OSCR

Factors Contributing to Short-Term Structural Variability in a Longitudinal MRI Dataset.

Code ↔ Paper

4 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. [1] § Results › Complementary Analysis I ↔ Patterns_longitudinal_manuscript/Step4_sPLS_final.R, lines 96–145 · score 0.91 · superior parietal lobule, superior occipital gyrus, angular gyrus, postcentral gyrus, medial segment, precuneus
  2. [2] § Results › Main Analysis ↔ Patterns_longitudinal_manuscript/Step4_sPLS_final.R, lines 96–145 · score 0.71 · middle temporal gyrus, middle occipital gyrus, inferior temporal, sPLS
  3. [3] § Method › Materials and Time‐Varying Predictors of Brain Changes ↔ Patterns_longitudinal_manuscript/Step4_sPLS_final.R, lines 47–94 · score 0.64 · room temperature, concentration, Estradiol, physical, Testosterone, PANAS
  4. [4] § Method › Materials and Time‐Varying Predictors of Brain Changes ↔ Patterns_longitudinal_manuscript/Step3_Imputation_2.0.R, lines 101–140 · score 0.56 · room temperature, Estradiol, Testosterone, PANAS, humidity, hours

Paper

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The authors' code

R · 419 lines · 21 KB · CC-BY-4.0 · 3 matches

  1. #spls analysis
  3. # Bioconductor install
  4. if (!requireNamespace("BiocManager", quietly = TRUE)){
  5. install.packages("BiocManager")
  6. }
  8. # Install BiocParallel
  9. BiocManager::install("BiocParallel")
  10. BiocManager::install(update = TRUE)
  12. # over GitHub
  13. install.packages("devtools") # restart no
  14. devtools::install_github("mixOmicsTeam/mixOmics", force = TRUE)
  16. #BiocManager::install("mixOmics")
  18. #install.packages("mixOmics")
  19. library(mixOmics) # import the mixOmics library
  22. install.packages("ggplot2")
  23. library(ggplot2)
  24. install.packages("MASS")
  25. library(MASS)
  26. install.packages("lattice")
  27. library(lattice)
  28. # -------------------------------------------------------------------------------------------------------------------------------
  30. ### Import my data
  31. install.packages("jsonlite")
  32. library(jsonlite)
  34. #######
  35. X_impu <- read.csv("/Users/Masterthesis_Mayla/Step8_spls/Data_for_spls/x_impu.csv")
  36. X_not_impu <- read.csv("/Users/Masterthesis_Mayla/Step8_spls/Data_for_spls/X_not_impu.csv")
  37. Y_ROI <- read.csv("/Users/Masterthesis_Mayla/Step8_spls/Data_for_spls/Y_ROI_final_Cobra_neuromorpho.csv")
  39. rm(Y_ROI)
  40. rm(X_impu)
  41. rm(X_not_impu)
  42. # change X and Y df into matrix
  43. X_impu <- as.matrix(X_impu[, sapply(X_impu, is.numeric)]) #change the numeric columns in a matrix
  44. Y_ROI <- as.matrix(Y_ROI[, sapply(Y_ROI, is.numeric)])
  45. X_not_impu <- as.matrix(X_not_impu[, sapply(X_not_impu, is.numeric)])
  47. ## Check Dimensions of dataframes
  49. dim(X_impu) # check the dimensions of the X dataframe
  50. dim(Y_ROI) # check the dimensions of the Y dataframe
  51. dim(X_not_impu)
  53. original_names <- c(
  54. "anxietyDuringScan", "atmPressume", "bloodPressure_diastolic_mmHg",
  55. "bloodPressure_systolic_mmHg", "cacao_last24hs_percentage", "cacao_last2hs_percentage",
  56. "caffein_last24hs_cups", "caffein_last2hs_cups", "calories_burned",
  57. "chocolate_last24hs_gramms", "chocolate_last2hs_gramms", "cigarettes_last24hs",
  58. "daysFromFirstScan", "dayDreams_last24hs", "easeOfConcentration_last24hs", "estradiol",
  59. "generalHealth_last24hs", "generalStress_last24hs", "hoursActiveSocialInteraction_last24hs",
  60. "hoursFree_last24hs", "hoursOfWork_last24hs", "hoursPassiveSocialInteraction_last24hs",
  61. "hoursSpentOutdoors_last24hs", "hoursSport_last24hs", "hoursUsingScreens_last24hs",
  62. "liquid_last24hs_liters", "marihuanaCigarettes_last24hs", "maxTemp_C", "menstrualCycleDay",
  63. "minTemp_C", "MR_HeliumLevel", "MR_Room_Humidity", "MR_Room_Temperature", "number_steps",
  64. "number_stories", "PANAS_afraid", "PANAS_active", "PANAS_alert", "PANAS_ashamed",
  65. "PANAS_attentive", "PANAS_determined", "PANAS_distressed", "PANAS_enthusiastic",
  66. "PANAS_excited", "PANAS_guilty", "PANAS_hostile", "PANAS_inspired", "PANAS_interested",
  67. "PANAS_irritable", "PANAS_jittery", "PANAS_nervous", "PANAS_proud", "PANAS_scared",
  68. "PANAS_strong", "PANAS_upset", "physicalPainDuringScan", "precip_form", "precip_mm",
  69. "relHumidity", "rememberDreams_fromLastNight", "ruminationDuringScan", "scanDate",
  70. "scanTime", "sleepQuality_lastNight", "sleptDuringScan", "sunshine_hrs", "sweets_last24hs_Likert",
  71. "testosterone", "time_bed", "time_slept_min", "walk_distance", "weight_kg", "wind_km_h"
  72. )
  74. # New names
  75. new_names <- c(
  76. "Anxiety during Scan", "Athmospheric pressure", "Blood pressure diastolic",
  77. "Blood pressure systolic", "Cacao intake (%) last 24 hs", "Cacao intake (%) last 2 hs",
  78. "Caffeine intake last 24 hs", "Caffeine intake last 2 hs", "Calories burned",
  79. "Chocolate intake (g) last 24 hs", "Chocolate intake (g) last 2 hs", "Cigarettes last 24 hs",
  80. "Days from first Scan", "Daydreams last 24 hs", "Ease of concentration", "Estradiol",
  81. "Genereal Health last 24 hs", "General Stress last 24 hs", "Active Social Interact last 24 hs",
  82. "Hours free last 24 hs", "Hours of Work last 24 hs", "Passive Social Interact last 24 hs",
  83. "Hours outdoors last 24 hs", "Hours Sport last 24 hs", "Hours using Screen last 24 hs",
  84. "liquid litres last 24 hs", "Marihuana Cigarettes last 24 hs", "Maximum Temperature",
  85. "Day of menstrual cycle", "Minimum Temperature", "MR Helium Level", "MR Room Humidity",
  86. "MR Room Temperature", "Number of steps", "Number of stories climbed", "PANAS afraid",
  87. "PANAS active", "PANAS alert", "PANAS ashamed", "PANAS attentive", "PANAS determined",
  88. "PANAS distressed", "PANAS enthusiastic", "PANAS excited", "PANAS guilty", "PANAS hostile",
  89. "PANAS inspired", "PANAS interested", "PANAS irritable", "PANAS jittery", "PANAS nervous",
  90. "PANAS proud", "PANAS scared", "PANAS strong", "PANAS upset", "Physical Pain during Scan",
  91. "Precipitation Form", "Precipitation mm", "Relative Humidity", "Remember Dreams", "Rumination during Scan",
  92. "Scandate", "Scantime", "Sleep Quality last Night", "Slept during Scan", "Sunshine hs", "Sweets last 24 hs",
  93. "Testosterone", "Time Bed", "Time slept min", "Walk Distance", "Weight kg", "Wind km/h"
  94. )
  96. # Create a named vector for mapping
  97. name_mapping <- setNames(new_names, original_names)
  99. # Replace column names in the dataframe
  100. colnames(X_impu) <- ifelse(colnames(X_impu) %in% names(name_mapping),
  101. name_mapping[colnames(X_impu)],
  102. colnames(X_impu))
  103. colnames(X_not_impu) <- ifelse(colnames(X_not_impu) %in% names(name_mapping),
  104. name_mapping[colnames(X_not_impu)],
  105. colnames(X_not_impu))
  108. original_names_Y <- c(
  109. "lStriatum", "lGloPal", "lTha", "lAntCerebLI_II", "lAntCerebLIII", "lAntCerebLIV", "lAntCerebLV", "lSupPostCerebLVI", "lSupPostCerebCI",
  110. "lSupPostCerebCII", "lSupPostCerebLVIIB", "lInfPostCerebLVIIIA", "lInfPostCerebLVIIIB", "lInfPostCerebLIX", "lInfPostCerebLX", "lAmy", "lHCA1",
  111. "lSub", "lCA4", "lCA2_3", "lStratum", "rStriatum", "rGloPal", "rTha", "rAntCerebLI_II", "rAntCerebLIII", "rAntCerebLIV", "rAntCerebLV",
  112. "rSupPostCerebLVI", "rSupPostCerebCI", "rSupPostCerebCII", "rSupPostCerebLVIIB", "rInfPostCerebLVIIIA", "rInfPostCerebLVIIIB", "rInfPostCerebLIX",
  113. "rInfPostCerebLX", "rAmy", "rHCA1", "rSub", "rCA4", "rCA2_3", "rStratum", "Right.Accumbens.Area.1", "Left.Accumbens.Area.1", "Right.Amygdala.1",
  114. "Left.Amygdala.1", "Right.Caudate.1", "Left.Caudate.1", "Right.Cerebellum.Exterior.1", "Left.Cerebellum.Exterior.1", "Right.Hippocampus.1",
  115. "Left.Hippocampus.1", "Right.Pallidum.1", "Left.Pallidum.1", "Right.Putamen.1", "Left.Putamen.1", "Right.Thalamus.Proper.1", "Left.Thalamus.Proper.1",
  116. "Right.Ventral.DC.1", "Left.Ventral.DC.1", "Optic.Chiasm.1", "Cerebellar.Vermal.Lobules.I.V.1", "Cerebellar.Vermal.Lobules.VI.VII.1",
  117. "Cerebellar.Vermal.Lobules.VIII.X.1", "Left.Basal.Forebrain.1", "Right.Basal.Forebrain.1", "Right.ACgG.anterior.cingulate.gyrus.1",
  118. "Left.ACgG.anterior.cingulate.gyrus.1", "Right.AIns.anterior.insula.1", "Left.AIns.anterior.insula.1", "Right.AOrG.anterior.orbital.gyrus.1",
  119. "Left.AOrG.anterior.orbital.gyrus.1", "Right.AnG.angular.gyrus.1", "Left.AnG.angular.gyrus.1", "Right.Calc.calcarine.cortex.1",
  120. "Left.Calc.calcarine.cortex.1", "Right.CO.central.operculum.1", "Left.CO.central.operculum.1", "Right.Cun.cuneus.1", "Left.Cun.cuneus.1",
  121. "Right.Ent.entorhinal.area.1", "Left.Ent.entorhinal.area.1", "Right.FO.frontal.operculum.1", "Left.FO.frontal.operculum.1", "Right.FRP.frontal.pole.1",
  122. "Left.FRP.frontal.pole.1", "Right.FuG.fusiform.gyrus.1", "Left.FuG.fusiform.gyrus.1", "Right.GRe.gyrus.rectus.1", "Left.GRe.gyrus.rectus.1",
  123. "Right.IOG.inferior.occipital.gyrus.1", "Left.IOG.inferior.occipital.gyrus.1", "Right.ITG.inferior.temporal.gyrus.1",
  124. "Left.ITG.inferior.temporal.gyrus.1", "Right.LiG.lingual.gyrus.1", "Left.LiG.lingual.gyrus.1", "Right.LOrG.lateral.orbital.gyrus.1",
  125. "Left.LOrG.lateral.orbital.gyrus.1", "Right.MCgG.middle.cingulate.gyrus.1", "Left.MCgG.middle.cingulate.gyrus.1", "Right.MFC.medial.frontal.cortex.1",
  126. "Left.MFC.medial.frontal.cortex.1", "Right.MFG.middle.frontal.gyrus.1", "Left.MFG.middle.frontal.gyrus.1", "Right.MOG.middle.occipital.gyrus.1",
  127. "Left.MOG.middle.occipital.gyrus.1", "Right.MOrG.medial.orbital.gyrus.1", "Left.MOrG.medial.orbital.gyrus.1",
  128. "Right.MPoG.postcentral.gyrus.medial.segment.1", "Left.MPoG.postcentral.gyrus.medial.segment.1", "Right.MPrG.precentral.gyrus.medial.segment.1",
  129. "Left.MPrG.precentral.gyrus.medial.segment.1", "Right.MSFG.superior.frontal.gyrus.medial.segment.1", "Left.MSFG.superior.frontal.gyrus.medial.segment.1",
  130. "Right.MTG.middle.temporal.gyrus.1", "Left.MTG.middle.temporal.gyrus.1", "Right.OCP.occipital.pole.1", "Left.OCP.occipital.pole.1",
  131. "Right.OFuG.occipital.fusiform.gyrus.1", "Left.OFuG.occipital.fusiform.gyrus.1", "Right.OpIFG.opercular.part.of.the.inferior.frontal.gyrus.1",
  132. "Left.OpIFG.opercular.part.of.the.inferior.frontal.gyrus.1", "Right.OrIFG.orbital.part.of.the.inferior.frontal.gyrus.1",
  133. "Left.OrIFG.orbital.part.of.the.inferior.frontal.gyrus.1", "Right.PCgG.posterior.cingulate.gyrus.1", "Left.PCgG.posterior.cingulate.gyrus.1",
  134. "Right.PCu.precuneus.1", "Left.PCu.precuneus.1", "Right.PHG.parahippocampal.gyrus.1", "Left.PHG.parahippocampal.gyrus.1",
  135. "Right.PIns.posterior.insula.1", "Left.PIns.posterior.insula.1", "Right.PO.parietal.operculum.1", "Left.PO.parietal.operculum.1",
  136. "Right.PoG.postcentral.gyrus.1", "Left.PoG.postcentral.gyrus.1", "Right.POrG.posterior.orbital.gyrus.1", "Left.POrG.posterior.orbital.gyrus.1",
  137. "Right.PP.planum.polare.1", "Left.PP.planum.polare.1", "Right.PrG.precentral.gyrus.1", "Left.PrG.precentral.gyrus.1", "Right.PT.planum.temporale.1",
  138. "Left.PT.planum.temporale.1", "Right.SCA.subcallosal.area.1", "Left.SCA.subcallosal.area.1", "Right.SFG.superior.frontal.gyrus.1",
  139. "Left.SFG.superior.frontal.gyrus.1", "Right.SMC.supplementary.motor.cortex.1", "Left.SMC.supplementary.motor.cortex.1",
  140. "Right.SMG.supramarginal.gyrus.1", "Left.SMG.supramarginal.gyrus.1", "Right.SOG.superior.occipital.gyrus.1",
  141. "Left.SOG.superior.occipital.gyrus.1", "Right.SPL.superior.parietal.lobule.1", "Left.SPL.superior.parietal.lobule.1",
  142. "Right.STG.superior.temporal.gyrus.1", "Left.STG.superior.temporal.gyrus.1", "Right.TMP.temporal.pole.1", "Left.TMP.temporal.pole.1",
  143. "Right.TrIFG.triangular.part.of.the.inferior.frontal.gyrus.1", "Left.TrIFG.triangular.part.of.the.inferior.frontal.gyrus.1",
  144. "Right.TTG.transverse.temporal.gyrus.1", "Left.TTG.transverse.temporal.gyrus.1"
  145. )
  147. # Changes for the loading plots
  148. new_names_Y_network_plot <- c(
  149. "L.Striatum", "L.GloPal", "L.Tha", "L.AntCereLI_II", "L.AntCereLIII", "L.AntCereLIV", "L.AntCereLV", "L.SupPoCereLVI",
  150. "L.SupPoCereCI", "L.SupPoCereCII", "L.SupPoCereLVIIB", "L.InfPoCereLVIIIA", "L.InfPoCereLVIIIB", "L.InfPoCereLIX",
  151. "L.InfPoCereLX", "L.Amy", "L.HCA1", "L.Sub", "L.CA4", "L.CA2_3", "L.Stratum", "R.Striatum", "R.GloPal", "R.Tha", "R.AntCereLI_II",
  152. "R.AntCereLIII", "R.AntCerebLIV", "R.AntCereLV", "R.SupPoCereLVI", "R.SupPoCereCI", "R.SupPoCereCII", "R.SupPoCereLVIIB",
  153. "R.InfPoCereLVIIIA", "R.InfPoCereLVIIIB", "R.InfPoCereLIX", "R.InfPoCereLX", "R.Amy", "R.HCA1", "R.Sub", "R.CA4", "R.CA2_3",
  154. "R.Stratum", "R.Accumb.Area", "L.Accumb.Area", "R.Amygdala", "L.Amygdala", "R.Caudate", "L.Caudate",
  155. "R.Cereb.Ext", "L.Cereb.Ext", "R.Hippoc", "L.Hippoc", "R.Pallidum", "L.Pallidum",
  156. "R.Putamen", "L.Putamen", "R.Thal.Prop", "L.Thal.Prop", "R.Ventral.DC", "L.Ventral.DC", "Optic.Chiasm",
  157. "Cereb.Vermal.Lob.I.V", "Cereb.Vermal.Lob.VI.VII", "Cereb.Vermal.Lob.VIII.X", "L.Basal.Forebrain",
  158. "R.Basal.Forebrain", "R.ACgG", "L.ACgG", "R.AIns",
  159. "L.AIns", "R.AOrG", "L.AOrG", "R.AnG",
  160. "L.AnG", "R.Calc", "L.Calc", "R.CO", "L.CO",
  161. "R.Cun", "L.Cun", "R.Ent", "L.Ent", "R.FO",
  162. "L.FO", "R.FRP", "L.FRP", "R.FuG", "L.FuG",
  163. "R.GRe", "L.GRe", "R.IOG", "L.IOG",
  164. "R.ITG", "L.ITG", "R.LiG", "L.LiG",
  165. "R.LOrG", "L.LOrG", "R.MCgG", "L.MCgG",
  166. "R.MFC", "L.MFC", "R.MFG", "L.MFG",
  167. "R.MOG", "L.MOG", "R.MOrG", "L.MOrG",
  168. "R.MPoG", "L.MPoG", "R.MPrG",
  169. "L.MPrG", "R.MSFG",
  170. "L.MSFG", "R.MTG", "L.MTG",
  171. "R.OCP", "L.OCP", "R.OFuG", "L.OFuG",
  172. "R.OpIFG", "L.OpIFG",
  173. "R.OrIFG", "L.OrIFG",
  174. "R.PCgG", "L.PCgG", "R.PCu", "L.PCu",
  175. "R.PHG", "L.PHG", "R.PIns", "L.PIns",
  176. "R.PO", "L.PO", "R.PoG", "L.PoG",
  177. "R.POrG", "L.POrG", "R.PP", "L.PP",
  178. "R.PrG", "L.PrG", "R.PT", "L.PT", "R.SCA",
  179. "L.SCA", "R.SFG", "L.SFG", "R.SMC",
  180. "L.SMC", "R.SMG", "L.SMG", "R.SOG",
  181. "L.SOG", "R.SPL", "L.SPL",
  182. "R.STG", "L.STG", "R.TMP", "L.TMP",
  183. "R.TrIFG", "L.TrIFG",
  184. "R.TTG", "L.TTG")
  189. name_mapping <- setNames(new_names_Y_network_plot, original_names_Y)
  190. colnames(Y_ROI) <- ifelse(colnames(Y_ROI) %in% names(name_mapping),
  191. name_mapping[colnames(Y_ROI)],
  192. colnames(Y_ROI))
  194. # -------------------------------------------------------------------------------------------
  195. # -------------------------------------------------------------------------------------------
  196. ### Multilevel ### (taking longitudinal data of all subjects into account)
  197. # -------------------------------------------------------------------------------------------
  198. # -------------------------------------------------------------------------------------------
  200. # Design matrix
  201. table(X_impu[, 1])
  202. # Number of measures:
  203. # Person 1: 50, Person 2: 12, Person 3: 50,
  204. # Person 4: 11, Person 5: 45, Person 6: 47,
  205. # Person 7: 40, Person 8: 49
  207. repeat.indiv <- c(rep(1, 50), rep(2, 12), rep(3, 50), rep(4, 11),
  208. rep(5, 45), rep(6, 47), rep(7, 40), rep(8, 49))
  210. # create Design matrix
  211. # basically contains the ‘Person’ column from my data
  212. # specifies for the spls command how the measurement repetition is handled
  213. design <- data.frame(sample = repeat.indiv)
  216. # --------------------------------------------------------------------------------------------------
  217. ### SPLS Multilevel Basic Model
  218. # --------------------------------------------------------------------------------------------------
  220. ### Basic Multilevel Model with imputed data: X_impu
  221. spls.4comp.multi.basic <- spls(X_impu, Y_ROI, ncomp = 4, mode = 'regression', multilevel = design)
  224. # --------------------------------------------------------------------------------------------------
  225. ### TUNING
  226. # --------------------------------------------------------------------------------------------------
  228. # set range of test values for number of variables to use from X dataframe
  229. list.keepX <- c(3:12)
  231. # set range of test values for number of variables to use from Y dataframe
  232. list.keepY <- c(5:164)
  234. # ---------------------------------------------------------
  236. ### Tune.splslevel ###
  238. ### Wrapper function to be able to understand the output
  240. tune.splslevel.wrapper <- function(X,
  241. Y,
  242. design,
  243. ncomp,
  244. test.keepX,
  245. test.keepY) {
  246. ncomps <- 1:ncomp # set range of ncomp values to use
  248. final.obj <- list() # initialise returned object
  250. # default values are -1 for debugging
  251. atX <- rep(-1, length(ncomps)-1) # already.tested.X
  252. atY <- rep(-1, length(ncomps)-1) # already.tested.Y
  253. for (ncomp in ncomps) {
  255. # previously calculated optimal keepX/Y value
  256. tmp.atX <- c(atX[1:ncomp-1])
  257. tmp.atY <- c(atY[1:ncomp-1])
  259. # need to be NULL for first iteration
  260. if (ncomp==1) { tmp.atX <- NULL; tmp.atY <- NULL }
  262. cat("=== NCOMP:", ncomp, "===\n")
  263. model <- suppressMessages(tune.splslevel(X, Y, multilevel=design,
  264. mode="regression",
  265. ncomp=ncomp,
  266. test.keepX = test.keepX,
  267. test.keepY = test.keepY,
  268. already.tested.X = tmp.atX,
  269. already.tested.Y = tmp.atY))
  271. # extract position in cor.value which corresponds to max
  272. opt.pos <- which(model$cor.value==max(model$cor.value), arr.ind=T)
  274. # set these for future iterations
  275. atX[ncomp] <- test.keepX[opt.pos[1]]
  276. atY[ncomp] <- test.keepY[opt.pos[2]]
  278. # add to returned object
  279. final.obj[["X"]][ncomp] <- test.keepX[opt.pos[1]]
  280. final.obj[["Y"]][ncomp] <- test.keepY[opt.pos[2]]
  281. final.obj[["cor"]][ncomp] <- max(model$cor.value)
  282. }
  283. # determine ncomp with optimal correlation
  284. cor.max <- which(final.obj[["cor"]]==max(final.obj[["cor"]]))
  286. final.obj[["opt.ncomp"]] <- cor.max
  288. return(final.obj)
  289. }
  292. # set range of test values for number of variables to use from X dataframe
  293. #list.keepX <- c(3:10)
  294. # set range of test values for number of variables to use from Y dataframe
  295. #list.keepY <- c(5:15)
  297. tune.spls.4comp.multi.wrapper <- tune.splslevel.wrapper(X = X_impu, Y = Y_ROI,
  298. design,
  299. ncomp = 4,
  300. test.keepX = list.keepX,
  301. test.keepY = list.keepY)
  302. #plot(tune.spls.multi.wrapper)
  305. # extract optimal number of variables for X and Y dataframe and number of components
  306. optimal.keepX <- tune.spls.4comp.multi.wrapper$X
  307. optimal.keepY <- tune.spls.4comp.multi.wrapper$Y
  308. optimal.ncomp <- length(optimal.keepX)
  309. # -----------------------------------------
  311. ### Final model
  313. # final: not imputed canonical
  314. spls.final.4comp.multilevel.canonical <- spls(X = X_not_impu, Y = Y_ROI, # generate a tuned sPLS model
  315. multilevel = design,
  316. ncomp = 4,
  317. keepX = optimal.keepX,
  318. keepY = optimal.keepY,
  319. mode = 'canonical')
  321. ### -----------------------------------------------------------------------------
  322. ### Plots ###
  323. ### mulitlevel spls tuned ###
  324. ### -----------------------------------------------------------------------------
  325. # extract loadings of Y variables to do a plot of the brain with regions visible that correlate positively with component 1 / negatively with component 1
  326. # same for component 2 (in one plot?)
  327. Loadings_Y_canonical <- spls.final.4comp.multilevel.canonical$loadings$Y
  328. write.csv(Loadings_Y, file = "/Users/Masterthesis_Mayla/Step8_spls/Results/Loadings_Y_final_4comp_cobneuro_canonical.csv", row.names = TRUE)
  329. Loadings_Y_regression <- spls.final.multilevel.regression$loadings$Y
  330. write.csv(Loadings_Y, file = "/Users/Masterthesis_Mayla/Step8_spls/Results/Loadings_Y_final_4comp_cobneuro_regression.csv", row.names = TRUE)
  332. dev.off()
  333. par(oma=c(0.5,7,0.5,1)) # bottom, left, top, right
  334. # Loadings
  335. ?plotLoadings
  336. plotLoadings(spls.final.4comp.multilevel.canonical, comp = 1, contrib = max, method = "median", subtitle = c('Questionnairy Loadings Comp 1', "Brain Loadings Comp 1"), size.name = 1.4)
  337. plotLoadings(spls.final.4comp.multilevel.canonical, comp = 2, contrib = max, method = "median", subtitle = c('Questionnairy Loadings Comp 2', "Brain Loadings Comp 2"), size.name = 1.4)
  338. plotLoadings(spls.final.4comp.multilevel.canonical, comp = 3, contrib = max, method = "median", subtitle = c('Questionnairy Loadings Comp 3', "Brain Loadings Comp 3"), size.name = 1.4)
  339. plotLoadings(spls.final.4comp.multilevel.canonical, comp = 4, contrib = max, method = "median", subtitle = c('Questionnairy Loadings Comp 4', "Brain Loadings Comp 4"), size.name = 1.4)
  343. # Correlation Cirle
  344. dev.off()
  345. ?plotVar
  346. #for component 1 and 2
  347. plotVar(spls.final.4comp.multilevel.canonical,
  348. cex = c(4,4), # font size of variable names
  349. # overlap = FALSE # creates two separate plots for X and Y; default is TRUE
  350. )
  351. # or component 1 and 3
  352. plotVar(spls.final.4comp.multilevel.canonical,
  353. comp = c(1,3),
  354. cex = c(4,4), # font size of variable names
  355. # overlap = FALSE # creates two separate plots for X and Y; default is TRUE
  356. )
  357. # or component 1 and 4
  358. plotVar(spls.final.4comp.multilevel.canonical,
  359. comp = c(1,4),
  360. cex = c(4,4), # font size of variable names
  361. # overlap = FALSE # creates two separate plots for X and Y; default is TRUE
  362. )
  363. # or component 2 and 3
  364. plotVar(spls.final.4comp.multilevel.canonical,
  365. comp = c(2,3),
  366. cex = c(4,4), # font size of variable names
  367. # overlap = FALSE # creates two separate plots for X and Y; default is TRUE
  368. )
  369. # or component 2 and 4
  370. plotVar(spls.final.4comp.multilevel.canonical,
  371. comp = c(2,4),
  372. cex = c(4,4), # font size of variable names
  373. # overlap = FALSE # creates two separate plots for X and Y; default is TRUE
  374. )
  375. # or component 4 and 3
  376. plotVar(spls.final.4comp.multilevel.canonical,
  377. comp = c(3,4),
  378. cex = c(4,4), # font size of variable names
  379. # overlap = FALSE # creates two separate plots for X and Y; default is TRUE
  380. )
  383. # Inividuals
  384. #plotIndiv(spls.final.multilevel.canonical, comp = c(1,2))
  385. #plotIndiv(spls.final.multilevel.canonical, rep.space = "XY",
  386. # group = design$sample,
  387. # col.per.group = color.mixo(1:8),
  388. # legend = TRUE, legend.title = 'Subject')
  390. # Variance explained
  391. spls.final.4comp.multilevel.canonical$prop_expl_var
  393. # Network Plot
  395. color.edge <- color.GreenRed(50) # set the colours of the connecting lines
  396. X11()
  397. par(oma=c(0.5,0.5,0.5,0.5)) # bottom, left, top, right
  399. dev.off()
  400. # To open a new window for Rstudio
  401. network(spls.final.4comp.multilevel.canonical, comp = 1:4,
  402. cutoff = 0.4, # only show connections with a correlation above 0.7
  403. shape.node = c("circle", "rectangle"),
  404. color.node = c("steelblue", "salmon2"),
  405. color.edge = color.edge,
  406. size.node = 1,
  407. cex.node.name = 0.7)
  411. ?cim
  412. X11()
  413. dev.off()
  414. par(oma=c(3,2,2,6))
  415. cim(spls.final.4comp.multilevel.canonical)

Step4_sPLS_final.R, under CC-BY-4.0 · at the source

Overview

Authors: Polona Kalc1, Mayla ter Veer1,2, Robert Dahnke1,3,4, Gabriel Ziegler5,6, Simone Kühn7, Christian Gaser1,3,4
ORCID iDs: Polona Kalc, Gabriel Ziegler
  1. Structural Brain Mapping Group, Department of Neurology Jena University Hospital Jena Germany
  2. Department of Biological Psychology and Cognitive Neuroscience Institute for Psychology, Friedrich‐Schiller University of Jena Jena Germany
  3. Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany
  4. German Center for Mental Health (DZPG), Jena‐Halle‐Magdeburg Germany
  5. German Center for Neurodegenerative Diseases (DZNE) Magdeburg Germany
  6. Institute of Cognitive Neurology and Dementia Research (IKND), Otto‐von‐Guericke University Magdeburg Germany
  7. Center for Environmental Neuroscience, Max Planck Institute for Human Development Berlin Germany
Institutions: Jena University Hospital (Germany); Friedrich Schiller University Jena (Germany); Deutsches Zentrum für Psychische Gesundheit (Germany); German Center for Neurodegenerative Diseases (Germany); Otto-von-Guericke-Universität Magdeburg (Germany); Max Planck Institute for Human Development (Germany)
Journal: Human brain mapping, volume 47, issue 4, article e70500
Dates: received 29 April 2025; accepted 3 March 2026; published online 12 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/hbm.70500 · PMID 41817026 · PMCID PMC13093422 · OpenAlex W7135074313
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), methods / tools (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning, Statistics
Keywords: longitudinal, partial least squares, structural MRI, within‐subject variance
MeSH: Brain*, Gray Matter*, Magnetic Resonance Imaging*, Female, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Male (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Carl‐Zeiss‐Stiftung (IMPULS P2019‐01‐006); Marie Skłodowska-Curie Innovative Training Network (SmartAge 859890 H2020‐MSCA‐ITN2019); European Union (ERC‐2022‐CoG‐BrainScape‐101086188); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (Pattern‐Cog ERAPERMED2021‐127)
Citations: cited by 1 paper (Europe PMC); 60 references in the paper

Abstract

When planning longitudinal magnetic resonance imaging (MRI) studies, it is advisable to consider various (confounding) factors that could influence brain structural changes over time. The goal of this study was to identify factors that contribute to intraindividual variability of brain structure within a short period of time. We employed multilevel sparse partial least squares regression to investigate the changes in regional gray matter volume in the longitudinal Day2day MRI dataset. The findings suggest that the changes in regional GM volume estimations were primarily driven by image quality, while the outdoor temperature and time since baseline appeared as the main predictors of volumetric changes in insular and diencephalic brain regions. We additionally investigated factors associated with variability in image quality. The findings underscore the importance of maintaining adequate participant arousal during scanning.

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 4 matches between paragraphs and lines of code.

figshare 28839635

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), ggplot2 (2 files), CAT12 (1 file), cowplot (1 file), GIfTI library for MATLAB (1 file), patchwork (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
6 files
At the source: doi.org/10.6084/m9.figshare.28839635

The paper's code and data availability statement is in the Data section.

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;
  • 6 scripts, each with its path and the digest of its content;
  • 4 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 Day2day data is available freely on request from Prof. Dr. Simone Kühn. The code to replicate the findings is available at Figshare (DOI: 10.6084/m9.figshare.28839635 (https://doi.org/10.6084/m9.figshare.28839635)).

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 8 MeSH terms, 4 funders, 59 references.

Cite

This paper

Kalc, P., ter Veer, M., Dahnke, R., Ziegler, G., Kühn, S., & Gaser, C. (2026). Factors Contributing to Short-Term Structural Variability in a Longitudinal MRI Dataset. Human brain mapping, 47(4), e70500. https://doi.org/10.1002/hbm.70500

BibTeX

@article{kalc2026factors,
author = {Kalc, Polona and ter Veer, Mayla and Dahnke, Robert and Ziegler, Gabriel and Kühn, Simone and Gaser, Christian},
title = {{Factors Contributing to Short-Term Structural Variability in a Longitudinal MRI Dataset}},
journal = {Human brain mapping},
year = {2026},
month = mar,
volume = {47},
number = {4},
pages = {e70500},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70500},
url = {https://doi.org/10.1002/hbm.70500},
pmid = {41817026},
pmcid = {PMC13093422}
}

RIS

TY - JOUR
AU - Kalc, Polona
AU - ter Veer, Mayla
AU - Dahnke, Robert
AU - Ziegler, Gabriel
AU - Kühn, Simone
AU - Gaser, Christian
TI - Factors Contributing to Short-Term Structural Variability in a Longitudinal MRI Dataset
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/03/01
VL - 47
IS - 4
SP - e70500
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70500
UR - https://doi.org/10.1002/hbm.70500
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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