Altered Reward-Related Resting-State Network Properties in Adolescent Cannabis Use and Depression

Altered Reward-Related Resting-State Network Properties in Adolescent Cannabis Use and Depression | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Altered Reward-Related Resting-State Network Properties in Adolescent Cannabis Use and Depression View ORCID Profile Tram N. B. Nguyen , View ORCID Profile Russell H. Tobe , View ORCID Profile Benjamin A. Ely , View ORCID Profile Vilma Gabbay doi: https://doi.org/10.1101/2025.11.01.25339294 Tram N. B. Nguyen 1 Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine , Bronx, NY, USA 2 Medical Scientist Training Program, Albert Einstein College of Medicine , Bronx, NY, USA 3 Nathan S. Kline Institute for Psychiatric Research , Orangeburg, NY, USA 4 Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine , Miami, FL, USA M.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tram N. B. Nguyen Russell H. Tobe 3 Nathan S. Kline Institute for Psychiatric Research , Orangeburg, NY, USA 5 Center for the Developing Brain, Child Mind Institute , New York, NY, USA 6 Department of Psychiatry, New York University Grossman School of Medicine , New York, NY, USA M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Russell H. Tobe Benjamin A. Ely 4 Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine , Miami, FL, USA Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Benjamin A. Ely Vilma Gabbay 3 Nathan S. Kline Institute for Psychiatric Research , Orangeburg, NY, USA 4 Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine , Miami, FL, USA M.D., M.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vilma Gabbay For correspondence: vxg595{at}med.miami.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Objective Cannabis use among adolescents with depression is prevalent. Reward dysfunction has been documented in each condition separately, yet rarely examined in their co-occurrence. Here, we investigated cannabis use and depression comorbidity in relation to resting-state properties of reward networks in a trans-diagnostic adolescent sample. Method Clinicians interviewed adolescents and assessed depression with the Children’s Depression Rating Scale. Cannabis use was characterized by self-report and toxicology screens. Neuroimaging scans were acquired and processed using the Human Connectome Project pipelines. Participant-level resting state data were parcellated, and Reward Expectancy and Reward Attainment network masks derived from a reward task were applied. Graph theoretical metrics, including Strength Centrality (C Str ), Eigenvector Centrality (C Eig ), and Local Efficiency (E Loc ), were estimated within each network. Group-level clinical correlates of network properties were assessed with non-parametric analyses (10,000 permutations), adjusted for age, sex, and family-wise error (FWE) rates (p FWE < 0.05). Results In the full sample (N = 131; 15.3 ± 2.2 years; 65.2% female), depression severity was associated with stronger E Loc of the ventral striatum within the Reward Attainment network. Among those who used cannabis (n = 38), heavier cannabis use was linked to weaker C Str of the anterior cingulate cortex and E Loc of the postcentral somatosensory area within the Reward Expectancy network. Exploratory analyses further revealed cannabis-related dorsolateral prefrontal and cerebellar dysconnectivity across the whole brain, as well as sex differences. Conclusions Our findings suggest that adolescent cannabis use and depression differentially influence resting-state reward network properties in co-occurring context. Additional research with larger cohort sizes is needed to corroborate findings. 1. Introduction Adolescent cannabis use is prevalent, with 34% of 12 th graders in the United States having tried cannabis and 13% reporting daily use for at least a month 1 . Alarmingly, adolescents with depression are more than twice as likely to use cannabis as their peers without depression 2 and face heightened risk for disordered use 3 , 4 . During adolescence, the endocannabinoid system plays a critical role in the maturation of neural circuits 5 , and disruptions of endocannabinoid modulation can potentiate depressive and substance use disorders 6 , 7 . Animal studies show that Δ 9 -tetrahydrocannabinol (THC), the primary psychoactive agent in cannabis, disrupts the adolescent reward system and induces depressive-like behaviors 5 , 8 – 10 . Consistent with these preclinical findings, youth who use cannabis exhibit motivational deficits 11 , including higher levels of anticipatory anhedonia 12 , reflecting diminished capacity to expect pleasurable rewards (i.e. reward expectancy). Neuroimaging studies in youth who use cannabis have also implicated alterations in reward circuitry, 13 – 15 a system central to the development and maintenance of depression 16 . Together, these data suggest that reward dysfunction may represent a shared mechanism underlying the co-occurrence of adolescent cannabis use and depression. However, direct neuroimaging investigation of their co-influence on the developing reward system is limited, as studies on cannabis use rarely account for depression, while those on depression typically exclude adolescents who use cannabis. Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful modality for studying functional brain networks without task engagement. In rs-fMRI, the most common analytic approach is seed-based functional connectivity. In the few existing studies on adolescent cannabis use, this method has revealed connectivity alterations in frontal, parietal/sensorimotor, and cingulate cortices 17 . Concurrently, impaired functional connectivity centered on cingulate, limbic, and striatal regions has been reported in adolescent depression 16 , 18 , 19 . While seed-based analyses have identified large-scale network dysconnectivity in each condition, they have provided only modest insight into the integrity of networks fundamental to reward processes. Addressing this methodological limitation, our group has implemented the fMRI Reward Flanker Task (RFT) to study neural processing during distinct phases of Reward Expectancy and Reward Attainment 20 , 21 . We then extracted network masks from the most strongly recruited regions in each reward phase and applied these reward network masks to resting-state data, employing graph theory to identify nodes where altered network topography was associated with depression and anxiety symptomatology 22 . Particularly, we detected significant associations between depression severity and altered striatal communication within the Reward Attainment network 22 . Graph theoretical approaches provide a framework to comprehensively characterize network organization and identify key information processing hubs 23 . Importantly, as graph theoretical approaches are data-driven, they offer a robust description of connectivity during sensitive developmental periods 24 and can reveal subtle anomalies not captured by seed-based methods. Capitalizing on this innovative approach, here we aimed to investigate resting-state properties of Reward Expectancy and Reward Attainment networks in relation to cannabis use and depression in adolescents. Based on prior findings, we hypothesized that: 1) Cannabis use would be associated with altered properties of the Reward Expectancy network; and 2) Depression severity would be associated with altered properties of the Reward Attainment network. Given well-established sex effects in cannabis use 15 and depression 25 , we also conducted exploratory sex-stratified analyses. Exploratory whole-brain analyses were also performed to assess neural organizations beyond a priori reward networks. 2. Methods 2.1. Participant Recruitment and Initial Assessments In our research program focusing on depression and related conditions, we recruited youth from the New York metropolitan region through clinician referrals and community-based advertisements. Study procedures were approved by the Institutional Review Boards (IRB) of the University of Miami Miller School of Medicine, Nathan Kline Institute for Psychiatric Research, Albert Einstein College of Medicine, and Icahn School of Medicine at Mount Sinai. General eligibility was assessed in initial phone interviews. For adolescents younger than 18 years, assent was obtained, and a parent/guardian signed informed consent. Adolescents 18 years and older provided their own informed consent. During the clinical assessment visit, a trained child and adolescent psychiatrist or a licensed clinical psychologist conducted semi-structured diagnostic interviews to determine psychiatric symptomatology and DSM-5 26 diagnoses for each participant. Participants younger than 18 years were evaluated with the Kiddie Schedule for Affective Disorders and Schizophrenia – Present and Lifetime Version for Children (K-SADS-PL) 27 , while those aged 18 years and older underwent the Mini International Neuropsychiatric Interview (MINI) 28 . On the scan day, fasting blood draws were performed to assess complete blood count, thyroid function, and liver function. A urine toxicology test was also administered to screen for amphetamines, barbiturates, benzodiazepines, cocaine, 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, opiates, oxycodone, propoxyphene, and THC. Female participants completed urine pregnancy tests. 2.2. Inclusion and Exclusion Criteria Eligible participants were 12-21 years old, of any sex, race, or ethnicity. Following a dimensional approach, participants with any level of cannabis use or depression were included, regardless of whether they met DSM-5 criteria for cannabis use disorder (CUD) or depressive disorders. All participants were required to be Tanner stage ≥4, and female participants were post-menarche. Participants were excluded if they had: a) a current or past diagnosis of pervasive development disorders, schizophrenia or other psychotic disorders, or substance use disorders other than CUD; b) current use of opioid, sedative, stimulant, and psychedelic substances as indicated on self-reports or urine toxicology tests; c) a full-scale IQ estimate below 80, as determined by the Kaufman Brief Intelligence Test (K-BIT); 29 d) any acute and/or chronic medical illness, including neurological disorders, as identified on medical history and blood test results; f) any MRI contraindications such as metallic implants, braces, and claustrophobia; and g) a positive pregnancy test. 2.3. Cannabis Use Quantification and Dimensional Symptom Measures Our assessment and quantification of cannabis use were detailed elsewhere 12 and in our Supplementary Methods . Briefly, clinician consensus was reached using combined information from interviews, self-reports, and urine tests to rate each participant based on use frequency and impact on daily function. For our analyses, we coded cannabis use on a 5-level scale: 0 = never used, 1 = tried once, 2 = low use, 3 = moderate use, and 4 = heavy use. Adolescents who did not use cannabis were defined as those who had never used or only tried it once. Depression severity was measured dimensionally with the clinician-rated Children’s Depression Rating Scale-Revised (CDRS-R) 30 , a validated 17-item instrument with established reliability and validity in adolescents 31 . Items are scored on 1–5 or 1–7 scales, yielding a total score range of 17–113, and specific examples of severity are provided for each item. Higher CDRS-R scores reflect more severe depression. We also captured related mood and anxiety symptoms using self-reported scales, including the Beck Scale for Suicide Ideation (BSSI) 32 , the Multidimensional Anxiety Scale for Children (MASC) 33 , and the Temporal Experience of Pleasure Scale (TEPS) 34 . The MASC (39 items; range 0–117) indexes anxiety severity, with higher scores indicating greater anxiety. The BSSI (19 items; range 0–38) evaluates suicidal ideation, with 14 follow-up items administered when any of the 5 screening item scores >0. The TEPS measures anticipatory (10 items; range 10–60) and consummatory (8 items; range 8–48) pleasure, with lower scores reflecting higher anhedonia. Anxiety, suicidality, and anhedonia symptom measures served to describe clinical profiles of the cohort and were not included in our primary fMRI analyses. 2.4. Neuroimaging Acquisition Participants completed an MRI clearance procedure to assess for metallic implants or prior exposure to metal. Neuroimaging scans of most participants in this study were performed on a 3T Siemens Skyra scanner (Siemens, Germany) equipped with a 16-channel head coil. A subset of 15 participants was scanned on the same scanner with a 32-channel head receive coil using identical sequences. In all fMRI analyses, coil type was included as a covariate of no interest (binarized as 16- vs. 32-channel). Imaging protocols aligned with those in the Human Connectome Project (HCP) Lifespan study 35 . Anatomical imaging included a T1-weighted MPRAGE sequence (TR = 2400 ms, TE = 2.06 ms, TI = 1000 ms, flip angle = 8°) with 224 sagittal slices, no interslice gap, a matrix size of 256 × 256, a FOV of 230 × 230 mm 2 , and an isotropic voxel size of 0.9 mm. In addition, a T2-weighted SPACE sequence was acquired (TR = 3200 ms, TE = 565 ms, flip angle = 120°), maintaining the same slice count, matrix, FOV, and voxel resolution as the T1-weighted scan. Functional imaging was acquired using gradient-recalled echo-planar imaging (EPI). A single resting-state scan (TR = 1000 ms, effective TE = 31.4 ms, flip angle = 60°) lasted 10 minutes and included 600 volumes of 60 slices aligned parallel to the AC-PC plane, with no interslice gaps, a multiband acceleration factor of 5, anterior-to-posterior phase encoding, a matrix size of 98 × 98, an FOV of 228 × 228 mm 2 , and an isotropic voxel size of 2.3 mm. Matched single-band EPI and spin-echo field maps were also acquired for registration and distortion correction. During the resting-state scan, participants were presented with a fixation cross and instructed to keep their eyes open and stay awake. Several task-based fMRI runs unrelated to this study were collected later in the neuroimaging session. 2.5. Processing of MRI Data We visually inspected all data prior to utilizing the HCP Preprocessing Pipelines v3.2 36 . Anatomical preprocessing involved correction for gradient nonlinearity and b 0 field distortions, followed by alignment to the AC-PC plane, co-registration, skull stripping, and bias field adjustment. Data were then transformed to Montreal Neurological Institute 152 non-linear 6th-generation (MNI152NLin6) space 37 , and cortical segmentation was performed using FreeSurfer. To facilitate surface-based analyses, cortical ribbon extraction was conducted. Functional preprocessing comprised gradient nonlinearity and EPI distortion correction, realignment for motion correction, transformation to MNI space, and intensity normalization. FreeSurfer’s default alignment was used for the initial mapping of functional data onto the cortical ribbon. We then generated participant-level dense timeseries in 32k-CIFTI grayordinate space, merging left and right cortical surfaces with subcortical volumes in MNI space for anatomically accurate representation 36 . Given the multimodal fMRI design, we applied the multi-run spatial ICA-FIX approach 38 – 40 to concatenated resting-state and task runs to identify and remove structured fMRI noise. We conducted mild high-pass filtering (default 2000s cutoff) on concatenated fMRI data and ran ICA using FSL MELODIC on minimally preprocessed whole-brain timeseries in MNI space. Independent components (ICs) were classified as “signal,” “noise,” or “unknown” using the FIX classifier, trained on the default HCP_hp2000.Rdata dataset, which achieves over 97% classification accuracy in locally acquired fMRI datasets. All “signal” and “unknown” ICs were reviewed by experienced neuroimagers in our group and manually reclassified as needed. Subsequently, “noise” ICs were removed from both MNI- and grayordinate-space data using a “soft” regression approach, ensuring that only unique variance associated with noise was eliminated. Following ICA-FIX denoising, cortical surface data in grayordinate space were aligned using the multimodal surface matching (MSMAll) approach 41 . This multimodal registration method, developed by the HCP 42 , enhances inter-participant correspondence by integrating shared structural and functional features. We applied MSMAll to all resting-state and task runs included in the multi-run ICA-FIX pipeline. Resting-state data were further denoised by regressing out 24 movement parameters (6 affine + temporal derivatives + squares of each) and the five largest principle components associated with white matter and cerebrospinal fluid (CompCor) 43 , identified using CONN Toolbox (RRID:SCR_009550) v17f 44 . Finally, bandpass temporal filtering (0.1 – 0.01Hz) was applied. 2.6. fMRI Analyses Participant-level processed resting-state data were parcellated using a variation of the Cole-Anticevic Brain Network Parcellation (CAB-NP), 45 which extends the cortical Glasser parcellation 46 to cover subcortical structures, slightly modified to include somatotopic subdivisions of the somatomotor strip. The resulting 750-node whole-brain data were further restricted to Reward Expectancy (114 nodes) and Reward Attainment (103 nodes) networks. These network masks were constructed as part of a prior study from nodes with 90 th percentile activation strength and their contralateral nodes across the two corresponding contrasts derived from the RFT 20 , 22 , 47 (see details in Supplementary Methods ). Consistent with prior work 22 , we used the Brain Connectivity Toolbox v2019–03–03 23 in MATLAB R2023b (MathWorks, Natick, MA) to estimate three participant-level weighted graph theoretical metrics within each reward network: Strength Centrality (C Str ), Eigenvector Centrality (C Eig ), and Local Efficiency (E Loc ). Specifically, C Str is the sum of all edge weights at a node, reflecting its overall connectivity. Nodes with high C Str maintain strong links with other nodes, indicating robust direct connectivity. C Eig is the eigenvector with the largest eigenvalue for each node and accounts for both a node’s connections and the importance of its neighbors. High C Eig values reflect connections to other highly connected, influential nodes. E Loc describes how efficiently information is exchanged among a node’s immediate neighbors when the node itself is removed. A high E Loc value suggests resilience of local processing despite node disruption. Together, these data-driven metrics captured various influential and communicative properties across nodes within each network 23 . At the group level, separate generalized linear models (GLM) were performed with each graph theoretical metric (i.e. C Str , C Eig , E Loc ) of each network (i.e. Reward Expectancy and Reward Attainment ) serving as the dependent variable. Non-parametric analyses were conducted in FSL PALM 48 version alpha119 with 10,000 permutations. In all models, independent variables were mean-centered, and correction for family-wise error (FWE) rates across multiple contrasts was applied. Results were considered significant at two-tailed p FWE < 0.05. To facilitate parallelized group-level analyses, we utilized the PALM-from-Excel MATLAB function 49 . All effect sizes were estimated based on t -statistics and their associated degrees of freedom (see Supplementary Methods ). To test our a priori hypotheses, we conducted group GLM analyses with cannabis use and depression severity (CDRS-R scores) both included as the responsible variables of interest. This allowed for the assessment of each effect while controlling for the other. In the full sample, the cannabis use variable was binarized to assess for group differences between adolescents who used cannabis and those who did not. Within the group of adolescents who use cannabis, the cannabis use variable included three levels (i.e. low use, moderate use, and heavy use) to examine cannabis use severity. In all models, age, sex, and coil type were included as covariates of no interest. Further, to explore sex differences, we repeated group-level analyses in female and male participants separately, with models adjusted for age and coil type. To explore the relationship between adolescent cannabis use, depression, and resting-state properties across the whole brain, we repeated the above analyses without participant-level reward network masking. This approach fundamentally changed the computation of C Str , C Eig , and E Loc metrics within the participant-level timeseries cross-correlation matrix (i.e. 750 × 750 for Whole Brain vs. 114 × 114 for Reward Expectancy and 103 × 103 for Reward Attainment ), thereby capturing properties of brain organization beyond reward-related systems. 2.7. Other Statistics In MATLAB R2023b, demographic and clinical profiles of participants were assessed for normality (continuous variables) and expected frequency (categorical variables). Appropriate group comparison and correlation analyses were sequentially conducted, with the significance threshold set at α = 0.05. 3. Results 3.1. Demographic and Clinical Profiles The 131 participants (age: 15.3 ± 2.2 years, 65.7% female) included in this study clinically presented across a spectrum of psychiatric illness severity, including those who met DSM criteria for mood, anxiety, and other comorbid psychiatric disorders, those who had subthreshold depression, and those who had no current or past psychiatric diagnoses. Among the 38 participants who used cannabis, 20 reported heavy use (5 met DSM-5 criteria for CUD), 3 reported moderate use, and 15 reported low use. Table 1 details the demographic and clinical information of study participants. View this table: View inline View popup Table 1. Demographic and clinical characteristics of study participants. 3.2. Primary Findings on Cannabis Use and Reward Network Properties There were no significant group differences in the properties of either reward network between adolescents with vs. without cannabis use. However, in adolescents who used cannabis, heavier cannabis use was correlated with weaker C Str of the right dorsal anterior cingulate cortex (ACC; Figure 1A ) and weaker E Loc of the left postcentral gyrus (somatosensory cortex; Figure 1B ) within the Reward Expectancy network. These findings were adjusted for depression severity, age, sex, and coil type. Download figure Open in new tab Figure 1. Significant associations between resting-state properties of reward networks and cannabis use and depression severity. 3.3. Primary Findings on Depression Severity and Reward Network Properties In the full sample, depression severity was positively correlated with Reward Attainment E Loc of the right ventral striatum ( Figure 1C ), controlling for cannabis use, age, sex, and coil type. When the analysis was restricted to the 38 adolescents who used cannabis, there were no significant associations between depression severity and reward network properties. Table 2 lists primary findings with cannabis use and depression. View this table: View inline View popup Download powerpoint Table 2. Significant alterations in resting-state properties of reward networks in association with adolescent cannabis use and depression. 3.4. Exploratory Sex-Stratified Results Cannabis use was reported by 31.4% of females ( n = 27/86) and 24.4% of males ( n = 11/45), with no significant difference between sexes ( p = 0.405). Female participants endorsed significantly more severe depression levels than male participants (CDRS-R of females: 36.4 ± 15.9 vs. males: 31.2 ± 15.5; p = 0.038). Male-specific results were significant only in the Reward Expectancy analyses. Relative to male youth who did not use cannabis, those who used cannabis showed weaker C Str and C Eig of two nodes in the right thalamus. Among male adolescents who used cannabis, heavier use was associated with weaker C Str of the left thalamus. These findings were controlled for depression severity, age, and coil type. In female adolescents, only results within the Reward Attainment network were significant. Across all female adolescents, depression severity was positively correlated with C Eig of the right ventral diencephalon, after adjusting for cannabis use, age, and coil type. In female adolescents who used cannabis, heavier cannabis use was associated with greater C Eig of the left posterior cingulate cortex (PCC), adjusting for depression severity, age, and coil type. Supplementary Figure S1 and Table S1 detail sex-specific findings. 3.5. Exploratory Results from Whole Brain Analyses Compared to adolescents without cannabis use, those with cannabis use exhibited stronger Whole Brain C Eig of the left lateral orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (dlPFC), and posterior inferior frontal junction. Within the group of adolescents who used cannabis, heavier cannabis use correlated with weaker Whole Brain C Str and E Loc of the same node within lobule X of the left cerebellum. There was no significant association between depression and Whole Brain resting-state properties. Exploratory whole-brain findings are detailed in Supplementary Figure S2 and Table S2 . 4. Discussion Bridging across resting-state and task fMRI paradigms, we examined how adolescent cannabis use and depression relate to the intrinsic architecture of reward networks. As hypothesized, cannabis use frequency corresponded to altered properties within the Reward Expectancy network, while depression severity was linked to altered properties of the Reward Attainment network. Exploratory analyses further revealed: a) male11specific alterations in Reward Expectancy and female-specific alterations in Reward Attainment network properties; b) relationships between cannabis use and Whole Brain resting-state properties. Together, these findings implicate aberrant resting-state organization of reward systems in the co-occurrence of adolescent cannabis use and depression. 4.1. Altered Resting-State Properties of Reward Networks in Adolescent Cannabis Use We observed that lower ACC strength centrality and reduced local efficiency in the somatosensory cortex within the Reward Expectancy network scaled with more frequent cannabis use. This is in line with prior longitudinal research showing baseline ACC-OFC hypoconnectivity predicted greater cannabis use over 18 months in adolescents, and that youth with CUD exhibited progressive ACC-prefrontal dysconnectivity relative to controls 50 , 51 . Although prior resting-state studies of adolescent cannabis use have not implicated somatosensory regions, our finding aligns with recent task-based evidence linking a sensorimotor-prominent cannabis risk network in adolescents to greater addiction severity and poorer treatment outcomes in adults with CUD 52 . While these findings and ours converge on disengagement of the ACC and alteration of somatosensory connectivity in youth with riskier cannabis use behaviors, our focus on the Reward Expectancy network adds specificity by localizing these impairments to a reward sub-system. Notably, our Reward Expectancy findings parallel our recent observation that adolescent cannabis use is associated with anticipatory anhedonia 12 and suggest a potential mechanism for such motivational deficits. In our sex-stratified analyses, results from the male-only sample echoed the primary findings, with cannabis-related alterations restricted to the Reward Expectancy network. Interestingly, however, female-only analyses revealed a unique positive correlation between cannabis use frequency and PCC eigenvector centrality within the Reward Attainment network. This finding suggests greater influence of the PCC, a key default mode network (DMN) node, on the circuitry underlying the experience of reward in female youth with heavier cannabis use. In contrast to this finding, one previous study found diminished DMN cohesion among female young adults who used cannabis more frequently 53 , while a separate adolescent study reported no significant sex × cannabis use interaction on PCC-based connectivity 54 . These discrepancies may reflect a female-specific developmental trajectory in which cannabinoid effects on PCC connectivity within circuits subserving consummatory reward processing emerge before generalizing to broader DMN dysfunction with continued cannabis use. Given the paucity of research on sex differences in resting-state connectivity and cannabis use, these findings highlight the need to examine how cannabinoids may differentially shape the maturation of neural reward circuitry across sexes. Our exploratory whole-brain analyses revealed greater global influence of the dlPFC and lateral OFC in adolescents with vs. without cannabis use, along with an association between use frequency and less efficient cerebellar lobule X communication. These results align well with two recent studies documenting greater frontal cortex-based connectivity 55 and diminished coupling between cerebellar lobule X and the superior frontal gyrus 56 in youth who used cannabis relative to those who did not. Collectively, these data point to a specific pattern of cannabis-related cerebellar suppression and frontal compensation, reflecting the heightened vulnerability of these CB 1 receptor-rich regions during adolescence 57 , 58 . 4.2. Altered Resting-State Properties of Reward Networks in Adolescent Depression Distinct from cannabis use, depression severity was positively correlated with local efficiency of the ventral striatum within the Reward Attainment network, reflecting greater striatal network integration in adolescents with worse depression. Adolescent depression has been consistently linked to cortico-striatal dysfunction across a wide range of neuroimaging modalities, including blunted task-evoked striatal responses to rewards 18 and striatal hyperconnectivity at rest 16 , 22 , 59 . Moreover, in a study examining an 11-node reward network in a large youth sample using resting-state fMRI, only elevated baseline striatal strength centrality predicted increased risk of depressive disorder at 3-year follow-up 60 . Consistent with this literature, we found heightened communication efficiency of the striatum with other reward processing regions at rest, potentially as a compensatory reorganization to offset diminished striatal activation during reward attainment. Our sex-stratified analyses additionally linked depression severity with ventral diencephalon influence within the Reward Attainment network in female adolescents. Though exploratory, this finding is consistent with reports of greater vulnerability to depression-related network alterations in female adolescents 61 , 62 and further maps this association onto the consummatory reward network. By using task fMRI to functionally localize reward-related networks that can then be examined during rest, our approach underscores the power of cross-modality integration to study reward circuitry with greater specificity in developmental psychopathology. 4.3. Clinical Relevance Our results are highly relevant to clinical settings. Significant disruptions of resting-state networks in co-morbid adolescent cannabis use and depression highlight the importance of screening for both conditions in routine care. These findings also support the utility of existing interventions. In individuals who use cannabis, motivational interviewing and cognitive behavioral therapy (CBT) are particularly effective in identifying motivational deficits and developing personalized, pragmatic strategies to reduce maladaptive coping mechanisms 63 , 64 . Behavioral activation therapy, a subtype of CBT that increases engagement in rewarding, values-consistent activities, can efficiently enhance enjoyable experiences in adolescents with depression 65 . Our various findings correlating cannabis use frequency and network alterations also support interventions grounded in harm-reduction principles, which prioritize reducing use frequency and promote safer-use education when abstinence is not feasible or desired 66 . Further, our findings dovetail with evidence identifying the ACC as a promising neuromodulation target for addiction in adults 67 . Extending this line of research to youth is critical, as studies on noninvasive brain stimulation for adolescent substance use and comorbid psychopathology remain scarce 68 . Ultimately, mitigating shared reward dysfunction in adolescents with co-occurring cannabis use and depression will require evidence-based treatments to simultaneously target both conditions. 4.4. Limitations and Future Directions There were several limitations in our study. First, the cross-sectional design of this study precludes inferences about causal relationships between detected neural alterations and cannabis use or depression. Future longitudinal studies acquiring data at multiple time points are needed to delineate trajectories of neural and clinical change. Second, the unequal cohort sizes between adolescents with and without cannabis use limited statistical power to detect group differences, with post hoc sensitivity analyses indicated 80% power to detect effects of d ≥ 0.54 (moderate). For correlation analyses, we achieved 80% power to detect effects of | r | ≥ 0.24 (small-to-moderate) in the full sample and | r | ≥ 0.42 (moderate-to-large) in adolescents with cannabis use. The proportion of adolescents who used cannabis in this sample (∼29%) was consistent with current epidemiology data 69 , enhancing the generalizability of our findings. While modest, our sample of participants who used cannabis ( n = 38) was also larger than most resting-state fMRI studies on adolescent cannabis use to date (i.e. typically <30 adolescents who used cannabis) 70 . Nevertheless, future studies should recruit larger cohorts of adolescents who use cannabis, especially those with heavy and disordered use patterns. Third, cannabis use information was primarily self-reported and thus may not be as accurate as quantitative methods based on biological samples, which we plan to incorporate in future work. Quantitative metrics could also facilitate improved dimensionality in correlative analyses. Fourth, our analyses did not adjust for psychosocial confounders such as socio-economic status, family history, and trauma history. Incorporating relevant substance co-use and psychosocial data into future analyses will also clarify factors contributing to brain maturation deviations in this high-risk population. 4.5. Conclusions Our study integrated hypothesis- and data-driven approaches to investigate properties of Reward Expectancy and Reward Attainment networks during rest in adolescents with co-occurring depression and cannabis use. We identified distinct patterns of reward network disruption associated with each condition and uncovered changes linked to cannabis use that were not previously reported, including sex differences. Taken together, these findings offer new insights into how cannabis use and depression both relate to intrinsic circuitry driving reward function in adolescence, a critical developmental window. Our results challenge the perceived low risks of cannabis use, and we advocate for education efforts and cessation/reduction support for adolescents, especially amid rapidly shifting cannabis policies and increasing product potency. Future research with larger cohorts of adolescents who use cannabis is needed to corroborate and extend these findings. Data Availability All data produced in the present study are available upon reasonable request to the corresponding author. Footnotes ↵ * Benjamin A. Ely and Vilma Gabbay are joint senior authors. Updated authorship: Benjamin A. Ely and Vilma Gabbay are joint senior authors. References 1. ↵ Miech RA , Johnston LD , Patrick ME , O’Malley PM. Monitoring the Future national survey results on drug use, 1975–2024: Overview and detailed results for secondary school students . Monitoring the Future Monograph Series. Institute for Social Research, University of Michigan ; 2025 . 2. ↵ Weinberger AH , Zhu J , Lee J , Anastasiou E , Copeland J , Goodwin RD. Cannabis use among youth in the United States, 2004-2016: Faster rate of increase among youth with depression . Drug Alcohol Depend . Apr 1 2020 ; 209 : 107894 . doi: 10.1016/j.drugalcdep.2020.107894 OpenUrl CrossRef PubMed 3. ↵ Marel C , Sunderland M , Mills KL , Slade T , Teesson M , Chapman C . Conditional probabilities of substance use disorders and associated risk factors: Progression from first use to use disorder on alcohol, cannabis, stimulants, sedatives and opioids . Drug Alcohol Depend . Jan 1 2019 ; 194 : 136 – 142 . doi: 10.1016/j.drugalcdep.2018.10.010 OpenUrl CrossRef 4. ↵ Groenman AP , Janssen TWP , Oosterlaan J . Childhood Psychiatric Disorders as Risk Factor for Subsequent Substance Abuse: A Meta-Analysis . J Am Acad Child Adolesc Psychiatry . Jul 2017 ; 56 ( 7 ): 556 – 569 . doi: 10.1016/j.jaac.2017.05.004 OpenUrl CrossRef PubMed 5. ↵ Parsons LH , Hurd YL . Endocannabinoid signalling in reward and addiction . Nat Rev Neurosci . Oct 2015 ; 16 ( 10 ): 579 – 94 . doi: 10.1038/nrn4004 OpenUrl CrossRef PubMed 6. ↵ Patel S , Hillard CJ . Role of endocannabinoid signaling in anxiety and depression . Curr Top Behav Neurosci . 2009 ; 1 : 347 – 71 . doi: 10.1007/978-3-540-88955-7_14 OpenUrl CrossRef PubMed 7. ↵ Lu HC , Mackie K . An Introduction to the Endogenous Cannabinoid System . Biol Psychiatry . Apr 1 2016 ; 79 ( 7 ): 516 – 25 . doi: 10.1016/j.biopsych.2015.07.028 OpenUrl CrossRef PubMed 8. ↵ Miller ML , Chadwick B , Dickstein DL , et al. Adolescent exposure to Delta(9)-tetrahydrocannabinol alters the transcriptional trajectory and dendritic architecture of prefrontal pyramidal neurons . Mol Psychiatry . Apr 2019 ; 24 ( 4 ): 588 – 600 . doi: 10.1038/s41380-018-0243-x OpenUrl CrossRef PubMed 9. ↵ Wenzel JM , Cheer JF . Endocannabinoid Regulation of Reward and Reinforcement through Interaction with Dopamine and Endogenous Opioid Signaling . Neuropsychopharmacology . Jan 2018 ; 43 ( 1 ): 103 – 115 . doi: 10.1038/npp.2017.126 OpenUrl CrossRef PubMed 10. ↵ Hurd YL , Manzoni OJ , Pletnikov MV , Lee FS , Bhattacharyya S , Melis M . Cannabis and the Developing Brain: Insights into Its Long-Lasting Effects . J Neurosci . Oct 16 2019 ; 39 ( 42 ): 8250 – 8258 . doi: 10.1523/JNEUROSCI.1165-19.2019 OpenUrl Abstract / FREE Full Text 11. ↵ Hinckley JD , Conner BT , Mauch R , et al. A Systematic Review: Investigating Biomarkers of Anhedonia and Amotivation in Depression and Cannabis Use . JAACAP Open . Sep 2025 ; 3 ( 3 ): 379 – 405 . doi: 10.1016/j.jaacop.2024.08.005 OpenUrl CrossRef PubMed 12. ↵ Nguyen TNB , Ely BA , Vitale A , et al. Cannabis Use is Related to Anhedonia in Adolescents With Diverse Mood and Anxiety Symptoms . JAACAP Open . 2025 ; doi: 10.1016/j.jaacop.2025.02.003 OpenUrl CrossRef 13. ↵ Boer OD , El Marroun H , Muetzel RL . Adolescent substance use initiation and long-term neurobiological outcomes: insights, challenges and opportunities . Mol Psychiatry . Feb 26 2024 ; doi: 10.1038/s41380-024-02471-2 OpenUrl CrossRef 14. Beyer E , Poudel G , Antonopoulos S , Thomson H , Lorenzetti V . Brain reward function in people who use cannabis: a systematic review . Front Behav Neurosci . 2023 ; 17 : 1323609 . doi: 10.3389/fnbeh.2023.1323609 OpenUrl CrossRef 15. ↵ Hammond CJ , Allick A , Park G , et al. A Meta-Analysis of fMRI Studies of Youth Cannabis Use: Alterations in Executive Control, Social Cognition/Emotion Processing, and Reward Processing in Cannabis Using Youth . Brain Sci . Sep 23 2022 ; 12 ( 10 ) doi: 10.3390/brainsci12101281 OpenUrl CrossRef 16. ↵ Ely BA , Nguyen TNB , Tobe RH , Walker AM , Gabbay V . Multimodal Investigations of Reward Circuitry and Anhedonia in Adolescent Depression . Front Psychiatry . 2021 ; 12 : 678709 . doi: 10.3389/fpsyt.2021.678709 OpenUrl CrossRef 17. ↵ Lichenstein SD , Manco N , Cope LM , et al. Systematic review of structural and functional neuroimaging studies of cannabis use in adolescence and emerging adulthood: evidence from 90 studies and 9441 participants . Neuropsychopharmacology . Apr 2022 ; 47 ( 5 ): 1000 – 1028 . doi: 10.1038/s41386-021-01226-9 OpenUrl CrossRef PubMed 18. ↵ Keren H , O’Callaghan G , Vidal-Ribas P , et al. Reward Processing in Depression: A Conceptual and Meta-Analytic Review Across fMRI and EEG Studies . Am J Psychiatry . Nov 1 2018 ; 175 ( 11 ): 1111 – 1120 . doi: 10.1176/appi.ajp.2018.17101124 OpenUrl CrossRef 19. ↵ Tse NY , Ratheesh A , Ganesan S , Zalesky A , Cash RFH . Functional dysconnectivity in youth depression: Systematic review, meta-analysis, and network-based integration . Neurosci Biobehav Rev . Oct 2023 ; 153 : 105394 . doi: 10.1016/j.neubiorev.2023.105394 OpenUrl CrossRef 20. ↵ Bradley KAL , Case JAC , Freed RD , Stern ER , Gabbay V . Neural correlates of RDoC reward constructs in adolescents with diverse psychiatric symptoms: A Reward Flanker Task pilot study . J Affect Disord . Jul 2017 ; 216 : 36 – 45 . doi: 10.1016/j.jad.2016.11.042 OpenUrl CrossRef PubMed 21. ↵ Liu Q , Ely BA , Stern ER , et al. Neural function underlying reward expectancy and attainment in adolescents with diverse psychiatric symptoms . NeuroImage: Clinical . 2022 : 103258 . 22. ↵ Ely BA , Liu Q , DeWitt SJ , Mehra LM , Alonso CM , Gabbay V . Data-driven parcellation and graph theory analyses to study adolescent mood and anxiety symptoms . Transl Psychiatry . May 3 2021 ; 11 ( 1 ): 266 . doi: 10.1038/s41398-021-01321-x OpenUrl CrossRef PubMed 23. ↵ Rubinov M , Sporns O . Complex network measures of brain connectivity: uses and interpretations . Neuroimage . Sep 2010 ; 52 ( 3 ): 1059 – 69 . doi: 10.1016/j.neuroimage.2009.10.003 OpenUrl CrossRef PubMed Web of Science 24. ↵ Fornito A , Zalesky A , Breakspear M . Graph analysis of the human connectome: promise, progress, and pitfalls . Neuroimage . Oct 15 2013 ; 80 : 426 – 44 . doi: 10.1016/j.neuroimage.2013.04.087 OpenUrl CrossRef PubMed Web of Science 25. ↵ Mohammadi S , Seyedmirzaei H , Salehi MA , et al. Brain-based Sex Differences in Depression: A Systematic Review of Neuroimaging Studies . Brain Imaging Behav . Oct 2023 ; 17 ( 5 ): 541 – 569 . doi: 10.1007/s11682-023-00772-8 OpenUrl CrossRef PubMed 26. ↵ Diagnostic and statistical manual of mental disorders : DSM-5™ . 5th edition . ed. DSM-5. American Psychiatric Publishing, a division of American Psychiatric Association ; 2013 . 27. ↵ Kaufman J , Birmaher B , Brent D , et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data . J Am Acad Child Adolesc Psychiatry . Jul 1997 ; 36 ( 7 ): 980 – 8 . doi: 10.1097/00004583-199707000-00021 OpenUrl CrossRef PubMed Web of Science 28. ↵ Sheehan DV , Lecrubier Y , Sheehan KH , et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10 . J Clin Psychiatry . 1998 ; 59 Suppl 20:22-33;quiz 34 – 57 . OpenUrl PubMed 29. ↵ Smith DK . Kaufman Brief Intelligence Test (K-BIT) . Diagnostique . 1999 ; 24 ( 1-4 ): 125 – 134 . doi: 10.1177/153450849902401-412 OpenUrl CrossRef 30. ↵ Poznanski EO , Mokros HB . Children’s Depression Rating Scale–Revised (CDRS-R) . WPS; 1996 . 31. ↵ Mayes TL , Bernstein IH , Haley CL , Kennard BD , Emslie GJ . Psychometric properties of the Children’s Depression Rating Scale-Revised in adolescents . J Child Adolesc Psychopharmacol . Dec 2010 ; 20 ( 6 ): 513 – 6 . doi: 10.1089/cap.2010.0063 OpenUrl CrossRef PubMed 32. ↵ Beck AT , Kovacs M , Weissman A . Assessment of suicidal intention: the Scale for Suicide Ideation . J Consult Clin Psychol . Apr 1979 ; 47 ( 2 ): 343 – 52 . doi: 10.1037//0022-006x.47.2.343 OpenUrl CrossRef PubMed Web of Science 33. ↵ March JS , Parker JD , Sullivan K , Stallings P , Conners CK . The Multidimensional Anxiety Scale for Children (MASC): factor structure, reliability, and validity . J Am Acad Child Adolesc Psychiatry . Apr 1997 ; 36 ( 4 ): 554 – 65 . doi: 10.1097/00004583-199704000-00019 OpenUrl CrossRef PubMed Web of Science 34. ↵ Gard DE , Gard MG , Kring AM , John OP . Anticipatory and consummatory components of the experience of pleasure: a scale development study . Journal of research in personality . 2006 ; 40 ( 6 ): 1086 – 1102 . OpenUrl CrossRef Web of Science 35. ↵ Harms MP , Somerville LH , Ances BM , et al. Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects . Neuroimage . Dec 2018 ; 183 : 972 – 984 . doi: 10.1016/j.neuroimage.2018.09.060 OpenUrl CrossRef PubMed 36. ↵ Glasser MF , Sotiropoulos SN , Wilson JA , et al. The minimal preprocessing pipelines for the Human Connectome Project . Neuroimage . Oct 15 2013 ; 80 : 105 – 24 . doi: 10.1016/j.neuroimage.2013.04.127 OpenUrl CrossRef PubMed Web of Science 37. ↵ Grabner G , Janke AL , Budge MM , Smith D , Pruessner J , Collins DL . Symmetric atlasing and model based segmentation: an application to the hippocampus in older adults . Med Image Comput Comput Assist Interv . 2006 ; 9 (Pt 2 ): 58 – 66 . doi: 10.1007/11866763_8 OpenUrl CrossRef 38. ↵ Griffanti L , Salimi-Khorshidi G , Beckmann CF , et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging . Neuroimage . Jul 15 2014 ; 95 : 232 – 47 . doi: 10.1016/j.neuroimage.2014.03.034 OpenUrl CrossRef PubMed Web of Science 39. Salimi-Khorshidi G , Douaud G , Beckmann CF , Glasser MF , Griffanti L , Smith SM . Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers . Neuroimage . Apr 15 2014 ; 90 : 449 – 68 . doi: 10.1016/j.neuroimage.2013.11.046 OpenUrl CrossRef PubMed Web of Science 40. ↵ Glasser MF , Coalson TS , Bijsterbosch JD , et al. Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data . Neuroimage . Nov 1 2018 ; 181 : 692 – 717 . doi: 10.1016/j.neuroimage.2018.04.076 OpenUrl CrossRef PubMed 41. ↵ Robinson EC , Jbabdi S , Glasser MF , et al. MSM: a new flexible framework for Multimodal Surface Matching . Neuroimage . Oct 15 2014 ; 100 : 414 – 26 . doi: 10.1016/j.neuroimage.2014.05.069 OpenUrl CrossRef PubMed 42. ↵ Coalson TS , Van Essen DC , Glasser MF . The impact of traditional neuroimaging methods on the spatial localization of cortical areas . Proc Natl Acad Sci U S A . Jul 3 2018 ; 115 ( 27 ): E6356 – E6365 . doi: 10.1073/pnas.1801582115 OpenUrl Abstract / FREE Full Text 43. ↵ Behzadi Y , Restom K , Liau J , Liu TT . A component based noise correction method (CompCor) for BOLD and perfusion based fMRI . Neuroimage . Aug 1 2007 ; 37 ( 1 ): 90 – 101 . doi: 10.1016/j.neuroimage.2007.04.042 OpenUrl CrossRef PubMed Web of Science 44. ↵ Whitfield-Gabrieli S , Nieto-Castanon A . Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks . Brain Connect . 2012 ; 2 ( 3 ): 125 – 41 . doi: 10.1089/brain.2012.0073 OpenUrl CrossRef PubMed 45. ↵ Ji JL , Spronk M , Kulkarni K , Repovs G , Anticevic A , Cole MW . Mapping the human brain’s cortical-subcortical functional network organization . Neuroimage . Jan 15 2019 ; 185 : 35 – 57 . doi: 10.1016/j.neuroimage.2018.10.006 OpenUrl CrossRef PubMed 46. ↵ Glasser MF , Coalson TS , Robinson EC , et al. A multi-modal parcellation of human cerebral cortex . Nature . Aug 11 2016 ; 536 ( 7615 ): 171 – 178 . doi: 10.1038/nature18933 OpenUrl CrossRef PubMed 47. ↵ Liu Q , Ely BA , Stern ER , et al. Neural function underlying reward expectancy and attainment in adolescents with diverse psychiatric symptoms . Neuroimage Clin . 2022 ; 36 : 103258 . doi: 10.1016/j.nicl.2022.103258 OpenUrl CrossRef PubMed 48. ↵ Winkler AM , Ridgway GR , Webster MA , Smith SM , Nichols TE . Permutation inference for the general linear model . Neuroimage . May 15 2014 ; 92 : 381 – 97 . doi: 10.1016/j.neuroimage.2014.01.060 OpenUrl CrossRef PubMed Web of Science 49. ↵ Palm-From-Excel . MathWorks File Exchange; 2024 . https://www.mathworks.com/matlabcentral/fileexchange/156777-palm-from-excel 50. ↵ Camchong J , Lim KO , Kumra S . Adverse Effects of Cannabis on Adolescent Brain Development: A Longitudinal Study . Cereb Cortex . Mar 1 2017 ; 27 ( 3 ): 1922 – 1930 . doi: 10.1093/cercor/bhw015 OpenUrl CrossRef 51. ↵ Camchong J , Collins PF , Becker MP , Lim KO , Luciana M . Longitudinal Alterations in Prefrontal Resting Brain Connectivity in Non-Treatment-Seeking Young Adults With Cannabis Use Disorder . Front Psychiatry . 2019 ; 10 : 514 . doi: 10.3389/fpsyt.2019.00514 OpenUrl CrossRef 52. ↵ Lichenstein SD , Kiluk BD , Potenza MN , et al. Identification and external validation of a problem cannabis risk network . Biol Psychiatry . Feb 3 2025 ; doi: 10.1016/j.biopsych.2025.01.022 OpenUrl CrossRef 53. ↵ Baacke KA , Sullivan RM , Shankula CA , Lisdahl KM . Network topology and cannabis use following two weeks of monitored abstinence: moderation of sex and patterns of use findings . Frontiers in Adolescent Medicine . 2025 ; 3 doi: 10.3389/fradm.2025.1549771 OpenUrl CrossRef 54. ↵ Ritchay MM , Huggins AA , Wallace AL , Larson CL , Lisdahl KM . Resting state functional connectivity in the default mode network: Relationships between cannabis use, gender, and cognition in adolescents and young adults . Neuroimage Clin . 2021 ; 30 : 102664 . doi: 10.1016/j.nicl.2021.102664 OpenUrl CrossRef 55. ↵ Ertl N , Lawn W , Mokrysz C , et al. Associations between regular cannabis use and brain resting-state functional connectivity in adolescents and adults . J Psychopharmacol . Sep 2023 ; 37 ( 9 ): 904 – 919 . doi: 10.1177/02698811231189441 OpenUrl CrossRef 56. ↵ Schnakenberg Martin AM , Kim DJ , Newman SD , et al. Altered cerebellar-cortical resting-state functional connectivity in cannabis users . J Psychopharmacol . Jul 2021 ; 35 ( 7 ): 823 – 832 . doi: 10.1177/02698811211019291 OpenUrl CrossRef PubMed 57. ↵ Kawamura Y , Fukaya M , Maejima T , et al. The CB1 cannabinoid receptor is the major cannabinoid receptor at excitatory presynaptic sites in the hippocampus and cerebellum . J Neurosci. Mar 15 2006; 26 ( 11 ): 2991 – 3001 . doi: 10.1523/JNEUROSCI.4872-05.2006 OpenUrl CrossRef 58. ↵ Tseng KY , Molla HM . Cannabinoid CB1 receptor-sensitive neurodevelopmental processes and trajectories . Mol Psychiatry . Aug 2025 ; 30 ( 8 ): 3792 – 3803 . doi: 10.1038/s41380-025-03057-2 OpenUrl CrossRef PubMed 59. ↵ Gabbay V , Ely BA , Li Q , et al. Striatum-based circuitry of adolescent depression and anhedonia . J Am Acad Child Adolesc Psychiatry . Jun 2013 ; 52 ( 6 ): 628 – 41 e13. doi: 10.1016/j.jaac.2013.04.003 OpenUrl CrossRef PubMed 60. ↵ Pan PM , Sato JR , Salum GA , et al. Ventral Striatum Functional Connectivity as a Predictor of Adolescent Depressive Disorder in a Longitudinal Community-Based Sample . Am J Psychiatry . Nov 1 2017 ; 174 ( 11 ): 1112 – 1119 . doi: 10.1176/appi.ajp.2017.17040430 OpenUrl CrossRef 61. ↵ Dorfschmidt L , Bethlehem RA , Seidlitz J , et al. Sexually divergent development of depression-related brain networks during healthy human adolescence . Sci Adv . May 27 2022 ; 8 ( 21 ): eabm7825 . doi: 10.1126/sciadv.abm7825 OpenUrl CrossRef 62. ↵ Jin J , Van Snellenberg JX , Perlman G , et al. Intrinsic neural circuitry of depression in adolescent females . J Child Psychol Psychiatry . Apr 2020 ; 61 ( 4 ): 480 – 491 . doi: 10.1111/jcpp.13123 OpenUrl CrossRef PubMed 63. ↵ Dennis M , Godley SH , Diamond G , et al. The Cannabis Youth Treatment (CYT) Study: main findings from two randomized trials . J Subst Abuse Treat . Oct 2004 ; 27 ( 3 ): 197 – 213 . doi: 10.1016/j.jsat.2003.09.005 OpenUrl CrossRef PubMed Web of Science 64. ↵ Calomarde-Gomez C , Jimenez-Fernandez B , Balcells-Olivero M , Gual A , Lopez-Pelayo H . Motivational Interviewing for Cannabis Use Disorders: A Systematic Review and Meta-Analysis . Eur Addict Res . 2021 ; 27 ( 6 ): 413 – 427 . doi: 10.1159/000515667 OpenUrl CrossRef PubMed 65. ↵ Tindall L , Kerrigan P , Li J , Hayward E , Gega L . Is behavioural activation an effective treatment for depression in children and adolescents? An updated systematic review and meta-analysis . Eur Child Adolesc Psychiatry . Dec 2024 ; 33 ( 12 ): 4133 – 4156 . doi: 10.1007/s00787-024-02429-3 OpenUrl CrossRef PubMed 66. ↵ Lin LA , Bonar EE , Coughlin LN . Toward a Harm Reduction Approach to Cannabis Use Disorder . Am J Psychiatry . Feb 1 2024 ; 181 ( 2 ): 98 – 99 . doi: 10.1176/appi.ajp.20230381 OpenUrl CrossRef 67. ↵ Zhao Y , Sallie SN , Cui H , et al. Anterior Cingulate Cortex in Addiction: New Insights for Neuromodulation . Neuromodulation . Oct 9 2020 ; doi: 10.1111/ner.13291 OpenUrl CrossRef 68. ↵ Doruk Camsari D , Kirkovski M , Croarkin PE . Therapeutic Applications of Noninvasive Neuromodulation in Children and Adolescents . Psychiatr Clin North Am . Sep 2018 ; 41 ( 3 ): 465 – 477 . doi: 10.1016/j.psc.2018.05.003 OpenUrl CrossRef PubMed 69. ↵ Miech RA , Johnston LD , Patrick ME , O’Malley PM. Monitoring the Future national survey results on drug use, 1975–2024: Overview and detailed results for secondary school students . Monitoring the Future Monograph Series. Institute for Social Research, University of Michigan ; 2025 . 70. ↵ Thomson H , Labuschagne I , Greenwood LM , et al. Is resting-state functional connectivity altered in regular cannabis users? A systematic review of the literature . Psychopharmacology (Berl) . May 2022 ; 239 ( 5 ): 1191 – 1209 . doi: 10.1007/s00213-021-05938-0 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted November 19, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Altered Reward-Related Resting-State Network Properties in Adolescent Cannabis Use and Depression Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Altered Reward-Related Resting-State Network Properties in Adolescent Cannabis Use and Depression Tram N. B. Nguyen , Russell H. Tobe , Benjamin A. Ely , Vilma Gabbay medRxiv 2025.11.01.25339294; doi: https://doi.org/10.1101/2025.11.01.25339294 Share This Article: Copy Citation Tools Altered Reward-Related Resting-State Network Properties in Adolescent Cannabis Use and Depression Tram N. B. Nguyen , Russell H. Tobe , Benjamin A. Ely , Vilma Gabbay medRxiv 2025.11.01.25339294; doi: https://doi.org/10.1101/2025.11.01.25339294 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Psychiatry and Clinical Psychology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9219) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffa940c390e8e2e',t:'MTc3OTQzOTg3OQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();