Resting state functional connectivity in dependent cannabis users: The moderating role of cannabis attitudes

BACKGROUND

The global increase in lenient cannabis policy has been paralleled by reduced harm perception, which has been associated with cannabis use initiation and persistent use. However, it is unclear how cannabis attitudes might affect the brain processes underlying cannabis use.

METHODS

Resting state functional connectivity (RSFC) within and between the executive control network (ECN), salience network (SN), and default mode network (DMN) was assessed in 110 near-daily cannabis users with cannabis use disorder (CUD) and 79 controls from The Netherlands and Texas, USA. Participants completed a questionnaire assessing the perceived benefits and harms of cannabis use from their personal, friends-family's, and country-state's perspectives and reported on their cannabis use (gram/week), CUD severity, and cannabis-related problems.

RESULTS

RSFC within the dorsal SN was lower in cannabis users than controls, while no group differences in between-network RSFC were observed. Furthermore, heavier cannabis use was associated with lower dorsal SN RSFC in the cannabis group. Perceived benefits and harms of cannabis - from personal, friends-family's, and country-state's perspectives - moderated associations of cannabis use, CUD severity, and cannabis use-related problems with within-network RSFC of the SN, ECN, and DMN. Personal perceived benefits and country-state perceived harms moderated the association between CUD severity and RSFC between the ventral and dorsal DMN.

CONCLUSIONS

This study highlights the importance of considering individual differences in the perceived harms and benefits of cannabis use as a factor in the associations between brain functioning and cannabis use, CUD severity, and cannabis use-related problems.

Associations between hair-derived cannabinoid levels, self-reported use, and cannabis-related problems

RATIONALE

As cannabis potency and cannabis use are increasing in newly legalized markets, it is increasingly important to measure and examine the effects of cannabinoid exposure.

OBJECTIVES

The current study aims to assess how hair-derived cannabinoid concentrations - offering insight into three-month cumulative exposure - are associated with common self-report measures of cannabis use and cannabis use-related problems.

METHODS

74 near-daily dependent cannabis users self-reported their quantity of cannabis use, cannabis use-related problems, and estimated cannabis potency. Hair samples were provided to quantify Δ9-THC, CBD, and CBN using LC-MS/MS and THC-consumption was verified by analyzing THC-COOH in hair using GC-MS/MS.

RESULTS

Cannabinoids were detectable in 95.95% of the hair samples from individuals who tested positive on a urine screen for cannabis. Δ9-THC concentrations were positively associated with measures of self-reported potency (relative potency, potency category, and perceived 'high'), but Δ9-THC, CBD, CBN concentrations and THC/CBD ratio were not associated with self-reported quantity of use. Self-reported potency, but not hair-derived concentrations, were associated with withdrawal and craving. Self-reported quantity of cannabis use, but not cannabinoid concentrations, were associated with cannabis use-related problems.

CONCLUSIONS

The use of hair-derived cannabinoid quantification is supported for detecting cannabis use in near-daily users, but the lack of associations between hair-derived cannabinoid concentrations and self-report measures of use does not support the use of hair analyses alone for quantification of cannabinoid exposure. Further research comparing hair-derived cannabinoid concentrations with other biological matrices (e.g. plasma) and self-report is necessary to further evaluate the validity of hair analyses for this purpose.

A cross-cultural fMRI investigation of cannabis approach bias in individuals with cannabis use disorder

Introduction

As cannabis policies and attitudes become more permissive, it is crucial to examine how the legal and social environment influence neurocognitive mechanisms underlying cannabis use disorder (CUD). The current study aimed to assess whether cannabis approach bias, one of the mechanisms proposed to underlie CUD, differed between environments with distinct recreational cannabis policies (Amsterdam, The Netherlands (NL) and Dallas, Texas, United States of America (TX)) and whether individual differences in cannabis attitudes affect those differences.

Methods

Individuals with CUD (NL-CUD: 64; TX-CUD: 48) and closely matched non-using controls (NL-CON: 50; TX-CON: 36) completed a cannabis approach avoidance task (CAAT) in a 3T MRI. The cannabis culture questionnaire was used to measure cannabis attitudes from three perspectives: personal, family/friends, and state/country attitudes.

Results

Individuals with CUD demonstrated a significant behavioral cannabis-specific approach bias. Individuals with CUD exhibited higher cannabis approach bias-related activity in clusters including the paracingulate gyrus, anterior cingulate cortex, and frontal medial cortex compared to controls, which was no longer significant after controlling for gender. Site-related differences emerged in the association between cannabis use quantity and cannabis approach bias activity in the putamen, amygdala, hippocampus, and insula, with a positive association in the TX-CUD group and a negative association in the NL-CUD group. This was not explained by site differences in cannabis attitudes.

Conclusions

Pinpointing the underlying mechanisms of site-related differences-including, but not limited to, differences in method of administration, cannabis potency, or patterns of substance co-use-is a key challenge for future research.

Working memory-related brain activity in cannabis use disorder: The role of cross-cultural differences in cannabis attitudes

Cannabis legislation and attitudes towards use are changing. Given that evidence from cultural neuroscience research suggests that culture influences the neurobiological mechanisms underlying behaviour, it is of great importance to understand how cannabis legislation and attitudes might affect the brain processes underlying cannabis use disorder. Brain activity of 100 dependent cannabis users and 84 controls was recorded during an N-back working memory (WM) task in participants from the Netherlands (NL; users = 60, controls = 52) and Texas, USA (TX; users = 40, controls = 32). Participants completed a cannabis culture questionnaire as a measure of perceived benefits (positive) and perceived harms (negative) of cannabis from their personal, friends-family's and country-state's perspectives. Amount of cannabis use (grams/week), DSM-5 CUD symptoms and cannabis use-related problems were assessed. Cannabis users self-reported more positive and less negative (personal and friends-family) cannabis attitudes than controls, with this effect being significantly larger in the TX cannabis users. No site difference in country-state attitudes was observed. TX cannabis users, compared with NL cannabis users, and those cannabis users perceiving more positive country-state attitudes showed a more positive association between grams/week and WM-related activity in the superior parietal lobe. NL cannabis users, compared with TX cannabis users, and those cannabis users with less positive personal attitudes showed a more positive association between grams/week and WM-load-related activity in the temporal pole. Both site and cultural attitudes moderated the association of quantity of cannabis use with WM- and WM-load-related activity. Importantly, differences in legislation did not align with perceived cannabis attitudes and appear to be differentially associated with cannabis use-related brain activity.

Functional hyperconnectivity between corticocerebellar networks and altered decision making in young adult cannabis users: Evidence from 7T and multivariate pattern analysis

Decision-making (DM) impairments are important predictors of cannabis initiation and continued use. In cannabis users, how decision-making abnormalities related to structural and functional connectivity in the brain are not fully understood. We employed a three-method multimodal image analysis and multivariate pattern analysis (MVPA) on high dimensional 7 tesla MRI images examining functional connectivity, white matter microstructure and gray matter volume in a group of cannabis users and non-users. Neuroimaging and cognitive analyses were performed on 92 CU and 92 age- matched NU from a total of 187 7T scans. CU were selected on the basis of their scores on the Semi-Structured Assessment for the Genetics of Alcoholism. The groups were first compared on a decision-making test and then on ICA based functional connectivity between corticocerebellar networks. An MVPA was done as a confirmatory analysis. The anatomy of these networks was then assessed using Diffusion Tensor imaging (DTI) and cortical volume analyses. Cannabis Users had significantly higher scores on the Iowa Gambling task (IGT) [Gambling task Percentage larger] and significantly lower scores on the [Gambling task reward Percentage smaller]. Left accumbens (L NAc) volume was significantly larger in cannabis users. DTI analysis between the groups yielded no significant (FWE corrected) differences. Resting state FC analysis of the left Cerebellum region 9 showed enhanced functional connectivity with the right nucleus accumbens and left pallidum and left putamen in CU. In addition, posterior cerebellum showed enhanced functional connectivity (FWE corrected) with 2 nodes of the DMN and left and right paracingulate (sub genual ACC) and the sub callosal cortex in CU. IGT percentage larger scores correlated with posterior cerebellar functional connectivity in non-user women. A multivariate pattern analysis confirmed this cerebellar hyperconnectivity in both groups. Our results demonstrate for the first time that deficits in DM observed in cannabis users are mirrored in hyper connectivity in corticocerebellar networks. Cortical volumes of some of the nodes of these networks showed increases in users. However, the underlying white matter was largely intact in CU. The observed DM deficits and hyper connectivity in resting networks may contribute to difficulties in quitting and/or facilitating relapse.

Cannabis Use Disorder Symptoms in Weekly Cannabis Users: A Network Comparison Between Daily Cigarette Users and Nondaily Cigarette Users

Background: Concurrent use (co-use) of cannabis and tobacco is common and associated with worse clinical outcomes compared with cannabis use only. The mechanisms and interactions of cannabis use disorder (CUD) symptoms underlying co-use remain poorly understood. Methods: We examined differences in the symptom presence and symptom network configurations between weekly cannabis users who use tobacco daily (co-users, n=789) or non- or nondaily (nondaily co-users, n=428). Results: First, we identified a range of symptoms (craving, failed reduce or quit attempts, neglected responsibilities, and negative social effects) that are most central to the highly interconnected CUD symptom network. Risky cannabis use was mostly associated with negative social and health effects, and independent of other CUD symptoms. Craving symptoms act as a bridge between different CUD and withdrawal symptoms. Among co-users, (1) craving is more strongly associated with negative psychosocial effects, (2) feelings of depression and negative health effects are more central to the network, and (3) the negative health effects are more strongly associated with failed attempts to reduce or quit attempts compared with nondaily co-users. Discussion: Our results go beyond existing findings focused on the mere increase in CUD symptom presence, and speak to the potential synergistic effects of co-use on dependence and withdrawal symptoms. We outline clinical implications with respect to targeting specific CUD symptoms in co-users, and point to future research to disentangle tobacco and cannabis craving symptoms.

An Interpretable and Predictive Connectivity-Based Neural Signature for Chronic Cannabis Use

BACKGROUND

Cannabis is one of the most widely used substances in the world, with usage trending upward in recent years. However, although the psychiatric burden associated with maladaptive cannabis use has been well established, reliable and interpretable biomarkers associated with chronic use remain elusive. In this study, we combine large-scale functional magnetic resonance imaging with machine learning and network analysis and develop an interpretable decoding model that offers both accurate prediction and novel insights into chronic cannabis use.

METHODS

Chronic cannabis users (n = 166) and nonusing healthy control subjects (n = 124) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic users and nonusers based on whole-brain functional connectivity. Network analysis was used to identify the most predictive regions and communities.

RESULTS

We obtained high (∼80% out-of-sample) accuracy across 4 different classification models, demonstrating that task-evoked connectivity can successfully differentiate chronic cannabis users from nonusers. We also identified key predictive regions implicating motor, sensory, attention, and craving-related areas, as well as a core set of brain networks that contributed to successful classification. The most predictive networks also strongly correlated with cannabis craving within the chronic user group.

CONCLUSIONS

This novel approach produced a neural signature of chronic cannabis use that is both accurate in terms of out-of-sample prediction and interpretable in terms of predictive networks and their relation to cannabis craving.

Gender differences in cannabis use disorder symptoms: A network analysis

BACKGROUND

While cannabis use in women is increasing worldwide, research into gender differences in cannabis use disorder (CUD) symptomology is lacking. In response to limited effectiveness of addiction treatment, research focus has been shifting from clinical diagnoses towards interactions between symptoms, as patterns of symptoms and their interactions could be crucial in understanding etiological mechanisms in addiction. The aim of this study was to evaluate the CUD symptom network and assess whether there are gender differences therein.

METHODS

A total of 1257 Dutch individuals reporting weekly cannabis use, including 745 men and 512 women, completed online questionnaires assessing DSM-5 CUD symptoms and additional items on plans to quit or reduce use, cigarette use, and the presence of psychological diagnoses. Gender differences were assessed for all variables and an Ising model estimation method was used to estimate CUD symptom networks in men and women using network comparison tests to assess differences.

RESULTS

There were gender differences in the prevalence of 6 of the 11 symptoms, but symptom networks did not differ between men and women. Cigarette use appeared to only be connected to the network through withdrawal, indicating a potential role of cigarette smoking in enhancing cannabis withdrawal symptoms. Furthermore, there were gender differences in the network associations of mood and anxiety disorders with CUD symptoms.

CONCLUSION

The association between smoking and withdrawal as well as gender differences in the role of comorbidities in the CUD network highlight the value of using network models to understand CUD and how symptom interactions might affect treatment.

The role of sex in the association between cannabis use and working memory-related brain activity

Although cannabis use patterns differ between men and women, studies on sex differences on the effects of cannabis on the brain and cognitive control are largely lacking. Working memory (WM) is a component of cognitive control believed to be involved in the development and maintenance of addiction. In this study, we evaluated the association between cannabis use and WM (load) related brain activity in a large sample, enabling us to assess sex effects in this association. The brain activity of 104 frequent cannabis users (63% men) and 85 controls (53% men) was recorded during an N‐back WM task. Behavioral results showed a significant interaction between WM load and group for both accuracy and reaction time, with cannabis users showing a relatively larger decrease in performance with increasing WM load. Cannabis users compared to controls showed a relatively smaller reduction in WM (load) related activity in the precuneus and posterior cingulate cortex at higher WM load. This WM (load) related activity was not associated with performance nor cannabis use and related problems. An exploratory analysis showed higher WM‐related activity in the superior frontal gyrus in men compared to women. While cannabis users showed higher WM (load) related activity in central nodes of the default mode network, this was not directly attributable to group specific worsening of performance under higher cognitive load. Further research is necessary to assess whether observed group differences increase with higher cognitive load, how group differences relate to measures of cannabis use, and how sex affects these group differences.

Joint risk prediction for hazardous use of alcohol, cannabis, and tobacco among adolescents: A preliminary study using statistical and machine learning

•

A risk prediction model for hazardous substance use among adolescents is proposed.

•

The model predicts risk of hazardous use of alcohol, cannabis, and tobacco jointly.

•

It uses statistical and machine learning methods for modeling of multiple outcomes.

•

It may help identify adolescent users at elevated risk of hazardous substance use.

For some, substance use during adolescence may be a stepping stone on the way to substance use disorders in adulthood. Risk prediction models may help identify adolescent users at elevated risk for hazardous substance use. This preliminary analysis used cross-sectional data (n = 270, ages 13–18) from the baseline dataset of a randomized controlled trial intervening with adolescent alcohol and/or cannabis use. Models were developed for jointly predicting quantitative scores on three measures of hazardous substance use (Rutgers Alcohol Problems Index, Adolescent Cannabis Problem Questionnaire, and Hooked on Nicotine Checklist) based on personal risk factors using two statistical and machine learning methods: multivariate covariance generalized linear models (MCGLM) and penalized multivariate regression with a lasso penalty. The predictive accuracy of a model was evaluated using root mean squared error computed via leave-one-out cross-validation. The final proposed model was an MCGLM model. It has eleven risk factors: age, early life stress, age of first tobacco use, age of first cannabis use, lifetime use of other substances, age of first use of other substances, maternal education, parental attachment, family cigarette use, family history of hazardous alcohol use, and family history of hazardous cannabis use. Different subsets of these risk factors feature in the three outcome-specific components of this joint model. The quantitative risk estimate provided by the proposed model may help identify adolescent substance users of cannabis, alcohol, and tobacco who may be at an elevated risk of developing hazardous substance use.

Unraveling the role of cigarette use in neural cannabis cue reactivity in heavy cannabis users

Cue reactivity is an important biomarker of cannabis use disorder (CUD). Despite high rates of cigarette and cannabis co‐use, its role in cannabis cue reactivity remains unclear. Using a visual functional magnetic resonance imaging cue reactivity paradigm, we investigated interactive effects of cannabis and cigarette use on cannabis cue relative to cigarette and neutral cue reactivity in a priori regions of interest—the amygdala, striatum, anterior cingulate cortex (ACC), ventral tegmental area (VTA), and orbitofrontal cortex—and a whole‐brain analysis. In our sample of cannabis users and controls closely matched on cigarette use, significant interactions between cannabis and cigarette use status emerged in the amygdala, striatum, ACC, frontal pole, and inferior frontal gyrus. Cannabis‐only users showed heightened cue reactivity in the amygdala compared with nonusing controls. Co‐users did not show heightened cue reactivity compared with cigarette smoking controls, although cue‐induced VTA activity was positively correlated with grams per week of cannabis. Cigarette smoking controls showed unexpectedly heightened cue reactivity compared to co‐users and nonsmoking controls. These findings and the high prevalence of cannabis and cigarette co‐use underscore the importance of considering cigarette smoking status when investigating the role of cue reactivity in heavy cannabis use.

A preliminary risk prediction model for cannabis use disorder

•

Substance use disorders are currently a major public health crisis in the US.

•

The prevalence of cannabis use disorder is rising due to legalization of cannabis.

•

This study built models to predict the risk of cannabis use disorder for a user.

•

Risk factors include personality traits, impulsivity and initial smoking enjoyment.

The ongoing trend toward legalization of cannabis for medicinal/recreational purposes is expected to increase the prevalence of cannabis use disorder (CUD). Thus, it is imperative to be able to predict the quantitative risk of developing CUD for a cannabis user based on their personal risk factors. Yet no such model currently exists. In this study, we perform preliminary analysis toward building such a model. The data come from n = 94 regular cannabis users recruited from Albuquerque, New Mexico during 2007–2010. As the data are cross-sectional, we only consider risk factors that remain relatively stable over time. We apply statistical and machine learning classification techniques that allow n to be small relative to the number of predictors. We use predictive accuracy estimated using leave-one-out-cross-validation to evaluate model performance. The final model is a LASSO logistic regression model consisting of the following seven risk factors: age; level of enjoyment from initial cigarette smoking; total score on Impulsive Sensation-Seeking Scale questionnaire; score on cognitive instability factor of Barratt Impulsivity Scale questionnaire; and scores on neuroticism, openness, and conscientiousness personality traits of Neuroticism, Extraversion, and Openness inventory. This model has an overall accuracy of 0.66 and the area under its receiver operating characteristic curve is 0.65. In summary, a preliminary relative risk model for predicting the quantitative risk of CUD is developed. It can be employed to identify users at high risk of CUD who may be provided with early intervention.

Alcohol use disorder and cannabis use disorder symptomatology in adolescents are differentially related to dysfunction in brain regions supporting face processing

Despite extensive behavioral evidence of impairments in face processing and expression recognition in adults with alcohol or cannabis use disorders (AUD/CUD), neuroimaging findings have been inconsistent. Moreover, relatively little work has examined the relationship of AUD or CUD symptoms with face or expression processing within adolescents. Given the high prevalence of alcohol and cannabis use during adolescence, understanding how these usage behaviors interact with neural mechanisms supporting face and expression processing could have important implications for youth social and emotional functioning. In this study, adolescents (N = 104) responded to morphed fearful and happy expressions during fMRI and their level of AUD and/or CUD symptoms were related to the BOLD response data. We found that AUD and CUD symptom severity were both negatively related to responses to faces generally. However, whereas this relationship was shown for AUD within ventromedial prefrontal cortex and lingual gyrus, it was shown for CUD within rostromedial prefrontal cortex including anterior cingulate cortex. Additionally, AUD symptom levels were associated with differential responses within medial temporal pole and inferior parietal lobule as a function of expression. These results have potential implications for understanding the social and emotional functioning of adolescents with AUD and CUD symptoms.

Threat Responsiveness as a Function of Cannabis and Alcohol Use Disorder Severity

Objective: Two of the most commonly abused substances by adolescents in the United States are alcohol and cannabis, both of which are associated with adverse medical and psychiatric outcomes throughout the lifespan. Both are assumed to impact the development of emotional processing although findings on the direction of this impact have been mixed. Preclinical animal work and some functional magnetic resonance imaging (fMRI) work with humans have suggested cannabis use disorder (CUD) and alcohol use disorder (AUD) are associated with increased threat responsiveness. However, other fMRI work has indicated CUD/AUD are associated with diminished threat responsiveness. In this study, we report on a study examining the relationship of severity of CUD/AUD and threat responsiveness in an adolescent population. Methods: The study involved 87 (43 male) adolescents with varying levels of CUD/AUD symptomatology (N = 45 above clinical cutoffs for CUD or AUD). They were scanned with fMRI during a looming threat task that involved images of threatening and neutral human faces or animals that appeared to be either looming or receding. Results: Increasing levels of CUD symptomatology were associated with decreased responding to looming stimuli within regions, including rostral frontal and fusiform gyrus as well as the amygdala. There were no relationships with AUD symptomatology. Conclusions: These data indicate that CUD in particular is associated with a decrease in responsiveness to the looming threat cue possibly relating to the putative neurotoxic impact of cannabis abuse.

Adolescents show differential dysfunctions related to Alcohol and Cannabis Use Disorder severity in emotion and executive attention neuro-circuitries

Alcohol and cannabis are two substances that are commonly abused by adolescents in the United States and which, when abused, are associated with negative medical and psychiatric outcomes across the lifespan. These negative psychiatric outcomes may reflect the detrimental impact of substance abuse on neural systems mediating emotion processing and executive attention. However, work indicative of this has mostly been conducted either in animal models or adults with Alcohol and/or Cannabis Use Disorder (AUD/CUD). Little work has been conducted in adolescent patients. In this study, we used the Affective Stroop task to examine the relationship in 82 adolescents between AUD and/or CUD symptom severity and the functional integrity of neural systems mediating emotional processing and executive attention. We found that AUD symptom severity was positively related to amygdala responsiveness to emotional stimuli and negatively related to responsiveness within regions implicated in executive attention and response control (i.e., dorsolateral prefrontal cortex, anterior cingulate cortex, precuneus) as a function of task performance. In contrast, CUD symptom severity was unrelated to amygdala responsiveness but positively related to responsiveness within regions including precuneus, posterior cingulate cortex, and inferior parietal lobule as a function of task performance. These data suggest differential impacts of alcohol and cannabis abuse on the adolescent brain.

•

Alcohol and cannabis are the most widely abused substances by adolescents.

•

AUDIT scores are related to amygdala hyperactivity to emotional stimuli.

•

AUDIT scores are related to hypoactivity in executive attention neuro-circuitry.

•

CUDIT scores are related to hyperactivity in executive attention neuro-circuitry.

Brain mechanisms of Change in Addictions Treatment: Models, Methods, and Emerging Findings

Increased understanding of "how" and "for whom" treatment works at the level of the brain has potential to transform addictions treatment through the development of innovative neuroscience-informed interventions. The 2015 Science of Change meeting bridged the fields of neuroscience and psychotherapy research to identify brain mechanisms of behavior change that are "common" across therapies, and "specific" to distinct behavioral interventions. Conceptual models of brain mechanisms underlying effects of Cognitive Behavioral Therapy, mindfulness interventions, and Motivational Interviewing were discussed. Presentations covered methods for integrating neuroimaging into psychotherapy research, and novel analytic approaches. Effects of heavy substance use on the brain, and recovery of brain functioning with sustained abstinence, which may be facilitated by cognitive training, were reviewed. Neuroimaging provides powerful tools for determining brain mechanisms underlying psychotherapy and medication effects, predicting and monitoring outcomes, developing novel interventions that target specific brain circuits, and identifying for whom an intervention will be effective.

Sex Effects of Marijuana on Brain Structure and Function

Background

Tetrahydrocannabinol (Δ⁹-THC), the primary ingredient in marijuana, exerts its effects across several neurological and biological systems that interact with the endocrine system. Thus, differential effects of Δ⁹-THC are likely to exist based on sex and hormone levels.

Methods

We reviewed the existing literature to determine sex-based effects of Δ⁹-THC on neural structure and functioning.

Results

The literature demonstrates differences in male and female marijuana users on brain structure, reward processing, attention, motor coordination, and sensitivity to withdrawal. However, inconsistencies exist in the literature regarding how marijuana affects men and women differentially, and more work is needed to understand these mechanisms. While extant literature remains inconclusive, differentiation between male and female marijuana users is likely due to neurological sexual dimorphism and differential social factors at play during development and adulthood.

Conclusions

Sex has important implications for marijuana use and the development of cannabis use disorders and should be considered in the development of prevention and treatment strategies.

Functional connectivity in inhibitory control networks and severity of cannabis use disorder

BACKGROUND

Loss of control is a prominent feature of cannabis use disorders (CUD) and involves orchestrated activity from several brain inhibitory control networks.

OBJECTIVES

In this study, we determined the associations between inhibitory control network activation and connectivity and CUD severity.

METHODS

To that end, we compared cannabis-dependent (N = 44) vs. nondependent (N = 30) users during a Stop Signal Task. First, we compared differences in neural response during response inhibition via general linear model analysis within a priori regions of interest. Second, we examined functional connectivity via psychophysiological interaction (PPI) analysis between the right frontal control network (seed region) and inhibitory control networks.

RESULTS

There was no significant difference in network activation between cannabis-dependent and nondependent users in any of the inhibitory control networks. However, preliminary findings using the PPI analysis showed that during successful response inhibition, cannabis-dependent users had greater connectivity between right frontal control network and substantia nigra/subthalamic nucleus (STN) network compared to nondependent users (small volume correction, FWE-corrected p < 0.05). Further, multiple regression analyses on the PPI maps showed modulatory effects of age of onset and quantity of cannabis use in the nondependent users.

CONCLUSIONS

Taken together, these findings suggest that functional connectivity between frontal control and substantia nigra/STN networks during response inhibition is sensitive to the effects of CUD severity unlike behavioral task performance and neural activation in inhibitory control networks. Further, modulators of this connectivity, such as onset and quantity of cannabis use, show attenuated effects with progression of CUD.

The Serotonin Link between Alcohol Use and Affective Disorders

Serotonin is imperative for the normal operations in the central nervous system. The serotonergic circuitry is implicated in many neuronal processes, and, especially so in mechanisms of emotional regulation and reward. Although function in the serotonergic circuitry has been shown to be abnormal in many pathological states like depression, anxiety, and addiction, its ubiquitous nature complicates efforts to pinpoint the exact loci of pathology. This becomes especially relevant when these conditions occur together, which they do frequently. In this review, we examine the literature on the role of serotonin in depression, anxiety, and addiction, identifying commonalities between these disorders to elucidate the mechanisms at work when they are comorbid. Specifically, we examine the role of serotonergic receptors, transporters, and networks in incidences of alcohol dependence that is comorbid with depression to facilitate a deeper understanding of these mechanisms necessary for the development of more effective and personalized treatments.

A baseline for the multivariate comparison of resting-state networks

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

The incentive salience of alcohol: translating the effects of genetic variant in CNR1

CONTEXT

The gene that codes for cannabinoid receptor 1 (CNR1) represents an important target for investigations designed to elucidate individual differences in the etiology of alcohol dependence.

OBJECTIVE

To achieve a better understanding of the role of the CNR1 gene in the etiology and treatment of alcohol dependence.

DESIGN

The present investigation spans multiple levels of analysis, including receptor binding in postmortem brain tissue, neuroimaging, human laboratory models, and analyses of treatment outcome data.

RESULTS

Findings indicate that the C allele of rs2023239 is associated with greater CB1 binding in the prefrontal cortex, greater alcohol cue-elicited brain activation in the midbrain and prefrontal cortex, greater subjective reward when consuming alcohol, and more positive outcomes after treatment with a medication that targets the mesocorticolimbic neurocircuitry. In addition, there were strong correlations between cue-elicited brain activation and alcohol consumption measures in individuals with the C allele.

CONCLUSION

Individuals with the C allele may be more susceptible to changes in the mesocorticolimbic neurocircuitry that is involved in the attribution of incentive salience after repeated exposure to alcohol.

Age and APOE-epsilon4 genotype influence the effect of physostigmine infusion on the in-vivo distribution volume of the muscarinic-2-receptor dependent tracer [18F]FP-TZTP

The apolipoprotein E-epsilon4 allele (APOE-epsilon4) confers greater susceptibility to age-related memory disorders. Abnormalities in the cholinergic system are likely contributors to these disorders with both age and APOE-epsilon4 genotype modifying behavioral and physiological responses to drugs that alter cholinergic pathway function. Recently, we reported a greater in vivo distribution volume of the F-18 labeled muscarinic-2 (M2) selective agonist, 3-(3-(3-[18F]Flouropropyl)thio)-1,2,5-thiadiazol-4-yl)-1,2,5,6-tetrahydro-1-methylpyridine ([18F]FP-TZTP), in aging healthy subjects with an APOE-epsilon4 allele. To examine the effects of aging and the APOE-epsilon4 allele on the response of the muscarinic component of cholinergic pathway to pharmacologic augmentation, two [18F]FP-TZTP PET scans were conducted in 19 subjects varying in age from 22 to 74 years, the first served as baseline for the second scan that was performed while the subjects were either infused with saline (n = 6) or with the acetylcholinesterase inhibitor physostigmine (6 with an APOE-epsilon4 allele and 7 without an APOE-epsilon4 allele). Using a multiple regression analysis, both AGE (beta = 0.621 +/- 0.135, B = 0.353 +/- 0.077, t(10) = 4.61, P < 0.001) and APOE-epsilon4 genotype (beta = 0.742, B = 14.8 +/- 2.69, t(10) = 5.51, P < 0.0003) were found to be significant contributors to subject response to physostigmine. The adjusted R2 for the model as a whole was 0.786 (F(2,10) = 23.00, P < 0.0002) with both increasing age and the presence of the APOE-epsilon4 allele modifying the response to physostigmine in the direction of larger decreases in [18F]FP-TZTP distribution volumes in all brain regions examined. The findings, particularly the absence of an interaction between AGE and APOE-epsilon4 genotype, contribute to the growing body of evidence that suggests that the APOE-epsilon4 genotype is likely to contribute to brain structure and function prior to aging.

Regional brain morphometry in patients with schizophrenia or bipolar disorder and their unaffected relatives

OBJECTIVE

Schizophrenia and psychotic bipolar disorder have a number of overlapping symptoms and risk factors, but it is not yet clear if the disorders are characterized by similar deviations in brain morphometry or whether any such deviations reflect the impact of shared susceptibility genes on brain structure. The authors used region-of-interest morphometry to volumetrically assess brain structures frequently implicated in psychotic illness in families affected with schizophrenia or psychotic bipolar disorder.

METHOD

Magnetic resonance imaging brain scans were obtained from 243 subjects, comprising 42 patients with schizophrenia or schizoaffective disorder, 57 of their unaffected first-degree relatives, 38 patients with psychotic bipolar disorder, 52 of their unaffected first-degree relatives, and 54 healthy comparison subjects. Most of the families affected with schizophrenia and all of the families affected with bipolar disorder were multiply affected with the illness. Volumetric measurements of the cerebrum, lateral ventricles, third ventricle, and hippocampus were completed with stereological methods.

RESULTS

Patients with schizophrenia had increased volume of the lateral and third ventricles and reduced hippocampal volume. None of these volumetric abnormalities was present in psychotic bipolar disorder. Unaffected relatives of patients with schizophrenia from multiply affected families had enlarged lateral ventricles but no other volumetric deviations. Unaffected relatives of patients with bipolar disorder showed preservation of ventricular and hippocampal volume.

CONCLUSIONS

Schizophrenia and psychotic bipolar disorder are characterized by morphometric distinctions in ventricular and hippocampal regions. Lateral ventricular enlargement represents a potential morphometric endophenotype for schizophrenia.

Is the P300 wave an endophenotype for schizophrenia? A meta-analysis and a family study

INTRODUCTION

We assessed the usefulness of the P300 wave as endophenotype for schizophrenia by means of a meta-analysis of the literature as well as our own family study.

METHOD

Meta-analysis: We conducted a systematic search for articles published between 1983 and 2003 that reported P300 measures in non-psychotic relatives of schizophrenic patients and in healthy controls. Meta-regression analyses were performed using a random effects procedure. The pooled standardized effect size (PSES) was calculated as the difference between the means of the two groups divided by the common standard deviation. Local study: We examined the P300 wave with a standard two-tone oddball paradigm in 30 patients with schizophrenia, 40 non-psychotic relatives, and 40 controls using linear mixed models.

RESULTS

Meta-analysis: We pooled 472 relatives and 513 controls. The P300 amplitude was significantly reduced in relatives (PSES = 0.61; 95% CI: 0.30 to 0.91; P < 0.001). The P300 latency was significantly delayed in relatives (PSES of -0.50; 95% CI: -0.88 to -0.13; P = 0.009]. Local study: The patients showed a trend for amplitude reductions (P = 0.06) and significant latency delays (P < 0.01). The relatives displayed normal amplitude but had significant latency delays (P = 0.01). The P300 amplitude and especially the P300 latency are promising alternative phenotypes for genetic research into schizophrenia.

Association between BDNF val66 met genotype and episodic memory

The val66 met polymorphism of brain derived neurotrophic factor (BDNF) has been associated with variability in episodic memory [Egan et al., 2003]. In an attempt to replicate this finding, we genotyped 206 individuals (92 affected with schizophrenia or a related disorder and 114 unaffected relatives) from the Maudsley Family Study for the BDNF val66 met polymorphism. We analyzed the effect of this polymorphism on episodic memory using the Wechsler Memory Scale, revised version (WMS-R) by regression analysis between the WMS delayed score of logical memory and genotype (corrected for age, sex, and IQ). We found the met66 allele conferred a lower score on the WMS delayed measure (R2 = 0.014 P = 0.09), which was not significant. When cases and unaffected relatives were analyzed separately, met66 was associated with a lower score on the WMS delayed measure in the relatives only (R2 = 0.077 P = 0.01), which is consistent with previous findings.