Fig. 1. Analysis workflow.

Dataset: Two datasets were collected on the same scanner using highly similar acquisition parameters: a phMRI dataset using the benzodiazepine alprazolam (green) and a developmental fMRI sample from the Philadelphia Neurodevelopmental Cohort (PNC) (purple). Preprocessing: Datasets were preprocessed using identical pipelines, which included removal of nuisance signal with aCompCor, global signal regression, and task regression. Connectivity matrix generation: Connectivity matrices were generated from standard atlases for placebo and drug sessions from the alprazolam dataset (n = 43; 86 sessions total) and for the PNC dataset (n = 879). Train and validate model: The alprazolam dataset was used to train a linear SVM classifier to distinguish drug and placebo sessions using 10-fold cross-validation. Apply model: The validated alprazolam model was applied to the PNC dataset, generating a distance metric that reflected each participant’s position on a continuum from “drug-like” (lower E:I) to “placebo-like” (higher E:I). Regress model output on age: This metric was then regressed on age using a generalized additive model with penalized splines that included covariates for sex, head motion, and attentiveness.