{"paper_id":"2ffa4d55-6c49-4332-aeb8-f6035bcdb6aa","body_text":"Cannabis Use and Glutamate across the Psychosis Spectrum: In Vivo Evidence from 7T Proton Magnetic Resonance Spectroscopy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cannabis Use and Glutamate across the Psychosis Spectrum: In Vivo Evidence from 7T Proton Magnetic Resonance Spectroscopy David Roalf, Tyler Moore, Jacquelyn Stifelman, Maggie Pecsok, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7802376/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Cannabis use is linked to elevated psychosis risk, yet the neurobiological mechanisms that couple use to symptom expression remain unclear. Because glutamatergic dysregulation has been implicated in both cannabis effects and psychosis vulnerability, we examined whether brain glutamate relates to dimensional symptoms as a function of cannabis use across the psychosis spectrum. Seventy-nine participants—typically developing controls, clinical high-risk individuals, and patients with psychosis—completed dimensional clinical assessments, detailed cannabis surveys, urine toxicology, and ultra-high-field 7T 1HMRS quantification of anterior cingulate cortex (ACC) glutamate levels. Linear models assessed the main and interactive effects of ACC glutamate and cannabis use on positive and negative symptoms. Self-reported cannabis use showed strong concordance with urine toxicology. Cannabis use was associated with higher positive and negative symptoms. Independently, higher ACC glutamate predicted greater positive and negative symptoms. Notably, lower glutamate levels were associated with higher positive symptoms in cannabis users. Exploratory analyses suggested interactions for depressive and manic symptoms, indicating that glutamatergic abnormalities may amplify the overall severity of cannabis-related symptoms. Sensitivity analyses revealed lower ACC glutamate in psychosis patients—especially cannabis users—highlighting diagnostic group differences and reinforcing the link between cannabis exposure and glutamatergic dysfunction. These findings implicate ACC glutamatergic dysfunction as a transdiagnostic correlate of symptom burden, particularly in those with psychosis who are cannabis users. Glutamate-targeted interventions and longitudinal designs will be needed to examine causal pathways linking cannabis exposure to psychosis-relevant outcomes. Biological sciences/Neuroscience Health sciences/Diseases/Psychiatric disorders/Schizophrenia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Recent changes in cannabis laws and the rise in nonmedical cannabis use have intensified concerns about the long-term effects of cannabis on mental health, particularly among youth. While cannabis use has been implicated in a range of psychiatric outcomes 1 , accumulating evidence highlights its association with dimensional psychopathology, including positive and negative psychosis-spectrum symptoms 2 and mood disturbances 3 . Epidemiological studies show that frequent use—especially of high-potency cannabis—and earlier age of initiation are linked to greater risk for adverse outcomes 4-7 , with a dose–response relationship, particularly in psychosis 8, 9 . Cannabis use during adolescence has been associated not only with the onset of psychotic disorders in early adulthood but also with elevated subclinical symptoms and poorer functional outcomes across multiple domains 10, 11 . In clinical high-risk for psychosis (CHR) samples 12-14 , cannabis use is associated with worsened subthreshold symptoms, and among those with established schizophrenia, it may exacerbate symptom severity and increase relapse risk 15-17 . These effects may be partly mediated by cannabis-induced disruption of the glutamatergic system 18, 19 , a key neurochemical pathway implicated in mood, psychosis, and neurodevelopmental disorders. Both cannabis use 18, 19 and psychiatric disorders 20-24 have been independently associated with glutamate dysregulation, yet the mechanistic interplay remains poorly understood. Here we begin to address this gap by integrating detailed self-reported cannabis use, urine drug screening, and ultra–high field 7T proton magnetic resonance spectroscopy (¹HMRS) of the ACC to examine glutamate (Glu) alterations associated with cannabis use across a transdiagnostic sample. The primary psychoactive component in cannabis, Δ9-tetrahydrocannabinol (THC), modulates neurometabolic and neurotransmitter systems—most notably dopamine and glutamate 19, 25 , both central to psychosis. Sustained cannabis use reduces glutamate signaling via Cannabinoid receptor 1 (CB1R) activation, and CB1R agonists induce psychosis-like behaviors 26 . Glutamate hypofunction 20, 21 contributes to dopamine dysregulation in schizophrenia, and recent studies have documented glutamatergic metabolite alterations in individuals with or at risk for schizophrenia 20, 22-24 and depression 27 . However, systematic in vivo investigations of glutamatergic alterations associated with cannabis use in humans using ultra-high field MRI are limited, hindering efforts to clarify how cannabis may heighten psychopathological vulnerability and symptoms. Glutamatergic alterations are central to the pathophysiology of psychosis. Historically, psychosis has been associated with dopaminergic dysfunction due to its link to positive symptoms 28 , but accumulating evidence emphasize the relevance of glutamate dysregulation in psychosis 29 . A growing literature implicates glutamate dysfunction in psychosis, especially in schizophrenia 30, 31 , including data from animal models 32-36 , postmortem studies 37-40 , genetic analyses 41-45 , peripheral biomarkers 46 , and in vivo 1 HMRS 31 . While early 1 HMRS meta-analyses at 3T reported higher Glu metabolites in cases vs. controls 47 , subsequent mega-analyses 20 , 7T GluCEST investigations 22, 24 , and 7T 1 HMRS 23 studies have demonstrated regionally lower Glu in individuals with psychosis, but see 48 . Region-specific effects include lower Glu in mPFC, frontal white matter, medial temporal lobe, and thalamus, with elevated Glu in cerebellum and basal ganglia. 47 Taken together, 1 HMRS studies reveal regionally heterogeneous findings in glutamatergic levels in CHR patients to those with threshold psychosis disorder, 30, 49-54 suggesting that glutamate alterations may be part of illness progression providing significant rationale for studying glutamate across the psychosis spectrum. Few studies have directly examined cannabis–glutamate associations across clinical dimensions of the psychosis spectrum, and existing work has been limited to 3T MRI. Early 1 HMRS work found that patients with early psychosis who used cannabis exhibited lower prefrontal Glu concentrations than both non‐using psychosis patients and healthy controls, along with a steeper age-related Glu decline, suggesting that cannabis may exacerbate glutamatergic dysfunction and influence disease progression 55 . Another 3T 1 HMRS study in early psychosis patients observed limited differences in Glutamate+Glutamine (Glx) between cannabis users and nonusers but identified associations between regional gray matter volume (caudate, hippocampus, cortex) and caudate Glx in cannabis users, implying that heavy cannabis use may alter structure–neurochemical connectivity in CB1R‐rich regions 56 . In healthy individuals, acute THC induced greater Glx surges in those who developed transient psychotic-like symptoms, particularly among participants with lower baseline Glx and greater prior cannabis exposure 57 . These findings collectively suggest that baseline glutamatergic state and cannabis responsiveness may interact to influence psychosis risk and expression. However, prior studies have been limited to 3T 1 HMRS, where sensitivity to glutamate is lower, and they have only measured cannabis–glutamate associations in categorical clinical groupings. In summary, converging epidemiological, preclinical, and neuroimaging evidence implicates glutamate as a key modulator of the cannabis–psychosis relationship. By integrating detailed cannabis use phenotyping, high–field 7T 1 HMRS measures, and dimensional clinical assessments across individuals who are typically developing, CHR or experience psychosis (PSY), our study aims to elucidate how cannabis use is associated with glutamatergic levels in those with and at risk for psychosis. In line with recent RDoC and NIMH initiatives, we assess clinical symptoms dimensionally rather than relying solely on categorical diagnoses. Dimensional measurement provides greater sensitivity to subthreshold variation 58, 59 , allowing us to capture the full spectrum of psychopathology and potential associations with cannabis use, including effects that may not meet diagnostic thresholds. This approach also facilitates transdiagnostic comparisons and improves power to detect brain–behavior associations by reducing heterogeneity within broad diagnostic categories. Understanding this interplay may identify neurobiological and clinical markers of risk and ultimately inform targeted prevention and intervention strategies for this high-risk population. Methods Participants To capture a broad range of psychopathology, we studied both healthy individuals and those with subthreshold and threshold psychiatric symptoms. The study sample (N = 79, ages 14–32, 35 female) included a typically-developing (TD) group (no Axis I diagnoses or subthreshold psychosis symptomatology, no history of psychotropic medication use; N = 31) and a clinical group composed of individuals classified by experienced clinician scientists (CK, MEC) as having psychosis spectrum disorders (N = 48), including 27 with CHR and 21 with schizophrenia spectrum and other psychotic disorders (PSY; N = 21). Although our primary analyses treat psychopathology as a continuous, transdiagnostic construct, demographic and clinical characteristics are displayed by categorical diagnosis for clarity and interpretability. This allows for evaluation of the representativeness of the sample and provides context for the dimensional analyses presented below. Complete demographic and clinical information are presented in Table 1 , stratified by cannabis user status and clinical diagnosis. In addition, sensitivity analyses are conducted by diagnostic group (Supplemental Results). Brief cognitive (Montreal Cognitive Assessment (MoCA 60 )) and functional assessments (Global Assessment of functioning (GAF 61 )) were also administered. Table 1 Participant demographics and clinical characteristics. This table summarizes demographic and clinical characteristics of the study sample. Demographic variables include age, sex distribution, and relevant clinical status or group designation. Mean (+/- s.d.) positive, negative, disorganized and generalized SIPS, GAF and MoCA scores are shown. Table 1 . Participant demographics stratifed by clinical diagnosis DxGroup N Age (years) Percent Female SIPS Positive SIPS Negative SIPS Disorganzied SIPS Genralized GAF MoCA TD Cannabis User No 16 20.7 ± 2.7 63% 1.3 ± 1.8 1.1 ± 1.0 0.4 ± 0.6 0.7 ± 0.9 87.4 ± 4.5 26.5 ± 2.8 Yes 15 22.8 ± 3.0 27% 1.7 ± 1.8 1.5 ± 1.8 0.3 ± 0.6 1.5 ± 2.0 81.2 ± 13.7 27.7 ± 1.6 CHR Cannabis User No 14 20.8 ± 3.6 50% 8.1 ± 4.7 7.6 ± 6.0 4.1 ± 2.5 3.7 ± 3.4 56.9 ± 8.0 24.8 ± 2.9 Yes 13 22.6 ± 2.8 31% 6.8 ± 3.1 7.6 ± 5.5 4.0 ± 3.0 3.2 ± 2.5 61.0 ± 12.2 27.0 ± 2.7 PSY Cannabis User No 5 26.8 ± 4.1 60% 14.0 ± 6.1 9.4 ± 5.6 5.4 ± 1.1 5.6 ± 3.6 47.4 ± 4.0 22.0 ± 3.9 Yes 16 24.8 ± 3.5 44% 19.7 ± 6.0 12.0 ± 2.7 7.6 ± 4.2 8.5 ± 6.0 42.4 ± 9.0 25.6 ± 2.4 Clinical Assessment All participants received detailed clinical assessments, including a semi-structured diagnostic interview to assess a broad spectrum of psychosis-relevant experiences, psychopathology, treatment history, and medications. Participants diagnostic assessments by highly-trained assessors using a computerized version of a modified and adapted Schedule for Affective Disorders and SZ for School Age Children – Present and Lifetime Version (K-SADS-4.0) 62 , which includes semi-structured assessments of mood, ADHD and substance use disorders. Psychosis spectrum symptoms were assessed using the Structured Interview for Prodromal Syndromes (SIPS, v. 4.0) , in which selected modules of the Structured Clinical Interview for DSM-IV (SCID-IV) 63 are integrated to facilitate differential diagnosis. The Scale of Prodromal Symptoms (SOPS) 64 , embedded within the SIPS 65 , describes and dimensionally rates the severity of positive, negative, disorganized, and general symptoms occurring within the past 6 months. To facilitate efficient data collection, the instruments were integrated into one administrative tool, the Computer-Assisted Psychopathology Assessment (CAPA) 62 . Interviewers underwent formal training including lectures and mock interviews, followed by supervised administration and performance evaluation until competency was established 62 . For minors, collateral information was provided by an independent interview with a parent or guardian. All information was integrated to yield consensus diagnoses and clinical symptom ratings at a clinical case conference led by at least two doctoral-level clinicians. CHR was defined as the presence of at least one positive symptom rated 3–5, or at least two negative and/or disorganized symptoms rated 3–6. Psychosis (PSY) patients are individuals with a DSM-5 schizophrenia spectrum disorder diagnosis (e.g., SZ). Clinical scores are shown in Table 1 . Estimation of Correlated Traits Clinical Factor scores . Clinical factor scores were derived using a framework that incorporated longitudinal computerized assessments (e.g. GOASSESS 66 , 67 and CAPA 68 ) used in our laboratory group since the landmark Philadelphia Neurodevelopmental Cohort study. 69 – 71 We used a bank of all clinically available data from our laboratory to inform a dimensional factor analysis (e.g. GOASSESS 66 , 67 and CAPA 68 ) and generate clinical factor scores for participants enrolled in the current study. Because the same instruments were not used across historical time points, historical data were harmonized across longitudinal visits by using only items that overlapped between GOASSESS and CAPA interviews. These included the screen items from the depression, mania, and ADHD sections of the GOASSESS/CAPA, as well as the PRIME 72 , 73 screen and a subset of SIPS items. Using this harmonized framework, dimensional clinical scores (correlated trait factor scores) were generated for each participant in the present study based on the CAPA data collected (described in the Clinical Assessment section). Dimensional clinical factors scores are shown, by diagnostic group, in Supplemental Table 2. Cannabis Use Cannabis use was measured using a computerized version of the Minnesota Center for Twin and Family Research self-report substance use assessment 74 , which was privately self-administered on a computer. The measure assessed lifetime use of several substances; for cannabis and alcohol, additional questions queried age at first use and frequency of past year use. To obtain more detailed data regarding patterns of cannabis use, several questions from the DFAQ-CU ( Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Inventory ) 75 were also administered. This measure provided data for individual methods of cannabis use, with visual depictions to indicate quantities of cannabis across forms (e.g., joint, flower). Summary measures were generated for frequency, age of onset, and quantity of cannabis use across methods of use, including increasingly popular methods (e.g., edibles, concentrates). The DFAQ-CU shows convergent, predictive, and discriminant validity, including high expected correlations (> 0.7) with other frequency measures (e.g., timeline follow-back), modulates correlations with cannabis use disorder symptoms and cannabis-related problems, and low correlations with alcohol use problems 75 , 76 . Consistent with prior research 77 , 78 , we classified individuals as cannabis users if they reported use more than once per month over the past year; for exploratory analyses we also classified individuals as frequent cannabis users if they reported using cannabis on a daily or nearly daily basis for the past 12 months. We also administered sections of the Cannabis Experiences Questionnaire (CEQ) that assess motivations for cannabis use and positive and negative experiences associated with use, consistent with prior studies in early psychosis 79 . Urine Drug Screen for Cannabis In line with expert consensus groups recommendations 80 , we also used a urine drug test to identify presence/absence of cannabis use. All urine samples (90ml) were collected using Redwood Toxicology iCUPs™, which detect one or more of the primary metabolites of THC. The immunoassay is binary (positive or negative). If the sample is below threshold, it is reported as negative; if above, it is reported as positive. Detection threshold for presence/absence is 50 ng/mL of THC-COOH. Cannabis metabolites are typically detected for up to 3 days after a single use in occasional users, while frequent users typically test positive for a much longer period—sometimes up to several weeks—due to the slow release of stored THC from fatty tissues and repeated, high-level exposure. Body fat percentage, metabolism, hydration level, and dosage all affect how long THC-COOH remains detectable in urine. Urine drug screening occurred prior to the 7T MRI session. If a participant reported cannabis use on the day of the MRI, or if acute intoxication was suspected, the study visit was rescheduled. 7T 1 HMRS Data Acquisition All 1 HMRS data were acquired on a whole-body 7 Tesla Terra MRI system (Siemens, Erlangen, Germany) equipped with a single-channel volume transmit and 32‐channel receive proton phased array head radiofrequency coil (Nova Medical, Wilmington, MA, USA). Prior to spectroscopy, high‐resolution structural images were obtained using a magnetization‐prepared rapid acquisition gradient echo (MP2RAGE) sequence (TE = 2.52 ms, TR = 5000 ms, TI1 = 700 ms, TI2 = 2500 ms, flip angle 1 = 7°, flip angle 2 = 5°, voxel size = 0.8 mm 3 ). These anatomical images served both for voxel placement and for later tissue‐segmentation corrections. Voxel Localization and Shimming 1 HMRS voxels (10 x 40 x 15 mm³) were manually placed in anterior cingulate cortex, using the mid-sagittal anatomical image as reference. Care was taken to align all voxel faces orthogonal to the cortical surface and to minimize inclusion of cerebrospinal fluid (CSF)‐rich sulcal regions. First‐ and second‐order B₀ shimming was performed manually over each voxel to achieve a full‐width at half‐maximum (FWHM) linewidth of ≤ 20 Hz for the unsuppressed water peak within the voxel. Spectroscopy Sequence Spectra were acquired using a point-resolved spectroscopy (PRESS) sequence optimized for 7T. Sequence parameters were as follows: TE = 23 ms, TR = 3,000 ms, number of averages (NA) = 64 (total acquisition time ≈ 3 min 24 s per voxel). Unsuppressed water reference scan preceded this acquisition for reference and calibration to quantify and correct for eddy‐currents. Sequence parameters of the unsuppressed scan (TE = 23 ms, TR = 3,000 ms, NA = 8) used identical localization and shimming settings. Data Processing and Quantification All spectroscopy data were exported in RDA format, which were coil-combined and averaged at the scanner. All spectra were processed, modeled, and quantified using Osprey (v2.9.0; Osprey Project, Johns Hopkins University) 81 running in MATLAB (R2023b; MathWorks, Natick, MA, USA). Spectral fitting was performed using the LCModel package (v6.3-1N) implemented in Osprey software. Prior to fitting, imported raw spectra were processed through the ‘OspreyProcess’ module, which includes eddy-current correction, frequency and phase alignment, water removal, frequency referencing, and initial phasing. Default parameters were utilized for modeling and quantification in ‘OspreyFit’, including a metabolite fit range of 0.5 to 4.0 ppm, a water fit range of 2.0 to 7.4 ppm, and a knot spacing of 0.4 ppm. The basis set provided for LCModel included simulated metabolite spectra for N-acetylaspartate (NAA), creatine (Cr), choline (Cho), glutamate (Glu), glutamine (Gln), myo-inositol (mI), γ-aminobutyric acid (GABA), and others, generated with density-matrix simulations at 7T incorporating sequence‐specific TE and TM. In addition to the basis set, default macromolecular and lipid components provided by Osprey were fitted to each spectrum. Each participant’s 1 HMRS voxel was then co-registered to a T 1 -weighted MP2RAGE provided for each individual. Co-registration in ‘OspreyCoReg’ also produces a voxel mask that is subsequently used in ‘OspreySeg’ to segment the voxel into gray matter, white matter, and CSF using SPM12. Example spectra and voxel overlap across participants are shown in Fig. 1 . In this module, fractional tissue volumes are also determined. Tissue- and relaxation-corrected metabolite molal concentrations were then estimated in ‘OspreyQuantify’ according to the Gasparovic method 82 . Only tissue-corrected metabolite estimates passing quality control (Cramér–Rao lower‐bound (CRLB) ≤ 20%) were included in subsequent analyses. Statistical Analysis Group differences in cannabis use rates were assessed using independent-samples t tests and chi-squared tests, as appropriate. To examine the effects of 1 HMRS glutamate, cannabis use, and their interaction on clinical symptom dimensions, we conducted a series of linear regression models. The primary outcome measures of interest were those associated with psychosis: positive and negative symptoms. Each of the measures was modeled as a continuous outcome. All linear models controlled for sex and age at scan. Cannabis use was coded dichotomously (0 = non-user, 1 = user), and glutamate was entered as a continuous variable. Interaction terms between glutamate and cannabis user status were included to assess modulation effects. Significance was assessed at p < 0.05, with trend-level effects noted at p < 0.10. Residuals were visually inspected for normality and homoscedasticity. Results are reported as F-statistics with associated degrees of freedom and p -values. Exploratory analyses in other symptom domains (depression, mania, and ADHD) were also conducted. As a sensitivity analysis, these analyses were repeated with categorical diagnostic group (TD, CHR, PSY; See Supplemental Results). Demographic data were compared across diagnostic groups (e.g., diagnosis, cannabis user status) using t-tests and chi-square as appropriate. Rates of cannabis use and urine drug screen data were compared across all three diagnostic groups using an omnibus Chi-square test, with planned post-hoc pairwise comparisons. Post-hoc comparisons were completed using simple slopes (‘sim_slopes’) and least squares means approach (‘lsmeans’) using the ‘interactions’ 83 and ‘lsmeans’ 84 libraries in R (V4.3.3), respectively. Results Self-reported cannabis use rates converge with results of urine toxicology screening Across the sample, 56% (n = 44) self-reported lifetime cannabis use, including 32% (n = 25) frequent users and 24% (n = 19) infrequent users, while 44% (n = 35) were cannabis non-users (Fig. 2 ). To assess the validity of self-reported cannabis use, we compared responses on cannabis use self-report assessments to results from urine toxicology across all individuals. 89% (70/79) of individuals had a valid urine toxicology screen. A chi-square test revealed a significant association between self-reported use and toxicology results, χ²(1) = 7.62, p < 0.01. The vast majority (92%) of cannabis non-users tested negative on the urine toxicology screen (Fig. 3 A), while self-reported cannabis users were more likely to test positive on the urine toxicology screen (Fig. 3 B), but overall rates of positive screens were low (38%), which is not unexpected given the typical window of cannabis metabolite detection. Indeed, self-reported frequent users (87%) were more likely to test positive on urine toxicology than infrequent users (13%; Fig. 3 C). Overall, these results indicate good concordance between self-report and biological verification of cannabis use. Note that two individuals who denied cannabis use tested positive on urine toxicology; these individuals were considered infrequent cannabis users in subsequent analyses. Cannabis use is higher in psychosis patients 76% of PSY participants, 48% of CHR participants, and 45% of TD participants reported cannabis use (Supplemental Fig. 1). A chi-square test of independence comparing all three groups was not significant (χ²(2) = 5.51, p = 0.06), but planned pairwise comparisons indicated that cannabis use was more prevalent in the PSY group compared to the TD group, χ²(1) = 3.78, p = 0.05. The rate of use between PSY and CHR groups showed nominally more cannabis use in PSY (χ²(1) = 2.80, p = 0.09), but CHR and TD groups did not differ in cannabis use, χ²(1) < 0.01, p = 0.99. Notably, analysis of frequency of cannabis use (Supplemental Fig. 2) in CHR (33%) and PSY (32%) showed similar rates of frequent use (3–4 times a week or daily). Glutamate and cannabis use are associated with dimensional clinical symptoms We examined the relationship of 1 HMRS glutamate levels, cannabis use, and their interaction with dimensional clinical symptoms across the entire sample. A significant Glu × cannabis interaction was observed for positive psychosis symptoms ( F (1,68) = 4.33, p = 0.041), indicating that the relationship between glutamate and positive symptom severity differed by cannabis use status (Fig. 4 A). Using a simple slopes follow-up analysis, in cannabis users, lower glutamate was significantly associated with greater positive symptom severity ( β = − 0.15, SE = 0.05, t (68) = − 3.11, p = 0.003), but this association was not significant in non-users ( β = 0.01, SE = 0.06, t (68) = 0.23, p = 0.82). These findings suggest that glutamate levels are differentially linked to positive psychosis symptoms depending on cannabis use history, with effects most pronounced among cannabis users. Additionally, both glutamate ( p = 0.012) and cannabis use ( p < 0.001) independently predicted greater positive symptoms. For negative psychosis symptoms (Fig. 4 B), glutamate ( p = 0.01) and cannabis use ( p = 0.02) were also significant predictors, but there was no interaction, suggesting additive effects of elevated glutamate and cannabis exposure. Follow-up sensitivity analysis comparing glutamate levels by psychosis diagnostic group showed similar results, including when stratified by cannabis use status (Supplemental Results). In the exploratory analysis of other dimensional symptoms, cannabis use was associated with greater depressive symptoms severity ( p = 0.011), and a trend-level interaction with glutamate ( p = 0.05), which suggested a potential cannabis-dependent relationship between glutamate and depressive symptoms (Fig. 5 A). There was no main effect of glutamate ( p = 0.22). A similar pattern emerged for manic symptoms, where cannabis use again predicted greater symptom burden ( p = 0.01), while glutamate showed a marginal association ( p = 0.09), but with no significant interaction (Fig. 5 B). In contrast, ADHD symptoms were not significantly associated with either glutamate or cannabis use (Fig. 5 C). Higher glutamate levels were also associated with overall functioning as measured by the GAF (F(1,66) = 4.50. p = 0.03), with cannabis non-users showing trend-level ( p = 0.06) higher GAF scores than cannabis users. Within the cannabis user group, there were no associations between glutamate level and age of onset, length of use, or paranoia scores on the CEQ. There was a trend level association for cannabis-associated euphoria, with lower levels of glutamate associated with higher reported CEQ euphoric scores ( p = 0.07). Analyses stratified by clinical diagnosis showed similar trends (Supplemental Results). Discussion We combined high-resolution 7T 1 HMRS with detailed cannabis use assessments to examine glutamatergic alterations in a transdiagnostic sample of patients with psychosis, clinical high risk for psychosis, and typically developing youth. Cannabis use was common overall but most prevalent among individuals with psychosis. Self-reported use showed strong concordance with urine toxicology results, especially among frequent users, supporting the validity of self-report in this cohort. As expected, psychosis participants exhibited elevated clinical symptomatology and lower global functioning compared to CHR and TD groups. Collectively, the results indicate that cannabis use is robustly associated with elevated symptoms across multiple clinical domains—particularly psychosis, but also depression and mania—and that glutamate levels contribute independently to symptom severity, with interaction effects most apparent for positive symptoms, and potentially depressive symptoms. These findings suggest that glutamatergic dysregulation may play a role in symptom expression among cannabis users, especially for the positive symptom dimension of psychosis. A novel finding of the current study was that anterior cingulate glutamate levels were significantly associated with positive psychosis symptoms only among cannabis users, suggesting that cannabis use modulates the glutamate–psychosis relationship. Specifically, lower glutamate predicted greater positive symptom severity in cannabis users but not in non-users. This pattern is consistent with evidence that cannabis can disrupt glutamatergic signaling in CB1 receptor–rich cortical regions, potentially leading to maladaptive reductions in excitatory tone or altered glutamate homeostasis in circuits already vulnerable to dysregulation in psychosis 85 . Such cannabis-related alterations could diminish glutamatergic efficiency, thereby linking lower glutamate levels to greater positive symptom expression among users. These results raise the possibility that ACC glutamate could serve as a biomarker of vulnerability among cannabis-using individuals on the psychosis spectrum, which could inform risk stratification and targeted interventions. Importantly, glutamate was also independently associated with negative symptoms and global functioning across groups, and cannabis use predicted worse outcomes on these measures, indicating broader relevance of glutamatergic dysfunction beyond cannabis-exposed individuals. Given prior reports of regionally specific glutamate reductions in early and chronic psychosis 22 , 23 , 85 , the additive and interactive effects of cannabis use observed here may reflect either direct neurochemical consequences of cannabis or shared neurodevelopmental risk factors, or a combination of both. These findings support a mechanistic framework in which cannabis use interacts with regional glutamatergic vulnerability to influence the severity and profile of psychopathology. Future longitudinal and ultra–high field imaging studies will be critical to further clarify causal pathways linking cannabis use, glutamate dysregulation, and clinical outcomes, and to determine whether glutamate-targeted pharmacological or behavioral interventions may be particularly beneficial for cannabis-using individuals with psychosis. We also found that cannabis use was associated with greater severity of depression and mania symptoms across the whole sample, highlighting the transdiagnostic clinical relevance of cannabis use. This is consistent with 1) meta-analytic evidence that heavy cannabis use increases risk for depressive disorders 86 ; and 2) epidemiological findings of high comorbidity between cannabis use and mood disorders 87 , 88 . The present associations may reflect overlapping neurobiological vulnerabilities—such as altered serotonergic and endocannabinoid signaling—that predispose individuals to both cannabis use and mood dysregulation, or the downstream effects of cannabis-related perturbations in neurotransmitter systems on affective processing. Self-reported cannabis use showed high concordance with urine toxicology results, supporting the validity of self-report in assessing cannabis exposure in psychiatric populations. Across the sample, 92% of individuals denying cannabis use tested negative on urine toxicology, and frequent users were far more likely to test positive than infrequent users (87% vs. 13%). This pattern replicates prior work showing that urine toxicology has high specificity and negative predictive value for cannabis 82 , 89 and that concordance is often strongest among individuals with frequent use 90 . Nonetheless, some discrepancies were observed, with two participants denying cannabis use testing positive on toxicology and overall low rates of positive screens among self-reported users (38%), underscoring important limitations of toxicology measures. Detection windows for urine THC metabolites are finite and may under-detect use in infrequent users 91 , while contextual factors, stigma, and recall error could bias self-reports 92 , 93 . These findings highlight the utility of pairing structured self-report cannabis use measures with biological verification, particularly in research on high-risk groups such as individuals with psychosis, where cannabis use is prevalent and clinically relevant 94 . Combining approaches may increase confidence in exposure classification while also capturing use patterns and frequency not discernible from toxicology alone. Notwithstanding the strengths of combining ultra–high field 7T 1 HMRS, biological and self-report measures of cannabis use, and dimensional clinical assessment across a transdiagnostic sample, several limitations should be noted. First, the cross-sectional design limits causal inference about the relationship between glutamate levels, cannabis use, and psychopathology symptom expression. Longitudinal studies are needed to determine whether cannabis-related glutamatergic alterations precede or result from symptom exacerbation. Second, although self-reported cannabis use showed strong concordance with urine toxicology results, the binary nature and limited detection window of the urine screen may underestimate recent use, particularly in infrequent users. Third, although the inclusion of clinical high-risk, psychosis-spectrum, and typically developing individuals enhances generalizability, the modest sample size, particularly within diagnostic subgroups, may limit statistical power to detect more nuanced interaction effects or subgroup-specific associations. Fourth, while advanced tissue correction and spectral quality control were employed, 1 HMRS glutamate measures remain unable to fully disentangle glutamate from glutamine, do not include information on inhibitory neurometabolites (e.g., GABA), and do not reflect synaptic neurotransmission directly. Lastly, potential confounding effects of other substances (e.g., alcohol, nicotine), antipsychotic medication 48 or comorbid psychiatric conditions were not fully modeled, and future work should expand the analytic framework to include polysubstance exposure and broader transdiagnostic factors. In conclusion, these findings suggest that cannabis use may interact with psychosis-related vulnerability to accentuate glutamatergic dysfunction, and that this neurochemical alteration may contribute to symptom expression, especially in the domain of positive symptoms. Understanding this relationship is critical to elucidate the neurobiological substrates underlying both acute and chronic cannabis effects on mental health. Declarations Acknowledgements We thank the patients and families who participated in this study. These data, in part, were presented at the 2025 Congress of the Schizophrenia International Research Society in Chicago, IL, USA. Availability of data and materials The data generated and/or analyzed during the current study is available. Please contact Dr. David Roalf with questions and considerations for data sharing upon reasonable requests. Author Information Contributors Statement All authors contributed to the writing, editing, and approval of this manuscript. DRR and JCS conceptualized and designed the research. MEC, CK, KR, SGR, RCG, JS, HR, AM, CM and REG, prepared and/or collected data on the assessment tools. DRR, AA, & KR performed aspects of the MRI experiments and analysis. DRR, TMM, KR, & JCS performed the statistical analyses. DRR wrote the first draft of the manuscript. All authors critically reviewed the manuscript’s content and approved the final version for publication. Corresponding author Correspondence to Dr. David Roalf Ethics declarations Ethics approval and consent to participate. Participants provided informed consent/assent and the study procedures were approved by the Institutional Review Boards at the Children’s Hospital of Philadelphia and the University of Pennsylvania. Participants’ privacy and confidentiality were ensured at every stage of the study. We confirm that all methods were performed in accordance with the relevant guidelines and regulations. Consent for publication. Not applicable Declaration of Competing Interest No authors have any competing interest to report with respect to this manuscript. Funding information Role of the Funding Source: This work was supported by the National Institute of Mental Health grants MH120174 (DRR), MH119185 (DRR), MH119219 (REG), U01 MH119738 (REG), MH117014 (RCG), MH131566 (DHW), and the Dowshen Program for Neuroscience at the University of Pennsylvania and the Lifespan Brain Institute (LiBI). References Petrilli K, Ofori S, Hines L, Taylor G, Adams S, Freeman TP. Association of cannabis potency with mental ill health and addiction: a systematic review. The Lancet Psychiatry 2022; 9(9): 736–750. D'Souza DC. Cannabis, cannabinoids and psychosis: a balanced view. World psychiatry 2023; 22(2): 231. Sorkhou M, Dent EL, George TP. Cannabis use and mood disorders: a systematic review. Frontiers in public health 2024; 12: 1346207. Robinson T, Ali MU, Easterbrook B, Hall W, Jutras-Aswad D, Fischer B. Risk-thresholds for the association between frequency of cannabis use and the development of psychosis: a systematic review and meta-analysis. Psychol Med 2023; 53(9): 3858–3868. Kiburi SK, Molebatsi K, Ntlantsana V, Lynskey MT. Cannabis use in adolescence and risk of psychosis: Are there factors that moderate this relationship? A systematic review and meta-analysis. Subst Abus 2021; 42(4): 527–542. Murray RM, Quigley H, Quattrone D, Englund A, Di Forti M. Traditional marijuana, high-potency cannabis and synthetic cannabinoids: increasing risk for psychosis. World psychiatry: official journal of the World Psychiatric Association (WPA) 2016; 15(3): 195–204. Di Forti M, Marconi A, Carra E, Fraietta S, Trotta A, Bonomo M et al. Proportion of patients in south London with first-episode psychosis attributable to use of high potency cannabis: a case-control study. The Lancet Psychiatry 2015; 2(3): 233–238. Marconi A, Di Forti M, Lewis CM, Murray RM, Vassos E. Meta-analysis of the association between the level of cannabis use and risk of psychosis. Schizophr Bull 2016; 42(5): 1262–1269. Di Forti M, Quattrone D, Freeman TP, Tripoli G, Gayer-Anderson C, Quigley H et al. The contribution of cannabis use to variation in the incidence of psychotic disorder across Europe (EU-GEI): a multicentre case-control study. The Lancet Psychiatry 2019; 6(5): 427–436. Jones JD, Calkins ME, Scott JC, Bach EC, Gur RE. Cannabis use, polysubstance use, and psychosis spectrum symptoms in a community-based sample of US youth. J Adolesc Health 2017; 60(6): 653–659. Stefanis NC, Delespaul P, Henquet C, Bakoula C, Stefanis C, Van Os J. Early adolescent cannabis exposure and positive and negative dimensions of psychosis. Addiction 2004; 99(10): 1333–1341. Carney R, Cotter J, Firth J, Bradshaw T, Yung A. Cannabis use and symptom severity in individuals at ultra high risk for psychosis: a meta-analysis. Acta Psychiatr Scand 2017; 136(1): 5–15. Santesteban-Echarri O, Liu L, Miller M, Bearden CE, Cadenhead KS, Cannon TD et al. Cannabis use and attenuated positive and negative symptoms in youth at clinical high risk for psychosis. Schizophr Res 2022; 248: 114–121. de Medeiros MW, Andrade JC, Haddad NM, Mendonça M, de Jesus LP, Fekih-Romdhane F et al. Cannabis use influences disorganized symptoms severity but not transition in a cohort of non-help-seeking individuals at-risk for psychosis from São Paulo, Brazil. Psychiatry Res 2024; 331: 115665. Schoeler T, Petros N, Di Forti M, Klamerus E, Foglia E, Ajnakina O et al. Effects of continuation, frequency, and type of cannabis use on relapse in the first 2 years after onset of psychosis: an observational study. The Lancet Psychiatry 2016; 3(10): 947–953. Schoeler T, Petros N, Di Forti M, Pingault J-B, Klamerus E, Foglia E et al. Association Between Continued Cannabis Use and Risk of Relapse in First-Episode Psychosis: A Quasi-Experimental Investigation Within an Observational Study. JAMA psychiatry 2016; 73(11): 1173–1179. Levi L, Bar-Haim M, Winter-van Rossum I, Davidson M, Leucht S, Fleischhacker WW et al. Cannabis use and symptomatic relapse in first episode schizophrenia: trigger or consequence? Data from the OPTIMISE Study. Schizophr Bull 2023; 49(4): 903–913. Colizzi M, McGuire P, Pertwee RG, Bhattacharyya S. Effect of cannabis on glutamate signalling in the brain: A systematic review of human and animal evidence. Neurosci Biobehav Rev 2016; 64: 359–381. Chowdhury KU, Holden ME, Wiley MT, Suppiramaniam V, Reed MN. Effects of Cannabis on Glutamatergic Neurotransmission: The Interplay between Cannabinoids and Glutamate. Cells 2024; 13(13): 1130. Merritt K, McCutcheon RA, Aleman A, Ashley S, Beck K, Block W et al. Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis. Mol Psychiatry 2023: 1–10. Merritt K, McGuire PK, Egerton A, Aleman A, Block W, Bloemen OJ et al. Association of age, antipsychotic medication, and symptom severity in schizophrenia with proton magnetic resonance spectroscopy brain glutamate level: a mega-analysis of individual participant-level data. JAMA psychiatry 2021; 78(6): 667–681. Roalf D, Nanga R, Rupert P, Hariharan H, Quarmley M, Calkins M et al. Glutamate imaging (GluCEST) reveals lower brain GluCEST contrast in patients on the psychosis spectrum. Mol Psychiatry 2017. Sydnor VJ, Roalf DR. A meta-analysis of ultra-high field glutamate, glutamine, GABA and glutathione 1HMRS in psychosis: implications for studies of psychosis risk. Schizophr Res 2020; 226: 61–69. Sydnor VJ, Larsen B, Kohler C, Crow AJ, Rush SL, Calkins ME et al. Diminished reward responsiveness is associated with lower reward network GluCEST: an ultra-high field glutamate imaging study. Mol Psychiatry 2021; 26(6): 2137–2147. Oleson EB, Hamilton LR, Gomez DM. Cannabinoid modulation of dopamine release during motivation, periodic reinforcement, exploratory behavior, habit formation, and attention. Front Synaptic Neurosci 2021; 13: 660218. Li Z, Mukherjee D, Duric B, Austin-Zimmerman I, Trotta G, Spinazzola E et al. Systematic review and meta-analysis on the effects of chronic peri-adolescent cannabinoid exposure on schizophrenia-like behaviour in rodents. Mol Psychiatry 2024: 1–11. Moriguchi S, Takamiya A, Noda Y, Horita N, Wada M, Tsugawa S et al. Glutamatergic neurometabolite levels in major depressive disorder: a systematic review and meta-analysis of proton magnetic resonance spectroscopy studies. Mol Psychiatry 2019; 24(7): 952–964. Lieberman J, Kane J, Alvir J. Provocative tests with psychostimulant drugs in schizophrenia. Psychopharmacology (Berl) 1987; 91(4): 415–433. Coyle JT. Glutamate and schizophrenia: beyond the dopamine hypothesis. Cell Mol Neurobiol 2006; 26(4): 363–382. Allen P, Chaddock CA, Egerton A, Howes OD, Barker G, Bonoldi I et al. Functional outcome in people at high risk for psychosis predicted by thalamic glutamate levels and prefronto-striatal activation. Schizophr Bull 2015; 41(2): 429–439. Baiano M, David A, Versace A, Churchill R, Balestrieri M, Brambilla P. Anterior cingulate volumes in schizophrenia: a systematic review and a meta-analysis of MRI studies. Schizophr Res 2007; 93(1–3): 1. Mohn AR, Gainetdinov RR, Caron MG, Koller BH. Mice with reduced NMDA receptor expression display behaviors related to schizophrenia. Cell 1999; 98(4): 427–436. Duncan GE, Moy SS, Perez A, Eddy DM, Zinzow WM, Lieberman JA et al. Deficits in sensorimotor gating and tests of social behavior in a genetic model of reduced NMDA receptor function. Behav Brain Res 2004; 153(2): 507–519. Jentsch JD, Redmond DE, Elsworth JD, Taylor JR, Youngren KD, Roth RH. Enduring cognitive deficits and cortical dopamine dysfunction in monkeys after long-term administration of phencyclidine. Science 1997; 277(5328): 953–955. Balla A, Koneru R, Smiley J, Sershen H, Javitt DC. Continuous phencyclidine treatment induces schizophrenia-like hyperreactivity of striatal dopamine release. Neuropsychopharmacology 2001; 25(2): 157–164. Javitt D. Glutamate as a therapeutic target in psychiatric disorders. Nature Publishing Group2004. Hu W, MacDonald ML, Elswick DE, Sweet RA. The glutamate hypothesis of schizophrenia: evidence from human brain tissue studies. Ann N Y Acad Sci 2015; 1338(1): 38–57. Beneyto M, Kristiansen LV, Oni-Orisan A, McCullumsmith RE, Meador-Woodruff JH. Abnormal glutamate receptor expression in the medial temporal lobe in schizophrenia and mood disorders. Neuropsychopharmacology 2007; 32(9): 1888–1902. Goff DC, Coyle JT. The emerging role of glutamate in the pathophysiology and treatment of schizophrenia. Am J Psychiatry 2001. Tsai G, Coyle JT. Glutamatergic mechanisms in schizophrenia. Annu Rev Pharmacol Toxicol 2002; 42(1): 165–179. Rubio MD, Wood K, Haroutunian V, Meador-Woodruff JH. Dysfunction of the ubiquitin proteasome and ubiquitin-like systems in schizophrenia. Neuropsychopharmacology 2013; 38(10): 1910–1920. Harrison PJ, Weinberger DR. Schizophrenia genes, gene expression, and neuropathology: on the matter of their convergence. Nature Publishing Group2005. Stefansson H, Petursson H, Sigurdsson E, Steinthorsdottir V, Bjornsdottir S, Sigmundsson T et al. Neuregulin 1 and susceptibility to schizophrenia. The American Journal of Human Genetics 2002; 71(4): 877–892. Moghaddam B. Bringing order to the glutamate chaos in schizophrenia. Neuron 2003; 40(5): 881–884. Wang Y-N, Figueiredo D, Sun X-D, Dong Z-Q, Chen W-B, Cui W-P et al. Controlling of glutamate release by neuregulin3 via inhibiting the assembly of the SNARE complex. Proceedings of the National Academy of Sciences 2018: 201716322. Palomino A, González-Pinto A, Aldama A, González-Gómez C, Mosquera F, González-García G et al. Decreased levels of plasma glutamate in patients with first-episode schizophrenia and bipolar disorder. Schizophr Res 2007; 95(1): 174–178. Merritt K, Egerton A, Kempton MJ, Taylor MJ, McGuire PK. Nature of Glutamate Alterations in Schizophrenia: A Meta-analysis of Proton Magnetic Resonance Spectroscopy Studies. JAMA psychiatry 2016; 73(7): 665–674. Girgis RR, de la Fuente-Sandoval C, Lewis-Fernández R, Reyes-Madrigal F, Wall MM, Hua J et al. Concerted elevations of cortical and striatal glutamate and GABA in antipsychotic-free individuals at clinical high-risk for psychosis. Neuropsychopharmacology 2025: 1–9. de la Fuente-Sandoval C, Leon-Ortiz P, Favila R, Stephano S, Mamo D, Ramirez-Bermudez J et al. Higher levels of glutamate in the associative-striatum of subjects with prodromal symptoms of schizophrenia and patients with first-episode psychosis. Neuropsychopharmacology 2011; 36(9): 1781–1791. Egerton A, Stone JM, Chaddock CA, Barker GJ, Bonoldi I, Howard RM et al. Relationship between brain glutamate levels and clinical outcome in individuals at ultra high risk of psychosis. Neuropsychopharmacology 2014. Uhl I, Mavrogiorgou P, Norra C, Forstreuter F, Scheel M, Witthaus H et al. 1H-MR spectroscopy in ultra-high risk and first episode stages of schizophrenia. J Psychiatr Res 2011; 45(9): 1135–1139. Stone JM, Day F, Tsagaraki H, Valli I, McLean MA, Lythgoe DJ et al. Glutamate dysfunction in people with prodromal symptoms of psychosis: relationship to gray matter volume. Biol Psychiatry 2009; 66(6): 533–539. Fusar-Poli P, Stone JM, Broome MR, Valli I, Mechelli A, McLean MA et al. Thalamic glutamate levels as a predictor of cortical response during executive functioning in subjects at high risk for psychosis. Arch Gen Psychiatry 2011; 68(9): 881. Tandon N, Bolo NR, Sanghavi K, Mathew IT, Francis AN, Stanley JA et al. Brain metabolite alterations in young adults at familial high risk for schizophrenia using proton magnetic resonance spectroscopy. Schizophr Res 2013. Rigucci S, Xin L, Klauser P, Baumann PS, Alameda L, Cleusix M et al. Cannabis use in early psychosis is associated with reduced glutamate levels in the prefrontal cortex. Psychopharmacology (Berl) 2018; 235: 13–22. Sami M, Worker A, Colizzi M, Annibale L, Das D, Kelbrick M et al. Association of cannabis with glutamatergic levels in patients with early psychosis: Evidence for altered volume striatal glutamate relationships in patients with a history of cannabis use in early psychosis. Translational psychiatry 2020; 10(1): 111. Colizzi M, Weltens N, McGuire P, Lythgoe D, Williams S, Van Oudenhove L et al. Delta-9-tetrahydrocannabinol increases striatal glutamate levels in healthy individuals: implications for psychosis. Mol Psychiatry 2020; 25(12): 3231–3240. Barch DM, Bustillo J, Gaebel W, Gur R, Heckers S, Malaspina D et al. Logic and justification for dimensional assessment of symptoms and related clinical phenomena in psychosis: relevance to DSM-5. Schizophr Res 2013; 150(1): 15–20. Phalen P, Millman Z, Rouhakhtar PR, Andorko N, Reeves G, Schiffman J. Categorical versus dimensional models of early psychosis. Early intervention in psychiatry 2022; 16(1): 42–50. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53(4): 695–699. Endicott J, Spitzer RL, Fleiss JL, Cohen J. The global assessment scale. A procedure for measuring overall severity of psychiatric disturbance. Arch Gen Psychiatry 1976; 33(6): 766–771. Calkins ME, Moore TM, Satterthwaite TD, Wolf DH, Turetsky BI, Roalf DR et al. Persistence of psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort: a prospective two-year follow‐up. World Psychiatry 2017; 16(1): 62–76. First MB, Spitzer RL, Gibbon M, Williams JBW. Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition (SCID-I/P) . Biometrics Research, New York State Psychiatric Institute: New York, 2002. McGlashan TH, Miller TJ, Woods SW,. Structured Interview for Prodromal Syndromes, Version 4.0. 2003. McGlashan T, Walsh B, Woods S. The psychosis-risk syndrome: handbook for diagnosis and follow-up . Oxford University Press2010. Calkins ME, Merikangas KR, Moore TM, Burstein M, Behr MA, Satterthwaite TD et al. The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative. 2015; 56: 1356–1369. Moore TM, Calkins ME, Wolf DH, Satterthwaite TD, Barzilay R, Scott JC et al. Estimation and Validation of the “c” Factor for Overall Cerebral Functioning in the Philadelphia Neurodevelopmental Cohort. Applied Sciences 2025; 15(4): 1697. Tang S, Yi J, Calkins M, Whinna D, Kohler C, Souders M et al. Psychiatric disorders in 22q11. 2 deletion syndrome are prevalent but undertreated. Psychol Med 2014; 44(06): 1267–1277. Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Valdez JN, Siegel SJ et al. Association of enhanced limbic response to threat with decreased cortical facial recognition memory response in schizophrenia. Am J Psychiatry 2010; 167(4): 418–426. Satterthwaite TD, Elliott MA, Ruparel K, Loughead J, Prabhakaran K, Calkins ME et al. Neuroimaging of the Philadelphia neurodevelopmental cohort. Neuroimage 2014; 86: 544–553. Calkins ME, Moore TM, Merikangas KR, Burstein M, Satterthwaite TD, Bilker WB et al. The psychosis spectrum in a young US community sample: findings from the Philadelphia Neurodevelopmental Cohort. World Psychiatry 2014; 13(3): 296–305. Kobayashi H, Nemoto T, Koshikawa H, Osono Y, Yamazawa R, Murakami M et al. A self-reported instrument for prodromal symptoms of psychosis: testing the clinical validity of the PRIME Screen-Revised (PS-R) in a Japanese population. Schizophr Res 2008; 106(2–3): 356. Miller T. The SIPS-Screen: a brief self-report screen to detect the schizophrenia prodrome. Schizophr Res 2004: 78. Han C, McGue MK, Iacono WG. Lifetime tobacco, alcohol and other substance use in adolescent Minnesota twins: univariate and multivariate behavioral genetic analyses. Addiction 1999; 94(7): 981–993. Cuttler C, Spradlin A. Measuring cannabis consumption: Psychometric properties of the Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Inventory (DFAQ-CU). PLoS One 2017; 12(5): e0178194. Gette JA, Littlefield AK, Victor SE, Schmidt AT, Garos S. Evaluation of the Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Questionnaire and its Relations to Cannabis-Related Problems. Cannabis (Albuquerque, NM) 2023; 6(3): 64–86. Meier MH, Caspi A, Ambler A, Harrington H, Houts R, Keefe RSE et al. Persistent cannabis users show neuropsychological decline from childhood to midlife. Proc Natl Acad Sci U S A 2012; 109(40): E2657–2664. Scott JC, Wolf DH, Calkins ME, Bach EC, Weidner J, Ruparel K et al. Cognitive functioning of adolescent and young adult cannabis users in the Philadelphia Neurodevelopmental Cohort. Psychol Addict Behav 2017; 31(4): 423–434. Bianconi F, Bonomo M, Marconi A, Kolliakou A, Stilo SA, Iyegbe C et al. Differences in cannabis-related experiences between patients with a first episode of psychosis and controls. Psychol Med 2016; 46(5): 995–1003. Lorenzetti V, Hindocha C, Petrilli K, Griffiths P, Brown J, Castillo-Carniglia Á et al. The iCannToolkit: a tool to embrace measurement of medicinal and non-medicinal cannabis use across licit, illicit and cross-cultural settings. Addiction (Abingdon, England) 2022; 117(6): 1523–1525. Oeltzschner G, Zöllner HJ, Hui SC, Mikkelsen M, Saleh MG, Tapper S et al. Osprey: Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data. J Neurosci Methods 2020; 343: 108827. Gasparovic C, Song T, Devier D, Bockholt HJ, Caprihan A, Mullins PG et al. Use of tissue water as a concentration reference for proton spectroscopic imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 2006; 55(6): 1219–1226. Long JA. interactions: Comprehensive, User-Friendly Toolkit for Probing Interactions. 2024. Lenth RV. Least-squares means: the R package lsmeans. Journal of statistical software 2016; 69: 1–33. Niznikiewicz M, Lin A, DeLisi LE. The Relationship of glutamate signaling to cannabis use and schizophrenia. Current Opinion in Psychiatry 2025; 38(3): 177–181. Lev-Ran S, Roerecke M, Le Foll B, George T, McKenzie K, Rehm J. The association between cannabis use and depression: a systematic review and meta-analysis of longitudinal studies. Psychol Med 2014; 44(4): 797–810. Feingold D, Weinstein A. Cannabis and depression. Cannabinoids and Neuropsychiatric Disorders 2020: 67–80. Schoeler T, Ferris J, Winstock AR. Rates and correlates of cannabis-associated psychotic symptoms in over 230,000 people who use cannabis. Translational psychiatry 2022; 12(1): 369. Salottolo K, McGuire E, Madayag R, Tanner AH, Carrick MM, Bar-Or D. Validity between self-report and biochemical testing of cannabis and drugs among patients with traumatic injury: brief report. Journal of Cannabis Research 2022; 4(1): 29. Skelton KR, Donahue E, Benjamin-Neelon SE. Validity of self-report measures of cannabis use compared to biological samples among women of reproductive age: a scoping review. BMC Pregnancy Childbirth 2022; 22(1): 344. Fink DS, Samples H, Malte CA, Olfson M, Wall MM, Alschuler DM et al. Cannabis legalization and increasing cannabis use in the United States: Data from urine toxicology testing in emergency room patients. International Journal of Drug Policy 2025; 138: 104765. Bharat C, Webb P, Wilkinson Z, McKetin R, Grebely J, Farrell M et al. Agreement between self-reported illicit drug use and biological samples: a systematic review and meta‐analysis. Addiction 2023; 118(9): 1624–1648. Palamar JJ, Le A, Guarino H, Mateu-Gelabert P. A comparison of the utility of urine-and hair testing in detecting self-reported drug use among young adult opioid users. Drug Alcohol Depend 2019; 200: 161–167. Oliver D, Chesney E, Cullen AE, Davies C, Englund A, Gifford G et al. Exploring causal mechanisms of psychosis risk. Neurosci Biobehav Rev 2024; 162: 105699. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files RoalfSupplementalMaterialCannabisMRSPSY10.07.2025.docx Supplemental Material Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 19 Jan, 2026 Review # 4 received at journal 18 Dec, 2025 Review # 2 received at journal 30 Nov, 2025 Reviewer # 4 agreed at journal 26 Nov, 2025 Review # 1 received at journal 26 Nov, 2025 Reviewer # 3 agreed at journal 19 Nov, 2025 Reviewer # 2 agreed at journal 16 Nov, 2025 Reviewer # 1 agreed at journal 12 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor assigned by journal 09 Oct, 2025 Submission checks completed at journal 09 Oct, 2025 First submitted to journal 08 Oct, 2025 Unknown event 08 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7802376\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":527033489,\"identity\":\"35bd233b-a315-4e88-8dfa-3dcbed6ee78f\",\"order_by\":0,\"name\":\"David Roalf\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"https://orcid.org/0000-0002-1728-9782\",\"institution\":\"University of Pennsylvania\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"David\",\"middleName\":\"\",\"lastName\":\"Roalf\",\"suffix\":\"\"},{\"id\":527033490,\"identity\":\"80814b36-ef72-4551-9c5f-b8f6aa25532e\",\"order_by\":1,\"name\":\"Tyler Moore\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-1384-0151\",\"institution\":\"Perelman School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tyler\",\"middleName\":\"\",\"lastName\":\"Moore\",\"suffix\":\"\"},{\"id\":527033491,\"identity\":\"0e00edb8-1a35-498b-a09a-7da1cef4886e\",\"order_by\":2,\"name\":\"Jacquelyn Stifelman\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jacquelyn\",\"middleName\":\"\",\"lastName\":\"Stifelman\",\"suffix\":\"\"},{\"id\":527033492,\"identity\":\"9ba3710d-5346-460c-82fe-c5466b703c9d\",\"order_by\":3,\"name\":\"Maggie Pecsok\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Maggie\",\"middleName\":\"\",\"lastName\":\"Pecsok\",\"suffix\":\"\"},{\"id\":527033493,\"identity\":\"e62acc4c-7fd6-4662-aee2-250ca95347fa\",\"order_by\":4,\"name\":\"Ally Atkins\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ally\",\"middleName\":\"\",\"lastName\":\"Atkins\",\"suffix\":\"\"},{\"id\":527033494,\"identity\":\"bcb64efd-a10a-41d8-9040-a063ab17456e\",\"order_by\":5,\"name\":\"Monica Calkins\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Pennsylvania\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Monica\",\"middleName\":\"\",\"lastName\":\"Calkins\",\"suffix\":\"\"},{\"id\":527033495,\"identity\":\"3cb4d04a-5f7c-4047-acb0-b9fefbb1c892\",\"order_by\":6,\"name\":\"Mariella De Biasi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mariella\",\"middleName\":\"\",\"lastName\":\"De Biasi\",\"suffix\":\"\"},{\"id\":527033496,\"identity\":\"1fe3388e-a1bc-43f8-9502-0da15bad300e\",\"order_by\":7,\"name\":\"Christian Kohler\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Christian\",\"middleName\":\"\",\"lastName\":\"Kohler\",\"suffix\":\"\"},{\"id\":527033497,\"identity\":\"bf9db860-3ee6-46a5-abd8-b45a17c23a22\",\"order_by\":8,\"name\":\"Christina Mastracchio\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Christina\",\"middleName\":\"\",\"lastName\":\"Mastracchio\",\"suffix\":\"\"},{\"id\":527033498,\"identity\":\"3b77575e-9c7c-457a-80e1-28e9617c0a8c\",\"order_by\":9,\"name\":\"Arianna Mordy\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Arianna\",\"middleName\":\"\",\"lastName\":\"Mordy\",\"suffix\":\"\"},{\"id\":527033499,\"identity\":\"251e65f0-3732-4456-b694-465615fb5afc\",\"order_by\":10,\"name\":\"Heather Robinson\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Heather\",\"middleName\":\"\",\"lastName\":\"Robinson\",\"suffix\":\"\"},{\"id\":527033500,\"identity\":\"9f4530e3-e5ed-45ee-a9dd-aeaa689209d2\",\"order_by\":11,\"name\":\"Ravinder Reddy\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-4580-2392\",\"institution\":\"Center for Metabolic Imaging in Precision Medicine (CAMIPM), Perelman School of Medicine at the University of Pennsylvania,\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ravinder\",\"middleName\":\"\",\"lastName\":\"Reddy\",\"suffix\":\"\"},{\"id\":527033501,\"identity\":\"055fd8cf-f002-4990-94c4-e7d457ee35f4\",\"order_by\":12,\"name\":\"Ravi Prakash Reddy Nanga\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-3644-5656\",\"institution\":\"Center for Metabolic Imaging in Precision Medicine (CAMIPM), Perelman School of Medicine at the University of Pennsylvania\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ravi\",\"middleName\":\"Prakash Reddy\",\"lastName\":\"Nanga\",\"suffix\":\"\"},{\"id\":527033502,\"identity\":\"293a7dd1-dfae-4159-bf6e-d9b25f19fabd\",\"order_by\":13,\"name\":\"Kosha Ruparel\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Pennsylvania\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kosha\",\"middleName\":\"\",\"lastName\":\"Ruparel\",\"suffix\":\"\"},{\"id\":527033503,\"identity\":\"f1fb43c2-9a60-4235-b081-dbe183f47d91\",\"order_by\":14,\"name\":\"Sage Rush\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sage\",\"middleName\":\"\",\"lastName\":\"Rush\",\"suffix\":\"\"},{\"id\":527033504,\"identity\":\"3819e260-6d0a-45ad-83d7-0097cc7e96ce\",\"order_by\":15,\"name\":\"Daniel Wolf\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-9731-8781\",\"institution\":\"University of Pennsylvania\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Daniel\",\"middleName\":\"\",\"lastName\":\"Wolf\",\"suffix\":\"\"},{\"id\":527033505,\"identity\":\"3eeee66e-0a81-4dcb-a392-d0ebaddc7aa5\",\"order_by\":16,\"name\":\"Ruben Gur\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-4082-8502\",\"institution\":\"University of Pennsylvania\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ruben\",\"middleName\":\"\",\"lastName\":\"Gur\",\"suffix\":\"\"},{\"id\":527033506,\"identity\":\"c4eaf832-9911-4d1c-9b3c-727ae314b54c\",\"order_by\":17,\"name\":\"Raquel Gur\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Pennsylvania\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Raquel\",\"middleName\":\"\",\"lastName\":\"Gur\",\"suffix\":\"\"},{\"id\":527033507,\"identity\":\"3377ab7d-a6f8-4bf0-95a8-3eb33de11b92\",\"order_by\":18,\"name\":\"J. Cobb Scott\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"J.\",\"middleName\":\"Cobb\",\"lastName\":\"Scott\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-10-07 20:00:25\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7802376/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7802376/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":96556228,\"identity\":\"f01ffa54-0cd7-4902-9135-ef6effbcf7a5\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":127689,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"CannabisMRSPSYmanuscript10.07.2025MP.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/25ca51fc44d6b83e62b9e561.docx\"},{\"id\":96556230,\"identity\":\"0a830e08-4af2-4321-a19e-4c33c25852df\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"pdf\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":421981,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure3.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/7e574ca9e4e7dc3c6ca1eb8a.pdf\"},{\"id\":96556240,\"identity\":\"333aaf41-df66-4f37-a708-02650fe4f751\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"pdf\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":442607,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure4.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/ba5aa9fdd32884c5a02454ed.pdf\"},{\"id\":96556238,\"identity\":\"eee9293b-fa8e-4cf6-8094-5135e1d443d5\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"pdf\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":450345,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure5.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/99323a6f7fef552afba9b174.pdf\"},{\"id\":96604678,\"identity\":\"9af07007-6c02-411a-80c5-c65fdd6c749a\",\"added_by\":\"auto\",\"created_at\":\"2025-11-24 09:14:33\",\"extension\":\"json\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":18203,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"2025MP002525.json\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/198e5bde246c2677510edc55.json\"},{\"id\":96556233,\"identity\":\"c74b326e-a082-4a4f-9aad-4d030dc59d11\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"docx\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":4222292,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"RoalfSupplementalMaterialCannabisMRSPSY10.07.2025.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/187ada19feefbc40fe5b47b5.docx\"},{\"id\":96556239,\"identity\":\"a12aa350-1d4b-4e06-86b7-b2ac41907899\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"xml\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":219589,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"2025MP0025250enriched.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/6c2a66fe344952a97bfe9ebf.xml\"},{\"id\":96604976,\"identity\":\"a5f68e06-3470-417d-b6cd-8cf55954ae5f\",\"added_by\":\"auto\",\"created_at\":\"2025-11-24 09:17:04\",\"extension\":\"pdf\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":973959,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure1SpectraOverlap.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/331e197348735425fbf29360.pdf\"},{\"id\":96556237,\"identity\":\"11d17220-aff4-49ec-8480-899242a7e22f\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"pdf\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":407442,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure2.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/5c1706e8775ffa5d59b073a7.pdf\"},{\"id\":96556243,\"identity\":\"a552ac0a-b40e-41db-8e58-82b20f72ff47\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:32\",\"extension\":\"pdf\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":421981,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure3.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/24d5602362c57ddcb687e958.pdf\"},{\"id\":96556231,\"identity\":\"ab538dc8-f794-476e-8699-4053e1730984\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"pdf\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":442607,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure4.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/60e631141354cd4a9e3cec5f.pdf\"},{\"id\":96556235,\"identity\":\"d496c393-c154-4629-88cf-9a6c9c1d275c\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"pdf\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":450345,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure5.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/2df4876150c856888695780c.pdf\"},{\"id\":96556244,\"identity\":\"1d5ad16a-509e-4d4d-b480-f9d5c7965c46\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:32\",\"extension\":\"xml\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":216082,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"2025MP0025250structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/1e9b56b2f96aa0e09a268edb.xml\"},{\"id\":96556236,\"identity\":\"ad57d007-5c3b-4b06-ae7a-c47d57c6ae5c\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"html\",\"order_by\":15,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":237119,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/13d1421e39bec5e0c6c1be2c.html\"},{\"id\":96556225,\"identity\":\"edb0f948-c325-4b9f-8ce7-475b02c33423\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":229788,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eIndividual metabolite quantification and voxel overlap in single-voxel spectroscopy (SVS) ¹H-MRS. \\u003c/strong\\u003ePanel A displays individual metabolite estimates obtained using the Osprey analysis pipeline. Each line represents the concentration of a specific metabolite quantified within the spectroscopy voxel for a representative participant. Key metabolites include glutamate (Glu), γ-aminobutyric acid (GABA), N-acetylaspartate (NAA), creatine (Cr), choline (Cho), myo-inositol (mI), and others derived from short-TE PRESS acquisitions. Panel B shows a voxel overlap heatmap across all participants, generated by co-registering individual SVS voxel placements to a common template space. Warmer colors indicate regions where greater numbers of participants’ voxels overlap, demonstrating the consistency of voxel localization across the sample and providing a measure of spatial coverage and reproducibility of the MRS acquisition.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/23c186cebc8012cc654f06d9.png\"},{\"id\":96556224,\"identity\":\"d48b4646-16b9-44ea-b58d-483daac9b5d4\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:30\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":19454,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFrequency of cannabis use in the transdiagnostic sample who underwent 7T MRI.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/2e9c3ae259d0f3a9661bea73.png\"},{\"id\":96556227,\"identity\":\"6fbd0dd7-cfdb-453f-ac7c-856738e1c852\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":45872,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComparison of self-reported use and urine toxicology screening. A. Many, but not a majority, of self-reported cannabis users tested positive on a urine toxicology screen. *Note two cannabis users did not complete urine toxicity screening. B. The vast majority of self-reported cannabis non-users tested negative on a urine toxicology screen. *Note seven cannabis non-users did not complete urine toxicity screening. C. Self-reported frequent cannabis users were more likely to test positive on a urine toxicology screen as compared to infrequent users.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/5e852cee90a1bb064d237976.png\"},{\"id\":96556234,\"identity\":\"d76bd44f-2583-49b7-b634-e305983fb352\",\"added_by\":\"auto\",\"created_at\":\"2025-11-23 11:45:31\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":42646,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAssociations between \\u003csup\\u003e1\\u003c/sup\\u003eHMRS glutamate level and positive (A) and negative (B) psychosis factor scores stratified by cannabis use status (yes/no). Simple slope significance (p) values are show for each association. *indicates significant interaction.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/fb6af15bf9e23780f713b667.png\"},{\"id\":96604903,\"identity\":\"b6946038-4a6a-4b01-9f88-f2ece7e81d6d\",\"added_by\":\"auto\",\"created_at\":\"2025-11-24 09:15:52\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":57413,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eExploratory associations between \\u003csup\\u003e1\\u003c/sup\\u003eHMRS glutamate level and depression (A), mania (B) and ADHD (C) factor scores stratified by cannabis use status (yes/no). Simple slope significance (p) values are show for each association. *indicates significant interaction.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/ba4e141cf7db1c3e6693488d.png\"},{\"id\":96607786,\"identity\":\"0a546a48-8c7d-4bce-87ee-04ce7735ac8b\",\"added_by\":\"auto\",\"created_at\":\"2025-11-24 09:27:44\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1478168,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/4a01cd99-8c13-4198-9876-02c74cef6431.pdf\"},{\"id\":96604921,\"identity\":\"f0676378-bc5e-48f0-9a31-2772b78280c6\",\"added_by\":\"auto\",\"created_at\":\"2025-11-24 09:16:07\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":4222292,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplemental Material\",\"description\":\"\",\"filename\":\"RoalfSupplementalMaterialCannabisMRSPSY10.07.2025.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7802376/v1/d3b8e35387eed1e1190455be.docx\"}],\"financialInterests\":\"The authors have declared there is \\u003cb\\u003eNO\\u003c/b\\u003e conflict of interest to disclose\",\"formattedTitle\":\"Cannabis Use and Glutamate across the Psychosis Spectrum: In Vivo Evidence from 7T Proton Magnetic Resonance Spectroscopy\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eRecent changes in cannabis laws and the rise in nonmedical cannabis use have intensified concerns about the long-term effects of cannabis on mental health, particularly among youth. While cannabis use has been implicated in a range of psychiatric outcomes\\u003csup\\u003e1\\u003c/sup\\u003e, accumulating evidence highlights its association with dimensional psychopathology, including positive and negative psychosis-spectrum symptoms\\u003csup\\u003e2\\u003c/sup\\u003e and mood disturbances\\u003csup\\u003e3\\u003c/sup\\u003e. Epidemiological studies show that frequent use\\u0026mdash;especially of high-potency cannabis\\u0026mdash;and earlier age of initiation are linked to greater risk for adverse outcomes\\u003csup\\u003e4-7\\u003c/sup\\u003e, with a dose\\u0026ndash;response relationship, particularly in psychosis\\u003csup\\u003e8, 9\\u003c/sup\\u003e. Cannabis use during adolescence has been associated not only with the onset of psychotic disorders in early adulthood but also with elevated subclinical symptoms and poorer functional outcomes across multiple domains\\u003csup\\u003e10, 11\\u003c/sup\\u003e. In clinical high-risk for psychosis (CHR) samples\\u003csup\\u003e12-14\\u003c/sup\\u003e, cannabis use is associated with worsened subthreshold symptoms, and among those with established schizophrenia, it may exacerbate symptom severity and increase relapse risk\\u003csup\\u003e15-17\\u003c/sup\\u003e. These effects may be partly mediated by cannabis-induced disruption of the glutamatergic system\\u003csup\\u003e18, 19\\u003c/sup\\u003e, a key neurochemical pathway implicated in mood, psychosis, and neurodevelopmental disorders. Both cannabis use\\u003csup\\u003e18, 19\\u003c/sup\\u003e and psychiatric disorders\\u003csup\\u003e20-24\\u003c/sup\\u003e have been independently associated with glutamate dysregulation, yet the mechanistic interplay remains poorly understood. Here we begin to address this gap by integrating detailed self-reported cannabis use, urine drug screening, and ultra\\u0026ndash;high field 7T proton magnetic resonance spectroscopy (\\u0026sup1;HMRS) of the ACC to examine glutamate (Glu) alterations associated with cannabis use across a transdiagnostic sample.\\u003c/p\\u003e\\n\\u003cp\\u003eThe primary psychoactive component in cannabis, \\u0026Delta;9-tetrahydrocannabinol (THC), modulates neurometabolic and neurotransmitter systems\\u0026mdash;most notably dopamine and glutamate\\u003csup\\u003e19, 25\\u003c/sup\\u003e, both central to psychosis. Sustained cannabis use reduces glutamate signaling \\u003cem\\u003evia\\u003c/em\\u003e Cannabinoid receptor 1 (CB1R) activation, and CB1R agonists induce psychosis-like behaviors\\u003csup\\u003e26\\u003c/sup\\u003e. Glutamate hypofunction\\u003csup\\u003e20, 21\\u003c/sup\\u003e contributes to dopamine dysregulation in schizophrenia, and recent studies have documented glutamatergic metabolite alterations in individuals with or at risk for schizophrenia\\u003csup\\u003e20, 22-24\\u003c/sup\\u003e and depression\\u003csup\\u003e27\\u003c/sup\\u003e. However, systematic \\u003cem\\u003ein vivo\\u003c/em\\u003e investigations of glutamatergic alterations associated with cannabis use in humans using ultra-high field MRI are limited, hindering efforts to clarify how cannabis may heighten psychopathological vulnerability and symptoms.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eGlutamatergic alterations are central to the pathophysiology of psychosis. Historically, psychosis has been associated with dopaminergic dysfunction due to its link to positive symptoms\\u003csup\\u003e28\\u003c/sup\\u003e, but accumulating evidence emphasize the relevance of glutamate dysregulation in psychosis\\u003csup\\u003e29\\u003c/sup\\u003e. A growing literature implicates glutamate dysfunction in psychosis, especially in schizophrenia\\u003csup\\u003e30, 31\\u003c/sup\\u003e, including data from animal models\\u003csup\\u003e32-36\\u003c/sup\\u003e, postmortem studies\\u003csup\\u003e37-40\\u003c/sup\\u003e, genetic analyses\\u003csup\\u003e41-45\\u003c/sup\\u003e, peripheral biomarkers\\u003csup\\u003e46\\u003c/sup\\u003e, and \\u003cem\\u003ein vivo\\u003c/em\\u003e \\u003csup\\u003e1\\u003c/sup\\u003eHMRS\\u003csup\\u003e31\\u003c/sup\\u003e. While early \\u003csup\\u003e1\\u003c/sup\\u003eHMRS meta-analyses at 3T reported higher Glu metabolites in cases vs. controls\\u003csup\\u003e47\\u003c/sup\\u003e, subsequent mega-analyses\\u003csup\\u003e20\\u003c/sup\\u003e, 7T GluCEST investigations\\u003csup\\u003e22, 24\\u003c/sup\\u003e, and 7T \\u003csup\\u003e1\\u003c/sup\\u003eHMRS\\u003csup\\u003e23\\u003c/sup\\u003e studies have demonstrated regionally lower Glu in individuals with psychosis, but see\\u003csup\\u003e48\\u003c/sup\\u003e. Region-specific effects include lower Glu in mPFC, frontal white matter, medial temporal lobe, and thalamus, with elevated Glu in cerebellum and basal ganglia.\\u003csup\\u003e47\\u003c/sup\\u003e Taken together, \\u003csup\\u003e1\\u003c/sup\\u003eHMRS studies reveal regionally heterogeneous findings in glutamatergic levels in CHR patients to those with threshold psychosis disorder,\\u003csup\\u003e30, 49-54\\u003c/sup\\u003e suggesting that glutamate alterations may be part of illness progression providing significant rationale for studying glutamate across the psychosis spectrum.\\u003c/p\\u003e\\n\\u003cp\\u003eFew studies have directly examined cannabis\\u0026ndash;glutamate associations across clinical dimensions of the psychosis spectrum, and existing work has been limited to 3T MRI. Early \\u003csup\\u003e1\\u003c/sup\\u003eHMRS work found that patients with early psychosis who used cannabis exhibited lower prefrontal Glu concentrations than both non‐using psychosis patients and healthy controls, along with a steeper age-related Glu decline, suggesting that cannabis may exacerbate glutamatergic dysfunction and influence disease progression\\u003csup\\u003e55\\u003c/sup\\u003e. Another 3T \\u003csup\\u003e1\\u003c/sup\\u003eHMRS study in early psychosis patients observed limited differences in Glutamate+Glutamine (Glx) between cannabis users and nonusers but identified associations between regional gray matter volume (caudate, hippocampus, cortex) and caudate Glx in cannabis users, implying that heavy cannabis use may alter structure\\u0026ndash;neurochemical connectivity in CB1R‐rich regions\\u003csup\\u003e56\\u003c/sup\\u003e. In healthy individuals, acute THC induced greater Glx surges in those who developed transient psychotic-like symptoms, particularly among participants with lower baseline Glx and greater prior cannabis exposure\\u003csup\\u003e57\\u003c/sup\\u003e. These findings collectively suggest that baseline glutamatergic state and cannabis responsiveness may interact to influence psychosis risk and expression. However, prior studies have been limited to 3T \\u003csup\\u003e1\\u003c/sup\\u003eHMRS, where sensitivity to glutamate is lower, and they have only measured cannabis\\u0026ndash;glutamate associations in categorical clinical groupings.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn summary, converging epidemiological, preclinical, and neuroimaging evidence implicates glutamate as a key modulator of the cannabis\\u0026ndash;psychosis relationship. By integrating detailed cannabis use phenotyping, high\\u0026ndash;field 7T \\u003csup\\u003e1\\u003c/sup\\u003eHMRS measures, and dimensional clinical assessments across individuals who are typically developing, CHR or experience psychosis (PSY), our study aims to elucidate how cannabis use is associated with glutamatergic levels in those with and at risk for psychosis. In line with recent RDoC and NIMH initiatives, we assess clinical symptoms dimensionally rather than relying solely on categorical diagnoses. Dimensional measurement provides greater sensitivity to subthreshold variation\\u003csup\\u003e58, 59\\u003c/sup\\u003e, allowing us to capture the full spectrum of psychopathology and potential associations with cannabis use, including effects that may not meet diagnostic thresholds. This approach also facilitates transdiagnostic comparisons and improves power to detect brain\\u0026ndash;behavior associations by reducing heterogeneity within broad diagnostic categories. Understanding this interplay may identify neurobiological and clinical markers of risk and ultimately inform targeted prevention and intervention strategies for this high-risk population.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003eParticipants\\u003c/span\\u003e To capture a broad range of psychopathology, we studied both healthy individuals and those with subthreshold and threshold psychiatric symptoms. The study sample (N\\u0026thinsp;=\\u0026thinsp;79, ages 14\\u0026ndash;32, 35 female) included a typically-developing (TD) group (no Axis I diagnoses or subthreshold psychosis symptomatology, no history of psychotropic medication use; N\\u0026thinsp;=\\u0026thinsp;31) and a clinical group composed of individuals classified by experienced clinician scientists (CK, MEC) as having psychosis spectrum disorders (N\\u0026thinsp;=\\u0026thinsp;48), including 27 with CHR and 21 with schizophrenia spectrum and other psychotic disorders (PSY; N\\u0026thinsp;=\\u0026thinsp;21). Although our primary analyses treat psychopathology as a continuous, transdiagnostic construct, demographic and clinical characteristics are displayed by categorical diagnosis for clarity and interpretability. This allows for evaluation of the representativeness of the sample and provides context for the dimensional analyses presented below. Complete demographic and clinical information are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, stratified by cannabis user status and clinical diagnosis. In addition, sensitivity analyses are conducted by diagnostic group (Supplemental Results). Brief cognitive (Montreal Cognitive Assessment (MoCA\\u003csup\\u003e\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e\\u003c/sup\\u003e)) and functional assessments (Global Assessment of functioning (GAF\\u003csup\\u003e\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u003c/sup\\u003e)) were also administered.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eParticipant demographics and clinical characteristics.\\u003c/b\\u003e This table summarizes demographic and clinical characteristics of the study sample. Demographic variables include age, sex distribution, and relevant clinical status or group designation. Mean (+/- s.d.) positive, negative, disorganized and generalized SIPS, GAF and MoCA scores are shown.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"10\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Participant demographics stratifed by clinical diagnosis\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eDxGroup\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eN\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eAge (years)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePercent Female\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSIPS Positive\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSIPS Negative\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSIPS Disorganzied\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSIPS Genralized\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eGAF\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eMoCA\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eTD\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eCannabis User\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e16\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e63%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e87.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e26.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e22.8 \\u0026plusmn; 3.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e27%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e81.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e27.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eCHR\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eCannabis User\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20.8 \\u0026plusmn; 3.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e50%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e7.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e3.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e56.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e24.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e13\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e22.6 \\u0026plusmn; 2.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e31%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e7.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e3.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e61.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e27.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePSY\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eCannabis User\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e26.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e60%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e14.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e9.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e5.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e5.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e47.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e22.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e16\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e24.8 \\u0026plusmn; 3.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e44%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e19.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e12.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e7.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e8.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e42.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e25.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003eClinical Assessment\\u003c/span\\u003e All participants received detailed clinical assessments, including a semi-structured diagnostic interview to assess a broad spectrum of psychosis-relevant experiences, psychopathology, treatment history, and medications. Participants diagnostic assessments by highly-trained assessors using a computerized version of a modified and adapted \\u003cem\\u003eSchedule for Affective Disorders and SZ for School Age Children \\u0026ndash; Present and Lifetime Version (K-SADS-4.0)\\u003c/em\\u003e\\u003csup\\u003e62\\u003c/sup\\u003e, which includes semi-structured assessments of mood, ADHD and substance use disorders. Psychosis spectrum symptoms were assessed using the \\u003cem\\u003eStructured Interview for Prodromal Syndromes (SIPS, v. 4.0)\\u003c/em\\u003e, in which selected modules of the Structured Clinical Interview for DSM-IV (SCID-IV)\\u003csup\\u003e\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e\\u003c/sup\\u003e are integrated to facilitate differential diagnosis. The \\u003cem\\u003eScale of Prodromal Symptoms (SOPS)\\u003c/em\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u003c/sup\\u003e, embedded within the SIPS\\u003csup\\u003e\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e\\u003c/sup\\u003e, describes and dimensionally rates the severity of positive, negative, disorganized, and general symptoms occurring within the past 6 months. To facilitate efficient data collection, the instruments were integrated into one administrative tool, the Computer-Assisted Psychopathology Assessment (CAPA)\\u003csup\\u003e\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e\\u003c/sup\\u003e. Interviewers underwent formal training including lectures and mock interviews, followed by supervised administration and performance evaluation until competency was established\\u003csup\\u003e\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e\\u003c/sup\\u003e. For minors, collateral information was provided by an independent interview with a parent or guardian. All information was integrated to yield consensus diagnoses and clinical symptom ratings at a clinical case conference led by at least two doctoral-level clinicians. CHR was defined as the presence of at least one positive symptom rated 3\\u0026ndash;5, or at least two negative and/or disorganized symptoms rated 3\\u0026ndash;6. Psychosis (PSY) patients are individuals with a DSM-5 schizophrenia spectrum disorder diagnosis (e.g., SZ). Clinical scores are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003eEstimation of Correlated Traits Clinical Factor scores\\u003c/span\\u003e. Clinical factor scores were derived using a framework that incorporated longitudinal computerized assessments (e.g. GOASSESS\\u003csup\\u003e\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e\\u003c/sup\\u003e and CAPA\\u003csup\\u003e\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e\\u003c/sup\\u003e) used in our laboratory group since the landmark Philadelphia Neurodevelopmental Cohort study.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR70\\\" citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e\\u003c/sup\\u003e We used a bank of all clinically available data from our laboratory to inform a dimensional factor analysis (e.g. GOASSESS\\u003csup\\u003e\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e\\u003c/sup\\u003e and CAPA\\u003csup\\u003e\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e\\u003c/sup\\u003e) and generate clinical factor scores for participants enrolled in the current study.\\u003c/p\\u003e\\u003cp\\u003eBecause the same instruments were not used across historical time points, historical data were harmonized across longitudinal visits by using only items that overlapped between GOASSESS and CAPA interviews. These included the screen items from the depression, mania, and ADHD sections of the GOASSESS/CAPA, as well as the PRIME\\u003csup\\u003e\\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e73\\u003c/span\\u003e\\u003c/sup\\u003e screen and a subset of SIPS items. Using this harmonized framework, dimensional clinical scores (correlated trait factor scores) were generated for each participant in the present study based on the CAPA data collected (described in the Clinical Assessment section). Dimensional clinical factors scores are shown, by diagnostic group, in Supplemental Table\\u0026nbsp;2.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003eCannabis Use\\u003c/span\\u003e Cannabis use was measured using a computerized version of the Minnesota Center for Twin and Family Research self-report substance use assessment\\u003csup\\u003e\\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e74\\u003c/span\\u003e\\u003c/sup\\u003e, which was privately self-administered on a computer. The measure assessed lifetime use of several substances; for cannabis and alcohol, additional questions queried age at first use and frequency of past year use. To obtain more detailed data regarding patterns of cannabis use, several questions from the \\u003cem\\u003eDFAQ-CU\\u003c/em\\u003e (\\u003cem\\u003eDaily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Inventory\\u003c/em\\u003e)\\u003csup\\u003e\\u003cem\\u003e\\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e\\u003c/em\\u003e\\u003c/sup\\u003e were also administered. This measure provided data for individual methods of cannabis use, with visual depictions to indicate quantities of cannabis across forms (e.g., joint, flower). Summary measures were generated for frequency, age of onset, and quantity of cannabis use across methods of use, including increasingly popular methods (e.g., edibles, concentrates). The DFAQ-CU shows convergent, predictive, and discriminant validity, including high expected correlations (\\u0026gt;\\u0026thinsp;0.7) with other frequency measures (e.g., timeline follow-back), modulates correlations with cannabis use disorder symptoms and cannabis-related problems, and low correlations with alcohol use problems\\u003csup\\u003e\\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e76\\u003c/span\\u003e\\u003c/sup\\u003e. Consistent with prior research\\u003csup\\u003e\\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e77\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e78\\u003c/span\\u003e\\u003c/sup\\u003e, we classified individuals as \\u003cspan type=\\\"ItalicUnderline\\\" class=\\\"ItalicUnderline\\\" name=\\\"Emphasis\\\"\\u003ecannabis users\\u003c/span\\u003e if they reported use more than once per month over the past year; for exploratory analyses we also classified individuals as \\u003cspan type=\\\"ItalicUnderline\\\" class=\\\"ItalicUnderline\\\" name=\\\"Emphasis\\\"\\u003efrequent cannabis users\\u003c/span\\u003e if they reported using cannabis on a daily or nearly daily basis for the past 12 months. We also administered sections of the \\u003cem\\u003eCannabis Experiences Questionnaire (CEQ)\\u003c/em\\u003e that assess motivations for cannabis use and positive and negative experiences associated with use, consistent with prior studies in early psychosis\\u003csup\\u003e\\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e79\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003eUrine Drug Screen for Cannabis\\u003c/span\\u003e In line with expert consensus groups recommendations\\u003csup\\u003e\\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e\\u003c/sup\\u003e, we also used a urine drug test to identify presence/absence of cannabis use. All urine samples (90ml) were collected using Redwood Toxicology iCUPs\\u0026trade;, which detect one or more of the primary metabolites of THC. The immunoassay is binary (positive or negative). If the sample is below threshold, it is reported as negative; if above, it is reported as positive. Detection threshold for presence/absence is 50 ng/mL of THC-COOH. Cannabis metabolites are typically detected for up to 3 days after a single use in occasional users, while frequent users typically test positive for a much longer period\\u0026mdash;sometimes up to several weeks\\u0026mdash;due to the slow release of stored THC from fatty tissues and repeated, high-level exposure. Body fat percentage, metabolism, hydration level, and dosage all affect how long THC-COOH remains detectable in urine. Urine drug screening occurred prior to the 7T MRI session. If a participant reported cannabis use on the day of the MRI, or if acute intoxication was suspected, the study visit was rescheduled.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003e7T\\u003c/span\\u003e\\u003csup\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003eHMRS Data Acquisition\\u003c/span\\u003e All \\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003eHMRS data were acquired on a whole-body 7 Tesla Terra MRI system (Siemens, Erlangen, Germany) equipped with a single-channel volume transmit and 32‐channel receive proton phased array head radiofrequency coil (Nova Medical, Wilmington, MA, USA). Prior to spectroscopy, high‐resolution structural images were obtained using a magnetization‐prepared rapid acquisition gradient echo (MP2RAGE) sequence (TE\\u0026thinsp;=\\u0026thinsp;2.52 ms, TR\\u0026thinsp;=\\u0026thinsp;5000 ms, TI1\\u0026thinsp;=\\u0026thinsp;700 ms, TI2\\u0026thinsp;=\\u0026thinsp;2500 ms, flip angle 1\\u0026thinsp;=\\u0026thinsp;7\\u0026deg;, flip angle 2\\u0026thinsp;=\\u0026thinsp;5\\u0026deg;, voxel size\\u0026thinsp;=\\u0026thinsp;0.8 mm\\u003csup\\u003e3\\u003c/sup\\u003e). These anatomical images served both for voxel placement and for later tissue‐segmentation corrections.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003eVoxel Localization and Shimming\\u003c/span\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003eHMRS voxels (10 x 40 x 15 mm\\u0026sup3;) were manually placed in anterior cingulate cortex, using the mid-sagittal anatomical image as reference. Care was taken to align all voxel faces orthogonal to the cortical surface and to minimize inclusion of cerebrospinal fluid (CSF)‐rich sulcal regions. First‐ and second‐order B₀ shimming was performed manually over each voxel to achieve a full‐width at half‐maximum (FWHM) linewidth of \\u0026le;\\u0026thinsp;20 Hz for the unsuppressed water peak within the voxel.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003eSpectroscopy Sequence\\u003c/span\\u003e Spectra were acquired using a point-resolved spectroscopy (PRESS) sequence optimized for 7T. Sequence parameters were as follows: TE\\u0026thinsp;=\\u0026thinsp;23 ms, TR\\u0026thinsp;=\\u0026thinsp;3,000 ms, number of averages (NA)\\u0026thinsp;=\\u0026thinsp;64 (total acquisition time\\u0026thinsp;\\u0026asymp;\\u0026thinsp;3 min 24 s per voxel). Unsuppressed water reference scan preceded this acquisition for reference and calibration to quantify and correct for eddy‐currents. Sequence parameters of the unsuppressed scan (TE\\u0026thinsp;=\\u0026thinsp;23 ms, TR\\u0026thinsp;=\\u0026thinsp;3,000 ms, NA\\u0026thinsp;=\\u0026thinsp;8) used identical localization and shimming settings.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003eData Processing and Quantification\\u003c/span\\u003e All spectroscopy data were exported in RDA format, which were coil-combined and averaged at the scanner. All spectra were processed, modeled, and quantified using Osprey (v2.9.0; Osprey Project, Johns Hopkins University)\\u003csup\\u003e\\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e81\\u003c/span\\u003e\\u003c/sup\\u003e running in MATLAB (R2023b; MathWorks, Natick, MA, USA). Spectral fitting was performed using the LCModel package (v6.3-1N) implemented in Osprey software. Prior to fitting, imported raw spectra were processed through the \\u0026lsquo;OspreyProcess\\u0026rsquo; module, which includes eddy-current correction, frequency and phase alignment, water removal, frequency referencing, and initial phasing. Default parameters were utilized for modeling and quantification in \\u0026lsquo;OspreyFit\\u0026rsquo;, including a metabolite fit range of 0.5 to 4.0 ppm, a water fit range of 2.0 to 7.4 ppm, and a knot spacing of 0.4 ppm. The basis set provided for LCModel included simulated metabolite spectra for N-acetylaspartate (NAA), creatine (Cr), choline (Cho), glutamate (Glu), glutamine (Gln), myo-inositol (mI), γ-aminobutyric acid (GABA), and others, generated with density-matrix simulations at 7T incorporating sequence‐specific TE and TM. In addition to the basis set, default macromolecular and lipid components provided by Osprey were fitted to each spectrum. Each participant\\u0026rsquo;s \\u003csup\\u003e1\\u003c/sup\\u003eHMRS voxel was then co-registered to a T\\u003csub\\u003e1\\u003c/sub\\u003e-weighted MP2RAGE provided for each individual. Co-registration in \\u0026lsquo;OspreyCoReg\\u0026rsquo; also produces a voxel mask that is subsequently used in \\u0026lsquo;OspreySeg\\u0026rsquo; to segment the voxel into gray matter, white matter, and CSF using SPM12. Example spectra and voxel overlap across participants are shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. In this module, fractional tissue volumes are also determined. Tissue- and relaxation-corrected metabolite molal concentrations were then estimated in \\u0026lsquo;OspreyQuantify\\u0026rsquo; according to the Gasparovic method\\u003csup\\u003e\\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e82\\u003c/span\\u003e\\u003c/sup\\u003e. Only tissue-corrected metabolite estimates passing quality control (Cram\\u0026eacute;r\\u0026ndash;Rao lower‐bound (CRLB)\\u0026thinsp;\\u0026le;\\u0026thinsp;20%) were included in subsequent analyses.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"BoldUnderline\\\" class=\\\"BoldUnderline\\\" name=\\\"Emphasis\\\"\\u003eStatistical Analysis\\u003c/span\\u003e Group differences in cannabis use rates were assessed using independent-samples t tests and chi-squared tests, as appropriate. To examine the effects of \\u003csup\\u003e1\\u003c/sup\\u003eHMRS glutamate, cannabis use, and their interaction on clinical symptom dimensions, we conducted a series of linear regression models. The primary outcome measures of interest were those associated with psychosis: positive and negative symptoms. Each of the measures was modeled as a continuous outcome. All linear models controlled for sex and age at scan. Cannabis use was coded dichotomously (0\\u0026thinsp;=\\u0026thinsp;non-user, 1\\u0026thinsp;=\\u0026thinsp;user), and glutamate was entered as a continuous variable. Interaction terms between glutamate and cannabis user status were included to assess modulation effects. Significance was assessed at \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, with trend-level effects noted at \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.10. Residuals were visually inspected for normality and homoscedasticity. Results are reported as F-statistics with associated degrees of freedom and \\u003cem\\u003ep\\u003c/em\\u003e-values. Exploratory analyses in other symptom domains (depression, mania, and ADHD) were also conducted. As a sensitivity analysis, these analyses were repeated with categorical diagnostic group (TD, CHR, PSY; See Supplemental Results). Demographic data were compared across diagnostic groups (e.g., diagnosis, cannabis user status) using t-tests and chi-square as appropriate. Rates of cannabis use and urine drug screen data were compared across all three diagnostic groups using an omnibus Chi-square test, with planned post-hoc pairwise comparisons. Post-hoc comparisons were completed using simple slopes (\\u0026lsquo;sim_slopes\\u0026rsquo;) and least squares means approach (\\u0026lsquo;lsmeans\\u0026rsquo;) using the \\u0026lsquo;interactions\\u0026rsquo;\\u003csup\\u003e83\\u003c/sup\\u003e and \\u0026lsquo;lsmeans\\u0026rsquo;\\u003csup\\u003e84\\u003c/sup\\u003e libraries in R (V4.3.3), respectively.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eSelf-reported cannabis use rates converge with results of urine toxicology screening\\u003c/h2\\u003e\\u003cp\\u003eAcross the sample, 56% (n\\u0026thinsp;=\\u0026thinsp;44) self-reported lifetime cannabis use, including 32% (n\\u0026thinsp;=\\u0026thinsp;25) frequent users and 24% (n\\u0026thinsp;=\\u0026thinsp;19) infrequent users, while 44% (n\\u0026thinsp;=\\u0026thinsp;35) were cannabis non-users (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). To assess the validity of self-reported cannabis use, we compared responses on cannabis use self-report assessments to results from urine toxicology across all individuals. 89% (70/79) of individuals had a valid urine toxicology screen. A chi-square test revealed a significant association between self-reported use and toxicology results, χ\\u0026sup2;(1)\\u0026thinsp;=\\u0026thinsp;7.62, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01. The vast majority (92%) of cannabis non-users tested negative on the urine toxicology screen (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA), while self-reported cannabis users were more likely to test positive on the urine toxicology screen (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB), but overall rates of positive screens were low (38%), which is not unexpected given the typical window of cannabis metabolite detection. Indeed, self-reported frequent users (87%) were more likely to test positive on urine toxicology than infrequent users (13%; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). Overall, these results indicate good concordance between self-report and biological verification of cannabis use. Note that two individuals who denied cannabis use tested positive on urine toxicology; these individuals were considered infrequent cannabis users in subsequent analyses.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eCannabis use is higher in psychosis patients\\u003c/h2\\u003e\\u003cp\\u003e76% of PSY participants, 48% of CHR participants, and 45% of TD participants reported cannabis use (Supplemental Fig.\\u0026nbsp;1). A chi-square test of independence comparing all three groups was not significant (χ\\u0026sup2;(2)\\u0026thinsp;=\\u0026thinsp;5.51, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.06), but planned pairwise comparisons indicated that cannabis use was more prevalent in the PSY group compared to the TD group, χ\\u0026sup2;(1)\\u0026thinsp;=\\u0026thinsp;3.78, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.05. The rate of use between PSY and CHR groups showed nominally more cannabis use in PSY (χ\\u0026sup2;(1)\\u0026thinsp;=\\u0026thinsp;2.80, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.09), but CHR and TD groups did not differ in cannabis use, χ\\u0026sup2;(1)\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.99. Notably, analysis of frequency of cannabis use (Supplemental Fig.\\u0026nbsp;2) in CHR (33%) and PSY (32%) showed similar rates of frequent use (3\\u0026ndash;4 times a week or daily).\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eGlutamate and cannabis use are associated with dimensional clinical symptoms\\u003c/h3\\u003e\\n\\u003cp\\u003eWe examined the relationship of \\u003csup\\u003e1\\u003c/sup\\u003eHMRS glutamate levels, cannabis use, and their interaction with dimensional clinical symptoms across the entire sample. A significant Glu \\u0026times; cannabis interaction was observed for positive psychosis symptoms (\\u003cem\\u003eF\\u003c/em\\u003e(1,68)\\u0026thinsp;=\\u0026thinsp;4.33, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.041), indicating that the relationship between glutamate and positive symptom severity differed by cannabis use status (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). Using a simple slopes follow-up analysis, in cannabis users, lower glutamate was significantly associated with greater positive symptom severity (\\u003cem\\u003eβ\\u003c/em\\u003e = \\u0026minus;\\u0026thinsp;0.15, \\u003cem\\u003eSE\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.05, \\u003cem\\u003et\\u003c/em\\u003e(68) = \\u0026minus;\\u0026thinsp;3.11, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.003), but this association was not significant in non-users (\\u003cem\\u003eβ\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.01, \\u003cem\\u003eSE\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.06, \\u003cem\\u003et\\u003c/em\\u003e(68)\\u0026thinsp;=\\u0026thinsp;0.23, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.82). These findings suggest that glutamate levels are differentially linked to positive psychosis symptoms depending on cannabis use history, with effects most pronounced among cannabis users. Additionally, both glutamate (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.012) and cannabis use (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) independently predicted greater positive symptoms. For negative psychosis symptoms (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB), glutamate (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.01) and cannabis use (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.02) were also significant predictors, but there was no interaction, suggesting additive effects of elevated glutamate and cannabis exposure. Follow-up sensitivity analysis comparing glutamate levels by psychosis diagnostic group showed similar results, including when stratified by cannabis use status (Supplemental Results).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn the exploratory analysis of other dimensional symptoms, cannabis use was associated with greater depressive symptoms severity (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.011), and a trend-level interaction with glutamate (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.05), which suggested a potential cannabis-dependent relationship between glutamate and depressive symptoms (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). There was no main effect of glutamate (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.22). A similar pattern emerged for manic symptoms, where cannabis use again predicted greater symptom burden (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.01), while glutamate showed a marginal association (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.09), but with no significant interaction (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB). In contrast, ADHD symptoms were not significantly associated with either glutamate or cannabis use (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC). Higher glutamate levels were also associated with overall functioning as measured by the GAF (F(1,66)\\u0026thinsp;=\\u0026thinsp;4.50. \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.03), with cannabis non-users showing trend-level (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.06) higher GAF scores than cannabis users. Within the cannabis user group, there were no associations between glutamate level and age of onset, length of use, or paranoia scores on the CEQ. There was a trend level association for cannabis-associated euphoria, with lower levels of glutamate associated with higher reported CEQ euphoric scores (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.07). Analyses stratified by clinical diagnosis showed similar trends (Supplemental Results).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eWe combined high-resolution 7T \\u003csup\\u003e1\\u003c/sup\\u003eHMRS with detailed cannabis use assessments to examine glutamatergic alterations in a transdiagnostic sample of patients with psychosis, clinical high risk for psychosis, and typically developing youth. Cannabis use was common overall but most prevalent among individuals with psychosis. Self-reported use showed strong concordance with urine toxicology results, especially among frequent users, supporting the validity of self-report in this cohort. As expected, psychosis participants exhibited elevated clinical symptomatology and lower global functioning compared to CHR and TD groups. Collectively, the results indicate that cannabis use is robustly associated with elevated symptoms across multiple clinical domains\\u0026mdash;particularly psychosis, but also depression and mania\\u0026mdash;and that glutamate levels contribute independently to symptom severity, with interaction effects most apparent for positive symptoms, and potentially depressive symptoms. These findings suggest that glutamatergic dysregulation may play a role in symptom expression among cannabis users, especially for the positive symptom dimension of psychosis.\\u003c/p\\u003e\\u003cp\\u003eA novel finding of the current study was that anterior cingulate glutamate levels were significantly associated with positive psychosis symptoms only among cannabis users, suggesting that cannabis use modulates the glutamate\\u0026ndash;psychosis relationship. Specifically, lower glutamate predicted greater positive symptom severity in cannabis users but not in non-users. This pattern is consistent with evidence that cannabis can disrupt glutamatergic signaling in CB1 receptor\\u0026ndash;rich cortical regions, potentially leading to maladaptive reductions in excitatory tone or altered glutamate homeostasis in circuits already vulnerable to dysregulation in psychosis\\u003csup\\u003e\\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e85\\u003c/span\\u003e\\u003c/sup\\u003e. Such cannabis-related alterations could diminish glutamatergic efficiency, thereby linking lower glutamate levels to greater positive symptom expression among users. These results raise the possibility that ACC glutamate could serve as a biomarker of vulnerability among cannabis-using individuals on the psychosis spectrum, which could inform risk stratification and targeted interventions.\\u003c/p\\u003e\\u003cp\\u003eImportantly, glutamate was also independently associated with negative symptoms and global functioning across groups, and cannabis use predicted worse outcomes on these measures, indicating broader relevance of glutamatergic dysfunction beyond cannabis-exposed individuals. Given prior reports of regionally specific glutamate reductions in early and chronic psychosis\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e85\\u003c/span\\u003e\\u003c/sup\\u003e, the additive and interactive effects of cannabis use observed here may reflect either direct neurochemical consequences of cannabis or shared neurodevelopmental risk factors, or a combination of both. These findings support a mechanistic framework in which cannabis use interacts with regional glutamatergic vulnerability to influence the severity and profile of psychopathology. Future longitudinal and ultra\\u0026ndash;high field imaging studies will be critical to further clarify causal pathways linking cannabis use, glutamate dysregulation, and clinical outcomes, and to determine whether glutamate-targeted pharmacological or behavioral interventions may be particularly beneficial for cannabis-using individuals with psychosis.\\u003c/p\\u003e\\u003cp\\u003eWe also found that cannabis use was associated with greater severity of depression and mania symptoms across the whole sample, highlighting the transdiagnostic clinical relevance of cannabis use. This is consistent with 1) meta-analytic evidence that heavy cannabis use increases risk for depressive disorders\\u003csup\\u003e\\u003cspan citationid=\\\"CR86\\\" class=\\\"CitationRef\\\"\\u003e86\\u003c/span\\u003e\\u003c/sup\\u003e; and 2) epidemiological findings of high comorbidity between cannabis use and mood disorders\\u003csup\\u003e\\u003cspan citationid=\\\"CR87\\\" class=\\\"CitationRef\\\"\\u003e87\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR88\\\" class=\\\"CitationRef\\\"\\u003e88\\u003c/span\\u003e\\u003c/sup\\u003e. The present associations may reflect overlapping neurobiological vulnerabilities\\u0026mdash;such as altered serotonergic and endocannabinoid signaling\\u0026mdash;that predispose individuals to both cannabis use and mood dysregulation, or the downstream effects of cannabis-related perturbations in neurotransmitter systems on affective processing.\\u003c/p\\u003e\\u003cp\\u003eSelf-reported cannabis use showed high concordance with urine toxicology results, supporting the validity of self-report in assessing cannabis exposure in psychiatric populations. Across the sample, 92% of individuals denying cannabis use tested negative on urine toxicology, and frequent users were far more likely to test positive than infrequent users (87% vs. 13%). This pattern replicates prior work showing that urine toxicology has high specificity and negative predictive value for cannabis\\u003csup\\u003e\\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e82\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR89\\\" class=\\\"CitationRef\\\"\\u003e89\\u003c/span\\u003e\\u003c/sup\\u003e and that concordance is often strongest among individuals with frequent use\\u003csup\\u003e\\u003cspan citationid=\\\"CR90\\\" class=\\\"CitationRef\\\"\\u003e90\\u003c/span\\u003e\\u003c/sup\\u003e. Nonetheless, some discrepancies were observed, with two participants denying cannabis use testing positive on toxicology and overall low rates of positive screens among self-reported users (38%), underscoring important limitations of toxicology measures. Detection windows for urine THC metabolites are finite and may under-detect use in infrequent users\\u003csup\\u003e\\u003cspan citationid=\\\"CR91\\\" class=\\\"CitationRef\\\"\\u003e91\\u003c/span\\u003e\\u003c/sup\\u003e, while contextual factors, stigma, and recall error could bias self-reports\\u003csup\\u003e\\u003cspan citationid=\\\"CR92\\\" class=\\\"CitationRef\\\"\\u003e92\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e93\\u003c/span\\u003e\\u003c/sup\\u003e. These findings highlight the utility of pairing structured self-report cannabis use measures with biological verification, particularly in research on high-risk groups such as individuals with psychosis, where cannabis use is prevalent and clinically relevant\\u003csup\\u003e\\u003cspan citationid=\\\"CR94\\\" class=\\\"CitationRef\\\"\\u003e94\\u003c/span\\u003e\\u003c/sup\\u003e. Combining approaches may increase confidence in exposure classification while also capturing use patterns and frequency not discernible from toxicology alone.\\u003c/p\\u003e\\u003cp\\u003eNotwithstanding the strengths of combining ultra\\u0026ndash;high field 7T \\u003csup\\u003e1\\u003c/sup\\u003eHMRS, biological and self-report measures of cannabis use, and dimensional clinical assessment across a transdiagnostic sample, several limitations should be noted. First, the cross-sectional design limits causal inference about the relationship between glutamate levels, cannabis use, and psychopathology symptom expression. Longitudinal studies are needed to determine whether cannabis-related glutamatergic alterations precede or result from symptom exacerbation. Second, although self-reported cannabis use showed strong concordance with urine toxicology results, the binary nature and limited detection window of the urine screen may underestimate recent use, particularly in infrequent users. Third, although the inclusion of clinical high-risk, psychosis-spectrum, and typically developing individuals enhances generalizability, the modest sample size, particularly within diagnostic subgroups, may limit statistical power to detect more nuanced interaction effects or subgroup-specific associations. Fourth, while advanced tissue correction and spectral quality control were employed, \\u003csup\\u003e1\\u003c/sup\\u003eHMRS glutamate measures remain unable to fully disentangle glutamate from glutamine, do not include information on inhibitory neurometabolites (e.g., GABA), and do not reflect synaptic neurotransmission directly. Lastly, potential confounding effects of other substances (e.g., alcohol, nicotine), antipsychotic medication\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e or comorbid psychiatric conditions were not fully modeled, and future work should expand the analytic framework to include polysubstance exposure and broader transdiagnostic factors.\\u003c/p\\u003e\\u003cp\\u003eIn conclusion, these findings suggest that cannabis use may interact with psychosis-related vulnerability to accentuate glutamatergic dysfunction, and that this neurochemical alteration may contribute to symptom expression, especially in the domain of positive symptoms. Understanding this relationship is critical to elucidate the neurobiological substrates underlying both acute and chronic cannabis effects on mental health.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank the patients and families who participated in this study.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThese data, in part, were presented at the 2025 Congress of the Schizophrenia International Research Society \\u0026nbsp;in Chicago, IL, USA.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data generated and/or analyzed during the current study is available. \\u0026nbsp;Please contact Dr. David Roalf with questions and considerations for data sharing upon reasonable requests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Information\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eContributors Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors contributed to the writing, editing, and approval of this manuscript. DRR and JCS conceptualized and designed the research. MEC, CK, KR, SGR, RCG, JS, HR, AM, CM and REG, prepared and/or collected data on \\u0026nbsp;the assessment tools. \\u0026nbsp;DRR, AA, \\u0026amp; KR performed aspects of the MRI experiments and analysis. DRR, TMM, KR, \\u0026amp; JCS performed the statistical analyses. DRR wrote the first draft of the manuscript. All authors critically reviewed the manuscript\\u0026rsquo;s content and approved the final version for publication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCorresponding author\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCorrespondence to Dr. David Roalf\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics declarations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eParticipants provided informed consent/assent and the study procedures were approved by the Institutional Review Boards at the Children\\u0026rsquo;s Hospital of Philadelphia and the University of Pennsylvania. Participants\\u0026rsquo; privacy and confidentiality were ensured at every stage of the study. We confirm that all methods were performed in accordance with the relevant guidelines and regulations.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of Competing Interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNo authors have any competing interest to report with respect to this manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding information\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRole of the Funding Source:\\u0026nbsp;\\u003c/strong\\u003eThis work was supported by the National Institute of Mental Health grants MH120174 (DRR), MH119185 (DRR), MH119219 (REG), U01 MH119738 (REG), MH117014 (RCG), MH131566 (DHW), and the Dowshen Program for Neuroscience at the University of Pennsylvania and the Lifespan Brain Institute (LiBI).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003ePetrilli K, Ofori S, Hines L, Taylor G, Adams S, Freeman TP. Association of cannabis potency with mental ill health and addiction: a systematic review. \\u003cem\\u003eThe Lancet Psychiatry\\u003c/em\\u003e 2022; 9(9): 736\\u0026ndash;750.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eD'Souza DC. Cannabis, cannabinoids and psychosis: a balanced view. \\u003cem\\u003eWorld psychiatry\\u003c/em\\u003e 2023; 22(2): 231.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSorkhou M, Dent EL, George TP. Cannabis use and mood disorders: a systematic review. \\u003cem\\u003eFrontiers in public health\\u003c/em\\u003e 2024; 12: 1346207.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRobinson T, Ali MU, Easterbrook B, Hall W, Jutras-Aswad D, Fischer B. Risk-thresholds for the association between frequency of cannabis use and the development of psychosis: a systematic review and meta-analysis. \\u003cem\\u003ePsychol Med\\u003c/em\\u003e 2023; 53(9): 3858\\u0026ndash;3868.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKiburi SK, Molebatsi K, Ntlantsana V, Lynskey MT. Cannabis use in adolescence and risk of psychosis: Are there factors that moderate this relationship? A systematic review and meta-analysis. \\u003cem\\u003eSubst Abus\\u003c/em\\u003e 2021; 42(4): 527\\u0026ndash;542.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMurray RM, Quigley H, Quattrone D, Englund A, Di Forti M. Traditional marijuana, high-potency cannabis and synthetic cannabinoids: increasing risk for psychosis. \\u003cem\\u003eWorld psychiatry: official journal of the World Psychiatric Association (WPA)\\u003c/em\\u003e 2016; 15(3): 195\\u0026ndash;204.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eDi Forti M, Marconi A, Carra E, Fraietta S, Trotta A, Bonomo M \\u003cem\\u003eet al.\\u003c/em\\u003e Proportion of patients in south London with first-episode psychosis attributable to use of high potency cannabis: a case-control study. \\u003cem\\u003eThe Lancet Psychiatry\\u003c/em\\u003e 2015; 2(3): 233\\u0026ndash;238.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMarconi A, Di Forti M, Lewis CM, Murray RM, Vassos E. Meta-analysis of the association between the level of cannabis use and risk of psychosis. \\u003cem\\u003eSchizophr Bull\\u003c/em\\u003e 2016; 42(5): 1262\\u0026ndash;1269.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eDi Forti M, Quattrone D, Freeman TP, Tripoli G, Gayer-Anderson C, Quigley H \\u003cem\\u003eet al.\\u003c/em\\u003e The contribution of cannabis use to variation in the incidence of psychotic disorder across Europe (EU-GEI): a multicentre case-control study. \\u003cem\\u003eThe Lancet Psychiatry\\u003c/em\\u003e 2019; 6(5): 427\\u0026ndash;436.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eJones JD, Calkins ME, Scott JC, Bach EC, Gur RE. Cannabis use, polysubstance use, and psychosis spectrum symptoms in a community-based sample of US youth. \\u003cem\\u003eJ Adolesc Health\\u003c/em\\u003e 2017; 60(6): 653\\u0026ndash;659.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eStefanis NC, Delespaul P, Henquet C, Bakoula C, Stefanis C, Van Os J. Early adolescent cannabis exposure and positive and negative dimensions of psychosis. \\u003cem\\u003eAddiction\\u003c/em\\u003e 2004; 99(10): 1333\\u0026ndash;1341.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCarney R, Cotter J, Firth J, Bradshaw T, Yung A. Cannabis use and symptom severity in individuals at ultra high risk for psychosis: a meta-analysis. \\u003cem\\u003eActa Psychiatr Scand\\u003c/em\\u003e 2017; 136(1): 5\\u0026ndash;15.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSantesteban-Echarri O, Liu L, Miller M, Bearden CE, Cadenhead KS, Cannon TD \\u003cem\\u003eet al.\\u003c/em\\u003e Cannabis use and attenuated positive and negative symptoms in youth at clinical high risk for psychosis. \\u003cem\\u003eSchizophr Res\\u003c/em\\u003e 2022; 248: 114\\u0026ndash;121.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ede Medeiros MW, Andrade JC, Haddad NM, Mendon\\u0026ccedil;a M, de Jesus LP, Fekih-Romdhane F \\u003cem\\u003eet al.\\u003c/em\\u003e Cannabis use influences disorganized symptoms severity but not transition in a cohort of non-help-seeking individuals at-risk for psychosis from S\\u0026atilde;o Paulo, Brazil. \\u003cem\\u003ePsychiatry Res\\u003c/em\\u003e 2024; 331: 115665.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSchoeler T, Petros N, Di Forti M, Klamerus E, Foglia E, Ajnakina O \\u003cem\\u003eet al.\\u003c/em\\u003e Effects of continuation, frequency, and type of cannabis use on relapse in the first 2 years after onset of psychosis: an observational study. \\u003cem\\u003eThe Lancet Psychiatry\\u003c/em\\u003e 2016; 3(10): 947\\u0026ndash;953.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSchoeler T, Petros N, Di Forti M, Pingault J-B, Klamerus E, Foglia E \\u003cem\\u003eet al.\\u003c/em\\u003e Association Between Continued Cannabis Use and Risk of Relapse in First-Episode Psychosis: A Quasi-Experimental Investigation Within an Observational Study. \\u003cem\\u003eJAMA psychiatry\\u003c/em\\u003e 2016; 73(11): 1173\\u0026ndash;1179.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLevi L, Bar-Haim M, Winter-van Rossum I, Davidson M, Leucht S, Fleischhacker WW \\u003cem\\u003eet al.\\u003c/em\\u003e Cannabis use and symptomatic relapse in first episode schizophrenia: trigger or consequence? Data from the OPTIMISE Study. \\u003cem\\u003eSchizophr Bull\\u003c/em\\u003e 2023; 49(4): 903\\u0026ndash;913.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eColizzi M, McGuire P, Pertwee RG, Bhattacharyya S. Effect of cannabis on glutamate signalling in the brain: A systematic review of human and animal evidence. \\u003cem\\u003eNeurosci Biobehav Rev\\u003c/em\\u003e 2016; 64: 359\\u0026ndash;381.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eChowdhury KU, Holden ME, Wiley MT, Suppiramaniam V, Reed MN. Effects of Cannabis on Glutamatergic Neurotransmission: The Interplay between Cannabinoids and Glutamate. \\u003cem\\u003eCells\\u003c/em\\u003e 2024; 13(13): 1130.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMerritt K, McCutcheon RA, Aleman A, Ashley S, Beck K, Block W \\u003cem\\u003eet al.\\u003c/em\\u003e Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis. \\u003cem\\u003eMol Psychiatry\\u003c/em\\u003e 2023: 1\\u0026ndash;10.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMerritt K, McGuire PK, Egerton A, Aleman A, Block W, Bloemen OJ \\u003cem\\u003eet al.\\u003c/em\\u003e Association of age, antipsychotic medication, and symptom severity in schizophrenia with proton magnetic resonance spectroscopy brain glutamate level: a mega-analysis of individual participant-level data. \\u003cem\\u003eJAMA psychiatry\\u003c/em\\u003e 2021; 78(6): 667\\u0026ndash;681.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRoalf D, Nanga R, Rupert P, Hariharan H, Quarmley M, Calkins M \\u003cem\\u003eet al.\\u003c/em\\u003e Glutamate imaging (GluCEST) reveals lower brain GluCEST contrast in patients on the psychosis spectrum. \\u003cem\\u003eMol Psychiatry\\u003c/em\\u003e 2017.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSydnor VJ, Roalf DR. A meta-analysis of ultra-high field glutamate, glutamine, GABA and glutathione 1HMRS in psychosis: implications for studies of psychosis risk. \\u003cem\\u003eSchizophr Res\\u003c/em\\u003e 2020; 226: 61\\u0026ndash;69.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSydnor VJ, Larsen B, Kohler C, Crow AJ, Rush SL, Calkins ME \\u003cem\\u003eet al.\\u003c/em\\u003e Diminished reward responsiveness is associated with lower reward network GluCEST: an ultra-high field glutamate imaging study. \\u003cem\\u003eMol Psychiatry\\u003c/em\\u003e 2021; 26(6): 2137\\u0026ndash;2147.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eOleson EB, Hamilton LR, Gomez DM. Cannabinoid modulation of dopamine release during motivation, periodic reinforcement, exploratory behavior, habit formation, and attention. \\u003cem\\u003eFront Synaptic Neurosci\\u003c/em\\u003e 2021; 13: 660218.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLi Z, Mukherjee D, Duric B, Austin-Zimmerman I, Trotta G, Spinazzola E \\u003cem\\u003eet al.\\u003c/em\\u003e Systematic review and meta-analysis on the effects of chronic peri-adolescent cannabinoid exposure on schizophrenia-like behaviour in rodents. \\u003cem\\u003eMol Psychiatry\\u003c/em\\u003e 2024: 1\\u0026ndash;11.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMoriguchi S, Takamiya A, Noda Y, Horita N, Wada M, Tsugawa S \\u003cem\\u003eet al.\\u003c/em\\u003e Glutamatergic neurometabolite levels in major depressive disorder: a systematic review and meta-analysis of proton magnetic resonance spectroscopy studies. \\u003cem\\u003eMol Psychiatry\\u003c/em\\u003e 2019; 24(7): 952\\u0026ndash;964.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLieberman J, Kane J, Alvir J. Provocative tests with psychostimulant drugs in schizophrenia. \\u003cem\\u003ePsychopharmacology (Berl)\\u003c/em\\u003e 1987; 91(4): 415\\u0026ndash;433.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCoyle JT. Glutamate and schizophrenia: beyond the dopamine hypothesis. \\u003cem\\u003eCell Mol Neurobiol\\u003c/em\\u003e 2006; 26(4): 363\\u0026ndash;382.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAllen P, Chaddock CA, Egerton A, Howes OD, Barker G, Bonoldi I \\u003cem\\u003eet al.\\u003c/em\\u003e Functional outcome in people at high risk for psychosis predicted by thalamic glutamate levels and prefronto-striatal activation. \\u003cem\\u003eSchizophr Bull\\u003c/em\\u003e 2015; 41(2): 429\\u0026ndash;439.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBaiano M, David A, Versace A, Churchill R, Balestrieri M, Brambilla P. Anterior cingulate volumes in schizophrenia: a systematic review and a meta-analysis of MRI studies. \\u003cem\\u003eSchizophr Res\\u003c/em\\u003e 2007; 93(1\\u0026ndash;3): 1.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMohn AR, Gainetdinov RR, Caron MG, Koller BH. Mice with reduced NMDA receptor expression display behaviors related to schizophrenia. \\u003cem\\u003eCell\\u003c/em\\u003e 1999; 98(4): 427\\u0026ndash;436.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eDuncan GE, Moy SS, Perez A, Eddy DM, Zinzow WM, Lieberman JA \\u003cem\\u003eet al.\\u003c/em\\u003e Deficits in sensorimotor gating and tests of social behavior in a genetic model of reduced NMDA receptor function. \\u003cem\\u003eBehav Brain Res\\u003c/em\\u003e 2004; 153(2): 507\\u0026ndash;519.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eJentsch JD, Redmond DE, Elsworth JD, Taylor JR, Youngren KD, Roth RH. Enduring cognitive deficits and cortical dopamine dysfunction in monkeys after long-term administration of phencyclidine. \\u003cem\\u003eScience\\u003c/em\\u003e 1997; 277(5328): 953\\u0026ndash;955.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBalla A, Koneru R, Smiley J, Sershen H, Javitt DC. Continuous phencyclidine treatment induces schizophrenia-like hyperreactivity of striatal dopamine release. \\u003cem\\u003eNeuropsychopharmacology\\u003c/em\\u003e 2001; 25(2): 157\\u0026ndash;164.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eJavitt D. Glutamate as a therapeutic target in psychiatric disorders. Nature Publishing Group2004.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHu W, MacDonald ML, Elswick DE, Sweet RA. The glutamate hypothesis of schizophrenia: evidence from human brain tissue studies. \\u003cem\\u003eAnn N Y Acad Sci\\u003c/em\\u003e 2015; 1338(1): 38\\u0026ndash;57.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBeneyto M, Kristiansen LV, Oni-Orisan A, McCullumsmith RE, Meador-Woodruff JH. Abnormal glutamate receptor expression in the medial temporal lobe in schizophrenia and mood disorders. \\u003cem\\u003eNeuropsychopharmacology\\u003c/em\\u003e 2007; 32(9): 1888\\u0026ndash;1902.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGoff DC, Coyle JT. The emerging role of glutamate in the pathophysiology and treatment of schizophrenia. \\u003cem\\u003eAm J Psychiatry\\u003c/em\\u003e 2001.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTsai G, Coyle JT. Glutamatergic mechanisms in schizophrenia. \\u003cem\\u003eAnnu Rev Pharmacol Toxicol\\u003c/em\\u003e 2002; 42(1): 165\\u0026ndash;179.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRubio MD, Wood K, Haroutunian V, Meador-Woodruff JH. Dysfunction of the ubiquitin proteasome and ubiquitin-like systems in schizophrenia. \\u003cem\\u003eNeuropsychopharmacology\\u003c/em\\u003e 2013; 38(10): 1910\\u0026ndash;1920.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHarrison PJ, Weinberger DR. Schizophrenia genes, gene expression, and neuropathology: on the matter of their convergence. Nature Publishing Group2005.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eStefansson H, Petursson H, Sigurdsson E, Steinthorsdottir V, Bjornsdottir S, Sigmundsson T \\u003cem\\u003eet al.\\u003c/em\\u003e Neuregulin 1 and susceptibility to schizophrenia. \\u003cem\\u003eThe American Journal of Human Genetics\\u003c/em\\u003e 2002; 71(4): 877\\u0026ndash;892.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMoghaddam B. Bringing order to the glutamate chaos in schizophrenia. \\u003cem\\u003eNeuron\\u003c/em\\u003e 2003; 40(5): 881\\u0026ndash;884.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWang Y-N, Figueiredo D, Sun X-D, Dong Z-Q, Chen W-B, Cui W-P \\u003cem\\u003eet al.\\u003c/em\\u003e Controlling of glutamate release by neuregulin3 via inhibiting the assembly of the SNARE complex. \\u003cem\\u003eProceedings of the National Academy of Sciences\\u003c/em\\u003e 2018: 201716322.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePalomino A, Gonz\\u0026aacute;lez-Pinto A, Aldama A, Gonz\\u0026aacute;lez-G\\u0026oacute;mez C, Mosquera F, Gonz\\u0026aacute;lez-Garc\\u0026iacute;a G \\u003cem\\u003eet al.\\u003c/em\\u003e Decreased levels of plasma glutamate in patients with first-episode schizophrenia and bipolar disorder. \\u003cem\\u003eSchizophr Res\\u003c/em\\u003e 2007; 95(1): 174\\u0026ndash;178.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMerritt K, Egerton A, Kempton MJ, Taylor MJ, McGuire PK. Nature of Glutamate Alterations in Schizophrenia: A Meta-analysis of Proton Magnetic Resonance Spectroscopy Studies. \\u003cem\\u003eJAMA psychiatry\\u003c/em\\u003e 2016; 73(7): 665\\u0026ndash;674.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGirgis RR, de la Fuente-Sandoval C, Lewis-Fern\\u0026aacute;ndez R, Reyes-Madrigal F, Wall MM, Hua J \\u003cem\\u003eet al.\\u003c/em\\u003e Concerted elevations of cortical and striatal glutamate and GABA in antipsychotic-free individuals at clinical high-risk for psychosis. \\u003cem\\u003eNeuropsychopharmacology\\u003c/em\\u003e 2025: 1\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ede la Fuente-Sandoval C, Leon-Ortiz P, Favila R, Stephano S, Mamo D, Ramirez-Bermudez J \\u003cem\\u003eet al.\\u003c/em\\u003e Higher levels of glutamate in the associative-striatum of subjects with prodromal symptoms of schizophrenia and patients with first-episode psychosis. \\u003cem\\u003eNeuropsychopharmacology\\u003c/em\\u003e 2011; 36(9): 1781\\u0026ndash;1791.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eEgerton A, Stone JM, Chaddock CA, Barker GJ, Bonoldi I, Howard RM \\u003cem\\u003eet al.\\u003c/em\\u003e Relationship between brain glutamate levels and clinical outcome in individuals at ultra high risk of psychosis. \\u003cem\\u003eNeuropsychopharmacology\\u003c/em\\u003e 2014.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eUhl I, Mavrogiorgou P, Norra C, Forstreuter F, Scheel M, Witthaus H \\u003cem\\u003eet al.\\u003c/em\\u003e 1H-MR spectroscopy in ultra-high risk and first episode stages of schizophrenia. \\u003cem\\u003eJ Psychiatr Res\\u003c/em\\u003e 2011; 45(9): 1135\\u0026ndash;1139.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eStone JM, Day F, Tsagaraki H, Valli I, McLean MA, Lythgoe DJ \\u003cem\\u003eet al.\\u003c/em\\u003e Glutamate dysfunction in people with prodromal symptoms of psychosis: relationship to gray matter volume. \\u003cem\\u003eBiol Psychiatry\\u003c/em\\u003e 2009; 66(6): 533\\u0026ndash;539.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFusar-Poli P, Stone JM, Broome MR, Valli I, Mechelli A, McLean MA \\u003cem\\u003eet al.\\u003c/em\\u003e Thalamic glutamate levels as a predictor of cortical response during executive functioning in subjects at high risk for psychosis. \\u003cem\\u003eArch Gen Psychiatry\\u003c/em\\u003e 2011; 68(9): 881.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTandon N, Bolo NR, Sanghavi K, Mathew IT, Francis AN, Stanley JA \\u003cem\\u003eet al.\\u003c/em\\u003e Brain metabolite alterations in young adults at familial high risk for schizophrenia using proton magnetic resonance spectroscopy. \\u003cem\\u003eSchizophr Res\\u003c/em\\u003e 2013.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRigucci S, Xin L, Klauser P, Baumann PS, Alameda L, Cleusix M \\u003cem\\u003eet al.\\u003c/em\\u003e Cannabis use in early psychosis is associated with reduced glutamate levels in the prefrontal cortex. \\u003cem\\u003ePsychopharmacology (Berl)\\u003c/em\\u003e 2018; 235: 13\\u0026ndash;22.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSami M, Worker A, Colizzi M, Annibale L, Das D, Kelbrick M \\u003cem\\u003eet al.\\u003c/em\\u003e Association of cannabis with glutamatergic levels in patients with early psychosis: Evidence for altered volume striatal glutamate relationships in patients with a history of cannabis use in early psychosis. \\u003cem\\u003eTranslational psychiatry\\u003c/em\\u003e 2020; 10(1): 111.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eColizzi M, Weltens N, McGuire P, Lythgoe D, Williams S, Van Oudenhove L \\u003cem\\u003eet al.\\u003c/em\\u003e Delta-9-tetrahydrocannabinol increases striatal glutamate levels in healthy individuals: implications for psychosis. \\u003cem\\u003eMol Psychiatry\\u003c/em\\u003e 2020; 25(12): 3231\\u0026ndash;3240.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBarch DM, Bustillo J, Gaebel W, Gur R, Heckers S, Malaspina D \\u003cem\\u003eet al.\\u003c/em\\u003e Logic and justification for dimensional assessment of symptoms and related clinical phenomena in psychosis: relevance to DSM-5. \\u003cem\\u003eSchizophr Res\\u003c/em\\u003e 2013; 150(1): 15\\u0026ndash;20.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePhalen P, Millman Z, Rouhakhtar PR, Andorko N, Reeves G, Schiffman J. Categorical versus dimensional models of early psychosis. \\u003cem\\u003eEarly intervention in psychiatry\\u003c/em\\u003e 2022; 16(1): 42\\u0026ndash;50.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eNasreddine ZS, Phillips NA, B\\u0026eacute;dirian V, Charbonneau S, Whitehead V, Collin I \\u003cem\\u003eet al.\\u003c/em\\u003e The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. \\u003cem\\u003eJ Am Geriatr Soc\\u003c/em\\u003e 2005; 53(4): 695\\u0026ndash;699.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eEndicott J, Spitzer RL, Fleiss JL, Cohen J. The global assessment scale. A procedure for measuring overall severity of psychiatric disturbance. \\u003cem\\u003eArch Gen Psychiatry\\u003c/em\\u003e 1976; 33(6): 766\\u0026ndash;771.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCalkins ME, Moore TM, Satterthwaite TD, Wolf DH, Turetsky BI, Roalf DR \\u003cem\\u003eet al.\\u003c/em\\u003e Persistence of psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort: a prospective two-year follow‐up. \\u003cem\\u003eWorld Psychiatry\\u003c/em\\u003e 2017; 16(1): 62\\u0026ndash;76.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFirst MB, Spitzer RL, Gibbon M, Williams JBW. \\u003cem\\u003eStructured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition (SCID-I/P)\\u003c/em\\u003e. Biometrics Research, New York State Psychiatric Institute: New York, 2002.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMcGlashan TH, Miller TJ, Woods SW,. Structured Interview for Prodromal Syndromes, Version 4.0. 2003.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMcGlashan T, Walsh B, Woods S. \\u003cem\\u003eThe psychosis-risk syndrome: handbook for diagnosis and follow-up\\u003c/em\\u003e. Oxford University Press2010.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCalkins ME, Merikangas KR, Moore TM, Burstein M, Behr MA, Satterthwaite TD \\u003cem\\u003eet al.\\u003c/em\\u003e The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative. 2015; 56: 1356\\u0026ndash;1369.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMoore TM, Calkins ME, Wolf DH, Satterthwaite TD, Barzilay R, Scott JC \\u003cem\\u003eet al.\\u003c/em\\u003e Estimation and Validation of the \\u0026ldquo;c\\u0026rdquo; Factor for Overall Cerebral Functioning in the Philadelphia Neurodevelopmental Cohort. \\u003cem\\u003eApplied Sciences\\u003c/em\\u003e 2025; 15(4): 1697.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTang S, Yi J, Calkins M, Whinna D, Kohler C, Souders M \\u003cem\\u003eet al.\\u003c/em\\u003e Psychiatric disorders in 22q11. 2 deletion syndrome are prevalent but undertreated. \\u003cem\\u003ePsychol Med\\u003c/em\\u003e 2014; 44(06): 1267\\u0026ndash;1277.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSatterthwaite TD, Wolf DH, Loughead J, Ruparel K, Valdez JN, Siegel SJ \\u003cem\\u003eet al.\\u003c/em\\u003e Association of enhanced limbic response to threat with decreased cortical facial recognition memory response in schizophrenia. \\u003cem\\u003eAm J Psychiatry\\u003c/em\\u003e 2010; 167(4): 418\\u0026ndash;426.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSatterthwaite TD, Elliott MA, Ruparel K, Loughead J, Prabhakaran K, Calkins ME \\u003cem\\u003eet al.\\u003c/em\\u003e Neuroimaging of the Philadelphia neurodevelopmental cohort. \\u003cem\\u003eNeuroimage\\u003c/em\\u003e 2014; 86: 544\\u0026ndash;553.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCalkins ME, Moore TM, Merikangas KR, Burstein M, Satterthwaite TD, Bilker WB \\u003cem\\u003eet al.\\u003c/em\\u003e The psychosis spectrum in a young US community sample: findings from the Philadelphia Neurodevelopmental Cohort. \\u003cem\\u003eWorld Psychiatry\\u003c/em\\u003e 2014; 13(3): 296\\u0026ndash;305.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKobayashi H, Nemoto T, Koshikawa H, Osono Y, Yamazawa R, Murakami M \\u003cem\\u003eet al.\\u003c/em\\u003e A self-reported instrument for prodromal symptoms of psychosis: testing the clinical validity of the PRIME Screen-Revised (PS-R) in a Japanese population. \\u003cem\\u003eSchizophr Res\\u003c/em\\u003e 2008; 106(2\\u0026ndash;3): 356.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMiller T. The SIPS-Screen: a brief self-report screen to detect the schizophrenia prodrome. \\u003cem\\u003eSchizophr Res\\u003c/em\\u003e 2004: 78.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHan C, McGue MK, Iacono WG. Lifetime tobacco, alcohol and other substance use in adolescent Minnesota twins: univariate and multivariate behavioral genetic analyses. \\u003cem\\u003eAddiction\\u003c/em\\u003e 1999; 94(7): 981\\u0026ndash;993.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCuttler C, Spradlin A. Measuring cannabis consumption: Psychometric properties of the Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Inventory (DFAQ-CU). \\u003cem\\u003ePLoS One\\u003c/em\\u003e 2017; 12(5): e0178194.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGette JA, Littlefield AK, Victor SE, Schmidt AT, Garos S. Evaluation of the Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Questionnaire and its Relations to Cannabis-Related Problems. \\u003cem\\u003eCannabis (Albuquerque, NM)\\u003c/em\\u003e 2023; 6(3): 64\\u0026ndash;86.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMeier MH, Caspi A, Ambler A, Harrington H, Houts R, Keefe RSE \\u003cem\\u003eet al.\\u003c/em\\u003e Persistent cannabis users show neuropsychological decline from childhood to midlife. \\u003cem\\u003eProc Natl Acad Sci U S A\\u003c/em\\u003e 2012; 109(40): E2657\\u0026ndash;2664.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eScott JC, Wolf DH, Calkins ME, Bach EC, Weidner J, Ruparel K \\u003cem\\u003eet al.\\u003c/em\\u003e Cognitive functioning of adolescent and young adult cannabis users in the Philadelphia Neurodevelopmental Cohort. \\u003cem\\u003ePsychol Addict Behav\\u003c/em\\u003e 2017; 31(4): 423\\u0026ndash;434.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBianconi F, Bonomo M, Marconi A, Kolliakou A, Stilo SA, Iyegbe C \\u003cem\\u003eet al.\\u003c/em\\u003e Differences in cannabis-related experiences between patients with a first episode of psychosis and controls. \\u003cem\\u003ePsychol Med\\u003c/em\\u003e 2016; 46(5): 995\\u0026ndash;1003.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLorenzetti V, Hindocha C, Petrilli K, Griffiths P, Brown J, Castillo-Carniglia \\u0026Aacute; \\u003cem\\u003eet al.\\u003c/em\\u003e The iCannToolkit: a tool to embrace measurement of medicinal and non-medicinal cannabis use across licit, illicit and cross-cultural settings. \\u003cem\\u003eAddiction (Abingdon, England)\\u003c/em\\u003e 2022; 117(6): 1523\\u0026ndash;1525.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eOeltzschner G, Z\\u0026ouml;llner HJ, Hui SC, Mikkelsen M, Saleh MG, Tapper S \\u003cem\\u003eet al.\\u003c/em\\u003e Osprey: Open-source processing, reconstruction \\u0026amp; estimation of magnetic resonance spectroscopy data. \\u003cem\\u003eJ Neurosci Methods\\u003c/em\\u003e 2020; 343: 108827.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGasparovic C, Song T, Devier D, Bockholt HJ, Caprihan A, Mullins PG \\u003cem\\u003eet al.\\u003c/em\\u003e Use of tissue water as a concentration reference for proton spectroscopic imaging. \\u003cem\\u003eMagnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine\\u003c/em\\u003e 2006; 55(6): 1219\\u0026ndash;1226.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLong JA. interactions: Comprehensive, User-Friendly Toolkit for Probing Interactions. 2024.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLenth RV. Least-squares means: the R package lsmeans. \\u003cem\\u003eJournal of statistical software\\u003c/em\\u003e 2016; 69: 1\\u0026ndash;33.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eNiznikiewicz M, Lin A, DeLisi LE. The Relationship of glutamate signaling to cannabis use and schizophrenia. \\u003cem\\u003eCurrent Opinion in Psychiatry\\u003c/em\\u003e 2025; 38(3): 177\\u0026ndash;181.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLev-Ran S, Roerecke M, Le Foll B, George T, McKenzie K, Rehm J. The association between cannabis use and depression: a systematic review and meta-analysis of longitudinal studies. \\u003cem\\u003ePsychol Med\\u003c/em\\u003e 2014; 44(4): 797\\u0026ndash;810.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFeingold D, Weinstein A. Cannabis and depression. \\u003cem\\u003eCannabinoids and Neuropsychiatric Disorders\\u003c/em\\u003e 2020: 67\\u0026ndash;80.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSchoeler T, Ferris J, Winstock AR. Rates and correlates of cannabis-associated psychotic symptoms in over 230,000 people who use cannabis. \\u003cem\\u003eTranslational psychiatry\\u003c/em\\u003e 2022; 12(1): 369.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSalottolo K, McGuire E, Madayag R, Tanner AH, Carrick MM, Bar-Or D. Validity between self-report and biochemical testing of cannabis and drugs among patients with traumatic injury: brief report. \\u003cem\\u003eJournal of Cannabis Research\\u003c/em\\u003e 2022; 4(1): 29.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSkelton KR, Donahue E, Benjamin-Neelon SE. Validity of self-report measures of cannabis use compared to biological samples among women of reproductive age: a scoping review. \\u003cem\\u003eBMC Pregnancy Childbirth\\u003c/em\\u003e 2022; 22(1): 344.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFink DS, Samples H, Malte CA, Olfson M, Wall MM, Alschuler DM \\u003cem\\u003eet al.\\u003c/em\\u003e Cannabis legalization and increasing cannabis use in the United States: Data from urine toxicology testing in emergency room patients. \\u003cem\\u003eInternational Journal of Drug Policy\\u003c/em\\u003e 2025; 138: 104765.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBharat C, Webb P, Wilkinson Z, McKetin R, Grebely J, Farrell M \\u003cem\\u003eet al.\\u003c/em\\u003e Agreement between self-reported illicit drug use and biological samples: a systematic review and meta‐analysis. \\u003cem\\u003eAddiction\\u003c/em\\u003e 2023; 118(9): 1624\\u0026ndash;1648.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePalamar JJ, Le A, Guarino H, Mateu-Gelabert P. A comparison of the utility of urine-and hair testing in detecting self-reported drug use among young adult opioid users. \\u003cem\\u003eDrug Alcohol Depend\\u003c/em\\u003e 2019; 200: 161\\u0026ndash;167.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eOliver D, Chesney E, Cullen AE, Davies C, Englund A, Gifford G \\u003cem\\u003eet al.\\u003c/em\\u003e Exploring causal mechanisms of psychosis risk. \\u003cem\\u003eNeurosci Biobehav Rev\\u003c/em\\u003e 2024; 162: 105699.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"molecular-psychiatry\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"mp\",\"sideBox\":\"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)\",\"snPcode\":\"41380\",\"submissionUrl\":\"https://mts-mp.nature.com/cgi-bin/main.plex\",\"title\":\"Molecular Psychiatry\",\"twitterHandle\":\"@molpsychiatry\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7802376/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7802376/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"Cannabis use is linked to elevated psychosis risk, yet the neurobiological mechanisms that couple use to symptom expression remain unclear. Because glutamatergic dysregulation has been implicated in both cannabis effects and psychosis vulnerability, we examined whether brain glutamate relates to dimensional symptoms as a function of cannabis use across the psychosis spectrum. Seventy-nine participants—typically developing controls, clinical high-risk individuals, and patients with psychosis—completed dimensional clinical assessments, detailed cannabis surveys, urine toxicology, and ultra-high-field 7T 1HMRS quantification of anterior cingulate cortex (ACC) glutamate levels. Linear models assessed the main and interactive effects of ACC glutamate and cannabis use on positive and negative symptoms. Self-reported cannabis use showed strong concordance with urine toxicology. Cannabis use was associated with higher positive and negative symptoms. Independently, higher ACC glutamate predicted greater positive and negative symptoms. Notably, lower glutamate levels were associated with higher positive symptoms in cannabis users. Exploratory analyses suggested interactions for depressive and manic symptoms, indicating that glutamatergic abnormalities may amplify the overall severity of cannabis-related symptoms. Sensitivity analyses revealed lower ACC glutamate in psychosis patients—especially cannabis users—highlighting diagnostic group differences and reinforcing the link between cannabis exposure and glutamatergic dysfunction. These findings implicate ACC glutamatergic dysfunction as a transdiagnostic correlate of symptom burden, particularly in those with psychosis who are cannabis users. Glutamate-targeted interventions and longitudinal designs will be needed to examine causal pathways linking cannabis exposure to psychosis-relevant outcomes.\",\"manuscriptTitle\":\"Cannabis Use and Glutamate across the Psychosis Spectrum: In Vivo Evidence from 7T Proton Magnetic Resonance Spectroscopy\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-23 11:45:26\",\"doi\":\"10.21203/rs.3.rs-7802376/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"revise\",\"date\":\"2026-01-19T10:14:06+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2025-12-18T14:35:50+00:00\",\"index\":4,\"fulltext\":\"This content is not available.\"},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2025-11-30T09:03:18+00:00\",\"index\":2,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2025-11-26T12:53:56+00:00\",\"index\":4,\"fulltext\":\"This content is not available.\"},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2025-11-26T11:43:34+00:00\",\"index\":1,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2025-11-19T08:37:03+00:00\",\"index\":3,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2025-11-17T00:55:38+00:00\",\"index\":2,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2025-11-13T04:14:56+00:00\",\"index\":1,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-11-11T16:29:04+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-10-09T10:12:48+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-10-09T09:55:19+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Molecular Psychiatry\",\"date\":\"2025-10-08T13:26:41+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksFailed\",\"content\":\"\",\"date\":\"2025-10-08T10:45:36+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"molecular-psychiatry\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"mp\",\"sideBox\":\"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)\",\"snPcode\":\"41380\",\"submissionUrl\":\"https://mts-mp.nature.com/cgi-bin/main.plex\",\"title\":\"Molecular Psychiatry\",\"twitterHandle\":\"@molpsychiatry\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"b2265a98-06ac-40a1-b142-9cc86b9fe261\",\"owner\":[],\"postedDate\":\"November 23rd, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[{\"id\":56018348,\"name\":\"Biological sciences/Neuroscience\"},{\"id\":56018349,\"name\":\"Health sciences/Diseases/Psychiatric disorders/Schizophrenia\"}],\"tags\":[],\"updatedAt\":\"2026-05-12T15:46:34+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-23 11:45:26\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7802376\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7802376\",\"identity\":\"rs-7802376\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}