Primary regional GMV baseline and longitudinal analyses

In R, use this scripts: NEW_Archer_IrritabilityVolume to create a clean dataset prior to running models in Mplus.

• Load data
• Clean dataset for Mplus
• (For longitudinal analyses, create sum score of baseline irritability)
• Write as dat file to use in Mplus

In Mplus, use the following scripts:

• Use Vol_1_Irr.inp to run the baseline analyses for the 68 cortical regions and 19 cortical regions
• Use Vol_1_Irr_long.inp to run the longitudinal analyses for the 68 cortical regions and 19 subcortical regions

The scripts above are examples of the 87 scripts used to analyze associations between regional gray matter volumes (GMV) and the irritability factor controlling for the primary covariates (age, sex, race/ethnicity, MRI model), as well as parent education, medication use, and total intracranial volume (ICV) at baseline and at third-year follow-up. Details below.

• Load the subject data from the .dat file
• Specifies variable names in the .dat file in the correct order
• Specifies which variables will be used in the analyses
• Specifies which variables are categorical
• Specifies notation for missing variables
• Applies post-stratification weights to account for the stratification of the sample in data collection sites
• Specifies ID variable
• Accounts for clustering within families
• Standardizes the brain volume variable
• Specifies the analysis estimator, iterations, and type
• Runs SEM models for individual brain volume according to the following formula: regional GMV = age + sex + race/ethnicity + MRI model + parent education + medication use + ICV + irritability (+ baseline irritability)

For specificity analyses, use the following scripts:

• Use Vol_1_Irr_anx.inp to run the baseline analyses for the 68 cortical regions and 19 cortical regions with anxiety symptoms as a covariate
• Use Vol_1_Irr_dep.inp to run the baseline analyses for the 68 cortical regions and 19 subcortical regions with depressive symptoms as a covariate
• Use Vol_1_Irr_tot.inp to run the baseline analyses for the 68 cortical regions and 19 subcortical regions with total psychopathology symptoms as a covariate

Primary regional CT and SA analyses

In R, use these scripts: Archer_IrritabilityCT and NEW_Archer_IrritabilityVolume to create a clean dataset prior to running models in Mplus.

• Load data
• Clean dataset for Mplus
• Write as dat file to use in Mplus

In Mplus, use the following scripts:

• Use CT_Irr_1 to run the baseline analyses for the 68 cortical regions
• Use SA_Irr_1 to run the baseline analyses for the 68 cortical regions

The script above is an example of the 68 scripts used to analyze associations between regional cortical thickness and surface area and the irritability factor controlling for the primary covariates (age, sex, race/ethnicity, MRI model), as well as parent education, medication use, and average cortical thickness and surface area, respectively, at baseline. Details below.

• Load the subject data from the .dat file
• Specifies variable names in the .dat file in the correct order
• Specifies which variables will be used in the analyses
• Specifies which variables are categorical
• Specifies notation for missing variables
• Applies post-stratification weights to account for the stratification of the sample in data collection sites
• Specifies ID variable
• Accounts for clustering within families
• Standardizes the brain cortical thickness variable
• Specifies the analysis estimator, iterations, and type
• Runs SEM models for individual cortical thickness/surface area according to the following formula: irritability = regional CT/SA + age + sex + race/ethnicity + MRI model + parent education + medication use + average CT/SA

Demographics (Table 1)

In R, use this script: Demo_Irritability.R to obtain the demographics for the final sample included in the primary analyses (N = 10,647).

• Load data from the .dat file and append column names
• Find mean and SD of sample age
• Find number and percentage of each sex
• Find number and percentage of each race/ethnicity category
• Find number and percentage of each income category
• Find number and percentage of each parental education category

Brain Figure (Figure 2)

See how to create the Brain Figure here.