Brain Volume Abnormalities in Youth at High Risk for Depression: Adolescent Brain and Cognitive Development Study

Pagliaccio, David; Alqueza, Kira L.; Marsh, Rachel; Auerbach, Randy P.

doi:10.1016/j.jaac.2019.09.032

Cited by 49 publications

(44 citation statements)

References 91 publications

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“…72,73 Furthermore, brain volume is associated with psychiatric conditions such as autism spectrum disorder, 74-76 schizophrenia 77,78 and depression. [79][80][81] In forthcoming studies, we will assess whether the DCC Low income, n (%) 11 (16.9) 5 (13.9) 6 (20.7) 0.47 ePRS = expression-based polygenic risk score; MAVAN = Maternal Adversity, Vulnerability and Neurodevelopment. *Data are presented as n (%) or mean ± standard deviation.…”

Section: Discussionmentioning

confidence: 99%

DCC gene network in the prefrontal cortex is associated with total brain volume in childhood

Morgunova

Pokhvisneva

Nolvi

et al. 2021

jpn

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Background: Genetic variation in the guidance cue DCC gene is linked to psychopathologies involving dysfunction in the prefrontal cortex. We created an expression-based polygenic risk score (ePRS) based on the DCC coexpression gene network in the prefrontal cortex, hypothesizing that it would be associated with individual differences in total brain volume. Methods: We filtered single nucleotide polymorphisms (SNPs) from genes coexpressed with DCC in the prefrontal cortex obtained from an adult postmortem donors database (BrainEAC) for genes enriched in children 1.5 to 11 years old (BrainSpan). The SNPs were weighted by their effect size in predicting gene expression in the prefrontal cortex, multiplied by their allele number based on an individual’s genotype data, and then summarized into an ePRS. We evaluated associations between the DCC ePRS and total brain volume in children in 2 community-based cohorts: the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) and University of California, Irvine (UCI) projects. For comparison, we calculated a conventional PRS based on a genome-wide association study of total brain volume. Results: Higher ePRS was associated with higher total brain volume in children 8 to 10 years old (β = 0.212, p = 0.043; n = 88). The conventional PRS at several different thresholds did not predict total brain volume in this cohort. A replication analysis in an independent cohort of newborns from the UCI study showed an association between the ePRS and newborn total brain volume (β = 0.101, p = 0.048; n = 80). The genes included in the ePRS demonstrated high levels of coexpression throughout the lifespan and are primarily involved in regulating cellular function. Limitations: The relatively small sample size and age differences between the main and replication cohorts were limitations. Conclusion: Our findings suggest that the DCC coexpression network in the prefrontal cortex is critically involved in whole brain development during the first decade of life. Genes comprising the ePRS are involved in gene translation control and cell adhesion, and their expression in the prefrontal cortex at different stages of life provides a snapshot of their dynamic recruitment.

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Section: Discussionmentioning

confidence: 99%

DCC gene network in the prefrontal cortex is associated with total brain volume in childhood

Morgunova

Pokhvisneva

Nolvi

et al. 2021

jpn

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“…Effect sizes of neuroimaging measures have been shown to have noticeable instability up to as many as 1000-2000 subjects (e.g., see Figure S1 in Miller et al 126 ). Indeed, large-scale studies, including studies that pool existing data such as ENIGMA as well as large population-representative samples 126,127 , are beginning to show that variability in structural and functional brain imaging accounts for only a small percentage of the explained variance of clinical phenotypes. Thus, similar to genetics literature, it appears that individual measures of structural brain alterations account for limited variance in complex phenotypes such as depression.…”

Section: Scientific and Clinical Relevancementioning

confidence: 99%

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

et al. 2020

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A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of largescale data sharing for mental health research.

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“…Researchers have identified a multitude of pathways that may explain the intergenerational transmission of depression-including genetic as well as environmental (e.g., caregiving quality, epigenetic) factors-although the relative contribution of these purported mechanisms still remains unknown (for reviews, see references (18,40,41). Critically, there is evidence of biological alterations in youth whose parents have a history of depression, including smaller gray matter volumes in subcortical structures (42,43), HPA-axis dysfunction (for a review see 40), and alterations in epigenetic markers (45,46), even when youth whose parents have a history of depression but do not endorse current depression symptoms themselves. That higher levels of family conflict were identified as an important predictor of both current and prospective symptoms of depression further supports the formulation that parental behaviors, which are informed by mental health status, are critical for scaffolding offspring brain development.…”

Section: Resultsmentioning

confidence: 99%

Multi-level Predictors of Depression Symptoms in the Adolescent Brain Cognitive Development (ABCD) Study

Shah

Mishra

et al. 2021

Preprint

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Objective: To identify multi-level factors that maximize prediction of depression symptoms in a diverse sample of children in the U.S. participating in the Adolescent Brain and Cognitive Development (ABCD) study. Methods: 8,507 participants (49.6% female, 75.2% white, ages 9-10) from ABCD provided complete data at baseline and 7,998 of these participants provided one-year follow-up data. Depression symptoms were measured with the Child Behavior Checklist. Predictive features included child demographic, environmental, and structural and resting-state fMRI variables, parental depression symptoms and demographic characteristics, and relevant site and scanner-related covariates. We used linear (elastic net regression, EN) and non-linear (gradient boosted trees, GBT) predictive models to identify which set of features maximized prediction of depression symptoms at baseline and, separately, at one-year follow-up. Results: Both linear and non-linear models achieved comparable results for predicting baseline (EN: MAE=3.628; R2=0.232; GBT: MAE=3.555; R2=0.229) and one-year follow-up (EN: MAE=4.116; R2=0.143; GBT: MAE=4.141; R2=0.1400) depression. Parental depression symptoms, family support, and child sleep duration were among the top predictors of concurrent and future child depression symptoms across both models. Although resting-state fMRI features were relatively weaker predictors, connectivity of the right caudate was consistently the strongest neural feature associated with depression symptoms at both timepoints. In contrast, brain features derived from structural MRI did not significantly predict child depression symptoms. Conclusions & Relevance: Parental mental health and child sleep quality are potentially modifiable risk factors for youth depression. Functional connectivity of the caudate is a relatively weaker predictor of depression symptoms but may represent a biomarker of depression risk.

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Brain Volume Abnormalities in Youth at High Risk for Depression: Adolescent Brain and Cognitive Development Study

Cited by 49 publications

References 91 publications

DCC gene network in the prefrontal cortex is associated with total brain volume in childhood

DCC gene network in the prefrontal cortex is associated with total brain volume in childhood

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

Multi-level Predictors of Depression Symptoms in the Adolescent Brain Cognitive Development (ABCD) Study

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