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

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 cerebellotemporo-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/functioninteractions in neural systems subserving cognitive and sensorimotor functions.

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping

“…Interest in utilizing sMRI data to analyze brain morphometry continues to grow. SMRI features have successfully been applied to examine the structure and function-alterations in different domains [32, 33], patient classifications [34, 35, 36], and in a multimodal study [37, 38]. In a scenario where data is only locally accessible and cannot be pooled in a central location, many established algorithms, like constrained SBM, can not operate.…”

Nicotine addiction and overweight affect intrinsic neural activity and neurotransmitter activity: A fMRI study of interaction effects

“…In addition, fALFF values that significantly altered in main and interaction effect analyses were used as input for spatial correlation with PET-and SPECT-derived maps in JuSpace (https://github.com/ juryxy/JuSpace) to examine specific neurotransmitter system changes underlying nicotine addiction and weight. 29,30 Independent z-score maps based on all 12 PET and SPECT maps implemented in JuSpace [the serotonergic system (5HT1a, 5HT1b, 5HT2a, SERT DASB HC30, and SERT MADAM); the dopaminergic system (D1, D2, DAT, FDOPA); the GABAergic system (GABAa); the μ-opioid receptor system (MU); the noradrenaline system (NAT)] of main effect and interaction effect were computed using Spearman correlations analysis (based on the Neuromorphometrics atlas; exact Pvalues, N = 10 000 permutations; adjusted for spatial autocorrelation). 30 To examine the association of fALFF change with nicotine addiction severity and BMI status, we carried out Spearman's correlation analyses between brain regions (altered in interaction effect analyses) and clinical data (smoking years, pack-year, FTND score, and BMI value).…”

“…2 JuSpace toolbox is allowed for examination of a variety of neurotransmitter systems. 30 We can better understand the relationship between INA changes and the underlying molecular features. 543 Our study reveals the interaction of nicotine addiction and overweight on brain activity from neuroimaging and molecular imaging perspectives, which may be the reason for the inconsistent results of previous studies on nicotine addicts and overweight populations, and suggests that future studies should use the other as a control variable for separate studies.…”

The relationship between cannabis use and taurine: A MRS and metabolomics study

“…This effect is thought to occur both in the periphery and in the brain. We focused on the dACC as it is part of the reward system [ 21 ] that has been previously found to show differential activation [ 22 ] and functional connectivity differences [ 23 – 25 ] in chronic CB users. Additionally, because previous studies have linked CB use and glutamate [ 2 , 26 ] an association between taurine and glutamate levels is hypothesized.…”