Multimodal MRI data fusion reveals distinct structural, functional and neurochemical correlates of heavy cannabis use

Heavy cannabis use (HCU) is linked to changes in both brain structure and function, even in individuals without a cannabis-use disorder or other major mental illnesses. This study used multimodal MRI to examine grey matter volume and intrinsic neural activity in 24 heavy users and 16 controls. Two distinct brain component patterns differed between groups: one involving the cerebellum, temporal, and thalamic regions (structural), and another involving frontal and parietal regions (functional). These patterns correlated with specific cannabis-use behaviors. Additionally, the structural changes were associated with the serotonergic system, while functional changes were linked to serotonergic, dopaminergic, and μ-opioid receptor systems, offering new insights into the neural mechanisms of heavy cannabis use.

Study at a glance

Abstract

Heavy cannabis use (HCU) is frequently associated with a plethora of cognitive, psychopathological and sensorimotor phenomena. Although HCU is frequent, specific patterns of abnormal brain structure and function underlying HCU in individuals presenting without cannabis-use disorder or other current and life-time major mental disorders are unclear at present. This multimodal magnetic resonance imaging (MRI) study examined resting-state functional MRI (rs-fMRI) and structural MRI (sMRI) data from 24 persons with HCU and 16 controls. Parallel independent component analysis (p-ICA) was used to examine covarying components among grey matter volume (GMV) maps computed from sMRI and intrinsic neural activity (INA), as derived from amplitude of low-frequency fluctuations (ALFF) maps computed from rs-fMRI data. Further, we used JuSpace toolbox for cross-modal correlations between MRI-based modalities with nuclear imaging derived estimates, to examine specific neurotransmitter system changes underlying HCU. We identified two transmodal components, which significantly differed between the HCU and controls (GMV: p = 0.01, ALFF p = 0.03, respectively). The GMV component comprised predominantly cerebello-temporo-thalamic regions, whereas the INA component included fronto-parietal regions. Across HCU, loading parameters of both components were significantly associated with distinct HCU behavior. Finally, significant associations between GMV and the serotonergic system as well as between INA and the serotonergic, dopaminergic and μ-opioid receptor system were detected. This study provides novel multimodal neuromechanistic insights into HCU suggesting co-altered structure/function-interactions in neural systems subserving cognitive and sensorimotor functions.