bioRxiv Science⌕ Search

Biology subjects

Heitzeg, M. M.

Publications and source records attributed to Heitzeg, M. M..

9 recordsLinked to original sources

Cognitive and affective neurodevelopment in youth exposed to deprivation and threat

BackgroundEarly life adversity (ELA) is associated with notable negative consequences across development. Experiences of deprivation may affect neurocognitive development, while experiences of threat may alter emotion processing. Deprivation and threat may also differentially influence reward processing. However, unique consequences of deprivation and threat beyond low family resources are debated. MethodsWe employed an exposure vs. control data analytic approach to isolate deprivation and threat influences from socioeconomic resources. Adolescent Brain Cognitive Development (ABCD(R)) Study youth exposed to neither deprivation nor threat (N=2408-2962) were matched to youth exposed to deprivation-only (N=638-721), threat-only (N=198-232), or threat non-exclusively (threat+: N=382-464) based on family income, parental education, race/ethnicity, sex, and age. Multivariate analyses were used to distinguish each ELA group from their respective control groups in the neurocognitive domain (resting-state connectomic maturation, cognitive task performance, and cortical grey matter thickness at two timepoints) and in the neuroaffective domain (nucleus accumbens and caudate activation to reward anticipation and amygdala and insula activation to fearful faces). ResultsIn the neurocognitive domain, similar latent variables (LVs) differentiated the deprivation and threat+ groups from their respective matched control groups. This LV corresponded to neurocognitive maturation, loading positively on cortical functional maturation and task performance, and negatively on cortical grey matter thickness. This LV was weaker in the deprivation and threat+ groups compared to controls. In the neuroaffective domain, no significant LVs were found. ConclusionBoth threat and deprivation exposure during childhood may delay neurocognitive development in early adolescence beyond their co-occurrence with low socioeconomic resources.

neuroscience↗

Somatomotor disconnection links sleep duration with socioeconomic context, screen time, cognition, and psychopathology

BackgroundSleep is critical for healthy brain development and emotional wellbeing, especially during adolescence when sleep, behavior, and neurobiology are rapidly evolving. Theoretical reviews and empirical research have historically focused on how sleep influences mental health through its impact on higher-order brain systems. No studies have leveraged data-driven network neuroscience methods to uncover interpretable, brain-wide signatures of sleep duration in adolescence, their socio-environmental origins, or their consequences for cognition and mental health. MethodsHere, we implement graph theory and component-based predictive modeling to examine how a multimodal index of sleep duration is associated with intrinsic brain architecture in 3,173 youth (11-12 years) from the Adolescent Brain Cognitive DevelopmentSM Study. ResultsWe demonstrate that network integration/segregation exhibit a strong, generalizable multivariate association with sleep duration. We next identify a single component of brain architecture centered on a single network as the dominant contributor of this relationship. This component is characterized by increasing disconnection of a lower-order system - the somatomotor network - from other systems, with shorter sleep duration. Finally, greater somatomotor disconnection is associated with lower socioeconomic resources, longer screen times, reduced cognitive/academic performance, and elevated externalizing problems. ConclusionsThese findings reveal a novel neural signature of shorter sleep in adolescence that is intertwined with environmental risk, cognition, and psychopathology. By robustly elucidating the key involvement of an understudied brain system in sleep, cognition, and psychopathology, this study can inform theoretical and translational research directions on sleep to promote neurobehavioral development and mental health during the adolescent transition.

neuroscience↗

Assessing neurocognitive maturation in early adolescence based on baby and adult functional brain landscapes

Adolescence is a period of growth in cognitive performance and functioning. Recently, data-driven measures of brain-age gap, which can index cognitive decline in older populations, have been utilized in adolescent data with mixed findings. Instead of using a data-driven approach, here we assess the maturation status of the brain functional landscape in early adolescence by directly comparing an individuals resting-state functional connectivity (rsFC) to the canonical early-life and adulthood communities. Specifically, we hypothesized that the degree to which a youths connectome is better captured by adult networks compared to infant/toddler networks is predictive of their cognitive development. To test this hypothesis across individuals and longitudinally, we utilized the Adolescent Brain Cognitive Development (ABCD) Study at baseline (9-10 years; n = 6,489) and 2-year-follow-up (Y2: 11-12 years; n = 5,089). Adjusted for demographic factors, our anchored rsFC score (AFC) was associated with better task performance both across and within participants. AFC was related to age and aging across youth, and change in AFC statistically mediated the age-related change in task performance. In conclusion, we showed that a model-fitting-free index of the brain at rest that is anchored to both adult and baby connectivity landscapes predicts cognitive performance and development in youth.

neuroscience↗

Variation in moment-to-moment brain state engagement changes across development and contributes to individual differences in executive function

Neural variability, or variation in brain signals, facilitates dynamic brain responses to ongoing demands. This flexibility is important during development from childhood to young adulthood, a period characterized by rapid changes in experience. However, little is known about how variability in the engagement of recurring brain states changes during development. Such investigations would require the continuous assessment of multiple brain states concurrently. Here, we leverage a new computational framework to study state engagement variability (SEV) during development. A consistent pattern of SEV changing with age was identified across cross-sectional and longitudinal datasets (N>3000). SEV developmental trajectories stabilize around mid-adolescence, with timing varying by sex and brain state. SEV successfully predicts executive function (EF) in youths from an independent dataset. Worse EF is further linked to alterations in SEV development. These converging findings suggest SEV changes over development, allowing individuals to flexibly recruit various brain states to meet evolving needs.

neuroscience↗

Socioeconomic resources in youth are linked to divergent patterns of network integration and segregation across the brain's transmodal axis

Socioeconomic resources (SER) calibrate the developing brain to the current context, which can confer or attenuate risk for psychopathology across the lifespan. Recent multivariate work indicates that SER levels powerfully influence intrinsic functional connectivity patterns across the entire brain. Nevertheless, the neurobiological meaning of these widespread alterations remains poorly understood, despite its translational promise for early risk identification, targeted intervention, and policy reform. In the present study, we leverage the resources of graph theory to precisely characterize multivariate and univariate associations between household SER and the functional integration and segregation (i.e., participation coefficient, within-module degree) of brain regions across major cognitive, affective, and sensorimotor systems during the resting state in 5,821 youth (ages 9-10 years) from the Adolescent Brain Cognitive Development (ABCD) Study. First, we establish that decomposing the brain into profiles of integration and segregation captures more than half of the multivariate association between SER and functional connectivity with greater parsimony (100-fold reduction in number of features) and interpretability. Second, we show that the topological effects of SER are not uniform across the brain; rather, higher SER levels are related to greater integration of somatomotor and subcortical systems, but greater segregation of default mode, orbitofrontal, and cerebellar systems. Finally, we demonstrate that the effects of SER are spatially patterned along the unimodal-transmodal gradient of brain organization. These findings provide critical interpretive context for the established and widespread effects of SER on brain organization, indicating that SER levels differentially configure the intrinsic functional architecture of developing unimodal and transmodal systems. This study highlights both sensorimotor and higher-order networks that may serve as neural markers of environmental stress and opportunity, and which may guide efforts to scaffold healthy neurobehavioral development among disadvantaged communities of youth.

neuroscience↗

Flexible adaptation of task-positive brain networks predicts efficiency of evidence accumulation

Efficiency of evidence accumulation (EEA), an individuals ability to selectively gather goal-relevant information to make adaptive choices, is thought to be a key neurocomputational mechanism associated with cognitive functioning and transdiagnostic risk for psychopathology. However, the neural basis of individual differences in EEA is poorly understood, especially regarding the role of largescale brain network dynamics. We leverage data from over 5,000 participants from the Human Connectome Project and Adolescent Brain Cognitive Development Study to demonstrate a strong association between EEA and flexible adaptation to cognitive demand in "task-positive" frontoparietal and dorsal attention networks, which explains 36%-39% of the variance across individuals in EEA. Notably, individuals with higher EEA displayed divergent task-positive network activation across n-back task conditions: higher activation under high cognitive demand (2-back) and lower activation under low demand (0-back). These findings suggest that brain networks flexible adaptation to cognitive demands is a key neural underpinning of EEA.

neuroscience↗

Charting Brain Growth and Aging at High Spatial Precision

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and use normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1,985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision making.

neuroscience↗

Evidence accumulation and associated error-related brain activity as computationally informed prospective predictors of substance use in emerging adulthood

RationaleSubstance use peaks during the developmental period known as emerging adulthood (ages 18-25), but not every individual who uses substances during this period engages in frequent or problematic use. Although individual differences in neurocognition appear to predict use severity, mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have yet to be identified. Here we aim to do so with an approach rooted in computational psychiatry, an emerging field in which formal models are used to identify candidate biobehavioral dimensions that confer risk for psychopathology. ObjectivesWe test whether lower efficiency of evidence accumulation (EEA), a computationally-characterized individual difference variable that drives performance on the go/no-go and other neurocognitive tasks, is a risk factor for substance use in emerging adults. Methods and ResultsIn an fMRI substudy within a sociobehavioral longitudinal study (n=106), we find that lower EEA and reductions in a robust neural-level correlate of EEA (error-related activations in salience network structures) measured at ages 18-21 are both prospectively related to greater substance use during ages 22-26, even after adjusting for other well-known risk factors. Results from Bayesian model comparisons corroborated inferences from conventional hypothesis testing and provided evidence that both EEA and its neuroimaging correlates contain unique predictive information about substance use involvement. ConclusionsThese findings highlight EEA as a computationally-characterized neurocognitive risk factor for substance use during a critical developmental period, with clear links to both neuroimaging measures and well-established formal theories of brain function.

neuroscience↗

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The ABCD Study is a collaborative effort, including a Coordinating Center, 21 data acquisition sites across the United States, and a Data Analysis and Informatics Center (DAIC). The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data will provide a resource of unprecedented scale and depth for studying typical and atypical development. Here, we describe the baseline neuroimaging processing and subject-level analysis methods used by the ABCD DAIC in the centralized processing and extraction of neuroanatomical and functional imaging phenotypes. Neuroimaging processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI.\n\nHighlightsO_LIAn overview of the MRI processing pipeline for the ABCD Study\nC_LIO_LIA discussion on the challenges of large, multisite population studies\nC_LIO_LIA methodological reference for users of publicly shared data from the ABCD Study\nC_LI

neuroscience↗