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Inefficient integration between effort and reward in anhedonia.

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  1. [1] § Material and methods › EEG recording and processing ↔ Analysis_code.R, lines 1–51 · score 0.76 · 276 ms, 324 ms, 376 ms, 424 ms, 450 ms, RewP

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  1. rm(list=ls())
  3. ###########################################################################################
  4. # load required packages
  5. pacman::p_load(R.matlab,dplyr,ggplot2,lme4,lmerTest,emmeans,effects,tidyverse,sjPlot,MASS)
  7. # set path to folder containing this script
  8. setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
  10. ### Load data: ----
  11. # 1) Behavioral data ----
  12. a1 <- read.csv('Behavior/behavior_data.csv')
  13. a2 <- read.csv('Behavior/choice_data.csv')
  14. r1 <- read.csv('Behavior/rating_data.csv')
  16. # 2) Neural signal data ----
  17. electrode <- c('FP1', 'FP2', 'F7', 'F3', 'FZ', 'F4', 'F8', 'FT7', 'FC3', 'FCZ',
  18. 'FC4', 'FT8', 'T7', 'C3', 'CZ', 'C4', 'T8', 'CP3', 'CPZ', 'CP4',
  19. 'P7', 'P3', 'PZ', 'P4', 'P8', 'O1', 'OZ', 'O2')
  20. # CueP3 [456 556] CPZ CP3 CP4 PZ P3 P4
  21. CueP3 = readMat('EEG/CueP3440600.mat' )
  22. CueP3 = CueP3$CueP3440600
  23. CueP3 = as.data.frame(CueP3)
  24. colnames(CueP3) <- electrode
  25. a1$CueP3 <- apply(cbind (CueP3$CP3, CueP3$CPZ, CueP3$CP4,CueP3$P3, CueP3$PZ, CueP3$P4),1, mean) # computes average activation in ROI
  26. # RewP [276 376] FCZ FC3 FC4
  27. RewP = readMat('EEG/RewP276376.mat')
  28. RewP = RewP$RewP276376
  29. RewP = as.data.frame(RewP)
  30. colnames(RewP)<- electrode
  31. a1$RewP <- apply(cbind ( RewP$FCz, RewP$FC3,RewP$FC4),1, mean) # computes average activation in ROI
  32. # feedback-P3 [324 424] PZ P3 P4
  33. fbP3 = readMat('EEG/fbP3324424.mat')
  34. fbP3 = fbP3$fbP3324424
  35. fbP3 = as.data.frame(fbP3)
  36. colnames(fbP3)<- electrode
  37. a1$fbP3 <- apply(cbind (fbP3$P3, fbP3$PZ, fbP3$P4),1, mean) # computes average activation in ROI
  38. # performP3 [250 450] PZ P3 P4
  39. PerformP3 = readMat('EEG/PerformP3250450.mat')
  40. PerformP3 = PerformP3$PerformP3250450
  41. PerformP3 = as.data.frame(PerformP3)
  42. colnames(PerformP3)<- electrode
  43. a1$PerformP3<- apply(cbind (PerformP3$Pz, PerformP3$P3, PerformP3$P4),1, mean)
  44. # ChoiceTheta [100 400] FCz
  45. theta = readMat('EEG/theta100400.mat')
  46. theta = theta$theta100400
  47. theta = as.data.frame(theta)
  48. frex = pracma::logspace(log10(1),log10(30),30) %>% round(digits = 4)
  49. colnames(theta) <- frex
  50. fidx <- c(which.min(abs(frex-4)), which.min(abs(frex-7)))
  51. a2$theta <- apply(theta[,fidx[1]:fidx[2]], 1, mean)
  53. # remove outliers
  54. for (i in levels(a1$Subject) )
  55. { print(i)
  56. a1[a1$Subject==i,]$CompleteRT <- outliers_mad(a1[a1$Subject==i,]$CompleteRT, 4)
  57. a1[a1$Subject==i,]$CueP3 <- outliers_mad(a1[a1$Subject==i,]$CueP3, 4)
  58. a1[a1$Subject==i,]$PerformP3 <- outliers_mad(a1[a1$Subject==i,]$PerformP3, 4)
  59. a1[a1$Subject==i,]$RewP <- outliers_mad(a1[a1$Subject==i,]$RewP, 4)
  60. a1[a1$Subject==i,]$fbP3 <- outliers_mad(a1[a1$Subject==i,]$fbP3, 4)
  61. }
  62. # as factor & standardize of effort phase
  63. a1$Subject <- as.factor(a1$Subject)
  65. a1 <- a1 %>% mutate(group = ifelse(group == 1,"CNT","ANH") )
  66. a1$group<-factor(a1$group, levels = c("CNT","ANH"))
  67. contrasts(a1$group) <- contr.sdif(2)
  69. a1<-a1%>%mutate(Complete=ifelse(Complete==0,0,1))
  71. a1<-a1%>%mutate(zEffort=scale(Effort, scale=TRUE, center=TRUE) )
  72. a1$zEffort <- as.vector(a1$zEffort)
  74. a1<-a1%>%mutate(zMagnitude=scale(Magnitude, scale=TRUE, center=TRUE) )
  75. a1$zMagnitude <- as.vector(a1$zMagnitude)
  77. a1 <- a1 %>% mutate(valence = ifelse(RewardType==0.0 ,"Loss", "Gain") )
  78. a1$valence<-factor(a1$valence, levels = c("Loss","Gain"))
  79. contrasts(a1$valence) <- contr.sdif(2)
  81. # remove outliers of choice phase
  82. a2 <- a2 %>% mutate( theta= ifelse(Subject==102,NaN, theta) ) # delete neural data of subject 102
  83. for (i in levels(a2$Subject) )
  84. { print(i)
  85. a2[a2$Subject==i,]$ChoiceRT <- outliers_mad(a2[a2$Subject==i,]$ChoiceRT, 4)
  86. a2[a2$Subject==i,]$theta <- outliers_mad(a2[a2$Subject==i,]$theta, 4)
  87. }
  88. # as factor & standardize of choice phase
  89. a2$Subject <- as.factor(a2$Subject)
  91. a2 <- a2 %>% mutate(group = ifelse(group == 1,"CNT","ANH") )
  92. a2$group<-factor(a2$group, levels = c("CNT","ANH"))
  93. contrasts(a2$group) <- contr.sdif(2)
  95. a2 <- a2 %>% mutate(Choice = ifelse(Choice==2,NaN, Choice))
  96. a2<-a2%>%mutate(Choice=ifelse(Choice==3,0,1))
  97. # a2$Choice <- as.factor(a2$Choice)
  98. # contrasts(a2$Choice) <- contr.sdif(2)
  100. a2<-a2%>%mutate(zEffort=scale(Effort, scale=TRUE, center=TRUE) );
  101. a2$zEffort <- as.vector(a2$zEffort)
  102. a2<-a2%>%mutate(zMagnitude=scale(Magnitude, scale=TRUE, center=TRUE) );
  103. a2$zMagnitude <- as.vector(a2$zMagnitude)
  105. for (i in levels(a2$Subject) )
  106. { print(i)
  107. a2$ztheta[a2$Subject==i] <- scale(a2$theta[a2$Subject==i], scale= TRUE, center=TRUE )
  108. # a2$zRT[a2$Subject==i] <- scale(a2$ChoiceRT[a2$Subject==i], scale= TRUE, center=TRUE )
  109. }
  111. # change the Rating data into longer format
  112. r2<-pivot_longer(data = r1,
  113. cols = c(Liking_Effort1, Liking_Effort2, Liking_Effort3, Liking_Effort4,Liking_Effort5),
  114. names_to = c("EffortLevel"), values_to = "Liking",
  115. names_pattern = "(1|2|3|4|5)") %>% dplyr::select(SubID,group,Liking)
  116. r3<-pivot_longer(data = r1,
  117. cols = c(Tired_Effort1, Tired_Effort2, Tired_Effort3, Tired_Effort4,Tired_Effort5 ),
  118. names_to = c("EffortLevel"), values_to = "Tired",
  119. names_pattern = "(1|2|3|4|5)") %>% dplyr::select(Tired)
  120. r4<-pivot_longer(data = r1,
  121. cols = c(PhyRequire_Effort1, PhyRequire_Effort2, PhyRequire_Effort3, PhyRequire_Effort4,PhyRequire_Effort5),
  122. names_to = c("EffortLevel"), values_to = "PhyRequire",
  123. names_pattern = "(1|2|3|4|5)") %>% dplyr::select(PhyRequire)
  124. r5<-pivot_longer(data = r1,
  125. cols = c(TimeStress_Effort1, TimeStress_Effort2, TimeStress_Effort3, TimeStress_Effort4,TimeStress_Effort5 ),
  126. names_to = c("EffortLevel"), values_to = "TimeStress",
  127. names_pattern = "(1|2|3|4|5)") %>% dplyr::select(TimeStress)
  128. r6<-pivot_longer(data = r1,
  129. cols = c(Perform_Effort1, Perform_Effort2, Perform_Effort3, Perform_Effort4,Perform_Effort5 ),
  130. names_to = c("EffortLevel"), values_to = "Perform",
  131. names_pattern = "(1|2|3|4|5)") %>% dplyr::select(Perform)
  132. r7<- cbind(r2,r3,r4,r5,r6)%>%mutate(EffortLevel = (rep(c(1,2,3,4,5),times = length(r1$SubID))))
  133. r8<-pivot_longer(data = r7,
  134. cols = c(Liking,Tired,PhyRequire,TimeStress,Perform),
  135. names_to = c("Items"), values_to = "Rating",
  136. names_pattern = "(Liking|Tired|PhyRequire|TimeStress|Perform)") %>% dplyr::select(-Items)%>%
  137. mutate(Items = (rep(c("Liking","Tired","PhyRequire","TimeStress","Perform"),times = length(r1$SubID)*5)))
  138. # as factor & standardize of rating data
  139. r8$SubID <- as.factor(r8$SubID)
  140. r8$group<-factor(r8$group, levels = c("CNT","ANH"))
  141. contrasts(r8$group) <- contr.sdif(2)
  142. r8<-r8%>%mutate(zEffort=scale(as.numeric(EffortLevel), scale=TRUE, center=TRUE) )
  143. r8$zEffort <- as.vector(r8$zEffort)
  145. ### Analyses: ----
  146. # 3.1 Results of rating data ----
  147. print (summary(mod_liking <- lmer(Rating ~ group*zEffort+(1| SubID) ,data= r8[r8$Items=="Liking",])))
  148. # Fixed effects:
  149. # Estimate Std. Error df t value Pr(>|t|)
  150. # (Intercept) 6.98250 0.13913 78.00000 50.186 <2e-16 ***
  151. # group2-1 -0.49500 0.27827 78.00000 -1.779 0.0792 .
  152. # zEffort -1.26604 0.06758 318.00000 -18.735 <2e-16 ***
  153. # group2-1:zEffort -0.36071 0.13515 318.00000 -2.669 0.0080 **
  154. print (summary(mod_effort <- lmer(Rating ~ group*zEffort+(1| SubID),data= r8[r8$Items=="PhyRequire",])))
  155. # Fixed effects:
  156. # Estimate Std. Error df t value Pr(>|t|)
  157. # (Intercept) 3.56250 0.11651 78.00000 30.576 <2e-16 ***
  158. # group2-1 0.09500 0.23302 78.00000 0.408 0.685
  159. # zEffort 2.09356 0.06241 318.00000 33.546 <2e-16 ***
  160. # group2-1:zEffort -0.07073 0.12482 318.00000 -0.567 0.571
  161. print (summary(mod_Tired <- lmer(Rating ~ group*zEffort+(1| SubID),data= r8[r8$Items=="Tired",])))
  162. # Fixed effects:
  163. # Estimate Std. Error df t value Pr(>|t|)
  164. # (Intercept) 3.62500 0.12297 78.00000 29.479 <2e-16 ***
  165. # group2-1 0.14000 0.24594 78.00000 0.569 0.571
  166. # zEffort 2.07588 0.06315 318.00000 32.874 <2e-16 ***
  167. # group2-1:zEffort -0.20511 0.12629 318.00000 -1.624 0.105
  168. print (summary(mod_TimeStress <- lmer(Rating ~ group*zEffort+(1| SubID),data= r8[r8$Items=="TimeStress",])))
  169. # Fixed effects:
  170. # Estimate Std. Error df t value Pr(>|t|)
  171. # (Intercept) 3.53500 0.12518 78.00000 28.240 <2e-16 ***
  172. # group2-1 0.28000 0.25035 78.00000 1.118 0.267
  173. # zEffort 2.04759 0.06376 318.00001 32.116 <2e-16 ***
  174. # group2-1:zEffort 0.13438 0.12751 318.00001 1.054 0.293
  175. print (summary(mod_Perform <- lmer(Rating ~ group*zEffort+(1| SubID),data= r8[r8$Items=="Perform",])))
  176. # Fixed effects:
  177. # Estimate Std. Error df t value Pr(>|t|)
  178. # (Intercept) 7.30500 0.13823 78.00000 52.847 <2e-16 ***
  179. # group2-1 -0.65000 0.27646 78.00000 -2.351 0.0212 *
  180. # zEffort -1.14934 0.06556 318.00000 -17.531 <2e-16 ***
  181. # group2-1:zEffort -0.13438 0.13112 318.00000 -1.025 0.3062
  182. tab_model(mod_liking,mod_effort,mod_Perform,mod_Tired,mod_TimeStress)
  184. # 3.2 Results of the effort-reward task
  185. # 3.2.1 Behavioral data ----
  186. # a) success rate
  187. print (summary(mod_complete <- glmer(data=a1, Complete~ group*zEffort*zMagnitude+(zEffort|Subject),family=binomial,control=glmerControl("bobyqa"))))
  188. # Fixed effects:
  189. # Estimate Std. Error z value Pr(>|z|)
  190. # (Intercept) 10.4117 1.2720 8.185 2.72e-16 ***
  191. # group2-1 -2.0493 1.5001 -1.366 0.172
  192. # zEffort -5.7306 0.9085 -6.308 2.83e-10 ***
  193. # zMagnitude -0.8350 0.5531 -1.510 0.131
  194. # group2-1:zEffort 1.3533 1.0770 1.257 0.209
  195. # group2-1:zMagnitude 0.2713 1.0985 0.247 0.805
  196. # zEffort:zMagnitude 0.5880 0.3963 1.484 0.138
  197. # group2-1:zEffort:zMagnitude -0.2196 0.7869 -0.279 0.780
  198. tab_model(mod_complete, transform = NULL)
  199. # b) completion RT
  200. print (summary(mod_CompleteRT <- lmer(CompleteRT~ group*zEffort*zMagnitude+(zEffort|Subject), data=a1, REML=FALSE, control=lmerControl("bobyqa"))))
  201. # Fixed effects:
  202. # Estimate Std. Error df t value Pr(>|t|)
  203. # (Intercept) 3.213e+00 2.481e-02 7.900e+01 129.510 < 2e-16 ***
  204. # group2-1 3.624e-02 4.962e-02 7.900e+01 0.730 0.46726
  205. # zEffort 1.621e+00 1.144e-02 7.900e+01 141.730 < 2e-16 ***
  206. # zMagnitude -9.360e-03 3.348e-03 1.169e+04 -2.796 0.00519 **
  207. # group2-1:zEffort 3.877e-02 2.288e-02 7.900e+01 1.695 0.09406 .
  208. # group2-1:zMagnitude -5.907e-03 6.696e-03 1.169e+04 -0.882 0.37770
  209. # zEffort:zMagnitude -7.221e-03 3.348e-03 1.169e+04 -2.157 0.03105 *
  210. # group2-1:zEffort:zMagnitude -4.386e-03 6.697e-03 1.169e+04 -0.655 0.51251
  211. tab_model(mod_CompleteRT)
  212. mylist <- list(zEffort=c(1,-1))
  213. emtrends(mod_CompleteRT, pairwise~zEffort, var="zMagnitude",at=mylist,pbkrtest.limit = 11850) %>% summary(infer = c(TRUE,TRUE))
  214. # $emtrends
  215. # zEffort zMagnitude.trend SE df lower.CL upper.CL t.ratio p.value
  216. # 1 -0.01658 0.00474 11696 -0.0259 -0.00730 -3.501 0.0005
  217. # -1 -0.00214 0.00474 11696 -0.0114 0.00714 -0.452 0.6515
  218. # 3.2.2 Neural signal data ----
  219. # a) Cue-P3
  220. print (summary(mod_cueP3 <- lmer(CueP3~ group*zEffort*zMagnitude+(zEffort|Subject),data = a1,REML=FALSE, control=lmerControl("bobyqa"))))
  221. # Fixed effects:
  222. # Estimate Std. Error df t value Pr(>|t|)
  223. # (Intercept) 4.983e+00 3.148e-01 7.991e+01 15.826 <2e-16 ***
  224. # group2-1 -7.539e-01 6.297e-01 7.991e+01 -1.197 0.2347
  225. # zEffort 1.196e+00 1.095e-01 7.974e+01 10.923 <2e-16 ***
  226. # zMagnitude 1.326e-01 7.528e-02 1.148e+04 1.762 0.0781 .
  227. # group2-1:zEffort -3.312e-01 2.191e-01 7.974e+01 -1.512 0.1345
  228. # group2-1:zMagnitude -3.143e-01 1.506e-01 1.148e+04 -2.088 0.0368 *
  229. # zEffort:zMagnitude 1.157e-01 7.529e-02 1.148e+04 1.536 0.1245
  230. # group2-1:zEffort:zMagnitude -2.900e-02 1.506e-01 1.148e+04 -0.193 0.8473
  231. tab_model(mod_cueP3)
  232. emtrends(mod_cueP3, pairwise~group, var="zMagnitude", infer=T,pbkrtest.limit = 11635)
  233. # $emtrends
  234. # group zMagnitude.trend SE df lower.CL upper.CL t.ratio p.value
  235. # CNT 0.2898 0.107 11483 0.0807 0.499 2.717 0.0066
  236. # ANH -0.0245 0.106 11481 -0.2329 0.184 -0.231 0.8175
  237. # b) RewP
  238. print (summary(mod_RewP <- lmer(RewP~ group*zEffort*zMagnitude*valence+(zEffort+zMagnitude+valence|Subject),data = a1,REML=FALSE, control=lmerControl("bobyqa"))))
  239. # Fixed effects:
  240. # Estimate Std. Error df t value Pr(>|t|)
  241. # (Intercept) 3.430e+00 3.514e-01 7.972e+01 9.760 2.94e-15 ***
  242. # group2-1 1.573e+00 7.028e-01 7.972e+01 2.239 0.02795 *
  243. # zEffort -3.711e-02 1.281e-01 7.933e+01 -0.290 0.77284
  244. # zMagnitude 5.384e-01 1.016e-01 7.913e+01 5.297 1.03e-06 ***
  245. # valence2-1 1.259e+00 2.074e-01 7.890e+01 6.073 4.16e-08 ***
  246. # group2-1:zEffort 3.077e-01 2.563e-01 7.933e+01 1.201 0.23341
  247. # group2-1:zMagnitude 3.242e-01 2.033e-01 7.913e+01 1.595 0.11473
  248. # zEffort:zMagnitude -2.387e-01 8.570e-02 1.127e+04 -2.786 0.00535 **
  249. # group2-1:valence2-1 -8.461e-01 4.148e-01 7.890e+01 -2.040 0.04471 *
  250. # zEffort:valence2-1 -1.115e-01 1.713e-01 1.126e+04 -0.651 0.51505
  251. # zMagnitude:valence2-1 4.207e-01 1.708e-01 1.125e+04 2.463 0.01377 *
  252. # group2-1:zEffort:zMagnitude 2.246e-01 1.714e-01 1.127e+04 1.311 0.19004
  253. # group2-1:zEffort:valence2-1 1.847e-01 3.427e-01 1.126e+04 0.539 0.58996
  254. # group2-1:zMagnitude:valence2-1 -5.454e-03 3.416e-01 1.125e+04 -0.016 0.98726
  255. # zEffort:zMagnitude:valence2-1 9.489e-02 1.714e-01 1.125e+04 0.554 0.57982
  256. # group2-1:zEffort:zMagnitude:valence2-1 3.700e-02 3.428e-01 1.125e+04 0.108 0.91404
  257. tab_model(mod_RewP)
  258. emtrends(mod_RewP, ~valence, var="zMagnitude",at=mylist,infer=T,pbkrtest.limit = 11557)
  259. # valence zMagnitude.trend SE df lower.CL upper.CL t.ratio p.value
  260. # Loss 0.328 0.134 238 0.0645 0.592 2.452 0.0149
  261. # Gain 0.749 0.134 238 0.4852 1.012 5.597 <.0001
  262. mylist <- list(zEffort=c(1,-1))
  263. emtrends(mod_RewP, ~zEffort, var="zMagnitude",at=mylist,pbkrtest.limit = 11557) %>% summary(infer = c(TRUE,TRUE))
  264. # zEffort zMagnitude.trend SE df lower.CL upper.CL t.ratio p.value
  265. # 1 0.300 0.135 244 0.0342 0.565 2.224 0.0271
  266. # -1 0.777 0.133 234 0.5147 1.040 5.834 <.0001
  267. emmeans(mod_RewP,pairwise~valence|group,infer=T,pbkrtest.limit = 11557)
  268. # $contrasts
  269. # group = CNT:
  270. # contrast estimate SE df lower.CL upper.CL t.ratio p.value
  271. # Loss - Gain -1.683 0.297 82.2 -2.27 -1.092 -5.662 <.0001
  272. #
  273. # group = ANH:
  274. # contrast estimate SE df lower.CL upper.CL t.ratio p.value
  275. # Loss - Gain -0.834 0.297 81.8 -1.43 -0.244 -2.811 0.0062
  276. # c) Feedback-P3
  277. print (summary(mod_fbP3 <- lmer(fbP3~ group*zEffort*zMagnitude*valence+(zMagnitude+valence|Subject),data = a1,REML=FALSE, control=lmerControl("bobyqa"))))
  278. # Fixed effects:
  279. # Estimate Std. Error df t value Pr(>|t|)
  280. # (Intercept) 5.317e+00 4.498e-01 7.990e+01 11.819 < 2e-16 ***
  281. # group2-1 1.623e+00 8.997e-01 7.990e+01 1.804 0.0750 .
  282. # zEffort 4.069e-02 8.250e-02 1.133e+04 0.493 0.6218
  283. # zMagnitude 5.189e-01 1.010e-01 7.921e+01 5.139 1.94e-06 ***
  284. # valence2-1 1.286e+00 2.145e-01 7.938e+01 5.995 5.70e-08 ***
  285. # group2-1:zEffort 2.241e-01 1.650e-01 1.133e+04 1.358 0.1744
  286. # group2-1:zMagnitude 1.447e-01 2.020e-01 7.921e+01 0.717 0.4757
  287. # zEffort:zMagnitude -1.884e-01 8.251e-02 1.134e+04 -2.283 0.0225 *
  288. # group2-1:valence2-1 -6.521e-01 4.291e-01 7.938e+01 -1.520 0.1325
  289. # zEffort:valence2-1 -2.472e-01 1.650e-01 1.133e+04 -1.498 0.1341
  290. # zMagnitude:valence2-1 7.090e-01 1.644e-01 1.133e+04 4.312 1.63e-05 ***
  291. # group2-1:zEffort:zMagnitude -1.080e-01 1.650e-01 1.134e+04 -0.655 0.5127
  292. # group2-1:zEffort:valence2-1 7.048e-01 3.299e-01 1.133e+04 2.136 0.0327 *
  293. # group2-1:zMagnitude:valence2-1 5.231e-02 3.289e-01 1.133e+04 0.159 0.8736
  294. # zEffort:zMagnitude:valence2-1 4.165e-01 1.650e-01 1.133e+04 2.525 0.0116 *
  295. # group2-1:zEffort:zMagnitude:valence2-1 2.822e-02 3.300e-01 1.133e+04 0.086 0.9319
  296. tab_model(mod_fbP3)
  297. mylist <- list(zEffort=c(1,-1),group=c("CNT","ANH"))
  298. emtrends(mod_fbP3, ~zEffort|valence, var="zMagnitude",at=mylist,pbkrtest.limit = 11557) %>% summary(infer = c(TRUE,TRUE))
  299. # valence = Loss:
  300. # zEffort zMagnitude.trend SE df lower.CL upper.CL t.ratio p.value
  301. # 1 -0.232 0.176 726 -0.579 0.114 -1.316 0.1886
  302. # -1 0.561 0.174 698 0.218 0.904 3.216 0.0014
  303. #
  304. # valence = Gain:
  305. # zEffort zMagnitude.trend SE df lower.CL upper.CL t.ratio p.value
  306. # 1 0.893 0.177 735 0.546 1.241 5.048 <.0001
  307. # -1 0.854 0.175 701 0.511 1.196 4.886 <.0001
  308. emtrends(mod_fbP3, ~group|valence, var="zEffort",pbkrtest.limit = 11557) %>% summary(infer = c(TRUE,TRUE))
  309. # valence = Loss:
  310. # group zEffort.trend SE df lower.CL upper.CL t.ratio p.value
  311. # CNT 0.230 0.165 11338 -0.0932 0.5535 1.395 0.1630
  312. # ANH 0.102 0.164 11345 -0.2196 0.4245 0.624 0.5329
  313. #
  314. # valence = Gain:
  315. # group zEffort.trend SE df lower.CL upper.CL t.ratio p.value
  316. # CNT -0.371 0.165 11338 -0.6954 -0.0475 -2.248 0.0246
  317. # ANH 0.206 0.166 11343 -0.1192 0.5302 1.241 0.2148
  318. # d) Performance-P3
  319. print (summary(mod_PerformP3 <- lmer(PerformP3~ group*zEffort*zMagnitude+(zEffort|Subject),data = a1,REML=FALSE, control=lmerControl("bobyqa"))))
  320. # Fixed effects:
  321. # Estimate Std. Error df t value Pr(>|t|)
  322. # (Intercept) 1.848e+00 2.556e-01 7.967e+01 7.229 2.62e-10 ***
  323. # group2-1 3.235e-02 5.112e-01 7.967e+01 0.063 0.950
  324. # zEffort 4.853e-01 1.173e-01 7.910e+01 4.137 8.70e-05 ***
  325. # zMagnitude 2.855e-02 7.177e-02 1.121e+04 0.398 0.691
  326. # group2-1:zEffort 7.277e-02 2.346e-01 7.910e+01 0.310 0.757
  327. # group2-1:zMagnitude 3.776e-02 1.435e-01 1.121e+04 0.263 0.792
  328. # zEffort:zMagnitude -8.869e-02 7.201e-02 1.122e+04 -1.232 0.218
  329. # group2-1:zEffort:zMagnitude 1.184e-01 1.440e-01 1.122e+04 0.822 0.411
  330. tab_model(mod_PerformP3)
  332. # 3.3 Results of the effort-based decision-making task
  333. # 3.3.1 Behavioral data ----
  334. # a) choice rate
  335. print (summary(mod_choice <- glmer(data=a2, Choice~ group*zEffort*zMagnitude+(zEffort+zMagnitude|Subject),family=binomial,control=glmerControl("bobyqa"))))
  336. # Fixed effects:
  337. # Estimate Std. Error z value Pr(>|z|)
  338. # (Intercept) 7.04504 0.66087 10.660 <2e-16 ***
  339. # group2-1 0.01721 1.09119 0.016 0.9874
  340. # zEffort -2.26011 0.23915 -9.451 <2e-16 ***
  341. # zMagnitude 3.86089 0.38644 9.991 <2e-16 ***
  342. # group2-1:zEffort 0.75108 0.40631 1.849 0.0645 .
  343. # group2-1:zMagnitude -0.40774 0.60951 -0.669 0.5035
  344. # zEffort:zMagnitude 0.05601 0.09038 0.620 0.5354
  345. # group2-1:zEffort:zMagnitude -0.11238 0.17478 -0.643 0.5202
  346. tab_model(mod_choice, transform = NULL)
  347. emtrends(mod_choice, ~group, var="zEffort", infer=T,pbkrtest.limit = 11635) %>% summary(infer = c(TRUE,TRUE))
  348. # group zEffort.trend SE df asymp.LCL asymp.UCL z.ratio p.value
  349. # CNT -2.64 0.318 Inf -3.26 -2.01 -8.277 <.0001
  350. # ANH -1.88 0.309 Inf -2.49 -1.28 -6.097 <.0001
  351. # d) decision RT
  352. print (summary(mod_choiceRT <- lmer(ChoiceRT~ group*zEffort*zMagnitude+(1+zEffort|Subject), data=a2, REML=FALSE, control=lmerControl("bobyqa"))))
  353. # Fixed effects:
  354. # Estimate Std. Error df t value Pr(>|t|)
  355. # (Intercept) 587.259 23.119 80.000 25.402 < 2e-16 ***
  356. # group2-1 -50.795 46.238 80.000 -1.099 0.275
  357. # zEffort 25.146 5.149 80.000 4.884 5.24e-06 ***
  358. # zMagnitude -3.812 4.234 7840.000 -0.900 0.368
  359. # group2-1:zEffort -9.285 10.298 80.000 -0.902 0.370
  360. # group2-1:zMagnitude -10.266 8.468 7840.000 -1.212 0.225
  361. # zEffort:zMagnitude -3.361 4.234 7840.000 -0.794 0.427
  362. # group2-1:zEffort:zMagnitude 12.979 8.469 7840.000 1.532 0.125
  363. tab_model(mod_choiceRT)
  364. # 3.3.2 Neural signal data ----
  365. # a) choice-theta
  366. print (summary(mod_Theta <- lmer(theta~ group*zEffort*zMagnitude+(zEffort |Subject),data = a2,REML=FALSE, control=lmerControl("bobyqa"))))
  367. # Fixed effects:
  368. # Estimate Std. Error df t value Pr(>|t|)
  369. # (Intercept) 4.633e-01 3.906e-02 7.905e+01 11.861 < 2e-16 ***
  370. # group2-1 -4.337e-02 7.812e-02 7.905e+01 -0.555 0.580340
  371. # zEffort -7.641e-03 1.402e-02 7.890e+01 -0.545 0.587195
  372. # zMagnitude -3.901e-02 1.105e-02 7.496e+03 -3.529 0.000419 ***
  373. # group2-1:zEffort 1.390e-02 2.803e-02 7.890e+01 0.496 0.621310
  374. # group2-1:zMagnitude 4.849e-02 2.211e-02 7.496e+03 2.193 0.028310 *
  375. # zEffort:zMagnitude -7.976e-03 1.106e-02 7.499e+03 -0.721 0.470785
  376. # group2-1:zEffort:zMagnitude 5.583e-02 2.212e-02 7.499e+03 2.524 0.011614 *
  377. tab_model(mod_Theta)
  378. mylist <- list(zEffort=c(1,-1),group=c("CNT","ANH"))
  379. emtrends(mod_Theta, ~zEffort | group, var="zMagnitude",at=mylist,pbkrtest.limit = 7651) %>% summary(infer = c(TRUE,TRUE))
  380. # group = CNT:
  381. # zEffort zMagnitude.trend SE df lower.CL upper.CL t.ratio p.value
  382. # 1 -0.09915 0.0223 7504 -0.1428 -0.05548 -4.450 <.0001
  383. # -1 -0.02737 0.0222 7501 -0.0710 0.01623 -1.231 0.2185
  384. #
  385. # group = ANH:
  386. # zEffort zMagnitude.trend SE df lower.CL upper.CL t.ratio p.value
  387. # 1 0.00517 0.0219 7501 -0.0378 0.04816 0.236 0.8136
  388. # -1 -0.03470 0.0220 7501 -0.0779 0.00847 -1.576 0.1151
  389. # b) Could theta predict participants' effort choices
  390. print (summary(mod_choice_theta <- glmer(data=a2, Choice~ group*ztheta+(1|Subject),family=binomial,control=glmerControl("bobyqa"))))
  391. # Fixed effects:
  392. # Estimate Std. Error z value Pr(>|z|)
  393. # (Intercept) 2.36822 0.16520 14.336 <2e-16 ***
  394. # group2-1 0.43769 0.32484 1.347 0.1778
  395. # ztheta -0.03028 0.03507 -0.864 0.3878
  396. # group2-1:ztheta 0.19158 0.07014 2.732 0.0063 **
  397. tab_model(mod_choice_theta, transform = NULL)
  398. emtrends(mod_choice_theta, ~ group, var="ztheta",at=mylist) %>% summary(infer = c(TRUE,TRUE))
  399. # group ztheta.trend SE df asymp.LCL asymp.UCL z.ratio p.value
  400. # CNT -0.1261 0.0455 Inf -0.215 -0.0368 -2.769 0.0056
  401. # ANH 0.0655 0.0533 Inf -0.039 0.1701 1.228 0.2195

Analysis_code.R, no license · at the source

Overview

Authors: Zhao Wang1, Shiyu Zhou1, Bo Gao1, Haohan Sang1, Ya Zheng2
ORCID iDs: Zhao Wang, Shiyu Zhou, Ya Zheng
  1. Department of Mental Health and Psychology, Dalian Medical University, Dalian, China
  2. Department of Psychology, Guangzhou University, Guangzhou, China
Institutions: Dalian Medical University (China); Guangzhou University (China)
Journal: Psychological medicine, volume 56, article e62
Dates: received 25 July 2025; accepted 30 December 2025; published online 3 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1017/s0033291726103249 · PMID 41773068 · PMCID PMC12969199 · OpenAlex W7133350916
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Machine learning, Preprocessing, Evoked potentials, Statistics
Keywords: anhedonia, effort, motivation, neural dynamics, reward
MeSH: Anhedonia*, Cues*, Decision Making*, Evoked Potentials*, Reward*, Adolescent, Adult, Case-Control Studies, Electroencephalography, Female, Humans, Male, Motivation, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: 2024 Tertiary Education Scientific Research Project of Guangzhou Municipal Education Bureau (2024312195); National Natural Science Foundation of China (National Science Foundation of China) (32571255)
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Background: Anhedonia is defined as a reduced interest in or inability to experience pleasure from reward-related activities. Recent studies have demonstrated deficient effort-based motivation in anhedonia, but the neural dynamics underlying the interface between effort and reward remain unclear.

Methods: To address this issue, we recruited an anhedonia (ANH) group (N = 40) and a control (CNT) group (N = 40) to complete two tasks: (1) an effort–reward task where participants earned varying rewards by exerting different levels of physical effort and (2) an effort-based decision-making task where they chose between a no-effort option for a smaller reward and a high-effort option for a larger reward. We recorded EEG during both tasks and analyzed the resulting neural responses.

Results: As expected, the ANH group showed reduced reward responses in both self-reported ratings and event-related potential (ERP) data in response to cue stimuli (indexed by the cue-P3) and reward feedback (indexed by the reward positivity). Importantly, the ANH group exhibited inefficient integration between effort and reward, showing an absent effort-discounting effect on the feedback-P3 during reward evaluation and a lack of reward-related theta modulation during effort-based decision-making.

Conclusions: Our findings suggest a neurodynamic motivation model in anhedonia that informs precise interventions for relevant neuropsychiatric disorders.

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

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OSF 6wzk5

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State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 10 files, 1 script
Software Heritage: not checked
Found in: “Data availability statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: emmeans (1 file), ggplot2 (1 file), lme4 (1 file), lmerTest (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
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2 files
At the source: osf.io/6wzk5/?view_only=d33469d402734b6f89d638f115dd6405

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

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Data availability statement

Data and code that support the findings of this study are available on Open Science Framework at https://osf.io/6wzk5/?view_only=d33469d402734b6f89d638f115dd6405.

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

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Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 14 MeSH terms, 2 funders, 55 references.

Cite

This paper

Wang, Z., Zhou, S., Gao, B., Sang, H., & Zheng, Y. (2026). Inefficient integration between effort and reward in anhedonia. Psychological medicine, 56, e62. https://doi.org/10.1017/s0033291726103249

BibTeX

@article{wang2026inefficient,
author = {Wang, Zhao and Zhou, Shiyu and Gao, Bo and Sang, Haohan and Zheng, Ya},
title = {{Inefficient integration between effort and reward in anhedonia}},
journal = {Psychological medicine},
year = {2026},
month = mar,
volume = {56},
pages = {e62},
publisher = {Cambridge University Press},
issn = {0033-2917},
doi = {10.1017/s0033291726103249},
url = {https://doi.org/10.1017/s0033291726103249},
pmid = {41773068},
pmcid = {PMC12969199}
}

RIS

TY - JOUR
AU - Wang, Zhao
AU - Zhou, Shiyu
AU - Gao, Bo
AU - Sang, Haohan
AU - Zheng, Ya
TI - Inefficient integration between effort and reward in anhedonia
T2 - Psychological medicine
J2 - Psychol Med
PY - 2026
DA - 2026/03/03
VL - 56
SP - e62
SN - 0033-2917
PB - Cambridge University Press
DO - 10.1017/s0033291726103249
UR - https://doi.org/10.1017/s0033291726103249
LA - en
ER -

CSL-JSON

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"family": "Wang",
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"page": "e62",
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