Group, subgroup, and person specific symptom associations in individuals at different levels of risk for psychosis:A combination of theory-based and data-driven approaches

Introduction Dynamics between symptoms may reveal insights into mechanisms underlying the development of psychosis. We combined a top-down (theory-based) and bottom-up (data-driven) approach to examine which symptom dynamics arise on group-level, on subgroup levels, and on individual levels in early clinical stages. We compared data-driven subgroups to theory-based subgroups, and explored how the data-driven subgroups differed from each other. Methods Data came from N = 96 individuals at risk for psychosis divided over four subgroups (n1 = 25, n2 = 27, n3 = 24, n4 = 20). Each subsequent subgroup represented a higher risk for psychosis (clinical stages 0-1b). All individuals completed 90 days of daily diaries, totaling 8640 observations. Confirmatory Subgrouping Group Iterative Multiple Model Estimation (CS-GIMME) and subgrouping (S-)-GIMME were used to examine group-level associations, respectively, theory-based and data-driven subgroups associations, and individual-specific associations between daily reports of depression, anxiety, stress, irritation, psychosis, and confidence. Results One contemporaneous group path between depression and confidence was identified. CS-GIMME identified several subgroup-specific paths and some paths that overlapped with other subgroups. S-GIMME identified two data-driven subgroups, with one subgroup reporting more psychopathology and lower social functioning. This subgroup contained most individuals from the higher stages and those with more severe psychopathology from the lower stages, and shared more connections between symptoms. Discussion Although subgroup-specific paths were recovered, no clear ordering of symptom patterns was found between different early clinical stages. Theory-based subgrouping distinguished individuals based on psychotic severity, whereas data-driven subgrouping distinguished individuals based on overall psychopathological severity. Future work should compare the predictive value of both methods

CONTEXT EFFECTS ON THE NEURAL CORRELATES OF EMOTION

Human emotions are inextricably linked to the context in which they occur, yet neuroscience research on emotion often overlooks the role of context in shaping the neural correlates of human emotions. This dissertation, through three studies, begins to address this gap. Study 1 (Chapter 2) investigated how language as a form of context influences the brain’s response to facial expressions of anger and disgust, showing distinct differences in neural activity related to disgust perception between Chinese Asian and White American participants. Language was found to play a significant role in shaping the neural correlates of emotion perception, particularly in the context of disgust for Chinese Asian participants. Study 2 (Chapter 3) examined the influence of social and cultural contexts on the neural correlates of fear and sadness, demonstrating that both cultural group membership and cultural attitudes are related to the brain’s processing of negative emotional experiences. Study 3 (Chapter 4) focused on individual and group level variation in emotion-related functional connectivity, finding both idiographic and nomothetic patterns of connectivity related to negative emotional experiences. It further highlighted the influence of cultural attitudes on the neural correlates of emotion. Overall, these studies illustrate the importance of considering diverse contextual variables in studying the neural basis of emotion. They show that social and cultural contexts influence how the human brain processes and represents emotions.Doctor of Philosoph

BRAIN Initiative: Cutting-Edge Tools and Resources for the Community.

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

Structured Estimation of Heterogeneous Time Series

How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al., (2022) introduced the multi-VAR approach for simultaneously estimating multiple-subject multivariate time series characterized by common and individualizing features using penalized estimation. This approach differs from many popular modeling approaches for multiple-subject time series in that qualitative and quantitative differences in a large number of individual dynamics are well-accommodated. The current work extends the multi-VAR framework to include new adaptive weighting schemes that greatly improve estimation performance. In a small set of simulation studies we compare adaptive multi-VAR with these new penalty weights to common alternative estimators in terms of path recovery and bias. Furthermore, we provide toy examples and code demonstrating the utility of multi-VAR under different heterogeneity regimes using the multivar package for R (Fisher, 2022)

Unsupervised Classification Reveals Degenerate Neural Representations of Emotion

Neural degeneracy refers to the idea that distinct neural systems are capable of performing the same functions (Noppeney, Friston, & Price, 2004). Consistent with neural degeneracy, the Theory of Constructed Emotion (TCE) suggests that emotions and other mental states arise from combinations of the brain’s domain-general intrinsic networks such as the default mode network, salience network, and frontoparietal control network (Clark-Polner, Johnson, & Barrett, 2017). A key prediction of degeneracy and the TCE is that the same emotion can emerge from distinct patterns of connectivity across time or across individuals (Barrett, 2017). This project specifically investigates the principle of neural degeneracy in emotion for the first time using a data-driven model building algorithm with unsupervised classification (S-GIMME; Gates, Lane, Varangis, Giovanello, & Guskiewicz, 2017) to quantify distinct patterns of between-network connectivity during self-generated experiences of anxiety and anger. Twenty-four subjects underwent an fMRI experiment in which they listened to unpleasant music and self-generated experiences of anxiety and anger. The hypotheses of this experiment were tested in four consecutive analysis steps. The first analysis step revealed that the S-GIMME procedure could roughly reproduce the experimental conditions in the present experiment by subgrouping individuals based on patterns of connectivity that differentiated anger and anxiety. The second analysis step revealed that this variation could be further subdivided into degenerate neural pathways within each emotion category. The third analysis step showed that subgroups revealed during the anger and anxiety conditions are distinct from those found during a task-positive control condition in which participants listened to neutral music but did not generate an emotional experience. Finally, the fourth analysis step provided a more stringent test of the degeneracy hypothesis by showing that distinct patterns of connectivity revealed in the previous analyses are not the result of stable individual differences that would also be present at rest. Taken together, these analyses show that different patterns of connectivity are associated with the experience of the same emotion.Doctor of Philosoph

Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes

AbstractAssociations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average networks between known groups. However, neural heterogeneity within groups may limit the ability to make inferences at the individual level as qualitatively distinct processes across individuals may be obscured in group averages. This study characterizes the heterogeneity of effective connectivity reward networks among 103 early adolescents and examines associations between individualized features and multiple behavioral and clinical outcomes. To characterize network heterogeneity, we used extended unified structural equation modeling to identify effective connectivity networks for each individual and an aggregate network. We found that an aggregate reward network was a poor representation of individuals, with most individual-level networks sharing less than 50% of the group-level network paths. We then used Group Iterative Multiple Model Estimation to identify a group-level network, subgroups of individuals with similar networks, and individual-level networks. We identified three subgroups that appear to reflect differences in network maturity, but this solution had modest validity. Finally, we found numerous associations between individual-specific connectivity features and behavioral reward functioning and risk for substance use disorders. We suggest that accounting for heterogeneity is necessary to use connectivity networks for inferences precise to the individual

An Idionomic Network Analysis of Psychological Processes and Outcomes

Background: Clinical psychology research emphasizing treatment packages targeted at DSM defined problems obscures individual differences and violates statistical assumptions regarding its applicability to individuals in the sample. An alternative approach maps the relationship between psychological processes and outcomes at the individual level before aggregating results. This study represents the first effort to undertake such an approach using a novel measure, the Process Based Assessment Tool (PBAT), that assesses functionally defined psychological processes linked to intervention and based on modern evolution science. Methods: Data on psychological variation, selection, and retention, domains of psychological distress, life satisfaction, and burnout, were collected twice daily for a 35day period using a smartphone application. These data were analyzed using the SGIMME statistical package to generate group, sub-group, and individual level network models. Results: S-GIMME models successfully converged for all participants. Network models directed at each of 7 outcomes yielded interpretable subgroups. Elements of the PBAT reliably produced directed pathways impacting elements of psychological distress within the sample. 17 of 18 elements of the PBAT appear in final models which maximized directed pathways toward each of the 7 targeted outcomes. Discussion: The PBAT demonstrated utility as a daily diary measure and reliably produced directed pathways impacting domains of psychological distress and well-being. Subgroup formation demonstrated consistency across outcomes directed models. Individual network models represent potential clinical utility

Dynamic symptom networks across different at-risk stages for psychosis:An individual and transdiagnostic perspective

The clinical staging model distinguishes different stages of mental illness. Early stages, are suggested to be more mild, diffuse and volatile in terms of expression of psychopathology than later stages. This study aimed to compare individual transdiagnostic symptom networks based on intensive longitudinal data between individuals in different early clinical stages for psychosis. It was hypothesized that with increasing clinical stage (i) density of symptom networks would increase and (ii) psychotic experiences would be more central in the symptom networks. Data came from a 90-day diary study, resulting in 8640 observations within N = 96 individuals, divided over four subgroups representing different early clinical stages (n1 = 25, n2 = 27, n3 = 24, n4 = 20). Sparse Time Series Chain Graphical Models were used to create individual contemporaneous and temporal symptom networks based on 10 items concerning symptoms of depression, anxiety, psychosis, non-specific and vulnerability domains. Network density and symptom centrality (strength) were calculated individually and compared between and within the four subgroups. Level of psychopathology increased with clinical stage. The symptom networks showed large between-individual variation, but neither network density not psychotic symptom strength differed between the subgroups in the contemporaneous (pdensity = 0.59, pstrength > 0.51) and temporal (pdensity = 0.75, pstrength > 0.35) networks. No support was found for our hypothesis that higher clinical stage comes with higher symptom network density or a more central role for psychotic symptoms. Based on the high inter-individual variability, our results highlight the importance of individualized assessment of symptom networks

Violence Exposure and Social Deprivation: Neural Connectivity Correlates and Protective Factors

Dimensions of early adversity, such as violence exposure and social deprivation, may have different effects associated with socioemotional functioning in the developing brain and different factors may be protective. This dissertation examined the downstream effects of childhood violence exposure and social deprivation in data from the Fragile Families and Child Wellbeing Study at birth, and ages 1, 3, 5, 9, and 15 years. Study one examined the association between violence exposure, social deprivation, and amygdala-prefrontal cortex white matter connectivity, a crucial circuit for emotion regulation. High violence exposure coupled with high social deprivation related to less amygdala–OFC white matter connectivity. Violence exposure was not associated with white matter connectivity when social deprivation was at mean or low levels (i.e., relatively socially supportive contexts). Therefore, social deprivation may exacerbate the effects of childhood violence exposure on the development of white matter connections involved in emotion processing and regulation. Conversely, social support may buffer against them. Study two investigated the association between violence exposure, social deprivation, and adolescent resting-state functional connectivity in two resting-state networks involved in socioemotional functioning (salience network, default mode network) using a person-specific modeling approach. Childhood violence exposure, but not social deprivation, was associated with reduced adolescent resting-state density of the salience and default mode networks. A data-driven algorithm, blinded to childhood adversity, identified youth with heightened violence exposure based on resting-state connectivity patterns. Childhood violence exposure was associated with adolescent functional connectivity heterogeneity, which may reflect person-specific neural plasticity and should be considered when attempting to understand the impacts of early adversity on the brain. Study three examined whether school connectedness was protective against violence exposure and social deprivation when predicting symptoms of internalizing and externalizing psychopathology and positive function and if school connectedness was uniformly protective against both dimensions of adversity. Results suggest that school connectedness is broadly related to better outcomes and may confer additional protection against social deprivation. These findings highlight the important role that the school environment can play for youth who have been exposed to adversity in other areas of their lives. Additionally, the interactive effect of school connectedness with social deprivation, but not violence exposure, supports modeling risk and resilience processes using dimensional frameworks to better identify specific groups of youth that may benefit from interventions that boost social connectedness at school in future research. Overall, this dissertation provides evidence for the complex and person-specific ways through which risk and resilience relate to development and points to considerations for future research. This research has implications for understanding how dimensions of adversity affect the brain and behavior during development and what factors can be protective, which can inform future neuroscience-informed policy interventions.PHDPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167957/1/lcgayle_1.pd

From Exercise Physiology to Network Physiology of Exercise

Exercise physiology (EP) and its main research directions, strongly influenced by reductionism from its origins, have progressively evolved toward Biochemistry, Molecular Biology, Genetics, and OMICS technologies. Although these technologies may be based on dynamic approaches, the dominant research methodology in EP, and recent specialties such as Molecular Exercise Physiology and Integrative Exercise Physiology, keep focused on non-dynamical bottom-up statistical inference techniques. Inspired by the new field of Network Physiology and Complex Systems Science, Network Physiology of Exercise emerges to transform the theoretical assumptions, the research program, and the practical applications of EP, with relevant consequences on health status, exercise, and sport performance. Through an interdisciplinary work with diverse disciplines such as bioinformatics, data science, applied mathematics, statistical physics, complex systems science, and nonlinear dynamics, Network Physiology of Exercise focuses the research efforts on improving the understanding of different exercise-related phenomena studying the nested dynamics of the vertical and horizontal physiological network interactions. After reviewing the EP evolution during the last decades and discussing their main theoretical and methodological limitations from the lens of Complex Networks Science, we explain the potential impact of the emerging field of Network Physiology of Exercise and the most relevant data analysis techniques and evaluation tools used until now