{"paper_id":"29cbc10b-03ff-458e-aaf1-473b4b7b26e9","body_text":"Page 1 of 34\n1 Title Page:\n2 Associations Between Resting State Functional Brain Connectivity and \n3 Childhood Anhedonia: A Reproduction and Replication Study \n4 Authors:\n5 Yi Zhou1 MSc\n6 Narun Pat2 PhD\n7 Michael C. Neale1 PhD\n8 Affiliations:\n9 1Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth \n10 University\n11 2 Department of Psychology, University of Otago, New Zealand\n12 Corresponding Author Email:\n13 zhouy33@vcu.edu\n14\n15 Short/Running Title: \n16 Anhedonia and Brain Connectivity in Children\n17\n18 6 Keywords:\n19 Childhood Anhedonia, rsfMRI Brain Connectivity, Depressed Mood, Reproducibility, \n20 Replicability, Development. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\nPage 2 of 34\n21 Abstract:\n22 Background: Previously, a study using a sample of the Adolescent Brain Cognitive \n23 Development (ABCD)® study from the earlier 1.0 release found differences in several \n24 resting state functional MRI (rsfMRI) brain connectivity measures associated with \n25 children reporting anhedonia. Here, we aim to reproduce, replicate, and extend the \n26 previous findings using data from the later ABCD study 4.0 release, which includes a \n27 significantly larger sample.\n28 Methods: To reproduce and replicate the previous authors’ findings, we analyzed data \n29 from the ABCD 1.0 release (n = 2437), in an independent subsample from the newer \n30 ABCD 4.0 release (n = 6456), and in the full ABCD 4.0 release sample (n = 8866). \n31 Additionally, we assessed whether using a multiple linear regression approach could \n32 improve replicability by controlling for the effects of comorbid psychiatric conditions and \n33 socio-demographic covariates.   \n34 Results: We could only replicate the significant association between anhedonia and the \n35 Within Cingulo-Opercular network connectivity measure in an independent subsample \n36 of the ABCD 4.0 data release. When using the larger full ABCD 4.0 sample, six out of \n37 the eleven previously reported associations remained significant. Accounting for socio-\n38 demographic covariates and comorbid conditions using multiple linear regression did \n39 not improve replicability but allowed for the identification of specific and independent \n40 effects of anhedonia on 16 rsfMRI connectivity measures in the full ABCD 4.0 release \n41 sample. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 3 of 34\n42 Conclusion: Replication of previous findings were limited. A multiple linear regression \n43 approach helped resolve the specificity of rsfMRI connectivity associations with \n44 anhedonia. \n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 4 of 34\n62 Main Article Text\n63 Introduction\n64 Anhedonia is defined as a markedly diminished interest or pleasure in previously \n65 enjoyable activities and is a transdiagnostic symptom that is a core component of major \n66 depressive disorders (MDD) (1) and schizophrenia (SZN) (2). Symptoms of anhedonia \n67 are also present in substance use disorders (3), PTSD (4), bipolar depression \n68 (Gałuszko-Węgielnik et al., 2019), and ADHD (5). Anhedonia in children and \n69 adolescents is a significant prognostic predictor of greater depression severity (6), \n70 treatment resistant depression (7), and suicidal behaviors (8). \n71 Functional neuroimaging approaches have been widely used to explore the \n72 neurocircuitry of anhedonia (9). Functional brain connectivity is a measure of the \n73 coactivation of different brain regions, which measures the degree of synchrony \n74 between the blood oxygen level dependent (BOLD) signals across time between \n75 regions in the brain (Lv et al., 2018). In other words, functional connectivity allows for \n76 the characterization of networks of brain activity rather than activity in single brain \n77 regions. Importantly, functional connectivity can be measured at rest.\n78 While there have been many studies of brain activity and connectivity in \n79 anhedonic adults, fewer have been conducted in children and adolescents. However, \n80 findings from these studies generally converge on the significance of disruptions in the \n81 reward, default mode, and salience networks (10–12). A recent study using the early 1.0 \n82 release of the Adolescent Brain Cognitive Development (ABCD) study data found \n83 several resting state functional MRI (rsfMRI) brain network connectivity measures \n84 associated with anhedonia in children aged 9-10 years old (13). Importantly, it was one \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 5 of 34\n85 of the largest studies of anhedonia in children with a sample size of ~2,500 participants \n86 including 215 children reporting past and/or present anhedonia. Presently, the latest 4.0 \n87 release of the ABCD study, which includes a significantly larger sample of participants \n88 with neuroimaging and behavioral data (n = 11,878), has been made available. Given \n89 that prior releases of the ABCD data are archived and publicly available, there is a \n90 significant opportunity to both  reproduce and replicate these findings.\n91 We define reproducibility as the ability to achieve exactly the same results as a \n92 previous study by using the same data and analytical approach, and replicability as the \n93 ability to achieve the same (or similar) results as a previous study in a different dataset \n94 (14). By our definition, reproducibility is better able to assess the consistency of results \n95 while replicability is better able to assess the generalizability of those results. The aims \n96 of this present study are to reproduce the previously reported associations between \n97 rsfMRI connectivity and childhood anhedonia using the ABCD 1.0 release sample, and \n98 to replicate those findings using an independent subset of the larger ABCD 4.0 release \n99 sample, excluding participants from the ABCD 1.0 release sample. \n100 Importantly, in depressive disorders, anhedonia is characterized as the loss of \n101 pleasure and interest that is distinct from feelings of sadness or other dysphoric \n102 moods(15).  Thus, there is great need to elucidate the specific neurobiological \n103 underpinnings associated with anhedonia, distinct from other comorbid symptoms, to \n104 better understand the underlying brain dysfunction. Thus, we also aim to extend our \n105 analyses and evaluate the specificity of rsfMRI connectivity associations with anhedonia \n106 by evaluating the effects of significantly comorbid psychiatric symptoms and diagnoses. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 6 of 34\n107 Methods and Materials\n108 All of our analyses were performed in R (version 4.0.3) and Rstudio . We used \n109 several scripts, including the utils.R and combat.R (16) scripts for data harmonization, \n110 which were provided by the previous authors immediately upon request. The code and \n111 data structures used for our study can be accessed from the associated NDA study. \n112 The code we used can also be found at our Open Science Framework repository \n113 (https://osf.io/vy85h/?view_only=0497385708874a6a9cce2bbfc5c30600). \n114 ABCD Study Data \n115 The ABCD® study is the largest longitudinal study of brain development in \n116 children in the United States (https://abcdstudy.org/). The study has collected structural \n117 and functional brain imaging measures as well as detailed psychiatric and behavioral \n118 data from almost 12,000 children starting from when they were 9-10 years old. Notably, \n119 data is released on a continuous basis. For this study, we used baseline data from the \n120 ABCD 1.0 and ABCD 4.0 releases.  \n121 rsfMRI Connectivity Measures and Quality Control (QC)\n122 Neuroimaging processing pipelines and analyses for the ABCD study are \n123 reviewed by (17). Briefly, the functional scans include twenty minutes of resting-state \n124 data acquired with eyes open and passive viewing of a crosshair (18). From the ABCD \n125 Data Repository, we obtained rsfMRI connectivity measures which were constructed \n126 using a seed-based correlational approach where regions of interest (ROIs) within \n127 Gordon parcellations (19) were grouped together into predefined cortical networks. \n128 Briefly, correlations between unique pairs of ROI’s were obtained and Fisher \n129 transformed into z-statistics. Connectivity measures represent the averaged Fisher-\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 7 of 34\n130 transformed correlations of all the unique pairs of ROIs either within a cortical network, \n131 between cortical networks, or between cortical networks and subcortical regions.  From \n132 the ABCD Data Repository, we obtained rsfMRI connectivity measures between 19 \n133 subcortical regions and 12 cortical networks (data structure: mrirscor02), as well as \n134 rsfMRI connectivity measures from within and between the 12 cortical networks (data \n135 structure: abcd_betnet02). Thus, there were 228 (12 x 19) subcortical ROI vs. cortical \n136 network rsfMRI connectivity measures and 78 (12C2 network pairs + 12 within network) \n137 within/between cortical network rsfMRI variables, for a total of 306 rsfMRI connectivity \n138 measures. \n139 For QC, we used the IQC_RSFMRI_GOOD_SER variable, which represents the \n140 number of rsfMRI runs that were complete, passed protocol compliance and QC, and \n141 had field maps acquired within 2 scans prior to the run that were complete and passed \n142 QC and protocol compliance. Like the previous authors’, we retained subjects who had \n143 IQC_RSFMRI_GOOD_SER values greater than or equal to four. \n144 For analyses using only ABCD 1.0 release sample, we also removed individuals \n145 who were scanned by “Philips Medical Systems” MRI machines because of a post-\n146 processing issue in the ABCD 1.0 release, which was resolved in later releases. When \n147 working with ABCD 4.0 release sample, we retained all the subjects who were scanned \n148 by Philips Medical Systems scanners because the post-processing errors identified in \n149 the ABCD 1.0 release had been fixed for the ABCD 4.0 release. \n150 Data Harmonization\n151 Different MRI scanners were used across the 21 sites in the ABCD study. The \n152 original authors harmonized the data across MRI scanners by using the ComBat tool \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 8 of 34\n153 (combat.R) to adjust for batch effects due to the different scanners used. Here, we do \n154 the same and harmonize the data separately for the subcortical ROI vs. cortical network \n155 and within/between cortical network rsfMRI measures as it is possible these two \n156 variable types may be affected by scanners differently(20). Note, before the \n157 harmonization step, listwise deletion of subjects with any missing rsfMRI data was done \n158 as data harmonization requires complete data. There were 23 different scanners used \n159 in the study for the ABCD 1.0 release and 29 different scanners for the ABCD 4.0 \n160 release. Thus 23 and 29 batch effects were used to adjust the ABCD 1.0 and 4.0 \n161 releases, respectively. \n162 Psychiatric Symptoms and Diagnoses \n163 The focus of this study were past/present symptoms of anhedonia. However, we \n164 were also interested in other psychiatric conditions that may be comorbid with \n165 anhedonia. Psychiatric data were obtained from the youth (data structure: \n166 abcd_ksad501) and parent (data structure: abcd_ksad01) Kiddie Schedule for Affective \n167 Disorders and Schizophrenia (KSADS) data structures from the ABCD study. Using \n168 both youth and parent KSADS items, we combined past and present items for the same \n169 symptom or diagnosis and consolidated some items into a single variable. For example, \n170 we consolidated 18 past and present suicide related diagnosis items into one single \n171 suicide thoughts and behavior variable, which was similarly done in another study (21). \n172 For psychiatric conditions besides anhedonia, we first selected the KSADS items \n173 representing psychiatric diagnoses and not individual symptoms. However, in both the \n174 Youth and Parents KSADS data, no diagnosis variables for major depressive disorder \n175 (MDD) were available. Thus, we selected two MDD related symptoms (besides \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 9 of 34\n176 anhedonia), irritability and depressed mood, from both the Youth and Parent data to be \n177 used in our analyses. Similarly, no diagnostic variable was available for ADHD. Thus, \n178 we created a representative variable, inattention_distracted_p, which is a combination \n179 of two prevalent ADHD related symptom items (Symptom - Difficulty sustaining attention \n180 since elementary school and/or Symptom - Easily distracted since elementary school) \n181 from the parent KSADS data.\n182 Statistical Analyses \n183 Student’s and Bayes Factor T-Tests\n184 Prior to statistical analyses, participants with missing data for psychiatric \n185 symptoms/diagnoses were removed. We then proceeded to identify and remove outliers \n186 for each rsfMRI connectivity measure as values 1.5 times greater than the interquartile \n187 range (IQR) of values. \n188 We performed Student’s T-Tests for each of the 306 rsfMRI measures between \n189 controls and individuals with past and/or present psychiatric symptoms/diagnoses of \n190 interest (the reference group consisted of individuals endorsing psychiatric \n191 symptoms/diagnoses, such as anhedonia). We applied the Benjamini-Hochberg \n192 adjustment for multiple testing corrections. Finally, we performed Bayes Factor T-Tests \n193 for each of the rsfMRI measures and reported natural logarithms of the Bayes Factors \n194 (lnBF). As with the previous authors’, lnBF values greater than 1.1 were considered \n195 significant. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 10 of 34\n196 Tetrachoric Correlations\n197 Tetrachoric correlations are suitable for use with binary or categorical variables \n198 as it assumes responses arise from an underlying normal distribution with thresholds \n199 that delineate response categories. The tetrachoric() function from the psych R package \n200 was used in our analyses. Tetrachoric correlations were used to estimate the \n201 correlations between pairs of 34 binary psychiatric variables. \n202 Multiple Linear Regressions \n203 For multiple linear regression analyses, the ABCD rsfMRI data (from both 1.0 \n204 and 4.0 releases) were harmonized for MRI scanner using the ComBat tool as \n205 previously described, except we also adjusted for batch effects with covariates (20). The \n206 covariates included during data harmonization were: age, sex, race/ethnicity, \n207 anhedonia, bipolar II, irritability, and depressed mood. \n208 We performed linear mixed effects modeling using the lmer4 package in R, a \n209 form of multiple linear regression, on the harmonized data. Individual rsfMRI \n210 connectivity measures were modeled as outcome variables while age, sex, \n211 race/ethnicity, anhedonia (reference group was the control group), depressed mood, \n212 irritability, and bipolar II disorder were modeled as independent explanatory variables.  \n213 Age, sex, and race/ethnicity were considered potential confounding variables. The \n214 independent effects of anhedonia, bipolar II, irritability, and depressed mood symptoms \n215 on rsfMRI connectivity measures were assessed by identifying corresponding \n216 statistically significant partial regression coefficients, after multiple testing corrections. \n217 Family ID was included as a random effect to control for the non-independence of \n218 values from participants who belonged to the same family. We adjusted for multiple \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 11 of 34\n219 comparisons with the Benjamini-Hochberg method to control for a False Discovery Rate \n220 of 0.05. \n221 Results\n222 Reproduction of previous findings\n223 To reproduce the previous authors’ results, we used the ABCD 1.0 release \n224 sample. Socio-demographic characteristics for this sample can be found in Table 1. \n225 Note that although the two groups appear to exhibit differences in a few of these \n226 characteristics, they were not controlled for statistically when we performed in our t-tests \n227 in order to remain consistent with the previous authors’ approach.  \n228 Table 1. Comparison of Socio-demographic Measures Between Controls and \n229 Those with Anhedonia in the ABCD 1.0 Release Sample. \nMeasures Control (N = 2209) Anhedonia (N = 215) Statistic p-value\n% Male 51.8 54 0.286 0.593\nMean Age (months) 120.6 119.9 1.183a 0.238\n% Asian 1.8 0.9 0.45 0.502\n% Black 8.2 18.1 22.296 <0.001\n% Hispanic 20 26.5 4.753 0.029\n% Other 9.3 9.8 0.012 0.911\n% White 60.8 44.7 20.386 <0.001\n% Bipolar II 0.8 7.9 64.358 <0.001\n% Depressed Mood 8.1 28.4 87.975 <0.001\n% Irritability 4.7 26.5 148.192 <0.001\n230 The proportion (%) of participants in each group for each measure are shown. Student’s \n231 t-test was done to compare age (in months) between control and anhedonia groups. \n232 Chi-square tests of independence were done for all other measures. Note there is a \n233 slightly lower number of controls here than reported below due to the exclusion of \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 12 of 34\n234 participants with missing sociodemographic and/or comorbid psychiatric syndrome and \n235 diagnoses measures. \n236 a Student’s t-statistic.\n237 We were able to successfully reproduce their findings (13). To assess how well we \n238 reproduced the previous authors’ results, we calculated Pearson correlations of the t-\n239 statistics and lnBF values between our values and those reported by the previous \n240 authors when comparing individuals with or without anhedonia (Fig. 1A and 1B).\n241 Figure 1 – Reproduction of previous statistical analyses. Pearson correlations \n242 between A) Student’s t-statistics and B) lnBF statistics derived from the previous \n243 author’s analyses and those derived from our replication analyses. LnBF – natural \n244 logarithm of Bayes Factors. \n245 Like the previous authors, we identified 215 individuals who endorsed past \n246 and/or present anhedonia and 2,222 controls who reported neither past nor present \n247 anhedonia at the baseline timepoint. In line with the previous authors findings, 11 \n248 rsfMRI connectivity measures were found to be associated with anhedonia using the \n249 lnBF(10) statistic (Table. 2; full table of results found in Supplementary Table. 1). To be \n250 consistent with the previous authors, individuals with anhedonia were the reference \n251 group. \n252 Table. 2 Reproducing Results of rsfMRI Network Connectivity Measures \n253 Associated with Anhedonia.\nrsfMRI Measure t-stat p-value p.adj lnBF Meancontrol SDcontrol Meananhedonia SDanhedonia\nDorsalAttentionLeftHippocampus -3.931 0 0.027 5.06 -0.129 0.095 -0.101 0.096\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 13 of 34\nWithinRetrosplenialTemporal 3.644 0 0.042 3.989 0.525 0.152 0.485 0.143\nCinguloParietalBrainStem -3.46 0.001 0.045 3.366 -0.001 0.091 0.023 0.095\nDefaultDorsalAttention -3.441 0.001 0.045 3.29 -0.129 0.063 -0.113 0.063\nSensorimotorHandBrainStem 3.304 0.001 0.051 2.854 0.11 0.106 0.084 0.096\nSalienceLeftVentraldc -3.294 0.001 0.051 2.809 -0.12 0.092 -0.097 0.094\nWithinCinguloOpercular 3.142 0.002 0.074 2.323 0.291 0.078 0.273 0.08\nRetrosplenialTemporalRightCerebellumCortex 3.084 0.002 0.079 2.151 0.127 0.136 0.097 0.127\nCinguloParietalRightPallidum 3.024 0.003 0.086 1.974 0.127 0.119 0.101 0.132\nSensorimotorHandRightHippocampus 2.965 0.003 0.094 1.809 0.099 0.099 0.078 0.097\nCinguloOpercularBrainStem 2.75 0.006 0.167 1.204 0.026 0.101 0.005 0.102\n254 The results of statistically significant univariate Student’s T-tests and Bayes Factor T-\n255 tests comparing differences in rsfMRI connectivity measures between controls and \n256 individuals reporting anhedonia at baseline using the ABCD 1.0 release sample (n = \n257 2437). Student’s t-statistics (t-stat), p-values, Benjamini-Hochberg adjusted p-values \n258 (p.adj), natural logarithms of Bayes Factors (lnBF), means (Mean) and standard \n259 deviations (SD) for the control and anhedonia groups are reported.  lnBF values greater \n260 than 1.1 were considered statistically significant.\n261 Replication of previous findings \n262 At the time of writing, the ABCD 4.0 release has been made available. We \n263 wanted to replicate the previous findings by using the full cohort, excluding the subjects \n264 used in the previous analyses, which is analogous to replication in an independent \n265 sample. In this sub-sample of the ABCD 4.0 release (which excludes participants from \n266 ABCD 1.0 release), we found 591 participants who endorsed past and/or present \n267 anhedonia and 5,865 controls who did not. Socio-demographic characteristics for this \n268 sample can be found in Table 3. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 14 of 34\n269 Table. 3 Comparison of Sociodemographic Measures Between Controls and \n270 Those with Anhedonia in the ABCD 4.0 Release, Excluding ABCD 1.0 Release, \n271 Sub-sample. \n272\nMeasures Control (N = 5863) Anhedonia (N = 591) Statistic p-value\n% Male 51.2 56.7 6.202 0.013\nMean Age (months)a 118.5 119.1 -1.705 0.089\n% Asian 2.3 0.8 4.703 0.03\n% Black 15.2 20.8 12.449 <0.001\n% Hispanic 20.5 26.4 11.087 0.001\n% Other 10.4 12.9 3.055 0.08\n% White 51.6 39.1 33.287 <0.001\n% Bipolar II 0.3 8.3 313.34 <0.001\n% Depressed Mood 6.4 31.8 431.052 <0.001\n% Irritability 4.4 27.9 484.563 <0.001\n273 The proportion (%) of participants in each group for each measure are shown. Student’s \n274 t-test was done to compare age (in months) between control and anhedonia groups. \n275 Chi-square tests of independence were done for all other measures. Note there is a \n276 slightly lower number of controls here than reported above due to the exclusion of \n277 participants with missing sociodemographic and/or comorbid psychiatric syndrome and \n278 diagnoses measures. \n279 a Student’s t-statistic.\n280 When comparing the groups using Student’s and Bayes Factor t-tests, 18 rsfMRI \n281 connectivity measures were found to be significantly associated with anhedonia \n282 according to the lnBF statistic (Table. 4; full table of results found in Supplementary \n283 Table 2). However, only the w ithinCinguloOpercular network rsfMRI connectivity \n284 identified by the previous authors, was also found to be significant. \n285 Table. 4 Replicating Results of rsfMRI Connectivity Measures Associated with \n286 Anhedonia.\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 15 of 34\nrsfMRI Connectivity Measure t-stat p-value p.adj lnBF Meancontrol SDcontrol Meananhedonia SDanhedonia\nVisualLeftHippocampus 4.328 0 0.005 6.273 0.056 0.089 0.039 0.09\nCinguloOpercularLeftAmygdala 4.02 0 0.009 5.002 0.07 0.094 0.053 0.096\nRetrosplenialTemporalRightThalamusProper 3.852 0 0.012 4.338 0.158 0.083 0.144 0.087\nAuditoryRightPutamen 3.574 0 0.022 3.321 0.124 0.107 0.107 0.107\nWithinSensorimotorHand -3.507 0 0.022 3.092 0.271 0.071 0.282 0.072\nCinguloParietalLeftThalamusProper 3.498 0 0.022 3.053 0.224 0.093 0.21 0.091\nSensorimotorHandRightPutamen 3.477 0.001 0.022 2.98 -0.011 0.105 -0.027 0.112\nRetrosplenialTemporalRightVentraldc 3.367 0.001 0.028 2.611 0.018 0.093 0.004 0.094\nVentralAttentionLeftCerebellumCortex -3.332 0.001 0.028 2.491 -0.004 0.076 0.007 0.08\nSensorimotorHandLeftPallidum 3.315 0.001 0.028 2.436 0.074 0.098 0.06 0.101\nCinguloOpercularLeftCaudate 3.274 0.001 0.029 2.306 0.018 0.092 0.004 0.094\nDorsalAttentionRightAmygdala -3.252 0.001 0.029 2.226 -0.009 0.078 0.002 0.076\nVisualRightPallidum 3.236 0.001 0.029 2.183 -0.032 0.076 -0.043 0.075\nCinguloOpercularRightAmygdala 3.213 0.001 0.029 2.111 -0.014 0.105 -0.029 0.11\nCinguloParietalRightCerebellumCortex 3.087 0.002 0.041 1.708 0.163 0.108 0.148 0.11\nSensorimotorHandRightAccumbensArea 3.068 0.002 0.041 1.661 0.224 0.093 0.211 0.091\nAuditorySensorimotorHand -3.048 0.002 0.042 1.603 0.12 0.057 0.128 0.058\nCinguloParietalLeftCaudate 2.987 0.003 0.048 1.407 0.259 0.079 0.249 0.077\nWithinCinguloOperculara 2.968 0.003 0.048 1.35 0.299 0.067 0.29 0.066\nAuditorySalience 2.931 0.003 0.052 1.244 0.042 0.067 0.034 0.068\n287 The results of statistically significant univariate Student’s T-tests and Bayes Factor T-\n288 tests comparing differences in rsfMRI connectivity measures between controls and \n289 individuals reporting anhedonia at baseline using an independent subsample of the \n290 ABCD 4.0 release, excluding ABCD 1.0 release, data (n = 6456). Student’s t-statistics \n291 (t-stat), p-values, Benjamini-Hochberg adjusted p-values (p.adj), natural logarithms of \n292 Bayes Factors (lnBF), means (Mean) and standard deviations (SD) for the control and \n293 anhedonia groups are reported. lnBF values greater than 1.1 were considered \n294 statistically significant.\n295 a replicated association between rsfMRI connectivity measure and anhedonia.\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 16 of 34\n296 To increase our power to detect genuine associations, we next performed our \n297 analyses on the full ABCD 4.0 release sample, including participants from the ABCD 1.0 \n298 release. In the full ABCD 4.0 release sample, there were 800 participants who endorsed \n299 past and/or present anhedonia and 8066 controls who did not. Socio-demographic \n300 characteristics for this sample can be found in Table 5.  \n301 Table 5. Comparison of Sociodemographic Measures Between Controls and \n302 Those with Anhedonia in the Full ABCD 4.0 Release Sample. \nMeasures Control (N = 8064) Anhedonia (N = 800) Statistic p-value\n% Malea 51.3 55.8 5.589 0.018\nMean Age (months) 119.1 119.3 -0.845 0.398\n% Asiana 2.2 0.9 5.363 0.021\n% Blacka 13.3 19.8 25.19 <0.001\n% Hispanica 20.4 26.6 16.579 <0.001\n% Other 10.1 12.1 2.916 0.088\n% Whitea 54 40.6 52.043 <0.001\n% Bipolar IIa 0.5 7.9 347.62 <0.001\n% Depressed Mooda 6.9 30.9 507.29 <0.001\n% Irritabilitya 4.4 27.4 627.02 <0.001\n303 The proportion (%) of participants in each group for each measure are shown. Student’s \n304 t-test was done to compare age (in weeks) between control and anhedonia groups. Chi-\n305 square tests of independence were done for all other measures. Note there is a slightly \n306 lower number of controls here than reported below due to the exclusion of participants \n307 with missing sociodemographic and/or comorbid psychiatric syndrome and diagnoses \n308 measures. \n309 a Student’s t-statistic.\n310 Notably, 6 out of the 11 rsfMRI connectivity measures identified by the previous \n311 authors were also significantly associated with anhedonia in this analysis (Table. 6; full \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 17 of 34\n312 table of results found in Supplementary Table 3). As well, 17 out of the 20 significant \n313 rsfMRI connectivity measures identified in the independent sub-sample of the ABCD 4.0 \n314 release (from Table. 4) were also found to be significant in the analyses using the full \n315 ABCD 4.0 release sample, including the within CinguloOpercular network connectivity \n316 measure (Table. 6). \n317 Table. 6 Significant Associations Between rsfMRI Connectivity Measures and \n318 Anhedonia in the Full ABCD 4.0 Sample.\nrsfMRI Connectivity Measure t-stat p-value p.adj lnBF Meancontrol SDcontrol Meananhedonia SDanhedonia\nRetrosplenialTemporalRightThalamusProperb 4.962 0 0 9.057 0.161 0.082 0.146 0.086\nCinguloOpercularLeftAmygdalab 4.252 0 0.003 5.816 0.072 0.093 0.057 0.094\nVentralAttentionLeftPutamen -4.207 0 0.003 5.626 -0.055 0.065 -0.045 0.067\nAuditoryRightPutamenb 4.085 0 0.003 5.125 0.125 0.106 0.109 0.105\nSensorimotorHandRightPutamenb 4.029 0 0.003 4.902 -0.01 0.104 -0.026 0.108\nSensorimotorHandLeftPallidumb 4.027 0 0.003 4.893 0.076 0.098 0.061 0.099\nWithinCinguloOpercularab 3.865 0 0.004 4.254 0.303 0.066 0.293 0.066\nVentralAttentionLeftCaudate 3.839 0 0.004 4.153 0.036 0.066 0.026 0.07\nCinguloParietalLeftThalamusProperb 3.836 0 0.004 4.142 0.224 0.092 0.211 0.092\nSensorimotorHandRightAccumbensAreab 3.787 0 0.005 3.961 0.225 0.093 0.212 0.092\nVentralAttentionLeftCerebellumCortexb -3.762 0 0.005 3.865 -0.007 0.075 0.003 0.08\nVisualLeftHippocampusb 3.691 0 0.006 3.605 0.056 0.088 0.044 0.089\nDefaultDorsalAttentiona -3.604 0 0.007 3.284 -0.127 0.051 -0.121 0.051\nCinguloOpercularRightAmygdalab 3.601 0 0.007 3.281 -0.013 0.103 -0.027 0.106\nRetrosplenialTemporalRightCerebellumCortexa 3.52 0 0.009 2.993 0.128 0.106 0.114 0.106\nWithinSensorimotorHandb -3.513 0 0.009 2.975 0.271 0.07 0.28 0.072\nWithinVisual 3.411 0.001 0.012 2.61 0.409 0.089 0.398 0.09\nCinguloOpercularBrainStema 3.358 0.001 0.013 2.436 0.025 0.074 0.016 0.074\nCinguloOpercularLeftCaudateb 3.315 0.001 0.013 2.3 0.02 0.09 0.008 0.093\nVisualRightPallidumb 3.31 0.001 0.013 2.283 -0.03 0.076 -0.039 0.076\nDorsalAttentionRightVentraldcb 3.312 0.001 0.013 2.282 0.046 0.067 0.038 0.069\nFrontoParietalVisual -3.307 0.001 0.013 2.266 -0.107 0.044 -0.102 0.044\nSensorimotorHandBrainStema 3.268 0.001 0.015 2.136 0.129 0.08 0.119 0.079\nSalienceLeftVentraldca -3.246 0.001 0.015 2.07 -0.121 0.071 -0.113 0.072\nFrontoParietalLeftCerebellumCortex -3.228 0.001 0.015 2.011 -0.063 0.065 -0.055 0.068\nDorsalAttentionLeftVentraldc 3.189 0.001 0.017 1.885 0.27 0.116 0.256 0.119\nVentralAttentionLeftThalamusProper 3.154 0.002 0.017 1.773 -0.096 0.11 -0.109 0.113\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 18 of 34\nCinguloParietalLeftCaudateb 3.149 0.002 0.017 1.76 0.26 0.078 0.251 0.076\nFrontoParietalRightPallidum -3.148 0.002 0.017 1.755 -0.035 0.058 -0.029 0.058\nRetrosplenialTemporalRightVentraldcb 3.114 0.002 0.018 1.657 0.021 0.092 0.01 0.095\nFrontoParietalLeftCaudate 3.111 0.002 0.018 1.645 0.084 0.044 0.079 0.044\nDorsalAttentionRightAmygdala -3.108 0.002 0.018 1.629 -0.012 0.077 -0.003 0.076\nCinguloOpercularLeftAccumbensArea 3.091 0.002 0.019 1.581 0.111 0.077 0.102 0.076\nSalienceVentralAttention 3.04 0.002 0.021 1.423 0.089 0.061 0.082 0.062\nDorsalAttentionLeftAmygdala -3.035 0.002 0.021 1.408 -0.076 0.064 -0.068 0.063\n319 The results of statistically significant univariate Student’s T-tests and Bayes Factor T-\n320 tests comparing differences in rsfMRI connectivity measures between controls and \n321 individuals reporting anhedonia at baseline using the full ABCD 4.0 release sample (n = \n322 8866). Student’s t-statistics (t-stat), p-values, Benjamini-Hochberg adjusted p-values \n323 (p.adj), natural logarithms of Bayes Factors (lnBF), means (Mean) and standard \n324 deviations (SD) for the control and anhedonia groups are reported.  lnBF values greater \n325 than 1.1 were considered statistically significant. \n326 a rsfMRI connectivity measures also reported by the previous authors as significantly \n327 associated with anhedonia.\n328 b rsfMRI connectivity measures that were also found to be significant in the replication \n329 analyses. \n330 It is important to note that individuals reporting anhedonia may also report other \n331 symptoms or psychiatric diagnoses. Thus, it is important to evaluate the specificity of \n332 the associations between rsfMRI connectivity and anhedonia. In order to identify \n333 psychiatric conditions significantly comorbid in individuals reporting anhedonia, we \n334 performed tetrachoric correlations between anhedonia and 33 additional psychiatric \n335 diagnoses and symptoms collected at baseline (Supplementary Figure. 1). Three \n336 psychiatric conditions exhibited correlation coefficients greater than or equal to 0.5 with \n337 anhedonia: irritability, depressed mood, and bipolar II disorder. In the full ABCD 4.0 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 19 of 34\n338 release sample, 32%, 28%, and 8% of the participants reporting anhedonia also \n339 reported depressed mood, irritability, and bipolar II disorder (Supplementary Table. 4).\n340 We next performed student’s and Bayes Factor t-tests to compare rsfMRI \n341 connectivity between individuals with and without depressed mood, irritability, and \n342 bipolar II, respectively (Supplementary Tables. 5-7). We found that several rsfMRI \n343 connectivity measures associated with anhedonia were also associated with these other \n344 psychiatric conditions.\n345 Multiple Linear Regression Approach \n346 While we were able to characterize which rsfMRI connectivity measures were \n347 specifically associated with anhedonia vs. shared with other psychiatric conditions, \n348 simple t-tests were not able to estimate the independent effects of each psychiatric \n349 condition on rsfMRI connectivity. Furthermore, t-tests are not able to control for \n350 potentially confounding sociodemographic variables such age, sex, and race/ethnicity. \n351 Chi-square tests of independence showed significant differences in race/ethnicity and \n352 sex between individuals reporting anhedonia and those who do not, across the different \n353 samples used in our analyses (Tables 1, 3, and 5). \n354 By using a multiple linear regression approach where a rsfMRI connectivity \n355 measure is modeled as the response (or outcome) variable, comorbid psychiatric \n356 conditions as well as confounding factors can be included as explanatory (or predictor) \n357 variables. Thus, multiple linear regression allows for the estimation of the main effects \n358 of anhedonia on rsfMRI connectivity, independent of the effects of depressed mood, \n359 irritability, and bipolar II disorder (and vice versa), and the effects of confounding \n360 covariates.  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 20 of 34\n361 Since the ABCD 1.0 release sample and the subsample of ABCD 4.0 release, \n362 excluding the ABCD 1.0 sample, were not matched on age, sex, or race/ethnicity, we \n363 hypothesized that controlling for these potential confounders may improve replicability. \n364 Thus, we first performed multiple linear regression in the ABCD 1.0 release sample and \n365 then in the ABCD 4.0 release sample, excluding the ABCD 1.0 release sample. The \n366 reference group was the control group without symptoms with anhedonia. In the ABCD \n367 1.0 release sample, only two rsfMRI connectivity measures exhibited statistically \n368 significant partial regression coefficients for anhedonia, after multiple testing corrections \n369 (Table 4; full multiple regression results found in Supplementary Table 8). \n370 Table. 4 rsfMRI Connectivity Measures with Significant Partial Regression \n371 Coefficients for Anhedonia in the ABCD 1.0 Sample.\nrsfMRI Connectivity Measure Effect of Anhedonia Std.Err t-value p-value p.adj R2\nDorsalAttentionLeftHippocampus 0.024 0.007 3.208 0.001 0.019 0.012\nCinguloParietalBrainStem 0.021 0.007 2.968 0.003 0.036 0.006\n372 Statistically significant effects of anhedonia as a predictor of rsfMRI connectivity \n373 measures from multiple linear regression tests are shown. The standard errors \n374 (Std.Err), t-values, p-values, Benjamini-Hochberg adjusted p-values (p.adj) for the \n375 effects of anhedonia, and overall coefficients of determination (R2) for each regression \n376 are reported. \n377  It is likely that including the additional explanatory variables into the model \n378 reduced the statistical power to detect significant associations in the relatively small \n379 sample. Accordingly, while more rsfMRI connectivity measures were significantly \n380 associated with anhedonia in the ABCD 4.0 release, excluding ABCD 1.0 release, sub-\n381 sample, we were unable to replicate the associations found using the ABCD 1.0 release \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 21 of 34\n382 sample (Table. 5). rsfMRI connectivity measures with significant partial regression \n383 coefficients for bipolar II and depressed mood were also identified for analyses in the \n384 ABCD 1.0 release sample (Supplementary Table. 8) and for depressed mood, irritability, \n385 and bipolar II in the ABCD 4.0 release, excluding ABCD 1.0 release, sub-sample \n386 (Supplementary Table. 9). No significant partial regression coefficients for irritability \n387 were detected in the ABCD 1.0 release sample. \n388 Table. 5 rsfMRI Connectivity Measures with Significant Partial Regression \n389 Coefficients for Anhedonia in the ABCD 4.0 Release, Excluding ABCD 1.0 \n390 Release, Sub-sample. \nrsfMRI Connectivity Measure Effect of Anhedonia Std.Err t-value\np-\nvalue p.adj R2\nAuditoryRightPutamen -0.013 0.005 -2.563 0.01\n0.03\n6\n0.04\n8\nDorsalAttentionRightHippocampus 0.009 0.004 2.524 0.012 0.04 0\nDorsalAttentionRightAmygdala 0.012 0.004 3.226 0.001\n0.00\n6\n0.00\n6\nRetrosplenialTemporalRightThalamusPrope\nr -0.01 0.004 -2.661 0.008\n0.02\n8\n0.02\n9\nVentralAttentionLeftCerebellumCortex 0.01 0.004 2.715 0.007\n0.02\n5\n0.00\n6\nVisualLeftHippocampus -0.013 0.004 -3.067 0.002\n0.00\n9\n0.04\n5\nAuditoryCinguloParietal -0.008 0.003 -2.465 0.014\n0.04\n6\n0.01\n8\nAuditorySensorimotorHand 0.007 0.003 2.614 0.009\n0.03\n2\n0.03\n3\nCinguloOpercularSalience -0.009 0.003 -2.903 0.004\n0.01\n5\n0.00\n2\nCinguloParietalSensorimotorHand -0.008 0.003 -2.626 0.009\n0.03\n1\n0.00\n6\nFrontoParietalVisual 0.006 0.002 2.732 0.006\n0.02\n3\n0.01\n1\nRetrosplenialTemporalSensorimotorHand -0.007 0.002 -2.88 0.004\n0.01\n5\n0.02\n1\nWithinSensorimotorHand 0.009 0.003 2.837 0.005\n0.01\n7\n0.03\n8\nWithinSalience -0.015 0.005 -2.802 0.005\n0.01\n9\n0.00\n3\n391 Statistically significant effects of anhedonia as a predictor of rsfMRI connectivity \n392 measures from multiple linear regression tests are shown. The standard errors \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 22 of 34\n393 (Std.Err), t-values, p-values, Benjamini-Hochberg adjusted p-values (p.adj) for the \n394 effects of anhedonia, and overall coefficients of determination (R2) for each regression \n395 are reported.  \n396 In order to maximize our power to detect associations between rsfMRI \n397 connectivity measures and anhedonia, we performed multiple linear regression \n398 analyses in the full ABCD 4.0 release sample. We found that 16 rsfMRI connectivity \n399 measures exhibited significant partial regression coefficients for anhedonia (Table. 6; \n400 full table of results found in Supplementary Table 10). Of note, 9 out of these 16 rsfMRI \n401 connectivity measures were previously identified to be uniquely associated with \n402 anhedonia from our t-tests. Furthermore, the Retrosplenial Temporal vs. Right \n403 Cerebellum Cortex and CinguloOpercular vs. Brainstem connectivity measures were \n404 reported to be significantly associated with anhedonia by the previous authors. \n405 Table. 6 rsfMRI Connectivity Measures with Significant Partial Regression \n406 Coefficients for Anhedonia in the Full ABCD 4.0 Release Sample. \nrsfMRI Connectivity Measure Effect of Anhedonia Std.Err t-value\np-\nvalue p.adj R2\nRetrosplenialTemporalRightThalamusPropera -0.012 0.003 -3.583 0\n0.00\n1\n0.02\n9\nVentralAttentionLeftCerebellumCortexa 0.009 0.003 3.161 0.002\n0.00\n6\n0.00\n8\nAuditoryRightPutamen -0.013 0.004 -3.034 0.002\n0.00\n9\n0.04\n5\nDorsalAttentionRightAmygdalaa 0.008 0.003 2.751 0.006 0.02\n0.00\n7\nVentralAttentionLeftPutamen 0.007 0.003 2.745 0.006 0.02\n0.01\n9\nCinguloOpercularSalience -0.007 0.003 -2.667 0.008\n0.02\n5\n0.00\n2\nWithinSensorimotorHanda 0.007 0.003 2.649 0.008\n0.02\n6\n0.03\n7\nVisualRightPalliduma -0.008 0.003 -2.615 0.009\n0.02\n9\n0.03\n1\nFrontoParietalVisuala 0.005 0.002 2.609 0.009\n0.02\n9\n0.01\n2\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 23 of 34\nWithinSalience -0.011 0.005 -2.449 0.014\n0.04\n4\n0.00\n1\nCinguloOpercularBrainStema -0.007 0.003 -2.437 0.015\n0.04\n5\n0.03\n9\nRetrosplenialTemporalSensorimotorHand -0.005 0.002 -2.422 0.015\n0.04\n7 0.02\nVisualRightVentraldc 0.008 0.003 2.412 0.016\n0.04\n8\n0.00\n7\nRetrosplenialTemporalRightCerebellumCortex\na -0.01 0.004 -2.407 0.016\n0.04\n9\n0.04\n1\nVentralAttentionLeftCaudatea -0.006 0.003 -2.404 0.016\n0.04\n9\n0.02\n5\nSensorimotorHandLeftPallidum -0.009 0.004 -2.398 0.017 0.05\n0.05\n6\n407 Statistically significant effects of anhedonia as a predictor of rsfMRI connectivity \n408 measures from multiple linear regression tests are shown. The standard errors \n409 (Std.Err), t-values, p-values, Benjamini-Hochberg adjusted p-values (p.adj) for the \n410 effects of anhedonia, and overall coefficients of determination (R2) for each regression \n411 are reported. \n412 a rsfMRI connectivity measures also found to be specifically associated with anhedonia \n413 from previously performed t-tests in the full ABCD 4.0 release sample. \n414 Two rsfMRI connectivity measures, the Auditory vs. Right Putamen and Ventral \n415 Attention vs. Left Putamen were previously found to be associated with both depressed \n416 mood and anhedonia from our t-tests but now only exhibit significant partial regression \n417 coefficients for anhedonia. Interestingly, the Sensorimotor-Hand vs. Left Pallidum \n418 connectivity measure exhibited significant partial regression coefficients for both \n419 anhedonia and depressed mood (Table. 6, Supplementary Table. 10), consistent with \n420 previous t-test results, suggesting the presence of significant independent effects of \n421 both symptoms on the same rsfMRI connectivity measure. \n422 The CinguloOpercular vs. Left Amygdala connectivity measure was also \n423 previously associated with both depressed mood and anhedonia, based on their t-tests, \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 24 of 34\n424 but now only exhibit significant partial regression coefficients for depressed mood \n425 (Supplementary Table. 10). Similarly, the Ventral Attention vs. Left Thalamus Proper \n426 connectivity measure was previously associated with both anhedonia and irritability \n427 based on t-tests, but now only exhibit significant partial regression coefficients for \n428 irritability (Supplementary Table. 10). Only one rsfMRI connectivity measure, Within \n429 Ventral Attention, exhibited a significant partial regression coefficient for bipolar II \n430 disorder (Supplementary Table. 10).\n431 Altogether, while multiple linear regression analyses did not improve replicability \n432 between the ABCD study sub-samples, it did allow us to estimate the independent \n433 effects of anhedonia on rsfMRI connectivity measures in the full ABCD 4.0 release \n434 sample and helped resolve the specificity of the effects of connectivity measures that \n435 were previously found to be associated with anhedonia and other psychiatric symptoms. \n436 Discussion\n437 Reproduction and replication of previous findings \n438 While we were able to successfully reproduce the previous authors’ findings, we \n439 were mostly unable to replicate them using a larger independent subset of the full \n440 ABCD 4.0 release sample. Interestingly, a recent study exploring the replicability of \n441 brain-behavior association studies using simulations and parametric bootstrapping \n442 methods found that relatively small sample sizes (n<500) produced results with \n443 significantly inflated effect sizes, low precision, and low replicability and it was only \n444 when the sample sizes were increased to the high hundreds or thousands were they \n445 able to produce stable effects that were significantly more replicable (22). Thus, it is \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 25 of 34\n446 possible that associations found by the original authors and in our replication analyses \n447 using relatively small samples are at risk of being driven by random sample variation. \n448 To maximize statistical power and to reduce inflated associations between \n449 rsfMRI connectivity associations with anhedonia, future analyses should be conducted \n450 using larger samples, such as in the full ABCD 4.0 release sample. Furthermore, Marek \n451 et al., 2022 found that controlling for socio-demographic covariates further reduced \n452 effect size inflation. Thus, the results from our multiple linear regression analyses using \n453 the full ABCD 4.0 release sample, where we control for socio-demographic covariates, \n454 are more likely to represent less-inflated and more replicable findings. \n455 Specificity of associations \n456 We found depressed mood, irritability, and bipolar II disorder to be significantly \n457 comorbid with anhedonia. Using a multiple linear regression approach in the full ABCD \n458 4.0 dataset, we were able to estimate the effects of anhedonia on rsfMRI connectivity \n459 measures independent of those comorbid conditions, allowing us to disentangle rsfMRI \n460 connectivity measures associated with more than one condition based on t-test results. \n461 In doing so, however, the interpretation of anhedonia requires careful consideration and \n462 reflection. \n463 As mentioned previously, while anhedonia and depressed mood are core \n464 symptoms of major depressive disorders, they are considered distinct processes (15). \n465 Alternatively, the hierarchical Taxonomy of Psychopathology (HiTOP) (23), a recently \n466 developed dimensional framework for psychopathology, has classified anhedonia as a \n467 symptom belonging to two high level sepctra of psychopathology; the internalizing and \n468 detachment spectra. Interestingly, low/depressed mood and irritability also fall under the \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 26 of 34\n469 internalizing spectrum whereas bipolar II falls under the thought disorder spectrum. \n470 Thus, when estimating the effects of anhedonia independent of other internalizing and \n471 thought disorder related symptoms, we could interpret the remaining effect to \n472 emphasize detachment processes. As detachment is a component of the psychosis \n473 super-spectrum (24) our findings may represent anhedonic neurocircuitry that may, in \n474 part, be related to schizophrenia, schizotypal personality, or other psychotic disorder \n475 processes. Further work is required to assess the extent to which differences in rsfMRI \n476 connectivity specific to anhedonia better associates with or even predicts dimensional \n477 measures of internalizing or detachment related psychopathology. \n478 We included race/ethnicity as a covariate in our multiple linear regression \n479 analyses and note they exhibited significant partial regression coefficients for many of \n480 the rsfMRI connectivity measures we analyzed. Furthermore, we found there were \n481 significantly higher proportions of Black and Hispanic participants in the anhedonia \n482 group compared to non-anhedonic controls in the full ABCD 4.0 release sample.  Race \n483 and ethnicity are social constructs representing complex social and cultural factors (25) \n484 deserving careful consideration. Several previous studies have reported significantly \n485 higher risk of anhedonia in Black and Hispanic compared to non-Hispanic White adults \n486 (26,27) and that these associations may, in part, be accounted for by socioeconomic \n487 factors, such as household income and education, as well as other social determinants \n488 of health (28), such as disparities in access to healthcare (26). In the ABCD sample, \n489 racial discrimination may be an important factor contributing to risk of anhedonia as well \n490 as differences in brain-based measures. For example, several recent studies have \n491 found that racial discrimination is associated with lower total brain volume (29) and \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 27 of 34\n492 alterations in pre-frontal white matter tracts in adults (30,31).  While out of the scope of \n493 this study, it will be very important to investigate how social determinants of health and \n494 other related factors contribute to differences in health and brain-based outcomes \n495 between different racial and ethnic groups during child and adolescent development.\n496 Limitations \n497 One major limitation of our study was the use of seed-based correlational \n498 methods to compute network connectivity measures in functionally-defined networks. As \n499 such, these network connectivity measures are averages over large and distributed \n500 networks where signals from sub-regions potentially highly associated with anhedonia \n501 may be drowned out by signals from sub-regions with low levels of association. Another \n502 concern is that the Gordon brain parcellations were produced using a boundary-\n503 mapping approach in adult brains (19) so whether they are generalizable to the brains \n504 of developing children is important to consider. For example, one study found that the \n505 functional topography of connectivity networks does change with age which was \n506 predictive of individual differences in executive function (32). One alternative method is \n507 to use a decomposition-based method, such as independent components analysis \n508 (ICA), to define functional connectivity measures (33). ICA is a data driven approach \n509 that extracts components that maximally explain the data and thus, may enhance \n510 predictive performance. One study took such an approach and found that using a \n511 decomposition-based, compared to a seed-based, extraction of functional networks \n512 during a social cognition task achieved significantly greater performance in predicting \n513 the degree of social anhedonia in around 70 adolescents/young adults with varying \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 28 of 34\n514 levels of schizotypy (34). Since the raw neuroimaging data from the ABCD data are \n515 publicly available, this may be a feasible approach to implement in a future study. \n516 Another limitation in our study is the use of a binary classifier for childhood \n517 anhedonia. Several studies have reported greater predictive performance of \n518 neuroimaging measures on anhedonia symptom scores (34,35) which suggests that \n519 functional neuroimaging measures may be more useful for predicting symptom severity \n520 rather than for disorder classification. Thus, it may be more reliable to investigate the \n521 associations between functional neuroimaging measures and clinical scales for \n522 assessing behavioral problems (such as the Child Behavior Checklist) or neurocognitive \n523 performance in individuals with anhedonia.  \n524 Finally, the DSM-V definition of anhedonia conflates two distinct reward \n525 processes: motivational (interest/wanting) and consummatory (pleasurable/liking) \n526 behaviors. These behaviors have been shown to have distinct neurobiological and \n527 behavioral components (9,36). We are limited in our study because we do not \n528 distinguish between these processes. However, the ABCD study data does include \n529 task-based functional neuroimaging of participants completing the monetary incentive \n530 delay task, which is able to assess the anticipatory, consummatory, and learning \n531 aspects of reward (37). These processes were studied previously by \n532 Pornpattananangkul et al., but exceeded the scope of this study. Nevertheless, \n533 exploration of brain connectivity specific to each of these reward-based components in \n534 subjects with anhedonia may help elucidate the underlying circuitry underlying this \n535 complex psychiatric condition. \n536 Acknowledgements\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 29 of 34\n537 Data used in the preparation of this article were obtained from the Adolescent Brain \n538 Cognitive Development SM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data \n539 Archive (NDA). This is a multisite, longitudinal study designed to recruit more than \n540 10,000 children age 9-10 and follow them over 10 years into early adulthood. The \n541 ABCD Study® is supported by the National Institutes of Health and additional federal \n542 partners under award numbers U01DA041048, U01DA050989, U01DA051016, \n543 U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, \n544 U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, \n545 U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, \n546 U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full \n547 list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of \n548 participating sites and a complete listing of the study investigators can be found at \n549 https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed \n550 and implemented the study and/or provided data but did not necessarily participate in \n551 the analysis or writing of this report. This manuscript reflects the views of the authors \n552 and may not reflect the opinions or views of the NIH or ABCD consortium investigators.\n553 The ABCD data repository grows and changes over time. The ABCD data used in this \n554 report came from http://dx.doi.org/10.15154/1523041. \n555 This manuscript reflects the views of the authors and may not reflect the opinions or \n556 views of the NIH or ABCD consortium investigators.\n557 This study received no external funding. Yi Zhou was supported by the department of \n558 Psychiatry at Virginia Commonwealth University. \n559 Disclosures\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 30 of 34\n560 The authors report no financial interests or potential conflicts of interest\n561 References \n562 1. American Psychiatric Association, American Psychiatric Association, editors. \n563 Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Washington, \n564 D.C: American Psychiatric Association; 2013. 947 p. \n565 2. Gutkovich Z, Morrissey RF, Espaillat RK, Dicker R. Anhedonia and pessimism in \n566 hospitalized depressed adolescents. Depress Res Treat. 2011;2011:795173. \n567 3. Garfield JBB, Lubman DI, Yücel M. Anhedonia in substance use disorders: a \n568 systematic review of its nature, course and clinical correlates. Aust N Z J Psychiatry. \n569 2014 Jan;48(1):36–51. \n570 4. Frewen PA, Dozois DJA, Lanius RA. 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CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\nPage 33 of 34\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint \n\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.24.22281441doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}