10 CIP patients and 12 NPCU were recruited for this study. All patients were recruited at the psychiatric inward of the University Policlinico Hospital of Milan, Italy, whereas the cannabis users were enrolled in the Milan catchment area. All patients were in stable pharmacological treatment. Either left- or right- handed participants were included. All participants had a habitual cannabis consumption and the type of drug, the frequency and the duration, as well socio-demographic, clinical and psychosocial parameters of dependency were measured. The study was approved by the local ethical committee.

1. Participants

1. Use the following inclusion criteria: For patients: age 18-45 years old, DSM-IV diagnosis of Cannabis-induced Psychotic Disorder, heavy cannabis consumption at the time of the study and in the previous 6 months. For NPCU: age 18-45 years old, no DSM-IV diagnosis, heavy cannabis consumption at the time of the study and in the previous 6 months.
2. Use the following exclusion criteria: a diagnosis of mental retardation, any current major medical or neurological illness, a history of traumatic head injury with loss of consciousness, and any other Axis I, including alcohol abuse, or Axis II disorders and pregnancy. Verify that psychotic symptoms do not precede the onset of the cannabis use and do not persist for a substantial period of time after the cessation of acute withdrawal or severe intoxication. Verify that there is no history of recurrent nonsubstance-related episodes.
3. To obtain informed consent read the consent form to the participants. Have both the participant and the investigator sign the consent form in duplicate. Store the consent form for records.
4. To evaluate the diagnosis of CIP patients, use the Structured Clinical Interview for Diagnosis (SCID-I) of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision (DSM-IV-TR)²⁷.
5. To establish the frequency and the duration of dependency, use the manual for the semistructured clinical interview for children and adolescents SCICA²⁸.

2. Clinical and psychosocial evaluation

NOTE: Several clinical and psychosocial scales were administered to all the participants.

1. To evaluate psychiatric symptoms, use the Brief Psychiatric Rating Scale (BPRS)²⁹, the Young Mania Rating Scale (YMRS)³⁰, the Montgomery-Åsberg Depression Rating Scale (MADRS)³¹, the Hamilton Depression Rating Scale (HAM-D)³² and the Hamilton Anxiety Rating Scale (HAM-A)³³.
2. To explore the presence of trauma or infection during or immediately after the partum, use the Murray-Lewis Obstetric Complications Scale (MLOCS)³⁴.
3. To assess experiences of neglect or abuse, use the Childhood Experience of Care and Abuse Questionnaire (CECA-Q)³⁵.
4. To estimate the Socio-economic status (SES), use the Socio Economic Status Scale of MacArthur³⁶.
5. Use the Neighbourhood Scale (NS)³⁷ to assess the specific characteristics of the neighbourhood, in terms of neighbourhood satisfaction (NS-A), sense of security (NS-B), level of degradation (NS-C), willingness on the part of fellow citizens to intervene in adverse situations (NS-D), and degree of acceptance of substances (NS-E).
6. Employ the Temperament and Character Inventory (TCI-125) for exploring personality traits^38,39.
7. To assess the quality of life and the global functioning use the Manchester Short Assessment of Quality of Life (MANSA)⁴⁰ and the Quality of Life Index (QL-index)⁴¹ and the Global Assessment of Functioning (GAF)²⁷ scales, respectively.
   NOTE: All socio-demographic and clinical data are summarized in Table 1.

3. Magnetic resonance imaging

1. Insert the participant in a supine position on the bed of the 3 Tesla MRI scanner.
2. Place a radio frequency (RF) coil over the participant’s head.
3. Provide earplugs and headphones to block background noise.
4. Attach foam pads to immobilize the head.
5. Instruct the subject to remain still.
6. Run MRI session from the workstation in the control room.
   1. Run a 3-plane gradient echo scan for alignment and localization and perform a shim procedure to generate a homogeneous, constant magnetic field.
   2. Start an echo-planar-imaging protocol for MRI. The acquisition parameters for the acquisition of high-resolution T1-weighted three-dimensional brain scan are already set in the imaging program and should not be changed. The parameters are: repetition time [TR] = 9.8, echo time [TE] = 4.6 ms, in plane voxel size= 0.9375 × 0.9375, matrix= 256 × 256, flip angle = 8°.
7. Remove the participant from the MR scanner room. Transfer the MR data to disk and close the session.
   NOTE: A total of 185 contiguous 1 mm sagittal slices extending superiorly from the inferior aspect of the cerebellum to encompass most of the brain were selected from a sagittal localizer scan.

4. Pre-processing steps

NOTE: A voxel-based morphometry analysis should be performed using Statistical Parametric Mapping (SPM12) implemented in MATLAB.

1. Perform the following pre-processing steps, shown in the Script_pre-processing script file, before carrying out group analyses.
   1. Segmentation: Process the structural image to distinguish and separate the white matter tissues, the grey matter tissues and the cerebrospinal fluid into different images. This separation is obtained thanks to the combination of probability maps, elaborated from the general knowledge of tissue distribution combined with model cluster analyses that identifies voxel distributions of specific tissues in the original image. Run the segment.mat batch file.
   2. DARTEL (Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra) tools: determine the nonlinear deformations for registering the GM and white matter images of all participants. Run the create_template.mat batch file.
   3. Normalization: during the spatial normalization phase, adapt MRI images to an anatomical standard template. This is because every subject has little differences in the form and organization of the brain such as the size and morphologic differences in structures. Run the normalize_to_MNI.mat batch file.
   4. Spatial Smoothing: after motion correction, perform an isotropic Gaussian kernel of 6 mm full width at half maximum Gaussian kernel to increase the signal-to-noise ratio and to account for subtle variations in anatomic structures. Run the normalize_to_MNI.mat batch file.
   5. Extract the total intracranial volume (ICV) using SPM12: it can be obtained by adding up the density values in GM, white matter, and CSF class images and multiplying by the voxel volumes.
      NOTE: Once the pre-processing is completed, it is possible to elaborate the data.
      NOTE: Please refer to the SPM manual (https://www.fil.ion.ucl.ac.uk/spm/doc/spm12_manual.pdf) that provides a detailed description of the pre-processing steps employed in this study and the SPM commands to use. Please also refer to the script and Matlab batches included in the supplementary materials with the exact pre-processing steps used for this study.

5. Statistical analyses

1. Perform chi-square tests (categorical variables) and two sample t tests (quantitative variables) for exploring differences between the two groups on demographic, clinical and psychosocial scale.
2. Perform a one-way Analysis of Variance (ANOVA), in the context of a General Linear Model (GLM) design to compare GM volumes between CIP patients and NPCU. Gender and age were used as controlling variables in all the analyses. Run the one-way ANOVA batch file.
3. Carry out whole-brain regression analyses, only for the CIP group, to explore whether the scores in all the clinical and psychosocial scales employed in this study were significantly correlated with GM volumes changes. Do not use any brain mask but consider all voxels. Run the Regression analysis batch file with the clinical scale of interest.
4. Convert stereotactic coordinates of the peak maxima of the suprathreshold clusters from the MNI spatial array (www.mni.mcgill.ca) to that of Talairach and Tournoux⁴².
   NOTE: In all the neuroanatomical analyses, the volumetric differences among subjects were considered by proportional scaling for the total intracranial volume (ICV).
   1. For the ANOVA, set the significance threshold to p < 0.001 uncorrected, with a minimum cluster size of k=30, whereas for the multiple regression analyses, a p < 0.05 peak Family-Wise Error (pFWE) corrected was considered significant and a minimum cluster size of k=10 was employed. The former threshold was considered due to the small sample size employed in this study and therefore the results emerged from this analysis must be considered as preliminary. The latter threshold is more stringent since the p-value is corrected for multiple comparisons.
      NOTE: Please refer to the VBM8 Manual for more details about post-processing steps (http://dbm.neuro.uni-jena.de/vbm8/VBM8-Manual.pdf). Please also refer to the Matlab batches named “one-way ANOVA” and “Regression analysis” included in the supplementary materials with the exact model used for this study. Due to the exploratory nature of this study, a formal sample size calculation would have been of little value and therefore it was not performed.