Alterations in resting-state functional connectivity relate to psychopathology trajectories during emerging adolescence.

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7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

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[1] § METHODS AND MATERIALS › Statistical analyses › Longitudinal multilevel modeling ↔ rsfc_p_JCPPA_analyses.R, lines 1–50 · score 0.75 · sensorimotor mouth, sensorimotor hand, cingulo parietal, auditory, retrosplenial, ABCD
[2] § RESULTS › Within‐ and between‐network RSFC relations with p‐factor trajectories ↔ rsfc_p_JCPPA_analyses.R, lines 1–50 · score 0.72 · cingulo opercular, ventral attention, dorsal attention, DMN VAN, frontoparietal, salience
[3] § METHODS AND MATERIALS › Resting‐state functional connectivity (RSFC) ↔ rsfc_p_JCPPA_df.R, the whole file · a weak match · score 0.57 · resting state scans, transformed, FD, frames, motion, RSFC
[4] § RESULTS › Within‐ and between‐network RSFC relations with p‐factor trajectories ↔ rsfc_p_JCPPA_analyses.R, lines 1282–1339 · score 0.57 · FDR correction, VAN CON, DMN CON, DMN DAN, Exploratory, factor scores
[5] § RESULTS › Unconditional linear and non‐linear growth models ↔ rsfc_p_JCPPA_analyses.R, lines 405–449 · score 0.56 · growth models, quadratic model, unconditional, fit, linear, trajectory
[6] § RESULTS › Within‐ and between‐network RSFC relations with p‐factor trajectories ↔ rsfc_p_JCPPA_analyses.R, lines 52–100 · score 0.55 · ventral attention network, dorsal attention network, salience network, cingulo, FPN, Connectivity
[7] § METHODS AND MATERIALS › Statistical analyses › Longitudinal multilevel modeling ↔ rsfc_p_JCPPA_df.R, the whole file · a weak match · score 0.54 · scanner dummies, MRI, FD, factor scores, intercept, baseline

Paper

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The authors' code

R · 1,815 lines · 92 KB · no license · 5 matches

#####Title: Alterations in resting-state functional connectivity relate to psychopathology trajectories during emerging adolescence
#####Author: Jenna Jones Devine
#####Date: 09-10-2025
# read in libraries
library(dplyr)
library(lme4)
library(lmerTest)
library(performance) # for ICC
library(broom.mixed) # tidy model outputs into dataframes
library(tidyverse)
library(ggplot2)
library(emmeans)
library(patchwork)
library(interactions)
library(psych)
library(circlize)
# setwd
dir = "C:/Users/jjonesdevine/OneDrive - Virginia Tech/rsfc_p_abcd/data"
setwd(dir)
## read in longform data (failed QA + site22 participants removed)
pfactor <- read.csv("rsfc_p_jcppa_long_FINAL.csv", header = TRUE)
##### RENAME AND RECODE VARIABLES #####
## recode wave from values of 1,2,3 to 0,1,2
pfactor$wave <- pfactor$wave - 1
## rename site id variable
pfactor <- rename(pfactor, site_id = site_id_l)
## change site id variable to factor
pfactor$site_id <- as.factor(pfactor$site_id)
## rename rsfc variables
# dmn connectivity
pfactor$dmn_dmn <- pfactor$rsfmri_c_ngd_dt_ngd_dt #dmn dmn
pfactor$dmn_fpn <- pfactor$rsfmri_c_ngd_dt_ngd_fo #dmn frontoparietal
pfactor$dmn_sa <- pfactor$rsfmri_c_ngd_dt_ngd_sa #dmn salience
pfactor$dmn_van <- pfactor$rsfmri_c_ngd_dt_ngd_vta #dmn ventral attention
pfactor$dmn_dan <- pfactor$rsfmri_c_ngd_dt_ngd_dla #dmn dorsal attention
pfactor$dmn_con <- pfactor$rsfmri_c_ngd_dt_ngd_cgc #dmn cingulo-opercular
pfactor$dmn_ad <- pfactor$rsfmri_c_ngd_dt_ngd_ad #dmn auditory
pfactor$dmn_ca <- pfactor$rsfmri_c_ngd_dt_ngd_ca #dmn cingulo-parietal
pfactor$dmn_n <- pfactor$rsfmri_c_ngd_dt_ngd_n #dmn none
pfactor$dmn_rspltp <- pfactor$rsfmri_c_ngd_dt_ngd_rspltp #dmn retrosplenial temoral
pfactor$dmn_smh <- pfactor$rsfmri_c_ngd_dt_ngd_smh #dmn sensorimotor hand
pfactor$dmn_smm <- pfactor$rsfmri_c_ngd_dt_ngd_smm #dmn sensorimotor mouth
pfactor$dmn_vs <- pfactor$rsfmri_c_ngd_dt_ngd_vs #dmn visual
# fpn connectivity
pfactor$fpn_fpn <- pfactor$rsfmri_c_ngd_fo_ngd_fo #fpn fpn
pfactor$fpn_sa <- pfactor$rsfmri_c_ngd_fo_ngd_sa #fpn salience
pfactor$fpn_van <- pfactor$rsfmri_c_ngd_fo_ngd_vta #fpn van
pfactor$fpn_dan <- pfactor$rsfmri_c_ngd_fo_ngd_dla #fpn dan
pfactor$fpn_con <- pfactor$rsfmri_c_ngd_fo_ngd_cgc #fpn con
pfactor$fpn_ad <- pfactor$rsfmri_c_ngd_fo_ngd_ad #fpn auditory
pfactor$fpn_ca <- pfactor$rsfmri_c_ngd_fo_ngd_ca #fpn cingulo parietal
pfactor$fpn_n <- pfactor$rsfmri_c_ngd_fo_ngd_n #fpn none
pfactor$fpn_rspltp <- pfactor$rsfmri_c_ngd_fo_ngd_rspltp #fpn retrosplenial temporal
pfactor$fpn_smh <- pfactor$rsfmri_c_ngd_fo_ngd_smh #fpn senosrimotor hand
pfactor$fpn_smm <- pfactor$rsfmri_c_ngd_fo_ngd_smm #fpn senosrimotor mouth
pfactor$fpn_vs <- pfactor$rsfmri_c_ngd_fo_ngd_vs #fpn visual
# salience network connectivity
pfactor$sa_sa <- pfactor$rsfmri_c_ngd_sa_ngd_sa #sa salience
pfactor$sa_van <- pfactor$rsfmri_c_ngd_sa_ngd_vta #sa van
pfactor$sa_dan <- pfactor$rsfmri_c_ngd_sa_ngd_dla #sa dan
pfactor$sa_con <- pfactor$rsfmri_c_ngd_sa_ngd_cgc #sa CON
pfactor$sa_ad <- pfactor$rsfmri_c_ngd_sa_ngd_ad #sa auditory
pfactor$sa_ca <- pfactor$rsfmri_c_ngd_sa_ngd_ca #sa cinuglo parietal
pfactor$sa_n <- pfactor$rsfmri_c_ngd_sa_ngd_n #sa none
pfactor$sa_rspltp <- pfactor$rsfmri_c_ngd_sa_ngd_rspltp #sa rspltp
pfactor$sa_smh <- pfactor$rsfmri_c_ngd_sa_ngd_smh #sa hand
pfactor$sa_smm <- pfactor$rsfmri_c_ngd_sa_ngd_smm #sa mouth
pfactor$sa_vs <- pfactor$rsfmri_c_ngd_sa_ngd_vs #sa visual
# ventral attention network connectivity
pfactor$van_van <- pfactor$rsfmri_c_ngd_vta_ngd_vta #van van
pfactor$van_dan <- pfactor$rsfmri_c_ngd_vta_ngd_dla #van dan
pfactor$van_con <- pfactor$rsfmri_c_ngd_vta_ngd_cgc #van con
pfactor$van_ad <- pfactor$rsfmri_c_ngd_vta_ngd_ad #van auditory
pfactor$van_ca <- pfactor$rsfmri_c_ngd_vta_ngd_ca #van cinula parietal
pfactor$van_n <- pfactor$rsfmri_c_ngd_vta_ngd_n #van none
pfactor$van_rspltp <- pfactor$rsfmri_c_ngd_vta_ngd_rspltp #van rspltp
pfactor$van_smh <- pfactor$rsfmri_c_ngd_vta_ngd_smh #van hand
pfactor$van_smm <- pfactor$rsfmri_c_ngd_vta_ngd_smm #van mouth
pfactor$van_vs <- pfactor$rsfmri_c_ngd_vta_ngd_vs #van visual
# dorsal attention network connectivity
pfactor$dan_dan <- pfactor$rsfmri_c_ngd_dla_ngd_dla #dan dan
pfactor$dan_con <- pfactor$rsfmri_c_ngd_dla_ngd_cgc #dan con
pfactor$dan_ad <- pfactor$rsfmri_c_ngd_dla_ngd_ad #dan auditory
pfactor$dan_ca <- pfactor$rsfmri_c_ngd_dla_ngd_ca #dan cingulo parietal
pfactor$dan_n <- pfactor$rsfmri_c_ngd_dla_ngd_n #dan none
pfactor$dan_rspltp <- pfactor$rsfmri_c_ngd_dla_ngd_rspltp #dan rspltp
pfactor$dan_smh <- pfactor$rsfmri_c_ngd_dla_ngd_smh #dan hand
pfactor$dan_smm <- pfactor$rsfmri_c_ngd_dla_ngd_smm #dan mouth
pfactor$dan_vs <- pfactor$rsfmri_c_ngd_dla_ngd_vs #dan visual
# cingulo oprecular network connectivity
pfactor$con_con <- pfactor$rsfmri_c_ngd_cgc_ngd_cgc #con con
pfactor$con_ad <- pfactor$rsfmri_c_ngd_cgc_ngd_ad #con auditory
pfactor$con_ca <- pfactor$rsfmri_c_ngd_cgc_ngd_ca #con cingulo parietal
pfactor$con_n <- pfactor$rsfmri_c_ngd_cgc_ngd_n #con none
pfactor$con_rspltp <- pfactor$rsfmri_c_ngd_cgc_ngd_rspltp #con rspltp
pfactor$con_smh <- pfactor$rsfmri_c_ngd_cgc_ngd_smh #con hand
pfactor$con_smm <- pfactor$rsfmri_c_ngd_cgc_ngd_smm #con mouth
pfactor$con_vs <- pfactor$rsfmri_c_ngd_cgc_ngd_vs #con visual
# auditory network connectivity
pfactor$ad_ad <- pfactor$rsfmri_c_ngd_ad_ngd_ad #ad auditory
pfactor$ad_ca <- pfactor$rsfmri_c_ngd_ad_ngd_ca #ad cingulo parietal
pfactor$ad_n <- pfactor$rsfmri_c_ngd_ad_ngd_n #ad none
pfactor$ad_rspltp <- pfactor$rsfmri_c_ngd_ad_ngd_rspltp #ad rspltp
pfactor$ad_smh <- pfactor$rsfmri_c_ngd_ad_ngd_smh #ad hand
pfactor$ad_smm <- pfactor$rsfmri_c_ngd_ad_ngd_smm #ad mouth
pfactor$ad_vs <- pfactor$rsfmri_c_ngd_ad_ngd_vs #ad visual
# cingulo parietal network connectivity
pfactor$ca_ca <- pfactor$rsfmri_c_ngd_ca_ngd_ca #ca cingulo parietal
pfactor$ca_n <- pfactor$rsfmri_c_ngd_ca_ngd_n #ca none
pfactor$ca_rspltp <- pfactor$rsfmri_c_ngd_ca_ngd_rspltp #ca rspltp
pfactor$ca_smh <- pfactor$rsfmri_c_ngd_ca_ngd_smh #ca hand
pfactor$ca_smm <- pfactor$rsfmri_c_ngd_ca_ngd_smm #ca mouth
pfactor$ca_vs <- pfactor$rsfmri_c_ngd_ca_ngd_vs #ca visual
# none network connectivity
pfactor$n_n <- pfactor$rsfmri_c_ngd_n_ngd_n #n none
pfactor$n_rspltp <- pfactor$rsfmri_c_ngd_n_ngd_rspltp #n rspltp
pfactor$n_smh <- pfactor$rsfmri_c_ngd_n_ngd_smh #n hand
pfactor$n_smm <- pfactor$rsfmri_c_ngd_n_ngd_smm #n mouth
pfactor$n_vs <- pfactor$rsfmri_c_ngd_n_ngd_vs #n visual
# retrosplenial temporal network connectivity
pfactor$rspltp_rspltp <- pfactor$rsfmri_c_ngd_rspltp_ngd_rspltp #rspltp rspltp
pfactor$rspltp_smh <- pfactor$rsfmri_c_ngd_rspltp_ngd_smh #rspltp hand
pfactor$rspltp_smm <- pfactor$rsfmri_c_ngd_rspltp_ngd_smm #rspltp mouth
pfactor$rspltp_vs <- pfactor$rsfmri_c_ngd_rspltp_ngd_vs #rspltp visual
# sensorimotor hand network connectivity
pfactor$smh_smh <- pfactor$rsfmri_c_ngd_smh_ngd_smh #smh sensorimotor hand
pfactor$smh_smm <- pfactor$rsfmri_c_ngd_smh_ngd_smm #smh sensorimotor mouth
pfactor$smh_vs <- pfactor$rsfmri_c_ngd_smh_ngd_vs #smh visual
# sensorimotor mouth network connectivity
pfactor$smm_smm <- pfactor$rsfmri_c_ngd_smm_ngd_smm #smm sensorimotor mouth
pfactor$smm_vs <- pfactor$rsfmri_c_ngd_smm_ngd_vs #smm visual
# visual network connectivity
pfactor$vs_vs <- pfactor$rsfmri_c_ngd_vs_ngd_vs #smm visual
##### MEAN CENTER AND RESCALE VARIABLES #####
## Center rsfc variables by subject
# dmn
pfactor$subj_dmn_dmn_cent <- pfactor$dmn_dmn - mean(pfactor$dmn_dmn, na.rm = TRUE)
pfactor$subj_dmn_fpn_cent <- pfactor$dmn_fpn - mean(pfactor$dmn_fpn, na.rm = TRUE)
pfactor$subj_dmn_sa_cent <- pfactor$dmn_sa - mean(pfactor$dmn_sa, na.rm = TRUE)
pfactor$subj_dmn_van_cent <- pfactor$dmn_van - mean(pfactor$dmn_van, na.rm = TRUE)
pfactor$subj_dmn_dan_cent <- pfactor$dmn_dan - mean(pfactor$dmn_dan, na.rm = TRUE)
pfactor$subj_dmn_con_cent <- pfactor$dmn_con - mean(pfactor$dmn_con, na.rm = TRUE)
pfactor$subj_dmn_ad_cent <- pfactor$dmn_ad - mean(pfactor$dmn_ad, na.rm = TRUE)
pfactor$subj_dmn_ca_cent <- pfactor$dmn_ca - mean(pfactor$dmn_ca, na.rm = TRUE)
pfactor$subj_dmn_n_cent <- pfactor$dmn_n - mean(pfactor$dmn_n, na.rm = TRUE)
pfactor$subj_dmn_rspltp_cent <- pfactor$dmn_rspltp - mean(pfactor$dmn_rspltp, na.rm = TRUE)
pfactor$subj_dmn_smh_cent <- pfactor$dmn_smh - mean(pfactor$dmn_smh, na.rm = TRUE)
pfactor$subj_dmn_smm_cent <- pfactor$dmn_smm - mean(pfactor$dmn_smm, na.rm = TRUE)
pfactor$subj_dmn_vs_cent <- pfactor$dmn_vs - mean(pfactor$dmn_vs, na.rm = TRUE)
# fpn
pfactor$subj_fpn_fpn_cent <- pfactor$fpn_fpn - mean(pfactor$fpn_fpn, na.rm = TRUE)
pfactor$subj_fpn_sa_cent <- pfactor$fpn_sa - mean(pfactor$fpn_sa, na.rm = TRUE)
pfactor$subj_fpn_van_cent <- pfactor$fpn_van - mean(pfactor$fpn_van, na.rm = TRUE)
pfactor$subj_fpn_dan_cent <- pfactor$fpn_dan - mean(pfactor$fpn_dan, na.rm = TRUE)
pfactor$subj_fpn_con_cent <- pfactor$fpn_con - mean(pfactor$fpn_con, na.rm = TRUE)
pfactor$subj_fpn_ad_cent <- pfactor$fpn_ad - mean(pfactor$fpn_ad, na.rm = TRUE)
pfactor$subj_fpn_ca_cent <- pfactor$fpn_ca - mean(pfactor$fpn_ca, na.rm = TRUE)
pfactor$subj_fpn_n_cent <- pfactor$fpn_n - mean(pfactor$fpn_n, na.rm = TRUE)
pfactor$subj_fpn_rspltp_cent <- pfactor$fpn_rspltp - mean(pfactor$fpn_rspltp, na.rm = TRUE)
pfactor$subj_fpn_smh_cent <- pfactor$fpn_smh - mean(pfactor$fpn_smh, na.rm = TRUE)
pfactor$subj_fpn_smm_cent <- pfactor$fpn_smm - mean(pfactor$fpn_smm, na.rm = TRUE)
pfactor$subj_fpn_vs_cent <- pfactor$fpn_vs - mean(pfactor$fpn_vs, na.rm = TRUE)
# sa
pfactor$subj_sa_sa_cent <- pfactor$sa_sa - mean(pfactor$sa_sa, na.rm = TRUE)
pfactor$subj_sa_van_cent <- pfactor$sa_van - mean(pfactor$sa_van, na.rm = TRUE)
pfactor$subj_sa_dan_cent <- pfactor$sa_dan - mean(pfactor$sa_dan, na.rm = TRUE)
pfactor$subj_sa_con_cent <- pfactor$sa_con - mean(pfactor$sa_con, na.rm = TRUE)
pfactor$subj_sa_ad_cent <- pfactor$sa_ad - mean(pfactor$sa_ad, na.rm = TRUE)
pfactor$subj_sa_ca_cent <- pfactor$sa_ca - mean(pfactor$sa_ca, na.rm = TRUE)
pfactor$subj_sa_n_cent <- pfactor$sa_n - mean(pfactor$sa_n, na.rm = TRUE)
pfactor$subj_sa_rspltp_cent <- pfactor$sa_rspltp - mean(pfactor$sa_rspltp, na.rm = TRUE)
pfactor$subj_sa_smh_cent <- pfactor$sa_smh - mean(pfactor$sa_smh, na.rm = TRUE)
pfactor$subj_sa_smm_cent <- pfactor$sa_smm - mean(pfactor$sa_smm, na.rm = TRUE)
pfactor$subj_sa_vs_cent <- pfactor$sa_vs - mean(pfactor$sa_vs, na.rm = TRUE)
# van
pfactor$subj_van_van_cent <- pfactor$van_van - mean(pfactor$van_van, na.rm = TRUE)
pfactor$subj_van_dan_cent <- pfactor$van_dan - mean(pfactor$van_dan, na.rm = TRUE)
pfactor$subj_van_con_cent <- pfactor$van_con - mean(pfactor$van_con, na.rm = TRUE)
pfactor$subj_van_ad_cent <- pfactor$van_ad - mean(pfactor$van_ad, na.rm = TRUE)
pfactor$subj_van_ca_cent <- pfactor$van_ca - mean(pfactor$van_ca, na.rm = TRUE)
pfactor$subj_van_n_cent <- pfactor$van_n - mean(pfactor$van_n, na.rm = TRUE)
pfactor$subj_van_rspltp_cent <- pfactor$van_rspltp - mean(pfactor$van_rspltp, na.rm = TRUE)
pfactor$subj_van_smh_cent <- pfactor$van_smh - mean(pfactor$van_smh, na.rm = TRUE)
pfactor$subj_van_smm_cent <- pfactor$van_smm - mean(pfactor$van_smm, na.rm = TRUE)
pfactor$subj_van_vs_cent <- pfactor$van_vs - mean(pfactor$van_vs, na.rm = TRUE)
# dan
pfactor$subj_dan_dan_cent <- pfactor$dan_dan - mean(pfactor$dan_dan, na.rm = TRUE)
pfactor$subj_dan_con_cent <- pfactor$dan_con - mean(pfactor$dan_con, na.rm = TRUE)
pfactor$subj_dan_ad_cent <- pfactor$dan_ad - mean(pfactor$dan_ad, na.rm = TRUE)
pfactor$subj_dan_ca_cent <- pfactor$dan_ca - mean(pfactor$dan_ca, na.rm = TRUE)
pfactor$subj_dan_n_cent <- pfactor$dan_n - mean(pfactor$dan_n, na.rm = TRUE)
pfactor$subj_dan_rspltp_cent <- pfactor$dan_rspltp - mean(pfactor$dan_rspltp, na.rm = TRUE)
pfactor$subj_dan_smh_cent <- pfactor$dan_smh - mean(pfactor$dan_smh, na.rm = TRUE)
pfactor$subj_dan_smm_cent <- pfactor$dan_smm - mean(pfactor$dan_smm, na.rm = TRUE)
pfactor$subj_dan_vs_cent <- pfactor$dan_vs - mean(pfactor$dan_vs, na.rm = TRUE)
# con
pfactor$subj_con_con_cent <- pfactor$con_con - mean(pfactor$con_con, na.rm = TRUE)
pfactor$subj_con_ad_cent <- pfactor$con_ad - mean(pfactor$con_ad, na.rm = TRUE)
pfactor$subj_con_ca_cent <- pfactor$con_ca - mean(pfactor$con_ca, na.rm = TRUE)
pfactor$subj_con_n_cent <- pfactor$con_n - mean(pfactor$con_n, na.rm = TRUE)
pfactor$subj_con_rspltp_cent <- pfactor$con_rspltp - mean(pfactor$con_rspltp, na.rm = TRUE)
pfactor$subj_con_smh_cent <- pfactor$con_smh - mean(pfactor$con_smh, na.rm = TRUE)
pfactor$subj_con_smm_cent <- pfactor$con_smm - mean(pfactor$con_smm, na.rm = TRUE)
pfactor$subj_con_vs_cent <- pfactor$con_vs - mean(pfactor$con_vs, na.rm = TRUE)
# ad
pfactor$subj_ad_ad_cent <- pfactor$ad_ad - mean(pfactor$ad_ad, na.rm = TRUE)
pfactor$subj_ad_ca_cent <- pfactor$ad_ca - mean(pfactor$ad_ca, na.rm = TRUE)
pfactor$subj_ad_n_cent <- pfactor$ad_n - mean(pfactor$ad_n, na.rm = TRUE)
pfactor$subj_ad_rspltp_cent <- pfactor$ad_rspltp - mean(pfactor$ad_rspltp, na.rm = TRUE)
pfactor$subj_ad_smh_cent <- pfactor$ad_smh - mean(pfactor$ad_smh, na.rm = TRUE)
pfactor$subj_ad_smm_cent <- pfactor$ad_smm - mean(pfactor$ad_smm, na.rm = TRUE)
pfactor$subj_ad_vs_cent <- pfactor$ad_vs - mean(pfactor$ad_vs, na.rm = TRUE)
# ca
pfactor$subj_ca_ca_cent <- pfactor$ca_ca - mean(pfactor$ca_ca, na.rm = TRUE)
pfactor$subj_ca_n_cent <- pfactor$ca_n - mean(pfactor$ca_n, na.rm = TRUE)
pfactor$subj_ca_rspltp_cent <- pfactor$ca_rspltp - mean(pfactor$ca_rspltp, na.rm = TRUE)
pfactor$subj_ca_smh_cent <- pfactor$ca_smh - mean(pfactor$ca_smh, na.rm = TRUE)
pfactor$subj_ca_smm_cent <- pfactor$ca_smm - mean(pfactor$ca_smm, na.rm = TRUE)
pfactor$subj_ca_vs_cent <- pfactor$ca_vs - mean(pfactor$ca_vs, na.rm = TRUE)
# n
pfactor$subj_n_n_cent <- pfactor$n_n - mean(pfactor$n_n, na.rm = TRUE)
pfactor$subj_n_rspltp_cent <- pfactor$n_rspltp - mean(pfactor$n_rspltp, na.rm = TRUE)
pfactor$subj_n_smh_cent <- pfactor$n_smh - mean(pfactor$n_smh, na.rm = TRUE)
pfactor$subj_n_smm_cent <- pfactor$n_smm - mean(pfactor$n_smm, na.rm = TRUE)
pfactor$subj_n_vs_cent <- pfactor$n_vs - mean(pfactor$n_vs, na.rm = TRUE)
# rspltp
pfactor$subj_rspltp_rspltp_cent <- pfactor$rspltp_rspltp - mean(pfactor$rspltp_rspltp, na.rm = TRUE)
pfactor$subj_rspltp_smh_cent <- pfactor$rspltp_smh - mean(pfactor$rspltp_smh, na.rm = TRUE)
pfactor$subj_rspltp_smm_cent <- pfactor$rspltp_smm - mean(pfactor$rspltp_smm, na.rm = TRUE)
pfactor$subj_rspltp_vs_cent <- pfactor$rspltp_vs - mean(pfactor$rspltp_vs, na.rm = TRUE)
# smh
pfactor$subj_smh_smh_cent <- pfactor$smh_smh - mean(pfactor$smh_smh, na.rm = TRUE)
pfactor$subj_smh_smm_cent <- pfactor$smh_smm - mean(pfactor$smh_smm, na.rm = TRUE)
pfactor$subj_smh_vs_cent <- pfactor$smh_vs - mean(pfactor$smh_vs, na.rm = TRUE)
# smm
pfactor$subj_smm_smm_cent <- pfactor$smm_smm - mean(pfactor$smm_smm, na.rm = TRUE)
pfactor$subj_smm_vs_cent <- pfactor$smm_vs - mean(pfactor$smm_vs, na.rm = TRUE)
#vs
pfactor$subj_vs_vs_cent <- pfactor$vs_vs - mean(pfactor$vs_vs, na.rm = TRUE)
## rescale variables
#dmn scale
pfactor$scale_subj_dmn_dmn_cent <- scale(pfactor$subj_dmn_dmn_cent)
pfactor$scale_subj_dmn_fpn_cent <- scale(pfactor$subj_dmn_fpn_cent)
pfactor$scale_subj_dmn_sa_cent <- scale(pfactor$subj_dmn_sa_cent)
pfactor$scale_subj_dmn_van_cent <- scale(pfactor$subj_dmn_van_cent)
pfactor$scale_subj_dmn_dan_cent <- scale(pfactor$subj_dmn_dan_cent)
pfactor$scale_subj_dmn_con_cent <- scale(pfactor$subj_dmn_con_cent)
pfactor$scale_subj_dmn_ad_cent <- scale(pfactor$subj_dmn_ad_cent)
pfactor$scale_subj_dmn_ca_cent <- scale(pfactor$subj_dmn_ca_cent)
pfactor$scale_subj_dmn_n_cent <- scale(pfactor$subj_dmn_n_cent)
pfactor$scale_subj_dmn_rspltp_cent <- scale(pfactor$subj_dmn_rspltp_cent)
pfactor$scale_subj_dmn_smh_cent <- scale(pfactor$subj_dmn_smh_cent)
pfactor$scale_subj_dmn_smm_cent <- scale(pfactor$subj_dmn_smm_cent)
pfactor$scale_subj_dmn_vs_cent <- scale(pfactor$subj_dmn_vs_cent)
#fpn scale
pfactor$scale_subj_fpn_fpn_cent <- scale(pfactor$subj_fpn_fpn_cent)
pfactor$scale_subj_fpn_sa_cent <- scale(pfactor$subj_fpn_sa_cent)
pfactor$scale_subj_fpn_van_cent <- scale(pfactor$subj_fpn_van_cent)
pfactor$scale_subj_fpn_dan_cent <- scale(pfactor$subj_fpn_dan_cent)
pfactor$scale_subj_fpn_con_cent <- scale(pfactor$subj_fpn_con_cent)
pfactor$scale_subj_fpn_ad_cent <- scale(pfactor$subj_fpn_ad_cent)
pfactor$scale_subj_fpn_ca_cent <- scale(pfactor$subj_fpn_ca_cent)
pfactor$scale_subj_fpn_n_cent <- scale(pfactor$subj_fpn_n_cent)
pfactor$scale_subj_fpn_rspltp_cent <- scale(pfactor$subj_fpn_rspltp_cent)
pfactor$scale_subj_fpn_smh_cent <- scale(pfactor$subj_fpn_smh_cent)
pfactor$scale_subj_fpn_smm_cent <- scale(pfactor$subj_fpn_smm_cent)
pfactor$scale_subj_fpn_vs_cent <- scale(pfactor$subj_fpn_vs_cent)
#sa scale
pfactor$scale_subj_sa_sa_cent <- scale(pfactor$subj_sa_sa_cent)
pfactor$scale_subj_sa_van_cent <- scale(pfactor$subj_sa_van_cent)
pfactor$scale_subj_sa_dan_cent <- scale(pfactor$subj_sa_dan_cent)
pfactor$scale_subj_sa_con_cent <- scale(pfactor$subj_sa_con_cent)
pfactor$scale_subj_sa_ad_cent <- scale(pfactor$subj_sa_ad_cent)
pfactor$scale_subj_sa_ca_cent <- scale(pfactor$subj_sa_ca_cent)
pfactor$scale_subj_sa_n_cent <- scale(pfactor$subj_sa_n_cent)
pfactor$scale_subj_sa_rspltp_cent <- scale(pfactor$subj_sa_rspltp_cent)
pfactor$scale_subj_sa_smh_cent <- scale(pfactor$subj_sa_smh_cent)
pfactor$scale_subj_sa_smm_cent <- scale(pfactor$subj_sa_smm_cent)
pfactor$scale_subj_sa_vs_cent <- scale(pfactor$subj_sa_vs_cent)
#van scale
pfactor$scale_subj_van_van_cent <- scale(pfactor$subj_van_van_cent)
pfactor$scale_subj_van_dan_cent <- scale(pfactor$subj_van_dan_cent)
pfactor$scale_subj_van_con_cent <- scale(pfactor$subj_van_con_cent)
pfactor$scale_subj_van_ad_cent <- scale(pfactor$subj_van_ad_cent)
pfactor$scale_subj_van_ca_cent <- scale(pfactor$subj_van_ca_cent)
pfactor$scale_subj_van_n_cent <- scale(pfactor$subj_van_n_cent)
pfactor$scale_subj_van_rspltp_cent <- scale(pfactor$subj_van_rspltp_cent)
pfactor$scale_subj_van_smh_cent <- scale(pfactor$subj_van_smh_cent)
pfactor$scale_subj_van_smm_cent <- scale(pfactor$subj_van_smm_cent)
pfactor$scale_subj_van_vs_cent <- scale(pfactor$subj_van_vs_cent)
#dan scale
pfactor$scale_subj_dan_dan_cent <- scale(pfactor$subj_dan_dan_cent)
pfactor$scale_subj_dan_con_cent <- scale(pfactor$subj_dan_con_cent)
pfactor$scale_subj_dan_ad_cent <- scale(pfactor$subj_dan_ad_cent)
pfactor$scale_subj_dan_ca_cent <- scale(pfactor$subj_dan_ca_cent)
pfactor$scale_subj_dan_n_cent <- scale(pfactor$subj_dan_n_cent)
pfactor$scale_subj_dan_rspltp_cent <- scale(pfactor$subj_dan_rspltp_cent)
pfactor$scale_subj_dan_smh_cent <- scale(pfactor$subj_dan_smh_cent)
pfactor$scale_subj_dan_smm_cent <- scale(pfactor$subj_dan_smm_cent)
pfactor$scale_subj_dan_vs_cent <- scale(pfactor$subj_dan_vs_cent)
#con scale
pfactor$scale_subj_con_con_cent <- scale(pfactor$subj_con_con_cent)
pfactor$scale_subj_con_ad_cent <- scale(pfactor$subj_con_ad_cent)
pfactor$scale_subj_con_ca_cent <- scale(pfactor$subj_con_ca_cent)
pfactor$scale_subj_con_n_cent <- scale(pfactor$subj_con_n_cent)
pfactor$scale_subj_con_rspltp_cent <- scale(pfactor$subj_con_rspltp_cent)
pfactor$scale_subj_con_smh_cent <- scale(pfactor$subj_con_smh_cent)
pfactor$scale_subj_con_smm_cent <- scale(pfactor$subj_con_smm_cent)
pfactor$scale_subj_con_vs_cent <- scale(pfactor$subj_con_vs_cent)
#ad scale
pfactor$scale_subj_ad_ad_cent <- scale(pfactor$subj_ad_ad_cent)
pfactor$scale_subj_ad_ca_cent <- scale(pfactor$subj_ad_ca_cent)
pfactor$scale_subj_ad_n_cent <- scale(pfactor$subj_ad_n_cent)
pfactor$scale_subj_ad_rspltp_cent <- scale(pfactor$subj_ad_rspltp_cent)
pfactor$scale_subj_ad_smh_cent <- scale(pfactor$subj_ad_smh_cent)
pfactor$scale_subj_ad_smm_cent <- scale(pfactor$subj_ad_smm_cent)
pfactor$scale_subj_ad_vs_cent <- scale(pfactor$subj_ad_vs_cent)
#ca scale
pfactor$scale_subj_ca_ca_cent <- scale(pfactor$subj_ca_ca_cent)
pfactor$scale_subj_ca_n_cent <- scale(pfactor$subj_ca_n_cent)
pfactor$scale_subj_ca_rspltp_cent <- scale(pfactor$subj_ca_rspltp_cent)
pfactor$scale_subj_ca_smh_cent <- scale(pfactor$subj_ca_smh_cent)
pfactor$scale_subj_ca_smm_cent <- scale(pfactor$subj_ca_smm_cent)
pfactor$scale_subj_ca_vs_cent <- scale(pfactor$subj_ca_vs_cent)
#n scale
pfactor$scale_subj_n_n_cent <- scale(pfactor$subj_n_n_cent)
pfactor$scale_subj_n_rspltp_cent <- scale(pfactor$subj_n_rspltp_cent)
pfactor$scale_subj_n_smh_cent <- scale(pfactor$subj_n_smh_cent)
pfactor$scale_subj_n_smm_cent <- scale(pfactor$subj_n_smm_cent)
pfactor$scale_subj_n_vs_cent <- scale(pfactor$subj_n_vs_cent)
#rspltp scale
pfactor$scale_subj_rspltp_rspltp_cent <- scale(pfactor$subj_rspltp_rspltp_cent)
pfactor$scale_subj_rspltp_smh_cent <- scale(pfactor$subj_rspltp_smh_cent)
pfactor$scale_subj_rspltp_smm_cent <- scale(pfactor$subj_rspltp_smm_cent)
pfactor$scale_subj_rspltp_vs_cent <- scale(pfactor$subj_rspltp_vs_cent)
#smh scale
pfactor$scale_subj_smh_smh_cent <- scale(pfactor$subj_smh_smh_cent)
pfactor$scale_subj_smh_smm_cent <- scale(pfactor$subj_smh_smm_cent)
pfactor$scale_subj_smh_vs_cent <- scale(pfactor$subj_smh_vs_cent)
#smm scale
pfactor$scale_subj_smm_smm_cent <- scale(pfactor$subj_smm_smm_cent)
pfactor$scale_subj_smm_vs_cent <- scale(pfactor$subj_smm_vs_cent)
#vs scale
pfactor$scale_subj_vs_vs_cent <- scale(pfactor$subj_vs_vs_cent)
#scale p
pfactor$scalep <- scale(pfactor$p)
##### UNCONDITIONAL LINEAR MODEL VS QUADRATIC MODEL #####
## unconditional linear model
m <- lmer(p ~ wave + (1|site_id) + (1|rel_family_id) + (1 + wave|id),
REML = FALSE,
data=pfactor,
control=lmerControl(optimizer = "bobyqa",optCtrl = list(maxfun=2e5)))
summary(m)
## unconditional quadratic model
mq <- lmer(p ~ wave + I(wave^2) + (1|site_id) + (1|rel_family_id) +(1 + wave + I(wave^2)|id),
REML = FALSE,
data=pfactor,
control=lmerControl(optimizer = "bobyqa",optCtrl = list(maxfun=2e5)))
summary(mq)
## confint for unconditional quad model
# use broom.mixed::tidy to save model outputs to dataframe
df_mq <- tidy(mq, conf.int = TRUE, conf.level = 0.95)
confint(mq)
## ICC for quad model
icc(mq) # adj. ICC = 0.781
# unadj. ICC = 0.781
## ANOVA comparing linear and quadratic model
anova(m,mq) # quad model is significant, suggests quadratic fits data trajectories better
##### CONDITIONAL MULTILEVEL QUAD GROWTH MODELS #####
##### DMN ####
# dmn dmn
model_dmndmn <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_dmn_cent + scale_subj_dmn_dmn_cent*wave +
scale_subj_dmn_dmn_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmndmn)
# dmn fpn
model_dmnfpn <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_fpn_cent + scale_subj_dmn_fpn_cent*wave +
scale_subj_dmn_fpn_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmnfpn)
# dmn sa
model_dmnsa <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_sa_cent + scale_subj_dmn_sa_cent*wave +
scale_subj_dmn_sa_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmnsa)
# dmn van
model_dmnvan <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_van_cent + scale_subj_dmn_van_cent*wave +
scale_subj_dmn_van_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmnvan)
# dmn dan
model_dmndan <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_dan_cent + scale_subj_dmn_dan_cent*wave +
scale_subj_dmn_dan_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmndan)
# dmn con
model_dmncon <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_con_cent + scale_subj_dmn_con_cent*wave +
scale_subj_dmn_con_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmncon)
# dmn ad
model_dmnad <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_ad_cent + scale_subj_dmn_ad_cent*wave +
scale_subj_dmn_ad_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmnad)
# dmn ca
model_dmnca <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_ca_cent + scale_subj_dmn_ca_cent*wave +
scale_subj_dmn_ca_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmnca)
# dmn n
model_dmnn <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_n_cent + scale_subj_dmn_n_cent*wave +
scale_subj_dmn_n_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmnn)
# dmn rspltp
model_dmnrspltp <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_rspltp_cent + scale_subj_dmn_rspltp_cent*wave +
scale_subj_dmn_rspltp_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmnrspltp)
# dmn smh
model_dmnsmh <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_smh_cent + scale_subj_dmn_smh_cent*wave +
scale_subj_dmn_smh_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmnsmh)
# dmn smm
model_dmnsmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_smm_cent + scale_subj_dmn_smm_cent*wave +
scale_subj_dmn_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmnsmm)
# dmn vs
model_dmnvs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_vs_cent + scale_subj_dmn_vs_cent*wave +
scale_subj_dmn_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmnvs)
##### FPN #####
# fpn fpn
model_fpnfpn <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_fpn_cent + scale_subj_fpn_fpn_cent*wave +
scale_subj_fpn_fpn_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpnfpn)
# fpn sa
model_fpnsa <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_sa_cent + scale_subj_fpn_sa_cent*wave +
scale_subj_fpn_sa_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpnsa)
# fpn van
model_fpnvan <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_van_cent + scale_subj_fpn_van_cent*wave +
scale_subj_fpn_van_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpnvan)
# fpn dan
model_fpndan <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_dan_cent + scale_subj_fpn_dan_cent*wave +
scale_subj_fpn_dan_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpndan)
# fpn con
model_fpncon <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_con_cent + scale_subj_fpn_con_cent*wave +
scale_subj_fpn_con_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpncon)
# fpn ad
model_fpnad <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_ad_cent + scale_subj_fpn_ad_cent*wave +
scale_subj_fpn_ad_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpnad)
# fpn ca
model_fpnca <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_ca_cent + scale_subj_fpn_ca_cent*wave +
scale_subj_fpn_ca_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpnca)
# fpn n
model_fpnn <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_n_cent + scale_subj_fpn_n_cent*wave +
scale_subj_fpn_n_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpnn)
# fpn rspltp
model_fpnrspltp <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_rspltp_cent + scale_subj_fpn_rspltp_cent*wave +
scale_subj_fpn_rspltp_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpnrspltp)
# fpn smh
model_fpnsmh <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_smh_cent + scale_subj_fpn_smh_cent*wave +
scale_subj_fpn_smh_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpnsmh)
# fpn smm
model_fpnsmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_smm_cent + scale_subj_fpn_smm_cent*wave +
scale_subj_fpn_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpnsmm)
# fpn vs
model_fpnvs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_fpn_vs_cent + scale_subj_fpn_vs_cent*wave +
scale_subj_fpn_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_fpnvs)
##### SA #####
# sa sa
model_sasa <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_sa_sa_cent + scale_subj_sa_sa_cent*wave +
scale_subj_sa_sa_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_sasa)
# sa van
model_savan <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_sa_van_cent + scale_subj_sa_van_cent*wave +
scale_subj_sa_van_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_savan)
# sa dan
model_sadan <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_sa_dan_cent + scale_subj_sa_dan_cent*wave +
scale_subj_sa_dan_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_sadan)
# sa con
model_sacon <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_sa_con_cent + scale_subj_sa_con_cent*wave +
scale_subj_sa_con_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_sacon)
# sa ad
model_saad <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_sa_ad_cent + scale_subj_sa_ad_cent*wave +
scale_subj_sa_ad_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_saad)
# sa ca
model_saca <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_sa_ca_cent + scale_subj_sa_ca_cent*wave +
scale_subj_sa_ca_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_saca)
# sa n
model_san <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_sa_n_cent + scale_subj_sa_n_cent*wave +
scale_subj_sa_n_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_san)
# sa rspltp
model_sarspltp <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_sa_rspltp_cent + scale_subj_sa_rspltp_cent*wave +
scale_subj_sa_rspltp_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_sarspltp)
# sa smh
model_sasmh <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_sa_smh_cent + scale_subj_sa_smh_cent*wave +
scale_subj_sa_smh_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_sasmh)
# sa smm
model_sasmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_sa_smm_cent + scale_subj_sa_smm_cent*wave +
scale_subj_sa_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_sasmm)
# sa vs
model_savs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_sa_vs_cent + scale_subj_sa_vs_cent*wave +
scale_subj_sa_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_savs)
##### VAN #####
# van van
model_vanvan <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_van_van_cent + scale_subj_van_van_cent*wave +
scale_subj_van_van_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vanvan)
# van dan
model_vandan <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_van_dan_cent + scale_subj_van_dan_cent*wave +
scale_subj_van_dan_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vandan)
# van con
model_vancon <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_van_con_cent + scale_subj_van_con_cent*wave +
scale_subj_van_con_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vancon)
# van ad
model_vanad <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_van_ad_cent + scale_subj_van_ad_cent*wave +
scale_subj_van_ad_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vanad)
# van ca
model_vanca <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_van_ca_cent + scale_subj_van_ca_cent*wave +
scale_subj_van_ca_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vanca)
# van n
model_vann <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_van_n_cent + scale_subj_van_n_cent*wave +
scale_subj_van_n_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vann)
# van rspltp
model_vanrspltp <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_van_rspltp_cent + scale_subj_van_rspltp_cent*wave +
scale_subj_van_rspltp_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vanrspltp)
# van smh
model_vansmh <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_van_smh_cent + scale_subj_van_smh_cent*wave +
scale_subj_van_smh_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vansmh)
# van smm
model_vansmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_van_smm_cent + scale_subj_van_smm_cent*wave +
scale_subj_van_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vansmm)
# van vs
model_vanvs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_van_vs_cent + scale_subj_van_vs_cent*wave +
scale_subj_van_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vanvs)
##### DAN #####
# dan dan
model_dandan <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dan_dan_cent + scale_subj_dan_dan_cent*wave +
scale_subj_dan_dan_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dandan)
# dan con
model_dancon <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dan_con_cent + scale_subj_dan_con_cent*wave +
scale_subj_dan_con_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dancon)
# dan ad
model_danad <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dan_ad_cent + scale_subj_dan_ad_cent*wave +
scale_subj_dan_ad_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_danad)
# dan ca
model_danca <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dan_ca_cent + scale_subj_dan_ca_cent*wave +
scale_subj_dan_ca_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_danca)
# dan n
model_dann <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dan_n_cent + scale_subj_dan_n_cent*wave +
scale_subj_dan_n_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dann)
# dan rspltp
model_danrspltp <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dan_rspltp_cent + scale_subj_dan_rspltp_cent*wave +
scale_subj_dan_rspltp_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_danrspltp)
# dan smh
model_dansmh <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dan_smh_cent + scale_subj_dan_smh_cent*wave +
scale_subj_dan_smh_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dansmh)
# dan smm
model_dansmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dan_smm_cent + scale_subj_dan_smm_cent*wave +
scale_subj_dan_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dansmm)
# dan vs
model_danvs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dan_vs_cent + scale_subj_dan_vs_cent*wave +
scale_subj_dan_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_danvs)
##### CON #####
# con con
model_concon <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_con_con_cent + scale_subj_con_con_cent*wave +
scale_subj_con_con_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_concon)
# con ad
model_conad <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_con_ad_cent + scale_subj_con_ad_cent*wave +
scale_subj_con_ad_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_conad)
# con ca
model_conca <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_con_ca_cent + scale_subj_con_ca_cent*wave +
scale_subj_con_ca_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_conca)
# con n
model_conn <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_con_n_cent + scale_subj_con_n_cent*wave +
scale_subj_con_n_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_conn)
# con rspltp
model_conrspltp <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_con_rspltp_cent + scale_subj_con_rspltp_cent*wave +
scale_subj_con_rspltp_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_conrspltp)
# con smh
model_consmh <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_con_smh_cent + scale_subj_con_smh_cent*wave +
scale_subj_con_smh_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_consmh)
# con smm
model_consmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_con_smm_cent + scale_subj_con_smm_cent*wave +
scale_subj_con_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_consmm)
# con vs
model_convs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_con_vs_cent + scale_subj_con_vs_cent*wave +
scale_subj_con_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_convs)
##### AD #####
# ad ad
model_adad <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ad_ad_cent + scale_subj_ad_ad_cent*wave +
scale_subj_ad_ad_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_adad)
# ad ca
model_adca <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ad_ca_cent + scale_subj_ad_ca_cent*wave +
scale_subj_ad_ca_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_adca)
# ad n
model_adn <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ad_n_cent + scale_subj_ad_n_cent*wave +
scale_subj_ad_n_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_adn)
# ad rspltp
model_adrspltp <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ad_rspltp_cent + scale_subj_ad_rspltp_cent*wave +
scale_subj_ad_rspltp_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_adrspltp)
# ad smh
model_adsmh <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ad_smh_cent + scale_subj_ad_smh_cent*wave +
scale_subj_ad_smh_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_adsmh)
# ad smm
model_adsmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ad_smm_cent + scale_subj_ad_smm_cent*wave +
scale_subj_ad_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_adsmm)
# ad vs
model_advs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ad_vs_cent + scale_subj_ad_vs_cent*wave +
scale_subj_ad_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_advs)
##### CA #####
# ca ca
model_caca <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ca_ca_cent + scale_subj_ca_ca_cent*wave +
scale_subj_ca_ca_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_caca)
# ca n
model_can <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ca_n_cent + scale_subj_ca_n_cent*wave +
scale_subj_ca_n_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_can)
# ca rspltp
model_carspltp <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ca_rspltp_cent + scale_subj_ca_rspltp_cent*wave +
scale_subj_ca_rspltp_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_carspltp)
# ca smh
model_casmh <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ca_smh_cent + scale_subj_ca_smh_cent*wave +
scale_subj_ca_smh_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_casmh)
# ca smm
model_casmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ca_smm_cent + scale_subj_ca_smm_cent*wave +
scale_subj_ca_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_casmm)
# ca vs
model_cavs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_ca_vs_cent + scale_subj_ca_vs_cent*wave +
scale_subj_ca_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_cavs)
##### N #####
# n n
model_nn <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_n_n_cent + scale_subj_n_n_cent*wave +
scale_subj_n_n_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_nn)
# n rspltp
model_nrspltp <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_n_rspltp_cent + scale_subj_n_rspltp_cent*wave +
scale_subj_n_rspltp_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_nrspltp)
# n smh
model_nsmh <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_n_smh_cent + scale_subj_n_smh_cent*wave +
scale_subj_n_smh_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_nsmh)
# n smm
model_nsmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_n_smm_cent + scale_subj_n_smm_cent*wave +
scale_subj_n_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_nsmm)
# n vs
model_nvs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_n_vs_cent + scale_subj_n_vs_cent*wave +
scale_subj_n_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_nvs)
##### RSPLTP #####
# rspltp rspltp
model_rspltprspltp <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_rspltp_rspltp_cent + scale_subj_rspltp_rspltp_cent*wave +
scale_subj_rspltp_rspltp_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_rspltprspltp)
# rspltp smh
model_rspltpsmh <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_rspltp_smh_cent + scale_subj_rspltp_smh_cent*wave +
scale_subj_rspltp_smh_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_rspltpsmh)
# rspltp smm
model_rspltpsmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_rspltp_smm_cent + scale_subj_rspltp_smm_cent*wave +
scale_subj_rspltp_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_rspltpsmm)
# rspltp vs
model_rspltpvs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_rspltp_vs_cent + scale_subj_rspltp_vs_cent*wave +
scale_subj_rspltp_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_rspltpvs)
##### SMH #####
# smh smh
model_smhsmh <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_smh_smh_cent + scale_subj_smh_smh_cent*wave +
scale_subj_smh_smh_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_smhsmh)
# smh smm
model_smhsmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_smh_smm_cent + scale_subj_smh_smm_cent*wave +
scale_subj_smh_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_smhsmm)
# smh vs
model_smhvs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_smh_vs_cent + scale_subj_smh_vs_cent*wave +
scale_subj_smh_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_smhvs)
##### SMM #####
# smm smm
model_smmsmm <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_smm_smm_cent + scale_subj_smm_smm_cent*wave +
scale_subj_smm_smm_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_smmsmm)
# smm vs
model_smmvs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_smm_vs_cent + scale_subj_smm_vs_cent*wave +
scale_subj_smm_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_smmvs)
#####VS #####
# vs vs
model_vsvs <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_vs_vs_cent + scale_subj_vs_vs_cent*wave +
scale_subj_vs_vs_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vsvs)
##### LOOP FOR TABLE #####
## MAIN EFFECTS
main_results <- list() # create empty list to attach values to in loop
for (v in 497:587) { # looping columns with scaled mean centered rsfc variables
var_name <- names(pfactor)[v] # creating list that will name variables based on loop
x <- pfactor[[v]] # assign loop variable static value to be used in the quadratic regression
model <- lmer(
scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
x + x*wave + x*I(wave^2) +
(1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e5))
)
coefs <- summary(model)$coefficients # summary of all important coefficients from model
main_results[[var_name]] <- c(
Estimate = coefs[11, 1],
SE = coefs[11, 2],
p_value = coefs[11, 5]
)
}
# Convert to data frame
main_effects <- data.frame(
variable = names(main_results),
do.call(rbind, main_results),
row.names = NULL
)
# save csv for posterity
write.csv(main_effects, file = "main_effects_FINAL.csv")
# read in saved csv
main_effects <- read.csv("main_effects_FINAL.csv", header = TRUE)
# only run if reading in data
main_effects <- main_effects[-1]
# add "ME" to variable column (represents the main effect for specified model)
main_effects$variable <- paste("ME_", main_effects$variable)
# CALCULATE CONFIDENCE INTERVALS (95%)
main_effects$lower <- main_effects$Estimate - 1.96*main_effects$SE
main_effects$upper <- main_effects$Estimate + 1.96*main_effects$SE
## INTERACTION EFFECTS
interaction_results <- list()
for (v in 497:587) {
var_name <- names(pfactor)[v]
x <- pfactor[[v]]
model <- lmer(
scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
x + x*wave + x*I(wave^2) +
(1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e5))
)
coefs <- summary(model)$coefficients
interaction_results[[var_name]] <- c(
Estimate = coefs[13, 1],
SE = coefs[13, 2],
p_value = coefs[13, 5]
)
}
# Convert to data frame
interaction_effects <- data.frame(
variable = names(interaction_results),
do.call(rbind, interaction_results),
row.names = NULL
)
# save csv for posterity
write.csv(interaction_effects, file = "interaction_effects_FINAL.csv")
# read in saved csv
interaction_effects <- read.csv("interaction_effects_FINAL.csv", header = TRUE)
interaction_effects <- interaction_effects[-1]
# add "IE" to variable column (represents the interaction effect for specified model)
interaction_effects$variable <- paste("IE_", interaction_effects$variable)
# CALCULATE CONFIDENCE INTERVALS (95%)
interaction_effects$lower <- interaction_effects$Estimate - 1.96*interaction_effects$SE
interaction_effects$upper <- interaction_effects$Estimate + 1.96*interaction_effects$SE
##### FDR CORRECTION #####
# merge into one big dataframe
results <- rbind(main_effects, interaction_effects)
# save csv for posterity
write.csv(results, file = "results_table_all_FINAL.csv")
## FDR CORRECTION
## first, FDR correcting for ALL analyses (all ME and IE)
results$adjp <- p.adjust(results$p_value, method = "BH")
sum(results$adjp<0.05) #4
write.csv(results, file = "results_FDR_all_FINAL.csv")
## next, looking at the hypothesized and exploratory results independently
# first, selected only hypothesized vs exploratory rows and created two dataframes
hypot <- results%>%filter(grepl("dmn_dmn", variable) | grepl("dmn_fpn", variable) | grepl("dmn_sa", variable) |
grepl("dmn_van", variable) | grepl("dmn_dan", variable) | grepl("dmn_con", variable) |
grepl("fpn_fpn", variable) | grepl("fpn_sa", variable) |
grepl("fpn_van", variable) | grepl("fpn_dan", variable) | grepl("fpn_con", variable) |
grepl("sa_sa", variable) | grepl("sa_van", variable) | grepl("sa_dan", variable) | grepl("sa_con", variable) |
grepl("van_van", variable) | grepl("van_dan", variable) | grepl("van_con", variable) |
grepl("dan_dan", variable) | grepl("dan_con", variable) | grepl("con_con", variable))%>%select(variable, Estimate, SE, p_value, upper, lower)
explor <- results%>%filter(!grepl("dmn_dmn", variable) & !grepl("dmn_fpn", variable) & !grepl("dmn_sa", variable) &
!grepl("dmn_van", variable) & !grepl("dmn_dan", variable) & !grepl("dmn_con", variable) &
!grepl("fpn_fpn", variable) & !grepl("fpn_sa", variable) &
!grepl("fpn_van", variable) & !grepl("fpn_dan", variable) & !grepl("fpn_con", variable) &
!grepl("sa_sa", variable) & !grepl("sa_van", variable) & !grepl("sa_dan", variable) & !grepl("sa_con", variable) &
!grepl("van_van", variable) & !grepl("van_dan", variable) & !grepl("van_con", variable) &
!grepl("dan_dan", variable) & !grepl("dan_con", variable) & !grepl("con_con", variable))%>%select(variable, Estimate, SE, p_value, upper, lower)
write.csv(hypot, file = "results_table_hypot_FINAL.csv") # write csv for posterity
write.csv(explor, file = "results_table_explor_FINAL.csv")
# FDR CORRECTIONS BY HYPOTHESIZED ANALYSES AND EXPLORATORY ANALYSES
hypot$adjp <- p.adjust(hypot$p_value, method = "BH")
sum(hypot$adjp<0.05) #11
explor$adjp <- p.adjust(explor$p_value, method = "BH")
sum(explor$adjp<0.05) #0
write.csv(hypot, file = "results_FDR_hypot_FINAL.csv")
write.csv(explor, file = "results_FDR_explor_FINAL.csv")
##### INTERACTION PLOTS FOR SIGNIFICANT FINDINGS #####
### models with significant slope effects
## DMN-VAN
dmnvan_plot <- interact_plot(model_dmnvan, pred = wave, modx = scale_subj_dmn_van_cent,
modx.values=c(-1,0,1), # -1SD, mean, +1SD
main.title = "DMN-VAN",
x.label = "Wave", y.label = "P Factor Scores",
line.thickness = 2, colors = c("red", "blue", "green")) + # change colors of lines
theme(legend.position="none",
axis.title = element_text(size = 18),
axis.text = element_text(size = 12),
plot.title = element_text(size = 24, hjust = .5),
panel.grid.major.x = element_line(color = "black", size = .5)) +
ylim(-.09, .12) # adjust y-axis to match with other plots
dmnvan_plot
emtrends(model_dmnvan, ~scale_subj_dmn_van_cent, var ="I(wave^2)", at=list(scale_subj_dmn_van_cent = c(-1, 0, 1)),
lmerTest.limit = 34774, delta.var = 0.001)
emtrends(model_dmnvan, ~scale_subj_dmn_van_cent, var ="I(wave^2)", at=list(scale_subj_dmn_van_cent = c(-1, 0, 1)),
lmerTest.limit = 34774, delta.var = 0.001)%>%test() #rerun wrapped in test() to get pvalues
## VAN-DAN
vandan_plot <- interact_plot(model_vandan, pred = wave, modx = scale_subj_van_dan_cent,
modx.values=c(-1,0,1), # -1SD, mean, +1SD
main.title = "VAN-DAN",
x.label = "Wave", y.label = "P Factor Scores",
line.thickness = 2, colors = c("red", "blue", "green")) + # change colors of lines
theme(legend.position="none",
axis.title = element_text(size = 18),
axis.text = element_text(size = 12),
plot.title = element_text(size = 24, hjust = .5),
panel.grid.major.x = element_line(color = "black", size = .5)) +
ylim(-.09, .12) # adjust y-axis to match with other plots
vandan_plot
emtrends(model_vandan, ~scale_subj_van_dan_cent, var ="I(wave^2)", at=list(scale_subj_van_dan_cent = c(-1, 0, 1)),
lmerTest.limit = 34774, delta.var = 0.001)
emtrends(model_vandan, ~scale_subj_van_dan_cent, var ="I(wave^2)", at=list(scale_subj_van_dan_cent = c(-1, 0, 1)),
lmerTest.limit = 34774, delta.var = 0.001)%>%test() #rerun wrapped in test() to get pvalues
## SN-CON
sacon_plot <- interact_plot(model_sacon, pred = wave, modx = scale_subj_sa_con_cent,
modx.values=c(-1,0,1), # -1SD, mean, +1SD
modx.labels = c("1 SD Below Mean","Mean","1 SD Above Mean"),
main.title = "SN-CON",
legend.main = "RSFC",
x.label = "Wave", y.label = "P Factor Scores",
line.thickness = 2, colors = c("red", "blue", "green")) + # change colors of lines
theme(legend.position="right",
axis.title = element_text(size = 18),
axis.text = element_text(size = 12),
plot.title = element_text(size = 24, hjust = .5),
panel.grid.major.x = element_line(color = "black", size = .5)) +
ylim(-.09, .12) # adjust y-axis to match with other plots
sacon_plot
emtrends(model_sacon, ~scale_subj_sa_con_cent, var ="I(wave^2)", at=list(scale_subj_sa_con_cent = c(-1, 0, 1)),
lmerTest.limit = 34774, delta.var = 0.001)
emtrends(model_sacon, ~scale_subj_sa_con_cent, var ="I(wave^2)", at=list(scale_subj_sa_con_cent = c(-1, 0, 1)),
lmerTest.limit = 34774, delta.var = 0.001)%>%test()
## patchwork() PLOTS
dmnvan_plot + vancon_plot + sacon_plot
##### DESCRIPTIVE STATS #####
## read in wideform data (QA failed + site 22 + missing baseline p-factor scores removed; includes all siblings)
pfactor_wide <- read.csv("rsfc_p_jcppa_wide_FINAL.csv", header = TRUE)
desc <- pfactor_wide%>%select("src_subject_id", "interview_age", "sex_numeric", "race_ethnicity",
"demo_comb_income_v2", "p_fs_ho1", "p_fs_ho2", "p_fs_ho3", "p_fs_ho4",
137:305, "rsfmri_meanmotion")
## set missing values to NA
desc[desc == 777] <- NA
desc[desc == 999] <- NA
# df for no missing pfactor scores
desc_nomiss <- desc%>%filter(!is.na(p_fs_ho1))%>%filter(!is.na(p_fs_ho2))%>%filter(!is.na(p_fs_ho3))%>%filter(!is.na(p_fs_ho4))
# df for at least one missing pfactor score
desc_miss <- desc%>%filter(is.na(p_fs_ho1) | is.na(p_fs_ho2) | is.na(p_fs_ho3) | is.na(p_fs_ho4))
### descriptives
# full sample
tab1 <- describe(desc) # descriptive means
table(desc$race_ethnicity) # count race data: White(1) = 5009; Black(2) = 1280; Hispanic(3) = 1889;
# Asian(4) = 186; Other(5) = 978; total n = 9344
table(desc$sex_numeric) # count sex data: M(0) = 4696; F(1) = 4658; total n = 9344
write.csv(tab1, "descriptives_full_FINAL.csv")
# no missing data
tab1_nomiss <- describe(desc_nomiss) # means
table(desc_nomiss$race_ethnicity) # count race data: White(1) = 4397; Black(2) = 903; Hispanic(3) = 1487;
# Asian(4) = 151; Other(5) = 805; total n = 7743
table(desc_nomiss$sex_numeric) # count sex data: M(0) = 3908; F(1) = 3835; total n = 7743
write.csv(tab1_nomiss, "descriptives_nomiss.csv")
# missing data
tab1_miss <- describe(desc_miss) # means
table(desc_miss$race_ethnicity) # count race data: White(1) = 612; Black(2) = 379; Hispanic(3) = 404;
# Asian(4) = 35; Other(5) = 173; total n = 1603
table(desc_miss$sex_numeric) # count sex data: M(0) = 790; F(1) = 815; total n = 1605
write.csv(tab1_miss, "descriptives_miss.csv")
## t-tests
desc_miss$group <- "missing" # add group column
desc_nomiss$group <- "full" # add group column
ttest_df <- rbind(desc_miss, desc_nomiss) # combine dataframes with group variable
# create a loop
ttest_df$group <- as.factor(ttest_df$group)
ttest_result <- list() # create empty list for loop
for (i in 2:180){ # loop for each relevant variable
var_name <- names(ttest_df)[i] # copy variable name from df in loop
x <- ttest_df[[i]]
#t-test for each variable grouped between missing data or no missing data
ttest <- t.test(x ~ ttest_df$group)
coef<-ttest
ttest_result[[var_name]] <- c(
t_stat = coef[["statistic"]],
p_value = coef[["p.value"]])
}
# Convert to data frame
ttest_outcome <- data.frame(
variable = names(ttest_result),
do.call(rbind, ttest_result),
row.names = NULL)
ttest_result
# save output for table
write.csv(ttest_outcome, file = "ttest_outcomes.csv")
## descriptive for medication variable
# read csv
desc_med <- read.csv("ttest_df_meds_JS.csv", header = TRUE)
desc_med <- desc_med%>%select(src_subject_id, meds_recode)
desc_med_df <- left_join(ttest_df, desc_med, by = "src_subject_id")
desc_med_df$group <- as.factor(desc_med_df$group)
t.test(meds_recode ~ group, data = desc_med_df)
# reduce med table to have fewer outputs to make easier to read .. just need med descriptives info for table
tab_med <- describe(desc_med_df)
table(desc_med_df$meds_recode)
desc_miss_med <- left_join(desc_miss, desc_med, by = "src_subject_id")
tab_med_miss <- describe(desc_miss_med)
table(desc_miss_med$meds_recode)
desc_nomiss_med <- left_join(desc_nomiss, desc_med, by = "src_subject_id")
tab_med_nomiss <- describe(desc_nomiss_med)
table(desc_nomiss_med$meds_recode)
## CHI SQUARE
desc$group <- ifelse(!is.na(desc$p_fs_ho1) & !is.na(desc$p_fs_ho2) & !is.na(desc$p_fs_ho3) & !is.na(desc$p_fs_ho4), "full", "miss")
sex_chi <- desc%>%select(sex_numeric, group)
sex_table <- table(sex_chi$sex_numeric, sex_chi$group)
print(sex_table)
chisq.test(sex_table)
eth_chi <- desc%>%select(race_ethnicity, group)
eth_table <- table(eth_chi$race_ethnicity, eth_chi$group)
white_chi <- eth_chi%>%filter(race_ethnicity == 1)
white_table <- table(white_chi$race_ethnicity, white_chi$group)
chisq.test(white_table)
black_chi <- eth_chi%>%filter(race_ethnicity == 2)
black_table <- table(black_chi$race_ethnicity, black_chi$group)
chisq.test(black_table)
his_chi <- eth_chi%>%filter(race_ethnicity == 3)
his_table <- table(his_chi$race_ethnicity, his_chi$group)
chisq.test(his_table)
asian_chi <- eth_chi%>%filter(race_ethnicity == 4)
asian_table <- table(asian_chi$race_ethnicity, asian_chi$group)
chisq.test(asian_table)
other_chi <- eth_chi%>%filter(race_ethnicity == 5)
other_table <- table(other_chi$race_ethnicity, other_chi$group)
chisq.test(other_table)
##### CHORD DIAGRAM ##########
# first, use code above to run & store models in R environment
## HYPOTHESIZED MODELS
# use broom.mixed::tidy to save model outputs to dataframe
df_dmndmn <- tidy(model_dmndmn, conf.int = TRUE, conf.level = 0.95)
df_dmnfpn <- tidy(model_dmnfpn, conf.int = TRUE, conf.level = 0.95)
df_dmnsa <- tidy(model_dmnsa, conf.int = TRUE, conf.level = 0.95)
df_dmnvan <- tidy(model_dmnvan, conf.int = TRUE, conf.level = 0.95)
df_dmndan <- tidy(model_dmndan, conf.int = TRUE, conf.level = 0.95)
df_dmncon <- tidy(model_dmncon, conf.int = TRUE, conf.level = 0.95)
df_fpnfpn <- tidy(model_fpnfpn, conf.int = TRUE, conf.level = 0.95)
df_fpnsa <- tidy(model_fpnsa, conf.int = TRUE, conf.level = 0.95)
df_fpnvan <- tidy(model_fpnvan, conf.int = TRUE, conf.level = 0.95)
df_fpndan <- tidy(model_fpndan, conf.int = TRUE, conf.level = 0.95)
df_fpncon <- tidy(model_fpncon, conf.int = TRUE, conf.level = 0.95)
df_sasa <- tidy(model_sasa, conf.int = TRUE, conf.level = 0.95)
df_savan <- tidy(model_savan, conf.int = TRUE, conf.level = 0.95)
df_sadan <- tidy(model_sadan, conf.int = TRUE, conf.level = 0.95)
df_sacon <- tidy(model_sacon, conf.int = TRUE, conf.level = 0.95)
df_vanvan <- tidy(model_vanvan, conf.int = TRUE, conf.level = 0.95)
df_vandan <- tidy(model_vandan, conf.int = TRUE, conf.level = 0.95)
df_vancon <- tidy(model_vancon, conf.int = TRUE, conf.level = 0.95)
df_dandan <- tidy(model_dandan, conf.int = TRUE, conf.level = 0.95)
df_dancon <- tidy(model_dancon, conf.int = TRUE, conf.level = 0.95)
df_concon <- tidy(model_concon, conf.int = TRUE, conf.level = 0.95)
# slope effects in one dataframe
chord_hypot <- rbind(df_dmndmn, df_dmnfpn, df_dmnsa, df_dmnvan, df_dmndan, df_dmncon,
df_fpnfpn, df_fpnsa, df_fpnvan, df_fpndan, df_fpncon,
df_sasa, df_savan, df_sadan, df_sacon,
df_vanvan, df_vandan, df_vancon,
df_dandan, df_dancon,
df_concon)%>%
filter(grepl("I", term) & grepl("scale_subj", term))%>%
select(term, estimate)
# intercept effects in one dataframe
chord_hypot_int <- rbind(df_dmndmn, df_dmnfpn, df_dmnsa, df_dmnvan, df_dmndan, df_dmncon,
df_fpnfpn, df_fpnsa, df_fpnvan, df_fpndan, df_fpncon,
df_sasa, df_savan, df_sadan, df_sacon,
df_vanvan, df_vandan, df_vancon,
df_dandan, df_dancon,
df_concon)%>%
filter(!grepl("I", term) & !grepl("wave:scale", term) & grepl("scale_subj", term))%>%
select(term, estimate)
# create network labels for chord diagram
net1 <- c("DMN", "DMN", "DMN", "DMN", "DMN", "DMN",
"FPN", "FPN", "FPN", "FPN", "FPN",
"SN", "SN", "SN", "SN",
"VAN", "VAN", "VAN",
"DAN", "DAN", "CON")
net2 <- c("DMN", "FPN", "SN", "VAN", "DAN", "CON",
"FPN", "SN", "VAN", "DAN", "CON",
"SN", "VAN", "DAN", "CON",
"VAN", "DAN", "CON",
"DAN", "CON", "CON")
chord_hypot <-cbind(net1, net2, chord_hypot)
chord_hypot_int <-cbind(net1, net2, chord_hypot_int)
# check that network labels match term value, then remove extra term column
chord_hypot <- chord_hypot%>%select(c(-3))
chord_hypot_int <- chord_hypot_int%>%select(c(-3))
## create chord diagram
col_fun_hypot = colorRamp2(c(-0.01, 0, 0.01), c("#3B75E9", "white", "#BF4146"))
chord_wd <- data.frame(c("DMN", "VAN", "SN"), c("VAN", "DAN", "CON"), c(3, 3, 3))
chord_ty <- data.frame(c("DMN", "VAN", "SN"), c("VAN", "DAN", "CON"), c(2, 2, 2))
chord_border <- data.frame(c("DMN", "VAN", "SN"), c("VAN", "DAN", "CON"), c(1, 1, 1))
chord_wd_int <- data.frame(c("DMN", "DMN", "DMN", "DMN", "SN", "VAN", "VAN", "DAN"), c("DMN", "VAN", "DAN", "CON", "CON", "DAN", "CON", "DAN"), c(3, 3, 3, 3, 3, 3, 3, 3))
chord_ty_int <- data.frame(c("DMN", "DMN", "DMN", "DMN", "SN", "VAN", "VAN", "DAN"), c("DMN", "VAN", "DAN", "CON", "CON", "DAN", "CON", "DAN"), c(2, 2, 2, 2, 2, 2, 2, 2))
chord_border_int <- data.frame(c("DMN", "DMN", "DMN", "DMN", "SN", "VAN", "VAN", "DAN"), c("DMN", "VAN", "DAN", "CON", "CON", "DAN", "CON", "DAN"), c(1, 1, 1, 1, 1, 1, 1, 1))
chordDiagram(chord_hypot,
annotationTrack = c("name", "grid"),
grid.col = "darkgrey", col = col_fun_hypot,
link.lty = chord_ty, link.lwd = chord_wd, link.border = chord_border,
link.zindex = rank(chord_hypot$estimate), transparency = 0.25)
title("Slope Effects")
chordDiagram(chord_hypot_int,
annotationTrack = c("name", "grid"),
grid.col = "darkgrey", col = col_fun_hypot,
link.lty = chord_ty_int, link.lwd = chord_wd_int, link.border = chord_border_int,
link.zindex = rank(chord_hypot_int$estimate), transparency = 0.25)
title("Intercept Effects")
##### GLHT TESTS #####
# dmn dmn
confint(model_dmndmn, level = 0.95, method = "Wald")
w1 <- c(0,0,0,0,0,0,0,0,0,0,1,0,1)
w2 <- c(0,0,0,0,0,0,0,0,0,0,1,0,2)
w3 <- c(0,0,0,0,0,0,0,0,0,0,1,0,3)
dmndmn_glht <- glht(model_dmndmn, linfct = rbind(w1, w2, w3))
summary(dmndmn_glht, test=adjusted('none'))
confint(dmndmn_glht, level = 0.95)
# dmn van
confint(model_dmnvan, level = 0.95, method = "Wald")
w1 <- c(0,0,0,0,0,0,0,0,0,0,1,0,1)
w2 <- c(0,0,0,0,0,0,0,0,0,0,1,0,2)
w3 <- c(0,0,0,0,0,0,0,0,0,0,1,0,3)
dmnvan_glht <- glht(model_dmnvan, linfct = rbind(w1, w2, w3))
summary(dmnvan_glht, test=adjusted('none'))
confint(dmnvan_glht, level = 0.95)
# dmn dan
confint(model_dmndan, level = 0.95, method = "Wald")
w1 <- c(0,0,0,0,0,0,0,0,0,0,1,0,1)
w2 <- c(0,0,0,0,0,0,0,0,0,0,1,0,2)
w3 <- c(0,0,0,0,0,0,0,0,0,0,1,0,3)
dmndan_glht <- glht(model_dmndan, linfct = rbind(w1, w2, w3))
summary(dmndan_glht, test=adjusted('none'))
confint(dmndan_glht, level = 0.95)
# dmn con
confint(model_dmncon, level = 0.95, method = "Wald")
w1 <- c(0,0,0,0,0,0,0,0,0,0,1,0,1)
w2 <- c(0,0,0,0,0,0,0,0,0,0,1,0,2)
w3 <- c(0,0,0,0,0,0,0,0,0,0,1,0,3)
dmncon_glht <- glht(model_dmncon, linfct = rbind(w1, w2, w3))
summary(dmncon_glht, test=adjusted('none'))
confint(dmncon_glht, level = 0.95)
# sn con
confint(model_sacon, level = 0.95, method = "Wald")
w1 <- c(0,0,0,0,0,0,0,0,0,0,1,0,1)
w2 <- c(0,0,0,0,0,0,0,0,0,0,1,0,2)
w3 <- c(0,0,0,0,0,0,0,0,0,0,1,0,3)
sacon_glht <- glht(model_sacon, linfct = rbind(w1, w2, w3))
summary(sacon_glht, test=adjusted('none'))
confint(sacon_glht, level = 0.95)
# van dan
confint(model_vandan, level = 0.95, method = "Wald")
w1 <- c(0,0,0,0,0,0,0,0,0,0,1,0,1)
w2 <- c(0,0,0,0,0,0,0,0,0,0,1,0,2)
w3 <- c(0,0,0,0,0,0,0,0,0,0,1,0,3)
vandan_glht <- glht(model_vandan, linfct = rbind(w1, w2, w3))
summary(vandan_glht, test=adjusted('none'))
confint(vandan_glht, level = 0.95)
# van con
confint(model_vancon, level = 0.95, method = "Wald")
w1 <- c(0,0,0,0,0,0,0,0,0,0,1,0,1)
w2 <- c(0,0,0,0,0,0,0,0,0,0,1,0,2)
w3 <- c(0,0,0,0,0,0,0,0,0,0,1,0,3)
vancon_glht <- glht(model_vancon, linfct = rbind(w1, w2, w3))
summary(vancon_glht, test=adjusted('none'))
confint(vancon_glht, level = 0.95)
# dan dan
confint(model_dandan, level = 0.95, method = "Wald")
w1 <- c(0,0,0,0,0,0,0,0,0,0,1,0,1)
w2 <- c(0,0,0,0,0,0,0,0,0,0,1,0,2)
w3 <- c(0,0,0,0,0,0,0,0,0,0,1,0,3)
dandan_glht <- glht(model_dandan, linfct = rbind(w1, w2, w3))
summary(dandan_glht, test=adjusted('none'))
confint(dandan_glht, level = 0.95)
##### SENSITIVITY ANALYESES #####
### MEDICATION
# dmn dmn
model_dmndmn_meds <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
meds_recode + scale_subj_dmn_dmn_cent + scale_subj_dmn_dmn_cent*wave +
scale_subj_dmn_dmn_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmndmn_meds)
# dmn van
model_dmnvan_meds <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
meds_recode + scale_subj_dmn_van_cent + scale_subj_dmn_van_cent*wave +
scale_subj_dmn_van_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmnvan_meds)
# dmn dan
model_dmndan_meds <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
scale_subj_dmn_dan_cent + scale_subj_dmn_dan_cent*wave +
scale_subj_dmn_dan_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmndan_meds)
# dmn con
model_dmncon_meds <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
meds_recode + scale_subj_dmn_con_cent + scale_subj_dmn_con_cent*wave +
scale_subj_dmn_con_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dmncon_meds)
# sa con
model_sacon_meds <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
meds_recode + scale_subj_sa_con_cent + scale_subj_sa_con_cent*wave +
scale_subj_sa_con_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_sacon_meds)
# van dan
model_vandan_meds <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
meds_recode + scale_subj_van_dan_cent + scale_subj_van_dan_cent*wave +
scale_subj_van_dan_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vandan_meds)
# van con
model_vancon_meds <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
meds_recode + scale_subj_van_con_cent + scale_subj_van_con_cent*wave +
scale_subj_van_con_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_vancon_meds)
# dan dan
model_dandan_meds <- lmer(scalep ~ wave + I(wave^2) + sex_numeric + interview_age + Achieva + Discovery + Ingenia + Prisma + rsfmri_meanmotion +
meds_recode + scale_subj_dan_dan_cent + scale_subj_dan_dan_cent*wave +
scale_subj_dan_dan_cent*I(wave^2) + (1|site_id) + (1|rel_family_id) + (1 + wave + I(wave^2)|id),
data = pfactor,
control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
summary(model_dandan_meds)
## MOTION AND MISSINGNESS
# GRH added for sensitivity #
#Removes for motion >= 0.2
pfactor <- pfactor%>%filter(rsfmri_meanmotion < 0.2)
## rerun models with new dataframe
#Groups by ID, then filters any who are missing p on waves 2 3 or 4
#We lose ~ 1084
pfactor2 <- pfactor%>%
group_by(src_subject_id) %>%
filter(any(wave == 1) &
(any(wave == 2 & !is.na(P)) |
any(wave == 3 & !is.na(P)) |
any(wave == 4 & !is.na(P)))) %>%
ungroup()
## rerun models with new dataframe

rsfc_p_JCPPA_analyses.R at commit 882ad37, no license · at the source

Overview

Authors: Jenna Jones Devine¹, Garrett R. Hosterman¹, Jolee Sloss¹, Adrienne L. Romer¹

ORCID iDs: Adrienne L. Romer

Department of Psychology Virginia Polytechnic Institute and State University Blacksburg Virginia USA

Institutions: Virginia Tech (United States)

Journal: JCPP advances, article e70135

Dates: received 12 September 2025; accepted 24 April 2026; published online 30 May 2026; in print May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1002/jcv2.70135 · PMID 42416655 · PMCID PMC13339399 · OpenAlex W7162869792

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: fMRI (modality), human (organism), developmental (subfield)

Methods: Connectivity, Statistics, fMRI & imaging

Keywords: adolescence, general psychopathology, longitudinal, p‐factor, resting‐state functional connectivity, transdiagnostic

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Brain and Behavior Research Foundation (32880); Virginia Polytechnic Institute and State University (452018)

Citations: cited by 2 papers (Europe PMC); 68 references in the paper

Abstract

Background: A general psychopathology “p‐factor” captures shared variation across psychiatric disorder categories and is associated with dysfunctions in cognitive control. Alterations in resting‐state functional connectivity (RSFC) of cognitive and attentional networks have been associated concurrently with the p‐factor in youth samples. However, we do not yet know whether these RSFC alterations prospectively relate to change in the p‐factor over time during the transition to adolescence, when many forms of disorder onset or worsen.

Methods: We examined whether baseline RSFC was prospectively related to the trajectory of p‐factor scores over three years in 9344 preadolescents (age 9–10 at baseline) from the Adolescent Brain Cognitive Development Adolescent Brain Cognitive DevelopmentSM (ABCD) study. Using longitudinal multilevel modeling, we tested whether baseline within‐ and between‐network RSFC of the default mode (DMN), frontoparietal (FPN), salience (SN), cingulo‐opercular (CON), and ventral and dorsal attention (VAN and DAN) networks were related to the intercept (between‐person differences) and slope (within‐person rate of change) of p‐factor scores over four ABCD study waves (3‐year timeframe).

Results: There was a significant nonlinear, quadratic trajectory of p‐factor scores over wave. Lower within‐DMN and within‐DAN connectivity and greater DMN‐DAN, DMN‐CON, and VAN‐CON connectivity were associated with higher between‐person levels of p‐factor scores, which persisted over time. Greater VAN‐DAN and SN‐CON connectivity and lower DMN‐VAN connectivity were prospectively associated with steeper within‐person rates of quadratic change in p‐factor scores over time.

Conclusion: These novel results identify specific alterations in RSFC within and between core networks involved in self‐referential processing, bottom‐up and town‐down attention, salience, and cognitive control that might contribute to general psychopathology development in early adolescence. Children with aberrant connectivity between the DMN and VAN, SN and CON, and VAN and DAN networks may be vulnerable to increases in general psychopathology during the transition to adolescence.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

Ageyr13/ABCD_RSFC_MLM

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 882ad37565b38cda2bcfa92c365ed62a9890adc6, 10 September 2025

Languages: R (2)

Size: 3 files, 2 scripts

Software Heritage: not archived

Found in: the text, “Longitudinal multilevel modeling”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: tidyverse (2 files), broom (1 file), circlize (1 file), easystats (1 file), emmeans (1 file), ggplot2 (1 file), lme4 (1 file), lmerTest (1 file), patchwork (1 file), psych (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

2 files

rsfc_p_JCPPA_analyses.R, R, 1,815 lines, 5 matches
rsfc_p_JCPPA_df.R, R, 70 lines, 2 matches

At the source: github.com/Ageyr13/ABCD_RSFC_MLM.git

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
2 scripts, each with its path and the digest of its content;
7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The data that support the findings of this study are openly available in NIMH Data Archive at http://dx.doi.org/10.15154/z563‐zd24 (http://dx.doi.org/10.15154/z563-zd24), reference number 2313.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, pages, dates, 4 authors, 6 keywords, 2 funders, 68 references.

Cite

This paper

Devine, J. J., Hosterman, G. R., Sloss, J., & Romer, A. L. (2026). Alterations in resting-state functional connectivity relate to psychopathology trajectories during emerging adolescence. JCPP advances, e70135. https://doi.org/10.1002/jcv2.70135

BibTeX

@article{devine2026alterations, author = {Devine, Jenna Jones and Hosterman, Garrett R. and Sloss, Jolee and Romer, Adrienne L.}, title = {{Alterations in resting-state functional connectivity relate to psychopathology trajectories during emerging adolescence}}, journal = {JCPP advances}, year = {2026}, month = may, pages = {e70135}, publisher = {Wiley}, issn = {2692-9384}, doi = {10.1002/jcv2.70135}, url = {https://doi.org/10.1002/jcv2.70135}, pmid = {42416655}, pmcid = {PMC13339399} }

RIS

TY - JOUR AU - Devine, Jenna Jones AU - Hosterman, Garrett R. AU - Sloss, Jolee AU - Romer, Adrienne L. TI - Alterations in resting-state functional connectivity relate to psychopathology trajectories during emerging adolescence T2 - JCPP advances J2 - JCPP Adv PY - 2026 DA - 2026/05/30 SP - e70135 SN - 2692-9384 PB - Wiley DO - 10.1002/jcv2.70135 UR - https://doi.org/10.1002/jcv2.70135 LA - en ER -

CSL-JSON

{ "id": "10.1002/jcv2.70135", "type": "article-journal", "title": "Alterations in resting-state functional connectivity relate to psychopathology trajectories during emerging adolescence", "container-title": "JCPP advances", "author": [ { "family": "Devine", "given": "Jenna Jones" }, { "family": "Hosterman", "given": "Garrett R." }, { "family": "Sloss", "given": "Jolee" }, { "family": "Romer", "given": "Adrienne L." } ], "container-title-short": "JCPP Adv", "page": "e70135", "DOI": "10.1002/jcv2.70135", "PMID": "42416655", "PMCID": "PMC13339399", "ISSN": "2692-9384", "publisher": "Wiley", "URL": "https://doi.org/10.1002/jcv2.70135", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 30 ] ] } }

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