Cannabis Grow

Functional Magnetic Resonance Imaging research has identified the striatum as a key hub in PE learning , along with the insula and anterior cingulate cortex . These regions also exhibit significant neurodevelopment during adolescence . Indeed, the first study to have examined PE in adolescents found that teenagers exhibit a similar, albeit stronger striatal response for positive PE compared to adults , and a more recent study found adolescents learn faster from reward PE than adults , suggesting that key PE learning systems are similar though perhaps more sensitive during adolescence. In the current study, we adopted a violations of expectations framework, which is reflective of both the PE calculation as well as the social context in which it occurs. Within this framework, we sought to determine whether adolescents show exaggerated behavioral responses to VoEs, and to extend this question to examine whether they demonstrate unique neural responses when they receive expected and unexpected social feedback from a friend. In adults, social learning tasks with relationship partners and conspecifics implicate a similar neural network as that involved in PE, suggesting that PE computations are conserved across domains. Adolescents spend much of their time with friends but it is unknown whether specific peer feedback , valence, and value from known peers influences behavior and recruitment of still-maturing neural regions. Although several studies have examined adolescent neural reactivity to unexpected peer feedback and peer evaluation ,trimming cannabis few have done so within the context of a close friend, whose opinion may yield more realistic behavioral and neural responses.

The goal of this study was to identify the neural mechanisms associated with learning social information from a friend using a novel social PE task in adolescents. Exploring this phenomenon in adolescents is important compared to other developmental age groups because adolescents often rely on their friends’ opinions to form perceptions of themselves , and learn many social skills necessary for development during this time from their friends. Because building up social expectations is challenging in a laboratory setting, we leveraged real-life social expectations between friends and manipulated them in the laboratory. We hypothesized that adolescents would: 1) demonstrate decreased reaction time when expectations were met compared to when they were violated ; 2) report feeling happier learning their friend reported something better than they expected compared to something worse than what they expected ; 3) activate brain regions previously associated with PE, such as the ventral striatum when they learned something better than expected from their friend; and 4) activate regions associated with socioemotional processing, such as the subgenual anterior cingulate cortex when they learned something worse than expected from their friend. Participants completed a Social Violations of Expectations task while undergoing a brain scan. In this task, we presented target participants with statements that were purportedly based on the friends’ responses to the Friendship Questionnaire. The responses were presented as if they represented the friend’s true response, but in fact they were manipulated. To create positive violations of expectations, we changed the friend’s responses to be greater than the target’s expectation . To meet the expectations of the participant, we modified the friend’s response to match the target’s prediction.

To create negative violations of expectations, we modified the friend’s response to be worse than the target’s expectation . Because the dyads were close friends, participants tended to have relatively positive expectations of their friends’ responses, which limited our ability to change valence and VoE value of the friend’s “responses”. Thus, during the session, each participant was presented with an average of 14.96 positive violations, 7.04 items where expectations were met, and 17.88 negative violations. The task was programmed in E-Prime 2.0 and was presented through an LCD Optoma projector connected via fiber optic cables. Participants were presented with all 40 statements from the Friendship Questionnaire and were asked to press a button on a 4-button button box with their right index finger to proceed to the next question/trial. The trial began with the presentation of the question, followed by the target’s expectation, a 2000-6000ms jittered interstimulus-interval , their friend’s “response,” a jittered ISI, and a request to press a button to proceed to the next trial followed by a 4000-8000ms jittered inter-trial-interval . Participants were allotted a maximum of 5000 ms to press the button before a jittered fixation cross appeared on the screen, and the subsequent trial began . Following the scan, participants completed a questionnaire that contained each statement from the Friendship Questionnaire, along with their predictions and their friend’s “responses.” For example, participants were reminded of the statement, “My friend thinks I’m nice” and were shown that they expected their friend to report 6, while their friend “reported” 10. They were asked to report how seeing this made them feel on a scale of 1 to 10 . The present study was a within-subjects, event related design. Our independent variable was the violation of expectation, operationalized by modifying the friend’s initial response to be better than, equal to, or worse than the target’s expectation. Our dependent variables were 1) behavioral response, 2) self-reported response, and 3) neural activation when targets experienced a violation of expectations. Behavioral response was measured by indexing reaction time to press a button on a response box to move on to the next trial. Self-reported responses were collected via survey following completion of the task. Neural activation was examined using a priori regions of interest .

Behavioral Analyses IBM SPSS Statistics Software, version 23.0 was used to analyze RT and self-report responses to social violations of expectations. The data were analyzed three ways: 1) effects of expected versus unexpected information; 2) effects of valence ; and 3) parametric effects of incremental differences between the expectations and the associated outcomes—which was represented by violations that ranged between 9 values less than their expectation and 9 values greater than their expectation . fMRI Analyses Images were collected using a 3-Tesla Siemens Trio MRI machine equipped with 16- channels at the Staglin Center for Cognitive Neuroscience at UCLA. Two structural MRI images were collected at the start of the scan: a T1-weighted magnetization-prepared rapid-acquisition gradient echo image , 2000 ms; echo time , 2100 ms; matrix, 256 x 256; and field of view, 250 mm) and a T2- weighted matched bandwidth high-resolution scan,vertical cannabis which was prescribed to the functional images. One functional run was collected and consisted of a maximum of 440 and an average of 382 T2*-weighted echo-planar images . The first three TRs of the run were automatically discarded. Data preprocessing and analyses were conducted using the FMRIB Software Library version 5.0 . Images were corrected for motion using MCFLIRT and denoised using multivariate exploratory linear optimized decomposition into independent components analysis. Data were smoothed using a 5-mm full width-half-maximum Gaussian kernel and filtered with a nonlinear high-pass filter . A three-step registration process was used to align individual participant data into standard Montreal Neurological Institute space. EPI images were first registered to the matched band width image, then to the MPRAGE image, and finally to MNI space. Two subjects’ data required truncating of the last 115 volumes due to excess motion in the latter half of the scan. Data were analyzed using a subtraction method to model violations of expectations, the difference between the outcome and the expectation .Six motion parameters were included as covariates in the model for each run for each of the participants. Statistical analyses were performed on each participant’s data using a general linear model to observe neural activation associated with change in expectations. Each participant’s data were modeled using a three-column regressor that contained the onset of each event , its duration, and a standard weight of 1 . Neuroimaging analyses were performed three ways to examine neural response to the following: 1) social versus non-social trials); 2) violations versus nonviolations of expectations; and 3) valence of information . To compare neural activation to violations versus nonviolations of expectations, we averaged all VoE values different from 0 and tested them against VoE0 . To compare neural activation by valence , violations between VoE-9 and VoE-1 were grouped and averaged as negative trials, non-violations were grouped and averaged to represent when expectations were not violated , and VoE+1 to VoE+9 were grouped and averaged as positive trials; respectively. Analyses were conducted using FMRI expert analysis tool , first at a lower level to represent one level of a condition within an individual subject, then at a second level using a fixed-effects model to represent all levels of a condition for one subject. Finally, data were analyzed at a group level analysis using FMRIB’s Local Analysis of Mixed Effects with automatic outlier detection to group all participants together and compare contrasts of interest. Structural ROI masks containing a cluster radius of 6 voxels were created for the amygdala, dorsal anterior cingulate cortex , insula, subgenual anterior cingulate cortex , and ventral striatum based on probability maps from Neurosynth . These masks were added as the Pre-threshold Masks for each respective ROI and were clustered at the voxel level with a Z threshold of 2.3 and probability threshold of p = .05. To compare the RT between trial types to information type , a two-way repeated measures analysis of variance was performed.

This analysis did not reveal a main effect of trial type or information, or an interaction . To determine whether there were differences between valences in the violations, we ran a two-way repeated measures ANOVA comparing trial type to valence of the violations . The results revealed no main effect of valence or interaction between trial type and valence 1 . Thus, we sought to determine whether there were parametric differences between VoE values. Because not all participants had the same number of valence and value trials, a cross classification analysis was performed by indexing each variable of interest by each individual trial. This analysis enabled us to account for the variation that naturally occurred within each participant’s responses, as well as between participants. A regression was performed on the reorganized cross-classified data to account for 1) each trial for each participant; and 2) each VoE value associated with each trial.The goal of this study was to characterize the neural correlates of social violations of expectations in adolescents based on feedback from a close friend. Behaviorally, we found that adolescents’ reaction times decreased linearly as social expectations transitioned from negative to increasingly positive violations. Self-reported happiness increased linearly for social information, as social expectations transitioned from negative to increasingly positive violations. Correlational analyses indicated participants who reported closer friendships were happier to learn they were accurate in their expectations about their friendship, while participants who were not as close were happier when their expectations were positively violated. Neurobiologically, participants demonstrated greater recruitment of the VS for social positive compared to social negative violations; and greater recruitment of the subACC and insula for social negative compared to social positive violations. These results are in accordance with previous literature, such that increased reaction time to negative social expectations may have reflected greater cognitive interference on these trials , especially when the stimuli are emotional in nature . This may have been true specifically for increasingly negative social violations compared to positive social violations, as positive social violations may not have been as surprising to participants who were close friends. Thus, we speculate that participants’ self reported closeness may have contributed to this result, whereby participants were more surprised to receive negative social feedback from their friend.Interestingly, we found participants who reported being closer friends reported feeling happier when expectations were not violated compared to when they were violated, and were increasingly happier as social expectations were increasingly positively violated. Reminding participants of their predictions and their friends’ responses may have felt threatening whereby participants knew their friend could have reported something worse than they expected. In turn, this may have amplified the happiness they reported when expectations were met, perhaps indicating they were relieved and were pleased to experience reciprocity or something better than what they expected. We posit that experiencing a violation of any kind is conflicting and emotionally arousing for an adolescent, as they are hyper-sensitive to peer feedback, and learning they are incorrect about their friendship may be disconcerting compared to learning they are correct.

More high school students smoked little cigars and cigarillos than cigarettes in 33 US states in 2015. Concern is growing about co-use of tobacco and marijuana among youth, particularly among African-American youth.In a 2015 survey, for example, one in four Florida high school students reported ever using cigars or cigar wraps to smoke marijuana. One colloquial term for this is a “blunt.” Adolescent cigar smokers were almost ten times more likely than adults to report that their usual brand offers a flavored variety. Since the US ban on flavored cigarettes , the number of unique LCC flavors more than doubled. Anticipating further regulation, the industry increasingly markets flavored LCCs with sensory and other descriptors that are not recognizable tastes.For example, after New York City prohibited the sale of flavored cigars, blueberry and strawberry cigarillos were marketed as blue and pink, but contained the same flavor ingredients as prohibited products. Among the proliferation of such “concept” flavors , anecdotal evidence suggests that references to marijuana are evident. Cigar marketing includes the colloquial term, “blunt”, in brand names and product labels . Other marketing techniques imply that some brands of cigarillos make it easier for users to replace the contents with marijuana.For example, the image of a zipper on the packaging for Splitarillos and claims about “EZ roll” suggest that products are easily manipulated for making blunts. We use the term “marijuana co-marketing” to refer to such tobacco industry marketing that may promote dual use of tobacco and marijuana and concurrent use . In addition to flavoring, low prices for LCCs also likely increase their appeal to youth. In California, 74% of licensed tobacco retailers sold cigarillos for less than $1 in 2013. Before Boston regulated cigar pack size and price in 2012,grow trays the median price for a popular brand of grape-flavored cigars was $1.19.

In 2012, 78% of US tobacco retailers sold single cigarillos, which suggests that the problem of cheap, combustible tobacco is widespread. Additionally, the magnitude of the problem is worse in some neighborhoods than others. Popular brands of flavored cigarillos cost significantly less in Washington DC block groups with a higher proportion of African Americans and in California census tracts with lower median household income.For the first time, this study examines neighborhood variation in the maximum pack size of cigarillos priced at $1 or less and assesses the prevalence of marijuana co-marketing in the retail environment for tobacco. School neighborhoods are the focus of this research because 78% of USA teens attend school within walking distance of a tobacco retailer. In addition, emerging research suggests that adolescents’ exposure to retail marketing is associated with greater curiosity about smoking cigars and higher odds of ever smoking blunts.In California, 79% of licensed tobacco retailers near public schools sold LCCs and approximately 6 in 10 of these LCC retailers sold cigar products labeled as blunts or blunt wraps or sold cigar products with a marijuana-related flavor descriptor. A greater presence of marijuana co-marketing in neighborhoods with a higher proportion of school-age youth and lower median household income raises concerns about how industry marketing tactics may contribute to disparities in LCC use. The study results also suggest that $1 buys significantly more cigarillos in California school neighborhoods with lower median household income. Policies to establish minimum pack sizes and prices could reduce the widespread availability of cheap cigar products and address disparities in disadvantaged areas.After Boston’s 2012 cigar regulation, the mean price for a grape-flavored cigar was $1.35 higher than in comparison communities.The industry circumvented sales restrictions in some cities by marketing even larger packs of cigarillos at the same low price, and the industry’s tipping point on supersized cigarillo packs for less than $1 is not yet known. The retail availability of 5- and 6-packs of LCCs for less than $1 observed near California schools underscores policy recommendations to establish minimum prices for multipacks .

A novel measure of marijuana co-marketing and a representative sample of retailers near schools are strengths of the current study. A limitation is that the study assessed the presence of marijuana co-marketing, but not the quantity. The protocol likely underestimates the prevalence of marijuana co-marketing near schools because we lacked a comprehensive list of LCC brands and flavor varieties. Indeed, state and local tobacco control policy research and enforcement would be greatly enhanced by access to a comprehensive list of tobacco products from the US Food and Drug Administration, including product name, category, identification number and flavor. Both a routinely updated list and product repository would be useful for tobacco control research, particularly for further identifying how packaging and product design reference marijuana use. This first assessment of marijuana co-marketing focused on brand and flavor names because of their appeal to youth.However, the narrow focus is a limitation that also likely underestimates the prevalence of marijuana co-marketing.Examples are pack imagery that refers to blunt making, such as the zipper on Splitarillos, as well as re-sealable packaging for cigarillos and blunt wraps, which is convenient for tobacco users who want to store marijuana.Coding for brands that are perforated to facilitate blunt making and marketing that refers to “EZ roll” should also be considered. Future research could assess marijuana co-marketing across a larger scope of tobacco/nicotine products.Advertising for vaping products also features compatibility with “herbs” and otherwise associates nicotine with words or images that refer to marijuana . Conducted before California legalized recreational marijuana use, the current study represents a baseline for understanding how retail marketing responds to a policy environment where restrictions on marijuana and tobacco are changing, albeit in opposite directions.The prevalence of marijuana co-marketing near schools makes it imperative to understand how tobacco marketing capitalizes on the appeal of marijuana to youth and other priority populations. How drying marijuana co-marketing contributes to dual and concurrent use of marijuana and tobacco warrants study, particularly for youth and young adults. In previous research, the prevalence of adult marijuana use in 50 California cities was positively correlated with the retail availability of blunts.

Whether this is correlated with blunt use by adolescents is not yet known. Consumer perception studies are necessary to assess whether marijuana co-marketing increases the appeal of cigar smoking or contributes to false beliefs about product ingredients. Research is also needed to understand how the tobacco industry exploits opportunities for marijuana co-marketing in response to policies that restrict sales of flavored tobacco products and to policies that legalize recreational marijuana use. Such assessments are essential to understand young people’s use patterns and to inform current policy concerns about how expanding retail environments for recreational marijuana will impact tobacco marketing and use.Despite their similar chemical structures, anandamide and 2-arachidonylglycerol are produced through distinct biochemical pathways. Formation of anandamide may result from hydrolysis of the phospholipid precursor Narachidonyl phosphatidylethanolamine, catalyzed by a Ž phosphodiesterase such as phospholipase D Di Marzo et . al., 1994; Cadas et al., 1997; Schmid, 2000 . 2-Arachidonylglycerol, on the other hand, may be produced by cleavage of 1,2-diacylglycerol generated by phospholipase Ž C acting on phosphatidylinositol bisphosphate Gammon et . al., 1989; Stella et al., 1997 , although the participation of Ž alternative pathways cannot be excluded Piomelli et al., . 2000 . The existence of different enzymatic routes for the formation of anandamide and 2-arachidonylglycerol suggests that under certain circumstances, these two endocannabinoid substances might operate independently of each other. This possibility is supported by two findings. In hippocampal slices, stimulation of glutamate-releasing fibers in the Schaffer collaterals increases the levels of Ž 2-arachidonylglycerol, but not those of anandamide Stella . et al., 1997 . Conversely, in vivo microdialysis experiments show that activation of striatal D -family dopamine 2 receptors enhances release of anandamide, but not of 2- Ž . arachidonylglycerol Giuffrida et al., 1999 . It is unclear, however, whether this discrepancy reflects different experimental conditions, regional segregation of the phospholipase C and phospholipase D pathways, or receptoractivated mechanisms linked to the generation of specific endocannabinoid lipids. To examine these different possibilities, in the present study we have investigated the receptor mechanisms underlying the formation of anandamide and 2-arachidonylglycerol in primary cultures of rat cortical neurons. Our findings indicate that the excitatory neurotransmitter glutamate triggers 2-arachidonylglycerol biosynthesis by allowing external Ca2q to enter cortical neurons through activated NMDA receptor channels. In these cells, 2-arachidonylglycerol formation is likely to be mediated by the phospholipase Crdiacylglycerol lipase pathway, because selective drug inhibitors of these enzyme activities prevent the accumulation of 2-arachidonylglycerol elicited 2q Ž . by a Ca ionophore Stella et al., 1997 . This conclusion is also in agreement with previous work showing that NMDA receptor activation stimulates phospholipase C and Ž diacylglycerol lipase activities in cultured neurons Nico- . letti et al., 1986; Farooqui et al., 1993 . Noteworthy, we found that activation of acetylcholine receptors with carbachol had little or no effect, per se, on 2-arachidonylglycerol levels.

This result, at variance with the ability of cholinergic agonists to stimulate phospholipase C activity in many tissues and to cause 2-arachidonylglycerol accu- Ž . mulation in vascular endothelium Mechoulam et al., 1998 , underscores the differences in 2-arachidonylglycerol biosynthesis between neurons and other non-neuronal cell types. The functional role of 2-arachidonylglycerol formation in NMDA receptor signaling is unclear at present. However, experiments with superfused hippocampal slices suggest that 2-arachidonylglycerol may act as a localized Ž feedback signal within the hippocampus Stella et al., . 1997 . Electrical stimulation of the Schaffer’s collaterals, a glutamatergic fiber tract that projects from CA3 to CA1 neurons, produced a marked increase in 2-arachidonylglycerol accumulation in the slices, which was prevented by the Naq channel blocker tetrodotoxin or by removal of 2q Ž . external Ca Stella et al., 1997 . In the same preparation, exogenous 2-arachidonylglycerol inhibited the induction of long-term potentiation at CA3–CA1 synapses by activating local cannabinoid CB receptors, whereas it had no 1 Ž . effect on basal synaptic transmission Stella et al., 1997 . The possible role of 2-arachidonylglycerol as a negative feedback regulator of N-methyl-D-aspartate-mediated responses, suggested by these results, is reinforced by the ability of cannabinoid drugs to reduce glutamate transmis- Ž . sion Shen et al., 1996; Shen and Thayer, 1999 , inhibit Ž long-term potentiation Collins et al., 1994; Terranova et . al., 1995; Misner and Sullivan, 1999 , and alleviate gluta- Ž . mate-induced neurotoxicity Nagayama et al., 1999 .In the present experiments, the simultaneous application of glutamate and carbachol, but not of either agent alone, caused a marked stimulation of anandamide biosynthesis in cortical neurons. Pharmacological experiments suggest that this synergistic effect may result from the coactivation of NMDA and a7 nicotinic receptors, and may depend both on membrane depolarization and on mobilization of Ca2q ions from intracellular stores. Though necessary for the response, membrane depolarization was insufficient per se to initiate anandamide biosynthesis.Thus, fatty acid ethanolamide biosynthesis in the former cultures might result, not from a direct effect of membrane depolarization, but from the action of neurotransmitters released in the extracellular medium. A similar indirect mechanism may be responsible for the release of anandamide induced in vivo by a depolarizing concentration of KCl, administered in dorsal striatum by Ž . reverse dialysis Giuffrida et al., 1999 . Receptor-dependent anandamide formation may be distinguished pharmacologically from that of other fatty acid ethanolamides that do not activate cannabinoid receptors. Indeed, though biosynthesis of all fatty acid ethanolamides is contingent on NMDA receptor occupation, anandamide formation requires the coactivation of NMDA and a7 nicotinic receptors, while oleylethanolamide and palmitylethanolamide formation requires the coactivation of NMDA and muscarinic receptors. Thus, glutamate and acetylcholine may elicit the biosynthesis of different fatty acid ethanolamides, depending on the complement of acetylcholine receptors expressed in their target neurons. The finding that palmitylethanolamide and oleylethanolamide may be produced by dissimilar molecular mechanisms and may exert biological effects that are not medi- Ž ated by cannabinoid receptors Calignano et al., 1998; . Jaggar et al., 1998 raises the possibility that they might serve signaling functions independent from those of anandamide. Testing this possibility will require a thorough investigation of the pharmacological effects of palmitylethanolamide and oleylethanolamide in neurons, as well as the identification of the putative cellular targets that may mediate these effects. In conclusion, biosynthesis of anandamide and 2- arachidonylglycerol in cortical neurons may be triggered by activation of membrane receptors for the neurotransmitters, glutamate and acetylcholine. This suggests that a key function of the endocannabinoid system in the brain may be to modulate the effects of primary neurotransmitters by a localized feedback action.

In closing, the present study found drinking behaviors did not significantly mediate the effect of pharmacotherapy on smoking behaviors across the span of 6 months in a sample of heavy drinking smokers engaged in a treatment tailored to promote smoking cessation and drinking reduction. While the main results of the trial showed compelling quit rates and superiority of varenicline plus placebo, compared to varenicline plus naltrexone, the study adds to the importance of targeting smoking and drinking concomitantly. The main findings for the drinking outcome showed a modest superiority of varenicline plus naltrexone on reductions in drinks per drinking day, compared to varenicline plus placebo. Since the main trial showed a complex picture with drinking and smoking results lacking alignment, it is perhaps unsurprising that drinking did not mediate medication effects on smoking. Nevertheless, smoking and drinking behaviors remained intertwined across the trial, primarily at the cross sectional level of analysis. Among participants engaged in smoking cessation and drinking reduction, our results generally confirmed the previous findings that smoking and drinking are intertwined and remain so throughout treatment. As such the concurrent use of alcohol and cigarettes remains a critical clinical target with the potential to result in dramatic improvements to health outcomes with successful smoking cessation and drinking reduction. The dissertation project examined both etiology and treatment of the unique subgroup of heavy drinking smokers.

Etiology of heavy drinking smokers was examined from a behavioral economics framework as one way in which to demonstrate the bidirectional nature of co-use, as well as nuanced complexities in the ways these substances exert cross substance effects . The results made salient how behavioral economic indices may be sensitive to cross-substance relationships. They also further explained the bidirectional relationship between cigarettes and alcohol,indoor grow cannabis showing that such relationships are asymmetrically stronger for smoking variables affecting alcohol demand, not the other way around. Treatment was examined through Chapter 2 and Chapter 3 , utilizing data drawn from the RCT described above. The translation of basic science and experimental pharmacology findings to clinical samples is imperative to elucidate our understanding the role of relevant biological variables such as sex hormones, and to further understand the mechanisms of action underlying pharmacological treatment response on clinical outcomes. Recent findings implicating menstrual cycle phase with smoking behavior and clinical responses to naltrexone have emerged from experimental pharmacology studies. However, these results have not been consistently extended to clinical trials. While our results did not show an effect of the ratio of P4/E2 on smoking outcomes, we did observe an effect of P4/E2 ratio on the drinking outcome of percent days abstinent. These findings are important as they underscore how sex hormones offer important information above and beyond comparing groups on the bases of sex. For Chapter 3, while our results did not reveal drinking outcomes to mediate the relationship between medication condition and smoking outcomes, we did see a consistent pattern throughout active medication phase, and follow-up, where greater drinking and smoking is consistently associated with greater use of the respective substances. These findings call attention to how the relationship between drinking and smoking can be maintained throughout a 6-month clinical trial and furthers the need for the development of treatment options that can target both alcohol and cigarette use in heavy drinking smokers.

Together, these studies use a translation framework that combines pharmacology, experimental psychology, and biomarkers of sex differences in order to address the clinical implications of the co-use of alcohol and cigarettes. These studies advance a precision medicine approach whereby the complimentary between smoking and drinking can be clinically targeted. These studies position the candidate to have a unique and impactful program of research drawing from experimental psychopharmacology, sex differences, advanced data analytic methods, and a patient-centered clinical framework. Proper adherence to combination antiretroviral medication therapy is critical for improving health outcomes in HIV . However, it is estimated that less than 40% of HIV+ individuals are retained in long-term healthcare management and only about one-quarter are virally suppressed in the U.S. . Further, approximately half of individuals prescribed antiretroviral medications do not fully adhere to their regimen . Suboptimal cART adherence is associated with increased viremia, immune suppression, increased risk of HIV transmission, and mortality . Numerous factors appear to be contributing to suboptimal cART adherence, including psychiatric comorbidities , lack of social support, severity of antiretroviral side effects, beliefs about self-efficacy and neurocognitive dysfunction . With regard to the latter, HIV-associated neurocognitive deficits and cART non-adherence appear to have a cyclical relationship; that is, neurocognitive deficits can interfere with medication taking behaviors , which in turn can accelerate disease progression and further impair neurocognitive functioning . The historic complexity of combination antiretroviral therapies may also impose an undue burden on cognitive abilities necessary for adherence . The medications comprising a cART regimen may involve differing dosages and/or administration schedules, which could increase the risk of errors that negatively impact overall adherence, particularly among individuals with neurocognitive difficulties. Although the pill burden of cART is on a downward trajectory, polypharmacy remains common and these challenges are important given that cART is the gold standard for HIV treatment in the United States.

Additionally, the prevalence of HIV associated neurocognitive disorders may have even risen slightly in the cART era, with approximately 50% of the population affected . The elevated rates of HAND and domain-based impairment may be due to increasing lifespans resultant from high cART efficacy, leading to longer exposure to both the presence of virus as well as potentially toxic long-term effects of the drug regimens, combined with comorbid processes such as cognitive aging . While HIV-associated neurocognitive deficits can be observed in a variety of neurocognitive domains, the prevalence of impairment in higher-order neurocognitive abilities,curing cannabis such as episodic memory in particular, are higher . In this study we therefore focus our attention on the role of episodic memory deficits in cART non-adherence. HIV-associated neural injury in frontal and temporal systems affect multiple aspects of episodic memory , including traditional retrospective memory for word lists, designs, and passages, as well as prospective, source, and temporal order memory . The profile of HIV-associated episodic memory deficits is heterogeneous , with prior studies showing evidence that numerous specific memory processes may be disrupted, including learning/acquisition, storage/consolidation, and retrieval . Anywhere from 20 to 40% of HIV+ individuals exhibit a primary retrieval profile of memory deficits consistent with injury to fronto-striato-thalamo-cortical circuits. This retrieval profile is characterized by difficulties in bringing previously stored information into conscious awareness and is evidenced by deficits in free recall of information that are ameliorated when structured retrieval cues are provided . A retrieval profile, which is sometimes referred to as a mixed encoding/retrieval profile, can suggest that an individual only partially encodes the target information. This is because stimuli that were processed only in part are difficult to spontaneously recall in their entirety, but may be accurately recognized when presented. HIV-associated deficits in learning and memory have been linked to poorer everyday functioning, including medication management skills and cART non-adherence. Deficits in acquisition and delayed free recall as a global composite index have been consistently associated with poor performance on laboratory-based medication management tasks across varying types of stimuli . Among the two studies that we found examining specific memory components in the context of adherence, measures of delayed free recall were more consistently associated with medication adherence , whereas indices of initial learning show more variable associations.

Yet the specificity of such delayed recall deficits is difficult to determine, as they may be a consequence of problems with encoding, forgetting, and/or retrieval. Thus, while learning and memory are consistently associated with medication management in the laboratory and daily life, little is known about the specific profile that may be driving these relationships. Identification of such profiles at the levels of both group data and individual participants is important in order to enhance the clinical identification of persons at risk for non-adherence and develop tailored compensatory mnemonic approaches to improve adherence. To that end, Wright and colleagues applied the item specific deficit approach , an item analytic method, to study the association between cART adherence and word list encoding, consolidation, and retrieval as measured by the California Verbal Learning Test in 75 HIV+ participants. The ISDA indices were developed as a novel method for categorizing encoding, consolidation and retrieval deficits. The ISDA encoding index is constructed by summing the number of items recalled in less than three of the five learning trials, the ISDA consolidation index reflects the number of items recalled during the learning trials but not recalled again during any of the recall trials , and retrieval deficits are indexed by the number of learned items recalled inconsistently across short- and long-delay trials. To control for potential group differences in learning, the consolidation and retrieval indices are each divided by the total words recalled at least once during the learning trials. When using the ISDA Wright and colleagues found that, compared to healthy adults, HIV+ individuals demonstrated poorer performance on the CVLT regardless of their level of antiretroviral adherence. Additionally, while both HIV+ groups demonstrated similar ISDA encoding deficits, only non-adherers demonstrated ISDA retrieval deficits compared to HIV− participants. Additionally, retrieval abilities, as measured by the ISDA indices, accounted for a greater proportion of variance in long-delay free recall performance for poor adherers than for good adherers. While the study conducted by Wright and colleagues provided initial evidence of an association between retrieval deficits and suboptimal adherence in HIV, no studies have evaluated the association between suboptimal adherence and traditional list learning profiles that also incorporate retention and recognition performance as is commonly done in clinical research and practice. At the group level, such profile-based approaches have a long tradition of utility in distinguishing between different neuropsychological disorders. For example, retrieval deficits in the context of significantly improved recognition are commonly associated with “subcortical” dementias . “Cortical” dementias , on the other hand, are sometimes differentiated by rapid forgetting, minimal improvement on recognition, and high rates of cued recall intrusions . Our approach here is to employ those same cognitive psychology approaches of learning and memory profile distinctions to better understand the cognitive architecture of non-adherence. To further enhance the clinical relevance of this group-level analytic approach, we also propose to classify individuals’ profiles as reflective of problems with encoding versus retrieval using an established data-based algorithm that has shown utility differentiating traditionally cortical versus subcortical diseases . This algorithm-based individual profile approach has proven to be a worthwhile complement to group-based analyses by allowing for clinically-relevant classification accuracy data and demonstrating the heterogeneity of profiles within these traditionally “cortical” and “subcortical” groups. To our knowledge, this would be the first study to apply this algorithm-based memory profile classification of individuals to understand an important everyday functioning outcome . Further, no study thus far has evaluated the HIV+ memory deficit profiles associated with non-adherence while using different types of verbal stimuli . The format in which information is presented may have a direct impact on memory performance, particularly in the context of medication management. For example, multiple studies have reported improved medication adherence when traditional prescriptions were enhanced with more descriptive instructions about the medication regimen instead of simply listing the information . The current study seeks to expand upon previous research to determine the associations between different episodic memory profiles and antiretroviral non-adherence by utilizing traditional CVLT scoring methods with the inclusion of retention and recognition indices and a concurrent measure of passage recall . In addition to standard group-level analyses of these clinical memory tasks, we also endeavored to look at individual profiles of word list learning as a risk factor for non-adherence using a previously established algorithm for classifying normal, encoding and retrieval profiles. Based on the literature reviewed above, we expect that antiretroviral non-adherence will be associated with a mixed encoding/retrieval profile. These associations may be particularly pronounced on word lists versus story memory given the former’s greater reliance on executive processes , such as strategic encoding, which is reliably impaired in HIV+ individuals and has been strongly associated with non-adherence .The study sample was comprised of 202 HIV+ participants recruited from the general San Diego area . The study was approved by the University of California, San Diego’s Human Research Protections Program.

Bivariate correlations were used to examine the relationship among behavioral economic indices within each substance and between pairing indices . An exploratory factor analysis was conducted using principle component analysis estimation method with an oblique rotation to allow for a multi-factorial solution with correlated factors. The PCA approach is consistent with previous research examining the latent structure of demand , with the rationale that characterizing total variance among indices was preferable due to the high levels of variability among associations between demand indices . Factor structure was determined by an Eigenvalue >1 and by further examination of the scree plot. When interpreting the rotated factor pattern, factor loading of 0.40 on the pattern matrix was the criteria used to determine if an item significantly loaded onto a given factor .A series of hierarchical multiple regressions with PROC REG were used to test our second and third aims in relation to same-substance and cross-substance associations between use severity/past 30-day use and latent factors of demand and demand indices. The primary outcomes were the latent factors of demand derived from the PCA. Due to the lack of existing research on the traditional five behavioral economic indices for alcohol and cigarettes in a sample of heavy drinking smokers, we ran parallel models including the five indices of demand as opposed to the latent factors of demand. These results are presented in the Supplementary Materials. In the lowest block were demographic characteristics . Due to the possible influence of income on choice behavior, income was included in this block of analyses.

The second block included same-substance predictors ,heavy duty propagation trays while the third block included cross-substance predictors . The same pattern of analyses was replicated for cigarette smoking indices, such that the second block included same-substance indicators of cigarette smoking and the third block included cross-substance indicators of alcohol use. To control alpha inflation an omnibus approach was used in the hierarchical regression, such that if the change in R2 was not significant, the block of coefficients was not considered further. This approach reduces alpha inflation by reducing the total number of tests. No correction for Type I error was implemented based on the rationale that Type I error needs to be considered at the level of families of hypotheses separately and not for the number of variables in the whole set of analyses reported . In the present analyses, the primary outcomes of the two factors of demand represent two families of hypotheses suggesting correction for Type I error may not be necessary. Results from full models including the three aforementioned blocks and reduced models, excluding non-significant blocks, are reported. Analyses were conducted in SAS University Edition version 9.4 .Power analyses for the final study sample of n=322 for the APT and n=334 for the CPT were conducted in G*Power 3.1 . We conducted a sensitivity analysis to determine the minimum effect size that could be reliably detected in the planned hierarchical multiple regressions with three sets of predictors in an F test for a fixed multiple regression with an R 2 increase setting the alpha level at p<.05 and power = .80. Across the APT and CPT, the results revealed the sample size afforded an 80% power to detect an effect size of f2 = .03 which is slightly above the small effect cut-off .In a large sample of heavy drinking smokers, this study examined the association between latent factors of demand for nicotine and alcohol, in terms of same-substance associations cross-substance associations between use severity/past 30 day use for each substance in relation to demand for the other substance .

In examining same-substance relationships reflected in the second block of the hierarchical regression models for alcohol, a relatively consistent pattern emerged such that greater alcohol use severity, as indexed by ADS and past 30-day use, was associated with greater derived Persistence and Amplitude values. For demand for cigarettes, there was a similar pattern of consistency in cigarette use variables, represented by FTND and past 30-day cigarette use, as these variables were associated with both Persistence and Amplitude. These findings were consistent with the literature and support the notion that use and dependence of a substance is related to demand for a substance that can be captured through hypothetical purchase tasks . Results from our final aim of testing cross-substance associations revealed an interesting pattern whereby cigarette dependence and use predicted Persistence values for alcohol, however not Amplitude values for alcohol. For Persistence, these effects were seen in the expected direction whereby greater FTND predicted greater Persistence values. The same pattern was not seen in predicting cigarette demand such that alcohol dependence and use did not significantly predict Persistence values over and above alcohol use factors. However, alcohol use and dependence significantly predicted Amplitude values. Notably, this alcohol variables in the final block reached statistical significance with change in R2 , but when examining the coefficients of this block, neither drinks per drinking day nor ADS scores were significant. Further, the additional amount of variance this cross-substance use block was able to predict in Amplitude was rather small in comparison to the significant cross-substance use block predicting Persistence values . Results of our principle component analysis aligned with previous literature supporting that Persistence reflects four main dimensions of the demand curve, maximum expenditure , price corresponding to maximum expenditure , first price suppressing consumption to zero , and the overall slope of the demand curve . This factor Persistence represents interrelated measures of sensitivity to escalating price that has been hypothesized to reflect how far, in terms of price, an individual is willing to spend on alcohol . The second factor Amplitude consisted of only one demand indices, Intensity, thus reflecting how much in consumption an individual is willing to consume . These findings suggest that the Persistence factor is operative in these findings for alcohol, suggesting that greater tobacco involvement is associated with insensitivity to the escalating response cost for alcohol.

This pattern did not transition to cigarette outcomes, where results indicate the alcohol involvement is associated with greater overall consumption when free as represented by the Amplitude factor. These results align in part with previous work examining alcohol demand in a sample of heavy drinking smokers. We found greater nicotine dependence to relatively consistently predict greater willingness to spend on alcohol reflected in the Persistence factor which is consistent with Amlung and colleagues findings of smokers experiencing greater alcohol Omax and Breakpoint than non-smokers. Additionally, our results support Yurasek and colleagues finding that Pmax for alcohol was also elevated among smokers. When examining additional comorbidities, a recent study found greater alcohol demand among those who co-use alcohol and cannabis . Furthermore, an early study of commodity specificity revealed that tobacco demand is fundamentally independent of food demand suggesting that purchase tasks are not simply capturing a generic reward sensitivity . In line with what has previously been suggested , there is the possibility of a general hypersensitivity to all rewards that individuals who co-use both alcohol and cigarettes may experience. If there were a generalized hypersensitivity to reward,vertical cannabis we would expect to see a consistent pattern across cigarettes and alcohol such that alcohol use would predict cigarette demand and vice versa. In our sample, we found nicotine dependence and use to be relatively consistent in predicting greater insensitivity to the escalating response cost for alcohol via Persistence factor while alcohol dependence and use only predicted Amplitude reflecting intensity of demand for cigarettes. These results imply from a behavioral economics framework, there may be a stronger effect of nicotine dependence on demand for alcohol than the other way around . There are various possibilities by which tobacco involvement would predict greater reinforcing value of alcohol. One possibility is a methodological issue such that is plausible that this sample had a more stable smoking pattern with more variability in alcohol use, which in turn may explain these effects. In samples that use cigarettes more sporadically, alcohol may have a stronger effect driving demand for cigarettes. Another possibility is asymmetric pharmacological interactions with alcohol potentiating nicotine’s effects but the opposite not being true to the same extent. From a behavioral economics standpoint, this is turn could mean there are asymmetical behavioral interactions, such that smoking is more of a complement than alcohol with smoking making drinking better to a larger extent than drinking makes smoking better. A final possibility is that smoking involvement is a proxy for other items, such as comorbid psychiatric issues or other risk factors, such as adverse childhood events. From this perspective, alcohol becomes more valuable because smokers tend to be more disadvantaged and otherwise vulnerable.

Results indicated significant correlations within demand indices for cigarettes among all demand indices, with the exception of Intensity and Pmax. When examining demand for alcohol, nearly all demand indices were highly correlated apart from intensity which did not correlate with Pmax, Breakpoint, and Elasticity. While each of these demand indices are functionally related all having been derived from the same demand curve, the construct of relative reinforcing efficacy value is proposed to be heterogenous in nature . Thus, the consistent patterns of correlations may serve as a reflection of the demand curve, and deviations in correlations within a substance may reflect a unique aspect of our co-use population where by demand indices for one substance, namely cigarettes, are more strongly inter-related than demand indices for alcohol. The higher correlations may also have been a result of the differences in pricing structure. The present study should be interpreted in light of its strengths including a large sample size and use of all five behavioral economic indices to examine the effects alcohol could have on all aspects of the demand curve for cigarettes and vice versa. Limitations include the use of ADS and FTND as self-report measures of use severity, as opposed to a formal AUD or TUD diagnoses. In addition, the APT used an early price structure that was modelled on a progressiveratio operant schedule, with a doubling of response requirements that leads to the inclusion of non-market prices. This approach includes large intervals between prices that can inflate variance and may be responsible, for example, in the within-task differences in correlations for the APT and CPT, which used a narrower range of market-compatible prices. In summary, our results show that latent factors of demand derived from behavioral economic indices may be sensitive to cross-substance relationships and specifically that such relationships are asymmetrically stronger for smoking variables affecting alcohol demand, not the other way around. Whether this is a function of differential pharmacological interactions between alcohol and nicotine or whether it is because smoking severity is a proxy for other factors that lead to higher alcohol reinforcing value cannot be inferred in the current study, but warrants subsequent examination. More broadly, understanding cross-commodity demand relationships has the potential to illuminate both overlapping and non-overlapping aspects of substance misuse. Various sex differences exist in relation to cigarette use. Prior evidence has suggested that female smokers are at increased risks for negative health consequences when compared to males , with women experiencing greater nicotine withdrawal symptoms, craving, and negative affect , poorer smoking cessation outcomes , and increased risk of mortality . These smoking differences may be related to changes in hormone levels because of menstrual cycle phase. Two hormones of interest that have been shown to vary across the menstrual cycle are progesterone and estradiol . The menstrual cycle can be divided into two phases: the follicular phase and the luteal phase . The follicular phase begins the first day of menses and extends until ovulation. At the beginning of the follicular phase, P4 and E2 levels are low. P4 levels reach their peak during the luteal phase. E2 begins to increase during the follicular phase, reaching a peak at the end of this phase which is signaled by ovulation occurring approximately day 14 of a typical 28 – 30- day menstrual cycle. During the luteal phase, E2 decreases reaching an intermediate secondary peak. By the late luteal phase, both P4 and E2 levels decrease . Changes in sex hormones, specifically P4 and E2, regulate numerous neurotransmitter systems, thus influencing a variety of behaviors, including those related to problematic substance use.

We hypothesized that amygdala engagement to aversive stimuli would show a positive correlation with accumulated lifetime ecstasy use; a negative correlation with SERT binding, as assessed with positron emission tomography imaging, in the amygdala; and a negative correlation with time since last use of ecstasy, suggesting recovery of amygdala response.Fourteen users of ecstasy and 12 non-using control subjects were recruited by flyers, advertisements posted on relevant websites, and word of mouth. Potential candidates were invited to a face to-face screening that involved assessment of history of alcohol, tobacco, and illicit drug use and Lifetime Drinking History, as well as screening of current and previous psychiatric symptoms using the Schedules for Clinical Assessment in Neuropsychiatry interview . All participants included in the present study, which focuses on fMRI investigations not presented previously, constitute of individuals who took part in a simultaneously conducted study with PET measurements of serotonergic markers—the PET results obtained in the larger sample have been published previously . To avoid acute drug effects in the ecstasy users, use of drugs was not allowed seven days before the PET and MRI scans, which were conducted on separate days at the Neurobiology Research Unit at Rigshospitalet and at the Danish Research Centre for Magnetic Resonance at Hvidovre Hospital, respectively. Abstinence was confirmed by urine screen on the PET and MRI days. Control individuals were excluded if they reported more than 15 lifetime exposures to cannabis or had any history of use of other illegal drugs,indoor garden table and urine screen on the day of the scan was carried out. Demographic and drug data are presented in Table 1.

No present or prior neurological or psychiatric disorders were allowed for any of the subjects, and all subjects had a normal neurological examination and were lifetime naïve to antidepressants and antipsychotics. The study was approved by the local Ethics Committee, Copenhagen and Frederiksberg, Denmark 01-124/04 with amendment 11-283038, and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants.Regression analysis was used to investigate possible correlations between reaction times and log2 to the lifetime intake of ecstasy tablets or log2 to the number of days since the last use of ecstasy. Due to our previous findings that the effects of cumulative ecstasy lifetime intake and time since last use of ecstasy on SERT binding follow a log2distribution, we expected a log2-linear relationship between RT and lifetime intake and time since last use . Analysis and results that include lifetime intake and/or number of days since last use of ecstasy will in this manuscript refer to “log2 to” the number of accumulated lifetime doses/time since last use, but in order to improve readability, in the Results and Discussion sections, we have left out “log2 to” from the text. Differences in RT were assessed using a repeated measures analysis of variance model, with group as between-subject factor and emotion as within-subject factor . The significance level was set to α=0.05. Post hoc t-tests were carried out in order to test paired comparisons between facial expressions. The Greenhouse–Geisser method was used to correct for non-sphericity.The degree of serotonin depletion as reflected by cerebral SERT binding was assessed with PET and the selective SERT radioligand, 11C-DASB, as described in more detail by Erritzoe et al. . In short, PET data were acquired on a GE-Advance scanner as a dynamic 90-minute emission recording after intravenous injection of the radiotracer, 11C-DASB. There was maximum of 1.6 months between the 11C-DASB PET and the fMRI experiment ; approximately half of the group had PET before MRI, and vice versa. The vast majority was scanned within one to two weeks; only two were scanned with an interval of more than a month. For each participant, a mean SERT binding value from the amygdala was calculated. To confirm previous results obtained in the overlapping study sample , we used linear regression analysis to test whether the log2 to the accumulated lifetime intake of ecstasy tablets correlated with SERT binding from the amygdala.

Since effects of lifetime ecstasy intake on SERT become smaller with increasing abstinence from ecstasy , we controlled for this by also including the log2 to the number of days since the last use of ecstasy in the analysis. Also using a linear regression analysis, we tested whether the log2 to the number of days since the last use of ecstasy correlated with SERT binding from the amygdala . The possible difference between ecstasy users and controls in amygdala SERT binding was tested with an independent samples t-test.Functional data were preprocessed and analyzed using the statistical parametric mapping software package . Preprocessing included spatial realignment, co-registration to the anatomical image, segmentation, and normalization to the standard Montreal Neurological Institute template, and smoothing using a symmetric 6-mm Gaussian kernel. Subject-level models were constructed using five emotional face regressors , together with regressors modulating the events by their RT values. In addition, the subject-level models included a regressor for incorrect sex discrimination answers, and 24 nuisance regressors to correct for movement artifacts, including first- and secondorder movement parameters and spin history effects . T-contrasts comparing BOLD responses to emotional and neutral images were created for each participant. These maps were used in group-level models assessing: the main effects of emotional face processing across all participants, differences in BOLD responses between ecstasy users and control subjects, correlations between BOLD responses and the log2 to the accumulated lifetime ecstasy intake in ecstasy users, correlations between brain activity and regional SERT binding in the amygdala in ecstasy users and control subjects, and correlations between BOLD responses and the log2 to the number of days since the last use of ecstasy in ecstasy users. Drug use data used for correlations with MRI data and PET data were acquired in MRI and PET scan days, respectively. In and , one subject was excluded due to lack of precise information about the number of days of abstinence prior to the MRI investigation day, but the person was eligible for other analysis due to a negative urine sample. Our a priori region of interest was the amygdala.

At first, we therefore restricted the correction for multiple comparisons to the amygdala ROI, as defined by the SPM anatomy toolbox ; p-values are provided as p . We set the significance level for activated voxels at p<0.05 corrected for multiple comparisons using the family-wise error correction . The entry threshold was set to p<0.001 uncorrected with an extent threshold of five contiguous voxels. Second, a whole-brain analysis was performed. The significance level for activated voxels was set at p<0.05 corrected for multiple comparisons . The threshold was the same as in the ROI analysis.To test whether associations between ecstasy usage and BOLD response were mediated by SERT,microgreens grow rack a path analysis was used to decompose the total effect of MDMA usage into direct and indirect effects. The direct effect of ecstasy exposure on the mean BOLD response across the amygdala is the conditional effect adjusting for SERT binding. The indirect effect of MDMA on the mean BOLD response is the difference in the ecstasy effect between a model, where SERT BP is controlled for compared to when it is not. This difference in effect is equivalent to the product between the effect that ecstasy has on SERT and the effect that SERT has on BOLD response. Linearity assumptions were assessed graphically. Standard errors of the indirect effect were calculated by the delta method and were validated by comparison with 95% quantiles from a parametric bootstrap.To our knowledge, this is the first study to examine the effects of long-term ecstasy use on the neural responses to emotional face expressions. Relative to neutral face stimuli, main effects of emotional processing were found bilaterally in the amygdala, showing increased neural activity, especially in response to fearful and angry faces. This concurs well with a number of studies showing that viewing emotional faces, fearful faces in particular, activates the amygdala . While there was no ecstasy effect on task performance, ecstasy users did, as hypothesized, show higher amygdala activity with increased lifetime ecstasy use during angry face processing; that is, the more ecstasy tablets the ecstasy users had taken during their lifetime, the more activation they displayed in amygdala when watching angry faces. In the ecstasy user group, SERT binding correlated negatively with amygdala activity in response to angry faces. Non-significant statistical trends for activity during processing of angry and sad face processing suggested that amygdala activity waned with increasing time since the last intake of ecstasy. Neither the analyses of emotional expressions other than anger nor the whole-brain analysis revealed any significant results. Thus, our results support the hypothesis that long-term ecstasy use alters the neural basis of emotional face processing. This effect is dose-dependently related to lifetime consumption of ecstasy and appears to be reduced with increased time since last use. Interestingly, the linear relationship was consistently expressed for angry faces but not for other aversive facial expressions. This observation is in line with the results of Bedi et al. who found that acute MDMA intake alters the amygdala response to angry, but not fearful, facial expressions. The limited sample size of this study does, however, not allow us to conclude that there is not an effect of lifetime ecstasy intake on processing of other aversive facial expressions.

While acute MDMA intake has been shown to diminish amygdala activation , we found the opposite effect in long-term ecstasy users. This supports our hypothesis that long-term ecstasy users are in a chronic, albeit potentially reversible, serotonin-depleted state and therefore in accordance with studies showing that serotonin depletion, as induced by acute tryptophan depletion, leads to elevated amygdala activity when processing negative facial expressions . When including the lifetime amphetamine use in the model, the effect of the lifetime intake of ecstasy tablets on amygdala activity was no longer significant. This may be due to high correlation between ecstasy and amphetamine use . Since the lifetime amphetamine use in itself did not have a significant effect on amygdala activity during angry face processing, our interpretation of the results is that the effect of ecstasy use on emotional processing would be present also in the absence of amphetamine use. The present study was carried out on a sub-sample of our previous study sample of chronic ecstasy users , and we confirmed a negative correlation between SERT binding and accumulated ecstasy use. Hence, it could be speculated that our present fMRI results, showing a positive correlation between lifetime use of ecstasy tablets and left amygdala activity, was mediated by SERT density; that is, a larger lifetime intake of ecstasy tablets was associated with lower SERT binding levels , possibly leading to a higher degree of amygdala activation during angry face processing. In the ecstasy user group, SERT binding was indeed negatively correlated with amygdala reactivity to angry faces, which is in line with Rhodes et al. , showing a negative correlation in the left amygdala between SERT density and activity during emotional face processing. Post hoc mediation analysis did, however, not support the mediation hypothesis, although these results need to be interpreted with caution given the small sample size and hence the low statistical power. In short, our study suggests that there are functional consequences of a chronically depleted serotonin system as indexed by lowered SERT. Of note, an augmented amygdala response to angry faces has also been observed in mood disorders and could within a population with reduced serotonergic tone represent a sub-clinical vulnerability marker for such conditions. In line with several other studies , we have recently reported that recovery of subcortical—but not cortical—SERT availability takes place after termination of ecstasy use. Importantly, here, we found trends showing that days of abstinence from ecstasy correlated negatively with left amygdala activity during angry face processing and with right amygdala activity during sad face processing. Since lifetime use of ecstasy tablets correlated positively with amygdala activity during angry face processing, the trend toward a negative correlation between days of abstinence from ecstasy and amygdala activity during angry face processing might be a potential sign of functional reversibility.There are limitations to the current study. Because of the cross-sectional nature of our study, it cannot be ruled out that the exaggerated amygdala response to angry faces and/or the low cerebral SERT among heavy ecstasy users represents preexisting traits associated with an increased preference for the use of ecstasy.

The final analysis in Table 4 addressed Hypothesis 4 by evaluating main and interaction effects for numbers of ARBs across eight time points using a 3 ethnic groups by 2 sexes by 2 LR categories by 8 time points mixed-design ANOVA that controlled for maximum drinks while using education group as a covariate. For all analyses, missing data were handled through a maximum likelihood procedure . At baseline, the 398 eligible participants were 18-year-old UCSD freshmen, of whom 62% were female with 40% EA, 20% Hispanic, and 41% of Asian descent . Table 2 presents the numbers of subjects across combinations of ethnicities, sex, and LR groups. The SRE values averaged 4 drinks across four possible effects actually experienced the first five times they drank. In the prior month, these students consumed on average 6 maximum and 4 usual drinks per occasion, with 4 drinking occasions per month. At baseline, 21% noted having experienced an ARB in the prior month, with an average of 0.33 such episodes . About 40% had used cannabis the prior month, and 87% were in the active intervention group in the prevention study. Table 3 presents statistical analyses associated with the 3 figures, while Table 4 shows results of a mixed-design ANOVA for the overall statistical analysis regarding Hypothesis 4.Figure 1 presents the average number of ARBs the month before each assessment for members of the three ethnic groups, with highest rates for EA students, lowest for Asian individuals, and an intermediate rate for Hispanic students. While not shown in the figures,weed growing system over the 55 weeks 43.0% had at least 1 ARB, including 13.3% with 1, 8.8% with 2, 2.5% each with 4, 5, or 6 ARBs, and 0 to 1.0% each with between 7 and a maximum of 36 ARBs.

As demonstrated by the absence of a significant ethnicity by time interaction in Table 4, general patterns of ups and downs in numbers of ARBs over 55-weeks were similar for the three groups where ARB values tended to diminish between January and March , increase in June in concert with a campus festival known for heavy drinking, decrease again over the summer when most students returned home , and rose after returning to school . However, regarding Figure 1, as shown in Table 3 the ANOVAs carried out at each time point revealed significant differences in the average number of ARBs across groups at every evaluation. After controlling for maximum drinks reported at each assessment and education group assignment, residual statistics for ethnic group differences in ARBs at each time point across EA, Hispanic, and Asian subjects remained significant at Times 2 and 4. The absence of a main effect for ethnicity in the mixed-design ANOVA in Table 4 indicates the possibility that other characteristics may have impacted results. Therefore, Figure 2 presents ARB trajectories for the three ethnic groups for female and male students separately. While not shown, during the 55 weeks, 48.0% of females and 34.7% of males reported at least one ARB, with an average of 0.30 and 0.19 ARBs per assessment, respectively. Ethnic group differences in ARBs were prominent among females, with the highest ARB rates for EA women, the lowest for Asian individuals, and intermediate, but relatively low, rates for Hispanics. Looking at each assessment for females, statistical analyses in Table 3 demonstrate significant ethnic differences in the rates of ARBs for raw ARB numbers at every assessment, which remained significant at times 4, 5 and 8 after controlling for education group and maximum drinks. Differences across ethnic groups were less apparent for males, and the ethnicity by sex by time interaction was significant in Table 4.

Also, for between subjects’ analyses where time was collapsed and average scores across the eight time points were used, there was a significant sex main effect, and the ethnicity by sex interaction was a trend . Next, the potential relationship of LR to the ethnic patterns of ARBs over time was evaluated in Figure 3. Here, high and low LR subjects showed the same general pattern of ARBs across time demonstrated in Figure 1, including highest rates of ARBs per assessment for EA, lowest for Asian, and intermediate rates for Hispanic students. However, ethnic group differences in ARBs were more robust for high LR subjects, with Table 3 revealing significant differences in raw ARB numbers across the three ethnic groups at every assessment, which remained significant at Time 7 after controlling for maximum drinks and education group assignment, with a trend at Time 4 . The only significant differences in raw ARB numbers for low LR subjects were noted at Time 2 and 4, each of which lost significance once residuals were used. In Table 4 the sex by LR by time interaction was significant and the LR group by time interaction for patterns of ARBs was a trend . Thus, the relationship of LR to differences in ARB patterns across ethnic groups was modest, and was most robust when considered in the context of sex effects. Regarding Hypothesis 4, as briefly alluded to above, the overall analysis in Table 4 indicates interactions regarding ARBs among ethnicity, sex, and LR in two 3-way interactions . However, the 4-way interaction was not significant. It is important to note that cross-sectional data in Figures 1 to 3 were analyzed after controlling for the education group in which students participated. Table 4 offers additional information about effects of educational group assignment, which was used as a covariate. Here, the education group by time interaction was a trend , and the education group main effect was significant, with controls having higher ARB frequencies than the active education participants.

Alcohol-related blackouts are highly prevalent phenomena associated with potentially severe problems . Recently, the prevalence of ARBs has reached alarming rates, especially in females and individuals with early onset drinking . The UCSD freshmen studied here are no exception to these trends as 43% of these students reported at least 1 ARB during the 55 weeks, including 48% in females and 35% in males. While the risk for these alcohol-related anterograde memory lapses increases with BACs , ARB vulnerabilities were also related to ethnic background, female sex, and levels of response to alcohol. The patterns and interactions among these characteristics are the focus of this paper.The current analyses added potentially useful data to the study of ARBs. The sample is relatively large, and subjects were assessed prospectively eight times over 55 weeks during a life-period likely to involve heavy drinking . Several assessments were scheduled at periods when the rates of ARBs were likely to change, including following a heavy drinking campus festival, summer break, and after returning to school as sophomores . Data were evaluated while controlling for maximum drinks, thus diminishing the possibility that ARB patterns simply reflected impacts of heavy drinking itself, and after controlling for possible effects of the prevention trial from which the data were extracted. The major questions focused on improving understanding of how ethnicity, sex,indoor farming systems and LR related to rates of ARBs. To address Hypothesis 1, evaluations began with documentation of expected ethnic group differences in rates of ARBs across time. Consistent with most prior studies, the highest rates were observed for students of EA origin, the lowest among Asian students, with an intermediate rate for Hispanic individuals. This pattern of the number of ARBs persisted after controlling for maximum drinks and the prevention group in which a person participated in the larger study. While fluctuations in ARBs across the year were fairly similar for the three ethnic groups , rates of ARBs were different across ethnicities. As suggested by several recent papers and predicted in the first part of Hypothesis 2, women had higher ARB rates. However, contrary to the second half of that hypothesis, the relationship of ethnicities to ARBs over time was different in females and males. The expected pattern of highest ARBs in EA students and lowest in Asian individuals was most obvious for females and less prominent for males. The mixed-design ANOVA in Table 4 demonstrated significant sex main effects, as well as ethnicity by sex by time and sex by LR by time interactions. The key role of sex in the rates of ARBs over 55 weeks and the interactions of sex with ethnicity might reflect several mechanisms. First, women develop higher BACs per drink , which may translate into higher risks for ARBs. The differences across ethnicities may be especially strong in women vs. men as Asian and Hispanic women may also have stronger culture-based prohibitions against heavier drinking than seen in EA cultures . Also, while more research is needed, considering recent documentation of potentially genetically-related physiologic characteristics that may relate to the BAC required for ARBs , higher rates of ARBs in EA women might reflect some sex-related biological mechanisms that contribute directly to the ARB risk. The first part of Hypothesis 3 was also supported in that a low LR was related to higher ARB rates in these subjects. However, the data in Figure 3 indicate that the relationships of ethnicity to ARBs differ in high- and low-LR subjects.

It is possible that greater differential in ethnicity-related ARB risks might be observed primarily in subjects with higher LRs where drinking quantities are not already elevated by a low sensitivity to alcohol.Finally regarding hypotheses, the prediction that the ethnic group status will interact with sex and LR to predict ARB propensity was partially supported. Table 4 demonstrates significant 3-way interactions for ethnicity by sex by time and sex by LR by time, but the overall 4-way interaction was not significant . Still, the findings underscore the contention that there is more to ARBs than just how much a person drinks, and support the prediction that ethnicity, sex and LR all relate to ARB patterns. The optimal understanding of how ARBs develop requires considering a range of characteristics, preferably in a prospective study . The complex relationships with which multiple factors relate to ARB risks may indicate opportunities for more focused and efficient prevention by identifying subgroups most likely to experience ARBs and who are most likely to gain from programs aimed at decreasing heavy drinking. The larger study from which these data were extracted and a smaller investigation at another university indicated that active education about alcohol-related risk factors are associated with less intense future drinking. In the current study, the significant active education group vs. control group main effect in Table 4 supports the conclusion that decreases in maximum drinks seen with participation in the educational videos were also associated with lower levels of ARBs over time . Thus, universities and other institutions interested in decreasing the risk for ARBs and associated problems might consider developing similar education programs and focusing their efforts on subgroups of subjects with the highest ARB risk. As is true for all research, it is important to recognize caveats regarding the current work. The data were extracted from a larger study evaluating different ways of decreasing heavy drinking among students, and consistent with a prior report focusing on heavy drinking , exposure to active intervention affected ARB rates, a factor that complicates interpretation of results. However, as shown in Table 4, the current results remained robust when prevention group assignment was used as a covariate in the mixed design ANOVA. The relationships among ethnicity, LR and changes in drinking over time are the focus of several other papers and, due to space constraints, are not discussed in detail here . Also regarding the larger study, the subjects were from a single California university, and the generalizability of results to other settings needs to be established, including gathering data on additional ethnic minorities as our analyses were limited to EA, Hispanic and Asian individuals. Next, the data were gathered on-line rather than in person by research staff with whom students had no personal contact, a step that might have affected the veracity of the responses, but the level of impact or direction of effect cannot be determined. Also regarding the larger study from which these data were extracted, to maximize the number of students receiving educational videos only 13% of the subjects were controls, and differences in numbers of subjects across groups may have impacted on current results.

All procedures were approved by the University of Georgia Animal Care and Use Committee, and followed the guidelines of the International Association of the Study of Pain and the National Institutes of Health. Rimonabant was obtained from NIDA. URB597 was purchased from Cayman . URB602 was synthesized by reacting diimidazole- 1-ylmethanone with biphenyl-3-yl amine in acetonitrile in the presence of 4-dimethylaminopyradine and subsequently with cyclohexanol as described previously. Animals were anesthetized with a mixture of sodium pentobarbital and ketamine. Stainless steel guide cannulae were unilaterally implanted above either the BLA or CeA using zero points from bregma, the midline suture and the surface of the skull, respectively. Cannulae were fixed to the skull using skull screws and dental acrylic. Five to seven days after surgery, rats were habituated to restraining tubes prior to testing. The latency to remove the tail from a radiant heat source was measured using the tail-flick test. Drug or vehicle was micro-injected using a micro-infusion pump over 60 s into either the BLA, CeA or deliberately off-site. In experiment, rimonabant or vehicle was micro-injected into either the BLA or CeA 5 min prior to foot shock. In experiment, URB597 , URB602 ,rolling tables grow or vehicle was administered to the BLA 32 min prior to foot shock. Doses and delays were selected based upon previous studies demonstrating efficacy of the identical drug treatments following micro-injection into the midbrain PAG.

SIA was induced by exposing rats to continuous foot shock using a Lafayette grid-shock apparatus and quantified behaviorally using the radiant heat tail-flick test. Removal of the tail from the heat source terminated application of thermal stimulation. Tail- flick latencies were monitored over 4 min immediately prior to exposure to the stressor to evaluate changes in basal nociceptive thresholds induced by pharmacological manipulations. Tail withdrawal latencies were measured at 2-min intervals before and after foot shock. A ceiling tail-flick latency of 10 s was employed to prevent tissue damage. In all studies, the experimenter was blinded to the experimental condition. Following testing, rats were euthanized with sodium pentobarbital and perfused with saline followed by formalin. Brains were removed, cryoprotected overnight, cryostat-cut , and mounted onto gelatin-subbed slides. Sections were dried and stained with cresyl violet. Injection sites were confirmed histologically using a light microscope. micro-injection sites were confirmed for thirty-seven animals in the BLA and fifteen animals in the CeA . Eleven animals were used as off-site controls. Only animals with histologically confirmed micro-injection sites were included in data for analysis. Tail flick data were blocked for each subject by averaging every two adjacent tail-flick latencies into a single mean, as described previously. Means of two-trial blocks, calculated for each subject, were subjected to repeated measures analysis of variance and ANOVA, as appropriate. Post hoc comparisons were performed using the Fisher’s protected least squares difference , with P < 0.05 considered significant.In all studies, baseline tail-flick latencies did not differ between groups prior to administration of drug or vehicle. Moreover, latencies recorded just prior to foot shock, following injection of drug or vehicle, were similar between groups, indicating that the injection alone was not sufficient to induce antinociception. In all studies, foot shock increased tail-flick latencies .

Post-shock tail-flick latencies were attenuated in rats receiving intra-BLA micro-injection of rimonabant relative to vehicle 5.764; P < 0.04. Intra-BLA micro-injection of rimonabant also decreased stress antinociception compared with off-site controls receiving the same dose 5.181; P < 0.04. By contrast, intra-CeA micro-injection of rimonabant did not alter tail-flick latencies relative to vehicle . Neither the FAAH inhibitor URB597 nor the MGL inhibitor URB602 altered SIA , at doses that markedly enhanced SIA following micro-injection into the midbrain PAG. The present study demonstrates that pharmacological blockade of CB1 receptors in the BLA attenuates nonopioid SIA induced by continuous foot shock. Micro-injection of rimonabant into the BLA, where CB1 receptors are dense, suppressed stress-induced antinociception relative to control conditions. By contrast, micro-injection of rimonabant into the CeA, where CB1 receptors are largely absent, failed to suppress nonopioid SIA. Micro-injection of rimonabant into regions outside the amygdala also failed to suppress SIA, suggesting that the actions of the cannabinoid antagonist were not due to diffusion to distal sites. Our data are consistent with the observation that CB1 agonists depress monosynaptic evoked inhibitory post-synaptic potentials in the BLA but not in the CeA. Our results, therefore, suggest that CB1 receptors in the BLA modulate local inhibitory networks in the BLA to ultimately regulate expression of SIA. Nonetheless, neither the FAAH inhibitor URB597 nor the MGL inhibitor URB602 enhanced SIA following micro-injection into the BLA, at doses that markedly potentiated SIA following micro-injection into the midbrain dPAG. These differences likely reflect differential modulatory roles of distinct endocannabinoids in the ascending “affective” pain pathway compared to descending pain modulatory systems. Anatomical studies suggest that CB1 is preferentially associated with GABAergic, as opposed to glutamatergic, synapses in the BLA. Nonetheless, in the lateral amygdala, endocannabinoids mediate reductions in both local inhibitory inputs as well as excitatory transmission, whose actions could exert opposing effects. Low frequency stimulation of the lateral amygdala also mobilizes endocannabinoids from BLA neurons to presynaptically induce a long-term depression of inhibitory GABAergic transmission.

Endocannabinoid LTDi in the BLA in turn, enhances excitatory synaptic transmission in the CeA. A specifific role for anandamide, but not 2-AG, in this form of neuronal plasticity is suggested by two complementary observations. First, LTDi is enhanced in FAAH−/− mice, which are impaired in their ability to metabolize anandamide. Second, endocannabinoid mobilization in LTDi apparently requires the activation of the adenylyl-cyclase-protein kinase A pathway in the BLA, but not the phospholipase C-diacylglycerol lipase pathway that is implicated in 2-AG formation.This latter finding is in contrast to our observations that 2-AG appears to be the primary endocannabinoid implicated in nonopioid SIA; a strong temporal correspondence exists between cannabinoid SIA and the accumulation of 2-AG,pipp grow racks but not anandamide, in the PAG. Liquid chromatography mass spectrometric studies are required to determine how foot shock-induced endocannabinoid mobilization in the BLA differs from that observed previously in the PAG. It is also possible that changes in SIA could be more prominent when supraspinally-mediated measures of antinociception are employed that are more sensitive to the affective dimensions of pain . Our data do not preclude the possibility that the doses of FAAH and MGL inhibitors employed here were unable to surmount high levels of FAAH and MGL activity at this site relative to the PAG. It is also possible that enzymes other than FAAH or MGL may participate in endocannabinoid deactivation at these sites. We propose that CB1 receptor activation in the BLA removes inhibitory control over projection neurons innervating the CeA. The CeA thus receives multi-modal sensory information, and in turn, coordinates appropriate behavioral, hormonal, and autonomic responses to stress via efferent projections. The lack of effect of the FAAH inhibitor in our study is consistent with previous work demonstrating that FAAH−/− mice and mice treated with URB597, administered systemically, show similar amygdalar activation in response to restraint stress compared to control mice. In the BLA, restraint stress produced a low level of Fos induction, which was unaffected by cannabinoid treatment, whereas, the combination of restraint stress and CB1 agonist administration produced robust Fos induction within the CeA. These data support a synergistic interaction between environmental stress and CB1 receptor activation in the amygdala that could contribute to the behavioral phenotype observed here. CB1 as well as FAAH and MGL immunore activity are abundant in the BLA, suggesting that incoming stimuli may trigger the on-demand formation of endocannabinoids to activate CB1 receptors prior to undergoing enzymatic hydrolysis. Activation of CB1 receptors in the BLA, in turn, reduce the inhibitory tone exerted on principal neurons, eventually propagating this signal to brain regions implicated in antinociception, most notably the PAG.In the dPAG, foot shock stress stimulates mobilization of the endocannabinoids, 2-AG and anandamide, and micro-injection of CB1 antagonists into this site virtually abolishes nonopioid SIA. Our results suggest that endocannabinoids may be differentially modulated by stress in the amygdala. For example, in the amygdala, anandamide levels are decreased after the first exposure to restraint stress whereas 2-AG levels are unchanged. Our results collectively suggest that the facilitatory effects of endocannabinoids in Specifically enhancing antinociceptive responses to foot shock stress occur downstream of receptor interactions in the BLA.

In a variety of ways, Colorado remains a state in transition. As is the case with many states around the nation, the economy continues to improve from the recession. Demographically, the state is becoming increasingly diverse. According to the census, more than one-fifth of state residents were Hispanic or Latino/a in 2015. Colorado’s population growth of nearly eight percent since 2010 ranks third behind only Texas and North Dakota. Politically, Colorado has been trending blue over the past several election cycles. Some have identified the migration of Californians into Colorado as a contributing factor to the state leaning further in the Democratic direction . The state awarded its Electoral College votes in 2008 and 2012 to the Democratic ticket, and the party appears poised to carry the state in 2016. A streak of two consecutive wins for the Democratic Party’s presidential candidates has not happened since Colorado voters supported Franklin Roosevelt in 1932 and 1936. Democrats emerged victorious in the last three gubernatorial elections and won three consecutive elections to the U.S. Senate beginning in 2004. The state’s march toward blue-state status was interrupted in 2014, however, as Republican Representative Cory Gardner defeated incumbent Democratic Senator Mark Udall in a competitive race. Republican candidates in this election cycle swept three of the remaining four statewide races, and the GOP regained control of one chamber of the state legislature. The single bright spot for Democrats on election night 2014 was the reelection of incumbent Governor John Hickenlooper who narrowly edged out Republican challenger Bob Beauprez. Colorado continues to receive national and international attention over the legalization of recreational marijuana, which has important budgetary implications. Though revenues associated with legalized recreational marijuana have fallen short of early government forecasts, marijuana tax revenue continues to grow steadily. The sale of marijuana to adults over the age of 21 became legal on January 1, 2014. By the end of the year, the state received $44 million in revenue from recreational marijuana. Combined with the preexisting medical marijuana market, the state received $76 million in 2014 .1 Despite an improving economy and a new stream of marijuana tax revenue, legislators remain wary about approving new spending measures. This is partly attributable to uncertainty regarding potential tax rebates mandated by the Taxpayer’s Bill of Rights . When submitting requests to the Office of State Planning and Budget , departments must outline a strategic plan to accompany their request. The governor’s budget request is submitted to the legislature in the fall. After consideration by the Joint Budget Committee , the full legislature typically passes the budget in May in time for the start of the fiscal year on July 1. The constitution mandates a balanced budget. Last November, the priorities reflected in Governor Hickenlooper’s proposed budget for the 2015–2016 fiscal year were similar to those in prior budgets drafted by the governor in collaboration with the OSPB. In his accompanying letter to the six-member JBC, the governor emphasized “enrollment and inflation increases for K-12 education, the return of General Fund support for transportation for the first time since FY 2007‒08 pursuant to S.B. 09‒228, increased caseload in the State’s Medicaid program, an anticipated decrease in the federal Medicaid match rate, the continuation of existing capital construction projects, and essential projects for the state’s information technology infrastructure” . Spending in just two areas—K-12 education and health and human services—constitute two-thirds of all proposed General Fund appropriations. Governor Hickenlooper’s budget letter detailed how economic progress in Colorado has outpaced the nationwide recovery. In that sense, the budget reflects a cautious optimism about the state’s economic well being. The Bureau of Labor Statistics reports that Colorado’s seasonally adjusted unemployment rate in December 2014 was just 4.0 percent. Tied for seventh lowest in the country , it is a product of 35 consecutive months of job growth. The national unemployment rate for the same month was 5.6 percent. After a decrease in gross state product in 2009, the state has logged increases averaging about 2.5 percent a year for the past four years, and per capita income levels have increased. Against this backdrop of economic improvement, the governor’s budget includes $26.8 billion in total spending with a General Fund allocation of $10.3 billion. The sums represent spending increases from the prior fiscal year of 7.0 percent in total funds and 9.6 percent from the General Fund .

To the contrary, it has been recognized that multiple measures are necessary to gain meaningful information even within a single modality. It is this profoundly multivariate nature of mental disorders that has driven researchers to, for example, conduct genome-wide association studies and acquire whole-brain neuroimaging data. When aiming to build predictive models, this complexity necessitates the use of methods suitable for high-dimensional datasets in which the number of variables may far exceed the number of samples . Generally, the so-called Curse of Dimensionality is addressed in three ways . First, unsupervised methods for dimensionality reduction – such as Principal Component Analysis – may be used. These algorithms apply more or less straightforward transformations to the input data to yield a lower-dimensional representation. Also, they can extract a wide range of predefined features from raw-data. For example, distance measures can be extracted from raw protein sequences for classification in a fully automated fashion.Second, techniques integrating dimensionality reduction and predictive model estimation may be applied. In essence, they use penalties for model complexity, thereby enforcing simpler, often lower-dimensional models. Simply speaking, models containing more parameters must enable proportionally better predictions to be preferred over simpler models. These algorithms are at the heart of predictive analytics projects and include well-known techniques such as Support Vector Machines and Gaussian Process Classifiers as well as the numerous tree algorithms . Third, feature-engineering – i.e. all methods aiming to create useful predictors from the input data – can be used. In short,sliding grow tables feature-engineering aims to transform the input data in a way that optimally represents the underlying problem to the predictive model.

An illustrative example comes from a recent study which constructed a model predicting psychosis onset in high-risk youths based on free speech samples. Whereas it would have been near impossible to build a model based on the actual recordings of participants’ speech, the team achieved high accuracy in a cross-validation framework using speech features extracted with a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity. While these results still await fully independent replication, the approach shows that transforming the input data using domain-knowledge can greatly foster the construction of a predictive model. Demonstrating the problem dependent nature of feature-engineering, it might have been much easier to decode, for example, participants’ gender from the actual recordings than from LSA measures given the difference in pitch between males and females. In that it links data acquisition and model algorithms, feature engineering is not primarily a preprocessing or dimensionality-reduction technique, but a conceptually decisive step of building a predictive model. While important for all modalities, feature-engineering often plays a particularly crucial role when constructing predictive models based on physiological or biophysical data. On the one hand, these data are often especially high-dimensional , thus often requiring dimensionality-reduction. On the other hand, alternative transformations of the raw-data can contain fundamentally different, non-redundant information. For example, the same fMRI raw-data – i.e. measures of changes in regional blood-oxygen levels – can be processed to yield numerous, non-redundant representations . In addition, domain-knowledge regarding the choice of relevant regions-of-interest or atlas parcellations also fundamentally affects the representation of information in neuroimaging data.As different parameters can be meaningful in the context of different disorders, these examples powerfully illustrate the fundamental importance of domain-knowledge in feature-engineering. The sources of domain knowledge needed to decide which data representations might be optimal with regard to the problem at hand may range from large-scale meta-analyses, reviews and other empirical evidence to clinical experience.

Taking the traditionally somewhat subjective “art of feature-engineering” a step further, are automated feature-engineering algorithms. The former are akin to other unsupervised methods for dimensionality reduction, but can learn meaningful transformations from large, unlabeled datasets . In short, these algorithms form high-level representations of more basic regularities in the data .For example, we might use large datasets of resting-state fMRI to automatically uncover regularities using unsupervised learning. These newly constructed features might then provide a lower-dimensional, more informative basis for model-building in future fMRI projects. Note that domain-knowledge is not provided directly, but learned from independent data sources in this framework. While these techniques appear highly efficient as no expert involvement is required, discovering high-level features for the massively multivariate measures commonly needed in psychiatry will require extraordinarily large – though possibly unlabeled – datasets as well as computational power beyond the capabilities of most institutions today. Considering the developments in other areas such as speech recognition, we believe, however, that the significance of automated feature-engineering techniques can only grow in the years to come. In many ways, the theory-driven approach to Computational Psychiatry is following an at least equally promising – albeit extreme opposite –strategy: This approach builds mechanistic models based on theory and available evidence. After a model is validated, model parameters encapsulate a theoretical, often mechanistic, understanding of the phenomena . In many ways, the resulting models thus constitute highly-formalized representations of domain-knowledge, custom-tailored to the problem at hand. Unlike virtually all other approaches to feature-engineering, computational models allow researchers to test the validity of data-representations while simultaneously fully explicating domain-knowledge. While certainly more scientifically satisfying and theoretically superior to feature-engineering, constructing valid models is far from simple. Thus, we believe that this technology will gain in importance to the degree that building valid models proofs feasible, further intertwining theoretical progress and Predictive Analytics. Having discussed feature-engineering in greater detail, it is important to point out that model construction algorithms are not limited to the use of one single data-representation.

To the contrary, it is a particular strength of this approach – with algorithms usually allowing for massively multivariate data and model integration –that multiple, meaningful data representations can be combined to enable valid predictions . Summarizing, the acquisition of high-dimensional data is regularly required to capture the massively multivariate nature of the processes underlying psychiatric disorders. Even on a single level of observation, we thus need to deal with the Curse of Dimensionality. To this end, model building commonly includes steps such as simple dimensionality-reduction techniques and penalizing model-complexity as part of machine learning algorithms. Most importantly, however,indoor grow trays feature-engineering is used to create data-representations from the input data which enable machine learning algorithms to build a valid model. Feature-engineering may draw on partially or fully formalized domain-knowledge or a combination thereof. This prominent role of domain-knowledge underlines the interdependence of classic scientific approaches seeking mechanistic insight fostering theoretical development and Predictive Analytics approaches in mental health. While theoretical progress and meta-analytic evidence aid the construction of optimal features, a predictive analytics approach, in turn, allows for a direct assessment of the clinical utility of group-level evidence and theoretical advances. Thus, it is evident that these two branches of research are not mutually exclusive, but complementary approaches when aiming to benefit patients.Substantially aggravating the problem of dimensionality discussed above, mental disorders are characterized by numerous, possibly interacting biological, intrapsychic, interpersonal and socio-cultural factors.Thus, a clinically useful patient representation must probably, in many cases, be massively multi-modal, i.e. include data from multiple levels of observation – possibly spanning the range from molecules to social interaction. All these modalities might contain non-redundant, possibly interacting sources of information with regard to the clinical question. In fact, it is this peculiarity – distinguishing psychiatry from most other areas of medicine – which has hampered research in general and translational efforts in particular for decades now. As applying a simple predictive modelling pipeline on a multi-level patient representation would increase the already large number of dimensions for a unimodal dataset by several orders of magnitude, it might seem that Predictive Analytics endeavors are likely to suffer from similar if not larger problems. Indeed, neither of the dimensionality-reduction, regularization or even feature-engineering approaches outlined above is capable of seamlessly integrating such ultra-high-dimensional data from so profoundly different modalities. Considering the tremendous theoretical problems of understanding phenomena on one level of observation, we also cannot rely on progress regarding the development of a valid theory spanning multiple levels of observation in the near future. Likewise, detailed domain-knowledge across levels of observation is extremely difficult to obtain as empirical evidence as well as expert opinions are usually specific to one modality. Given the extreme amounts of data and the combinatorial explosion due to their potential interactions, fully automated feature-engineering approaches across levels of observation also appear unlikely in the near future. Finally, the often qualitatively different data sources alone – including genetics, proteomics, psychometry, and neuroimaging data as well as ambulatory assessments and information from various, increasingly popular wearable sensors – would make this a herculean task. A somewhat trivial solution would be to limit the predictive model to a single level of observation. If high-accuracy predictions can be obtained in this way – which might be considered unlikely at least for the most difficult clinical questions – such unimodal models are always preferable due to their comparatively high efficiency. Apart from the inherent multi-modal nature of mental disorders which might render unimodal models less accurate, it is, however, exactly these efficiency considerations which obviate the need for Predictive Analytics research to consider multiple levels of observation. In order to identify the most efficient combination of data sources in a principled way in the absence of detailed cross-modal expert knowledge and evidence, we have to learn it from the data. To this end, a plethora of machine learning approaches which can be broadly described as model integration techniques – have been developed.

Probably, the most intuitive way to combine information from different high-dimensional sources is by voting. In this framework, a predictive model is trained for each modality and the majority vote is used as the overall model prediction. In a binary classification – if we wish to predict therapeutic response from five multivariate data sources – we first train a model for each modality. Then, we count the number of models predicting a response and the number of models predicting no response . The final prediction of therapeutic response is given by the option receiving more votes across modalities. Here, again, a model is trained for each modality. The predictions are, however, not combined by voting, but used as input to another machine learning algorithm which constructs a final model with the unimodal predictions as features . In addition to these simple approaches, numerous other techniques Model Averaging, Bagging, Boosting or more sophisticated ensemble algorithms exist – each with different strengths and weaknesses which affect the computational infrastructure needed and interact with data structure within and across modalities. That said, most Predictive Analytics practitioners would agree that models – in the field – are most often constructed by evaluating a large number of approaches, i.e. by trial-and-error relying on computational power. However, it cannot be emphasized enough that this strategy must rely on the training data only. At no time and in no form, may the test set – i.e. the samples later used to evaluate model performance – be used in this process. Only in this way, we guarantee a valid estimation of predictive power in practice. Note that the techniques for model combination can generally be used also to construct predictive models from unimodal multivariate datasets as well . Given the multi-modal nature of psychiatric disorders, however, they hold particular value for cross-modal model integration.Importantly, the construction of models from multi-modal data does not mean that the final predictive model used in the clinic must also be multi-modal. To the contrary, by training models with multi-modal data, we not only guarantee maximum predictive power, but also gain empirical evidence regarding the utility of each modality. Analyzing the final model, we can investigate which modalities contribute substantial, non-redundant information. In an independent sample, we could then train a model based only on those modalities most important in the first model. With this iterative process, we can obtain not only the most accurate, but also the most efficient combination of modalities and variables in a principled manner. Thus, final models might only consist of very few modalities and variables fostering their widespread use also from a health economics point of view.

DISOPRED, also part of the PSIPRED package, predicted that most of the FUN sequence can be defined as disordered, though the first 50 amino acids of FUN are not predicted to be disordered . In sum, FUN transcript is found in developing leaves and tassels, which fits with the phenotype of the mutant. FUN transcript is also found in developing ears, which is surprising since an ear phenotype was not observed. Higher levels of FUN during leaf development are associated with larger auricles. FUN is predicted to be a disordered protein that localises to the nucleus. Attempts were then made to validate these predictions. Y2H, using the FUN protein as bait, and a cDNA library of immature ears and tassels as prey, retrieved a list of 234 possible interactors. Using the A. thaliana homologues of these proteins in a GO term biological process analysis found enrichment for proteins involved in negative regulation organ, specifically flower, development; and proteins involved in negative regulation of nucleic acid metabolic processes . The A. thaliana homologues were enriched in transcription factor activity and hydrolase activity according to a GO term analysis based on molecular function , and most of these predicted interacting partners were nuclear or cytosol localised. The GRMZM numbers were also run through the GO term analysis prediction software provided by AgriGO and were found to be enriched in GTP and GTPase binding . A table of selected genes returned by the Y2H can be seen in Figure 5-7A. One such gene is ZmDWF1. This brassinosteroid synthesis protein was found to have a synergistic interaction in double mutant analysis with fun . Another gene implicated in the brassinosteroids pathway was BSL1 that is known as an inhibitor of BRI1.

The strongest confidence hit for the Y2H as an interactor for FUN was tubulin,cannabis drying rack ideas but this may be a false positive due to the high concentration of this protein in a cell.In order to test the idea that FUN is nuclear-localized, I carried out a transformation experiment using Nicotiana benthamiana. The entire FUN protein was fused to an N terminus YFP as described in Methods. The FUN-YFP fusion was found to localise to the nucleus in transformed N. benthamiana pavement cells. This result was observed in two separate transformations. Though not all nuclei in the samples were found to fluoresce under YFP excitation, this is likely due to imperfect transformation efficiency and is normal in this kind of experiment109. 25 nuclei expressing YFP were photographed and many more observed during the course of this experiment; YFP expression was not observed in any other subcellular regions. In order to confirm that the YFP expression was nuclear, the leaves were also examined under 405nm excitation. Since the leaves had been infiltrated with DAPI prior to examination, this caused the DNA to fluoresce. YFP fluorescence was shown to overlap with this DAPI fluorescence . As further confirmation, the sample was also examined under bright field and the YFP fluorescence was thus seen to overlap with clearly visible nuclei . This was observed at the microscope, as well as by overlapping micrographs using ImageJ. Close inspection of individual transformed nuclei revealed a nuclear speckle pattern . In order to make an antibody to the FUN protein, the purified protein has to be injected into a living animal and the antibody produced must then be purified. To this end, the third exon of FUN was amplified by primers 53xF/R and cloned into pENTR. Recombining with pDEST17 was unsuccessful. I hypothesised that FUN may be toxic, which would explain why the expression plasmid would not grow, though pENTR would grow, so bacteria were grown at lower temperatures. This was also unsuccessful, so a smaller fragment out of the exon was used, amplified from cDNA with primers An2F/R . This 600bp fragment was successfully cloned into pDEST17. Interestingly, the other fragments attempted that contained the conserved GAKHIL motif did not clone into pDEST17 under the conditions used, which could imply a toxicity of this domain, since all fragments that did not contain the GAKHIL motif were successfully cloned.

The newly made plasmid pDEST17-An2F/R was then cloned into Rosetta cells and grown into a 100ml culture overnight. This culture was used to spike 1 litre of fresh LB. After 4 hours of growth at 37°C this culture reached 0.42 OD600nm and 200μl of 1M IPTG were added. This culture was then shaken at room temperature for 5 hours and spun at 8000 RCF for 15 minutes at 4°C. The resulting pellet was resuspended in 75ml of lysis buffer . This was spun at 12000 RCF for 15 minutes and the supernatant co-incubated with 2.5ml of 50% Ni++ beads in EtOH slurry. The beads were spun and washed several times using wash buffer and were finally eluted using elution buffers at pH 4.4, 3.8 and 3twice each. Most protein was recovered from the second pH 4.4 and first 3.8 elutions as measured by nanodrop. These samples were then run on an SDS gel and the correct size band was observed for An2F/R samples, but nothing was observed for An1F/R. Thus the protein produced by plasmid pDEST17-An2F/R was purified. It was then resuspended in 6M urea by dialysis in a side-A-lyser cassette and sent to Cocalico for injection into guinea pigs. Test-bleeds and pre-bleeds were returned from the company. The pDEST17- An2F/R protein product was blotted onto nitrocellulose and incubated in 2ml of PBS with 2μl of test-bleed or 2μl of pre-bleed for 2 hours before washing and incubating in PBS and anti-Guinea Pig Alkaline Phosphotase fusion antibody. Finally this was incubated in NETN and NBT/BCIP to give an output of purple colour if the antibody is reacting to the protein . Later, second bleeds were returned by the company, and this dot-blot process was repeated, along with the sera from another antibody as a control . Thus, it was confirmed that the guinea pigs were producing appropriate antibody. In order to purify the terminal bleeds, a GST-tagged version of the protein was made. The pENTR-An2F/R was therefore combined with pDEST-15,grow trays 4×4 transformed into Rosetta cells and induced and spun down as for the pDEST17- An2F/R purification step. This pellet was resuspended in NETN and put through a French press twice at ~1000lbs of pressure before 2x 20s of sonication to shear DNA. Triton X-100 was then added to 0.5%. This cell lysate was then spun at 12,000 RCF for 15mins and the supernatant was added to 2ml of 50% glutathione-sepharose beads equilibrated in 10ml NETN. After co-incubation during which the GST-tagged protein should bind to the beads, the beads are spun down and washed several times with 0.2M Borate pH8. The washed beads are then transferred to a column and cross-linked to the beads using DMP solution before washing with 0.1M glycine-Cl pH2.5 to remove non-covalently linked molecules and a final wash with 1xTBS . The terminal bleeds were thawed and 1/10 volume 10xTBS was added. This TBS-serum was then incubated in a column containing beads bound to GST for 30min at 4°C. Thus any antigens specific to GST that might be in the guinea pig sera would bind to these beads and the eluate from this column should be free of such contaminants. This eluate was incubated in the column described in the preceding paragraph and incubated at 4°C for 1 hour. Thus any antibodies specific to the FUN fragment should bind to these beads. The column was washed several times with TBS and 0.1 x TBS to remove any unbound molecules. Finally the column was eluted with 0.1M glycine-Cl pH2.5. 6ml of this 0.1M glycine-Cl pH2.5 was added to the column, and each sequential 800μl was collected separately. There were two guinea pigs murdered for this experiment designated 13 and 14. The 2nd elution for GP13 and the 3rd elution for GP14 had the highest concentrations of protein as measured by nanodrop. Purified antibody GP14-3 was then used for immunoblots on a transverse section of normal immature tassel, cross section of normal immature tassel and on normal SAM tissue.

Tissue was sectioned from wax using a microtome. Sections were laid on a slide, and the wax dissolved with Histoclear. Histoclear was then washed off with 100% EtOH and then the tissue was slowly rehydrated step-wise with lower concentrations of EtOH until finally pure water. The slides were then boiled in citrate buffer and left to stand for 10min. Slides were then incubated in PBS and then blocking solution . Next, the slides were incubated in 2ml of blocking solution and 2μl of antibody overnight, before incubating with the secondary antibody GP-AP, washing, and finally NETN and NBT/BCIP. Slides were examined under a light microscope and signal was observed at the base of the pedicel in the longitudinal section , and in the L2 in the transverse section , of the tassel. A similar localisation pattern was observed by in situ for TS1 RNA35. No signal was observed in the longitudinal section of the SAM, though it is possible that there is signal at the base of the axillary meristem . As a control, a longtitudinal section of a fun tassel was also used for an immunoblot . A similar localisation pattern was observed in this mutant control as was seen in the normal tassel casting doubt on the reliability of the wild-type localisation patterns since the fun mutation causes a truncation of the protein before the region that the antibody was designed to . This apparent non-specificity prompted a western blot using antibody GP14-3. No band was observed at the predicted size of 90kDa, and a 30kDa band was observed in both mutant and normal samples . Due to this lack of specificity, it was decided to make another antibody, using the entire third exon of FUN. The pENTR clone containing the 53xF/R insert was amplified by Phusion with primers AV53 and AV54 to create a DNA fragment containing the third exon of FUN flanked by appropriate restriction sites. The fragment was then cut with XhoI and BamHI, purified by phenol:choloroform extraction and ligated into the His-tagged expression vector pet. After verification by sequencing, this plasmid was transformed into Rosetta cells, grown in a 1L culture and induced with IPTG and purified with Ni++ beads as described above. This time, most protein was eluted at the 3rd pH3 elution. As before, this was dialysed into 6M urea and sent to Cocalico for injection into Guinea Pigs 15 and 16. In a dot plot, the test bleeds from GP15 and 16 showed strong reactivity against the antigens sent to Cocalico, no reactivity with control purified Tru1 protein, and a little reactivity with purified Bd1 protein, possibly due to the His tag on this protein. The secondary bleeds showed similar results. Recombination of pENTR-53xF/R and pDEST15 was unsuccessful, so pgex5x-2, a GST-fusion expression plasmid, was used instead. pENTR-53xF/R was amplified by Phusion with primers AV63 and AV64 for blunt end cloning. This PCR fragment was incubated with Polynucleotide Kinase to remove phosphoryl groups at the 3’ end and phosphorylate the 5’ end. pgex5x- 2 was cut with SmaI and incubated with Shrimp Alkaline Phosphotase to remove both 5’ and 3’ phosphate groups. The cut plasmid was then ligated to the PCR product with T4 ligase at 16°C overnight. Sequencing showed successful recombination, so the pgex5x-2-53xF/R plasmid was transformed into Rosetta cells and grown in a 1litre culture. This culture was induced with IPTG and made into a column, as described above. The pellet and supernatant of this Rosetta-pgex5x-2-53xF/R were used in a dot plot against an anti-GST antibody to test if the protein made was soluble, which it was .The pgex5x-2-53xF/R column was then used to purify sera from GP15 and GP16 .This western also found a >100kDa band in WT crude extract which was not present in fun crude extract, and a ~34kDa band that was present in both WT and fun crude extract . Crude extract of protein was carried out by grinding 2g of tissue in liquid nitrogen and adding 1ml of loading buffer , which was centrifuged before loading into gel.