Resting State Connectivity Patterns May Predict Depression, ADHD in Children

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The study provides strong evidence for the utility of resting-state fMRI measures in predicting the development of attentional or mood disorders in children.

Neuroimaging study data published in JAMA Psychiatry suggest that certain resting-state brain connectivity patterns may be important biomarkers for the early identification of children at risk for major depressive disorder or attention-deficit/hyperactivity disorder.

Susan Whitfield-Gabrieli, PhD, of the Helen Wills Neuroscience Institute at the University of California at Berkeley and the department of psychology, Northeastern University in Boston, Massachusetts, led the study, which sought to determine the ability of neuroimaging of resting state networks to predict psychiatric symptoms in children.

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The study enrolled children from an existing developmental longitudinal study on late-emerging reading disabilities at Vanderbilt University in Nashville, Tennessee, between 2010 and 2013. Children attended study visits at age 7 years and subsequently at 1-year intervals for 4 years. The investigators abstracted Child Behavior Checklist (CBCL) scores and resting-state functional magnetic resonance imaging (rs-fMRI) data from the baseline and 4-year follow-up timepoints. The fMRI data were used to examine the functional connectivity between a seed region in the medial prefrontal cortex (MPFC) and other regions implicated in mood regulation, including the dorsolateral prefrontal cortex (DLPFC) and subgenual anterior cingulate cortex (sgACC).

Analysis of covariance was performed to test the prognostic capacity of MPFC-DLPFC connectivity for CBCL scores across 4 years. Exploratory analysis focused on internalization symptoms, including anxiety/depression, withdrawn behavior, and somatic complaints. Leave-1-out cross-validation was utilized to reduce confounding and ensure the robustness of the prediction model. In addition, via logistic regression, the investigators identified correlations between CBCL scores and specific connectivity patterns.

Baseline data were available from 94 children (43.6% girls) aged 7 years, and follow-up data were available for 54 of the original participants (59.3% girls). Between age 7 and 11 years, 14 children (26%) displayed significant changes in internalizing scores, while 8 children (15%) exhibited changes in attentional problem scores.

Less positive MPFC-DLFPC connectivity at baseline was associated with improvement in attentional problems at the 4-year timepoint (P =.04). In contrast, weaker left DLPFC- sgACC connectivity predicted worsening of CBCL internalization subscales at follow-up (P =.01), particularly anxiety/depression (P =.005) and withdrawn behavior (P =.01). This association was replicated in an independent sample of children with (n=25) and without (n=18) familial risk for major depressive disorder (P <.001). In logistic regression analyses, sgACC-DLPFC connectivity was a stronger predictor than baseline CBCL score for progression to a subclinical score on internalization at 4 years (P =.01).

As a study limitation, investigators noted that the small cohort size prevented further analysis of patients who moved between diagnostic categories. In addition, no data were available on patients who progressed to psychiatric diagnoses after age 11 years. Even so, these findings provide strong evidence for the utility of resting-state fMRI measures in predicting the development of attentional or mood disorders in children.

The investigators implicated key nodes of the default mode and central executive networks in their findings. They concluded, “The variations in functional connectivity occurred in neural systems that are known to be salient for attention or mood.”

Disclosure: Several study authors declared affiliations with the pharmaceutical industry. Please see the original reference for a full list of authors’ disclosures


Whitfield-Gabrieli S, Wendelken C, Nieto-Castañón A, et al. Association of intrinsic brain architecture with changes in attentional and mood symptoms during development [published online December 26, 2019]. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2019.4208