Cortical Morphology Differences in Subjects at Increased Vulnerability for Developing a Psychotic Disorder: A Comparison between Subjects with Ultra-High Risk and 22q11.2 Deletion Syndrome.

INTRODUCTION: Subjects with 22q11.2 deletion syndrome (22q11DS) and subjects with ultra-high risk for psychosis (UHR) share a risk of approximately 30% to develop a psychotic disorder. Studying these groups helps identify biological markers of pathophysiological processes involved in the development of psychosis. Total cortical surface area (cSA), total cortical grey matter volume (cGMV), cortical thickness (CT), and local gyrification index (LGI) of the cortical structure have a distinct neurodevelopmental origin making them important target markers to study in relation to the development of psychosis. MATERIALS AND METHODS: Structural T1-weighted high resolution images were acquired using a 3 Tesla Intera MRI system in 18 UHR subjects, 18 22q11DS subjects, and 24 matched healthy control (HC) subjects. Total cSA, total cGMV, mean CT, and regional vertex-wise differences in CT and LGI were assessed using FreeSurfer software. The Positive and Negative Syndrome Scale was used to assess psychotic symptom severity in UHR and 22q11DS subjects at time of scanning. RESULTS: 22q11DS subjects had lower total cSA and total cGMV compared to UHR and HC subjects. The 22q11DS subjects showed bilateral lower LGI in the i) prefrontal cortex, ii) precuneus, iii) precentral gyrus and iv) cuneus compared to UHR subjects. Additionally, lower LGI was found in the left i) fusiform gyrus and right i) pars opercularis, ii) superior, and iii) inferior temporal gyrus in 22q11DS subjects compared to HC. In comparison to 22q11DS subjects, the UHR subjects had lower CT of the insula. For both risk groups, positive symptom severity was negatively correlated to rostral middle frontal gyrus CT. CONCLUSION: A shared negative correlation between positive symptom severity and rostral middle frontal gyrus CT in UHR and 22q11DS may be related to their increased vulnerability to develop a psychotic disorder. 22q11DS subjects were characterised by widespread lower degree of cortical gyrification linked to early and postnatal neurodevelopmental pathology. No implications for early neurodevelopmental pathology were found for the UHR subjects, although they did have distinctively lower insula CT which may have arisen from defective pruning processes during adolescence. Implications of these findings in relation to development of psychotic disorders are in need of further investigation in longitudinal studies.

Grey Matter Changes Associated with Heavy Cannabis Use: A Longitudinal sMRI Study.

Cannabis is the most frequently used illicit drug worldwide. Cross-sectional neuroimaging studies suggest that chronic cannabis exposure and the development of cannabis use disorders may affect brain morphology. However, cross-sectional studies cannot make a conclusive distinction between cause and consequence and longitudinal neuroimaging studies are lacking. In this prospective study we investigate whether continued cannabis use and higher levels of cannabis exposure in young adults are associated with grey matter reductions. Heavy cannabis users (N = 20, age baseline M = 20.5, SD = 2.1) and non-cannabis using healthy controls (N = 22, age baseline M = 21.6, SD = 2.45) underwent a comprehensive psychological assessment and a T1- structural MRI scan at baseline and 3 years follow-up. Grey matter volumes (orbitofrontal cortex, anterior cingulate cortex, insula, striatum, thalamus, amygdala, hippocampus and cerebellum) were estimated using the software package SPM (VBM-8 module). Continued cannabis use did not have an effect on GM volume change at follow-up. Cross-sectional analyses at baseline and follow-up revealed consistent negative correlations between cannabis related problems and cannabis use (in grams) and regional GM volume of the left hippocampus, amygdala and superior temporal gyrus. These results suggests that small GM volumes in the medial temporal lobe are a risk factor for heavy cannabis use or that the effect of cannabis on GM reductions is limited to adolescence with no further damage of continued use after early adulthood. Long-term prospective studies starting in early adolescence are needed to reach final conclusions.

123I-iododexetimide preferentially binds to the muscarinic receptor subtype M1 in vivo.

UNLABELLED: The muscarinic M1 receptor (M1R) is highly involved in cognition, and selective M1 agonists have procognitive properties. Loss of M1R has been found in postmortem brain tissue for several neuropsychiatric disorders and may be related to symptoms of cognitive dysfunction. (123)I-iododexetimide is used for imaging muscarinic acetylcholine receptors (mAchRs). Considering its high brain uptake and intense binding in M1R-rich brain areas, (123)I-iododexetimide may be an attractive radiopharmaceutical to image M1R. To date, the binding affinity and selectivity of (123)I-iododexetimide for the mAchR subtypes has not been characterized, nor has its brain distribution been studied intensively. Therefore, this study aimed to address these topics. METHODS: The in vitro affinity and selectivity of (127)I-iododexetimide (cold-labeled iododexetimide), as well as its functional antagonist properties (guanosine 5'-[γ-(35)S-thio]triphosphate [GTPγ(35)S] assay), were assessed on recombinant human M1R-M5R. Distributions of (127)I-iododexetimide and (123)I-iododexetimide in the brain were evaluated using liquid chromatography-mass spectrometry and storage phosphor imaging, respectively, ex vivo in rats, wild-type mice, and M1-M5 knock-out (KO) mice. Inhibition of (127)I-iododexetimide and (123)I-iododexetimide binding in M1R-rich brain areas by the M1R/M4R agonist xanomeline, or the antipsychotics olanzapine (M1R antagonist) and haloperidol (low M1R affinity), was assessed in rats ex vivo. RESULTS: In vitro, (127)I-iododexetimide displayed high affinity for M1R (pM range), with modest selectivity over other mAchRs. In biodistribution studies on rats, ex vivo (127)I-iododexetimide binding was much higher in M1R-rich brain areas, such as the cortex and striatum, than in cerebellum (devoid of M1Rs). In M1 KO mice, but not M2-M5 KO mice, (127)I-iododexetimide binding was strongly reduced in the frontal cortex compared with wild-type mice. Finally, acute administration of both an M1R/M4R agonist xanomeline and the M1R antagonist olanzapine was able to inhibit (123)I-iododexetimide ex vivo, and (123)I-iododexetimide binding in M1-rich brain areas in rats, whereas administration of haloperidol had no effect. CONCLUSION: The current results suggest that (123)I-iododexetimide preferentially binds to M1R in vivo and can be displaced by M1R ligands. (123)I-iododexetimide may therefore be a useful imaging tool as a way to further evaluate M1R changes in neuropsychiatric disorders, as a potential stratifying biomarker, or as a clinical target engagement biomarker to assess M1R.

Relationship between working-memory network function and substance use: a 3-year longitudinal fMRI study in heavy cannabis users and controls.

Deficient executive functions play an important role in the development of addiction. Working-memory may therefore be a powerful predictor of the course of drug use, but chronic substance use may also impair working-memory. The aim of this 3-year longitudinal neuro-imaging study was to investigate the relationship between substance use (e.g. alcohol, cannabis, nicotine, illegal psychotropic drugs) and working-memory network function over time in heavy cannabis users and controls. Forty-nine participants performed an n-back working-memory task at baseline and at 3-year follow-up. At follow-up, there were 22 current heavy cannabis users, 4 abstinent heavy cannabis users and 23 non-cannabis-using controls. Tensor-independent component analysis (Tensor-ICA) was used to investigate individual differences in working-memory network functionality over time. Within the group of cannabis users, cannabis-related problems remained stable, whereas alcohol-related problems, nicotine dependence and illegal psychotropic substance use increased over time. At both measurements, behavioral performance and network functionality during the n-back task did not differ between heavy cannabis users and controls. Although n-back accuracy improved, working-memory network function remained stable over time. Within the group of cannabis users, working-memory network functionality was not associated with substance use. These results suggest that sustained moderate to heavy levels of cannabis, nicotine, alcohol and illegal psychotropic substance use do not change working-memory network functionality. Moreover, baseline network functionality did not predict cannabis use and related problems three years later, warranting longitudinal studies in more chronic or dependent cannabis users.

Pharmacological Interventions for the MATRICS Cognitive Domains in Schizophrenia: What's the Evidence?

Schizophrenia is a disabling, chronic psychiatric disorder with a prevalence rate of 0.5-1% in the general population. Symptoms include positive (e.g., delusions, hallucinations), negative (e.g., blunted affect, social withdrawal), as well as cognitive symptoms (e.g., memory and attention problems). Although 75-85% of patients with schizophrenia report cognitive impairments, the underlying neuropharmacological mechanisms are not well understood and currently no effective treatment is available for these impairments. This has led to the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative, which established seven cognitive domains that are fundamentally impaired in schizophrenia. These domains include verbal learning and memory, visual learning and memory, working memory, attention and vigilance, processing speed, reasoning and problem solving, and social cognition. Recently, a growing number of studies have been conducted trying to identify the underlying neuropharmacological mechanisms of cognitive impairments in schizophrenia patients. Specific cognitive impairments seem to arise from different underlying neuropharmacological mechanisms. However, most review articles describe cognition in general and an overview of the mechanisms involved in these seven separate cognitive domains is currently lacking. Therefore, we reviewed the underlying neuropharmacological mechanisms focusing on the domains as established by the MATRICS initiative which are considered most crucial in schizophrenia.