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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.

Social learning theory posits that adolescents learn to adopt social norms by observing the behaviors of others and internalizing the associated outcomes. However, the underlying neural processes by which social learning occurs is less well-understood, despite extensive neurobiological reorganization and a peak in social influence sensitivity during adolescence. Forty-four adolescents (Mage = 12.2 years) completed an fMRI scan while observing their older sibling within four years of age (Mage = 14.3 years) of age complete a risky decision-making task. Group iterative multiple model estimation (GIMME) was used to examine patterns of directional brain region connectivity supporting social learning. We identified group-level neural pathways underlying social observation including the anterior insula to the anterior cingulate cortex and mentalizing regions to social cognition regions. We also found neural states based on adolescent sensitivity to social learning via age, gender, modeling, differentiation, and behavior. Adolescents who were more likely to be influenced elicited neurological up-regulation whereas adolescents who were less likely to be socially influenced elicited neurological down-regulation during risk-taking. These findings highlight patterns of how adolescents process information while a salient influencer takes risks, as well as salient neural pathways that are dependent on similarity factors associated with social learning theory.

Using traces of behaviors to predict outcomes is useful in varied contexts ranging from buyer behaviors to behaviors collected from smart-home devices. Increasingly, higher education systems have been using Learning Management System (LMS) digital data to capture and understand students’ learning and well-being. Researchers in the social sciences are increasingly interested in the potential of using digital log data to predict outcomes and design interventions. Using LMS data for predicting the likelihood of students’ success in for-credit college courses provides a useful example of how social scientists can use these techniques on a variety of data types. Here, we provide a primer on how LMS data can be feature-mapped and analyzed to accomplish these goals. We begin with a literature review summarizing current approaches to analyzing LMS data, then discuss ethical issues of privacy when using demographic data and equitable model building. In the second part of the paper, we provide an overview of popular machine learning algorithms and review analytic considerations such as feature generation, assessment of model performance, and sampling techniques. Finally, we conclude with an empirical example demonstrating the ability of LMS data to predict student success, summarizing important features and assessing model performance across different model specifications.

Researchers across varied fields increasingly are collecting and analyzing intensive longitudinal data (ILD) to examine processes across time at the individual level. Two types of relations are typically examined: lagged and contemporaneous. Lagged relations capture how variables at a prior time point can be used to explain variance in variables at a later time point. These are always modeled using auto- and cross-regressions by means of vector autoregression (VAR). By contrast, there are two types of relations commonly used to model the contemporaneous relations, which model how variables relate instantaneously. Until now, researchers must opt to either model contemporaneous relations as undirected relations among residuals (e.g., partial or full correlations) or as directed relations among the variables (e.g., paths or regressions). The choice for how to model contemporaneous relations has implications for inferences as well as the potential to introduce bias in the VAR lagged relations if the wrong type of relation is used. This article introduces a novel data-driven method, hybrid-group iterative multiple model estimation (GIMME), that provides a solution to the problem of having to choose one or the other type of contemporaneous relation to model. The modeling framework utilized in hybrid-GIMME allows for both types of contemporaneous relations in addition to the standard VAR relations. Both simulated and empirical data were used to test the performance of hybrid-GIMME. Results suggest this is a robust method for recovering contemporaneous relations in an exploratory manner, particularly with an ample number of time points per person. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Altered stress-reactivity may represent a general risk factor for psychopathology. In a broad at-risk sample, we examined (a) how stress and mild, daily expressions of psychopathology were interrelated over time, (b) whether we could detect subgroups with similar dynamics between stress and daily expressions of psychopathology (i.e., stress-reactivity), and (c) whether stress-reactivity was associated with psychopathology and social functioning. One hundred twenty-two young adults (43.4% women, mean age 23.6) at risk for developing a wide range of psychopathology completed a 6-month daily diary study. We used group iterative multiple model estimation (GIMME) to identify temporal associations between event stress and 11 mild expressions of psychopathology (e.g., feeling down, restlessness) at group, subgroup, and individual levels. Stress was associated with feeling irritated during the same day for >70% of individuals, and with feeling down and worrying during the same day for >50% of individuals. No stable subgroups characterized by similar daily stress-reactivity were identified. Instead, we observed 71 different stress-reactivity patterns in 122 individuals. Average daily event stress, but not overall stress-reactivity (weighted stress-response), was associated with psychopathology severity and social dysfunction. This study showed important similarities, as well as many differences between individuals, in terms of the impact of stress on mild expressions of psychopathology in daily life. Clustering based on similar stress-reactivity did not lead to stable subgroups. Finally, average daily stress levels, but not daily stress-reactivity, were associated with psychopathologic severity and social dysfunction. Findings highlight the importance of considering heterogeneity in stress-reactivity, but also challenges for identifying generalizable processes in doing so. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Objective: Skin cancer is the most frequently diagnosed cancer and rates are increasing because of global warming. This article reports a meta-analysis of randomized controlled trials of behavioral interventions to reduce exposure to ultraviolet radiation (UVR). The review aimed to (a) quantify the magnitude of intervention effects on indoor tanning, sun exposure, and sunscreen use, and (b) determine which intervention strategies maximize behavior change. Method: Out of 17,437 records identified via literature searches, 190 independent tests (N = 89,365) met the inclusion criteria. Sample, intervention, and methodological characteristics, and change techniques were coded, and random effects meta-analyses and metaregressions were conducted. Results: The sample-weighted average effect size across all studies was d+ = .193 (95% confidence interval, CI [.161, .226]), and there were significant effects on indoor tanning, sun exposure, and sunscreen use (d+ = .080, .149, and .196, respectively). However, there was evidence of publication bias, and trim and fill analyses indicated that the corrected effects for sun exposure and sunscreen use were of very small magnitude (d+ ~ .06) and were not significantly different from zero for indoor tanning (d+ = -.011, 95% CI [-.096, .074]). Metaregression analyses identified several intervention strategies that predicted effect sizes. For instance, interventions delivered individually that promoted alternatives to tanning were associated with larger effect sizes for indoor tanning. Conclusion: Interventions to date have had only a modest impact on behavioral exposure to UVR. The present findings offer new insights into how the effectiveness of future interventions can be improved. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Cognitive flexibility, the ability to appropriately adjust behavior in a changing environment, has been challenging to operationalize and validate in cognitive neuroscience studies. Here, we investigate neural activation and directed functional connectivity underlying cognitive flexibility using an fMRI-adapted version of the Flexible Item Selection Task (FIST) in adults (n = 32, ages 19–46 years). The fMRI-adapted FIST was reliable, showed comparable performance to the computer-based version of the task, and produced robust activation in frontoparietal, anterior cingulate, insular, and subcortical regions. During flexibility trials, participants directly engaged the left inferior frontal junction, which influenced activity in other cortical and subcortical regions. The strength of intrinsic functional connectivity between select brain regions was related to individual differences in performance on the FIST, but there was also significant individual variability in functional network topography supporting cognitive flexibility. Taken together, these results suggest that the FIST is a valid measure of cognitive flexibility, which relies on computations within a broad corticosubcortical network driven by inferior frontal junction engagement.

Objective Psychopathology research has relied on discrete diagnoses, which neglects the unique manifestations of each individual's pathology. Borderline personality disorder combines interpersonal, affective, and behavioral regulation impairments making it particularly ill-suited to a "one size fits all" diagnosis. Clinical assessment and case formulation involve understanding and developing a personalized model for each patient's contextualized dynamic processes, and research would benefit from a similar focus on the individual. Method We use group iterative multiple model estimation, which estimates a model for each individual and identifies general or shared features across individuals, in both a mixed-diagnosis sample (N = 78) and a subsample with a single diagnosis (n = 24). Results We found that individuals vary widely in their dynamic processes in affective and interpersonal domains both within and across diagnoses. However, there was some evidence that dynamic patterns relate to transdiagnostic baseline measures. We conclude with descriptions of 2 person-specific models as an example of the heterogeneity of dynamic processes. Conclusions The idiographic models presented here join a growing literature showing that the individuals differ dramatically in the total patterning of these processes, even as key processes are shared across individuals. We argue that these processes are best estimated in the context of person-specific models, and that so doing may advance our understanding of the contextualized dynamic processes that could identify maintenance mechanisms and treatment targets. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Psychological researchers often seek to obtain cluster solutions from sparse count matrices (e.g., social networks; counts of symptoms that are in common for 2 given individuals; structural brain imaging). Increasingly, community detection methods are being used to subset the data in a data-driven manner. While many of these approaches perform well in simulation studies and thus offer some improvement upon traditional clustering approaches, there is no readily available approach for evaluating the robustness of these solutions in empirical data. Researchers have no way of knowing if their results are due to noise. We describe here 2 approaches novel to the field of psychology that enable evaluation of cluster solution robustness. This tutorial also explains the use of an associated R package, perturbR, which provides researchers with the ability to use the methods described herein. In the first approach, the cluster assignment from the original matrix is compared against cluster assignments obtained by randomly perturbing the edges in the matrix. Stable cluster solutions should not demonstrate large changes in the presence of small perturbations. For the second approach, Monte Carlo simulations of random matrices that have the same properties as the original matrix are generated. The distribution of quality scores ("modularity") obtained from the cluster solutions from these matrices are then compared with the score obtained from the original matrix results. From this, one can assess if the results are better than what would be expected by chance. perturbR automates these 2 methods, providing an easy-to-use resource for psychological researchers. We demonstrate the utility of this package using benchmark simulated data generated from a previous study and then apply the methods to publicly available empirical data obtained from social networks and structural neuroimaging. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Introduction: Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at functional connectivity maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This article introduces HRF-GIMME, an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on functional magnetic resonance imaging data collected by using event-related designs. Critically, hemodynamic response function (HRF)-GIMME incorporates person-specific modeling of the HRF to accommodate known variability in onset delay and shape. Methods: After an introduction of the technical aspects of HRF-GIMME, the performance of HRF-GIMME is evaluated via both a simulation study and application to empirical data. The simulation study assesses the sensitivity and specificity of HRF-GIMME by using data simulated from one slow and two rapid event-related designs, and HRF-GIMME is then applied to two empirical data sets from similar designs to evaluate performance in recovering known neural circuitry. Results: HRF-GIMME showed high sensitivity and specificity across all simulated conditions, and it performed well in the recovery of expected relations between convolved task vectors and brain regions in both simulated and empirical data, particularly for the slow event-related design. Conclusion: Results from simulated and empirical data indicate that HRF-GIMME is a powerful new tool for obtaining directed functional connectivity maps of intrinsic and task-related connections that is able to uncover what is common across the sample as well as crucial individual-level path connections and estimates. IMPACT STATEMENT: Group iterative multiple model estimation (GIMME) is a reliable method for creating functional connectivity maps of the connections between brain regions across time, and it is able to detect what is common across the sample and what is shared between subsets of participants, as well as individual-level path estimates. However, historically, GIMME does not model task-related effects. The novel HRF-GIMME algorithm enables the modeling of direct and modulatory task effects through individual-level estimation of the hemodynamic response function (HRF), presenting a powerful new tool for assessing task effects on functional connectivity networks in functional magnetic resonance imaging data.

Data-driven model searches provide the opportunity to quantify person-specific processes using ambulatory assessment data. Here, the search space typically includes all potential relations among variables, meaning that all variables can potentially explain variability in all other variables. Oftentimes, this is unrealistic. For example, weather is unlikely to be predicted by someone’s emotional state, whereas the reverse might be true. Allowing for specification of exogenous variables, or variables that are not predicted within the system, permits more realistic models and allows the researcher to model contextual change processes via the use of moderation variables. We use two sets of daily diary data to demonstrate the capabilities of allowing for the specification of exogenous variables in GIMME (Group Iterative Multiple Model Estimation), a model search algorithm that allows for models with idiographic, individual-level as well as subgroup- and group-level processes with intensive longitudinal data. First, using data collected from individuals diagnosed with personality disorders, we show results where weather-related and temporal basis variables are specified as exogenous, and reports on affect and behavior are endogenous. Next, we demonstrate the modeling of treatment effects in an intervention study, looking at data from a 6-week meditation workshop in midlife adults. Finally, we use the meditation intervention data to demonstrate modeling moderation effects, where relationships between two endogenous variables are dependent on the current stage of the study for a given participant (i.e., currently attending meditation classes or not). We end by presenting adaptive LASSO as a method for probing results obtained from GIMME.

We provide a brief overview of two R packages that can conduct exploratory factor analysis (EFA): psych and EFAutilities. After introducing EFA and the exemplar data used in this paper we discuss b...

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.

Connectivity modeling in functional neuroimaging has become widely used method of analysis for understanding functional architecture. One method for deriving directed connectivity models is Group Iterative Multiple Model Estimation (GIMME; Gates and Molenaar, 2012). GIMME looks for commonalities across the sample to detect signal from noise and arrive at edges that exist across the majority in the group (“group-level edges”) and individual-level edges. In this way, GIMME obtains generalizable results via the group-level edges while also allowing for between subject heterogeneity in connectivity, moving the field closer to obtaining reliable personalized connectivity maps. In this article, we present a novel extension of GIMME, confirmatory subgrouping GIMME, which estimates subgroup-level edges for a priori known groups (e.g. typically developing controls vs. clinical group). Detecting edges that consistently exist for individuals within predefined subgroups aids in interpretation of the heterogeneity in connectivity maps and allows for subgroup-specific inferences. We describe this algorithm, as well as several methods to examine the results. We present an empirical example that finds similarities and differences in resting state functional connectivity among four groups of children: typically developing controls (TDC), children with autism spectrum disorder (ASD), children with Inattentive (ADHD-I) and Combined (ADHD-C) Type ADHD. Findings from this study suggest common involvement of the left Broca's area in all the clinical groups, as well as several unique patterns of functional connectivity specific to a given disorder. Overall, the current approach and proof of principle findings highlight a novel and reliable tool for capturing heterogeneity in complex mental health disorders.

Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, otherwise only a fraction of the system dynamics will be recovered. In this paper we introduce a Square-Root Second-Order Extended Kalman Filtering approach for estimating smoothly time-varying parameters. This approach is capable of handling dynamic factor models where the relations between variables underlying the processes of interest change in a manner that may be difficult to specify in advance. We examine the performance of our approach in a Monte Carlo simulation and show the proposed algorithm accurately recovers the unobserved states in the case of a bivariate dynamic factor model with time-varying dynamics and treatment effects. Furthermore, we illustrate the utility of our approach in characterizing the time-varying effect of a meditation intervention on day-to-day emotional experiences.

Unified structural equation modeling (uSEM) implemented in the Group Iterative Multiple Model Estimation (GIMME) framework has recently been widely used for characterizing within-person network dynamics of behavioral and functional neuroimaging variables. Previous studies have established that GIMME accurately recovers the presence of relations between variables. However, recovery of relation directionality is less consistent, which is concerning given the importance of directionality estimates for many research questions. There is evidence that strong autoregressive relations may aid directionality recovery and indirect evidence that a novel version of GIMME allowing for multiple solutions could improve recovery when such relations are weak, but it remains unclear how these strategies perform under a range of study conditions. Using comprehensive simulations that varied the strength of autoregressive relations among other factors, this study evaluated the directionality recovery of two GIMME search strategies: 1) estimating autoregressive relations by default in the null model (GIMME-AR), and 2) generating multiple solution paths (GIMME-MS). Both strategies recovered directionality best – and were roughly equivalent in performance – when autoregressive relations were strong (e.g., β = .60). When they were weak (β

There recently has been growing interest in the study of psychological and neurological processes at an individual level. One goal in such endeavors is to construct person-specific dynamic assessments using time series techniques such as Vector Autoregressive (VAR) models. However, two problems exist with current VAR specifications: 1) VAR models are restricted in that contemporaneous relations are typically modeled either as undirected relations among residuals or directed relations among observed variables, but not both; 2) current estimation frameworks are limited by the reliance on stepwise model building procedures. This study adopts a new modeling approach. We first extended the current unified SEM (uSEM) framework, a widely used structural VAR model, to a hybrid representation (i.e., “huSEM”) to include both undirected and directed contemporaneous effects, and then replaced the stepwise modeling with a LASSO-type regularization for a global search of the optimal sparse model. Our simulation study showed that regularized huSEM performed uniformly the best over alternative VAR representations and/or modeling approaches, with respect to accurately recovering the presence and directionality of hybrid relations and reliably removing false relations when the data are generated to have two types of contemporaneous relations. The present study to our knowledge is the first application of the recently developed regularized SEM technique to the estimation of huSEM, which points to a promising future for statistical learning in psychometric models.

There recently has been growing interest in the study of psychological and neurological processes at an individual level. One goal in such endeavors is to construct person-specific dynamic assessments using time series techniques such as Vector Autoregressive (VAR) models. However, two problems exist with current VAR specifications: (1) VAR models are restricted in that contemporaneous relations are typically modeled either as undirected relations among residuals or directed relations among observed variables, but not both; (2) current estimation frameworks are limited by the reliance on stepwise model building procedures. This study adopts a new modeling approach. We first extended the current unified SEM (uSEM) framework, a widely used structural VAR model, to a hybrid representation (i.e., "huSEM") to include both undirected and directed contemporaneous effects, and then replaced the stepwise modeling with a LASSO-type regularization for a global search of the optimal sparse model. Our simulation study showed that regularized huSEM performed uniformly the best over alternative VAR representations and/or modeling approaches, with respect to accurately recovering the presence and directionality of hybrid relations and reliably removing false relations when the data are generated to have two types of contemporaneous relations. The present study to our knowledge is the first application of the recently developed regularized SEM technique to the estimation of huSEM, which points to a promising future for statistical learning in psychometric models.

Researchers and many educators agree that the ability to self-regulate learning is important for academic success. Yet, many students struggle to anticipate learning difficulties and adjust accordingly. Further, despite theorizing that self-regulated learning involves adaptation across learning cycles, few researchers have examined students’ evaluative judgments, their implications for students’ behavior in a subsequent learning cycle, or their effects on achievement. Utilizing data from a large, introductory college biology course, we examined how struggling students’ evaluative judgments made after a first unit exam predicted changes in learning behaviors as well as how those changes predicted performance on a subsequent exam. We used natural language processing to analyze data from a reflective essay written after a first unit exam, identifying language that reflected evaluation of prior studying and plans to adapt learning. Then, we utilized digital traces of learning behaviors to measure students’ actual adaptation of their use of learning resources. Results from a path analysis revealed students’ evaluations predicted how extensively they discussed plans to adapt their learning process. Plans to adapt described in written reflections predicted an increase in the frequency of desirable learning behaviors, which in turn predicted higher subsequent exam scores, after controlling for previous exam performance. These findings provide empirical evidence of multiple theorized self-regulated learning processes, including how evaluations of learning at the end of a learning cycle can inform planning and behavior changes in a subsequent learning cycle, and that increases in the enactment of effective learning strategies predicts improved performance in complex learning tasks.

Hostile conflict in marriage can increase risks for disease and mortality. Physiological synchrony between partners—e.g., the linkage between their autonomic fluctuations—appears to capture engagement, or an inability to disengage from an exchange, and thus may amplify the health risks of noxious interactions such as marital conflict. Prior work has not examined the unique health correlates of this physiological signature. To test associations between couples’ heart rate variability (HRV) synchrony during conflict and inflammation, 43 married couples engaged in a marital problem discussion while wearing heart monitors and provided four blood samples; they repeated this protocol at a second visit. When couples’ moment-to-moment HRV changes tracked more closely together during conflict, they had higher levels of three inflammatory markers (i.e., IL-6, stimulated TNF-α, and sVCAM-1) across the day. Stronger HRV synchrony during conflict also predicted greater negative affect reactivity. Synchrony varied within couples, and was related to situational factors rather than global relationship traits. These data highlight partners’ HRV linkage during conflict as a novel social-biological pathway to inflammation-related disease.

Depressed patients show abnormalities in brain connectivity at rest, including hyperconnectivity within the default mode network (DMN). However, there is well-known heterogeneity in the clinical presentation of depression that is overlooked when averaging connectivity data. We used data-driven parsing of neural connectivity to reveal subgroups among 80 depressed patients completing resting state fMRI. Directed functional connectivity paths (eg, region A influences region B) within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation, a method shown to accurately recover the direction and presence of connectivity paths in individual participants. Individuals were clustered using community detection on neural connectivity estimates. Subgroups were compared on network features and on clinical and biological/demographic characteristics that influence depression prognosis. Two subgroups emerged. Subgroup A, containing 71% of the patients, showed a typical pattern of connectivity across DMN nodes, as previously reported in depressed patients on average. Subgroup B exhibited an atypical connectivity profile lacking DMN connectivity, with increased dorsal anterior cingulate-driven connectivity paths. Subgroup B members had an over-representation of females (87% of Subgroup B vs 65% of Subgroup A; χ2=3.89, p=0.049), comorbid anxiety diagnoses (42.9% of Subgroup B vs 17.5% of Subgroup A; χ2=5.34, p=.02), and highly recurrent depression (63.2% of Subgroup B vs 31.8% of Subgroup A; χ2=5.38, p=.02). Neural connectivity-based categorization revealed an atypical pattern of connectivity in a depressed patient subset that would be overlooked in group comparisons of depressed and healthy participants, and tracks with clinically relevant phenotypes including anxious depression and episodic recurrence. Data-driven parsing suggests heterogeneous substrates of depression; ideally, future work building on these findings will inform personalized treatment.

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.

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; χ 2 = 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.

In this article, the most commonly used algorithms for causal search on fMRI data are reviewed and discussed, with particular attention paid to aspects of the algorithms useful for substantive neuroimaging researchers. Classic algorithms, such as PC and GES, as well as more contemporary algorithms, such as IMaGES–LOFS and GIMME, are compared and contrasted. One major difference between algorithms, the use of lagged variables to infer direction vs. the assumption of non-normality to infer direction is discussed, with eye on the impact those choices have on substantive findings. The algorithms share some commonalities as well as differences. For instance, some use lagged variables to assist in ascertaining directionality, whereas others rely on aspects of non-normality in the data to infer directionality. Attention is given to the impact of these choices on the reliability of results.

Traditional diagnostic systems for neurodevelopmental disorders define diagnostic categories that are heterogeneous in behavior and underlying neurobiological alterations. The goal of this study was to parse heterogeneity in a core executive function (EF), cognitive flexibility, in children with a range of abilities (N = 132; children with autism spectrum disorder, attention-deficit/hyperactivity disorder [ADHD], and typically developing children) using directed functional connectivity profiles derived from resting-state functional magnetic resonance imaging data. Brain regions activated in response to a cognitive flexibility task in adults were used to guide region-of-interest selection to estimate individual connectivity profiles in this study. We expected to find subgroups of children who differed in their network connectivity metrics and symptom measures. Unexpectedly, we did not find a stable or valid subgrouping solution, which suggests that categorical models of the neural substrates of cognitive flexibility in children may be invalid. Exploratory analyses revealed dimensional associations between network connectivity metrics and ADHD symptomatology and EF ability across the entire sample. Results shed light on the validity of conceptualizing the neural substrates of cognitive flexibility categorically in children. Ultimately, this work may provide a foundation for the development of a revised nosology focused on neurobiological substrates as an alternative to traditional symptom-based classification systems.

Deep learning has revolutionized predictive modeling in computer vision and in natural language processing but is not commonly applied to psychology data. We provide an overview of machine learning and deep learning for psychologists who have a working knowledge of linear regression. We first help psychologists decide when to use deep learning by discussing how machine learning and deep learning may be used to answer both prediction-focused and causal-focused research questions. We then provide an overview of machine learning and present a general recipe for building machine learning algorithms. We define deep learning algorithms as a subset of machine learning algorithms that can extract successively more and more abstract representations of a data set by using many layers of processing. Finally, we present three basic deep learning models that generalize linear regression: The feedforward neural network, the convolutional neural network, and the recurrent neural network. We describe how these models may be applied to answer prediction-focused research questions using common data types collected by psychologists.

Autism spectrum disorders (ASDs) are neurodevelopmental disorders associated with atypical brain connectivity. Although language abilities vary widely, they are impaired or atypical in most children with ASDs. Underlying brain mechanisms, however, are not fully understood. The present study examined intrinsic functional connectivity (iFC) of the extended language network in a cohort of 52 children and adolescents with ASDs (ages 8-18 years), using resting-state functional magnetic resonance imaging. We found that, in comparison to typically developing peers (n = 50), children with ASDs showed increased connectivity between some language regions. In addition, seed-to-whole brain analyses revealed increased connectivity of language regions with posterior cingulate cortex (PCC) and visual regions in the ASD group. Post hoc effective connectivity analyses revealed a mediation effect of PCC on the iFC between bilateral inferior frontal and visual regions in an ASD subgroup. This finding qualifies and expands on previous reports of recruitment of visual areas in language processing in ASDs. In addition, increased iFC between PCC and visual regions was linked to lower language scores in this ASD subgroup, suggesting that increased connectivity with visual cortices, mediated by default mode regions, may be detrimental to language abilities. Autism Res 2019, 12: 1344-1355. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: We examined the functional connectivity between regions of the language network in children with autism spectrum disorders (ASDs) compared to typically developing peers. We found connectivity to be intact between core language in the ASD group, but also showed abnormally increased connectivity between regions of an extended language network. Additionally, connectivity was increased with regions associated with brain networks responsible for self-reflection and visual processing.

Researchers across many domains of psychology increasingly wish to arrive at personalized and generalizable dynamic models of individuals' processes. This is seen in psychophysiological, behavioral, and emotional research paradigms, across a range of data types. Errors of measurement are inherent in most data. For this reason, researchers typically gather multiple indicators of the same latent construct and use methods, such as factor analysis, to arrive at scores from these indices. In addition to accurately measuring individuals, researchers also need to find the model that best describes the relations among the latent constructs. Most currently available data-driven searches do not include latent variables. We present an approach that builds from the strong foundations of group iterative multiple model estimation (GIMME), the idiographic filter, and model implied instrumental variables with two-stage least squares estimation (MIIV-2SLS) to provide researchers with the option to include latent variables in their data-driven model searches. The resulting approach is called latent variable GIMME (LV-GIMME). GIMME is utilized for the data-driven search for relations that exist among latent variables. Unlike other approaches such as the idiographic filter, LV-GIMME does not require that the latent variable model to be constant across individuals. This requirement is loosened by utilizing MIIV-2SLS for estimation. Simulated data studies demonstrate that the method can reliably detect relations among latent constructs, and that latent constructs provide more power to detect effects than using observed variables directly. We use empirical data examples drawn from functional MRI and daily self-report data. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance the understanding of healthy and diseased brain circuit function through technological innovation. Core principles for this goal include the validation and dissemination of the myriad innovative technologies, tools, methods, and resources emerging from BRAIN-funded research. Innovators, BRAIN funding agencies, and non-Federal partners are working together to develop strategies for making these products usable, available, and accessible to the scientific community. Here, we describe several early strategies for supporting the dissemination of BRAIN technologies. We aim to invigorate a dialogue with the neuroscience research and funding community, interdisciplinary collaborators, and trainees about the existing and future opportunities for cultivating groundbreaking research products into mature, integrated, and adaptable research systems. Along with the accompanying Society for Neuroscience 2019 Mini-Symposium, “BRAIN Initiative: Cutting-Edge Tools and Resources for the Community,” we spotlight the work of several BRAIN investigator teams who are making progress toward providing tools, technologies, and services for the neuroscience community. These tools access neural circuits at multiple levels of analysis, from subcellular composition to brain-wide network connectivity, including the following: integrated systems for EM- and florescence-based connectomics, advances in immunolabeling capabilities, and resources for recording and analyzing functional connectivity. Investigators describe how the resources they provide to the community will contribute to achieving the goals of the NIH BRAIN Initiative. Finally, in addition to celebrating the contributions of these BRAIN-funded investigators, the Mini-Symposium will illustrate the broader diversity of BRAIN Initiative investments in cutting-edge technologies and resources.

Objective: We conducted a meta-analysis of physical activity interventions among cancer survivors in order to (a) quantify the magnitude of intervention effects on physical activity, and (b) determine what combination of intervention strategies maximizes behavior change. \ud Methods: Out of 32,626 records that were located using computerized searches, 138 independent tests (N = 13,050) met the inclusion criteria for the review. We developed a bespoke taxonomy of 34 categories of techniques designed to promote psychological change, and categorized sample, intervention, and methodological characteristics. Random effects meta-analysis and meta-regressions were conducted; effect size data were also submitted to Meta-CART analysis.\ud Results: The sample-weighted average effect size for physical activity interventions was d+ = .35, equivalent to an increase of 1,149 steps per day. Effect sizes exhibited both publication bias and small sample bias but remained significantly different from zero, albeit of smaller magnitude (d+ ≥ .20), after correction for bias. Meta-CART analysis indicated that the major difference in effectiveness was attributable to supervised versus unsupervised programs (d+ = .49 vs. .26). Greater contact time was associated with larger effects in supervised programs. For unsupervised programs, establishing outcome expectations, greater contact time, and targeting overweight or sedentary participants each predicted greater program effectiveness, whereas prompting barrier identification and providing workbooks were associated with smaller effect sizes.\ud Conclusion: The present review indicates that interventions have a small but significant effect on physical activity among cancer survivors, and offers insights into how the effectiveness of future interventions might be improved.

The scientific literacy and conceptual understanding demands of the 21st century have necessitated fundamental changes in science education, including changes from traditional lecture to more active learning pedagogies. The affordances of such pedagogies can benefit students, but only when they are able to enact effective and efficient self-regulated learning processing. More research is needed to understand how and when students should self-regulate during science learning, as well as how to help those students who struggle to do so. In this study, we leveraged multimodal online interaction trace data from 408 college students enrolled in an introductory biology class to investigate the temporal nature of self-regulation during science education. Using latent profile analyses, we found differences in self-regulatory processing predicted course performance, with implications for the development of systems for identifying and supporting students who are likely to struggle in active learning science education environments.

Individuals in both clinical and research settings increasingly provide data across numerous time points. Examples include measurements collected from wearable technology (e.g., accelerometers), psychophysiological measures, coded observations, social media behaviors, and daily diary data. When numerous observations are available for each individual, the data fall under the class of time series data and can be examined from within a dynamic systems perspective. We provide a broad overview of current analytic methods for quantifying relations among dyads using this data type. The techniques include those from within a linear modeling framework, approaches that include a measurement model, and methods for examining cyclical relations. We also discuss some special topics, such as methods that allow for models to shift across time and that accommodate heterogeneity across individuals. Finally, methods that account for similar shapes of nonlinear curves across time are described. From this breadth of options, we hope to help guide practitioners, clinicians, and researchers in choosing the optimal method for their data and line of questions. To further aid in this choice, we indicate programs available for each technique. Example dyads presented here range from mother–infant, patient–caretaker, and husband–wife; however, the analytic methods can be applied to any type of dyad.

Theories of adolescent neurodevelopment have largely focused on group-level descriptions of neural changes that help explain increases in risk behavior that are stereotypical of the teen years. However, because these models are concerned with describing the “average” individual, they can fail to account for important individual or within-group variability. New methodological developments now offer the possibility of accounting for both group trends and individual differences within the same modeling framework. Here we apply GIMME, a model-based approach which uses both group and individual-level information to construct functional connectivity maps, to investigate risky behavior and neural changes across development. Adolescents (N = 30, Mage = 13.22), young adults (N = 23, Mage = 19.19), and adults (N = 31, Mage = 43.93) completed a risky decision-making task during an fMRI scan, and functional networks were constructed for each individual. We took two subgrouping approaches: 1) a confirmatory approach where we searched for functional connections that distinguished between our a priori age categories, and 2) an exploratory approach where we allowed an unsupervised algorithm to sort individuals freely. Contrary to expectations, we show that age is not the most influence contributing to network configurations. The implications for developmental theories and methodologies are discussed.