Stephanie Lane

The Separation of Between-person and Within-person Components of Individual Change Over Time: A Latent Curve Model with Structured Residuals

OBJECTIVE Although recent statistical and computational developments allow for the empirical testing of psychological theories in ways not previously possible, one particularly vexing challenge remains: how to optimally model the prospective, reciprocal relations between 2 constructs as they developmentally unfold over time. Several analytic methods currently exist that attempt to model these types of relations, and each approach is successful to varying degrees. However, none provide the unambiguous separation over time of between-person and within-person components of stability and change, components that are often hypothesized to exist in the psychological sciences. Our goal in this article is to propose and demonstrate a novel extension of the multivariate latent curve model to allow for the disaggregation of these effects. METHOD We begin with a review of the standard latent curve models and describe how these primarily capture between-person differences in change. We then extend this model to allow for regression structures among the time-specific residuals to capture within-person differences in change. RESULTS We demonstrate this model using an artificial data set generated to mimic the developmental relation between alcohol use and depressive symptomatology spanning 5 repeated measures. CONCLUSIONS We obtain a specificity of results from the proposed analytic strategy that is not available from other existing methodologies. We conclude with potential limitations of our approach and directions for future research.

Clarifying the nature of startle habituation using latent curve modeling

Startle habituation is present in all startle studies, whether as a dependent variable, discarded habituation block, or ignored nuisance. However, there is still much that remains unknown about startle habituation, including the following: (1) what is the nature of the startle habituation curve?; (2) at what point does startle habituation approach an asymptote?; and (3) are there gender differences in startle habituation? The present study investigated these three questions in a sample of 94 undergraduates using both traditional means-based statistical methods and latent curve modeling. Results provided new information about the nature of the startle habituation curve, indicated that the optimal number of habituation trials with a 100dB startle stimulus is 13, and showed that females display greater startle reactivity but habituate toward the same level as males.

Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood

BACKGROUND There is well-known heterogeneity in affective mechanisms in depression that may extend to positive affect. We used data-driven parsing of neural connectivity to reveal subgroups present across depressed and healthy individuals during positive processing, informing targets for mechanistic intervention. METHODS Ninety-two individuals (68 depressed patients, 24 never-depressed control subjects) completed a sustained positive mood induction during functional magnetic resonance imaging. Directed functional connectivity paths within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation (GIMME), a method shown to accurately recover the direction and presence of connectivity paths in individual participants. During model selection, individuals were clustered using community detection on neural connectivity estimates. Subgroups were externally tested across multiple levels of analysis. RESULTS Two connectivity-based subgroups emerged: subgroup A, characterized by weaker connectivity overall, and subgroup B, exhibiting hyperconnectivity (relative to subgroup A), particularly among ventral affective regions. Subgroup predicted diagnostic status (subgroup B contained 81% of patients; 50% of control subjects; χ2 = 8.6, p = .003) and default mode network connectivity during a separate resting-state task. Among patients, subgroup B members had higher self-reported symptoms, lower sustained positive mood during the induction, and higher negative bias on a reaction-time task. Symptom-based depression subgroups did not predict these external variables. CONCLUSIONS Neural connectivity-based categorization travels with diagnostic category and is clinically predictive, but not clinically deterministic. Both patients and control subjects showed heterogeneous, and overlapping, profiles. The larger and more severely affected patient subgroup was characterized by ventrally driven hyperconnectivity during positive processing. Data-driven parsing suggests heterogeneous substrates of depression and possible resilience in control subjects in spite of biological overlap.

Unsupervised Classification During Time-Series Model Building

ABSTRACT Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects. However, it can be difficult to make inferences from the nuances in varied individual-level patterns. The present article introduces an algorithm that arrives at subgroups of individuals that have similar dynamic models. Importantly, the researcher does not need to decide the number of subgroups. The final models contain reliable group-, subgroup-, and individual-level patterns that enable generalizable inferences, subgroups of individuals with shared model features, and individual-level patterns and estimates. We show that integrating community detection into the GIMME algorithm improves upon current standards in two important ways: (1) providing reliable classification and (2) increasing the reliability in the recovery of individual-level effects. We demonstrate this method on functional MRI from a sample of former American football players.

Automated Selection of Robust Individual-Level Structural Equation Models for Time Series Data

In order to analyze intensive longitudinal data collected across multiple individuals, researchers frequently have to decide between aggregating all individuals or analyzing each individual separately. This paper presents an R package, gimme, which allows for the automatic specification of individual-level structural equation models that combine group-, subgroup-, and individual-level information. This R package is a complement of the GIMME program currently available via a combination of MATLAB and LISREL. By capitalizing on the flexibility of R and the capabilities of the existing structural equation modeling package lavaan, gimme allows for the automated specification and estimation of group-, subgroup-, and individual-level relations in time series data from within a structural equation modeling framework. Applications include daily diary data as well as functional magnetic resonance imaging data.

Uncovering general, shared, and unique temporal patterns in ambulatory assessment data.

Abstract Intensive longitudinal data provide psychological researchers with the potential to better understand individual-level temporal processes. While the collection of such data has become increasingly common, there are a comparatively small number of methods well-suited for analyzing these data, and many methods assume homogeneity across individuals. A recent development rooted in structural equation and vector autoregressive modeling, Subgrouping Group Iterative Multiple Model Estimation (S-GIMME), provides one method for arriving at individual-level models composed of processes shared by the sample, a subset of the sample, and a given individual. As this algorithm was motivated and validated for use with neuroimaging data, its performance is less understood in the context of ambulatory assessment data. Here, we evaluate the performance of the S-GIMME algorithm across various conditions frequently encountered with daily diary (compared to neuroimaging) data; namely, a smaller number of variables, a lower number of time points, and smaller autoregressive effects. We demonstrate, for the first time, the importance of the autoregressive effects in recovering data-generating connections and directions, and the ability to use S-GIMME with lengths of data commonly seen in daily diary studies. We demonstrate the use of S-GIMME with an empirical example evaluating the general, shared, and unique temporal processes associated with a sample of individuals with borderline personality disorder (BPD). Finally, we underscore the need for methods such as S-GIMME moving forward given the increasing use of intensive longitudinal data in psychological research, and the potential for these data to provide novel insights into human behavior and mental health.

Evaluating the Use of the Automated Unified Structural Equation Model for Daily Diary Data

Evaluating the Use of the Automated Unified Structural Equation Model for Daily Diary Data Stephanie T. Lane & Kathleen M. Gates To cite this article: Stephanie T. Lane & Kathleen M. Gates (2017) Evaluating the Use of the Automated Unified Structural Equation Model for Daily Diary Data, Multivariate Behavioral Research, 52:1, 126-127, DOI: 10.1080/00273171.2016.1265439 To link to this article: http://dx.doi.org/10.1080/00273171.2016.1265439While there is a wealth of resources delineating both why and how researchers should collect time series data, fewer resources speak to the analysis of time series data within psychological science (Hamaker, Ceulemans, Grasman, & Tuerlinckx, 2015). Researchers may concatenate time series across individuals to arrive at one model, or researchers may analyze each individual separately, utilizing no information common to the sample. Recent research suggests that a superior approach may be to arrive at individual-level models but utilize information shared across the sample (Gates & Molenaar, 2012). The group iterative multiple model estimation (GIMME) framework presents an automated procedure for estimating individual-level structural equation models composed of both lagged and contemporaneous effects (see Figure 1) using the unified SEM (Kim, Zhu, Chang, Bentler, & Ernst, 2007). In the GIMME framework, each individual model is composed of group-, (potentially) subgroup-, and individual-level paths. The performance of GIMME has been well documented in the context of resting-state fMRI data (Mumford & Ramsey, 2014), which is characterized by sizable autoregressive (AR) effects. By contrast, daily diary data are often characterized by much smaller, potentially nonsignificant AR effects, particularly if the construct under study occurs faster than the time scale of observation. It is unknown whether the inclusion of small autoregressive effects will allow for the reliable recovery of the lagged and contemporaneous relationships. A simulation study was conducted to investigate the performance of GIMME under conditions encountered in daily diary research. A variety of simulation factors were manipulated: number of variables (5, 10); number of timepoints (30, 60, 90, 120); number of individuals (25, 75, 150); and the presence of the lagged relationships at the start of model estimation. Contemporaneous relationships were set to β = 0.5, and the AR effects were set to be small, β = 0.2. GIMME’s performance