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Genetic and environmental influences on data missingness in developmental cognitive neuroscience.

Code ↔ Paper

5 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.

The 5 matches
  1. [1] § Methods › Associations between missingness and other traits ↔ prepdata_missingBT.R, lines 141–186 · score 0.79 · gestational age, parental education, parental age, family income, sex
  2. [2] § Methods › Twin analysis ↔ bt-missing_univariate.R, lines 255–336 · score 0.68 · fitting nested model, AE model, ACE model, covariance, CI
  3. [3] § Methods › Twin analysis ↔ univariate liability threshold model.R, lines 206–265 · score 0.65 · AE model, nested model, ACE model, thresholds, CI, fitting
  4. [4] § Methods › Twin analysis ↔ univariate liability threshold model.R, lines 1–39 · score 0.59 · liability threshold model, univariate liability threshold, variable, Twin
  5. [5] § Results › Experiment-level missingness ↔ univariate liability threshold model.R, lines 41–79 · score 0.55 · univariate liability threshold, Twin correlations, zero, model, DZ, MZ

Paper

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The authors' code

R · 297 lines · 7.3 KB · no license · 3 matches

  1. #==========
  2. # univariate twin analysis
  3. # used to test liability threshold models for experiment-level missing
  4. #
  5. # author: Giorgia Bussu
  6. # project: BT missing data
  7. # version: June 2024
  8. #==========
  9. rm(list=ls())
  12. require(OpenMx)
  13. source('C:/Users/myfolder/twin_modelling/tutorial/miFunctions.R')
  15. ###############################################################################################################
  16. ### prepare data:
  17. ### import and check data
  19. data <- read.csv(file='twin_bin_sensitivity_data.csv',header=T,
  20. sep=',')
  21. names(data);dim(data)
  23. ### select variable for analysis:
  25. Vars <- 'fomo_bin'
  26. nv <- 1
  27. ntv <- nv*2
  28. nth <- 1
  29. (selVars <- paste(Vars,c(rep(1,nv),rep(2,nv)),sep=''))
  31. ### select subsets for analysis:
  33. mz <- subset(data,zygosity=='MZ',selVars)
  34. dz <- subset(data,zygosity=='DZ',selVars)
  36. # convert the variable to a factor:
  38. mz <- mxFactor(x=mz[,selVars],levels=c(0:nth))
  39. dz <- mxFactor(x=dz[,selVars],levels=c(0:nth))
  41. ##### assumptions testing and twin correlations #####
  43. ### fit a fully saturated model
  45. # matrix for the mean, which is fixed to 0:
  47. expMean <- mxMatrix(type='Zero',nrow=1,ncol=ntv,name='ExpMean')
  49. # matrices for the thresholds:
  51. expThreshMZ <- mxMatrix(type='Full',nrow=1,ncol=ntv,free=T,values=1,
  52. labels=labFull('th_mz',1,ntv),
  53. name='ExpThreshMZ')
  54. expThreshDZ <- mxMatrix(type='Full',nrow=1,ncol=ntv,free=T,values=1,
  55. labels=labFull('th_dz',1,ntv),
  56. name='ExpThreshDZ')
  58. # twin correlations:
  60. expCorMZ <- mxMatrix(type='Stand',nrow=ntv,ncol=ntv,free=T,values=.5,
  61. label='cor_mz',lbound=-.9999,ubound=.9999,
  62. name='ExpCorMZ')
  63. expCorDZ <- mxMatrix(type='Stand',nrow=ntv,ncol=ntv,free=T,values=.5,
  64. label='cor_dz',lbound=-.9999,ubound=.9999,
  65. name='ExpCorDZ')
  67. # specify observed data:
  69. dataMZ <- mxData(mz,type='raw')
  70. dataDZ <- mxData(dz,type='raw')
  72. # specify objectives:
  74. objMZ <- mxExpectationNormal(covariance='ExpCorMZ',
  75. means='ExpMean',dimnames=selVars,
  76. thresholds='ExpThreshMZ')
  77. objDZ <- mxExpectationNormal(covariance='ExpCorDZ',
  78. means='ExpMean',dimnames=selVars,
  79. thresholds='ExpThreshDZ')
  81. # specify estimation method:
  83. funcML <- mxFitFunctionML()
  85. # specify the data groups:
  87. modelMZ <- mxModel('MZ',expMean,expThreshMZ,expCorMZ,dataMZ,objMZ,
  88. funcML)
  89. modelDZ <- mxModel('DZ',expMean,expThreshDZ,expCorDZ,dataDZ,objDZ,
  90. funcML)
  92. # combine data groups:
  94. multi <- mxFitFunctionMultigroup(c('MZ','DZ'))
  96. # confidence intervals:
  98. ci <- mxCI(c('MZ.ExpCorMZ','DZ.ExpCorDZ'))
  100. # combine all model objects:
  102. SatModel <- mxModel('Sat',modelMZ,modelDZ,ci,multi)
  104. # fit the model:
  106. SatFit <- mxTryHardOrdinal(SatModel,intervals=T)
  107. summary(SatFit)
  109. ### test assumptions
  111. # assumption 1: equal thresholds within twin pairs:
  113. SatModel2 <- mxModel(SatFit,name='Sat2')
  114. SatModel2 <- omxSetParameters(SatModel2,labels=labFull('th_mz',nth,ntv),
  115. free=T,values=1,newlabel='th_mz')
  116. SatModel2 <- omxSetParameters(SatModel2,labels=labFull('th_dz',nth,ntv),
  117. free=T,values=1,newlabel='th_dz')
  118. SatFit2 <- mxTryHardOrdinal(SatModel2,intervals=T)
  119. summary(SatFit2)
  121. # assumption 2:
  123. SatModel3 <- mxModel(SatFit2,name='Sat3')
  124. SatModel3 <- omxSetParameters(SatModel3,labels=c('th_mz','th_dz'),free=T,
  125. values=1,newlabel='th')
  126. SatFit3 <- mxTryHardOrdinal(SatModel3,intervals=T)
  127. summary(SatFit3)
  129. # compare the fit of the models:
  131. mxCompare(SatFit,c(SatFit2,SatFit3))
  133. # significance correlations
  134. # assumption 2:
  136. SatModel_corr <- mxModel(SatFit3,name='SatCorr')
  137. SatModel_corr <- omxSetParameters(SatModel_corr,labels=c('cor_dz'),free=F,
  138. values=0)
  139. SatFit_corr <- mxTryHardOrdinal(SatModel_corr,intervals=T)
  140. summary(SatFit_corr)
  142. mxCompare(SatFit3,SatFit_corr)
  145. ##### ACE model #####
  147. ### full ACE model
  149. # path coefficients:
  151. pathA <- mxMatrix(type='Full',nrow=nv,ncol=nv,free=T,values=1,label='a_1_1',
  152. name='a')
  153. pathC <- mxMatrix(type='Full',nrow=nv,ncol=nv,free=T,values=1,label='c_1_1',
  154. name='c')
  155. pathE <- mxMatrix(type='Full',nrow=nv,ncol=nv,free=T,values=1,label='e_1_1',
  156. name='e')
  158. # calculate variance components:
  160. varA <- mxAlgebra(a^2,name='A')
  161. varC <- mxAlgebra(c^2,name='C')
  162. varE <- mxAlgebra(e^2,name='E')
  164. # calculate the total variance:
  166. varP <- mxAlgebra(A+C+E,name='V')
  168. # constrain the total variance to equal 1:
  170. matU <- mxMatrix(type='Unit',nrow=nv,ncol=nv,name='U')
  171. varConst <- mxConstraint(V==U,name='VarConst')
  173. # mean:
  175. expMean <- mxMatrix(type='Zero',nrow=1,ncol=ntv,name='ExpMean')
  177. # threshold:
  179. expThresh <- mxMatrix(type='Full',nrow=nth,ncol=ntv,free=T,values=1,label='th',
  180. name='ExpThresh')
  182. # variance/covariance:
  184. expCovMZ <- mxAlgebra(rbind(cbind(V,A+C),
  185. cbind(A+C,V)),name='ExpCovMZ')
  186. expCovDZ <- mxAlgebra(rbind(cbind(V,0.5%x%A+C),
  187. cbind(0.5%x%A+C,V)),name='ExpCovDZ')
  189. # convert the variance components to proportions:
  191. estVC <- mxAlgebra(cbind(A/V,C/V,E/V),name='EstVC')
  193. # observed data:
  195. dataMZ <- mxData(mz,type='raw')
  196. dataDZ <- mxData(dz,type='raw')
  198. # objectives:
  200. objMZ <- mxExpectationNormal(covariance='ExpCovMZ',means='ExpMean',dimnames=selVars,
  201. thresholds='ExpThresh')
  202. objDZ <- mxExpectationNormal(covariance='ExpCovDZ',means='ExpMean',dimnames=selVars,
  203. thresholds='ExpThresh')
  204. funcML <- mxFitFunctionML()
  206. # specify data groups:
  208. pars <- list(pathA,pathC,pathE,varA,varC,varE,varP,matU,expMean,expThresh)
  210. modelMZ <- mxModel('MZ',pars,varConst,expCovMZ,estVC,dataMZ,objMZ,funcML)
  211. modelDZ <- mxModel('DZ',pars,varConst,expCovDZ,estVC,dataDZ,objDZ,funcML)
  213. # combine data groups:
  215. multi <- mxFitFunctionMultigroup(c('MZ','DZ'))
  217. # confidence intervals:
  219. ci <- mxCI(c('MZ.EstVC'))
  221. # combine all model objects:
  223. ModelACE <- mxModel('ACE',modelMZ,modelDZ,multi,ci)
  225. # fit the model:
  227. FitACE <- mxTryHardOrdinal(ModelACE,intervals=T)
  228. summary(FitACE)
  230. # compare fit to the saturated model:
  232. mxCompare(SatFit,FitACE)
  234. ### nested models:
  236. # AE model:
  238. ModelAE <- mxModel(FitACE,name='AE')
  239. ModelAE <- omxSetParameters(ModelAE,label='c_1_1',free=F,values=0)
  240. FitAE <- mxTryHardOrdinal(ModelAE,intervals=T)
  241. aefit<-summary(FitAE)
  243. # CE model:
  245. ModelCE <- mxModel(FitACE,name='CE')
  246. ModelCE <- omxSetParameters(ModelCE,label='a_1_1',free=F,values=0)
  247. FitCE <- mxTryHardOrdinal(ModelCE,intervals=T)
  248. cefit<-summary(FitCE)
  250. # E model:
  252. ModelE <- mxModel(FitACE,name='E')
  253. ModelE <- omxSetParameters(ModelE,labels=c('a_1_1','c_1_1'),free=F,values=0)
  254. FitE <- mxTryHardOrdinal(ModelE,intervals=T)
  255. summary(FitE)
  257. # compare fit with the ACE model:
  259. mxCompare(FitACE,c(FitAE,FitCE,FitE))
  261. # AC for p purposes
  262. ModelAC <- mxModel(FitACE,name='AC')
  263. ModelAC <- omxSetParameters(ModelAC,label='e_1_1',free=F,values=0)
  264. FitAC <- mxTryHardOrdinal(ModelAC,intervals=T)
  265. summary(FitAC)

univariate liability threshold model.R at commit ad713aa, no license · at the source

Overview

Authors: G Bussu1, A M Portugal1, C Viktorsson1, I Hardiansyah1,2, T Falck-Ytter1,2
ORCID iDs: G Bussu, A M Portugal, C Viktorsson, T Falck-Ytter
  1. Development and Neurodiversity Lab, Department of Psychology, Uppsala University, Uppsala, Sweden
  2. Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women’s and Children’s Health, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
Institutions: Uppsala University (Sweden); Region Stockholm (Sweden); Karolinska Institutet (Sweden); Stockholm Health Care Services (Sweden)
Journal: Communications psychology, volume 4, issue 1, article 70
Dates: received 23 January 2025; accepted 7 April 2026; published online 22 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44271-026-00457-0 · PMID 42020721 · PMCID PMC13102918 · OpenAlex W7155199275
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: Psychology, Visual system, Predictive markers
Topic: Cognitive Abilities and Testing (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: Knut och Alice Wallenbergs Stiftelse (KAW 2024.0228); Marianne and Marcus Wallenberg Foundation; Riksbankens Jubileumsfond (Stiftelsen Riksbankens Jubileumsfond) (NHS14-1802:1)
Citations: cited by 1 paper (Europe PMC); 47 references in the paper

Abstract

Missing data are common in social and clinical sciences and understanding the causes and patterns of missing data is important for selecting analysis approach and for the interpretation of the remaining data. Yet, knowledge about the factors influencing data loss is limited. Here, we assessed the contribution of genes and environments to data missingness across three experiments of infant brain and behavioural development. The sample consisted of 594 infant twins (330 monozygotic, 152 female, 178 male infants; 264 dizygotic, 132 female, 132 male infants) who were assessed with electroencephalography (EEG), pupillometry, and gaze tracking technologies at 5 months of age. Substantial familial factors (additive genetics and/or shared environment) for data missingness were found across all experiments. The amount of missing data showed only a low correlation across the experiments, suggesting a high degree of specificity in the factors contributing to missingness. The results underscore the need to adopt and improve procedural and analytical strategies that minimise data loss and its negative impacts on study conclusions.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

OSF rqwvc

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 0 files, 0 scripts
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
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At the source:

Zenodo 19333394

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (1 file), psych (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
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6 files
At the source:

brainhabit/bt_missingdata

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: ad713aa6f528e3b9a01871e817ceb39b355f1d84, 17 October 2024
Languages: R (5)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (1 file), psych (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
6 files
At the source: github.com/brainhabit/BT_missingdata/tree/cpCode

Code availability

The analytic code necessary to attempt to replicate the findings presented here is publicly available at 10.5281/zenodo.19333394.

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

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data availability

The study used behavioural data and derivates of genetic testing. The data contain pseudonymized personal information as defined by GDPR (EU law) and cannot be openly shared. Access is available through a controlled access procedure for researchers who: (a) demonstrate a legitimate scientific purpose aligned with the original participant consent and ethics approval; (b) obtain approval from their institutional ethics board; (c) have the capacity to ensure GDPR‑compliant secure data handling; and (d) sign a Data Sharing Agreement specifying conditions for use, storage, and destruction of the data. To request access, contact the corresponding author (TFY). The numerical data underlying Fig. 1 are available here: https://osf.io/bg8zx/files/osfstorage (https://eur01.safelinks.protection.outlook.com/?url=https://osf.io/bg8zx/files/osfstorage&data=05|02||80f306a61ff74fdf7c8008de8e37841d|bff7eef1cf4b4f32be3da1dda043c05d|0|0|639104566733914692|Unknown|TWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ==|0|||&sdata=4MTfOwF2tZb4QfHFo4XF6rNaY/Bvh02WWXpzkbjJuLQ=&reserved=0).

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

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 3 funders, 35 references.

Cite

This paper

Bussu, G., Portugal, A. M., Viktorsson, C., Hardiansyah, I., & Falck-Ytter, T. (2026). Genetic and environmental influences on data missingness in developmental cognitive neuroscience. Communications psychology, 4(1), 70. https://doi.org/10.1038/s44271-026-00457-0

BibTeX

@article{bussu2026genetic,
author = {Bussu, G and Portugal, A M and Viktorsson, C and Hardiansyah, I and Falck-Ytter, T},
title = {{Genetic and environmental influences on data missingness in developmental cognitive neuroscience}},
journal = {Communications psychology},
year = {2026},
month = apr,
volume = {4},
number = {1},
pages = {70},
publisher = {Nature Publishing Group},
issn = {2731-9121},
doi = {10.1038/s44271-026-00457-0},
url = {https://doi.org/10.1038/s44271-026-00457-0},
pmid = {42020721},
pmcid = {PMC13102918}
}

RIS

TY - JOUR
AU - Bussu, G
AU - Portugal, A M
AU - Viktorsson, C
AU - Hardiansyah, I
AU - Falck-Ytter, T
TI - Genetic and environmental influences on data missingness in developmental cognitive neuroscience
T2 - Communications psychology
J2 - Commun Psychol
PY - 2026
DA - 2026/04/22
VL - 4
IS - 1
SP - 70
SN - 2731-9121
PB - Nature Publishing Group
DO - 10.1038/s44271-026-00457-0
UR - https://doi.org/10.1038/s44271-026-00457-0
LA - en
ER -

CSL-JSON

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