Laura Koenders

Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium

The profile of brain structural abnormalities in schizophrenia is still not fully understood, despite decades of research using brain scans. To validate a prospective meta-analysis approach to analyzing multicenter neuroimaging data, we analyzed brain MRI scans from 2028 schizophrenia patients and 2540 healthy controls, assessed with standardized methods at 15 centers worldwide. We identified subcortical brain volumes that differentiated patients from controls, and ranked them according to their effect sizes. Compared with healthy controls, patients with schizophrenia had smaller hippocampus (Cohen’s d=−0.46), amygdala (d=−0.31), thalamus (d=−0.31), accumbens (d=−0.25) and intracranial volumes (d=−0.12), as well as larger pallidum (d=0.21) and lateral ventricle volumes (d=0.37). Putamen and pallidum volume augmentations were positively associated with duration of illness and hippocampal deficits scaled with the proportion of unmedicated patients. Worldwide cooperative analyses of brain imaging data support a profile of subcortical abnormalities in schizophrenia, which is consistent with that based on traditional meta-analytic approaches. This first ENIGMA Schizophrenia Working Group study validates that collaborative data analyses can readily be used across brain phenotypes and disorders and encourages analysis and data sharing efforts to further our understanding of severe mental illness.

Cannabis dependence, cognitive control and attentional bias for cannabis words

One of the characteristics of people suffering from addictive behaviors is the tendency to be distracted by drug cues. This attentional bias for drug cues is thought to lead to increased craving and drug use, and may draw individuals into a vicious cycle of drug addiction. In the current study we developed a Dutch version of the cannabis Stroop task and measured attentional bias for cannabis words in a group of heavy cannabis users and matched controls. The classical Stroop task was used as a global measure of cognitive control and we examined the relationship between cognitive control, cannabis-related problems, cannabis craving and cannabis attentional bias. Using our version of the cannabis Stroop task, a group of heavy cannabis users showed attentional bias to cannabis words, whereas a control group of non-users did not. Furthermore, within the group of cannabis users, those who were clinically recognized as dependent showed a stronger attentional bias than the heavy, non-dependent users. Cannabis users who displayed reduced cognitive control (as measured with the classical Stroop task) showed increased session-induced craving. Contrary to expectations, however, cognitive control did not appear to modulate the relationship between attentional bias to cannabis words (cannabis Stroop task) and cannabis dependence. This study confirmed the relationship between cannabis dependence and attentional bias and extends this by highlighting a moderating role for cognitive control, which may make some more vulnerable to craving.

Cannabis use and transition to psychosis in individuals at ultra-high risk: review and meta-analysis

BACKGROUND Previous research has established the relationship between cannabis use and psychotic disorders. Whether cannabis use is related to transition to psychosis in patients at ultra-high risk (UHR) for psychosis remains unclear. The present study aimed to review the existing evidence on the association between cannabis use and transition to psychosis in UHR samples. METHOD A search of PsychInfo, Embase and Medline was conducted from 1996 to August 2015. The search yielded 5559 potentially relevant articles that were selected on title and abstract. Subsequently 36 articles were screened on full text for eligibility. Two random-effects meta-analyses were performed. First, we compared transition rates to psychosis of UHR individuals with lifetime cannabis use with non-cannabis-using UHR individuals. Second, we compared transition rates of UHR individuals with a current DSM-IV cannabis abuse or dependence diagnosis with lifetime users and non-using UHR individuals. RESULTS We found seven prospective studies reporting on lifetime cannabis use in UHR subjects (n = 1171). Of these studies, five also examined current cannabis abuse or dependence. Lifetime cannabis use was not significantly associated with transition to psychosis [odds ratio (OR) 1.14, 95% confidence interval (CI) 0.856-1.524, p = 0.37]. A second meta-analysis yielded an OR of 1.75 (95% CI 1.135-2.710, p = 0.01), indicating a significant association between current cannabis abuse or dependence and transition to psychosis. CONCLUSIONS Our results show that cannabis use was only predictive of transition to psychosis in those who met criteria for cannabis abuse or dependence, tentatively suggesting a dose-response relationship between current cannabis use and transition to psychosis.

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.

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.

Cue-induced striatal activity in frequent cannabis users independently predicts cannabis problem severity three years later

Cannabis is the most frequently used illicit drug worldwide, but little is known about the mechanisms underlying continued cannabis use. Cue-reactivity (the physical, psychological, behavioural and neural reaction to substance-related cues) might be related to continued cannabis use. In this 3-year prospective neuroimaging study we investigated whether cannabis cue-induced brain activity predicted continued cannabis use and associated problem severity 3 years later. In addition, baseline brain activations were compared between dependent and non-dependent cannabis users at follow-up. Analyses were focussed on brain areas known to be important in cannabis cue-reactivity: anterior cingulate cortex, orbitofrontal cortex, ventral tegmental area, amygdala and striatum. At baseline, 31 treatment-naive frequent cannabis users performed a cue-reactivity functional magnetic resonance imaging task. Of these participants, 23 completed the 3-year follow-up. None of the cue-induced region of interest activations predicted the amount of cannabis use at follow-up. However, cue-induced activation in the left striatum (putamen) significantly and independently predicted problem severity at follow-up (p < 0.001) as assessed with the Cannabis Use Disorder Identification Test. Also, clinically dependent cannabis users at follow-up showed higher baseline activation at trend level in the left striatum compared with non-dependent users. This indicates that neural cue-reactivity in the dorsal striatum is an independent predictor of cannabis use-related problems. Given the relatively small sample size, these results are preliminary and should be replicated in larger samples of cannabis users.

Longitudinal study of hippocampal volumes in heavy cannabis users

Background: Cannabis exposure, particularly heavy cannabis use, has been associated with neuroanatomical alterations in regions rich with cannabinoid receptors such as the hippocampus in some but not in other (mainly cross-sectional) studies. However, it remains unclear whether continued heavy cannabis use alters hippocampal volume, and whether an earlier age of onset and/or a higher dosage exacerbate these changes. Methods: Twenty heavy cannabis users (mean age 21 years, range 18–24 years) and 23 matched non-cannabis using healthy controls were submitted to a comprehensive psychological assessment and magnetic resonance imaging scan at baseline and at follow-up (average of 39 months post-baseline; standard deviation=2.4). Cannabis users started smoking around 16 years and smoked on average five days per week. A novel aspect of the current study is that hippocampal volume estimates were obtained from manual tracing the hippocampus on T1-weighted anatomical magnetic resonance imaging scans, using a previously validated protocol. Results: Compared to controls, cannabis users did not show hippocampal volume alterations at either baseline or follow-up. Hippocampal volumes increased over time in both cannabis users and controls, following similar trajectories of increase. Cannabis dose and age of onset of cannabis use did not affect hippocampal volumes. Conclusions: Continued heavy cannabis use did not affect hippocampal neuroanatomical changes in early adulthood. This contrasts with prior evidence on alterations in this region in samples of older adult cannabis users. In young adults using cannabis at this level, cannabis use may not be heavy enough to affect hippocampal neuroanatomy.

Structural MRI Differences between Patients with and without First Rank Symptoms: A Delusion?

Objective It has been suggested that specific psychotic symptom clusters may be explained by patterns of biological abnormalities. The presence of first rank symptoms (FRS) has been associated with cognitive abnormalities, e.g., deficits in self-monitoring or in the experience of agency, suggesting that a specific network of neural abnormalities might underlie FRS. Here, we investigate differences in cortical and subcortical brain volume between patients with and without FRS. Methods Three independent patient samples (referred to as A, B, and C) with different mean ages and in different illness stages were included, leading to a total of 348 patients within the schizophrenia-spectrum. All underwent magnetic resonance imaging of the brain. In addition, the presence of FRS was established using a diagnostic interview. Patients with (FRS+, A: n = 63, B: n = 129, and C: n = 96) and without FRS (FRS−, A: n = 35, B: n = 17, and C: n = 8) were compared on global and local cortical volumes as well as subcortical volumes, using a whole brain (cerebrum) approach. Results Nucleus accumbens volume was significantly smaller in FRS+ as compared with FRS− in sample A (p < 0.005). Furthermore, FRS+ showed a smaller volume of the pars-opercularis relative to FRS− in sample B (p < 0.001). No further significant differences were found in cortical and subcortical volumes between FRS+ and FRS− in either one of the three samples after correction for multiple comparison. Conclusion Brain volume differences between patients with and without FRS are, when present, subtle, and not consistent between three independent samples. Brain abnormalities related to FRS may be too subtle to become visible through structural brain imaging.