The human gut microbiome has emerged as a potential key factor involved in the manifestation of physical and mental health. Despite an explosion of cross-disciplinary interest in researching the gut ...microbiome, there remains to be a gold-standard method for operationalizing gut microbiome alpha diversity. Given researchers' interest in examining the relationships among gut microbiome alpha diversity and health-related outcomes of interest, a way of operationalizing the microbiome that yields a numeric value, which could be used in common statistical approaches, is needed. Thus, the current study aims to provide methodological guidance for how to operationalize microbiome alpha diversity. Findings suggest that alpha diversity of the human gut microbiome is comprised of two sub-constructs (richness and evenness), and we propose a step-by-step method of creating alpha diversity composite measures based on this key insight. Finally, we demonstrate that our empirically derived richness and evenness composite measures are significantly associated with health-related variables of interest (alcohol use, symptoms of depression) among a human clinical sample.
The striatum is critical for the incremental learning of values associated with behavioral actions. The prefrontal cortex (PFC) represents abstract rules and explicit contingencies to support rapid ...behavioral adaptation in the absence of cumulative experience. Here we test two alternative models of the interaction between these systems, and individual differences thereof, when human subjects are instructed with prior information about reward contingencies that may or may not be accurate. Behaviorally, subjects are overly influenced by prior instructions, at the expense of learning true reinforcement statistics. Computational analysis found that this pattern of data is best accounted for by a confirmation bias mechanism in which prior beliefs--putatively represented in PFC--influence the learning that occurs in the striatum such that reinforcement statistics are distorted. We assessed genetic variants affecting prefrontal and striatal dopaminergic neurotransmission. A polymorphism in the COMT gene (rs4680), associated with prefrontal dopaminergic function, was predictive of the degree to which participants persisted in responding in accordance with prior instructions even as evidence against their veracity accumulated. Polymorphisms in genes associated with striatal dopamine function (DARPP-32, rs907094, and DRD2, rs6277) were predictive of learning from positive and negative outcomes. Notably, these same variants were predictive of the degree to which such learning was overly inflated or neglected when outcomes are consistent or inconsistent with prior instructions. These findings indicate dissociable neurocomputational and genetic mechanisms by which initial biases are strengthened by experience.
What are the genetic and neural components that support adaptive learning from positive and negative outcomes? Here, we show with genetic analyses that three independent dopaminergic mechanisms ...contribute to reward and avoidance learning in humans. A polymorphism in the DARPP-32 gene, associated with striatal dopamine function, predicted relatively better probabilistic reward learning. Conversely, the C957T polymorphism of the DRD2 gene, associated with striatal D2 receptor function, predicted the degree to which participants learned to avoid choices that had been probabilistically associated with negative outcomes. The Val/Met polymorphism of the COMT gene, associated with prefrontal cortical dopamine function, predicted participants' ability to rapidly adapt behavior on a trial-to-trial basis. These findings support a neurocomputational dissociation between striatal and prefrontal dopaminergic mechanisms in reinforcement learning. Computational maximum likelihood analyses reveal independent gene effects on three reinforcement learning parameters that can explain the observed dissociations.
Recent research has suggested that marijuana use is associated with volumetric and shape differences in subcortical structures, including the nucleus accumbens and amygdala, in a dose-dependent ...fashion. Replication of such results in well controlled studies is essential to clarify the effects of marijuana. To that end, this retrospective study examined brain morphology in a sample of adult daily marijuana users (n = 29) versus nonusers (n = 29) and a sample of adolescent daily users (n = 50) versus nonusers (n = 50). Groups were matched on a critical confounding variable, alcohol use, to a far greater degree than in previously published studies. We acquired high-resolution MRI scans, and investigated group differences in gray matter using voxel-based morphometry, surface-based morphometry, and shape analysis in structures suggested to be associated with marijuana use, as follows: the nucleus accumbens, amygdala, hippocampus, and cerebellum. No statistically significant differences were found between daily users and nonusers on volume or shape in the regions of interest. Effect sizes suggest that the failure to find differences was not due to a lack of statistical power, but rather was due to the lack of even a modest effect. In sum, the results indicate that, when carefully controlling for alcohol use, gender, age, and other variables, there is no association between marijuana use and standard volumetric or shape measurements of subcortical structures.
Alcohol, nicotine, and cannabis are among the most commonly used drugs. A prolonged and combined use of these substances can alter normal brain wiring in different ways depending on the consumed ...cocktail mixture. Brain connectivity alterations and their change with time can be assessed using functional magnetic resonance imaging (fMRI) because of its spatial and temporal content. Here, we estimated dynamic functional network connectivity (dFNC) as derived from fMRI data to investigate the effects of single or combined use of alcohol, nicotine, and cannabis. Data from 534 samples were grouped according to their substance use combination as controls (CTR), smokers (SMK), drinkers (DRN), smoking-and-drinking subjects (SAD), marijuana users (MAR), smoking-and-marijuana users (SAM), marijuana-and-drinking users (MAD), and users of all three substances (ALL). The DRN group tends to exhibit decreased connectivity mainly in areas of sensorial and motor control, a result supported by the dFNC outcome and the alcohol use disorder identification test. This trend dominated the SAD group and in a weaker manner MAD and ALL. Nicotine consumers were characterized by an increment of connectivity between dorsal striatum and sensorimotor areas. Where possible, common and separate effects were identified and characterized by the analysis of dFNC data. Results also suggest that a combination of cannabis and nicotine have more contrasting effects on the brain than a single use of any of these substances. On the other hand, marijuana and alcohol might follow an additive effect trend. We concluded that all of the substances have an impact on brain connectivity, but the effect differs depending on the dFNC state analyzed.
Heavy cannabis users display smaller amygdalae and hippocampi than controls, and genetic variation accounts for a large proportion of variance in liability to cannabis dependence (CD). A single ...nucleotide polymorphism in the cannabis receptor-1 gene (CNR1), rs2023239, has been associated with CD diagnosis and intermediate phenotypes, including abstinence-induced withdrawal, cue-elicited craving, and parahippocampal activation to cannabis cues. This study compared hippocampal and amygdalar volumes (potential CD intermediate phenotypes) between heavy cannabis users and healthy controls, and analyzed interactions between group, rs2023239 variation, and the volumes of these structures. Ninety-four heavy cannabis users participated, of whom 37 (14 men, 23 women; mean age=27.8) were matched to 37 healthy controls (14 men, 23 women; mean age=27.3) for case-control analyses. Controlling for total intracranial volume and other confounding variables, matched cannabis users had smaller bilateral hippocampi (left, p=0.002; right, p=0.001) and left amygdalae (p=0.01) than controls. When genotype was considered in the case-control analyses, there was a group by genotype interaction, such that the rs2023239 G allele predicted lower volume of bilateral hippocampi among cannabis users relative to controls (both p<0.001). This interaction persisted when all 94 cannabis users were compared to controls. There were no group by genotype interactions on amygdalar volume. These data replicate previous findings of reduced hippocampal and amygdalar volume among heavy cannabis users, and suggest that CNR1 rs2023239 variation may predispose smaller hippocampal volume after heavy cannabis use. This association should be tested in future studies of brain volume differences in CD.
Recent microbiome-brain axis findings have shown evidence of the modulation of microbiome community as an environmental mediator in brain function and psychiatric illness. This work is focused on the ...role of the microbiome in understanding a rarely investigated environmental involvement in schizophrenia (SZ), especially in relation to brain circuit dysfunction. We leveraged high throughput microbial 16s rRNA sequencing and functional neuroimaging techniques to enable the delineation of microbiome-brain network links in SZ. N = 213 SZ and healthy control subjects were assessed for the oral microbiome. Among them, 139 subjects were scanned by resting-state functional magnetic resonance imaging (rsfMRI) to derive brain functional connectivity. We found a significant microbiome compositional shift in SZ beta diversity (weighted UniFrac distance, p = 6 × 10−3; Bray-Curtis distance p = 0.021). Fourteen microbial species involving pro-inflammatory and neurotransmitter signaling and H2S production, showed significant abundance alterations in SZ. Multivariate analysis revealed one pair of microbial and functional connectivity components showing a significant correlation of 0.46. Thirty five percent of microbial species and 87.8 % of brain functional network connectivity from each component also showed significant differences between SZ and healthy controls with strong performance in classifying SZ from healthy controls, with an area under curve (AUC) = 0.84 and 0.87, respectively. The results suggest a potential link between oral microbiome dysbiosis and brain functional connectivity alteration in relation to SZ, possibly through immunological and neurotransmitter signaling pathways and the hypothalamic-pituitary-adrenal axis, supporting for future work in characterizing the role of oral microbiome in mediating effects on SZ brain functional activity.
Objective: This study compared two mechanisms by which mindfulness may reduce hazardous drinking: effortful control and craving, "top-down" and "bottom-up" processes, respectively. These ...relationships were compared in a secondary analysis of a randomized controlled trial of mindfulness-based relapse prevention (MBRP) versus relapse prevention (RP) treatments to explore if they differed based on more explicit versus subtle mindfulness training. Method: A total of 182 individuals (48.4% female; 21-60 years old) who reported drinking >14/21 drinks/week (for females/males, respectively) in the past 3 months but who wished to quit/reduce their drinking were recruited from Denver and Boulder, CO, United States. Participants were randomly assigned to either 8 weeks of MBRP or RP treatment and completed assessments at baseline, halfway through treatment, and at the end of treatment. The Five-Factor Mindfulness Questionnaire-Short Form, Alcohol Urge Questionnaire, and Effortful Control Scale completed halfway through treatment assessed the predictor, dispositional mindfulness, and mediators, craving and effortful control, respectively. The Alcohol Use Disorder Identification Task was completed after treatment and measured hazardous drinking. Cross-group path analyses were conducted including both mediators/treatments in the same model. Results: Comparing models with and without equality constraints across treatments, no paths significantly differed based on a chi-square test of difference, χ2(5) = 5.11, p = .40, and only the indirect effect of craving was significant (B = −1.01, p = .01). Conclusions: Findings suggest mindfulness may be associated with hazardous drinking reductions through craving but not effortful control and this indirect relationship works similarly across treatments engendering mindfulness explicitly and implicitly.
Public Health Significance Statement
This study indicated that effortful attentional control is not a significant mediator of the association between dispositional mindfulness and harmful alcohol use. In contrast, craving did mediate this relationship. The indirect relationship between mindfulness and harmful alcohol use via craving did not differ between mindfulness-based relapse prevention versus relapse prevention treatment, suggesting that reductions in craving may be affected by even slight increases in mindfulness and indicate the benefit of implementing mindfulness in alcohol treatment.
The rapidly growing legal cannabis market includes new and highly potent products, the effects of which, to our knowledge, have not previously been examined in biobehavioral research studies because ...of federal restrictions on cannabis research.
To use federally compatible, observational methods to study high-∆9-tetrahydrocannabinol (THC) legal market forms of cannabis.
In this cohort study with a between-groups design that was conducted in a community and university setting, cannabis flower users and concentrate users were randomly assigned to higher- vs lower-THC products within user groups. Participants completed a baseline and an experimental mobile laboratory assessment that included 3 points: before, immediately after, and 1 hour after ad libitum legal market flower and concentrate use. Of the 133 individuals enrolled and assessed, 55 regular flower cannabis users (41.4%) and 66 regular concentrate cannabis users (49.6%) complied with the study's cannabis use instructions and had complete data across primary outcomes.
Flower users were randomly assigned to use either 16% or 24% THC flower and concentrate users were randomly assigned to use either 70% or 90% THC concentrate that they purchased from a dispensary.
Primary outcome measures included plasma cannabinoids, subjective drug intoxication, and neurobehavioral tasks testing attention, memory, inhibitory control, and balance.
A total of 121 participants completed the study for analysis: 55 flower users (mean SD age, 28.8 8.1 years; 25 women 46%) and 66 concentrate users (mean SD age, 28.3 10.4 years; 30 women 45%). Concentrate users compared with flower users exhibited higher plasma THC levels and 11-hydroxyΔ9-THC (THC's active metabolite) across all points. After ad libitum cannabis administration, mean plasma THC levels were 0.32 (SE = 0.43) μg/mL in concentrate users (to convert to millimoles per liter, multiply by 3.18) and 0.14 (SE = 0.16) μg/mL in flower users. Most neurobehavioral measures were not altered by short-term cannabis consumption. However, delayed verbal memory (F1,203 = 32.31; P < .001) and balance function (F1,203 = 18.88; P < .001) were impaired after use. Differing outcomes for the type of product (flower vs concentrate) or potency within products were not observed.
This study provides information about the association of pharmacological and neurobehavioral outcomes with legal market cannabis. Short-term use of concentrates was associated with higher levels of THC exposure. Across forms of cannabis and potencies, users' domains of verbal memory and proprioception-focused postural stability were primarily associated with THC administration.
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