Association of Childhood Violence Exposure With Adolescent Neural Network Density

Goetschius, Leigh G.; Hein, Tyler C.; McLanahan, Sara; Brooks‐Gunn, Jeanne; McLoyd, Vonnie C.; Dotterer, Hailey L.; Lopez-Duran, Nestor L.; Mitchell, Colter; Hyde, Luke W.; Monk, Christopher S.; Beltz, Adriene M.

doi:10.1001/jamanetworkopen.2020.17850

Cited by 42 publications

(31 citation statements)

References 58 publications

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“…The subgrouping using community detection provides the model with more known priors that refines and improves the search for individual connections ( Beltz & Gates, 2017 ). Because each subgroup is defined by similarities in features, each is best described by their shared network features (e.g., Goetschius et al, 2020 ). Using both individual and subgroup level network features increase reliability of estimates for both individual and subgroups in comparison to other network approaches ( Gates et al, 2017 , Gates and Molenaar, 2012 , Smith et al, 2011 ).…”

Section: Methodsmentioning

confidence: 99%

Resting-state network topology characterizing callous-unemotional traits in adolescence

Winters

Sakai

Carter

2021

NeuroImage: Clinical

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Section: Methodsmentioning

confidence: 99%

Resting-state network topology characterizing callous-unemotional traits in adolescence

Winters

Sakai

Carter

2021

NeuroImage: Clinical

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“…Indeed, a number of recent empirical studies designed specifically to evaluate propositions of dimensional models yield evidence consistent with these ideas. This includes work supporting the predicted distinctions between threat and deprivation in their associations with a range of developmental outcomes, such as amygdala reactivity to threat, aversive learning, cognitive control, and even pubertal timing (Goetschius et al, 2020;Hein et al, 2020;Lambert, King, Monahan, & McLaughlin, 2017;Machlin, Miller, Snyder, McLaughlin, & Sheridan, 2019;Miller, Machlin, McLaughlin, & Sheridan, 2020;Miller et al, 2018;Peckins et al, 2020;Rosen, Meltzoff, Sheridan, & McLaughlin, 2019;Sheridan, Peverill, & McLaughlin, 2017;Sheridan et al, 2020;Sumner, Colich, Uddin, Armstrong, & McLaughlin, 2019;Sun, Fang, Wan, Su, & Tao, 2020;Wolf & Suntheimer, 2019). Perhaps the strongest evidence comes from systematic reviews and meta-analyses that document clearly divergent associations of threat and deprivation with neural structure and function (McLaughlin, Weissman, & Bitran, 2019) and measures of biological aging, including pubertal timing and cellular aging (Colich, Rosen, Williams, & McLaughlin, 2020).…”

Section: Problem 3: Consistent Differences In the Downstream Consequences Of Different Dimensions Of Early Experience Have Been Observedmentioning

confidence: 98%

The Value of Dimensional Models of Early Experience: Thinking Clearly About Concepts and Categories

McLaughlin

Sheridan

Humphreys³

et al. 2021

Perspect Psychol Sci

213

167

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We review the three prevailing approaches—specificity, cumulative risk, and dimensional models—to conceptualizing the developmental consequences of early-life adversity and address fundamental problems with the characterization of these frameworks in a recent Perspectives on Psychological Science piece by Smith and Pollak. We respond to concerns raised by Smith and Pollak about dimensional models of early experience and highlight the value of these models for studying the developmental consequences of early-life adversity. Basic dimensions of adversity proposed in existing models include threat/harshness, deprivation, and unpredictability. These models identify core dimensions of early experience that cut across the categorical exposures that have been the focus of specificity and cumulative risk approaches (e.g., abuse, institutional rearing, chronic poverty); delineate aspects of early experience that are likely to influence brain and behavioral development; afford hypotheses about adaptive and maladaptive responses to different dimensions of adversity; and articulate specific mechanisms through which these dimensions exert their influences, conceptualizing experience-driven plasticity within an evolutionary-developmental framework. In doing so, dimensional models advance specific falsifiable hypotheses, grounded in neurodevelopmental and evolutionary principles, that are supported by accumulating evidence and provide fertile ground for empirical studies on early-life adversity.

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“…During model generation, GIMME identifies shared connectivity patterns while accounting for individual heterogeneity using a community detection algorithm (walktrap) (Gates & Molenaar, 2012), which simulations have proven to be a reliable method of detecting subgroups of network patterns (Gates et al, 2017;Pons & Latapy, 2005). Because each subgroup is defined by similarities in features, each is best described by their shared network features (e.g., Goetschius et al, 2020). Using both individual and subgroup level network features increase reliability of estimates for both individual and subgroups in comparison to other network approaches (Gates et al, 2017;Gates & Molenaar, 2012;Smith et al, 2011).…”

Section: Inventory Of Callous-unemotional Traits (Icu)mentioning

confidence: 99%

Resting-state network topology characterizing callous-unemotional traits in adolescence

Winters¹,

Sakai²,

Carter

2021

Preprint

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Background: Callous-unemotional (CU) traits, a youth antisocial phenotype, are hypothesized to associate with aberrant connectivity (dis-integration) across the salience (SAL), default mode (DMN), and frontoparietal (FPN) networks. However, CU traits have a heterogeneous presentation and previous research has not modeled individual heterogeneity in resting-state connectivity amongst adolescents with CU traits. The present study models individual-specific network maps and examines topological features of individual and subgroup maps in relation to CU traits. Methods: Participants aged 13-17 completed resting-state functional connectivity and the inventory of callous-unemotional traits as part of the Nathan Klein Rockland study. A sparse network approach (GIMME) was used to derive individual-level and subgroup maps of all participants. We then examined heterogeneous network features associated with CU traits. Results: Higher rates of CU traits increased probability of inclusion in one subgroup, which had the highest mean level of CU traits. Analysis of network features reveals less density within the FPN and greater density between DMN-FPN associated with CU traits. Discussion: Findings indicate heterogenous person-specific connections and some subgroup connections amongst adolescents associate with CU traits. Higher CU traits associate with lower density in the FPN, which has been associated with attention and inhibition, and higher density between the DMN-FPN, which have been linked with cognitive control, social working memory, and empathy. Our findings suggest less efficiency in FPN function which, when considered mechanistically, could result in difficulty suppressing DMN when task-positive networks are engaged. This is an area for further exploration but could explain cognitive and socio-affective impairments in CU traits.

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Association of Childhood Violence Exposure With Adolescent Neural Network Density

Cited by 42 publications

References 58 publications

Resting-state network topology characterizing callous-unemotional traits in adolescence

Resting-state network topology characterizing callous-unemotional traits in adolescence

The Value of Dimensional Models of Early Experience: Thinking Clearly About Concepts and Categories

Resting-state network topology characterizing callous-unemotional traits in adolescence

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