Main text results
Figure 1 Effort costs by diagnostic group
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: expectedRewardlm_log ~ (1 + is3Back) + (1 + is3Back | subject_id)
## Data: filter(choiceData, decision == "exit", travel_task == "nback")
##
## REML criterion at convergence: 2496.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3454 -0.4075 0.0495 0.4610 7.5812
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject_id (Intercept) 0.35984 0.5999
## is3Back 0.02602 0.1613 -0.05
## Residual 0.11061 0.3326
## Number of obs: 3162, groups: subject_id, 79
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.42581 0.06802 77.73526 20.960 < 2e-16 ***
## is3Back -0.09179 0.02179 74.24208 -4.212 7.04e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## is3Back -0.088
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: expectedRewardlm_log ~ (1 + isLarge) + (1 + isLarge | subject_id)
## Data: filter(choiceData, decision == "exit", travel_task == "bp")
##
## REML criterion at convergence: 2872.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4155 -0.3678 0.0538 0.4274 4.9180
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject_id (Intercept) 0.3892 0.6239
## isLarge 0.0361 0.1900 -0.13
## Residual 0.1249 0.3533
## Number of obs: 3140, groups: subject_id, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.35622 0.07034 78.91596 19.281 <2e-16 ***
## isLarge -0.05550 0.02486 76.83212 -2.233 0.0285 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## isLarge -0.156
##
## Call:
## lm(formula = "cost_3back ~ diagnostic_group + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0652 -0.6949 -0.0334 0.3937 4.6250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05680 0.20225 0.281 0.780
## diagnostic_groupMDD -0.09886 0.25081 -0.394 0.695
## three_back_d_prime -0.12103 0.11695 -1.035 0.304
## demographics_age -0.12327 0.12608 -0.978 0.331
## years_education 0.05535 0.12219 0.453 0.652
##
## Residual standard error: 1.018 on 74 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.02838, Adjusted R-squared: -0.02414
## F-statistic: 0.5403 on 4 and 74 DF, p-value: 0.7066
##
## Call:
## lm(formula = "cost_3back ~ diagnostic_group + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_all_data %>% filter(current_depressed ==
## "comparison" | current_depressed == "current"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0739 -0.5899 -0.0528 0.4145 4.6932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05539 0.20359 0.272 0.786
## diagnostic_groupMDD -0.19497 0.26629 -0.732 0.467
## three_back_d_prime -0.16996 0.12258 -1.387 0.170
## demographics_age -0.10850 0.12724 -0.853 0.397
## years_education 0.04907 0.14050 0.349 0.728
##
## Residual standard error: 1.015 on 64 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.04847, Adjusted R-squared: -0.011
## F-statistic: 0.815 on 4 and 64 DF, p-value: 0.5203
##
## Call:
## lm(formula = "cost_large ~ diagnostic_group + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3378 -0.6159 -0.2214 0.5042 3.2170
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02917 0.19735 -0.148 0.883
## diagnostic_groupMDD 0.07235 0.24472 0.296 0.768
## three_back_d_prime 0.14950 0.11411 1.310 0.194
## demographics_age -0.03430 0.12303 -0.279 0.781
## years_education 0.07449 0.11922 0.625 0.534
##
## Residual standard error: 0.9938 on 74 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.03229, Adjusted R-squared: -0.02002
## F-statistic: 0.6172 on 4 and 74 DF, p-value: 0.6516
##
## Call:
## lm(formula = "cost_large ~ diagnostic_group + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_all_data %>% filter(current_depressed ==
## "comparison" | current_depressed == "current"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2683 -0.5605 -0.1542 0.5103 2.4024
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.022224 0.190810 -0.116 0.908
## diagnostic_groupMDD 0.008047 0.249574 0.032 0.974
## three_back_d_prime 0.115235 0.114890 1.003 0.320
## demographics_age -0.043933 0.119256 -0.368 0.714
## years_education 0.050761 0.131683 0.385 0.701
##
## Residual standard error: 0.9512 on 64 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.02225, Adjusted R-squared: -0.03885
## F-statistic: 0.3642 on 4 and 64 DF, p-value: 0.8332
##
## robust F-test (as if non-random weights)
##
## data: from rlm(formula = cost_3back ~ diagnostic_group + three_back_d_prime + from demographics_age + years_education, data = all_params_survey_composites_all_data)
## F = 0.30742, p-value = 0.5809
## alternative hypothesis: true diagnostic_groupMDD is not equal to 0
##
## robust F-test (as if non-random weights)
##
## data: from rlm(formula = cost_large ~ diagnostic_group + three_back_d_prime + from demographics_age + years_education, data = all_params_survey_composites_all_data)
## F = 0.2746, p-value = 0.6018
## alternative hypothesis: true diagnostic_groupMDD is not equal to 0
## # A tibble: 4 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group cost_3back_seeking n percent
## <dbl> <dbl> <int> <dbl>
## 1 0 0 21 NA
## 2 0 1 6 NA
## 3 1 0 41 NA
## 4 1 1 12 NA
## # A tibble: 80 × 2
## # Groups: diagnostic_group, cost_3back_seeking [4]
## diagnostic_group cost_3back_seeking
## <dbl> <dbl>
## 1 0 0
## 2 1 1
## 3 1 1
## 4 0 0
## 5 1 0
## 6 0 0
## 7 1 0
## 8 1 0
## 9 0 0
## 10 1 0
## # ℹ 70 more rows
## # A tibble: 4 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group cost_large_seeking n percent
## <chr> <dbl> <int> <dbl>
## 1 MDD 0 36 60
## 2 MDD 1 17 28.3
## 3 comparison 0 19 70.4
## 4 comparison 1 8 29.6
## # A tibble: 80 × 2
## # Groups: diagnostic_group, cost_large_seeking [4]
## diagnostic_group cost_large_seeking
## <dbl> <dbl>
## 1 0 1
## 2 1 1
## 3 1 1
## 4 0 0
## 5 1 1
## 6 0 0
## 7 1 1
## 8 1 0
## 9 0 0
## 10 1 0
## # ℹ 70 more rows
## # A tibble: 4 × 7
## # Groups: diagnostic_group [2]
## diagnostic_group param_name cost_effort cost_effort_sd cost_effort_sem
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 MDD cost_3back 11.2 15.5 2.13
## 2 MDD cost_large 8.84 18.7 2.57
## 3 comparison cost_3back 13.1 12.8 2.45
## 4 comparison cost_large 8.52 19.8 3.81
## # ℹ 2 more variables: diagnostic_group_plot <chr>, effort_type <chr>
Table 2 Symptom effort cost regressions (MDD group only)
##
## Call:
## lm(formula = "cost_3back ~ HRSD_total + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) HRSD_total three_back_d_prime demographics_age
## -0.06325 -0.36680 -0.13222 -0.05621
## years_education
## -0.11293
##
## Call:
## lm(formula = "cost_3back ~ anhedonia_symptoms + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anhedonia_symptoms three_back_d_prime demographics_age
## -0.01725 -0.17249 -0.09102 -0.14971
## years_education
## -0.07296
##
## Call:
## lm(formula = "cost_3back ~ anxiety_symptoms + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anxiety_symptoms three_back_d_prime demographics_age
## -0.04945 -0.49699 -0.15064 -0.10190
## years_education
## -0.11944
##
## Call:
## lm(formula = "cost_3back ~ apathy_behavioral_symptoms + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## -0.02970 -0.19433
## three_back_d_prime demographics_age
## -0.08399 -0.15408
## years_education
## -0.09735
##
## Call:
## lm(formula = "cost_3back ~ apathy_social_symptoms + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_social_symptoms three_back_d_prime
## -0.008377 0.009328 -0.081933
## demographics_age years_education
## -0.163026 -0.068575
##
## Call:
## lm(formula = "cost_3back ~ cog_function_symptoms + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) cog_function_symptoms three_back_d_prime
## -0.007303 -0.300094 -0.075919
## demographics_age years_education
## -0.067843 -0.085159
##
## Call:
## lm(formula = "cost_3back ~ depressed_mood_symptoms + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) depressed_mood_symptoms three_back_d_prime
## -0.03971 -0.25857 -0.14393
## demographics_age years_education
## -0.13682 -0.09660
##
## Call:
## lm(formula = "cost_3back ~ physical_anergia_slowing_symptoms + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## -0.02984 -0.22393
## three_back_d_prime demographics_age
## -0.12133 -0.12508
## years_education
## -0.10352
##
## Call:
## lm(formula = "cost_large ~ HRSD_total + BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) HRSD_total BMI demographics_age
## 0.02188 0.03602 0.06853 -0.06337
## years_education
## 0.15653
##
## Call:
## lm(formula = "cost_large ~ anhedonia_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anhedonia_symptoms
## 0.04816 0.41748
## larger_percent_req_presses_completed BMI
## 0.12688 0.05498
## demographics_age years_education
## -0.04344 0.15699
##
## Call:
## lm(formula = "cost_large ~ anxiety_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anxiety_symptoms
## 0.01546 -0.04102
## larger_percent_req_presses_completed BMI
## 0.11272 0.13018
## demographics_age years_education
## -0.06256 0.13830
##
## Call:
## lm(formula = "cost_large ~ apathy_behavioral_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## 0.05227 0.39405
## larger_percent_req_presses_completed BMI
## 0.16246 0.09442
## demographics_age years_education
## -0.10381 0.20061
##
## Call:
## lm(formula = "cost_large ~ apathy_social_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_social_symptoms
## 0.01777 0.13762
## larger_percent_req_presses_completed BMI
## 0.12069 0.10320
## demographics_age years_education
## -0.07114 0.18315
##
## Call:
## lm(formula = "cost_large ~ cog_function_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) cog_function_symptoms
## 0.02548 0.27011
## larger_percent_req_presses_completed BMI
## 0.14713 0.04900
## demographics_age years_education
## -0.09696 0.15986
##
## Call:
## lm(formula = "cost_large ~ depressed_mood_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) depressed_mood_symptoms
## 0.03243 0.13358
## larger_percent_req_presses_completed BMI
## 0.12153 0.11667
## demographics_age years_education
## -0.08966 0.16287
##
## Call:
## lm(formula = "cost_large ~ physical_anergia_slowing_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## 0.02645 0.16358
## larger_percent_req_presses_completed BMI
## 0.15846 0.10179
## demographics_age years_education
## -0.10035 0.17896
##
## Call:
## lm(formula = "cost_3back ~ anhedonia_symptoms + anxiety_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anhedonia_symptoms anxiety_symptoms three_back_d_prime
## -0.05094 -0.05513 -0.48289 -0.15152
## demographics_age years_education
## -0.09939 -0.11884
##
## Call:
## lm(formula = "cost_3back ~ apathy_behavioral_symptoms + anxiety_symptoms + \n three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## -0.05426 -0.05743
## anxiety_symptoms three_back_d_prime
## -0.48095 -0.14897
## demographics_age years_education
## -0.10124 -0.12579
##
## Call:
## lm(formula = "cost_3back ~ apathy_social_symptoms + anxiety_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_social_symptoms anxiety_symptoms
## -0.048850 0.008784 -0.496981
## three_back_d_prime demographics_age years_education
## -0.150404 -0.101940 -0.117622
##
## Call:
## lm(formula = "cost_3back ~ cog_function_symptoms + anxiety_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) cog_function_symptoms anxiety_symptoms
## -0.049566 0.001991 -0.498226
## three_back_d_prime demographics_age years_education
## -0.150856 -0.102377 -0.119461
##
## Call:
## lm(formula = "cost_3back ~ depressed_mood_symptoms + anxiety_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) depressed_mood_symptoms anxiety_symptoms
## -0.04003 0.12688 -0.56623
## three_back_d_prime demographics_age years_education
## -0.12989 -0.10622 -0.11345
##
## Call:
## lm(formula = "cost_3back ~ physical_anergia_slowing_symptoms + anxiety_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## -0.04626 0.06395
## anxiety_symptoms three_back_d_prime
## -0.53080 -0.14412
## demographics_age years_education
## -0.10857 -0.11333
## symptom_vars Estimate SE t p p_fdr r2 adj_r2
## 1 Anhedonia -0.172 0.147 -1.180 0.24506 0.286 NA NA
## 2 Anxiety -0.497 0.134 -3.700 0.00056 0.004 0.246 0.182
## 3 Behavioral apathy -0.194 0.151 -1.284 0.20541 0.286 NA NA
## 4 Social apathy 0.009 0.149 0.063 0.95035 0.950 NA NA
## 5 Cognitive function symptoms -0.300 0.144 -2.079 0.04300 0.150 NA NA
## 6 Depressed mood/suicidality -0.259 0.166 -1.556 0.12630 0.253 NA NA
## 7 Physical anergia/slowing -0.224 0.151 -1.483 0.14474 0.253 NA NA
## 8 Overall depression -0.367 0.164 -2.237 0.03000 NA 0.120 0.045
## 9 Anhedonia 0.417 0.142 2.933 0.00500 0.035 0.193 0.103
## 10 Anxiety -0.041 0.155 -0.264 0.79300 0.793 NA NA
## 11 Behavioral apathy 0.394 0.146 2.708 0.01000 0.035 0.173 0.082
## 12 Social apathy 0.138 0.147 0.934 0.35500 0.492 NA NA
## 13 Cognitive function symptoms 0.270 0.162 1.666 0.10300 0.240 NA NA
## 14 Depressed mood/suicidality 0.134 0.165 0.811 0.42200 0.492 NA NA
## 15 Physical anergia/slowing 0.164 0.164 0.997 0.32400 0.492 NA NA
## 16 Overall depression 0.036 0.170 0.212 0.83300 NA NA NA
## 17 Anhedonia -0.055 0.136 -0.400 0.68774 0.991 NA NA
## 18 Behavioral apathy -0.057 0.142 -0.400 0.68874 0.991 NA NA
## 19 Social apathy 0.009 0.133 0.070 0.94744 0.991 NA NA
## 20 Cognitive function symptoms 0.002 0.170 0.010 0.99069 0.991 NA NA
## 21 Depressed mood/suicidality 0.127 0.190 0.670 0.50800 0.991 NA NA
## 22 Physical anergia/slowing 0.064 0.162 0.390 0.69556 0.991 NA NA
## model
## 1 Cognitive, MDD
## 2 Cognitive, MDD
## 3 Cognitive, MDD
## 4 Cognitive, MDD
## 5 Cognitive, MDD
## 6 Cognitive, MDD
## 7 Cognitive, MDD
## 8 Cognitive, MDD
## 9 Physical, MDD
## 10 Physical, MDD
## 11 Physical, MDD
## 12 Physical, MDD
## 13 Physical, MDD
## 14 Physical, MDD
## 15 Physical, MDD
## 16 Physical, MDD
## 17 Cognitive controlling for anxiety, MDD
## 18 Cognitive controlling for anxiety, MDD
## 19 Cognitive controlling for anxiety, MDD
## 20 Cognitive controlling for anxiety, MDD
## 21 Cognitive controlling for anxiety, MDD
## 22 Cognitive controlling for anxiety, MDD
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:02 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrrrl}
## \hline
## symptom\_vars & Estimate & SE & t & p & p\_fdr & r2 & adj\_r2 & model \\
## \hline
## Anhedonia & -0.17 & 0.15 & -1.18 & 0.245 & 0.286 & & & Cognitive, MDD \\
## Anxiety & -0.50 & 0.13 & -3.70 & 0.001 & 0.004 & 0 & 0.182 & Cognitive, MDD \\
## Behavioral apathy & -0.19 & 0.15 & -1.28 & 0.205 & 0.286 & & & Cognitive, MDD \\
## Social apathy & 0.01 & 0.15 & 0.06 & 0.950 & 0.950 & & & Cognitive, MDD \\
## Cognitive function symptoms & -0.30 & 0.14 & -2.08 & 0.043 & 0.150 & & & Cognitive, MDD \\
## Depressed mood/suicidality & -0.26 & 0.17 & -1.56 & 0.126 & 0.253 & & & Cognitive, MDD \\
## Physical anergia/slowing & -0.22 & 0.15 & -1.48 & 0.145 & 0.253 & & & Cognitive, MDD \\
## Overall depression & -0.37 & 0.16 & -2.24 & 0.030 & & 0 & 0.045 & Cognitive, MDD \\
## Anhedonia & 0.42 & 0.14 & 2.93 & 0.005 & 0.035 & 0 & 0.103 & Physical, MDD \\
## Anxiety & -0.04 & 0.15 & -0.26 & 0.793 & 0.793 & & & Physical, MDD \\
## Behavioral apathy & 0.39 & 0.15 & 2.71 & 0.010 & 0.035 & 0 & 0.082 & Physical, MDD \\
## Social apathy & 0.14 & 0.15 & 0.93 & 0.355 & 0.492 & & & Physical, MDD \\
## Cognitive function symptoms & 0.27 & 0.16 & 1.67 & 0.103 & 0.240 & & & Physical, MDD \\
## Depressed mood/suicidality & 0.13 & 0.17 & 0.81 & 0.422 & 0.492 & & & Physical, MDD \\
## Physical anergia/slowing & 0.16 & 0.16 & 1.00 & 0.324 & 0.492 & & & Physical, MDD \\
## Overall depression & 0.04 & 0.17 & 0.21 & 0.833 & & & & Physical, MDD \\
## Anhedonia & -0.06 & 0.14 & -0.40 & 0.688 & 0.991 & & & Cognitive controlling for anxiety, MDD \\
## Behavioral apathy & -0.06 & 0.14 & -0.40 & 0.689 & 0.991 & & & Cognitive controlling for anxiety, MDD \\
## Social apathy & 0.01 & 0.13 & 0.07 & 0.947 & 0.991 & & & Cognitive controlling for anxiety, MDD \\
## Cognitive function symptoms & 0.00 & 0.17 & 0.01 & 0.991 & 0.991 & & & Cognitive controlling for anxiety, MDD \\
## Depressed mood/suicidality & 0.13 & 0.19 & 0.67 & 0.508 & 0.991 & & & Cognitive controlling for anxiety, MDD \\
## Physical anergia/slowing & 0.06 & 0.16 & 0.39 & 0.696 & 0.991 & & & Cognitive controlling for anxiety, MDD \\
## \hline
## \end{tabular}
## \end{table}
##
## Results of a comparison of two overlapping correlations based on dependent groups
##
## Comparison between r.jk (HRSD_total, cost_3back) = -0.2936 and r.jh (HRSD_total, cost_large) = -0.0288
## Difference: r.jk - r.jh = -0.2648
## Related correlation: r.kh = 0.0936
## Data: all_params_survey_composites_mdd_data %>% as.data.frame(.): j = HRSD_total, k = cost_3back, h = cost_large
## Group size: n = 53
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
##
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
## z = -1.4205, p-value = 0.1555
## Null hypothesis retained
## 95% confidence interval for r.jk - r.jh: -0.6513 0.1039
## Null hypothesis retained (Interval includes 0)
##
## Results of a comparison of two overlapping correlations based on dependent groups
##
## Comparison between r.jk (HRSD_total, cost_3back) = -0.2936 and r.jh (HRSD_total, cost_large) = -0.0288
## Difference: r.jk - r.jh = -0.2648
## Related correlation: r.kh = 0.0936
## Data: all_params_survey_composites_mdd_data %>% as.data.frame(.): j = HRSD_total, k = cost_3back, h = cost_large
## Group size: n = 53
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
##
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
## z = -1.4205, p-value = 0.1555
## Null hypothesis retained
## 95% confidence interval for r.jk - r.jh: -0.6513 0.1039
## Null hypothesis retained (Interval includes 0)
##
## Results of a comparison of two overlapping correlations based on dependent groups
##
## Comparison between r.jk (anxiety_symptoms, cost_3back) = -0.4551 and r.jh (anxiety_symptoms, cost_large) = -0.0689
## Difference: r.jk - r.jh = -0.3862
## Related correlation: r.kh = 0.0936
## Data: all_params_survey_composites_mdd_data %>% as.data.frame(.): j = anxiety_symptoms, k = cost_3back, h = cost_large
## Group size: n = 53
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
##
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
## z = -2.1545, p-value = 0.0312
## Null hypothesis rejected
## 95% confidence interval for r.jk - r.jh: -0.8060 -0.0381
## Null hypothesis rejected (Interval does not include 0)
##
## Results of a comparison of two overlapping correlations based on dependent groups
##
## Comparison between r.jk (anxiety_symptoms, cost_3back) = -0.4551 and r.jh (anxiety_symptoms, cost_large) = -0.0689
## Difference: r.jk - r.jh = -0.3862
## Related correlation: r.kh = 0.0936
## Data: all_params_survey_composites_mdd_data %>% as.data.frame(.): j = anxiety_symptoms, k = cost_3back, h = cost_large
## Group size: n = 53
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
##
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
## z = -2.1545, p-value = 0.0312
## Null hypothesis rejected
## 95% confidence interval for r.jk - r.jh: -0.8060 -0.0381
## Null hypothesis rejected (Interval does not include 0)
##
## Results of a comparison of two overlapping correlations based on dependent groups
##
## Comparison between r.jk (anhedonia_symptoms, cost_3back) = -0.1703 and r.jh (anhedonia_symptoms, cost_large) = 0.3401
## Difference: r.jk - r.jh = -0.5104
## Related correlation: r.kh = 0.0936
## Data: all_params_survey_composites_mdd_data %>% as.data.frame(.): j = anhedonia_symptoms, k = cost_3back, h = cost_large
## Group size: n = 53
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
##
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
## z = -2.7098, p-value = 0.0067
## Null hypothesis rejected
## 95% confidence interval for r.jk - r.jh: -0.9067 -0.1456
## Null hypothesis rejected (Interval does not include 0)
##
## Results of a comparison of two overlapping correlations based on dependent groups
##
## Comparison between r.jk (anhedonia_symptoms, cost_3back) = -0.1703 and r.jh (anhedonia_symptoms, cost_large) = 0.3401
## Difference: r.jk - r.jh = -0.5104
## Related correlation: r.kh = 0.0936
## Data: all_params_survey_composites_mdd_data %>% as.data.frame(.): j = anhedonia_symptoms, k = cost_3back, h = cost_large
## Group size: n = 53
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
##
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
## z = -2.7098, p-value = 0.0067
## Null hypothesis rejected
## 95% confidence interval for r.jk - r.jh: -0.9067 -0.1456
## Null hypothesis rejected (Interval does not include 0)
##
## Results of a comparison of two overlapping correlations based on dependent groups
##
## Comparison between r.jk (apathy_behavioral_symptoms, cost_3back) = -0.1735 and r.jh (apathy_behavioral_symptoms, cost_large) = 0.3328
## Difference: r.jk - r.jh = -0.5063
## Related correlation: r.kh = 0.0936
## Data: all_params_survey_composites_mdd_data %>% as.data.frame(.): j = apathy_behavioral_symptoms, k = cost_3back, h = cost_large
## Group size: n = 53
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
##
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
## z = -2.6860, p-value = 0.0072
## Null hypothesis rejected
## 95% confidence interval for r.jk - r.jh: -0.9017 -0.1409
## Null hypothesis rejected (Interval does not include 0)
##
## Results of a comparison of two overlapping correlations based on dependent groups
##
## Comparison between r.jk (apathy_behavioral_symptoms, cost_3back) = -0.1735 and r.jh (apathy_behavioral_symptoms, cost_large) = 0.3328
## Difference: r.jk - r.jh = -0.5063
## Related correlation: r.kh = 0.0936
## Data: all_params_survey_composites_mdd_data %>% as.data.frame(.): j = apathy_behavioral_symptoms, k = cost_3back, h = cost_large
## Group size: n = 53
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
##
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
## z = -2.6860, p-value = 0.0072
## Null hypothesis rejected
## 95% confidence interval for r.jk - r.jh: -0.9017 -0.1409
## Null hypothesis rejected (Interval does not include 0)
## Call:
## rlm(formula = cost_3back ~ HRSD_total + three_back_d_prime +
## demographics_age + years_education, data = all_params_survey_composites_mdd_data)
## Converged in 6 iterations
##
## Coefficients:
## (Intercept) HRSD_total three_back_d_prime demographics_age
## -0.1209451688 -0.3273035350 -0.0724907262 -0.0003927324
## years_education
## -0.0991113717
##
## Degrees of freedom: 52 total; 47 residual
## (8 observations deleted due to missingness)
## Scale estimate: 0.677
##
## robust F-test (as if non-random weights)
##
## data: from rlm(formula = cost_3back ~ HRSD_total + three_back_d_prime + from demographics_age + years_education, data = all_params_survey_composites_mdd_data)
## F = 5.3882, p-value = 0.02466
## alternative hypothesis: true HRSD_total is not equal to 0
## Call:
## rlm(formula = cost_3back ~ anxiety_symptoms + three_back_d_prime +
## demographics_age + years_education, data = all_params_survey_composites_mdd_data)
## Converged in 9 iterations
##
## Coefficients:
## (Intercept) anxiety_symptoms three_back_d_prime demographics_age
## -0.07415041 -0.43346121 -0.07807354 -0.05411385
## years_education
## -0.11912061
##
## Degrees of freedom: 52 total; 47 residual
## (8 observations deleted due to missingness)
## Scale estimate: 0.553
##
## robust F-test (as if non-random weights)
##
## data: from rlm(formula = cost_3back ~ anxiety_symptoms + three_back_d_prime + from demographics_age + years_education, data = all_params_survey_composites_mdd_data)
## F = 11.938, p-value = 0.001176
## alternative hypothesis: true anxiety_symptoms is not equal to 0
## Call:
## rlm(formula = cost_large ~ anhedonia_symptoms + three_back_d_prime +
## demographics_age + years_education, data = all_params_survey_composites_mdd_data)
## Converged in 6 iterations
##
## Coefficients:
## (Intercept) anhedonia_symptoms three_back_d_prime demographics_age
## 0.01612359 0.34704259 0.28695738 -0.01325075
## years_education
## 0.02471366
##
## Degrees of freedom: 52 total; 47 residual
## (8 observations deleted due to missingness)
## Scale estimate: 0.607
##
## robust F-test (as if non-random weights)
##
## data: from rlm(formula = cost_large ~ anhedonia_symptoms + three_back_d_prime + from demographics_age + years_education, data = all_params_survey_composites_mdd_data)
## F = 11.362, p-value = 0.001506
## alternative hypothesis: true anhedonia_symptoms is not equal to 0
## Call:
## rlm(formula = cost_large ~ apathy_behavioral_symptoms + three_back_d_prime +
## demographics_age + years_education, data = all_params_survey_composites_mdd_data)
## Converged in 6 iterations
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## 0.01062014 0.34262530
## three_back_d_prime demographics_age
## 0.24339175 -0.08874926
## years_education
## 0.07460394
##
## Degrees of freedom: 52 total; 47 residual
## (8 observations deleted due to missingness)
## Scale estimate: 0.725
##
## robust F-test (as if non-random weights)
##
## data: from rlm(formula = cost_large ~ apathy_behavioral_symptoms + three_back_d_prime + from demographics_age + years_education, data = all_params_survey_composites_mdd_data)
## F = 8.6671, p-value = 0.005022
## alternative hypothesis: true apathy_behavioral_symptoms is not equal to 0
Figure 3 Relationship of individual MDD symptom domains with overall
exit threshold
## # A tibble: 2 × 4
## diagnostic_group_plot threshold_mean threshold_sd threshold_sem
## <chr> <dbl> <dbl> <dbl>
## 1 Comparison 8.37 2.85 0.549
## 2 MDD 8.93 2.82 0.387
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Table 3 Symptom overall exit threshold regressions (MDD group
only)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: expectedRewardlm_log ~ 1 + diagnostic_group + travel_task + (1 |
## subject_id)
## Data: filter(choiceData, decision == "exit", effort_level == "low") %>%
## mutate(travel_task = ifelse(travel_task == "nback", 0.5, -0.5))
##
## REML criterion at convergence: 3276.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.9719 -0.4163 0.0542 0.4700 5.0169
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 0.3353 0.5791
## Residual 0.1452 0.3811
## Number of obs: 3200, groups: subject_id, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.316e+00 1.121e-01 7.795e+01 11.739 < 2e-16 ***
## diagnostic_groupMDD 1.326e-01 1.377e-01 7.794e+01 0.963 0.339
## travel_task 6.301e-02 1.355e-02 3.120e+03 4.651 3.44e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dg_MDD
## dgnstc_gMDD -0.814
## travel_task -0.002 0.002
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ HRSD_total + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) HRSD_total demographics_age years_education
## -0.04545 -0.33173 0.04315 -0.04850
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ anhedonia_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anhedonia_symptoms demographics_age years_education
## -0.021044 -0.254613 -0.037919 0.006792
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ anxiety_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anxiety_symptoms demographics_age years_education
## -0.031950 -0.343306 -0.005883 -0.033536
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ apathy_behavioral_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## -0.03928 -0.30491
## demographics_age years_education
## -0.04848 -0.03358
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ apathy_social_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_social_symptoms demographics_age
## -0.008926 -0.103745 -0.065000
## years_education
## -0.019870
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ cog_function_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) cog_function_symptoms demographics_age
## -0.0033919 -0.2100672 0.0001945
## years_education
## -0.0041068
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ depressed_mood_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) depressed_mood_symptoms demographics_age
## -0.05761 -0.40362 0.01392
## years_education
## -0.03739
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ physical_anergia_slowing_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## -0.029490 -0.315235
## demographics_age years_education
## 0.005778 -0.052344
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:02 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrl}
## \hline
## symptom\_vars & Estimate & SE & t & p & p\_fdr & group \\
## \hline
## Anhedonia & -0.26 & 0.14 & -1.80 & 0.078 & 0.109 & Overall threshold, MDD \\
## Anxiety & -0.34 & 0.14 & -2.43 & 0.019 & 0.066 & Overall threshold, MDD \\
## Behavioral apathy & -0.30 & 0.15 & -2.09 & 0.042 & 0.074 & Overall threshold, MDD \\
## Social apathy & -0.10 & 0.14 & -0.73 & 0.470 & 0.470 & Overall threshold, MDD \\
## Cognitive function symptoms & -0.21 & 0.15 & -1.44 & 0.157 & 0.184 & Overall threshold, MDD \\
## Depressed mood/suicidality & -0.40 & 0.15 & -2.68 & 0.010 & 0.066 & Overall threshold, MDD \\
## Physical anergia/slowing & -0.32 & 0.14 & -2.20 & 0.033 & 0.074 & Overall threshold, MDD \\
## Overall depression & -0.33 & 0.16 & -2.09 & 0.042 & & Overall threshold, MDD \\
## \hline
## \end{tabular}
## \end{table}
Additional task measures, travel task performance
##
## Call:
## lm(formula = "cost_3back ~ one_back_d_prime + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1371 -0.6777 -0.0697 0.3823 4.5886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.008729 0.114991 -0.076 0.940
## one_back_d_prime -0.028729 0.150084 -0.191 0.849
## three_back_d_prime -0.106984 0.147223 -0.727 0.470
## demographics_age -0.116288 0.130346 -0.892 0.375
## years_education 0.072799 0.120815 0.603 0.549
##
## Residual standard error: 1.019 on 74 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.02682, Adjusted R-squared: -0.02578
## F-statistic: 0.5099 on 4 and 74 DF, p-value: 0.7286
##
## Call:
## lm(formula = "cost_3back ~ change_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_all_data %>% mutate(change_d_prime = three_back_d_prime -
## one_back_d_prime))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8905 -0.7169 -0.0744 0.3599 4.6924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.007814 0.115038 -0.068 0.946
## change_d_prime -0.042467 0.133354 -0.318 0.751
## demographics_age -0.115134 0.130398 -0.883 0.380
## years_education 0.053817 0.119459 0.451 0.654
##
## Residual standard error: 1.02 on 75 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.01281, Adjusted R-squared: -0.02668
## F-statistic: 0.3244 on 3 and 75 DF, p-value: 0.8077
##
## Call:
## lm(formula = "HRSD_total ~ one_back_d_prime + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.45677 -0.72829 -0.06801 0.67137 1.88557
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.14575 0.12152 -1.199 0.2364
## one_back_d_prime -0.12231 0.15766 -0.776 0.4418
## three_back_d_prime -0.05899 0.16385 -0.360 0.7204
## demographics_age 0.32955 0.15067 2.187 0.0337 *
## years_education -0.10164 0.12776 -0.796 0.4303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8721 on 47 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1645, Adjusted R-squared: 0.09336
## F-statistic: 2.313 on 4 and 47 DF, p-value: 0.07133
##
## Call:
## lm(formula = "anxiety_symptoms ~ one_back_d_prime + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9432 -0.7617 -0.1691 0.5824 1.9846
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08173 0.13830 -0.591 0.557
## one_back_d_prime 0.02127 0.17943 0.119 0.906
## three_back_d_prime -0.15120 0.18647 -0.811 0.422
## demographics_age 0.11622 0.17147 0.678 0.501
## years_education -0.10090 0.14539 -0.694 0.491
##
## Residual standard error: 0.9925 on 47 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.0569, Adjusted R-squared: -0.02336
## F-statistic: 0.7089 on 4 and 47 DF, p-value: 0.5899
##
## Call:
## lm(formula = "cost_large ~ larger_percent_req_presses_completed + smaller_percent_req_presses_completed + \n demographics_age + BMI + years_education",
## data = all_params_survey_composites_all_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1558 -0.6204 -0.1159 0.6635 3.0792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01598 0.11647 0.137 0.891
## larger_percent_req_presses_completed 0.02759 0.12652 0.218 0.828
## smaller_percent_req_presses_completed -0.12310 0.12138 -1.014 0.314
## demographics_age 0.02762 0.13894 0.199 0.843
## BMI -0.05407 0.13109 -0.412 0.681
## years_education 0.08658 0.12255 0.707 0.482
##
## Residual standard error: 1.021 on 72 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.02684, Adjusted R-squared: -0.04074
## F-statistic: 0.3972 on 5 and 72 DF, p-value: 0.8492
##
## Call:
## lm(formula = "HRSD_total ~ larger_percent_req_presses_completed + smaller_percent_req_presses_completed + \n demographics_age + BMI + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.55489 -0.54182 -0.02477 0.39638 1.85979
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10865 0.12423 -0.875 0.3865
## larger_percent_req_presses_completed -0.24017 0.14103 -1.703 0.0955 .
## smaller_percent_req_presses_completed 0.08426 0.13168 0.640 0.5255
## demographics_age 0.31005 0.16026 1.935 0.0593 .
## BMI 0.03530 0.14501 0.243 0.8088
## years_education -0.16494 0.12834 -1.285 0.2053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8745 on 45 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.1801, Adjusted R-squared: 0.08905
## F-statistic: 1.977 on 5 and 45 DF, p-value: 0.1003
##
## Call:
## lm(formula = "anhedonia_symptoms ~ larger_percent_req_presses_completed + smaller_percent_req_presses_completed + \n demographics_age + BMI + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.53697 -0.82470 -0.04322 0.66815 1.69145
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.05917 0.13094 -0.452 0.6536
## larger_percent_req_presses_completed -0.15230 0.14865 -1.025 0.3111
## smaller_percent_req_presses_completed 0.37223 0.13879 2.682 0.0102 *
## demographics_age 0.03320 0.16892 0.197 0.8451
## BMI 0.13038 0.15284 0.853 0.3982
## years_education 0.04207 0.13527 0.311 0.7573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9217 on 45 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.1683, Adjusted R-squared: 0.07587
## F-statistic: 1.821 on 5 and 45 DF, p-value: 0.1279
##
## Call:
## lm(formula = "apathy_behavioral_symptoms ~ larger_percent_req_presses_completed + smaller_percent_req_presses_completed + \n demographics_age + BMI + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.85336 -0.82687 -0.01601 0.71479 1.82757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08035 0.13703 -0.586 0.561
## larger_percent_req_presses_completed -0.18542 0.15556 -1.192 0.240
## smaller_percent_req_presses_completed 0.18908 0.14524 1.302 0.200
## demographics_age 0.13942 0.17677 0.789 0.434
## BMI 0.05933 0.15994 0.371 0.712
## years_education -0.10571 0.14156 -0.747 0.459
##
## Residual standard error: 0.9646 on 45 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.09593, Adjusted R-squared: -0.004527
## F-statistic: 0.9549 on 5 and 45 DF, p-value: 0.4554
Supplementary results
SI Section 2 Power analysis
##
## t test power calculation
##
## n1 = 27
## n2 = 52
## d = 0.6729908
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
##
## Multiple regression power calculation
##
## u = 4
## v = 47
## f2 = 0.2537713
## sig.level = 0.05
## power = 0.8
Table S2 Confirmatory symptom factors alpha and diagnostic group
differences
##
## Welch Two Sample t-test
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$anhedonia_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$anhedonia_symptoms
## t = 3.0711, df = 55.459, p-value = 0.003301
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1482794 0.7049381
## sample estimates:
## mean of x mean of y
## 0.1323958 -0.2942130
##
## Welch Two Sample t-test
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$anxiety_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$anxiety_symptoms
## t = 7.3855, df = 83.523, p-value = 1.042e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.6016277 1.0450474
## sample estimates:
## mean of x mean of y
## 0.1972253 -0.6261122
##
## Welch Two Sample t-test
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$appetite_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$appetite_symptoms
## t = 5.5006, df = 76.735, p-value = 4.793e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.4467225 0.9537174
## sample estimates:
## mean of x mean of y
## 0.09133304 -0.60888690
##
## Welch Two Sample t-test
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$apathy_behavioral_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$apathy_behavioral_symptoms
## t = 6.1246, df = 58.794, p-value = 8.116e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.4985683 0.9824836
## sample estimates:
## mean of x mean of y
## 0.1868617 -0.5536643
##
## Welch Two Sample t-test
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$apathy_emotional_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$apathy_emotional_symptoms
## t = -1.2161, df = 44.077, p-value = 0.2304
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4941871 0.1222131
## sample estimates:
## mean of x mean of y
## -0.04923185 0.13675514
##
## Welch Two Sample t-test
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$apathy_social_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$apathy_social_symptoms
## t = 3.3035, df = 58.024, p-value = 0.001639
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1892841 0.7713901
## sample estimates:
## mean of x mean of y
## 0.1490701 -0.3312670
##
## Welch Two Sample t-test
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$cog_function_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$cog_function_symptoms
## t = 6.8804, df = 80.293, p-value = 1.186e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.5892615 1.0688123
## sample estimates:
## mean of x mean of y
## 0.2572873 -0.5717496
##
## Welch Two Sample t-test
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$depressed_mood_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$depressed_mood_symptoms
## t = 7.5076, df = 66.346, p-value = 1.958e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.5372711 0.9265112
## sample estimates:
## mean of x mean of y
## 0.1056741 -0.6262171
##
## Welch Two Sample t-test
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$effortful_control_trait and filter(survey_composites_all, diagnostic_group == "comparison")$effortful_control_trait
## t = -6.2854, df = 80.504, p-value = 1.581e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7494402 -0.3890203
## sample estimates:
## mean of x mean of y
## -0.1766577 0.3925726
##
## Welch Two Sample t-test
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$need_for_cognition and filter(survey_composites_all, diagnostic_group == "comparison")$need_for_cognition
## t = -0.79414, df = 58.151, p-value = 0.4303
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3788531 0.1636245
## sample estimates:
## mean of x mean of y
## -0.03339755 0.07421678
##
## Welch Two Sample t-test
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$physical_anergia_slowing_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$physical_anergia_slowing_symptoms
## t = 7.616, df = 84.886, p-value = 3.357e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.5680044 0.9693671
## sample estimates:
## mean of x mean of y
## 0.1572312 -0.6114546
##
## Wilcoxon rank sum test with continuity correction
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$anxiety_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$anxiety_symptoms
## W = 1369, p-value = 2.88e-07
## alternative hypothesis: true location shift is not equal to 0
##
## Wilcoxon rank sum test with continuity correction
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$cog_function_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$cog_function_symptoms
## W = 1366, p-value = 2.165e-07
## alternative hypothesis: true location shift is not equal to 0
##
## Wilcoxon rank sum test with continuity correction
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$depressed_mood_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$depressed_mood_symptoms
## W = 1415, p-value = 2.249e-08
## alternative hypothesis: true location shift is not equal to 0
##
## Wilcoxon rank sum test with continuity correction
##
## data: filter(survey_composites_all, diagnostic_group == "MDD")$physical_anergia_slowing_symptoms and filter(survey_composites_all, diagnostic_group == "comparison")$physical_anergia_slowing_symptoms
## W = 1349, p-value = 7.634e-07
## alternative hypothesis: true location shift is not equal to 0
## symptom_domain alpha ci_lower ci_upper N
## 2.5%...1 Anhedonia 0.8881544 0.8465827 0.9183880 60
## 2.5%...2 Anxiety 0.8990130 0.8554390 0.9295986 60
## 2.5%...3 Appetite symptoms 0.5175524 0.1968581 0.7018284 60
## 2.5%...4 Behavioral apathy 0.7165040 0.5384167 0.8184744 60
## 2.5%...5 Emotional apathy 0.5440075 0.2903538 0.6778799 60
## 2.5%...6 Social apathy 0.7102790 0.5490083 0.8118024 60
## 2.5%...7 Cognitive function symptoms 0.8388931 0.7578042 0.8902282 60
## 2.5%...8 Depressed mood/suicidality 0.8619862 0.8000124 0.8950651 60
## 2.5%...9 Effortful control (trait) 0.8366242 0.7493455 0.8862872 60
## 2.5%...10 Need for cognition (trait) 0.9024553 0.8530278 0.9335145 60
## 2.5%...11 Physical anergia/slowing 0.7642539 0.6406399 0.8366202 60
## MDD comparison t df p
## 2.5%...1 0.13239583 -0.29421295 3.071124 55.45930 3.300634e-03
## 2.5%...2 0.19722534 -0.62611219 7.385479 83.52310 1.042103e-10
## 2.5%...3 0.09133304 -0.60888690 5.500618 76.73487 4.793399e-07
## 2.5%...4 0.18686169 -0.55366427 6.124615 58.79406 8.116450e-08
## 2.5%...5 -0.04923185 0.13675514 -1.216138 44.07666 2.304054e-01
## 2.5%...6 0.14907014 -0.33126699 3.303490 58.02403 1.639259e-03
## 2.5%...7 0.14907014 -0.57174957 6.880371 80.29316 1.186429e-09
## 2.5%...8 0.10567413 -0.62621705 7.507581 66.34578 1.958057e-10
## 2.5%...9 -0.17665767 0.39257259 -6.285420 80.50381 1.581409e-08
## 2.5%...10 -0.03339755 0.07421678 -0.794140 58.15109 4.303429e-01
## 2.5%...11 0.15723118 -0.61145459 7.615969 84.88611 3.357030e-11
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:02 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrrrrr}
## \hline
## symptom\_domain & alpha & ci\_lower & ci\_upper & N & MDD & comparison & t & df & p \\
## \hline
## Anhedonia & 0.888 & 0.847 & 0.918 & 60 & 0.132 & -0.294 & 3.07 & 55.46 & 0.003 \\
## Anxiety & 0.899 & 0.855 & 0.930 & 60 & 0.197 & -0.626 & 7.39 & 83.52 & 0.000 \\
## Appetite symptoms & 0.518 & 0.197 & 0.702 & 60 & 0.091 & -0.609 & 5.50 & 76.73 & 0.000 \\
## Behavioral apathy & 0.717 & 0.538 & 0.818 & 60 & 0.187 & -0.554 & 6.12 & 58.79 & 0.000 \\
## Emotional apathy & 0.544 & 0.290 & 0.678 & 60 & -0.049 & 0.137 & -1.22 & 44.08 & 0.230 \\
## Social apathy & 0.710 & 0.549 & 0.812 & 60 & 0.149 & -0.331 & 3.30 & 58.02 & 0.002 \\
## Cognitive function symptoms & 0.839 & 0.758 & 0.890 & 60 & 0.149 & -0.572 & 6.88 & 80.29 & 0.000 \\
## Depressed mood/suicidality & 0.862 & 0.800 & 0.895 & 60 & 0.106 & -0.626 & 7.51 & 66.35 & 0.000 \\
## Effortful control (trait) & 0.837 & 0.749 & 0.886 & 60 & -0.177 & 0.393 & -6.29 & 80.50 & 0.000 \\
## Need for cognition (trait) & 0.902 & 0.853 & 0.934 & 60 & -0.033 & 0.074 & -0.79 & 58.15 & 0.430 \\
## Physical anergia/slowing & 0.764 & 0.641 & 0.837 & 60 & 0.157 & -0.611 & 7.62 & 84.89 & 0.000 \\
## \hline
## \end{tabular}
## \end{table}
SI Section 11 Fatigue effects
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "expectedRewardlm_log ~ 1 + is3Back*round_trial_num + \n (1 + is3Back + round_trial_num | subject_id)"
## Data: choiceData %>% filter(decision == "exit", travel_task == "nback") %>%
## group_by(round, subject_id) %>% mutate(round_trial_num = scale(seq(round)))
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 2476.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2161 -0.4071 0.0487 0.4570 7.4309
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject_id (Intercept) 0.359979 0.59998
## is3Back 0.026510 0.16282 -0.05
## round_trial_num 0.001028 0.03207 0.20 -0.42
## Residual 0.108662 0.32964
## Number of obs: 3162, groups: subject_id, 79
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.426e+00 6.803e-02 7.774e+01 20.958 < 2e-16 ***
## is3Back -9.172e-02 2.188e-02 7.418e+01 -4.192 7.55e-05 ***
## round_trial_num -2.295e-02 9.417e-03 1.690e+02 -2.437 0.0158 *
## is3Back:round_trial_num -2.132e-02 1.236e-02 2.913e+03 -1.725 0.0847 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) is3Bck rnd_t_
## is3Back -0.088
## rond_trl_nm 0.078 -0.136
## is3Bck:rn__ 0.000 0.000 -0.645
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "expectedRewardlm_log ~ 1 + isLarge*round_trial_num + \n (1 + isLarge + round_trial_num | subject_id)"
## Data: choiceData %>% filter(decision == "exit", travel_task == "bp") %>%
## group_by(round, subject_id) %>% mutate(round_trial_num = scale(seq(round)))
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 2874.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4106 -0.3662 0.0623 0.4258 4.9968
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject_id (Intercept) 0.3886532 0.62342
## isLarge 0.0363699 0.19071 -0.12
## round_trial_num 0.0004854 0.02203 0.37 -0.84
## Residual 0.1242067 0.35243
## Number of obs: 3140, groups: subject_id, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.356e+00 7.029e-02 7.892e+01 19.294 < 2e-16 ***
## isLarge -5.527e-02 2.490e-02 7.685e+01 -2.219 0.02940 *
## round_trial_num -2.522e-02 9.637e-03 1.872e+02 -2.617 0.00959 **
## isLarge:round_trial_num 1.566e-02 1.328e-02 2.895e+03 1.179 0.23838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) isLarg rnd_t_
## isLarge -0.152
## rond_trl_nm 0.095 -0.187
## isLrg:rnd__ 0.000 0.000 -0.677
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "expectedRewardlm_log ~ 1 + is3Back*round_trial_num*diagnostic_group + \n (1 + is3Back + round_trial_num | subject_id)"
## Data: choiceData %>% filter(decision == "exit", travel_task == "nback") %>%
## group_by(round, subject_id) %>% mutate(round_trial_num = scale(seq(round)))
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 2492.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1942 -0.4100 0.0457 0.4595 7.4476
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject_id (Intercept) 0.364079 0.60339
## is3Back 0.026614 0.16314 -0.06
## round_trial_num 0.001066 0.03264 0.20 -0.43
## Residual 0.108685 0.32967
## Number of obs: 3162, groups: subject_id, 79
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.388e+00 1.170e-01 7.675e+01
## is3Back -1.222e-01 3.753e-02 7.397e+01
## round_trial_num -2.456e-02 1.623e-02 1.691e+02
## diagnostic_groupMDD 5.703e-02 1.442e-01 7.675e+01
## is3Back:round_trial_num -2.843e-02 2.128e-02 2.911e+03
## is3Back:diagnostic_groupMDD 4.645e-02 4.623e-02 7.372e+01
## round_trial_num:diagnostic_groupMDD 2.486e-03 1.996e-02 1.671e+02
## is3Back:round_trial_num:diagnostic_groupMDD 1.077e-02 2.614e-02 2.911e+03
## t value Pr(>|t|)
## (Intercept) 11.862 <2e-16 ***
## is3Back -3.257 0.0017 **
## round_trial_num -1.513 0.1322
## diagnostic_groupMDD 0.395 0.6937
## is3Back:round_trial_num -1.336 0.1816
## is3Back:diagnostic_groupMDD 1.005 0.3183
## round_trial_num:diagnostic_groupMDD 0.125 0.9010
## is3Back:round_trial_num:diagnostic_groupMDD 0.412 0.6804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) is3Bck rnd_t_ dg_MDD is3B:__ i3B:_M r__:_M
## is3Back -0.095
## rond_trl_nm 0.076 -0.142
## dgnstc_gMDD -0.811 0.077 -0.061
## is3Bck:rn__ 0.000 0.000 -0.643 0.000
## is3Bck:_MDD 0.077 -0.812 0.115 -0.095 0.000
## rnd_t_:_MDD -0.062 0.115 -0.813 0.076 0.523 -0.142
## i3B:__:_MDD 0.000 0.000 0.524 0.000 -0.814 0.000 -0.643
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "expectedRewardlm_log ~ 1 + isLarge*round_trial_num*diagnostic_group + \n (1 + isLarge + round_trial_num | subject_id)"
## Data: choiceData %>% filter(decision == "exit", travel_task == "bp") %>%
## group_by(round, subject_id) %>% mutate(round_trial_num = scale(seq(round)))
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 2884.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4081 -0.3715 0.0584 0.4192 4.8838
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject_id (Intercept) 0.3831772 0.61901
## isLarge 0.0367286 0.19165 -0.14
## round_trial_num 0.0005003 0.02237 0.45 -0.81
## Residual 0.1240621 0.35222
## Number of obs: 3140, groups: subject_id, 80
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.214e+00 1.202e-01 7.804e+01
## isLarge -8.042e-02 4.327e-02 7.781e+01
## round_trial_num -2.472e-02 1.696e-02 2.053e+02
## diagnostic_groupMDD 2.151e-01 1.476e-01 7.798e+01
## isLarge:round_trial_num 4.690e-02 2.340e-02 2.897e+03
## isLarge:diagnostic_groupMDD 3.800e-02 5.300e-02 7.690e+01
## round_trial_num:diagnostic_groupMDD -1.405e-03 2.062e-02 1.990e+02
## isLarge:round_trial_num:diagnostic_groupMDD -4.611e-02 2.841e-02 2.897e+03
## t value Pr(>|t|)
## (Intercept) 10.097 8.17e-16 ***
## isLarge -1.859 0.0669 .
## round_trial_num -1.457 0.1465
## diagnostic_groupMDD 1.457 0.1492
## isLarge:round_trial_num 2.005 0.0451 *
## isLarge:diagnostic_groupMDD 0.717 0.4756
## round_trial_num:diagnostic_groupMDD -0.068 0.9457
## isLarge:round_trial_num:diagnostic_groupMDD -1.623 0.1047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) isLarg rnd_t_ dg_MDD isL:__ iL:_MD r__:_M
## isLarge -0.169
## rond_trl_nm 0.115 -0.177
## dgnstc_gMDD -0.814 0.138 -0.093
## isLrg:rnd__ 0.000 0.000 -0.677 0.000
## isLrg:d_MDD 0.138 -0.816 0.145 -0.169 0.000
## rnd_t_:_MDD -0.094 0.146 -0.823 0.116 0.557 -0.180
## isL:__:_MDD 0.000 0.000 0.557 0.000 -0.824 0.000 -0.677
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "expectedRewardlm_log ~ 1 + is3Back*round_trial_num*diagnostic_group + \n (1 + is3Back + round_trial_num | subject_id)"
## Data: choiceData %>% filter(decision == "exit", travel_task == "nback") %>%
## group_by(round, subject_id) %>% mutate(round_trial_num = scale(seq(round)))
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 2492.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1942 -0.4100 0.0457 0.4595 7.4476
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject_id (Intercept) 0.364079 0.60339
## is3Back 0.026614 0.16314 -0.06
## round_trial_num 0.001066 0.03264 0.20 -0.43
## Residual 0.108685 0.32967
## Number of obs: 3162, groups: subject_id, 79
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.388e+00 1.170e-01 7.675e+01
## is3Back -1.222e-01 3.753e-02 7.397e+01
## round_trial_num -2.456e-02 1.623e-02 1.691e+02
## diagnostic_groupMDD 5.703e-02 1.442e-01 7.675e+01
## is3Back:round_trial_num -2.843e-02 2.128e-02 2.911e+03
## is3Back:diagnostic_groupMDD 4.645e-02 4.623e-02 7.372e+01
## round_trial_num:diagnostic_groupMDD 2.486e-03 1.996e-02 1.671e+02
## is3Back:round_trial_num:diagnostic_groupMDD 1.077e-02 2.614e-02 2.911e+03
## t value Pr(>|t|)
## (Intercept) 11.862 <2e-16 ***
## is3Back -3.257 0.0017 **
## round_trial_num -1.513 0.1322
## diagnostic_groupMDD 0.395 0.6937
## is3Back:round_trial_num -1.336 0.1816
## is3Back:diagnostic_groupMDD 1.005 0.3183
## round_trial_num:diagnostic_groupMDD 0.125 0.9010
## is3Back:round_trial_num:diagnostic_groupMDD 0.412 0.6804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) is3Bck rnd_t_ dg_MDD is3B:__ i3B:_M r__:_M
## is3Back -0.095
## rond_trl_nm 0.076 -0.142
## dgnstc_gMDD -0.811 0.077 -0.061
## is3Bck:rn__ 0.000 0.000 -0.643 0.000
## is3Bck:_MDD 0.077 -0.812 0.115 -0.095 0.000
## rnd_t_:_MDD -0.062 0.115 -0.813 0.076 0.523 -0.142
## i3B:__:_MDD 0.000 0.000 0.524 0.000 -0.814 0.000 -0.643
Table S4 Demographic factors by diagnostic group
## # A tibble: 2 × 5
## diagnostic_group age_mean age_sd age_min age_max
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 MDD 26.9 11.1 18 61
## 2 comparison 27.1 9.64 19 59
##
## Welch Two Sample t-test
##
## data: filter(survey_data, diagnostic_group == "MDD")$demographics_age and filter(survey_data, diagnostic_group == "comparison")$demographics_age
## t = -0.083011, df = 57.224, p-value = 0.9341
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.884586 4.495697
## sample estimates:
## mean of x mean of y
## 26.91667 27.11111
## # A tibble: 2 × 13
## diagnostic_group years_education_mean years_education_sd years_education_min
## <chr> <dbl> <dbl> <dbl>
## 1 MDD 14.7 2.15 9
## 2 comparison 16.0 2.28 13
## # ℹ 9 more variables: years_education_max <dbl>, years_edu_father_mean <dbl>,
## # years_edu_father_sd <dbl>, years_edu_father_min <dbl>,
## # years_edu_father_max <dbl>, years_edu_mother_mean <dbl>,
## # years_edu_mother_sd <dbl>, years_edu_mother_max <dbl>,
## # years_edu_mother_min <dbl>
##
## Welch Two Sample t-test
##
## data: filter(survey_data, diagnostic_group == "MDD")$years_education and filter(survey_data, diagnostic_group == "comparison")$years_education
## t = -2.5109, df = 47.719, p-value = 0.01548
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.3478076 -0.2595998
## sample estimates:
## mean of x mean of y
## 14.73333 16.03704
##
## Welch Two Sample t-test
##
## data: filter(survey_data, diagnostic_group == "MDD")$years_edu_father and filter(survey_data, diagnostic_group == "comparison")$years_edu_father
## t = -1.1731, df = 61.926, p-value = 0.2452
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.5988753 0.6766531
## sample estimates:
## mean of x mean of y
## 14.81667 15.77778
##
## Welch Two Sample t-test
##
## data: filter(survey_data, diagnostic_group == "MDD")$years_edu_mother and filter(survey_data, diagnostic_group == "comparison")$years_edu_mother
## t = 0.086473, df = 61.824, p-value = 0.9314
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.310699 1.429218
## sample estimates:
## mean of x mean of y
## 15.13333 15.07407
##
## Pearson's product-moment correlation
##
## data: all_params_survey_composites_all_data$cost_3back and all_params_survey_composites_all_data$years_education
## t = 0.43714, df = 78, p-value = 0.6632
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1721513 0.2662603
## sample estimates:
## cor
## 0.04943543
##
## Pearson's product-moment correlation
##
## data: all_params_survey_composites_all_data$cost_large and all_params_survey_composites_all_data$years_education
## t = 0.89987, df = 78, p-value = 0.371
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1210472 0.3140873
## sample estimates:
## cor
## 0.1013656
##
## Pearson's product-moment correlation
##
## data: all_params_survey_composites_mdd_data$cost_3back and all_params_survey_composites_mdd_data$years_education
## t = -0.54366, df = 51, p-value = 0.589
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3392415 0.1984576
## sample estimates:
## cor
## -0.07590809
##
## Pearson's product-moment correlation
##
## data: all_params_survey_composites_mdd_data$cost_large and all_params_survey_composites_mdd_data$years_education
## t = 1.1555, df = 51, p-value = 0.2533
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1155635 0.4122172
## sample estimates:
## cor
## 0.1597193
## # A tibble: 2 × 5
## diagnostic_group income_childhood_mean income_childhood_sd
## <chr> <dbl> <dbl>
## 1 MDD 5.07 1.98
## 2 comparison 6 1.52
## # ℹ 2 more variables: income_childhood_min <dbl>, income_childhood_max <dbl>
##
## Welch Two Sample t-test
##
## data: filter(survey_data, diagnostic_group == "MDD")$income_childhood and filter(survey_data, diagnostic_group == "comparison")$income_childhood
## t = -2.3504, df = 61.727, p-value = 0.02196
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.7229162 -0.1391528
## sample estimates:
## mean of x mean of y
## 5.068966 6.000000
## # A tibble: 5 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group demographics_gender n percent
## <chr> <chr> <int> <dbl>
## 1 MDD Other or prefer not to say (write in) 3 5
## 2 MDD female 38 63.3
## 3 MDD male 19 31.7
## 4 comparison female 15 55.6
## 5 comparison male 12 44.4
## # A tibble: 2 × 3
## `Other or prefer not to say (write in)` female male
## <dbl> <int> <int>
## 1 3 38 19
## 2 0 15 12
##
## Pearson's Chi-squared test
##
## data: gender_diagnostic_group_chi
## X-squared = 2.3881, df = 2, p-value = 0.303
## # A tibble: 10 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group race n percent
## <chr> <chr> <int> <dbl>
## 1 MDD Asian 13 21.7
## 2 MDD Black or African American 8 13.3
## 3 MDD More than one race 6 10
## 4 MDD Other or prefer not to say 6 10
## 5 MDD White 27 45
## 6 comparison Asian 8 29.6
## 7 comparison Black or African American 3 11.1
## 8 comparison More than one race 1 3.70
## 9 comparison Other or prefer not to say 2 7.41
## 10 comparison White 13 48.1
## # A tibble: 2 × 5
## Asian Black or African Ame…¹ `More than one race` Other or prefer not …² White
## <int> <int> <int> <int> <int>
## 1 13 8 6 6 27
## 2 8 3 1 2 13
## # ℹ abbreviated names: ¹`Black or African American`,
## # ²`Other or prefer not to say`
##
## Pearson's Chi-squared test
##
## data: race_diagnostic_group_chi
## X-squared = 1.6556, df = 4, p-value = 0.7988
## # A tibble: 5 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group ethnicity n percent
## <chr> <chr> <int> <dbl>
## 1 MDD Latino or Hispanic 8 13.3
## 2 MDD Not Latino or Hispanic 49 81.7
## 3 MDD Other or prefer not to say 3 5
## 4 comparison Latino or Hispanic 4 14.8
## 5 comparison Not Latino or Hispanic 23 85.2
## # A tibble: 2 × 3
## `Latino or Hispanic` `Not Latino or Hispanic` `Other or prefer not to say`
## <int> <int> <dbl>
## 1 8 49 3
## 2 4 23 0
##
## Pearson's Chi-squared test
##
## data: ethnicity_diagnostic_group_chi
## X-squared = 1.4075, df = 2, p-value = 0.4947
## # A tibble: 17 × 5
## # Groups: diagnostic_group, total_income_category [17]
## diagnostic_group total_income_category total_income_category_…¹ n percent
## <chr> <chr> <dbl> <int> <dbl>
## 1 MDD $100,000 to $149,999 6 6 10
## 2 MDD $200,000 or more 8 4 6.67
## 3 MDD $25,000 to $34,999 2 7 11.7
## 4 MDD $35,000 to $49,999 3 8 13.3
## 5 MDD $50,000 to $74,999 4 9 15
## 6 MDD $75,000 to $99,999 5 5 8.33
## 7 MDD Less than $25,000 1 12 20
## 8 MDD Prefer not to answer NA 9 15
## 9 comparison $100,000 to $149,999 6 3 11.1
## 10 comparison $150,000 to $199,999 7 1 3.70
## 11 comparison $200,000 or more 8 2 7.41
## 12 comparison $25,000 to $34,999 2 3 11.1
## 13 comparison $35,000 to $49,999 3 2 7.41
## 14 comparison $50,000 to $74,999 4 8 29.6
## 15 comparison $75,000 to $99,999 5 5 18.5
## 16 comparison Less than $25,000 1 1 3.70
## 17 comparison Prefer not to answer NA 2 7.41
## # ℹ abbreviated name: ¹total_income_category_ordinal
## # A tibble: 2 × 8
## `6` `8` `2` `3` `4` `5` `1` `7`
## <int> <int> <int> <int> <int> <int> <int> <dbl>
## 1 6 4 7 8 9 5 12 0
## 2 3 2 3 2 8 5 1 1
##
## Pearson's Chi-squared test
##
## data: total_income_category_diagnostic_group_chi
## X-squared = 9.4437, df = 7, p-value = 0.2223
## # A tibble: 13 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group occupational_status_str n percent
## <chr> <chr> <int> <dbl>
## 1 MDD disabled 3 5
## 2 MDD homemaker 2 3.33
## 3 MDD seeking employment, laid off, etc. 6 10
## 4 MDD student full-time 9 15
## 5 MDD student part-time 3 5
## 6 MDD working & student 21 35
## 7 MDD working full-time 9 15
## 8 MDD working part-time 7 11.7
## 9 comparison seeking employment, laid off, etc. 2 7.41
## 10 comparison student full-time 3 11.1
## 11 comparison working & student 9 33.3
## 12 comparison working full-time 11 40.7
## 13 comparison working part-time 2 7.41
## # A tibble: 2 × 8
## disabled homemaker `seeking employment, laid off, etc.` `student full-time`
## <dbl> <dbl> <int> <int>
## 1 3 2 6 9
## 2 0 0 2 3
## # ℹ 4 more variables: `student part-time` <dbl>, `working & student` <int>,
## # `working full-time` <int>, `working part-time` <int>
##
## Pearson's Chi-squared test
##
## data: occupational_status_diagnostic_group_chi
## X-squared = 9.6488, df = 7, p-value = 0.2094
## # A tibble: 7 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group married n percent
## <chr> <chr> <int> <dbl>
## 1 MDD divorced or separated 2 3.33
## 2 MDD married 2 3.33
## 3 MDD other 8 13.3
## 4 MDD single 48 80
## 5 comparison married 3 11.1
## 6 comparison other 3 11.1
## 7 comparison single 21 77.8
## # A tibble: 2 × 4
## `divorced or separated` married other single
## <dbl> <int> <int> <int>
## 1 2 2 8 48
## 2 0 3 3 21
##
## Pearson's Chi-squared test
##
## data: married_diagnostic_group_chi
## X-squared = 2.9443, df = 3, p-value = 0.4003
## # A tibble: 10 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group alcohol_frequency_factor n percent
## <chr> <chr> <int> <dbl>
## 1 MDD 2-3 times a week 9 15
## 2 MDD 2-4 times a month 20 33.3
## 3 MDD 4 or more times a week 2 3.33
## 4 MDD Monthly or less 14 23.3
## 5 MDD Never 15 25
## 6 comparison 2-3 times a week 6 22.2
## 7 comparison 2-4 times a month 5 18.5
## 8 comparison 4 or more times a week 1 3.70
## 9 comparison Monthly or less 7 25.9
## 10 comparison Never 8 29.6
## # A tibble: 2 × 5
## `2-3 times a week` `2-4 times a month` `4 or more times a week`
## <int> <int> <int>
## 1 9 20 2
## 2 6 5 1
## # ℹ 2 more variables: `Monthly or less` <int>, Never <int>
##
## Pearson's Chi-squared test
##
## data: alcohol_frequency_diagnostic_group_chi
## X-squared = 2.1958, df = 4, p-value = 0.6998
## # A tibble: 9 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group alcohol_amount_factor n percent
## <chr> <chr> <int> <dbl>
## 1 MDD 0 15 25
## 2 MDD 1 or 2 20 33.3
## 3 MDD 3 or 4 16 26.7
## 4 MDD 5 or 6 6 10
## 5 MDD 7, 8, or 9 3 5
## 6 comparison 0 8 29.6
## 7 comparison 1 or 2 10 37.0
## 8 comparison 3 or 4 5 18.5
## 9 comparison 5 or 6 4 14.8
## # A tibble: 2 × 5
## `0` `1 or 2` `3 or 4` `5 or 6` `7, 8, or 9`
## <int> <int> <int> <int> <dbl>
## 1 15 20 16 6 3
## 2 8 10 5 4 0
##
## Pearson's Chi-squared test
##
## data: alcohol_amount_diagnostic_group_chi
## X-squared = 2.4628, df = 4, p-value = 0.6513
## # A tibble: 8 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group caffeine_amount_factor n percent
## <chr> <chr> <int> <dbl>
## 1 MDD 1 cup 29 48.3
## 2 MDD 2-3 cups 13 21.7
## 3 MDD 4-5 cups 2 3.33
## 4 MDD 6 or more cups 1 1.67
## 5 MDD None 15 25
## 6 comparison 1 cup 11 40.7
## 7 comparison 2-3 cups 9 33.3
## 8 comparison None 7 25.9
## # A tibble: 2 × 5
## `1 cup` `2-3 cups` `4-5 cups` `6 or more cups` None
## <int> <int> <dbl> <dbl> <int>
## 1 29 13 2 1 15
## 2 11 9 0 0 7
##
## Pearson's Chi-squared test
##
## data: caffeine_amount_diagnostic_group_chi
## X-squared = 2.5921, df = 4, p-value = 0.6282
## # A tibble: 4 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group tobacco_use_factor n percent
## <chr> <chr> <int> <dbl>
## 1 MDD Does not use tobacco 48 80
## 2 MDD Uses tobacco 12 20
## 3 comparison Does not use tobacco 26 96.3
## 4 comparison Uses tobacco 1 3.70
## # A tibble: 2 × 2
## `Does not use tobacco` `Uses tobacco`
## <int> <int>
## 1 48 12
## 2 26 1
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tobacco_use_diagnostic_group_chi
## X-squared = 2.7142, df = 1, p-value = 0.09946
## # A tibble: 5 × 12
## demographic_factor Mean_MDD SD_MDD Min_MDD Max_MDD Mean_comparison
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Age 26.9 11.1 18 61 27.1
## 2 Income childhood 5.07 1.98 1 8 6
## 3 Years of education (mother) 15.1 3.41 5 20 15.1
## 4 Years of education (father) 14.8 4.08 0 20 15.8
## 5 Years of education (self) 14.7 2.15 9 21 16.0
## # ℹ 6 more variables: SD_comparison <dbl>, Min_comparison <dbl>,
## # Max_comparison <dbl>, t <dbl>, df <dbl>, p <dbl>
## result
## cor...1 Cognitive effort cost vs years of education, MDD group
## cor...2 Cognitive effort cost vs years of education, all participants
## cor...3 Physical effort cost vs years of education, MDD group
## cor...4 Physical effort cost vs years of education, all participants
## cor t df p
## cor...1 -0.07590809 -0.5436607 51 0.5890437
## cor...2 0.04943543 0.4371364 78 0.6632203
## cor...3 0.15971929 1.1554571 51 0.2532880
## cor...4 0.10136563 0.8998721 78 0.3709582
## # A tibble: 44 × 9
## demographic_factor response n_MDD percent_MDD n_comparison percent_comparison
## <chr> <chr> <int> <dbl> <int> <dbl>
## 1 Gender Other o… 3 5 NA NA
## 2 Gender female 38 63.3 15 55.6
## 3 Gender male 19 31.7 12 44.4
## 4 Race Asian 13 21.7 8 29.6
## 5 Race Black o… 8 13.3 3 11.1
## 6 Race More th… 6 10 1 3.70
## 7 Race Other o… 6 10 2 7.41
## 8 Race White 27 45 13 48.1
## 9 Latine ethnicity Latino … 8 13.3 4 14.8
## 10 Latine ethnicity Not Lat… 49 81.7 23 85.2
## # ℹ 34 more rows
## # ℹ 3 more variables: chisq <dbl>, df <int>, p <dbl>
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:02 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrrrrrrr}
## \hline
## demographic\_factor & Mean\_MDD & SD\_MDD & Min\_MDD & Max\_MDD & Mean\_comparison & SD\_comparison & Min\_comparison & Max\_comparison & t & df & p \\
## \hline
## Age & 26.92 & 11.08 & 18.00 & 61.00 & 27.11 & 9.64 & 19.00 & 59.00 & -0.08 & 57.22 & 0.93 \\
## Income childhood & 5.07 & 1.98 & 1.00 & 8.00 & 6.00 & 1.52 & 3.00 & 9.00 & -2.35 & 61.73 & 0.02 \\
## Years of education (mother) & 15.13 & 3.41 & 5.00 & 20.00 & 15.07 & 2.73 & 11.00 & 20.00 & 0.09 & 61.82 & 0.93 \\
## Years of education (father) & 14.82 & 4.08 & 0.00 & 20.00 & 15.78 & 3.26 & 12.00 & 20.00 & -1.17 & 61.93 & 0.25 \\
## Years of education (self) & 14.73 & 2.15 & 9.00 & 21.00 & 16.04 & 2.28 & 13.00 & 20.00 & -2.51 & 47.72 & 0.02 \\
## \hline
## \end{tabular}
## \end{table}
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:02 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrr}
## \hline
## result & cor & t & df & p \\
## \hline
## Cognitive effort cost vs years of education, MDD group & -0.08 & -0.54 & 51 & 0.59 \\
## Cognitive effort cost vs years of education, all participants & 0.05 & 0.44 & 78 & 0.66 \\
## Physical effort cost vs years of education, MDD group & 0.16 & 1.16 & 51 & 0.25 \\
## Physical effort cost vs years of education, all participants & 0.10 & 0.90 & 78 & 0.37 \\
## \hline
## \end{tabular}
## \end{table}
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:02 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{llrrrrrrr}
## \hline
## demographic\_factor & response & n\_MDD & percent\_MDD & n\_comparison & percent\_comparison & chisq & df & p \\
## \hline
## Gender & Other or prefer not to say (write in) & 3 & 5.00 & & & 2.39 & 2 & 0.30 \\
## Gender & female & 38 & 63.33 & 15 & 55.56 & 2.39 & 2 & 0.30 \\
## Gender & male & 19 & 31.67 & 12 & 44.44 & 2.39 & 2 & 0.30 \\
## Race & Asian & 13 & 21.67 & 8 & 29.63 & 1.66 & 4 & 0.80 \\
## Race & Black or African American & 8 & 13.33 & 3 & 11.11 & 1.66 & 4 & 0.80 \\
## Race & More than one race & 6 & 10.00 & 1 & 3.70 & 1.66 & 4 & 0.80 \\
## Race & Other or prefer not to say & 6 & 10.00 & 2 & 7.41 & 1.66 & 4 & 0.80 \\
## Race & White & 27 & 45.00 & 13 & 48.15 & 1.66 & 4 & 0.80 \\
## Latine ethnicity & Latino or Hispanic & 8 & 13.33 & 4 & 14.81 & 1.41 & 2 & 0.49 \\
## Latine ethnicity & Not Latino or Hispanic & 49 & 81.67 & 23 & 85.19 & 1.41 & 2 & 0.49 \\
## Latine ethnicity & Other or prefer not to say & 3 & 5.00 & & & 1.41 & 2 & 0.49 \\
## Total household income & \$100,000 to \$149,999 & 6 & 10.00 & 3 & 11.11 & 9.44 & 7 & 0.22 \\
## Total household income & \$200,000 or more & 4 & 6.67 & 2 & 7.41 & 9.44 & 7 & 0.22 \\
## Total household income & \$25,000 to \$34,999 & 7 & 11.67 & 3 & 11.11 & 9.44 & 7 & 0.22 \\
## Total household income & \$35,000 to \$49,999 & 8 & 13.33 & 2 & 7.41 & 9.44 & 7 & 0.22 \\
## Total household income & \$50,000 to \$74,999 & 9 & 15.00 & 8 & 29.63 & 9.44 & 7 & 0.22 \\
## Total household income & \$75,000 to \$99,999 & 5 & 8.33 & 5 & 18.52 & 9.44 & 7 & 0.22 \\
## Total household income & Less than \$25,000 & 12 & 20.00 & 1 & 3.70 & 9.44 & 7 & 0.22 \\
## Total household income & Prefer not to answer & 9 & 15.00 & 2 & 7.41 & 9.44 & 7 & 0.22 \\
## Total household income & \$150,000 to \$199,999 & & & 1 & 3.70 & 9.44 & 7 & 0.22 \\
## Relationship status & divorced or separated & 2 & 3.33 & & & 2.94 & 3 & 0.40 \\
## Relationship status & married & 2 & 3.33 & 3 & 11.11 & 2.94 & 3 & 0.40 \\
## Relationship status & other & 8 & 13.33 & 3 & 11.11 & 2.94 & 3 & 0.40 \\
## Relationship status & single & 48 & 80.00 & 21 & 77.78 & 2.94 & 3 & 0.40 \\
## Alcohol frequency & 2-3 times a week & 9 & 15.00 & 6 & 22.22 & 2.20 & 4 & 0.70 \\
## Alcohol frequency & 2-4 times a month & 20 & 33.33 & 5 & 18.52 & 2.20 & 4 & 0.70 \\
## Alcohol frequency & 4 or more times a week & 2 & 3.33 & 1 & 3.70 & 2.20 & 4 & 0.70 \\
## Alcohol frequency & Monthly or less & 14 & 23.33 & 7 & 25.93 & 2.20 & 4 & 0.70 \\
## Alcohol frequency & Never & 15 & 25.00 & 8 & 29.63 & 2.20 & 4 & 0.70 \\
## Alcohol amount & 0 & 15 & 25.00 & 8 & 29.63 & 2.46 & 4 & 0.65 \\
## Alcohol amount & 1 or 2 & 20 & 33.33 & 10 & 37.04 & 2.46 & 4 & 0.65 \\
## Alcohol amount & 3 or 4 & 16 & 26.67 & 5 & 18.52 & 2.46 & 4 & 0.65 \\
## Alcohol amount & 5 or 6 & 6 & 10.00 & 4 & 14.81 & 2.46 & 4 & 0.65 \\
## Alcohol amount & 7, 8, or 9 & 3 & 5.00 & & & 2.46 & 4 & 0.65 \\
## Caffeine amount & 1 cup & 29 & 48.33 & 11 & 40.74 & 2.59 & 4 & 0.63 \\
## Caffeine amount & 2-3 cups & 13 & 21.67 & 9 & 33.33 & 2.59 & 4 & 0.63 \\
## Caffeine amount & 4-5 cups & 2 & 3.33 & & & 2.59 & 4 & 0.63 \\
## Caffeine amount & 6 or more cups & 1 & 1.67 & & & 2.59 & 4 & 0.63 \\
## Caffeine amount & None & 15 & 25.00 & 7 & 25.93 & 2.59 & 4 & 0.63 \\
## Tobacco use & 1 cup & 29 & 48.33 & 11 & 40.74 & 2.71 & 1 & 0.10 \\
## Tobacco use & 2-3 cups & 13 & 21.67 & 9 & 33.33 & 2.71 & 1 & 0.10 \\
## Tobacco use & 4-5 cups & 2 & 3.33 & & & 2.71 & 1 & 0.10 \\
## Tobacco use & 6 or more cups & 1 & 1.67 & & & 2.71 & 1 & 0.10 \\
## Tobacco use & None & 15 & 25.00 & 7 & 25.93 & 2.71 & 1 & 0.10 \\
## \hline
## \end{tabular}
## \end{table}
Demographic factors by diagnostic group within task data sample
## # A tibble: 2 × 5
## diagnostic_group age_mean age_sd age_min age_max
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 MDD 26.0 9.95 18 60
## 2 comparison 27.1 9.64 19 59
##
## Welch Two Sample t-test
##
## data: filter(survey_has_task_data, diagnostic_group == "MDD")$demographics_age and filter(survey_has_task_data, diagnostic_group == "comparison")$demographics_age
## t = -0.46594, df = 53.941, p-value = 0.6431
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.692059 3.545308
## sample estimates:
## mean of x mean of y
## 26.03774 27.11111
## # A tibble: 2 × 13
## diagnostic_group years_education_mean years_education_sd years_education_min
## <chr> <dbl> <dbl> <dbl>
## 1 MDD 14.8 2.12 9
## 2 comparison 16.0 2.28 13
## # ℹ 9 more variables: years_education_max <dbl>, years_edu_father_mean <dbl>,
## # years_edu_father_sd <dbl>, years_edu_father_min <dbl>,
## # years_edu_father_max <dbl>, years_edu_mother_mean <dbl>,
## # years_edu_mother_sd <dbl>, years_edu_mother_max <dbl>,
## # years_edu_mother_min <dbl>
##
## Welch Two Sample t-test
##
## data: filter(survey_has_task_data, diagnostic_group == "MDD")$years_education and filter(survey_has_task_data, diagnostic_group == "comparison")$years_education
## t = -2.401, df = 49.169, p-value = 0.02018
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.3208255 -0.2060787
## sample estimates:
## mean of x mean of y
## 14.77358 16.03704
##
## Welch Two Sample t-test
##
## data: filter(survey_has_task_data, diagnostic_group == "MDD")$years_edu_father and filter(survey_has_task_data, diagnostic_group == "comparison")$years_edu_father
## t = -0.75028, df = 59.068, p-value = 0.4561
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.229376 1.013443
## sample estimates:
## mean of x mean of y
## 15.16981 15.77778
##
## Welch Two Sample t-test
##
## data: filter(survey_has_task_data, diagnostic_group == "MDD")$years_edu_mother and filter(survey_has_task_data, diagnostic_group == "comparison")$years_edu_mother
## t = 0.4624, df = 62.413, p-value = 0.6454
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.070343 1.714647
## sample estimates:
## mean of x mean of y
## 15.39623 15.07407
## # A tibble: 2 × 5
## diagnostic_group income_childhood_mean income_childhood_sd
## <chr> <dbl> <dbl>
## 1 MDD 5.14 1.97
## 2 comparison 6 1.52
## # ℹ 2 more variables: income_childhood_min <dbl>, income_childhood_max <dbl>
##
## Welch Two Sample t-test
##
## data: filter(survey_has_task_data, diagnostic_group == "MDD")$income_childhood and filter(survey_has_task_data, diagnostic_group == "comparison")$income_childhood
## t = -2.1218, df = 62.935, p-value = 0.03779
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.67528921 -0.05020099
## sample estimates:
## mean of x mean of y
## 5.137255 6.000000
## # A tibble: 5 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group demographics_gender n percent
## <chr> <chr> <int> <dbl>
## 1 MDD Other or prefer not to say (write in) 3 5.66
## 2 MDD female 36 67.9
## 3 MDD male 14 26.4
## 4 comparison female 15 55.6
## 5 comparison male 12 44.4
## # A tibble: 2 × 3
## `Other or prefer not to say (write in)` female male
## <dbl> <int> <int>
## 1 3 36 14
## 2 0 15 12
##
## Pearson's Chi-squared test
##
## data: gender_diagnostic_group_chi_has_task
## X-squared = 3.7466, df = 2, p-value = 0.1536
## # A tibble: 10 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group race n percent
## <chr> <chr> <int> <dbl>
## 1 MDD Asian 12 22.6
## 2 MDD Black or African American 7 13.2
## 3 MDD More than one race 6 11.3
## 4 MDD Other or prefer not to say 4 7.55
## 5 MDD White 24 45.3
## 6 comparison Asian 8 29.6
## 7 comparison Black or African American 3 11.1
## 8 comparison More than one race 1 3.70
## 9 comparison Other or prefer not to say 2 7.41
## 10 comparison White 13 48.1
## # A tibble: 2 × 5
## Asian Black or African Ame…¹ `More than one race` Other or prefer not …² White
## <int> <int> <int> <int> <int>
## 1 12 7 6 4 24
## 2 8 3 1 2 13
## # ℹ abbreviated names: ¹`Black or African American`,
## # ²`Other or prefer not to say`
##
## Pearson's Chi-squared test
##
## data: race_diagnostic_group_chi_has_task
## X-squared = 1.6306, df = 4, p-value = 0.8033
## # A tibble: 5 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group ethnicity n percent
## <chr> <chr> <int> <dbl>
## 1 MDD Latino or Hispanic 6 11.3
## 2 MDD Not Latino or Hispanic 44 83.0
## 3 MDD Other or prefer not to say 3 5.66
## 4 comparison Latino or Hispanic 4 14.8
## 5 comparison Not Latino or Hispanic 23 85.2
## # A tibble: 2 × 3
## `Latino or Hispanic` `Not Latino or Hispanic` `Other or prefer not to say`
## <int> <int> <dbl>
## 1 6 44 3
## 2 4 23 0
##
## Pearson's Chi-squared test
##
## data: ethnicity_diagnostic_group_chi_has_task
## X-squared = 1.713, df = 2, p-value = 0.4246
## # A tibble: 17 × 5
## # Groups: diagnostic_group, total_income_category [17]
## diagnostic_group total_income_category total_income_category_…¹ n percent
## <chr> <chr> <dbl> <int> <dbl>
## 1 MDD $100,000 to $149,999 6 6 11.3
## 2 MDD $200,000 or more 8 4 7.55
## 3 MDD $25,000 to $34,999 2 5 9.43
## 4 MDD $35,000 to $49,999 3 6 11.3
## 5 MDD $50,000 to $74,999 4 7 13.2
## 6 MDD $75,000 to $99,999 5 5 9.43
## 7 MDD Less than $25,000 1 11 20.8
## 8 MDD Prefer not to answer NA 9 17.0
## 9 comparison $100,000 to $149,999 6 3 11.1
## 10 comparison $150,000 to $199,999 7 1 3.70
## 11 comparison $200,000 or more 8 2 7.41
## 12 comparison $25,000 to $34,999 2 3 11.1
## 13 comparison $35,000 to $49,999 3 2 7.41
## 14 comparison $50,000 to $74,999 4 8 29.6
## 15 comparison $75,000 to $99,999 5 5 18.5
## 16 comparison Less than $25,000 1 1 3.70
## 17 comparison Prefer not to answer NA 2 7.41
## # ℹ abbreviated name: ¹total_income_category_ordinal
## # A tibble: 2 × 8
## `6` `8` `2` `3` `4` `5` `1` `7`
## <int> <int> <int> <int> <int> <int> <int> <dbl>
## 1 6 4 5 6 7 5 11 0
## 2 3 2 3 2 8 5 1 1
##
## Pearson's Chi-squared test
##
## data: total_income_category_diagnostic_group_chi_has_task
## X-squared = 9.0186, df = 7, p-value = 0.2513
## # A tibble: 13 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group occupational_status_str n percent
## <chr> <chr> <int> <dbl>
## 1 MDD disabled 2 3.77
## 2 MDD homemaker 2 3.77
## 3 MDD seeking employment, laid off, etc. 5 9.43
## 4 MDD student full-time 9 17.0
## 5 MDD student part-time 3 5.66
## 6 MDD working & student 19 35.8
## 7 MDD working full-time 7 13.2
## 8 MDD working part-time 6 11.3
## 9 comparison seeking employment, laid off, etc. 2 7.41
## 10 comparison student full-time 3 11.1
## 11 comparison working & student 9 33.3
## 12 comparison working full-time 11 40.7
## 13 comparison working part-time 2 7.41
## # A tibble: 2 × 8
## disabled homemaker `seeking employment, laid off, etc.` `student full-time`
## <dbl> <dbl> <int> <int>
## 1 2 2 5 9
## 2 0 0 2 3
## # ℹ 4 more variables: `student part-time` <dbl>, `working & student` <int>,
## # `working full-time` <int>, `working part-time` <int>
##
## Pearson's Chi-squared test
##
## data: occupational_status_diagnostic_group_chi_has_task
## X-squared = 10.394, df = 7, p-value = 0.1673
## # A tibble: 7 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group married n percent
## <chr> <chr> <int> <dbl>
## 1 MDD divorced or separated 2 3.77
## 2 MDD married 1 1.89
## 3 MDD other 6 11.3
## 4 MDD single 44 83.0
## 5 comparison married 3 11.1
## 6 comparison other 3 11.1
## 7 comparison single 21 77.8
## # A tibble: 2 × 4
## `divorced or separated` married other single
## <dbl> <int> <int> <int>
## 1 2 1 6 44
## 2 0 3 3 21
##
## Pearson's Chi-squared test
##
## data: married_diagnostic_group_chi_has_task
## X-squared = 4.1241, df = 3, p-value = 0.2484
## # A tibble: 10 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group alcohol_frequency_factor n percent
## <chr> <chr> <int> <dbl>
## 1 MDD 2-3 times a week 7 13.2
## 2 MDD 2-4 times a month 17 32.1
## 3 MDD 4 or more times a week 2 3.77
## 4 MDD Monthly or less 14 26.4
## 5 MDD Never 13 24.5
## 6 comparison 2-3 times a week 6 22.2
## 7 comparison 2-4 times a month 5 18.5
## 8 comparison 4 or more times a week 1 3.70
## 9 comparison Monthly or less 7 25.9
## 10 comparison Never 8 29.6
## # A tibble: 2 × 5
## `2-3 times a week` `2-4 times a month` `4 or more times a week`
## <int> <int> <int>
## 1 7 17 2
## 2 6 5 1
## # ℹ 2 more variables: `Monthly or less` <int>, Never <int>
##
## Pearson's Chi-squared test
##
## data: alcohol_frequency_diagnostic_group_chi_has_task
## X-squared = 2.2692, df = 4, p-value = 0.6864
## # A tibble: 9 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group alcohol_amount_factor n percent
## <chr> <chr> <int> <dbl>
## 1 MDD 0 13 24.5
## 2 MDD 1 or 2 17 32.1
## 3 MDD 3 or 4 15 28.3
## 4 MDD 5 or 6 6 11.3
## 5 MDD 7, 8, or 9 2 3.77
## 6 comparison 0 8 29.6
## 7 comparison 1 or 2 10 37.0
## 8 comparison 3 or 4 5 18.5
## 9 comparison 5 or 6 4 14.8
## # A tibble: 2 × 5
## `0` `1 or 2` `3 or 4` `5 or 6` `7, 8, or 9`
## <int> <int> <int> <int> <dbl>
## 1 13 17 15 6 2
## 2 8 10 5 4 0
##
## Pearson's Chi-squared test
##
## data: alcohol_amount_diagnostic_group_chi_has_task
## X-squared = 2.1862, df = 4, p-value = 0.7016
## # A tibble: 7 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group caffeine_amount_factor n percent
## <chr> <chr> <int> <dbl>
## 1 MDD 1 cup 27 50.9
## 2 MDD 2-3 cups 11 20.8
## 3 MDD 4-5 cups 1 1.89
## 4 MDD None 14 26.4
## 5 comparison 1 cup 11 40.7
## 6 comparison 2-3 cups 9 33.3
## 7 comparison None 7 25.9
## # A tibble: 2 × 4
## `1 cup` `2-3 cups` `4-5 cups` None
## <int> <int> <dbl> <int>
## 1 27 11 1 14
## 2 11 9 0 7
##
## Pearson's Chi-squared test
##
## data: caffeine_amount_diagnostic_group_chi_has_task
## X-squared = 2.0351, df = 3, p-value = 0.5651
## # A tibble: 4 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group tobacco_use_factor n percent
## <chr> <chr> <int> <dbl>
## 1 MDD Does not use tobacco 43 81.1
## 2 MDD Uses tobacco 10 18.9
## 3 comparison Does not use tobacco 26 96.3
## 4 comparison Uses tobacco 1 3.70
## # A tibble: 2 × 2
## `Does not use tobacco` `Uses tobacco`
## <int> <int>
## 1 43 10
## 2 26 1
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tobacco_use_diagnostic_group_chi_has_task
## X-squared = 2.3076, df = 1, p-value = 0.1287
## # A tibble: 5 × 12
## demographic_factor Mean_MDD SD_MDD Min_MDD Max_MDD Mean_comparison
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Age 26.0 9.95 18 60 27.1
## 2 Income childhood 5.14 1.97 1 8 6
## 3 Years of education (mother) 15.4 3.33 5 20 15.1
## 4 Years of education (father) 15.2 3.73 4 20 15.8
## 5 Years of education (self) 14.8 2.12 9 21 16.0
## # ℹ 6 more variables: SD_comparison <dbl>, Min_comparison <dbl>,
## # Max_comparison <dbl>, t <dbl>, df <dbl>, p <dbl>
## # A tibble: 42 × 9
## demographic_factor response n_MDD percent_MDD n_comparison percent_comparison
## <chr> <chr> <int> <dbl> <int> <dbl>
## 1 Gender Other o… 3 5.66 NA NA
## 2 Gender female 36 67.9 15 55.6
## 3 Gender male 14 26.4 12 44.4
## 4 Race Asian 12 22.6 8 29.6
## 5 Race Black o… 7 13.2 3 11.1
## 6 Race More th… 6 11.3 1 3.70
## 7 Race Other o… 4 7.55 2 7.41
## 8 Race White 24 45.3 13 48.1
## 9 Latine ethnicity Latino … 6 11.3 4 14.8
## 10 Latine ethnicity Not Lat… 44 83.0 23 85.2
## # ℹ 32 more rows
## # ℹ 3 more variables: chisq <dbl>, df <int>, p <dbl>
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:03 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrrrrrrr}
## \hline
## demographic\_factor & Mean\_MDD & SD\_MDD & Min\_MDD & Max\_MDD & Mean\_comparison & SD\_comparison & Min\_comparison & Max\_comparison & t & df & p \\
## \hline
## Age & 26.04 & 9.95 & 18.00 & 60.00 & 27.11 & 9.64 & 19.00 & 59.00 & -0.47 & 53.94 & 0.64 \\
## Income childhood & 5.14 & 1.97 & 1.00 & 8.00 & 6.00 & 1.52 & 3.00 & 9.00 & -2.12 & 62.94 & 0.04 \\
## Years of education (mother) & 15.40 & 3.33 & 5.00 & 20.00 & 15.07 & 2.73 & 11.00 & 20.00 & 0.46 & 62.41 & 0.65 \\
## Years of education (father) & 15.17 & 3.73 & 4.00 & 20.00 & 15.78 & 3.26 & 12.00 & 20.00 & -0.75 & 59.07 & 0.46 \\
## Years of education (self) & 14.77 & 2.12 & 9.00 & 21.00 & 16.04 & 2.28 & 13.00 & 20.00 & -2.40 & 49.17 & 0.02 \\
## \hline
## \end{tabular}
## \end{table}
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:03 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{llrrrrrrr}
## \hline
## demographic\_factor & response & n\_MDD & percent\_MDD & n\_comparison & percent\_comparison & chisq & df & p \\
## \hline
## Gender & Other or prefer not to say (write in) & 3 & 5.66 & & & 3.75 & 2 & 0.15 \\
## Gender & female & 36 & 67.92 & 15 & 55.56 & 3.75 & 2 & 0.15 \\
## Gender & male & 14 & 26.42 & 12 & 44.44 & 3.75 & 2 & 0.15 \\
## Race & Asian & 12 & 22.64 & 8 & 29.63 & 1.63 & 4 & 0.80 \\
## Race & Black or African American & 7 & 13.21 & 3 & 11.11 & 1.63 & 4 & 0.80 \\
## Race & More than one race & 6 & 11.32 & 1 & 3.70 & 1.63 & 4 & 0.80 \\
## Race & Other or prefer not to say & 4 & 7.55 & 2 & 7.41 & 1.63 & 4 & 0.80 \\
## Race & White & 24 & 45.28 & 13 & 48.15 & 1.63 & 4 & 0.80 \\
## Latine ethnicity & Latino or Hispanic & 6 & 11.32 & 4 & 14.81 & 1.71 & 2 & 0.42 \\
## Latine ethnicity & Not Latino or Hispanic & 44 & 83.02 & 23 & 85.19 & 1.71 & 2 & 0.42 \\
## Latine ethnicity & Other or prefer not to say & 3 & 5.66 & & & 1.71 & 2 & 0.42 \\
## Total household income & \$100,000 to \$149,999 & 6 & 11.32 & 3 & 11.11 & 9.02 & 7 & 0.25 \\
## Total household income & \$200,000 or more & 4 & 7.55 & 2 & 7.41 & 9.02 & 7 & 0.25 \\
## Total household income & \$25,000 to \$34,999 & 5 & 9.43 & 3 & 11.11 & 9.02 & 7 & 0.25 \\
## Total household income & \$35,000 to \$49,999 & 6 & 11.32 & 2 & 7.41 & 9.02 & 7 & 0.25 \\
## Total household income & \$50,000 to \$74,999 & 7 & 13.21 & 8 & 29.63 & 9.02 & 7 & 0.25 \\
## Total household income & \$75,000 to \$99,999 & 5 & 9.43 & 5 & 18.52 & 9.02 & 7 & 0.25 \\
## Total household income & Less than \$25,000 & 11 & 20.75 & 1 & 3.70 & 9.02 & 7 & 0.25 \\
## Total household income & Prefer not to answer & 9 & 16.98 & 2 & 7.41 & 9.02 & 7 & 0.25 \\
## Total household income & \$150,000 to \$199,999 & & & 1 & 3.70 & 9.02 & 7 & 0.25 \\
## Relationship status & divorced or separated & 2 & 3.77 & & & 4.12 & 3 & 0.25 \\
## Relationship status & married & 1 & 1.89 & 3 & 11.11 & 4.12 & 3 & 0.25 \\
## Relationship status & other & 6 & 11.32 & 3 & 11.11 & 4.12 & 3 & 0.25 \\
## Relationship status & single & 44 & 83.02 & 21 & 77.78 & 4.12 & 3 & 0.25 \\
## Alcohol frequency & 2-3 times a week & 7 & 13.21 & 6 & 22.22 & 2.27 & 4 & 0.69 \\
## Alcohol frequency & 2-4 times a month & 17 & 32.08 & 5 & 18.52 & 2.27 & 4 & 0.69 \\
## Alcohol frequency & 4 or more times a week & 2 & 3.77 & 1 & 3.70 & 2.27 & 4 & 0.69 \\
## Alcohol frequency & Monthly or less & 14 & 26.42 & 7 & 25.93 & 2.27 & 4 & 0.69 \\
## Alcohol frequency & Never & 13 & 24.53 & 8 & 29.63 & 2.27 & 4 & 0.69 \\
## Alcohol amount & 0 & 13 & 24.53 & 8 & 29.63 & 2.19 & 4 & 0.70 \\
## Alcohol amount & 1 or 2 & 17 & 32.08 & 10 & 37.04 & 2.19 & 4 & 0.70 \\
## Alcohol amount & 3 or 4 & 15 & 28.30 & 5 & 18.52 & 2.19 & 4 & 0.70 \\
## Alcohol amount & 5 or 6 & 6 & 11.32 & 4 & 14.81 & 2.19 & 4 & 0.70 \\
## Alcohol amount & 7, 8, or 9 & 2 & 3.77 & & & 2.19 & 4 & 0.70 \\
## Caffeine amount & 1 cup & 27 & 50.94 & 11 & 40.74 & 2.04 & 3 & 0.57 \\
## Caffeine amount & 2-3 cups & 11 & 20.75 & 9 & 33.33 & 2.04 & 3 & 0.57 \\
## Caffeine amount & 4-5 cups & 1 & 1.89 & & & 2.04 & 3 & 0.57 \\
## Caffeine amount & None & 14 & 26.42 & 7 & 25.93 & 2.04 & 3 & 0.57 \\
## Tobacco use & 1 cup & 27 & 50.94 & 11 & 40.74 & 2.31 & 1 & 0.13 \\
## Tobacco use & 2-3 cups & 11 & 20.75 & 9 & 33.33 & 2.31 & 1 & 0.13 \\
## Tobacco use & 4-5 cups & 1 & 1.89 & & & 2.31 & 1 & 0.13 \\
## Tobacco use & None & 14 & 26.42 & 7 & 25.93 & 2.31 & 1 & 0.13 \\
## \hline
## \end{tabular}
## \end{table}
Table S5 MVT Group-level parameter posterior distributions
## # A tibble: 48,000 × 3
## params params_val param_name
## <chr> <dbl> <chr>
## 1 mu[1] 0.110 Inverse Tempurature
## 2 mu[2] 5.16 Cognitive Low Effort Travel Cost
## 3 mu[3] 13.7 Cognitive Effort Cost
## 4 mu[4] 20.5 Physical Low Effort Travel Cost
## 5 mu[5] 10.6 Physical Effort Cost
## 6 rho[3,5] 0.178 Cog. vs. Phys. corr
## 7 mu[1] 0.0220 Inverse Tempurature
## 8 mu[2] 0.173 Cognitive Low Effort Travel Cost
## 9 mu[3] 14.1 Cognitive Effort Cost
## 10 mu[4] 5.07 Physical Low Effort Travel Cost
## # ℹ 47,990 more rows
## # A tibble: 6 × 5
## param_name mean lower upper p
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Cog. vs. Phys. corr 0.0532 -0.240 0.345 0.365
## 2 Cognitive Effort Cost 11.7 7.10 16.5 0
## 3 Cognitive Low Effort Travel Cost 11.0 -6.69 27.2 0.0975
## 4 Inverse Tempurature -0.0215 -0.198 0.154 0.408
## 5 Physical Effort Cost 8.59 3.03 14.2 0.00112
## 6 Physical Low Effort Travel Cost 18.2 0.00396 35.1 0.025
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:03 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrr}
## \hline
## param\_name & mean & lower & upper & p \\
## \hline
## Cog. vs. Phys. corr & 0.05 & -0.24 & 0.34 & 0.365 \\
## Cognitive Effort Cost & 11.71 & 7.10 & 16.47 & 0.000 \\
## Cognitive Low Effort Travel Cost & 10.97 & -6.69 & 27.19 & 0.098 \\
## Inverse Tempurature & -0.02 & -0.20 & 0.15 & 0.408 \\
## Physical Effort Cost & 8.59 & 3.03 & 14.21 & 0.001 \\
## Physical Low Effort Travel Cost & 18.18 & 0.00 & 35.06 & 0.025 \\
## \hline
## \end{tabular}
## \end{table}
Figure S6 Model diagnostics
## rhat param
## mu[1] 1.035430 mu[1]
## mu[2] 1.009580 mu[2]
## mu[3] 1.004196 mu[3]
## mu[4] 1.004990 mu[4]
## mu[5] 1.002398 mu[5]
## tau[1] 1.001854 tau[1]
## tau[2] 1.003917 tau[2]
## tau[3] 1.001334 tau[3]
## tau[4] 1.003982 tau[4]
## tau[5] 1.001617 tau[5]
## L_u[1,1] NA L_u[1,1]
## L_u[2,1] 1.011969 L_u[2,1]
## L_u[3,1] 1.001869 L_u[3,1]
## L_u[4,1] 1.007323 L_u[4,1]
## L_u[5,1] 1.000609 L_u[5,1]
## L_u[1,2] NA L_u[1,2]
## L_u[2,2] 1.006583 L_u[2,2]
## L_u[3,2] 1.001386 L_u[3,2]
## L_u[4,2] 1.004470 L_u[4,2]
## L_u[5,2] 1.000860 L_u[5,2]
## L_u[1,3] NA L_u[1,3]
## L_u[2,3] NA L_u[2,3]
## L_u[3,3] 1.002079 L_u[3,3]
## L_u[4,3] 1.005208 L_u[4,3]
## L_u[5,3] 1.000563 L_u[5,3]
## L_u[1,4] NA L_u[1,4]
## L_u[2,4] NA L_u[2,4]
## L_u[3,4] NA L_u[3,4]
## L_u[4,4] 1.002405 L_u[4,4]
## L_u[5,4] 1.000802 L_u[5,4]
## L_u[1,5] NA L_u[1,5]
## L_u[2,5] NA L_u[2,5]
## L_u[3,5] NA L_u[3,5]
## L_u[4,5] NA L_u[4,5]
## L_u[5,5] 1.000845 L_u[5,5]
## rho[1,1] NA rho[1,1]
## rho[2,1] 1.011969 rho[2,1]
## rho[3,1] 1.001869 rho[3,1]
## rho[4,1] 1.007323 rho[4,1]
## rho[5,1] 1.000609 rho[5,1]
## rho[1,2] 1.011969 rho[1,2]
## rho[2,2] NA rho[2,2]
## rho[3,2] 1.001200 rho[3,2]
## rho[4,2] 1.002018 rho[4,2]
## rho[5,2] 1.000615 rho[5,2]
## rho[1,3] 1.001869 rho[1,3]
## rho[2,3] 1.001200 rho[2,3]
## rho[3,3] NA rho[3,3]
## rho[4,3] 1.001284 rho[4,3]
## rho[5,3] 1.000502 rho[5,3]
## rho[1,4] 1.007323 rho[1,4]
## rho[2,4] 1.002018 rho[2,4]
## rho[3,4] 1.001284 rho[3,4]
## rho[4,4] NA rho[4,4]
## rho[5,4] 1.001794 rho[5,4]
## rho[1,5] 1.000609 rho[1,5]
## rho[2,5] 1.000615 rho[2,5]
## rho[3,5] 1.000502 rho[3,5]
## rho[4,5] 1.001794 rho[4,5]
## rho[5,5] NA rho[5,5]
## [1] 1.03543
## Warning: Removed 16 rows containing non-finite outside the scale range
## (`stat_bin()`).
Figure S7 Posterior predictive check and log likelihood
Note that think chunk is very slow to run
##
## Welch Two Sample t-test
##
## data: filter(log_sum_exp_loglik, diagnostic_group == "MDD")$log_sum_exp and filter(log_sum_exp_loglik, diagnostic_group == "comparison")$log_sum_exp
## t = 0.5563, df = 36.279, p-value = 0.5814
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -16.60529 29.16261
## sample estimates:
## mean of x mean of y
## -147.5654 -153.8441
## # A tibble: 1 × 1
## prob_stay
## <dbl>
## 1 0.901
## prob_stay sim
## 1 0.8996570 1
## 2 0.8972451 2
## 3 0.9027110 3
## 4 0.9009726 4
## 5 0.9025387 5
## 6 0.9009413 6
## # A tibble: 2 × 2
## diagnostic_group prob_stay
## <chr> <dbl>
## 1 MDD 0.898
## 2 comparison 0.907
## # A tibble: 2 × 5
## diagnostic_group threshold_mean threshold_sd threshold_sem
## <chr> <dbl> <dbl> <dbl>
## 1 MDD 5.23 2.80 0.385
## 2 comparison 4.77 2.88 0.555
## # ℹ 1 more variable: diagnostic_group_plot <chr>
## # A tibble: 2 × 5
## diagnostic_group threshold_mean threshold_sd diagnostic_group_plot
## <chr> <dbl> <dbl> <chr>
## 1 MDD 4.82 2.68 MDD
## 2 comparison 4.49 2.90 Comparison
## # ℹ 1 more variable: threshold_sem <dbl>
## # A tibble: 4 × 7
## # Groups: diagnostic_group [2]
## diagnostic_group travel_task change_exit change_exit_sd change_exit_sem
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 MDD bp -0.178 0.974 0.134
## 2 MDD nback -0.322 0.768 0.107
## 3 comparison bp -0.224 1.11 0.214
## 4 comparison nback -0.456 0.862 0.166
## # ℹ 2 more variables: diagnostic_group_plot <chr>, effort_type <chr>
## # A tibble: 4 × 7
## # Groups: diagnostic_group [2]
## diagnostic_group travel_task change_exit_mean change_exit_sd effort_type
## <chr> <chr> <dbl> <dbl> <chr>
## 1 MDD bp -0.190 0.938 Physical
## 2 MDD nback -0.329 0.743 Cognitive
## 3 comparison bp -0.384 0.965 Physical
## 4 comparison nback -0.386 0.713 Cognitive
## # ℹ 2 more variables: diagnostic_group_plot <chr>, change_exit_sem <dbl>
Figure S8 Posterior predictive check probability of exiting by
expected reward level
## # A tibble: 82 × 8
## # Groups: effort_level_plot, travel_task_plot, diagnostic_group_plot [8]
## effort_level_plot travel_task_plot diagnostic_group_plot
## <fct> <chr> <chr>
## 1 Low Cognitive effort Comparison
## 2 Low Cognitive effort Comparison
## 3 Low Cognitive effort Comparison
## 4 Low Cognitive effort Comparison
## 5 Low Cognitive effort Comparison
## 6 Low Cognitive effort Comparison
## 7 Low Cognitive effort Comparison
## 8 Low Cognitive effort Comparison
## 9 Low Cognitive effort Comparison
## 10 Low Cognitive effort MDD
## # ℹ 72 more rows
## # ℹ 5 more variables: relative_expectedReward_binned <fct>, n_subj <int>,
## # p_exit <dbl>, p_exit_sem <dbl>,
## # relative_expectedReward_binned_numeric <dbl>
## # A tibble: 82 × 7
## # Groups: effort_level_plot, travel_task_plot, diagnostic_group_plot [8]
## effort_level_plot travel_task_plot diagnostic_group_plot
## <fct> <chr> <chr>
## 1 Low Cognitive effort Comparison
## 2 Low Cognitive effort Comparison
## 3 Low Cognitive effort Comparison
## 4 Low Cognitive effort Comparison
## 5 Low Cognitive effort Comparison
## 6 Low Cognitive effort Comparison
## 7 Low Cognitive effort Comparison
## 8 Low Cognitive effort Comparison
## 9 Low Cognitive effort Comparison
## 10 Low Cognitive effort MDD
## # ℹ 72 more rows
## # ℹ 4 more variables: relative_expectedReward_binned <fct>, n_subj <int>,
## # p_exit <dbl>, p_exit_sem <dbl>
## # A tibble: 40 × 7
## # Groups: travel_task_plot, diagnostic_group_plot [4]
## travel_task_plot diagnostic_group_plot relative_expectedReward_binned n_subj
## <chr> <chr> <fct> <int>
## 1 Cognitive effort Comparison (-1.4,-1] 27
## 2 Cognitive effort Comparison (-1,-0.6] 27
## 3 Cognitive effort Comparison (-0.6,-0.2] 27
## 4 Cognitive effort Comparison (-0.2,0.2] 27
## 5 Cognitive effort Comparison (0.2,0.6] 27
## 6 Cognitive effort Comparison (0.6,1] 27
## 7 Cognitive effort Comparison (1,1.4] 27
## 8 Cognitive effort Comparison (1.4,1.8] 27
## 9 Cognitive effort Comparison (1.8,2.2] 25
## 10 Cognitive effort MDD (-1.8,-1.4] 48
## # ℹ 30 more rows
## # ℹ 3 more variables: p_exit_effort_diff <dbl>, p_exit_effort_diff_sem <dbl>,
## # relative_expectedReward_binned_numeric <dbl>
## # A tibble: 40 × 7
## # Groups: travel_task_plot, diagnostic_group_plot [4]
## travel_task_plot diagnostic_group_plot relative_expectedReward_binned n_subj
## <chr> <chr> <fct> <int>
## 1 Cognitive effort Comparison (-1.4,-1] 27
## 2 Cognitive effort Comparison (-1,-0.6] 27
## 3 Cognitive effort Comparison (-0.6,-0.2] 27
## 4 Cognitive effort Comparison (-0.2,0.2] 27
## 5 Cognitive effort Comparison (0.2,0.6] 27
## 6 Cognitive effort Comparison (0.6,1] 27
## 7 Cognitive effort Comparison (1,1.4] 27
## 8 Cognitive effort Comparison (1.4,1.8] 27
## 9 Cognitive effort Comparison (1.8,2.2] 25
## 10 Cognitive effort MDD (-1.8,-1.4] 48
## # ℹ 30 more rows
## # ℹ 3 more variables: p_exit_effort_diff <dbl>, p_exit_effort_diff_sem <dbl>,
## # relative_expectedReward_binned_numeric <dbl>
## Warning: The `guide` argument in `scale_*()` cannot be `FALSE`. This was deprecated in
## ggplot2 3.3.4.
## ℹ Please use "none" instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Table S6 Symptom regressions, control for psychotropic medication
use, MDD
##
## Call:
## lm(formula = "cost_3back ~ diagnostic_group + BMI + demographics_age + years_education + \n psychotropic_medication",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) diagnostic_groupMDD
## 0.10667 -0.02332
## BMI demographics_age
## 0.24356 -0.12905
## years_education psychotropic_medicationTRUE
## -0.01339 -0.25532
##
## Call:
## lm(formula = "cost_3back ~ HRSD_total + three_back_d_prime + demographics_age + \n years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) HRSD_total
## 0.10999 -0.38602
## three_back_d_prime demographics_age
## -0.12777 -0.02193
## years_education psychotropic_medicationTRUE
## -0.11980 -0.33483
##
## Call:
## lm(formula = "cost_3back ~ anhedonia_symptoms + three_back_d_prime + demographics_age + \n years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anhedonia_symptoms
## 0.15231 -0.19536
## three_back_d_prime demographics_age
## -0.08534 -0.12015
## years_education psychotropic_medicationTRUE
## -0.07780 -0.32450
##
## Call:
## lm(formula = "cost_3back ~ anxiety_symptoms + three_back_d_prime + demographics_age + \n years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anxiety_symptoms
## 0.05860 -0.48928
## three_back_d_prime demographics_age
## -0.14527 -0.08534
## years_education psychotropic_medicationTRUE
## -0.12152 -0.20427
##
## Call:
## lm(formula = "cost_3back ~ apathy_behavioral_symptoms + three_back_d_prime + demographics_age + \n years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## 0.11913 -0.19926
## three_back_d_prime demographics_age
## -0.07804 -0.12953
## years_education psychotropic_medicationTRUE
## -0.10198 -0.28402
##
## Call:
## lm(formula = "cost_3back ~ apathy_social_symptoms + three_back_d_prime + demographics_age + \n years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_social_symptoms
## 0.134396 0.008767
## three_back_d_prime demographics_age
## -0.076215 -0.139758
## years_education psychotropic_medicationTRUE
## -0.072467 -0.271567
##
## Call:
## lm(formula = "cost_3back ~ cog_function_symptoms + three_back_d_prime + demographics_age + \n years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) cog_function_symptoms
## 0.14372 -0.30402
## three_back_d_prime demographics_age
## -0.06977 -0.04200
## years_education psychotropic_medicationTRUE
## -0.08934 -0.28713
##
## Call:
## lm(formula = "cost_3back ~ depressed_mood_symptoms + three_back_d_prime + demographics_age +\n years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) depressed_mood_symptoms
## 0.1012 -0.2571
## three_back_d_prime demographics_age
## -0.1379 -0.1141
## years_education psychotropic_medicationTRUE
## -0.1002 -0.2675
##
## Call:
## lm(formula = "cost_3back ~ physical_anergia_slowing_symptoms + three_back_d_prime + demographics_age + \n years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## 0.13592 -0.24124
## three_back_d_prime demographics_age
## -0.11763 -0.09489
## years_education psychotropic_medicationTRUE
## -0.11050 -0.31826
## symptom_vars Estimate SE t p p_fdr
## 1 Anhedonia -0.195 0.148 -1.320 0.19201 0.227
## 2 Anxiety -0.489 0.135 -3.620 0.00073 0.005
## 3 Behavioral apathy -0.199 0.151 -1.316 0.19480 0.227
## 4 Social apathy 0.009 0.149 0.059 0.95340 0.953
## 5 Cognitive function symptoms -0.304 0.144 -2.107 0.04100 0.144
## 6 Depressed mood/suicidality -0.257 0.166 -1.546 0.12902 0.226
## 7 Physical anergia/slowing -0.241 0.151 -1.593 0.11794 0.226
## 8 Diagnostic group difference -0.023 0.286 -0.080 0.93500 NA
## 9 Overall depression -0.386 0.164 -2.354 0.02300 NA
##
## Call:
## lm(formula = "cost_large ~ diagnostic_group + BMI + demographics_age + years_education + \n psychotropic_medication",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) diagnostic_groupMDD
## -0.062502 0.116635
## BMI demographics_age
## -0.078202 0.033796
## years_education psychotropic_medicationTRUE
## 0.116884 0.007464
##
## Call:
## lm(formula = "cost_large ~ HRSD_total + BMI + demographics_age + years_education + \n psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) HRSD_total
## 0.009122 0.037634
## BMI demographics_age
## 0.068412 -0.065715
## years_education psychotropic_medicationTRUE
## 0.156351 0.025957
##
## Call:
## lm(formula = "cost_large ~ anhedonia_symptoms + larger_percent_req_presses_completed + BMI + \n demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anhedonia_symptoms
## 0.02543 0.41991
## larger_percent_req_presses_completed BMI
## 0.12038 0.05175
## demographics_age years_education
## -0.04595 0.15650
## psychotropic_medicationTRUE
## 0.04595
##
## Call:
## lm(formula = "cost_large ~ anxiety_symptoms + larger_percent_req_presses_completed + BMI + \n demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anxiety_symptoms
## 0.03041 -0.03935
## larger_percent_req_presses_completed BMI
## 0.11701 0.13182
## demographics_age years_education
## -0.06101 0.13893
## psychotropic_medicationTRUE
## -0.02978
##
## Call:
## lm(formula = "cost_large ~ apathy_behavioral_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## 0.07993 0.39470
## larger_percent_req_presses_completed BMI
## 0.17047 0.09776
## demographics_age years_education
## -0.10068 0.20139
## psychotropic_medicationTRUE
## -0.05535
##
## Call:
## lm(formula = "cost_large ~ apathy_social_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_social_symptoms
## 0.03713 0.13766
## larger_percent_req_presses_completed BMI
## 0.12625 0.10558
## demographics_age years_education
## -0.06891 0.18365
## psychotropic_medicationTRUE
## -0.03883
##
## Call:
## lm(formula = "cost_large ~ cog_function_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) cog_function_symptoms
## 0.04548 0.27020
## larger_percent_req_presses_completed BMI
## 0.15288 0.05143
## demographics_age years_education
## -0.09467 0.16036
## psychotropic_medicationTRUE
## -0.04010
##
## Call:
## lm(formula = "cost_large ~ depressed_mood_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) depressed_mood_symptoms
## 0.06020 0.13567
## larger_percent_req_presses_completed BMI
## 0.12956 0.11992
## demographics_age years_education
## -0.08684 0.16384
## psychotropic_medicationTRUE
## -0.05524
##
## Call:
## lm(formula = "cost_large ~ physical_anergia_slowing_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## 0.04235 0.16321
## larger_percent_req_presses_completed BMI
## 0.16293 0.10380
## demographics_age years_education
## -0.09844 0.17928
## psychotropic_medicationTRUE
## -0.03193
## symptom_vars Estimate SE t p p_fdr
## 1 Anhedonia 0.420 0.145 2.903 0.006 0.035
## 2 Anxiety -0.039 0.158 -0.249 0.804 0.804
## 3 Behavioral apathy 0.395 0.147 2.682 0.010 0.035
## 4 Social apathy 0.138 0.149 0.925 0.360 0.491
## 5 Cognitive function symptoms 0.270 0.164 1.648 0.106 0.247
## 6 Depressed mood/suicidality 0.136 0.167 0.813 0.421 0.491
## 7 Physical anergia/slowing 0.163 0.166 0.984 0.331 0.491
## 8 Diagnostic group differences 0.117 0.293 0.400 0.692 NA
## 9 Overall depression 0.038 0.173 0.218 0.828 NA
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ diagnostic_group + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) diagnostic_groupMDD
## -0.18731 0.57077
## demographics_age years_education
## 0.02891 0.08469
## psychotropic_medicationTRUE
## -0.56763
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ HRSD_total + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) HRSD_total
## 0.27565 -0.37749
## demographics_age years_education
## 0.11115 -0.05494
## psychotropic_medicationTRUE
## -0.63297
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ anhedonia_symptoms + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anhedonia_symptoms
## 0.297392 -0.293246
## demographics_age years_education
## 0.018310 0.007971
## psychotropic_medicationTRUE
## -0.621701
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ anxiety_symptoms + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anxiety_symptoms
## 0.23308 -0.32562
## demographics_age years_education
## 0.03400 -0.03052
## psychotropic_medicationTRUE
## -0.51090
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ apathy_behavioral_symptoms + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## 0.2528603 -0.3131187
## demographics_age years_education
## -0.0003691 -0.0336097
## psychotropic_medicationTRUE
## -0.5678708
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ apathy_social_symptoms + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_social_symptoms
## 0.27985 -0.11405
## demographics_age years_education
## -0.01801 -0.02146
## psychotropic_medicationTRUE
## -0.56030
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ cog_function_symptoms + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) cog_function_symptoms
## 0.287810 -0.218643
## demographics_age years_education
## 0.050205 -0.003499
## psychotropic_medicationTRUE
## -0.564134
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ depressed_mood_symptoms + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) depressed_mood_symptoms
## 0.22101 -0.39779
## demographics_age years_education
## 0.05800 -0.03576
## psychotropic_medicationTRUE
## -0.53844
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ physical_anergia_slowing_symptoms + demographics_age + years_education + psychotropic_medication",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## 0.29320 -0.35422
## demographics_age years_education
## 0.06746 -0.05847
## psychotropic_medicationTRUE
## -0.63110
## symptom_vars Estimate SE t p p_fdr
## 1 Anhedonia -0.293 0.137 -2.15 0.03679 0.052
## 2 Anxiety -0.326 0.138 -2.36 0.02237 0.052
## 3 Behavioral apathy -0.313 0.141 -2.22 0.03112 0.052
## 4 Social apathy -0.114 0.138 -0.82 0.41389 0.414
## 5 Cognitive function symptoms -0.219 0.142 -1.54 0.12919 0.151
## 6 Depressed mood/suicidality -0.398 0.146 -2.73 0.00889 0.047
## 7 Physical anergia/slowing -0.354 0.138 -2.57 0.01330 0.047
## 8 Diagnostic group difference 0.571 0.280 2.04 0.04500 NA
## 9 Overall depression -0.377 0.153 -2.47 0.01700 NA
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:05 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrl}
## \hline
## symptom\_vars & Estimate & SE & t & p & p\_fdr & model \\
## \hline
## Anhedonia & -0.20 & 0.15 & -1.32 & 0.192 & 0.227 & Cognitive, MDD, controlling for medication status \\
## Anxiety & -0.49 & 0.14 & -3.62 & 0.001 & 0.005 & Cognitive, MDD, controlling for medication status \\
## Behavioral apathy & -0.20 & 0.15 & -1.32 & 0.195 & 0.227 & Cognitive, MDD, controlling for medication status \\
## Social apathy & 0.01 & 0.15 & 0.06 & 0.953 & 0.953 & Cognitive, MDD, controlling for medication status \\
## Cognitive function symptoms & -0.30 & 0.14 & -2.11 & 0.041 & 0.144 & Cognitive, MDD, controlling for medication status \\
## Depressed mood/suicidality & -0.26 & 0.17 & -1.55 & 0.129 & 0.226 & Cognitive, MDD, controlling for medication status \\
## Physical anergia/slowing & -0.24 & 0.15 & -1.59 & 0.118 & 0.226 & Cognitive, MDD, controlling for medication status \\
## Diagnostic group difference & -0.02 & 0.29 & -0.08 & 0.935 & & Cognitive, MDD, controlling for medication status \\
## Overall depression & -0.39 & 0.16 & -2.35 & 0.023 & & Cognitive, MDD, controlling for medication status \\
## Anhedonia & 0.42 & 0.14 & 2.90 & 0.006 & 0.035 & Physical, MDD, controlling for medication status \\
## Anxiety & -0.04 & 0.16 & -0.25 & 0.804 & 0.804 & Physical, MDD, controlling for medication status \\
## Behavioral apathy & 0.40 & 0.15 & 2.68 & 0.010 & 0.035 & Physical, MDD, controlling for medication status \\
## Social apathy & 0.14 & 0.15 & 0.93 & 0.360 & 0.491 & Physical, MDD, controlling for medication status \\
## Cognitive function symptoms & 0.27 & 0.16 & 1.65 & 0.106 & 0.247 & Physical, MDD, controlling for medication status \\
## Depressed mood/suicidality & 0.14 & 0.17 & 0.81 & 0.421 & 0.491 & Physical, MDD, controlling for medication status \\
## Physical anergia/slowing & 0.16 & 0.17 & 0.98 & 0.331 & 0.491 & Physical, MDD, controlling for medication status \\
## Diagnostic group differences & 0.12 & 0.29 & 0.40 & 0.692 & & Physical, MDD, controlling for medication status \\
## Overall depression & 0.04 & 0.17 & 0.22 & 0.828 & & Physical, MDD, controlling for medication status \\
## Anhedonia & -0.29 & 0.14 & -2.15 & 0.037 & 0.052 & Overall threshold, MDD, controlling for medication status \\
## Anxiety & -0.33 & 0.14 & -2.36 & 0.022 & 0.052 & Overall threshold, MDD, controlling for medication status \\
## Behavioral apathy & -0.31 & 0.14 & -2.22 & 0.031 & 0.052 & Overall threshold, MDD, controlling for medication status \\
## Social apathy & -0.11 & 0.14 & -0.82 & 0.414 & 0.414 & Overall threshold, MDD, controlling for medication status \\
## Cognitive function symptoms & -0.22 & 0.14 & -1.54 & 0.129 & 0.151 & Overall threshold, MDD, controlling for medication status \\
## Depressed mood/suicidality & -0.40 & 0.15 & -2.73 & 0.009 & 0.047 & Overall threshold, MDD, controlling for medication status \\
## Physical anergia/slowing & -0.35 & 0.14 & -2.57 & 0.013 & 0.047 & Overall threshold, MDD, controlling for medication status \\
## Diagnostic group difference & 0.57 & 0.28 & 2.04 & 0.045 & & Overall threshold, MDD, controlling for medication status \\
## Overall depression & -0.38 & 0.15 & -2.47 & 0.017 & & Overall threshold, MDD, controlling for medication status \\
## \hline
## \end{tabular}
## \end{table}
Table S7 Diagnostic group difference MVT model
## parameter mean lower upper
## 1 Cognitive Effort Cost -1.03089146 -8.0531570 6.0841557
## 2 Physical Effort Cost -1.07283622 -8.6938460 6.3133390
## 3 Cognitive Low Effort Cost 1.80856687 -7.3042835 10.7165000
## 4 Physical Low Effort Cost -3.06065765 -12.0215075 5.8964177
## 5 Inverse temp. (log) 0.09744694 -0.2435479 0.4565613
## 6 Current-Cognitive Effort Cost -1.39405656 -8.4888198 5.7580815
## 7 Current-Physical Effort Cost -1.73207197 -9.0957313 5.5997160
## 8 Current-Cognitive Low Effort Cost 2.52235296 -6.5296033 11.7729250
## 9 Current-Physical Low Effort Cost -3.72601884 -13.0095250 5.4034660
## 10 Current-Inverse temp. (log) 0.09442715 -0.2903971 0.4869201
## pd
## 1 0.385125
## 2 0.394500
## 3 0.339500
## 4 0.255500
## 5 0.289750
## 6 0.350625
## 7 0.317625
## 8 0.290250
## 9 0.214625
## 10 0.322000
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:05 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrr}
## \hline
## parameter & mean & lower & upper & pd \\
## \hline
## Cognitive Effort Cost & -1.03 & -8.05 & 6.08 & 0.39 \\
## Physical Effort Cost & -1.07 & -8.69 & 6.31 & 0.39 \\
## Cognitive Low Effort Cost & 1.81 & -7.30 & 10.72 & 0.34 \\
## Physical Low Effort Cost & -3.06 & -12.02 & 5.90 & 0.26 \\
## Inverse temp. (log) & 0.10 & -0.24 & 0.46 & 0.29 \\
## Current-Cognitive Effort Cost & -1.39 & -8.49 & 5.76 & 0.35 \\
## Current-Physical Effort Cost & -1.73 & -9.10 & 5.60 & 0.32 \\
## Current-Cognitive Low Effort Cost & 2.52 & -6.53 & 11.77 & 0.29 \\
## Current-Physical Low Effort Cost & -3.73 & -13.01 & 5.40 & 0.21 \\
## Current-Inverse temp. (log) & 0.09 & -0.29 & 0.49 & 0.32 \\
## \hline
## \end{tabular}
## \end{table}
Table S8 Self-reported overall depression results
##
## Call:
## lm(formula = "cost_3back ~ PHQ9_total + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) PHQ9_total three_back_d_prime demographics_age
## -0.03569 -0.32093 -0.15242 -0.14535
## years_education
## -0.07703
##
## Call:
## lm(formula = "cost_large ~ PHQ9_total + BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) PHQ9_total BMI demographics_age
## 0.02371 0.08015 0.05937 -0.06306
## years_education
## 0.15395
##
## Call:
## lm(formula = "PHQ9_total ~ one_back_d_prime + three_back_d_prime + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) one_back_d_prime three_back_d_prime demographics_age
## -0.080377 -0.158874 -0.118309 0.104893
## years_education
## -0.002144
##
## Call:
## lm(formula = "PHQ9_total ~ larger_percent_req_presses_completed + smaller_percent_req_presses_completed + \n demographics_age + BMI + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) larger_percent_req_presses_completed
## -0.072706 -0.143664
## smaller_percent_req_presses_completed demographics_age
## 0.009081 0.133746
## BMI years_education
## 0.112560 -0.045412
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ PHQ9_total + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) PHQ9_total demographics_age years_education
## -0.043745 -0.435753 -0.004650 -0.008445
## task_vars Estimate SE t p
## 1 Cognitive effort cost -0.321 0.150 -2.130 0.03805
## 2 Physical effort cost 0.080 0.154 0.520 0.60604
## 3 1-Back D' -0.159 0.172 -0.922 0.36108
## 4 3-Back D' -0.118 0.179 -0.661 0.51192
## 5 Smaller number of presses completed (%) 0.009 0.148 0.061 0.95127
## 6 Larger number of presses completed (%) -0.144 0.158 -0.908 0.36884
## 7 Overall exit threshold -0.436 0.138 -3.151 0.00277
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:05 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrr}
## \hline
## task\_vars & Estimate & SE & t & p \\
## \hline
## Cognitive effort cost & -0.32 & 0.15 & -2.13 & 0.038 \\
## Physical effort cost & 0.08 & 0.15 & 0.52 & 0.606 \\
## 1-Back D' & -0.16 & 0.17 & -0.92 & 0.361 \\
## 3-Back D' & -0.12 & 0.18 & -0.66 & 0.512 \\
## Smaller number of presses completed (\%) & 0.01 & 0.15 & 0.06 & 0.951 \\
## Larger number of presses completed (\%) & -0.14 & 0.16 & -0.91 & 0.369 \\
## Overall exit threshold & -0.44 & 0.14 & -3.15 & 0.003 \\
## \hline
## \end{tabular}
## \end{table}
Table S9 Symptom effort cost regressions, current MDD and all
participants
##
## Call:
## lm(formula = "cost_3back ~ anhedonia_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) anhedonia_symptoms three_back_d_prime demographics_age
## -0.01513 -0.13210 -0.13300 -0.11932
## years_education
## 0.04879
##
## Call:
## lm(formula = "cost_3back ~ anxiety_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) anxiety_symptoms three_back_d_prime demographics_age
## -0.04192 -0.40092 -0.16655 -0.09191
## years_education
## -0.02311
##
## Call:
## lm(formula = "cost_3back ~ apathy_behavioral_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## -0.03002 -0.21268
## three_back_d_prime demographics_age
## -0.14038 -0.14521
## years_education
## 0.02719
##
## Call:
## lm(formula = "cost_3back ~ apathy_social_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) apathy_social_symptoms three_back_d_prime
## -0.01005 -0.01828 -0.12551
## demographics_age years_education
## -0.12443 0.06413
##
## Call:
## lm(formula = "cost_3back ~ cog_function_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) cog_function_symptoms three_back_d_prime
## -0.01576 -0.23586 -0.12314
## demographics_age years_education
## -0.08618 0.02428
##
## Call:
## lm(formula = "cost_3back ~ depressed_mood_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) depressed_mood_symptoms three_back_d_prime
## -0.03406 -0.23330 -0.15360
## demographics_age years_education
## -0.09488 0.02089
##
## Call:
## lm(formula = "cost_3back ~ physical_anergia_slowing_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## -0.02852 -0.21461
## three_back_d_prime demographics_age
## -0.14780 -0.11353
## years_education
## 0.01044
## symptom_vars Estimate SE t p p_fdr
## 1 Anhedonia -0.132 0.118 -1.115 0.268 0.313
## 2 Anxiety -0.401 0.116 -3.443 0.001 0.007
## 3 Behavioral apathy -0.213 0.122 -1.740 0.086 0.120
## 4 Social apathy -0.018 0.121 -0.151 0.880 0.880
## 5 Cognitive function symptoms -0.236 0.117 -2.024 0.047 0.120
## 6 Depressed mood/suicidality -0.233 0.134 -1.747 0.085 0.120
## 7 Physical anergia/slowing -0.215 0.123 -1.745 0.085 0.120
##
## Call:
## lm(formula = "cost_large ~ anhedonia_symptoms + larger_percent_req_presses_completed + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) anhedonia_symptoms
## 0.009963 0.264696
## larger_percent_req_presses_completed demographics_age
## 0.030609 -0.057190
## years_education
## 0.140114
##
## Call:
## lm(formula = "cost_large ~ anxiety_symptoms + larger_percent_req_presses_completed + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) anxiety_symptoms
## -0.008744 -0.039698
## larger_percent_req_presses_completed demographics_age
## 0.003277 -0.044470
## years_education
## 0.097326
##
## Call:
## lm(formula = "cost_large ~ apathy_behavioral_symptoms + larger_percent_req_presses_completed + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## 0.01584 0.21163
## larger_percent_req_presses_completed demographics_age
## 0.04018 -0.02624
## years_education
## 0.14525
##
## Call:
## lm(formula = "cost_large ~ apathy_social_symptoms + larger_percent_req_presses_completed + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) apathy_social_symptoms
## -0.004436 0.024709
## larger_percent_req_presses_completed demographics_age
## 0.010389 -0.045251
## years_education
## 0.112185
##
## Call:
## lm(formula = "cost_large ~ cog_function_symptoms + larger_percent_req_presses_completed + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) cog_function_symptoms
## -0.001547 0.145199
## larger_percent_req_presses_completed demographics_age
## 0.042246 -0.068122
## years_education
## 0.131423
##
## Call:
## lm(formula = "cost_large ~ depressed_mood_symptoms + larger_percent_req_presses_completed + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) depressed_mood_symptoms
## 0.006346 0.100285
## larger_percent_req_presses_completed demographics_age
## 0.019162 -0.061636
## years_education
## 0.125040
##
## Call:
## lm(formula = "cost_large ~ physical_anergia_slowing_symptoms + larger_percent_req_presses_completed + \n demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## -0.0005863 0.0589896
## larger_percent_req_presses_completed demographics_age
## 0.0255322 -0.0502092
## years_education
## 0.1220117
## symptom_vars Estimate SE t p p_fdr
## 1 Anhedonia 0.265 0.115 2.30 0.024 0.168
## 2 Anxiety -0.040 0.126 -0.32 0.753 0.835
## 3 Behavioral apathy 0.212 0.124 1.71 0.091 0.318
## 4 Social apathy 0.025 0.118 0.21 0.835 0.835
## 5 Cognitive function symptoms 0.145 0.122 1.19 0.239 0.558
## 6 Depressed mood/suicidality 0.100 0.135 0.74 0.459 0.803
## 7 Physical anergia/slowing 0.059 0.131 0.45 0.654 0.835
##
## Call:
## lm(formula = "cost_3back ~ HRSD_total + larger_percent_req_presses_completed + demographics_age + \n years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) HRSD_total
## -0.07521 -0.28767
## larger_percent_req_presses_completed demographics_age
## -0.03168 -0.05825
## years_education
## -0.18904
##
## Call:
## lm(formula = "cost_3back ~ anhedonia_symptoms + three_back_d_prime + demographics_age + \n years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) anhedonia_symptoms three_back_d_prime demographics_age
## -0.1030 -0.1306 -0.1405 -0.1594
## years_education
## -0.1260
##
## Call:
## lm(formula = "cost_3back ~ anxiety_symptoms + three_back_d_prime + demographics_age + \n years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) anxiety_symptoms three_back_d_prime demographics_age
## -0.1021 -0.4972 -0.2233 -0.1135
## years_education
## -0.1148
##
## Call:
## lm(formula = "cost_3back ~ apathy_behavioral_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## -0.1190 -0.2154
## three_back_d_prime demographics_age
## -0.1237 -0.1642
## years_education
## -0.1831
##
## Call:
## lm(formula = "cost_3back ~ apathy_social_symptoms + three_back_d_prime + demographics_age + \n years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) apathy_social_symptoms three_back_d_prime
## -0.1168 -0.0733 -0.1358
## demographics_age years_education
## -0.1697 -0.1687
##
## Call:
## lm(formula = "cost_3back ~ cog_function_symptoms + three_back_d_prime + demographics_age + \n years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) cog_function_symptoms three_back_d_prime
## -0.09488 -0.33097 -0.15480
## demographics_age years_education
## -0.06255 -0.11401
##
## Call:
## lm(formula = "cost_3back ~ depressed_mood_symptoms + three_back_d_prime + demographics_age + \n years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) depressed_mood_symptoms three_back_d_prime
## -0.1123 -0.2030 -0.1810
## demographics_age years_education
## -0.1541 -0.1557
##
## Call:
## lm(formula = "cost_3back ~ physical_anergia_slowing_symptoms + three_back_d_prime + \n demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## -0.09746 -0.16786
## three_back_d_prime demographics_age
## -0.16051 -0.14496
## years_education
## -0.14388
## symptom_vars Estimate SE t p p_fdr
## 1 Anhedonia -0.131 0.168 -0.776 0.44286 0.517
## 2 Anxiety -0.497 0.150 -3.321 0.00203 0.014
## 3 Behavioral apathy -0.215 0.163 -1.318 0.19545 0.456
## 4 Social apathy -0.073 0.167 -0.440 0.66370 0.664
## 5 Cognitive function symptoms -0.331 0.166 -1.990 0.05405 0.189
## 6 Depressed mood/suicidality -0.203 0.182 -1.110 0.27283 0.477
## 7 Physical anergia/slowing -0.168 0.180 -0.930 0.35736 0.500
## 8 Overall depression -0.288 0.209 -1.380 0.17600 NA
##
## Call:
## lm(formula = "cost_large ~ HRSD_total + larger_percent_req_presses_completed + BMI + \n demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) HRSD_total
## -0.0778 0.2976
## larger_percent_req_presses_completed BMI
## 0.1916 0.1419
## demographics_age years_education
## -0.1308 0.1566
##
## Call:
## lm(formula = "cost_large ~ anhedonia_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) anhedonia_symptoms
## -0.08827 0.53877
## larger_percent_req_presses_completed BMI
## 0.15524 0.09170
## demographics_age years_education
## -0.03141 0.03133
##
## Call:
## lm(formula = "cost_large ~ anxiety_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) anxiety_symptoms
## -0.04789 0.05461
## larger_percent_req_presses_completed BMI
## 0.14258 0.13710
## demographics_age years_education
## -0.07326 0.14362
##
## Call:
## lm(formula = "cost_large ~ apathy_behavioral_symptoms + BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## -0.04762 0.40964
## BMI demographics_age
## 0.05109 -0.06456
## years_education
## 0.19787
##
## Call:
## lm(formula = "cost_large ~ apathy_social_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) apathy_social_symptoms
## -0.04564 0.02140
## larger_percent_req_presses_completed BMI
## 0.14160 0.13646
## demographics_age years_education
## -0.06788 0.14813
##
## Call:
## lm(formula = "cost_large ~ cog_function_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) cog_function_symptoms
## -0.05442 0.45234
## larger_percent_req_presses_completed BMI
## 0.21681 0.03978
## demographics_age years_education
## -0.14319 0.10036
##
## Call:
## lm(formula = "cost_large ~ depressed_mood_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) depressed_mood_symptoms
## -0.05555 0.22893
## larger_percent_req_presses_completed BMI
## 0.15138 0.13875
## demographics_age years_education
## -0.09479 0.15511
##
## Call:
## lm(formula = "cost_large ~ physical_anergia_slowing_symptoms + larger_percent_req_presses_completed + \n BMI + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## -0.09932 0.36264
## larger_percent_req_presses_completed BMI
## 0.24099 0.12014
## demographics_age years_education
## -0.12951 0.14650
## symptom_vars Estimate SE t p p_fdr
## 1 Anhedonia 0.539 0.133 4.06 0.000 0.000
## 2 Anxiety 0.055 0.157 0.35 0.729 0.851
## 3 Behavioral apathy 0.410 0.136 3.01 0.005 0.018
## 4 Social apathy 0.021 0.151 0.14 0.888 0.888
## 5 Cognitive function symptoms 0.452 0.161 2.81 0.008 0.019
## 6 Depressed mood/suicidality 0.229 0.160 1.43 0.163 0.228
## 7 Physical anergia/slowing 0.363 0.168 2.16 0.038 0.066
## 8 Overall depression 0.298 0.192 1.55 0.131 NA
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:05 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrl}
## \hline
## symptom\_vars & Estimate & SE & t & p & p\_fdr & effort\_type \\
## \hline
## Anhedonia & -0.13 & 0.12 & -1.11 & 0.268 & 0.313 & Cognitive, all \\
## Anxiety & -0.40 & 0.12 & -3.44 & 0.001 & 0.007 & Cognitive, all \\
## Behavioral apathy & -0.21 & 0.12 & -1.74 & 0.086 & 0.120 & Cognitive, all \\
## Social apathy & -0.02 & 0.12 & -0.15 & 0.880 & 0.880 & Cognitive, all \\
## Cognitive function symptoms & -0.24 & 0.12 & -2.02 & 0.047 & 0.120 & Cognitive, all \\
## Depressed mood/suicidality & -0.23 & 0.13 & -1.75 & 0.085 & 0.120 & Cognitive, all \\
## Physical anergia/slowing & -0.21 & 0.12 & -1.75 & 0.085 & 0.120 & Cognitive, all \\
## Anhedonia & 0.27 & 0.12 & 2.30 & 0.024 & 0.168 & Physical, all \\
## Anxiety & -0.04 & 0.13 & -0.32 & 0.753 & 0.835 & Physical, all \\
## Behavioral apathy & 0.21 & 0.12 & 1.71 & 0.091 & 0.318 & Physical, all \\
## Social apathy & 0.03 & 0.12 & 0.21 & 0.835 & 0.835 & Physical, all \\
## Cognitive function symptoms & 0.14 & 0.12 & 1.19 & 0.239 & 0.558 & Physical, all \\
## Depressed mood/suicidality & 0.10 & 0.14 & 0.74 & 0.459 & 0.803 & Physical, all \\
## Physical anergia/slowing & 0.06 & 0.13 & 0.45 & 0.654 & 0.835 & Physical, all \\
## Anhedonia & -0.13 & 0.17 & -0.78 & 0.443 & 0.517 & Cognitive, current MDD \\
## Anxiety & -0.50 & 0.15 & -3.32 & 0.002 & 0.014 & Cognitive, current MDD \\
## Behavioral apathy & -0.21 & 0.16 & -1.32 & 0.195 & 0.456 & Cognitive, current MDD \\
## Social apathy & -0.07 & 0.17 & -0.44 & 0.664 & 0.664 & Cognitive, current MDD \\
## Cognitive function symptoms & -0.33 & 0.17 & -1.99 & 0.054 & 0.189 & Cognitive, current MDD \\
## Depressed mood/suicidality & -0.20 & 0.18 & -1.11 & 0.273 & 0.477 & Cognitive, current MDD \\
## Physical anergia/slowing & -0.17 & 0.18 & -0.93 & 0.357 & 0.500 & Cognitive, current MDD \\
## Overall depression & -0.29 & 0.21 & -1.38 & 0.176 & & Cognitive, current MDD \\
## Anhedonia & 0.54 & 0.13 & 4.06 & 0.000 & 0.000 & Physical, current MDD \\
## Anxiety & 0.06 & 0.16 & 0.35 & 0.729 & 0.851 & Physical, current MDD \\
## Behavioral apathy & 0.41 & 0.14 & 3.01 & 0.005 & 0.018 & Physical, current MDD \\
## Social apathy & 0.02 & 0.15 & 0.14 & 0.888 & 0.888 & Physical, current MDD \\
## Cognitive function symptoms & 0.45 & 0.16 & 2.81 & 0.008 & 0.019 & Physical, current MDD \\
## Depressed mood/suicidality & 0.23 & 0.16 & 1.43 & 0.163 & 0.228 & Physical, current MDD \\
## Physical anergia/slowing & 0.36 & 0.17 & 2.16 & 0.038 & 0.066 & Physical, current MDD \\
## Overall depression & 0.30 & 0.19 & 1.55 & 0.131 & & Physical, current MDD \\
## \hline
## \end{tabular}
## \end{table}
Table S10 Inverse tempurature symptom regressions
##
## Call:
## lm(formula = "inv_temp ~ HRSD_total + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) HRSD_total demographics_age years_education
## -0.01604 0.03340 -0.23534 0.12781
##
## Call:
## lm(formula = "inv_temp ~ anhedonia_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anhedonia_symptoms demographics_age years_education
## -0.003354 0.269524 -0.252575 0.122074
##
## Call:
## lm(formula = "inv_temp ~ anxiety_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) anxiety_symptoms demographics_age years_education
## -0.01692 0.04076 -0.23146 0.12703
##
## Call:
## lm(formula = "inv_temp ~ apathy_behavioral_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## 0.006054 0.234155
## demographics_age years_education
## -0.236757 0.153129
##
## Call:
## lm(formula = "inv_temp ~ apathy_social_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) apathy_social_symptoms demographics_age
## -0.01966 0.01212 -0.22444
## years_education
## 0.12535
##
## Call:
## lm(formula = "inv_temp ~ cog_function_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) cog_function_symptoms demographics_age
## -0.020046 -0.005086 -0.222946
## years_education
## 0.122002
##
## Call:
## lm(formula = "inv_temp ~ depressed_mood_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) depressed_mood_symptoms demographics_age
## 0.00712 0.20968 -0.26521
## years_education
## 0.14512
##
## Call:
## lm(formula = "inv_temp ~ physical_anergia_slowing_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data)
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## -0.02665 -0.08527
## demographics_age years_education
## -0.20552 0.10631
## symptom_vars Estimate SE t p p_fdr
## 1 Anhedonia 0.270 0.137 1.970 0.05464 0.382
## 2 Anxiety 0.041 0.146 0.280 0.78076 0.972
## 3 Behavioral apathy 0.234 0.145 1.620 0.11166 0.391
## 4 Social apathy 0.012 0.140 0.087 0.93119 0.972
## 5 Cognitive function symptoms -0.005 0.145 -0.035 0.97200 0.972
## 6 Depressed mood/suicidality 0.210 0.154 1.360 0.18011 0.420
## 7 Physical anergia/slowing -0.085 0.146 -0.585 0.56129 0.972
## 8 Overall depression 0.033 0.161 0.207 0.83700 NA
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:05 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrr}
## \hline
## symptom\_vars & Estimate & SE & t & p & p\_fdr \\
## \hline
## Anhedonia & 0.270 & 0.137 & 1.970 & 0.055 & 0.382 \\
## Anxiety & 0.041 & 0.146 & 0.280 & 0.781 & 0.972 \\
## Behavioral apathy & 0.234 & 0.145 & 1.620 & 0.112 & 0.391 \\
## Social apathy & 0.012 & 0.140 & 0.087 & 0.931 & 0.972 \\
## Cognitive function symptoms & -0.005 & 0.145 & -0.035 & 0.972 & 0.972 \\
## Depressed mood/suicidality & 0.210 & 0.154 & 1.360 & 0.180 & 0.420 \\
## Physical anergia/slowing & -0.085 & 0.146 & -0.585 & 0.561 & 0.972 \\
## Overall depression & 0.033 & 0.161 & 0.207 & 0.837 & \\
## \hline
## \end{tabular}
## \end{table}
Table S11 Diagnostic group differences in travel task
performance
## # A tibble: 2 × 3
## diagnostic_group missed_response_rate_mean missed_response_rate_sd
## <chr> <dbl> <dbl>
## 1 MDD 1.45 2.13
## 2 comparison 1.47 2.00
## # A tibble: 80 × 4
## # Groups: diagnostic_group [2]
## diagnostic_group subject_id req_presses diagnostic_group_plot
## <chr> <chr> <dbl> <chr>
## 1 MDD 0HIKRQQFWP 38 MDD
## 2 MDD 41KCAHBAYJ 64 MDD
## 3 MDD 4FSEWB4OMT 22 MDD
## 4 MDD 5L85OKOB72 45 MDD
## 5 MDD 6JLYFJNQBT 63 MDD
## 6 MDD 6XAQZ7HIWS 37 MDD
## 7 MDD 803SBNRVGQ 66 MDD
## 8 MDD 8REMYRDIT1 76 MDD
## 9 MDD AHYMJHC33Q 61 MDD
## 10 MDD AIAIIKLPKU 61 MDD
## # ℹ 70 more rows
##
## Call:
## lm(formula = "missed_response_rate ~ diagnostic_group", data = NBackData_missed_response_rate_subj)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4695 -1.4485 -0.9396 0.4021 7.7784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.46948 0.40186 3.657 0.000465 ***
## diagnostic_groupMDD -0.02093 0.49533 -0.042 0.966398
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.088 on 77 degrees of freedom
## Multiple R-squared: 2.32e-05, Adjusted R-squared: -0.01296
## F-statistic: 0.001786 on 1 and 77 DF, p-value: 0.9664
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "rt ~ 1 + n_back*correct*target*diagnostic_group + demographics_age + \n (1 + n_back + target + correct | subject_id)"
## Data: NBackData_lm %>% mutate(rt = scale(log(rt))[, 1])
##
## REML criterion at convergence: 68776.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.1930 -0.3295 0.0371 0.4526 4.7968
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject_id (Intercept) 0.26077 0.5107
## n_back 0.07397 0.2720 0.18
## target 0.01747 0.1322 -0.07 -0.15
## correct 0.04023 0.2006 -0.82 -0.42 -0.29
## Residual 0.70965 0.8424
## Number of obs: 27285, groups: subject_id, 79
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.403e-01 1.000e-01 7.874e+01
## n_back 2.447e-01 6.266e-02 1.255e+02
## correct -9.987e-02 5.366e-02 7.254e+01
## target -7.006e-02 4.288e-02 1.854e+02
## diagnostic_groupMDD -1.663e-01 1.233e-01 7.863e+01
## demographics_age 1.990e-01 4.087e-02 6.672e+01
## n_back:correct -4.933e-02 6.921e-02 5.265e+03
## n_back:target 2.884e-01 6.763e-02 7.569e+03
## correct:target 9.585e-01 7.006e-02 3.063e+03
## n_back:diagnostic_groupMDD 8.825e-03 7.692e-02 1.234e+02
## correct:diagnostic_groupMDD 9.007e-02 6.566e-02 7.116e+01
## target:diagnostic_groupMDD 6.134e-02 5.242e-02 1.776e+02
## n_back:correct:target -4.967e-02 1.365e-01 1.155e+04
## n_back:correct:diagnostic_groupMDD -1.966e-01 8.417e-02 4.955e+03
## n_back:target:diagnostic_groupMDD -8.085e-02 8.212e-02 7.350e+03
## correct:target:diagnostic_groupMDD -2.730e-01 8.517e-02 2.450e+03
## n_back:correct:target:diagnostic_groupMDD 1.698e-01 1.657e-01 1.007e+04
## t value Pr(>|t|)
## (Intercept) 2.402 0.018666 *
## n_back 3.905 0.000153 ***
## correct -1.861 0.066763 .
## target -1.634 0.104008
## diagnostic_groupMDD -1.349 0.181246
## demographics_age 4.870 7.17e-06 ***
## n_back:correct -0.713 0.476105
## n_back:target 4.264 2.03e-05 ***
## correct:target 13.682 < 2e-16 ***
## n_back:diagnostic_groupMDD 0.115 0.908844
## correct:diagnostic_groupMDD 1.372 0.174443
## target:diagnostic_groupMDD 1.170 0.243492
## n_back:correct:target -0.364 0.715968
## n_back:correct:diagnostic_groupMDD -2.335 0.019565 *
## n_back:target:diagnostic_groupMDD -0.985 0.324862
## correct:target:diagnostic_groupMDD -3.206 0.001365 **
## n_back:correct:target:diagnostic_groupMDD 1.025 0.305461
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "rt ~ 1 + n_back*correct*target + demographics_age + \n (1 + n_back + target + correct | subject_id)"
## Data: NBackData_lm %>% mutate(rt = scale(log(rt))[, 1])
##
## REML criterion at convergence: 68768
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.1931 -0.3280 0.0360 0.4516 4.8568
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subject_id (Intercept) 0.26350 0.5133
## n_back 0.07391 0.2719 0.19
## target 0.01788 0.1337 -0.04 -0.15
## correct 0.04264 0.2065 -0.82 -0.44 -0.25
## Residual 0.70982 0.8425
## Number of obs: 27285, groups: subject_id, 79
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.303e-01 5.869e-02 7.938e+01 2.221 0.0292 *
## n_back 2.504e-01 3.634e-02 1.229e+02 6.891 2.55e-10 ***
## correct -4.135e-02 3.146e-02 7.609e+01 -1.314 0.1927
## target -2.932e-02 2.483e-02 1.709e+02 -1.181 0.2393
## demographics_age 2.063e-01 4.110e-02 6.927e+01 5.020 3.84e-06 ***
## n_back:correct -1.799e-01 3.943e-02 5.437e+03 -4.563 5.16e-06 ***
## n_back:target 2.293e-01 3.839e-02 8.157e+03 5.974 2.41e-09 ***
## correct:target 7.752e-01 3.998e-02 2.339e+03 19.387 < 2e-16 ***
## n_back:correct:target 7.057e-02 7.745e-02 1.001e+04 0.911 0.3622
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) n_back corrct target dmgrp_ n_bck:c n_bck:t crrct:
## n_back 0.124
## correct -0.687 -0.142
## target -0.056 -0.013 0.071
## demgrphcs_g -0.006 0.013 -0.009 -0.022
## n_bck:crrct 0.063 -0.402 -0.214 -0.103 -0.018
## n_back:trgt 0.029 -0.129 -0.091 -0.259 0.012 0.390
## corrct:trgt 0.060 -0.067 -0.119 -0.596 0.026 0.152 0.352
## n_bck:crrc: -0.025 0.206 0.111 0.278 -0.006 -0.219 -0.740 -0.313
## Data: NBackData_lm %>% mutate(rt = scale(log(rt))[, 1])
## Models:
## nback_rt_lmer: "rt ~ 1 + n_back*correct*target + demographics_age + \n (1 + n_back + target + correct | subject_id)"
## nback_rt_diagnostic_group_lmer: "rt ~ 1 + n_back*correct*target*diagnostic_group + demographics_age + \n (1 + n_back + target + correct | subject_id)"
## npar AIC BIC logLik deviance Chisq Df
## nback_rt_lmer 20 68764 68929 -34362 68724
## nback_rt_diagnostic_group_lmer 28 68762 68992 -34353 68706 18.384 8
## Pr(>Chisq)
## nback_rt_lmer
## nback_rt_diagnostic_group_lmer 0.01852 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "d_prime ~ 1 + n_back*diagnostic_group + demographics_age + (1 | subject_id)"
## Data: d_prime_subj %>% inner_join(survey_data %>% select(diagnostic_group,
## subject_id = participant_ID, demographics_age))
##
## REML criterion at convergence: 404.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.54048 -0.45076 0.02682 0.41128 2.40859
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 0.507 0.7120
## Residual 0.364 0.6033
## Number of obs: 158, groups: subject_id, 79
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.760e+00 3.154e-01 8.700e+01 8.752
## n_backthree_back -1.162e+00 1.642e-01 7.700e+01 -7.079
## diagnostic_groupMDD -7.609e-03 2.216e-01 1.136e+02 -0.034
## demographics_age -4.388e-04 9.563e-03 7.600e+01 -0.046
## n_backthree_back:diagnostic_groupMDD 1.167e-01 2.024e-01 7.700e+01 0.577
## Pr(>|t|)
## (Intercept) 1.44e-13 ***
## n_backthree_back 5.88e-10 ***
## diagnostic_groupMDD 0.973
## demographics_age 0.964
## n_backthree_back:diagnostic_groupMDD 0.566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) n_bck_ dg_MDD dmgrp_
## n_bckthr_bc -0.260
## dgnstc_gMDD -0.496 0.371
## demgrphcs_g -0.822 0.000 0.042
## n_bck_:_MDD 0.211 -0.811 -0.457 0.000
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: "d_prime ~ 1 + n_back + demographics_age + (1 | subject_id)"
## Data: d_prime_subj %>% inner_join(survey_data %>% select(diagnostic_group,
## subject_id = participant_ID, demographics_age))
##
## REML criterion at convergence: 402.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.63117 -0.45549 0.01894 0.41930 2.34203
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 0.5002 0.7073
## Residual 0.3609 0.6007
## Number of obs: 158, groups: subject_id, 79
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7584915 0.2721107 81.8977562 10.137 4.05e-16 ***
## n_backthree_back -1.0856110 0.0955834 78.0000001 -11.358 < 2e-16 ***
## demographics_age -0.0005554 0.0094938 77.0000004 -0.059 0.953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) n_bck_
## n_bckthr_bc -0.176
## demgrphcs_g -0.923 0.000
## Data: d_prime_subj %>% inner_join(survey_data %>% select(diagnostic_group, ...
## Models:
## d_prime_lmer: "d_prime ~ 1 + n_back + demographics_age + (1 | subject_id)"
## d_prime_diagnostic_group_lmer: "d_prime ~ 1 + n_back*diagnostic_group + demographics_age + (1 | subject_id)"
## npar AIC BIC logLik deviance Chisq Df
## d_prime_lmer 5 399.20 414.51 -194.6 389.20
## d_prime_diagnostic_group_lmer 7 402.79 424.23 -194.4 388.79 0.4095 2
## Pr(>Chisq)
## d_prime_lmer
## d_prime_diagnostic_group_lmer 0.8148
##
## Call:
## lm(formula = "req_presses ~ 1 + diagnostic_group + demographics_age",
## data = req_presses_group %>% inner_join(survey_data %>% select(subject_id = participant_ID,
## demographics_age), by = "subject_id"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.5560 -11.8741 -0.0489 13.4430 29.7799
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.9060 5.6330 10.280 4.24e-16 ***
## diagnostic_groupMDD -2.0028 3.6399 -0.550 0.5838
## demographics_age -0.3340 0.1768 -1.889 0.0627 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.37 on 77 degrees of freedom
## Multiple R-squared: 0.0467, Adjusted R-squared: 0.02194
## F-statistic: 1.886 on 2 and 77 DF, p-value: 0.1586
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "percent_completed_presses ~ 1 + button_press_trial_type*diagnostic_group + demographics_age + BMI + (1 | subject_id)"
## Data:
## percent_completed_small_large_presses_per_travel %>% inner_join(survey_data %>%
## select(subject_id = participant_ID, demographics_age, BMI),
## by = "subject_id")
##
## REML criterion at convergence: 24267.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.6244 -0.0946 0.0861 0.2577 2.4084
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 12.22 3.496
## Residual 85.33 9.237
## Number of obs: 3311, groups: subject_id, 78
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 102.78872 2.11869
## button_press_trial_typelarge -1.01497 0.54463
## diagnostic_groupMDD 0.27172 0.96865
## demographics_age 0.01439 0.04870
## BMI -0.14991 0.06677
## button_press_trial_typelarge:diagnostic_groupMDD -2.37882 0.67500
## df t value Pr(>|t|)
## (Intercept) 81.25813 48.515 < 2e-16
## button_press_trial_typelarge 3236.62765 -1.864 0.062470
## diagnostic_groupMDD 100.41088 0.281 0.779661
## demographics_age 77.29964 0.295 0.768459
## BMI 76.85720 -2.245 0.027625
## button_press_trial_typelarge:diagnostic_groupMDD 3237.35447 -3.524 0.000431
##
## (Intercept) ***
## button_press_trial_typelarge .
## diagnostic_groupMDD
## demographics_age
## BMI *
## button_press_trial_typelarge:diagnostic_groupMDD ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) btt___ dg_MDD dmgrp_ BMI
## bttn_prss__ -0.126
## dgnstc_gMDD -0.311 0.278
## demgrphcs_g -0.462 0.003 0.088
## BMI -0.700 -0.003 -0.049 -0.197
## btt___:_MDD 0.104 -0.807 -0.347 0.000 -0.001
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "percent_completed_presses ~ 1 + diagnostic_group + demographics_age + BMI + (1 | subject_id)"
## Data:
## percent_completed_small_large_presses_per_travel %>% filter(button_press_trial_type ==
## "large") %>% inner_join(survey_data %>% select(subject_id = participant_ID,
## demographics_age, BMI), by = "subject_id")
##
## REML criterion at convergence: 12570.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.5623 0.0186 0.0559 0.1502 3.4728
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 34.37 5.863
## Residual 105.00 10.247
## Number of obs: 1657, groups: subject_id, 78
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 104.87942 3.49098 80.05817 30.043 <2e-16 ***
## diagnostic_groupMDD -1.48991 1.50979 79.32874 -0.987 0.3267
## demographics_age 0.01318 0.08101 79.09911 0.163 0.8711
## BMI -0.26996 0.11076 77.81782 -2.437 0.0171 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dg_MDD dmgrp_
## dgnstc_gMDD -0.296
## demgrphcs_g -0.467 0.093
## BMI -0.706 -0.052 -0.196
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## "percent_completed_presses ~ 1 + diagnostic_group + demographics_age + BMI + (1 | subject_id)"
## Data:
## percent_completed_small_large_presses_per_travel %>% filter(button_press_trial_type ==
## "small") %>% inner_join(survey_data %>% select(subject_id = participant_ID,
## demographics_age, BMI), by = "subject_id")
##
## REML criterion at convergence: 11116.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -13.5032 0.0887 0.1049 0.1186 0.1330
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 0.00 0.000
## Residual 48.29 6.949
## Number of obs: 1654, groups: subject_id, 78
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.856e+01 8.655e-01 1.650e+03 113.878 <2e-16 ***
## diagnostic_groupMDD -9.797e-03 3.580e-01 1.650e+03 -0.027 0.978
## demographics_age 7.012e-03 1.917e-02 1.650e+03 0.366 0.715
## BMI 2.131e-02 2.701e-02 1.650e+03 0.789 0.430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dg_MDD dmgrp_
## dgnstc_gMDD -0.284
## demgrphcs_g -0.474 0.074
## BMI -0.734 -0.031 -0.153
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Table S12 Overall exit threshold relationship to symptoms, current
MDD and all participants
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ HRSD_total + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) HRSD_total demographics_age years_education
## -0.02144 -0.41835 0.01907 -0.15767
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ anhedonia_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) anhedonia_symptoms demographics_age years_education
## -0.04351 -0.24431 -0.06292 -0.08777
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ anxiety_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) anxiety_symptoms demographics_age years_education
## -0.0517 -0.3101 -0.0356 -0.1391
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ apathy_behavioral_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## -0.07392 -0.28418
## demographics_age years_education
## -0.08102 -0.17648
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ apathy_social_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) apathy_social_symptoms demographics_age
## -0.06048 -0.19872 -0.09446
## years_education
## -0.19503
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ cog_function_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) cog_function_symptoms demographics_age
## -0.05449 -0.16120 -0.03027
## years_education
## -0.13436
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ depressed_mood_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) depressed_mood_symptoms demographics_age
## -0.05516 -0.43592 -0.02256
## years_education
## -0.15448
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ physical_anergia_slowing_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_mdd_data %>% filter(current_depressed ==
## "current"))
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## -0.02014 -0.32686
## demographics_age years_education
## -0.03019 -0.14507
## symptom_vars Estimate SE t p p_fdr
## 1 Anhedonia -0.244 0.164 -1.49 0.14466 0.203
## 2 Anxiety -0.310 0.161 -1.93 0.06107 0.147
## 3 Behavioral apathy -0.284 0.160 -1.77 0.08411 0.147
## 4 Social apathy -0.199 0.159 -1.25 0.21853 0.255
## 5 Cognitive function symptoms -0.161 0.173 -0.93 0.35639 0.356
## 6 Depressed mood/suicidality -0.436 0.164 -2.66 0.01130 0.079
## 7 Physical anergia/slowing -0.327 0.172 -1.90 0.06466 0.147
## 8 Overall depression -0.418 0.197 -2.12 0.04000 NA
## group
## 1 Overall threshold, current MDD
## 2 Overall threshold, current MDD
## 3 Overall threshold, current MDD
## 4 Overall threshold, current MDD
## 5 Overall threshold, current MDD
## 6 Overall threshold, current MDD
## 7 Overall threshold, current MDD
## 8 Overall threshold, current MDD
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ anhedonia_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) anhedonia_symptoms demographics_age years_education
## -0.003707 -0.033977 -0.004637 0.046770
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ anxiety_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) anxiety_symptoms demographics_age years_education
## -0.012359 -0.131912 0.006211 0.020304
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ apathy_behavioral_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) apathy_behavioral_symptoms
## -0.01611 -0.14350
## demographics_age years_education
## -0.01949 0.02340
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ apathy_social_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) apathy_social_symptoms demographics_age
## -0.003806 -0.047624 -0.010819
## years_education
## 0.039698
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ cog_function_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) cog_function_symptoms demographics_age
## -0.004405 -0.101785 0.009494
## years_education
## 0.032048
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ depressed_mood_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) depressed_mood_symptoms demographics_age
## -0.027266 -0.216718 0.025029
## years_education
## 0.009194
##
## Call:
## lm(formula = "overall_thresh_log_subj ~ physical_anergia_slowing_symptoms + demographics_age + years_education",
## data = all_params_survey_composites_all_data)
##
## Coefficients:
## (Intercept) physical_anergia_slowing_symptoms
## -0.012644 -0.132645
## demographics_age years_education
## 0.001364 0.013301
## symptom_vars Estimate SE t p p_fdr
## 1 Anhedonia -0.034 0.118 -0.287 0.77492 0.775
## 2 Anxiety -0.132 0.124 -1.068 0.28897 0.506
## 3 Behavioral apathy -0.144 0.123 -1.167 0.24702 0.506
## 4 Social apathy -0.048 0.118 -0.405 0.68661 0.775
## 5 Cognitive function symptoms -0.102 0.119 -0.856 0.39478 0.553
## 6 Depressed mood/suicidality -0.217 0.132 -1.648 0.10352 0.506
## 7 Physical anergia/slowing -0.133 0.123 -1.077 0.28478 0.506
## group
## 1 Overall threshold, all
## 2 Overall threshold, all
## 3 Overall threshold, all
## 4 Overall threshold, all
## 5 Overall threshold, all
## 6 Overall threshold, all
## 7 Overall threshold, all
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:06 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrl}
## \hline
## symptom\_vars & Estimate & SE & t & p & p\_fdr & group \\
## \hline
## Anhedonia & -0.26 & 0.14 & -1.80 & 0.08 & 0.11 & Overall threshold, MDD \\
## Anxiety & -0.34 & 0.14 & -2.43 & 0.02 & 0.07 & Overall threshold, MDD \\
## Behavioral apathy & -0.30 & 0.15 & -2.09 & 0.04 & 0.07 & Overall threshold, MDD \\
## Social apathy & -0.10 & 0.14 & -0.73 & 0.47 & 0.47 & Overall threshold, MDD \\
## Cognitive function symptoms & -0.21 & 0.15 & -1.44 & 0.16 & 0.18 & Overall threshold, MDD \\
## Depressed mood/suicidality & -0.40 & 0.15 & -2.68 & 0.01 & 0.07 & Overall threshold, MDD \\
## Physical anergia/slowing & -0.32 & 0.14 & -2.20 & 0.03 & 0.07 & Overall threshold, MDD \\
## Overall depression & -0.33 & 0.16 & -2.09 & 0.04 & & Overall threshold, MDD \\
## \hline
## \end{tabular}
## \end{table}
## % latex table generated in R 4.3.2 by xtable 1.8-4 package
## % Tue Nov 5 13:31:06 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrl}
## \hline
## symptom\_vars & Estimate & SE & t & p & p\_fdr & group \\
## \hline
## Anhedonia & -0.24 & 0.16 & -1.49 & 0.145 & 0.203 & Overall threshold, current MDD \\
## Anxiety & -0.31 & 0.16 & -1.93 & 0.061 & 0.147 & Overall threshold, current MDD \\
## Behavioral apathy & -0.28 & 0.16 & -1.77 & 0.084 & 0.147 & Overall threshold, current MDD \\
## Social apathy & -0.20 & 0.16 & -1.25 & 0.219 & 0.255 & Overall threshold, current MDD \\
## Cognitive function symptoms & -0.16 & 0.17 & -0.93 & 0.356 & 0.356 & Overall threshold, current MDD \\
## Depressed mood/suicidality & -0.44 & 0.16 & -2.66 & 0.011 & 0.079 & Overall threshold, current MDD \\
## Physical anergia/slowing & -0.33 & 0.17 & -1.90 & 0.065 & 0.147 & Overall threshold, current MDD \\
## Overall depression & -0.42 & 0.20 & -2.12 & 0.040 & & Overall threshold, current MDD \\
## Anhedonia & -0.03 & 0.12 & -0.29 & 0.775 & 0.775 & Overall threshold, all \\
## Anxiety & -0.13 & 0.12 & -1.07 & 0.289 & 0.506 & Overall threshold, all \\
## Behavioral apathy & -0.14 & 0.12 & -1.17 & 0.247 & 0.506 & Overall threshold, all \\
## Social apathy & -0.05 & 0.12 & -0.41 & 0.687 & 0.775 & Overall threshold, all \\
## Cognitive function symptoms & -0.10 & 0.12 & -0.86 & 0.395 & 0.553 & Overall threshold, all \\
## Depressed mood/suicidality & -0.22 & 0.13 & -1.65 & 0.104 & 0.506 & Overall threshold, all \\
## Physical anergia/slowing & -0.13 & 0.12 & -1.08 & 0.285 & 0.506 & Overall threshold, all \\
## \hline
## \end{tabular}
## \end{table}
