ECG) heart rate variability (HRV) and questionnaire data. In total, 1,089 participant's data of ECG recordings of healthy resting state HRV were recorded and grouped into age categories. In addition to this, another 224 participants completed an online survey that included the following questionnaires: Yale-Brown Obsessive Compulsive Scale (Y-BOCS); Toronto Alexithymia Scale 20 (TAS-20); Acceptance and Action Questionnaire (AAQII); Depression, Anxiety, and Stress Scale 21 (DAS21); Multi-dimensional Assessment of Interoceptive Awareness Scale (MAIA); and the Self-as-Context Scale (SAC).

Results

Heart rate variability was shown to decrease with age when controlling for BMI and gender. In the two SEMs produced, it was found that OCD and alexithymia were causally associated with autism and depression indirectly through psychological inflexibility, SAC, and ISen interoception.

Conclusion

The results are discussed in relation to the limitations of the DSM with its categorical focus of protocols for syndromes and provide support for more flexible ideographic approaches in diagnosing and treating mental health and autism within the Extended Evolutionary Meta-Model (EEMM). Graph theory approaches are discussed in their capacity to depict the processes of change potentially even at the level of the relational frame.