Association of cannabis with glutamatergic levels in patients with early psychosis: Evidence for altered volume striatal glutamate relationships in patients with a history of cannabis use in early psychosis

Targeting corticostriatal transmission for the treatment of cannabinoid use disorder

It is generally assumed that the rewarding effects of cannabinoids are mediated by cannabinoid CB1 receptors (CB1Rs) the activation of which disinhibits dopaminergic neurons in the ventral tegmental area (VTA). However, this mechanism cannot fully explain novel results indicating that dopaminergic neurons also mediate the aversive effects of cannabinoids in rodents, and previous results showing that preferentially presynaptic adenosine A2A receptor (A2AR) antagonists counteract self-administration of Δ-9-tetrahydrocannabinol (THC) in nonhuman primates (NHPs). Based on recent experiments in rodents and imaging studies in humans, we propose that the activation of frontal corticostriatal glutamatergic transmission constitutes an additional and necessary mechanism. Here, we review evidence supporting the involvement of cortical astrocytic CB1Rs in the activation of corticostriatal neurons and that A2AR receptor heteromers localized in striatal glutamatergic terminals mediate the counteracting effects of the presynaptic A2AR antagonists, constituting potential targets for the treatment of cannabinoid use disorder (CUD).

Glutamate and N-Acetylaspartate Alterations Observed in Early Phase Psychosis: A Systematic Review of Proton Magnetic Resonance Spectroscopy Studies

Glutamate and N-acetylaspartate have been investigated in the neuropathology of chronic schizophrenia, with fewer studies focusing on early phase psychosis. Additionally, there has been little review and synthesis of the literature focused on multiple brain regions. This systematic review aims to provide a clear report of the current state of research on glutamate and n-acetylaspartate concentrations in early phase psychosis (defined as the first five years following psychosis onset) in multiple brain regions. Existing literature was searched systematically to compile reports of glutamate/glutamate+glutamine (Glx) and n-acetylaspartate absolute levels and ratios in both male and female individuals with early phase psychosis. Reports on glutamate/Glx concentrations in the medial prefrontal region and thalamus were varied, but the majority of reports suggested no alterations in EPP. No studies reported glutamate alterations in the hippocampus or cerebellum. There was no evidence for n-acetylaspartate alterations in the caudate, basal ganglia, and medial prefrontal cortex, and minimal evidence for NAA reductions in the thalamus, anterior cingulate cortex, and hippocampus. Future research should focus on the regions that are less commonly reported, and should aim to explore possible confounds, such as medication status and substance use.

Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study

BACKGROUND AND HYPOTHESIS

Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs).

STUDY DESIGN

Total 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat.

STUDY RESULTS

The accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%).

CONCLUSION

We built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.