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 PMID:38247369  

Preliminary examination of the effects of an early parenting intervention on amygdala-orbitofrontal cortex resting-state functional connectivity among high-risk children: A randomized clinical trial.

Marta Korom | Emilio A Valadez | Nim Tottenham | Mary Dozier | Jeffrey M Spielberg
Development and psychopathology | 2025

We examined the long-term causal effects of an evidence-based parenting program delivered in infancy on children's emotion regulation and resting-state functional connectivity (rs-fc) during middle childhood. Families were referred to the study by Child Protective Services (CPS) as part of a diversion from a foster care program. A low-risk group of families was also recruited. CPS-involved families were randomly assigned to receive the target (Attachment and Biobehavioral Catch-up, ABC) or a control intervention (Developmental Education for Families, DEF) before infants turned 2. Both interventions were home-based, manualized, and 10-sessions long. During middle childhood, children underwent a 6-min resting-state functional MRI scan. Amygdala seed-based rs-fc analysis was completed with intervention group as the group-level predictor of interest. Fifty-seven children (NABC = 21; NDEF = 17; NCOMP = 19; Mage = 10.02 years, range = 8.08-12.14) were scanned successfully. The DEF group evidenced negative left amygdala↔OFC connectivity, whereas connectivity was near zero in the ABC and comparison groups (ABCvsDEF: Cohen's d = 1.17). ABC may enhance high-risk children's regulatory neurobiology outcomes ∼8 years after the intervention was completed.

Pubmed ID: 38247369

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Associated grants

  • Agency: NIMH NIH HHS, United States
    Id: K23 MH130751
  • Agency: NIMH NIH HHS, United States
    Id: R01 MH074374

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Graph Theory GLM (GTG) MATLAB Toolbox (tool)

RRID:SCR_014075

A software application that calculates and runs a GLM on graph theory properties derived from brain networks. The GLM accepts continuous and categorical between-participant predictors and categorical within-participant predictors. Significance is determined via non-parametric permutation tests. Both fully connected and thresholded networks are tested. The toolbox also provides a data processing path for resting state and (block design) task fMRI data. Options for partialing nuisance signals include local and total white matter signal and PCA of white matter/ventricular signal. For task fMRI, connectivity matrices are computed for each condition by dividing up the timeseries. To compensate for HDR-related delay, the timeseries is deconvolved, allowing for division at the actual onset/offset times.

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