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68. The Impact of Cannabis Use on Resting-State Networks in the Psychosis Risk Period: Cross-Sectional and Longitudinal Implications of Use
Cannabis is used widely across the stages of psychotic illness, from individuals who are chronically ill to those at clinical high-risk (CHR). While there is now an established link between cannabis use and psychosis symptoms, our understanding of potential mechanism remains limited, particularly with respect to CHR states immediately preceding the onset of psychotic disorders. In this context, resting-state networks remain an important target for elucidating our mechanistic understanding in this area. Resting-state networks are a critical target as they are sensitive to active compounds such as tetrahydrocannabinol (THC) in normative populations and predict outcomes such as psychosis onset in CHR individuals. Additionally resting-state network features change both subtly and dramatically as a function of the adolescent/young-adult development that may be critical to understanding the interplay of developmental and environmental pathogenic mechanisms in the prodrome. In the present study we will examine resting-state network differences between CHR individuals who use cannabis (n=51), CHR individuals who do not use cannabis (n=23), and typically developing individuals (TD; n=31) who do not use cannabis (Aim 1). Using graph theory analyses, we will examine how network structure may be impacted by cannabis use in major resting state networks (e.g., default mode network). In a subset of the sample where two time points of imaging data are available (66 total; CHR cannabis users, n=31; CHR non-cannabis users, n=14; and TD, n=21), we will assess the impact of cannabis use over a one-year period on the development of resting-state networks (Aim 2). Networks will be evaluated with respect to group differences as well as links with emerging positive and negative symptoms in the cross-sectional study and be employed to better understanding normative and pathological developmental brain changes in the longitudinal analyses.