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A Data Driven Investigation Of Childhood Adversity And Neural Development%3a Examining Longitudinal Clustering Of Deprivation%2c Threat%2c And Neural Structure Using Network Analysis by Lucy A. Lurie
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1A Data-driven Investigation Of Childhood Adversity And Neural Development: Examining Longitudinal Clustering Of Deprivation, Threat, And Neural Structure Using Network Analysis
By Lucy A. Lurie, Feng Shi, Matthew Peverill, Katie A. McLaughlin and Margaret Sheridan
Childhood adversity exposure is common and is associated with unfavorable physical and mental health outcomes (Cicchetti & Toth, 1995; McLaughlin et al., 2012). While cumulative risk models of adversity have been instrumental in demonstrating that children with more adversity exposure are at greatest risk for unfavorable outcomes (Felitti et al., 1998), they do not elucidate the mechanisms by which adversity confers this risk. The Dimensional Model of Adversity and Psychopathology (DMAP) proposes two dimensions of adversity exposure – deprivation and threat – which differentially impact developmental outcomes through separate neurodevelopmental pathways (McLaughlin, Sheridan, & Lambert, 2014; Sheridan & McLaughlin, 2014). The model proposes that deprivation is associated with neural structure in areas of the brain that support complex cognitive functions (e.g., executive function, language, associative learning), like the frontoparietal control and dorsal attention networks. Another recent conceptual model proposes that early deprivation exposure may impact early-developing visual regions in the ventral visual stream that may scaffold the development of higher-order cognitive skills (Rosen et al., 2019). The DMAP further proposes that threat exposures are associated with structure in areas of the brain that support fear learning, emotion regulation, and threat perception like the limbic network, salience network, default mode network, amygdala, and hippocampus. Previous hypothesis-driven work has supported that deprivation and threat exposures differentially predict neural structure separately in childhood and adolescence (Busso et al., 2017; McLaughlin et al., 2016; 2019; McLaughlin, Sheridan, Winter, et al., 2014; Rosen et al., 2018; Sheridan, Copeland, et al., 2019). There is, however, a lack of consistent longitudinal evidence supporting the association between adversity exposures and neurodevelopmental trajectories. Furthermore, data-driven tests of the DMAP could support its relevance in understanding developmental outcomes following adversity exposures relative to cumulative risk models (e.g., Sheridan, Shi, et al., 2019). In the present study, we will take a network analytical approach to examine clusters of adversity exposures and structural neural development in a longitudinal neuroimaging sample of children and adolescents. The study involved two study visits starting with youth aged 8-16 years in the Seattle area. Participants were recruited for increased likelihood of maltreatment exposure. At the first visit, participants reported on lifetime deprivation and threat exposures and underwent structural magnetic resonance imaging (MRI). Approximately two years later, subjects completed a follow-up neuroimaging assessment using the same scanner.
“A Data-driven Investigation Of Childhood Adversity And Neural Development: Examining Longitudinal Clustering Of Deprivation, Threat, And Neural Structure Using Network Analysis” Metadata:
- Title: ➤ A Data-driven Investigation Of Childhood Adversity And Neural Development: Examining Longitudinal Clustering Of Deprivation, Threat, And Neural Structure Using Network Analysis
- Authors: Lucy A. LurieFeng ShiMatthew PeverillKatie A. McLaughlinMargaret Sheridan
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- Internet Archive ID: osf-registrations-w7ptx-v1
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The book is available for download in "data" format, the size of the file-s is: 0.14 Mbs, the file-s for this book were downloaded 2 times, the file-s went public at Fri Aug 20 2021.
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