Compared to the national data, the WSU sample pre-RML appears to be more white, more likely to be in a fraternity or sorority, more likely to live off campus or in a fraternity/sorority house, and less likely to live with parents. We will see based on the regressions that although these variables are associated with higher likelihood of marijuana use, they are also associated with a lower likelihood of increasing use after RML. To the extent that differences in composition between the WSU and national samples affect differences in the trend of marijuana use, we expect that such differences are likely to bias against an observed relative increase in use at WSU. We also compare pre-RML marijuana use between the WSU sample and the two national samples. Fig. 2 shows the percentage of respondents each year who have used marijuana in the past 30 days for all three samples. The NCHA national data is only through 2011.For the national NCHA data after 2011 and for the WSU data after 2012, we forecast each series based on the data through 2012. Forecasts are generated using best-fit double exponential smoothing to account both for levels and for changing trends.Both the national NCHA and NSDUH data show an increase over the period before 2012 and are consistently within 1 and 4 percentage points of each other. The WSU series starts out slightly lower than both national series but with a nearly parallel trend and remains in the range of both national series through 2012. Readers will note the relatively large increase in the WSU series between 2008 and 2010, which corresponds to changes in Washington’s MML laws. Though the magnitude is smaller, we observe an increase at this same time in both the national samples. It may be the case that national changes affected students both in and out of Washington. Any long-term effects of such national changes are reflected in the NSDUH data. The forecasts for both the WSU and the national NCHA samples are almost parallel to the actual trend in the NSDUH and the 95-percent confidence interval for each forecast contains the other forecast as well as the NSDUH actual values. It appears that the increase in marijuana use at WSU after 2008 may have been a one-time jump,grow tent indoor a proposition more fully examined in the conclusion of the paper.

It is also relevant to note that both national samples are “contaminated” with observations from WSU and from others in Washington and Colorado.9 If RML increases marijuana use for college students, as we expect, then including Washington and Colorado students in the national samples biases against finding an effect in the difference-in differences analysis.Estimates for the logit regressions on the probability of having used marijuana in the past 30 days are reported in Table 3. The far-left column shows the basic regression, controlling only for a linear trend. Column 2 shows the results of the regression with demographic controls added. Columns 3 and 4 show the results with more co-variates added, some potentially endogenous. Controlling for a predicted increase of about 1.2 percentage points each year, we find that marijuana use among WSU students increased between 2.0 and 3.5 percentage points after RML and remained higher through 2015. Each estimate across specifications is statistically different from zero with at least 95-percent confidence. We find no evidence that legal sales had an additional impact on the proportion of marijuana users. The additional change after legal sales is consistently positive but not statistically different from zero at conventional levels; t-scores for these differences range from 0.43 to 0.88 . This regression model also provides estimates of relative marijuana use among WSU students. Male students are between 2 and 7 percentage points more likely to have used marijuana than females. Black and white students are the most likely to use marijuana compared to other races with Asian students being the least likely. In results not shown , we also see a decreasing likelihood of marijuana use with age of about 3 percentage points per year after age 20. After controlling for GPA, Greek membership, residence, and international status, 1st-year undergraduates are the most likely to use marijuana by between 3 and 5 percentage points over students of other years. International students are between 4 and 7 percentage points less likely to use marijuana than domestic students. Students with a 4.0 GPA are between 3 and 10 percentage points less likely to use than other students. Students in fraternities or sororities are between 4 and 12 percentage points more likely than other students.

Finally, the likelihood of marijuana use is positively correlated with the use of tobacco, alcohol, and illegal drugs.different subgroups and present the results in To better understand the impact of RML, we repeat the analysis for Table 4. Results of these regressions are generally consistent across all four specifications for each group. For brevity, we report only the results that include controls for age, sex, race, and year in school . The proportion of each group that reported having used marijuana before 2014 is included at the bottom of each column. Though the estimates differ greatly in magnitude, and only a few of the estimates are statistically significant at conventional levels, all groups are associated with a positive increase above the trend in marijuana use after RML. The results suggest marijuana use by underage students increased at least as much as that by legal-age students after RML. The estimates for the increase in underage students’ likelihood of using marijuana are large and statistically significant with a p-value < .01, while the estimates for legal-age students are smaller and not statistically different from zero. Using a chi-squared test after estimation, the differences between the two groups’ estimates for 2014 and 2015 have p-values of 0.206 and 0.955, respectively. We also note that the difference between the estimates for 2015 and 2014 for legal-age students is marginally statistically significant with a p-value=0.081, indicating that legal-age students waited to use marijuana until after they could obtain it from authorized distributors. The subgroup analysis provides insight into which groups are driving the changes overall. There is a relatively large increase in likelihood of marijuana use for Black and Hispanic Students, although only Hispanic students showed changes that are statistically significant with a p-value < 0.05. The likelihood of marijuana use among Black and Hispanic students increased in 2014 by 15.8 and 14 percentage points, respectively. This change represents an 88-percent increase in recent users for Black students and a 93-percent increase for Hispanic students. This is 8–9 times the estimated effect for Asian and white students. This relatively large increase is made more significant by the fact that it occurs over a previously non-increasing trend for both groups. In fact, though not statistically different from zero, Black and Hispanic students are the only groups with estimated negative trends over this time.

In other words, both groups started out with a proportion of marijuana users that remained essentially constant since 2005 until RML, after which Black and Hispanic students were among the most likely students to have used marijuana. Females are the group with the next highest increase after RML that is statistically significant with a p-value < 0.05.Results from the logit regressions on the likelihood of using tobacco, alcohol, or illegal drugs are reported in Table 5. Again for brevity, we report only the results for the regressions that include controls only for age, sex, race, and year in school. For convenience, we report again the estimates for marijuana use from column 2 of Table 3. On average, the yearly trends in the likelihood of use for tobacco, alcohol, and illegal drugs are in the opposite direction and significantly smaller in magnitude than the yearly increase of 1.2 percentage points in marijuana use. No significant changes occur in 2014. In 2015, the only significant changes include a 2.4-percentage-point decrease in the likelihood of using tobacco and a 2.2-percentage-point increase in the likelihood of using other illegal drugs. These results imply a possible substitute/complement effect or a spillover effect on norms against other illegal drugs, though the changes did not occur until a full year after the major changes in marijuana use. Additionally, relative to the changes for marijuana, the changes for tobacco and illegal drugs are not as robust to alternative specifications and estimation methods . We see no evidence that RML or legal sales affected the use of alcohol. Overall, grow tent hydroponic our results do not support any systematic changes in other substances that occur parallel with changes in marijuana use. This supports a conjecture that RML was the cause of the changes we find for marijuana.Results of the OLS regressions with respect to regularity of marijuana use are presented in Table 6. In 2014, we find an increase of about 0.5 days in the past 30 days above a linear trend of between 0.13 and 0.16 days per year.This increase is statistically significant across specifications with at least 95-percent confidence. The estimates for after legal sales are smaller than for after RML and are not statistically significant at conventional levels. Though the magnitude of the estimates in 2015 are not significantly lower than in 2014, the lack of a significant increase in 2015 could indicate that the effect of RML on frequency is short-lived and the equilibrium trends in frequency are unaffected by legalization. Alternatively, this may indicate that a proportion of students who began using before legal sales of marijuana are more likely to use it more frequently than those who waited.

The calculation of the difference-in-differences estimations are reported in Table 7. Using the national NCHA forecast as a counterfactual, the estimated effect of RML is an increase of 8.6 percentage points. Using the NSDUH, the estimated effect is 9.6 percentage points. These estimates are both statistically significant with over 99-percent confidence and are 3–5 times larger than the estimated increase over a linear trend in the regressions. Although limited by not accounting for covariate changes over time, the difference-in-differences estimations suggest that the increase over a linear trend in the regressions may be a conservative estimate of the effect of RML on the likelihood of using marijuana.Substance use among young adults is a major public health concern and is associated with academic problems. The bulk of research in this area has focused on undergraduate students, as alcohol and marijuana use among this population are fairly common . In addition to academic difficulties, alcohol and marijuana use are associated with other negative consequences during the college years, including risky sexual behaviors, social and interpersonal problems, injury, and impaired driving . Longitudinal research has shown that alcohol and marijuana use during college might have long-term consequences after college graduation. Heavy drinking and marijuana use during college are associated with post-college substance abuse and dependence, unemployment, less prestigious employment, and lower income . Marijuana use during college and the immediate post-college years, particularly heavy use, is associated with several negative health outcomes at ages 24 and 27, including emotional problems, injury, illness, decreased quality of life, and less service utilization for physical and mental health problems . Degree non-completion as a consequence of substance use has been found in longitudinal studies of high school and college students. Adolescents who use alcohol, tobacco, and marijuana during ninth grade are less likely to complete high school than non-drug users . One study integrated data from three longitudinal studies and found that daily marijuana use during adolescence was significantly associated with decreased odds of both high school and college completion . In a study of college students, frequent marijuana use during the course of college was associated with increased likelihood of dropping out . Despite evidence of associations between alcohol and marijuana use and high school and undergraduate degree non-completion, the possible impact on graduate degree completion has not been explored. An increasing number of college graduates are enrolling in graduate school, with almost 40% of college graduates pursing a graduate degree within four years of graduation . However, only 50% to about 75% of those who enter graduate school ultimately complete their degree, with differences by degree type and academic discipline .

Gilman et al. used a similar multi-modal approach and found that gray matter density , shape , and volume of the left nucleus accumbens was significantly different for recreational users and controls. More specifically, in users, the left and right accumbens showed a negative relationship between shape and marijuana use frequency, such that the accumbens showed more inward deflections with more frequent recent use, as well as greater volume. However, in an attempt to replicate these findings, Weiland et al. found that the results of Gilman et al. held only when participants were not matched in terms of alcohol use. When matched for alcohol use, marijuana users and controls showed no significant effects in terms of cortical or sub-cortical morphometry. Moreover, when Weiland et al. examined the effect sizes observed in 11 prior studies, they found a net zero effect for the effects of marijuana on brain morphometry . Notably the results of the current study take into consideration the potentially confounding factors of alcohol and tobacco use as well as gender, age, and years of education. A recent paper using some of the same sample from the HCP examined how genetic vs. environmental factors might contribute to brain volume as a function of marijuana use. They found that marijuana use was associated with smaller volume of the left amygdala and right ventral striatum. However, their analyses suggested that the results for the amygdala are likely driven by shared genetic factors as compared to environmental factors as both marijuana user twins and their non-user twin showed reduced volume compared to concordant non-user twins. While the current study did not directly examine the role of genetics and shared environment in mediating the effects of marijuana on the brain, our analyses accounted for family structure. When accounting for family structure, we found no effects of marijuana on the amygdala, and a trend for a more inward deflection of the right accumbens, but no accompanying difference in volume. However, the analysis of Pagliaccio et al. trim bin tray was limited in sample size for certain sibships, in particular, monozygotic twins discordant for marijuana use.

With the upcoming complete HCP dataset consisting of 1200 participants, it will be important to update the analyses of Pagliaccio and colleagues, as well as adding sub-cortical shape as a measure, to see if a causal relationship arises with a larger sample .As the current study is not longitudinal but rather cross-sectional, it cannot speak to whether the use of marijuana causes changes in neural structures. Such an analysis will require longitudinal data as is to-be collected by the new national ABCD study funded by NIH. Nonetheless, the current study can provide hints as to potential effects of marijuana use due to its large sample size and because family structure was controlled for in the analyses. In addition, despite the 100-fold increase in the number of marijuana users from most studies and the high-quality of imaging data, the data on marijuana use history from the HCP is relatively sparse. Whereas alcohol and tobacco were assessed in terms of recent and past use, questions of marijuana use were restricted to self-report measures of the age of first use and the number of times used in the lifetime. As such, it was not possible to accurately examine the effects of duration of use or more specifically, the effects of time since last use. As noted in the Materials and methods Section, the age of first use and number of times used data was coarsely coded using relatively arbitrary ranges. In particular, the number of times used score presented widely different categories for participants to select, ranging from 1–5 times used to “more than 1000.” Moreover, no data was available regarding the recency of this use. Additionally, while tobacco and alcohol were controlled for using scores selected to best represent the impact of chronic, co-morbid substance use, it is possible that alternative metrics would change the representation of variance due to these substances. As it stands, the alcohol and tobacco use scores used in this presented significant co-variance with age of first use and times used, highlighting both the need to control for these factors and the importance of a data-set large enough to separate the effects of each variable.

While participants were excluded from the HCP for major psychiatric or neurological illness participants underwent a psychiatric screening as part of the SSAGA, and psychiatric symptoms were assessed with the NIH Toolbox and the Achenbach Adult Self-Report questionnaire . Examining the interactions of psychiatric symptoms and marijuana use was beyond the scope of the current study, but future studies should examine these effects. Previous studies have shown that adolescent marijuana use is co-morbid with a number of psychopathologies including childhood trauma , depression , attention deficit hyperactivity disorder , and psychosis . Furthermore, the HCP contains information about parental psychopathology. While much of the psychiatric information available in the HCP has less information than a targeted study of psychopathology, there is enough information for future studies to assess multivariate effects of marijuana use variables and co-morbid psychopathology and other substance use. Lastly, while the advanced imaging analyses used in this study provide powerful ways to non-invasively understand the anatomical changes occurring with a brain, they are limited in that they cannot speak to the mechanisms whereby marijuana use might influence brain structure. specifically, they cannot elucidate the microscopic changes responsible for the more macroscopic GM and WM impacts . For example, while shape changes of the accumbens and hippocampus might reflect inflammation, marijuana has been found to have anti-inflammatory properties . Macroscopic morphological changes could be caused by neuronal loss or changes in cytoarchitecture such as neuronal size, dendritic spine density, dendritic length, or synaptic protein levels . As such, morphometry studies can strongly inform where such changes are occurring, but cannot pinpoint the microscopic causes of these structural changes. It is important to note that the two major components of marijuana, Δ-9-tetrahydrocannabinol and cannabidiol , have opposite effects behaviorally, symptomatically, and in terms of functional activation of all of the regions-of-interest for the current study . With legalization of marijuana comes more accurate assays of THC and CBD concentrations, and thus, future research can and should focus on examining whether THC and CBD have dissociable effects on brain morphometry .Parkinson’s disease is the second most common neurodegenerative disorder worldwide, affecting 1e2% of the population over the age of 65.

The condition is characterised by the progressive loss of dopaminergic neurons from the substantia nigra pars compacta.There are a number of studies that link the development of PD with the exposure of certain pesticides such as rotenone.As a result, rotenone is commonly used to create in vivo and in vitro models to study the disease.H2O2 is a compound commonly used to model oxidative stress in vitro and in vivo. As mitochondrial dysfunction and oxidative stress are thought to contribute to cell death in PD, we aimed to assess the effects of both rotenone and H2O2 on SH-SY5Y neuroblastoma cells. The SH-SY5Y neuroblastoma cell line has been previously used to create a cellular model of PD.The cells share many biochemical and functional characteristics with mature dopaminergic neurons and have the ability to differentiate into adopaminergic phenotype. As tyrosine hydroxylase and dopamine seem to be central to the pathogenesis of PD and dopaminergic neurons are specifically targeted in the condition, we opted to utilise a cell line that had been transfected with human TH isoform 1.There were two main aims to be addressed in this study, firstly we aimed to compare the effects of rotenone and H2O2 treatment on cell viability and TH expression and once we had established these changes we would then assess the ability of a number of potentially neuroprotective compounds to protect against this toxicity. Cinnamon is a spice commonly used in food throughout the world. The spice has been demonstrated to have anti-diabetic and anti-inflammatory effects as well as some neuroprotective properties.For instance a previous study demonstrated that treatment with cinnamon prevented the development of PD like symptoms and pathology in 1-methyl-4-phenyl-1,2,3,6-tetra hydropyridine treated mice, however the effect of cinnamon on rotenone is yet to be investigated. Hemp seed and its oil have been used as both a food and medicine in China for at least 3000 years and hemp seed extracts have been found to demonstrate antioxidant and antiaging effects as well as improve cognitive impairment induced by chemicals in mice.In addition to all of this, epidemiological studies suggest societies that commonly use curcumin, cinnamon and hemp seed appear to demonstrate a lower incidence of PD and neurodegenerative disorders.We included the use of curcumin within our study as a positive control as this substance has been shown previously to provide protection against rotenone and H2O2 toxicity.Polygonum cuspidatum is widely distributed in the world and has been shown to possess antiviral, antimicrobial, anti-inflammatory,pollen trim tray neuroprotective, and cardioprotective properties,however these properties are yet to be investigated using a cellular model of PD.

In this study we assessed the effect of rotenone and H2O2 on SHSY5Y cell survival and TH protein expression. We also evaluated the protective effects of curcumin, cinnamaldehyde, and constituents isolated from hemp seed and polygonum cuspidatum .Methylthiazolyldiphenyl-tetrazolium bromide powder was used as a means to assess cell viability as previously described.Briefly, at the conclusion of the 24 h treatment duration, MTT was added to culture medium at a final concentration of 0.5 mg/ml and the plate was mixed gently for 1 min before 2 h incubation in a 37 C, 5% CO2 incubator. After the incubation, media was removed and 100 mL of DMSO was added to each well for 10e15 min while shaking. The intensity of the purple colour produced in each well was measured colourimetrically using a plate reader at 595 nm. The values of absorbance are expressed as a proportion of the controls.The Trypan Blue assay was used as another means of visually assessing cell viability to support the MTT assay findings. Cells were seeded into 12 well plates and treated as described above. When the treatment protocol had ended the media was removed from the wells and 30 mL of Trypan Blue was added and left for 30 s. After 30 s an image of the plate was taken using ‘Cell Pad’. While not quantitative this protocol provided a means of visually assessing the viability of cell cultures.Whole cell lysates were used for western blotting experiments to analyse total TH and Poly-ADP ribose-polymerase protein levels. Cells were seeded in a 24 well plate and treated as described above. At the end of the treatment protocol media was removed and 110 mL of 2% SDS stop buffer with inhibitors , 2% SDS, 2 mM EDTA, 1 mM Na orthovanadate, 1 mM Na fluoride, 10 mM Na pyrophosphate was added. The lysed cells were collected and heated for 10 min at 100 C. Samples were then frozen and stored at -20 C for later analysis. Samples were prepared for electrophoresis by dilution with sample buffer . Samples were run on an 8 or 10% SDS-polyacylamide gel and transferred to nitrocellulose membrane . To minimise non-specific binding membranes were blocked with 5% skim milk in Tris-buffered saline containing 0.075% tween 20 for 1.5 h at room temperature. Membranes were then incubated with anti-PARP-1 or anti-total TH antibodies for 1 h at room temperature. Blots were washed and exposed to appropriate secondary antibody for 1 h at room temperature. Blots were then exposed to enhanced chemiluminescence detection reagent for 1 h and developed using a LAS 4000 imaging system . Later, membranes were washed and then immunoblotted with b-actin antibody as a marker of the total protein loaded per lane.Quantitation of tTH and PARP-1 were normalised relative to b-actin levels.This study demonstrates that rotenone and H2O2 have markedly different effects on SH-SY5Y cells. H2O2 treatment does not appear to have any significant effect on TH protein expression and the cell death induced by H2O2 can be prevented by a number of compounds tested. In contrast, rotenone treatment was associated with an increase in TH protein levels and its toxicity could not be prevented with any of the compounds.

In the present study natural fiber composites,containing 10%,20% and 30% hemp fibers as reinforcement and mixture of virgin and recycled high density polyethylene as matrix,have been fabricated.The tensile and flexural properties of the composites are evaluated and compared with samples made of 100% virgin HDPE and 50-50 mixture of virgin and recycled HDPE.In the last years,considering the increasing environmental awareness and the introduction of new rules and regulations to reduce the environmental impact and reach sustainability,the new products must minimize the environmental impact,so the interest and the subsequent use of eco-friendly materials is grooving more and more in different application fields.This aspect is particularly relevant in the composite manufacturing field,where both synthetic fibers and petroleum polymers are still widely used.In this contest,the use of natural fibers as reinforcement is reaching an increasing attention,indeed a lot of researches have aimed on the study of a range of recyclable materials based on natural fibers such as flax,ramie,sisal,hemp and many more in order to study their possible use as interesting substitute over the conventional ones.Among various kinds of natural fibres,the hemp one is one of the most promising because of its interesting properties such as the low density,low cost and high specific mechanical properties.In addition,the hemp plant is characterized by the ability of extracting heavy metals from the soil makes and the environmental conditions required from its cultivation allow the easy growth of this plant around the world.Regarding the matrix used for the natural fibre composites manufacturing,commercial greenhouse supplies its selection is limited by the temperature at which natural fibres degrade.Both thermoset and thermoplastic polymers are coupled with natural fibres and each one highlights its peculiarity characteristics: thermoplastics are capable of being repeatedly softened by the application of heat and hardened by cooling and have the potential to be the most easily recycled,on the other hand a better emphasis of the fibres mechanical properties are generally achieved by using thermosets as matrix.

In fact,thermoset polymers are particularly attractive as matrix materials for natural fibre reinforced composite production as they generally have reactive functional groups that make them compatible with hydrophilic fibre surfaces.Among these,the epoxy is one of the most interesting polymer resins that used as matrix for the natural fibre composites shows very high mechanical properties of the final product.Several applications as interior and insulation components of hemp/epoxy composites was largely found in literature in particular in the automotive and building sectors,whilst there are few applications as structural components and few works aimed on the study of manufacturing processes to produce hemp composites characterized by low density and high specific mechanical properties.Based on this overview,this work is focused on the manufacturing of lightweight hemp/epoxy composites through a new process easily implemented in industrial production by using hemp fabrics characterized by a large mesh size.To assess their mechanical performances,tensile and bending tests were performed and coupled with FE simulations.Therefore,due to the no optimum control in the fabric superimposition,even though the fibre weight content is the same for each sample typology,the difference in the thickness vales is directly connected with the difference in the density and in the fibre volume fraction values.Ideally,considering for the B type a good alignment able to obtain the same thickness of the A type sample and also considering that the B type samples contained half number of tows then the A type,the ideal values of the density and of the fabric volume fraction should be half than the ones of the A type.Figs.8 and 9 shows the tensile and bending stress-strain curves for the specimens under investigation.The composites with density of around 0.74 g/cm3 are characterized by a tensile strength of 40 MPa and a tensile modulus of 3000 MPa,whilst the composites with a density of around 0.47 g/cm3 showed a tensile strength of 24 MPa and a tensile modulus of 1650 MPa; the tensile properties of the B type are approximately half of the ones of the A type.Looking at Fig.9,it is possible to note that all curves show the same behavior up to the failure,but when the maximum stress value was reached and then the specimens started to fail,the behavior changes.

This is observed by comparing the stress-strain curves and the type of failure of the specimens show in Fig.10.The curves of the A type show a significant drop in the stress and then a brittle failure of the specimens was observed,whilst the curves of the B type show a slighter decrease in the stress coupled with a sliding failure.This is due to the higher presence of resin on the bottom surface of the A type specimens.The composites with a density of 0.77 g/cm3 showed a bending strength of 40 MPa and a bending modulus of 3100 MPa,instead of the samples with a density of around 0.47 g/cm3 that was characterized by a bending strength of 22 MPa and a bending modulus of 1200 MPa.It is also possible to observe that the flexural mechanical response variability is in any case quite limited.However,a slightly greater value was reached for the B type specimens due to above said misalignment problems that mainly occurs for this specimen typology.This is also corroborated from the results of the FE simulations of the three-point bending tests where the eventual misalignment error was considered.Illicit marijuana use in the United States has been a longstanding public health concern for both adolescents and adults.As many as 44% of college-aged individuals endorse having used marijuana at some point in their life,and 21% of college-aged individuals report marijuana use in the past 30 days.Marijuana intoxication is associated with motor coordination deficits,euphoria,impaired temporal estimation,and a variety of other psychological phenomena.Marijuana use has also been associated with more specific cognitive deficits,even after acute intoxication has subsided,and with the development of severe psychopathology.Furthermore,chronic marijuana use has been related to adverse physiological consequences in the cardiovascular and respiratory systems.Adolescence and young adulthood represent periods of the lifespan when increased risk-taking occurs,including the use of illicit substances,such as marijuana.The combination of an innate propensity for risk-taking and use of a judgment-altering substance is a striking example of the immediate public health concern over marijuana use in young-adults.This concern is particularly pertinent in light of recent efforts in support of marijuana’s legalization in the United States.A challenge for the field is to identify which chemical systems and associated information processing networks are most affected by chronic marijuana use.

The main psychoactive component of marijuana,Δ9 -tetrahydrocannabinol,acts as an agonist in central nervous system cannabinoid receptors and in other peripheral cell types,primarily immune cells.In the CNS,CB1 receptor density is high in the basal ganglia,particularly in the dorsal striatum.Cannabinoid receptor signaling acts on multiple neurotransmitters through a variety of biochemical cascades,including inhibition of voltage-dependent calcium channels and by directly inhibiting vesicle release.Both excitatory and inhibitory neurotransmitters,including glutamate,γ-aminobutyric acid and dopamine,are either directly or indirectly affected by CB1 receptor activation.For marijuana and other drugs of abuse and dependence,the dorsal striatum has been hypothesized to play a key role in the transition from intermittent drug use to compulsive habit-based drug-taking via mechanisms that underlie long-term synaptic plasticity.Exogenous activation of CB1 receptors,as occurs with marijuana intoxication,inhibits the release of glutamate as well as GABA in both the dorsal and ventral striatum.This inhibition facilitates the development of long-term depression in the striatum,which is a critical component in the altered synaptic plasticity that accompanies drug addiction.Thus,the manner in which corticostriatal functional connectivity is altered in the context of marijuana use is of interest,as is metabolic activity within the chemical systems that contribute to those alterations.Magnetic resonance spectroscopy is a widely used tool,allowing for in vivo characterizations of various brain metabolites.MRS data is acquired either from single voxel or multiple voxels.The SVS method typically benefits from high spectral resolution and signal-to-noise ratio.MRSI has better spatial resolution compared to SVS,but typically has a much more limited spectral resolution.To the best of our knowledge,only four other studies utilizing some form of MRS to examine marijuana users have been published,and the methods of these studies are relatively heterogeneous.The existing studies are summarized in Table 1.

Individuals ages 16-to-42 years were studied with either SVS or MRSI.In two of the studies,only males were examined.In most cases,marijuana use was reported at 20 or more days per month.Lower levels of Glu,N-acetylaspartate,and myo-inositol were observed in marijuana users compared to controls in regions known to be associated with substance use,cannabis dry rack including the basal ganglia,thalamus,cingulate cortex,dorsolateral prefrontal cortex,and the striatum as well as posterior cortical regions.The methods,ages of subjects,and extent of current marijuana use in the samples tested vary considerably across studies as summarized in Table 1.As disruptions in glutamate activity have been implicated in the development of addiction,we hypothesized disruptions in glutamate concentrations in marijuana users compared to controls.Several lines of evidence suggest inhibition of glutamate excitotoxicity by marijuana.In addition,based on the MRS literature described above related to the basal ganglia of adult marijuana users and literature describing the inhibitory effects of CB1 receptors on glutamate release,we specifically hypothesized that young-adult MJU subjects would show lower levels of Glu + glutamine in the basal ganglia compared to their non-using counterparts.We did not have a specific hypothesis regarding concentrations of other metabolites given that other researchers have not concentrated their assessments on the striatum.However,the limited available literature suggested the possibility of altered mIns as well as NAA levels in users versus controls.Twenty-seven marijuana users were recruited into the study through local advertisements on the University of Minnesota-Twin Cities campus.Marijuana users’ ages ranged from 18-to-21 years,with a mean and standard deviation of 19.5 ± 0.6 years.Exclusion criteria are described below.Twenty-six healthy young adult non-users,who were participants in a large,longitudinal study of normal brain development,served as a control sample.Control participants’ ages ranged from 13-to-24 years,with a mean and standard deviation of 19.3 ± 3.1 years.The recruitment strategy for the control sample has been described elsewhere.Briefly,participants younger than 18 years of age were recruited through a database of research volunteers throughout the Metro community,through post-cards mailed to University of Minnesota civil service employees,and through local advertisements.Participants over the age of 18 years were recruited using on-campus advertisements.During the controls’ third longitudinal follow-up visit,MRS was added to the protocol as time allowed.Thus,the control sample described in this study has a broader age range than the MJU sample,a feature that was considered in the statistical approach described below.A description of the study was initially given to both the MJU and control participants over the phone.Interested participants were then invited to complete a brief phone screening to ascertain study eligibility.Exclusion criteria included major physical,neurological or psychiatric illness,substance use disorders,head injuries resulting in loss of consciousness >20 min,mental retardation,learning disabilities,current use of psychoactive medications,non-native English speaking,vision or hearing that was not normal or corrected to normal,complications at birth,current pregnancy,and MRI contraindications.Inclusion criteria for MJU participants included current use of marijuana at least five times per week for at least one year,and an age of onset of use prior to the age of 17 years.Marijuana users were also excluded if they were daily cigarette smokers,or if their alcohol use exceeded four drinks for females and five drinks for males on more than two occasions per week.Marijuana users were asked to refrain from drug use for at least 12 h prior to their visit to avoid acute intoxication during study procedures.Participants provided written informed consent and all study procedures were approved by the University of Minnesota’s Institutional Review Board.After the phone interview,eligible participants were invited to the University of Minnesota’s Center for Neurobehavioral Development for an in-person screening session to further ascertain eligibility and to verify information given over the phone.The Kiddie Schedule for Affective Disorders and Schizophrenia,Present and Lifetime version was used to assess for current or past Diagnostic and Statistical Manual,Fourth Edition axis I disorders,including childhood disorders given the relative youth of the sample.The presence or absence of DSM-IV disorders was confirmed by case consensus meetings with staff members including a license-eligible clinical psychologist.In addition,a two-subtest version of the Wechsler Abbreviated Scale of Intelligence was administered to yield estimated full scale IQ.Participants who met all inclusion criteria after the in-person interview were invited back for a comprehensive neuropsychological testing battery and an MRI scan.

As evidence for adverse consequences of marijuana use during adolescence on brain functioning accumulates,such research has the potential to improve prevention and intervention efforts through better education,thus reducing marijuana use and associated negative consequences.To guide recruitment,the Adolescent Brain Cognitive Development Study required a method for identifying children at high risk for early-onset substance use that may be utilized during the recruitment process.In this context,childhood risk refers to characteristics identified at ages 9 or 10 years that predict adverse outcomes in adolescence,and “high risk” refers to a categorical classification of some children as having increased risk compared to others.The construction of a brief measure for childhood substance use risk involves the identification of characteristics that predict early-onset substance use in mid to late adolescence.The identification and evaluation of optimal items for a brief childhood measure to serve as a high-risk screener ideally involves data from several large prospective studies with assessments initiated prior to the typical age of onset of substance use.To inform ABCD Study recruitment,secondary analyses are needed with datasets collected prior to ABCD Study initiation.In this context,a set of analyses with available data focused on a specific substance use outcome was determined to be most likely to be informative and feasible.While other substance use outcomes are also important,early-onset marijuana use is a relevant target.Marijuana is the most commonly used illicit drug by adolescents,and regular marijuana use identifies youth likely to develop cannabis use disorder.In these secondary data analyses,the definition of early-onset marijuana use was defined by the initiation of regular use as indicated in the available datasets.The studies contributing datasets were the Center for Education and Drug Abuse Research,cannabis drying the Pittsburgh Youth Study,the Pittsburgh Girls Study,and the Michigan Longitudinal Study.

In the studies contributing data to the secondary analyses described here,the definitions of regular marijuana use differed by sample due to measurement variations.The variations in the definitions of regular marijuana use were as follows: ; five or more use occasions in the past year and; six or more occasions in the past year.By efficiently identifying children at high risk for early-onset marijuana use,a brief and effective measure of childhood risk measure could be utilized as a screen to identify high risk children in prevention research,primary medical care,and mental health clinic settings.The present analyses were specifically undertaken to inform the development a childhood high risk screen for use in the ABCD Study.The ABCD Study is the National Institute of Healths’ large-scale prospective population study of the biological and environmental factors that influence young people’s ability to successfully navigate adolescence.The study has a special emphasis on the risk and protective factors that influence marijuana and other substance use,and subsequent health problems including substance use disorders.Utilizing data from previously conducted studies,the present study was thus undertaken to develop and establish the efficiency of a short measure to identify youth at high risk for early-onset marijuana use with optimal features for use in the ABCD Study.To achieve this goal,the risk level of a potential participant needs to be determined at the time of recruitment and prior to their scheduling for the extensive ABCD Study assessment protocol.Consequently,the optimal ABCD Study high risk screen has several characteristics: extreme brevity,including less than ten items; lack of sensitive items that may raise confidentiality concerns at this early stage of considering participation; consistency with prior research.These characteristics were taken into consideration in the analyses that follow.Historically,studies focusing on mental disorders such as schizophrenia,alcohol use disorder,and major depressive disorder,have used positive family history as a risk marker.Family history has been demonstrated to identify children at high risk of later substance use disorders in many prospective studies.However,a detailed family history may involve the parent being asked to disclose their own socially undesirable,embarrassing or,in some cases,illegal behavior.

There have been alternative strategies to acquire this information,such as the use of publicly available records of drunk driving or other drug offenses,or the use of hospital records to identify parental diagnosis.Obtaining such records would not be feasible in the initial recruitment phase of the ABCD Study.Regardless of the method for obtaining this information,requesting this information at the point of introducing the ABCD Study raises the real possibility that the parent will decline study involvement.Few longitudinal studies have formulated and tested measures for identifying high risk children likely to exhibit early-onset marijuana use.There have been several approaches developed for predicting substance use disorders,but relatively few have targeted the adolescent developmental period.One of the risk measures developed to identify high risk children is the SUD Transmissible Liability Index developed by Vanyukov,Tarter,Clark and colleagues,using longitudinal data from the CEDAR study.Although the TLI is sophisticated in its development,it is long,uses different portions of existing instruments,and is under copyright.In addition,the TLI did not focus on the age 15 outcome of marijuana use,and the publications did not use Receiver Operating Characteristic Area Under the Curve analyses to determine an optimal threshold score.Another screening instrument,the DSM Guided Cannabis Screen has unknown predictive value because it was constructed using cross-sectional data from a small clinical sample aged 14–59.Therefore,the current study fills a significant gap in the empirical literature.This report describes the process and results of secondary data analyses to prospectively identify a brief screening measure applicable to age 9–10-year-old children that would predict early-onset marijuana use in the 5–7 years following the initial screening measurement.To acquire data useful for developing this screening measure,we needed to identify population-based prospective studies which began assessments in late childhood,had been continued at least through ages 14–17,included marijuana use variables at both age periods,measured domains previously identified in the literature as predictive of adolescent substance use disorder outcomes,and had a sufficient number of measures in these domains that were shared across these studies so that screening validation could be replicated across different demographic groups.

The objectives of these secondary data analyses were as follows: To develop a brief screener for 9–10-year-old boys and girls to predict early-onset marijuana and other substance use in mid adolescence with demonstrated predictive utility across four longitudinal data sets; To dichotomize the outcome variable,which will reduce shrinkage,improve replicability and practical utility.; To replicate findings across construction and validation samples.The advantage of this dual analysis approach is that we could construct a screener that considers shrinkage that typically happens between construction of a screener and subsequent validation in another sample.In summary,the objective was to develop a brief and feasible approach to the identification of children at increased risk for early onset marijuana use that may inform the ABCD Study recruitment procedures.To ascertain replication of results,we used four existing longitudinal data sets.These data sets were utilized to build construction and validation samples for each sex,resulting in nine independent analyses.The four longitudinal data sets were from the CEDAR,PYS,PGS,and MLS.Where possible,we used both parent and child as informants,which is particularly important for externalizing behavior that is concealing in nature,because parents usually have less knowledge of the behavior compared to the child.The overall sample consisted of 882 boys and 368 girls.At the initiation of the study,81.3% of the boys were White,and 18.7% Non-White,and 74.7% of the girls were White,25.3% Non-White.Sample selection: Families were ascertained through two methods.The first involved recruitment through all district courts of fathers living in the area convicted for drunk driving with a biological son between the ages of 3 and 5 years old.Fathers were also required to be living with the boy and his biological mother.The second group were required to have the same family composition,but were ascertained through the same neighborhoods as the court-recruited families.Door to door canvassing was carried out to recruit two subgroups: families where neither parent met a lifetime substance use disorder diagnosis ; families where father met criteria for an alcohol use disorder but were not involved with the court.In addition to the original 3-5-year-old son and his biological parents,a female sibling within the range of 3–11,when present,was also recruited.If other siblings in the 3-11-year age range were also present in the home,they were recruited as well.Assessment at T1 for this study : average ages: 10.55 for boys and 10.61 for girls.

Where possible,we used both parent and child as informants,which is particularly important for the externalizing behaviors that are concealing in nature,because parents often are not aware of this type of child behavior.The outcome of interest was child self-report of marijuana use at about age 14.Attrition was 10%.The potential items for analyses were identified by examining prior research,prior analyses with the available datasets,greenhouse benches particularly the extensive analyses with CEDAR data,identifying pertinent items available in the four longitudinal projects used in these secondary analyses,and deliberations on the acceptability of areas of inquiry for potential participants during the recruitment process.Based on these considerations,the constructs represented by the pool of items to be considered included child externalizing behaviors,child internalizing behaviors,and parent tobacco smoking.Child externalizing behaviors.In the case of the ABCD Study design,we are projecting from ages 9–10,when marijuana use typically is minimal and not a viable risk item for screening purposes.Therefore,for candidate items on child externalizing behaviors,we considered non-substance use characteristics that other studies have found to predict early-onset substance use in mid adolescence,particularly child externalizing behaviors.Potential externalizing behaviors considered were vandalism,lying,and disobedience at school.Child internalizing behaviors.In addition,we examined whether selected internalizing behaviors augmented predictions.After examining potential internalizing items’ correlations with both the tentative screener and with the outcome variable,we initially focused on the following items : unhappy,sad or depressed; too fearful or anxious; secretive or keep things to oneself; self-conscious or easily embarrassed.After considering which internalizing items correlated with the externalizing screener at that point,we finally focused on: unhappy,sad or depressed; too fearful or anxious.Parent smoking.For candidate items on parent behaviors,parent smoking was also considered a viable candidate.This candidate item for the screener was available in the 4 study data sets.The predicted outcome was marijuana use by ages 14–15 with a frequency that indicated greater than experimental use.The available outcome categories varied across the studies,including monthly use in CEDAR,use at five times or more in the past year in the PYS and PGS,and 6 or more times during the past year in the MLS.The presence of marijuana use at or above these thresholds for the depicted ages defined “early-onset marijuana use” in these secondary analyses.The evaluations of individual items and their combinations in relations to early-onset marijuana use were undertaken with Receiver Operating Characteristic statistics.This approach is typically used in evaluating screening for diseases,with several examples in the prior literature focusing on substance use frequency in relation to adolescent substance use disorders.Using ROC statistics,the evaluation of the prediction power of a screen is usually based on a 2 by 2 table,as illustrated in Fig.1.The quality of a screen is indicated by four parameters: Sensitivity and specificity,which refer to True Positives/,and True Negatives/,respectively,and Positive predictive value and negative predictive value,which refer to True Positives/True Positives + False Positives and True Negatives/True Negatives + False Negatives,respectively.

Area Under the Curve analyses were used to establish whether the prediction is better than chance; and what the optimal cut-off is to minimize false negative and false positive errors.AUC can range from 0.5 to 1.0,when sensitivity and specificity are considered equally important.In practice,AUC tends to be lower than 1.0,meaning that one cannot correctly classify all future marijuana users or correctly classify all future non-marijuana users.The general rule is that the higher the sensitivity,the lower the specificity.Lowering the cut-off score can increase sensitivity,but with the consequence that there will be more false positives.Where sample sizes from study sites were sufficient,we created two subgroups,labeled “construction” and “validation” samples,using a randomization method,the SPSS random variable generation function.This partitioning of the samples was done to avoid idiosyncratic findings.Sufficient sample sizes were available to take this approach for CEDAR boys,PYS,and PGS,but not for CEDAR girls,MLS boys,or MLS girls.To support scale construction yet allow for validation in these limited samples,weightings were applied so that there were more subjects assigned to the construction sub-sample than to the validation sub-sample.We searched for equivalent predictor items of interest in each dataset.This is very important because we needed construct convergence among the four longitudinal datasets.We used prorating in cases where there were missing items so that we would maximize the numbers of participants.Note that sample sizes varied somewhat due to missing cases for each analysis.