Unsupervised classification reveals consistency and degeneracy in neural network patterns of emotion.

In the present study, we used an unsupervised classification algorithm to reveal both consistency and degeneracy in neural network connectivity during anger and anxiety. Degeneracy refers to the ability of different biological pathways to produce the same outcomes. Previous research is suggestive of degeneracy in emotion, but little research has explicitly examined whether degenerate functional connectivity patterns exist for emotion categories such as anger and anxiety. Twenty-four subjects underwent functional magnetic resonance imaging (fMRI) while listening to unpleasant music and self-generating experiences of anger and anxiety. A data-driven model building algorithm with unsupervised classification (subgrouping Group Iterative Multiple Model Estimation) identified patterns of connectivity among 11 intrinsic networks that were associated with anger vs anxiety. As predicted, degenerate functional connectivity patterns existed within these overarching consistent patterns. Degenerate patterns were not attributable to differences in emotional experience or other individual-level factors. These findings are consistent with the constructionist account that emotions emerge from flexible functional neuronal assemblies and that emotion categories such as anger and anxiety each describe populations of highly variable instances.

Focusing personality assessment on the person: Modeling general, shared, and person specific processes in personality and psychopathology.

Personality and psychopathology are composed of dynamic and interactive processes among diverse psychological systems, manifesting over time and in response to an individual's natural environment. Ambulatory assessment techniques promise to revolutionize assessment practices by allowing access to the dynamic data necessary to study these processes directly. Assessing manifestations of personality and psychopathology naturalistically in an individual's own ecology allows for dynamic modeling of key behavioral processes. However, advances in dynamic data collection have highlighted the challenges of both fully understanding an individual (via idiographic models) and how s/he compares with others (as seen in nomothetic models). Methods are needed that can simultaneously model idiographic (i.e., person-specific) processes and nomothetic (i.e., general) structure from intensive longitudinal personality assessments. Here we present a method, group iterative multiple model estimation (GIMME) for simultaneously studying general, shared (i.e., in subgroups), and person-specific processes in intensive longitudinal behavioral data. We first provide an introduction to the GIMME method, followed by a demonstration of its use in a sample of individuals diagnosed with personality disorder who completed daily diaries over 100 consecutive days. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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

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. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Unsupervised Classification During Time-Series Model Building.

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.

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.