Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived <scp>COVID</scp>‐19 economic adversity

Hardi, Felicia A.; Goetschius, Leigh G.; McLoyd, Vonnie C.; Lopez-Duran, Nestor L.; Mitchell, Colter; Hyde, Luke W.; Beltz, Adriene M.; Monk, Christopher S.

doi:10.1111/jcpp.13749

Cited by 8 publications

(4 citation statements)

References 78 publications

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“…There were also no associations observed between network metrics and depression at age 17 (before the pandemic), suggesting that these effects could be unique to the circumstance of heightened stress during COVID, or could indicate that these brain-depression findings only emerge later in development when rates of depression continue to increase. Interestingly, we found no association between network connectivity and anxiety symptoms, echoing the potentially differential neural mechanisms relating to anxiety and depression during this period ( Hardi et al, 2022b ), thus indicating a future direction to further tease apart the distinct etiology preceding these mental health disorders.…”

Section: Discussionmentioning

confidence: 74%

Early childhood household instability, adolescent structural neural network architecture, and young adulthood depression: A 21-year longitudinal study

Hardi

Goetschius

Tillem

et al. 2023

Developmental Cognitive Neuroscience

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Section: Discussionmentioning

confidence: 74%

Early childhood household instability, adolescent structural neural network architecture, and young adulthood depression: A 21-year longitudinal study

Hardi

Goetschius

Tillem

et al. 2023

Developmental Cognitive Neuroscience

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“…This method, which simultaneously minimizes similarities between individuals within the same group and maximizes differences between subgroups, can be used to classify individuals for prediction of risk factors or prospective outcomes. For example, network models using functional connectivity identified adolescents who were susceptible to anxiety six years later (Hardi et al 2023a) and those who were exposed to violence early in life (Goetschius et al 2020a). A similar approach has also been taken to distinguish clinically depressed from nondepressed individuals on the basis of brain function (Drysdale et al 2017, Price et al 2017.…”

Section: Data Pipeline Improvementsmentioning

confidence: 99%

“…There are two ways to accomplish this. First, split-half reliability can be done by subdividing data sets in two (Hardi et al 2023a, Pronk et al 2022). Second, one may randomly draw subsamples of participants from the sample to assess the stability of the original findings (Hardi et al 2023a, Tomasi & Volkow 2021.…”

Section: Replication Testingmentioning

confidence: 99%

“…First, split-half reliability can be done by subdividing data sets in two (Hardi et al 2023a, Pronk et al 2022). Second, one may randomly draw subsamples of participants from the sample to assess the stability of the original findings (Hardi et al 2023a, Tomasi & Volkow 2021. Thus, while not as optimal as out-of-sample replication, within-sample replication allows for the assessment of replicability in samples that are more distinct.…”

Section: Replication Testingmentioning

confidence: 99%

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Poverty, Brain Development, and Mental Health: Progress, Challenges, and Paths Forward

Monk

Hardi

2023

Annu. Rev. Dev. Psychol.

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Poverty is associated with changes in brain development and elevates the risk for psychopathology in childhood, adolescence, and adulthood. Although the field is rapidly expanding, there are methodological challenges that raise questions about the validity of current findings. These challenges include the interrelated issues of reliability, effect size, interindividual heterogeneity, and replicability. To address these issues, we propose a multipronged approach that spans short-, medium-, and long-term solutions, including changes to data pipelines along with more comprehensive data acquisition of environment, brain, and mental health. Additional suggestions are to use open science approaches, more robust statistical analyses, and replication testing. Furthermore, we propose increased integration between advanced analytical approaches using large samples and neuroscience models in intervention research to enhance the interpretability of findings. Collectively, these approaches will expand the application of neuroimaging findings and provide a foundation for eventual policy changes designed to improve conditions for children in poverty. Expected final online publication date for the Annual Review of Developmental Psychology, Volume 5 is December 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Latent Profiles of Childhood Adversity, Adolescent Mental Health, and Neural Network Connectivity

Hardi,

Beltz,

McLoyd

et al. 2024

JAMA Netw Open

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ImportanceAdverse childhood experiences are pervasive and heterogeneous, with potential lifelong consequences for psychiatric morbidity and brain health. Existing research does not capture the complex interplay of multiple adversities, resulting in a lack of precision in understanding their associations with neural function and mental health.ObjectivesTo identify distinct childhood adversity profiles and examine their associations with adolescent mental health and brain connectivity.Design, Setting, and ParticipantsThis population-based birth cohort used data for children who were born in 20 large US cities between 1998 and 2000 and participated in the Future Families and Child Well-Being Study. Families were interviewed when children were born and at ages 1, 3, 5, 9, and 15 years. At age 15 years, neuroimaging data were collected from a subset of these youths. Data were collected from February 1998 to April 2017. Analyses were conducted from March to December 2023.ExposuresLatent profiles of childhood adversity, defined by family and neighborhood risks across ages 0 to 9 years.Main Outcomes and MeasuresInternalizing and externalizing symptoms at age 15 years using parent- and youth-reported measures. Profile-specific functional magnetic resonance imaging connectivity across the default mode network (DMN), salience network (SN), and frontoparietal network (FPN).ResultsData from 4210 individuals (2211 [52.5%] male; 1959 [46.5%] Black, 1169 [27.7%] Hispanic, and 786 [18.7%] White) revealed 4 childhood adversity profiles: low-adversity (1230 individuals [29.2%]), medium-adversity (1973 [46.9%]), high-adversity (457 [10.9%]), and high maternal depression (MD; 550 [13.1%]). High-adversity, followed by MD, profiles had the highest symptoms. Notably, internalizing symptoms did not differ between these 2 profiles (mean difference, 0.11; 95% CI, −0.03 to 0.26), despite the MD profile showing adversity levels most similar to the medium-adversity profile. In the neuroimaging subsample of 167 individuals (91 [54.5%] female; 128 [76.6%] Black, 11 [6.6%] Hispanic, and 20 [12.0%] White; mean [SD] age, 15.9 [0.5] years), high-adversity and MD profiles had the highest DMN density relative to other profiles (F(3,163) = 11.14; P &lt; .001). The high-adversity profile had lower SN density relative to the low-adversity profile (mean difference, −0.02; 95% CI, −0.04 to −0.003) and the highest FPN density among all profiles (F(3,163) = 18.96; P &lt; .001). These differences were specific to brain connectivity during an emotion task, but not at rest.Conclusions and RelevanceIn this cohort study, children who experienced multiple adversities, or only elevated MD, had worse mental health and different neural connectivity in adolescence. Interventions targeting multiple risk factors, with a focus on maternal mental health, could produce the greatest benefits.

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Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID‐19 economic adversity

Cited by 8 publications

References 78 publications

Early childhood household instability, adolescent structural neural network architecture, and young adulthood depression: A 21-year longitudinal study

Early childhood household instability, adolescent structural neural network architecture, and young adulthood depression: A 21-year longitudinal study

Poverty, Brain Development, and Mental Health: Progress, Challenges, and Paths Forward

Latent Profiles of Childhood Adversity, Adolescent Mental Health, and Neural Network Connectivity

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