Specifying exogeneity and bilinear effects in data-driven model searches

Dynamic processes in behavioral activation therapy for anhedonic adolescents: Modeling common and patient-specific relations

OBJECTIVE

Behavioral activation (BA) is a brief intervention for depression encouraging gradual and systematic re-engagement with rewarding activities and behaviors. Given this treatment focus, BA may be particularly beneficial for adolescents with prominent anhedonia, a predictor of poor treatment response and common residual symptom. We applied group iterative multiple model estimation (GIMME) to ecological momentary assessment (EMA) treatment data to investigate common and person-specific processes during BA for anhedonic adolescents.

METHOD

Thirty-nine adolescents (Mage = 15.7 years old, 67% female, 81% White) with elevated anhedonia (Snaith-Hamilton Pleasure Scale) were enrolled in a 12-week BA trial, with weekly anhedonia assessments. EMA surveys were triggered every other week (2-3 surveys per day) throughout treatment assessing current positive affect (PA) and negative affect (NA), engagement in pleasurable activities and social interactions, anticipatory pleasure, rumination, and recent pleasurable and stressful experiences.

RESULTS

A multilevel model revealed significant decreases in anhedonia, t(25.5) = -4.76, p < .001, over the 12-week trial. GIMME results indicated substantial heterogeneity in variable networks across patients. PA was the variable with the greatest number (22% of all paths vs. 11% for NA) of predictive paths to other symptoms (i.e., highest out-degree). Higher PA (but not NA) out-degree was associated with greater anhedonia improvement, t(25.8) = -2.22, p = .035.

CONCLUSIONS

Results revealed substantial heterogeneity in variable relations across patients, which may obscure the search for common processes of change in BA. PA may be a particularly important treatment target for anhedonic adolescents in BA. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Detecting Changes in Correlation Networks with Application to Functional Connectivity of fMRI Data

Research questions in the human sciences often seek to answer if and when a process changes across time. In functional MRI studies, for instance, researchers may seek to assess the onset of a shift in brain state. For daily diary studies, the researcher may seek to identify when a person's psychological process shifts following treatment. The timing and presence of such a change may be meaningful in terms of understanding state changes. Currently, dynamic processes are typically quantified as static networks where edges indicate temporal relations among nodes, which may be variables reflecting emotions, behaviors, or brain activity. Here we describe three methods for detecting changes in such correlation networks from a data-driven perspective. Networks here are quantified using the lag-0 pair-wise correlation (or covariance) estimates as the representation of the dynamic relations among variables. We present three methods for change point detection: dynamic connectivity regression, max-type method, and a PCA-based method. The change point detection methods each include different ways to test if two given correlation network patterns from different segments in time are significantly different. These tests can also be used outside of the change point detection approaches to test any two given blocks of data. We compare the three methods for change point detection as well as the complementary significance testing approaches on simulated and empirical functional connectivity fMRI data examples.

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.