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Objectives Increasing evidence implicates cannabis consumption as a key risk factor in the development of psychosis, but the mechanisms underpinning this relationship remain understudied. This study proposes to determine whether sleep disruption acts as a mediator of the cannabis-to-psychosis relationship. Study Design This longitudinal study assessed measures of cannabis use frequency, sleep quality (SQ), and psychotic-like experiences (PLEs) were collected using self-reported questionnaires. Data were collected from September 2012 to September 2018. Data were collected from a general population sample of adolescents who entered the seventh grade in 31 schools in the Greater Montreal area. The study uses data collected on an annual basis from 3801 high school students from grades 7 to 11. The aforementioned measures were measured using the Detection of Alcohol and Drug Problems in Adolescents questionnaire, a SQ Likert scale, and measures the Psychotic-Like Experiences Questionnaire for Children. Study Results Results show a reciprocal 1-year cross-lagged effect of cannabis use and sleep (β = −0.076, 95% CI = −0.037 to −0.018, P = .000), of sleep on cannabis use (β = −.016, 95% CI = −0.025 to −0.006, P = .007), of sleep on PLEs (β = −0.077, 95%CI = −0.014 to −0.051, P = .000), and of PLEs on sleep (β = −0.027, 95% CI = −0.037 to −0.018, P = .000). We additionally found a 2 years indirect lagged-effect of cannabis use on PLEs (β = 0.068, 95% CI = 0.024 to 0.113, P = .011) mediated by 1-year sleep (β = 0.006, 95% CI = 0.003 to 0.009, P = .001). Conclusions Our results suggest sleep disruptions simultaneously aggravate, and are aggravated by, cannabis addiction and PLEs. The longitudinal sleep-mediated effect of cannabis use on PLEs encourages further research into the role of sleep as a potential therapeutic target in the prevention of cannabis-related psychosis.

The externalizing psychopathological dimension is associated with alterations in adolescents’ functional brain connectivity. The current study aims to identify the functional correlates of the unique variability in conduct problems within the context of the broad externalizing dimension. The broad externalizing dimension and unique variability in conduct problems were estimated using a bifactor model. Resting-state data were available for a sample of 125 adolescents. Based on multiresolution parcellation of functional brain networks atlas, major resting-state functional brain networks and the connectivity correlates of unique conduct problems and the broad externalizing dimension were established. The broad externalizing dimension was related to connectivity alterations in the ventral attention/salience network, while unique variability in conduct problems dimension was related to connectivity alterations in the cerebellum crusi as well as the mesolimbic network. The current study is a first step toward the identification of functional resting-state network correlates of broad and specific variability in the externalizing dimension.

Although the peak onset of depressive symptoms occurs during adolescence, very few studies have directly examined depression-related changes in resting-state (RS) default mode network activity during adolescence, controlling for potential neural markers of risk.This study used data from a longitudinal adolescent cohort to investigate age-specific, persistent (i.e., lagged), and dynamic associations between RS functional connectivity within the default mode network and depressive symptoms during adolescence using a random intercept cross-lagged panel framework. The Neuroventure sample consisted of 151 adolescents ages 12-14 at study entry without any neurological illness who were assessed three times during a 5-year follow-up with 97% follow-up across the three assessments. Depressive symptoms were measured using the depression subscale of the Brief Symptoms Inventory. RS functional magnetic resonance imaging data were collected using a 3T Siemens Magnetom Trio scanner in a single 6-minute sequence.After controlling for relationships between random intercepts, future depression risk was predicted by RS couplings in the perigenual anterior cingulate cortex and anterior dorsomedial prefrontal cortex (β = -0.69, p = .014) and in the left inferior parietal lobule and anterior superior frontal gyrus (β = -0.43, p = .035). Increases in depressive symptoms at previous time points significantly predicted changes in functional connectivity between the posterior cingulate cortex and the precuneus and posterior middle temporal gyrus (β = 0.37, p = .039) and between the dorsal precuneus and posterior middle temporal gyrus (β = 0.47, p = .036).This study was able to disassociate the RS brain markers of depression from those that appear to follow early-onset depression.