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Official websites use. Share sensitive information only on official, secure websites. A recent shift in legal and social attitudes toward marijuana use has also spawned a surge of interest in understanding the effects of marijuana use on the brain. There is considerable evidence that an adolescent onset of marijuana use negatively impacts white matter coherence. On the other hand, a recent well-controlled study demonstrated no effects of marijuana use on the morphometry of subcortical or cortical structures when users and non-users were matched for alcohol use. Regardless, most studies have involved small, carefully selected samples, so the ability to generalize to larger populations is limited. In an attempt to address this issue, we examined the effects of marijuana use on white matter integrity and cortical and subcortical morphometry using data from the Human Connectome Project HCP consortium. The HCP data consists of ultra-high resolution neuroimaging data from a large community sample, including adults reporting recreational marijuana use. Rather than just contrasting two groups of individuals who vary significantly in marijuana usage as typifies prior studies, we leveraged the large sample size provided by the HCP data to examine parametric effects of recreational marijuana use. Our results indicate that the earlier the age of onset of marijuana use, the lower was white matter coherence. Age of onset also also affected the shape of the accumbens, while the number of lifetime uses impacted the shape of the amygdala and hippocampus. Marijuana use had no effect on cortical volumes. These findings suggest subtle but significant effects of recreational marijuana use on brain structure. Effects of marijuana use on brain morphology in recreational users were investigated. Age of first use and lifetime times used impacted shape of the hippocampus and accumbens. Previous epidemiological studies have revealed strong negative impacts of marijuana use, suggesting that marijuana has similar potential for abuse as other illicit substances e. Volkow et al. However, several focused empirical studies have countered these results, finding instead no significant effect of marijuana use on subcortical brain morphometry and only an uncertain effect on cognition e. The past two decades have seen shifts in legal and societal attitudes toward marijuana use, with 23 states and the District of Columbia legalizing medical marijuana and four states legalizing recreational marijuana Marijuana Resource Center: State Laws Related to Marijuana, ; moreover, perceptions of the risk of regular marijuana use have decreased, even amongst adolescents, particularly in Colorado, recreational marijuana is now legal Schuermeyer et al. As increases in the potency of marijuana have accompanied these shifts in attitudes Volkow et al. Adolescence is a sensitive period for brain development with white matter myelination and gray matter pruning, and, critically, an increase in the number of cannabinoid receptors that respond to marijuana Jacobus and Tapert, While preliminary studies of the effects of marijuana use on white matter integrity showed no significant effects in adolescents or adults DeLisi et al. Importantly, these effects have been observed longitudinally, suggesting a causation between marijuana use and white matter changes Bava et al. However, most of these studies have relied on small sample sizes i. Moreover, the majority of these studies all examined the effects of heavy use e. As many white matter tracts continue to develop in adolescence and young adulthood Lebel and Beaulieu, , with maximal change in such development during this time frame Simmonds et al. Studies of the effects of marijuana use on cortical and subcortical morphometrics in humans have typically focused on the amygdala and hippocampus Rocchetti et al. At least some, but far from all, of the evidence suggests an influence of marijuana on brain structure. For example, marijuana users compared to nonusers have been found to have reduced amygdala volume Churchwell et al. On the other hand, a recent meta-analysis of 14 studies of marijuana users compared to nonusers found no summary changes in amygdala volume, but did observe a consistent pattern of reduced hippocampal volume Rocchetti et al. In addition, a large number of studies with animals and humans have shown that marijuana affects the structure of the nucleus accumbens Gilman et al. Hence, there is evidence in the existing literature to suggest the possibility that marijuana influences the structure of these regions, all of which are known to be affected in addiction Koob and Volkow, Nonetheless, a recent well-controlled study by Weiland et al. They compared morphometry in a sample of adult and adolescent daily users of marijuana to nonusers matching the groups for alcohol use , while controlling for other confounding variables of tobacco use, depression, impulsivity, age, and gender. Importantly, they found no group differences in measures of brain morphometry for the nucleus accumbens, amygdala, hippocampus, cerebellum, or 35 cortical regions in each hemisphere. Interestingly, when they simply controlled for the amount of alcohol use, rather than matching users and nonusers, they replicated several findings of Gilman and colleagues. Furthermore, when examining effect size across previous studies, they found that the literature demonstrates a mean lack of effect. Given the discrepancies in the literature, we wanted to re-examine this issue using a large representative sample. To this end, we analyzed extremely high-quality multi-modal neuroimaging data from participants in the Human Connectome Project HCP who reported using marijuana at least once during their lives Van Essen et al. The participants in this sample consist of twins and their non-twin siblings who have no history of major psychiatric illness, but vary greatly in terms of race, education, income, BMI, and the degree of recreational drug use. A recent study used this HCP dataset to disentangle causal effects of marijuana use on regional brain volume from shared genetic effects and found that it was mainly shared genetic effects explained differences in bran volumes Pagliaccio et al. However, this study did not investigate the effects of marijuana use on white matter integrity or the shape of subcortical regions, which was the focus of the current study. Rather than investigating extremes of marijuana use i. Nevertheless, a comparison of users and non-users was also performed as a replication of prior work. Data were only considered if they had structural e. We further restricted analyses to individuals who had reported using marijuana at least once in their lifetime, resulting in participants in the final sample. An overview of the participant recruitment strategy is described in detail elsewhere Van Essen et al. Our goal is to capture a broad range of variability in healthy individuals with respect to behavioral, ethnic, and socioeconomic diversity p. The data included in this study consisted of individuals from different families, ranging from 1 to 4 members per family, with a mean number of 1. Sibships with individuals having severe neurodevelopmental disorders, documented neuropsychiatric disorders, diabetes, or high blood pressure were excluded, as were twins born before 34 weeks gestation and non-twins born before 37 weeks Van Essen et al. Demographic, medical, family history, personality, cognitive, and lifestyle information is collected from each subject over two weeks of phone and in-person interviews as well as through written assessments e. Age of first use was reverse scored so that an earlier age of first use was scored more highly, in line with the times used measure. Age, gender, tobacco and alcohol usage, and years of education were included as covariates in all analyses. For the DTI data analyses, an index of motion was included as a covariate, as previous work has shown motion to contribute to spurious effects in group analyses Yendiki et al. Motion was calculated following Yendiki et al. The Total Motion Index was defined for each subject i as:. Covariates were converted to Z -scores for all of the marijuana use analyses. While the focus of the current study was the identification of linear effects of marijuana use on brain morphometry, we conducted a group comparison of users and non-users as a confirmatory analysis of prior group studies that have investigated morphometry for a review, see Jacobus and Tapert, Users were defined as those who both started using marijuana before the age of 18 and have used marijuana more than times in their lifetime. Non-users were those who reported zero lifetime uses of marijuana. The final sample for this analysis included users and non-users. Structural MRI scans were acquired at 0. Diffusion images were acquired at very-high spatial resolution 1. Data downloaded from the HCP for the present study had undergone a minimal preprocessing pipeline, described in detail elsewhere Glasser et al. Structural data analyzed in the current study were the result of the PreFreeSurfer and FreeSurfer pipelines. Briefly, T1w and T2w images were corrected for gradient distortion, aligned and averaged i. The undistorted T1w and T2w images were then registered together in order to perform bias field correction, and finally, were non-linearly aligned to MNI space. Diffusion data analyzed in the current study were the result of the Diffusion Preprocessing pipeline. Diffusion images underwent b 0 intensity normalization, EPI distortion correction with FSL's topup , eddy current and motion correction, and gradient nonlinearity correction. Although the family structure of the HCP and similar studies present unique opportunities for investigating the heritability of brain structures Pagliaccio et al. Extensions to permutation methods have been developed that allow for types of designs that contain well-structured non-independence between observations like paired tests and repeated measures Winkler et al. More recently, it has been demonstrated that multi-level exchangeability blocks can be defined to allow for permutation tests on voxel-wise brain data with complex sibships such as those contained in the HCP Winkler et al. The exchangeability block file used in the current study consisted of separate columns that coded the family type e. Rather than allowing for permutations amongst all individuals, permutations were constrained at both the whole-block level e. In this manner, heritability—or more specifically the non-independence due to heritability—is treated as a nuisance variable, but without directly modeling the heritability. All voxel-wise analyses involving related individuals were carried out using multi-level block permutation methods in FSL's PALM tool version 94a Winkler et al. Nested exchangeability blocks were defined which restricted permutations to the same family type, which allowed us to account for family structure without directly modeling these complicated repeated-measures factors. Nevertheless, as a further control for heritability, we conducted follow-up analyses by randomly selecting one user from each family such that there were no siblings within the subsample, resulting in marijuana users. Identical analyses were performed using the full user sample and the unrelated subsample. These analyses were conducted using FSL's randomise package, which performs permutation methods, without using exchangeability blocks. Following the PALM analyses, permutations were performed. Briefly, the diffusion tensor model Pierpaoli and Basser, was fit at each voxel yielding maps for fractional anisotropy FA; fraction of anisotropic diffusion , axial diffusivity AD; diffusivity along the principal axis , radial diffusivity RD; average of diffusion along the 2 secondary axes , and mean diffusivity MD; mean of all diffusivity axes. This last step creates a skeleton which represents the centers of all fiber bundles that are generally common to all participants in the study. The skeleton and nonlinear registration parameters were generated using the FA maps, and these transformations were subsequently applied to the other diffusion parameter maps. Voxel-wise statistics were then performed in this skeleton space. The effect of marijuana use on white matter was assessed with a linear parametric analysis across level of marijuana use. Participants reported a wide-range of levels of marijuana use in terms of age of first use as well as the number of times used over the lifetime, as shown in Fig. Age of first use and times used were included as factors of interest and, as described above, all analyses included alcohol and tobacco use, age, gender, years of education, and Total Motion Index as covariates. For a table of correlations between all covariates see Table 1. Histograms depicting self-report measures of marijuana use including age of first use A and times used in lifetime B. Correlation matrix for imaging covariates. Those rows shaded in red represent marijuana use measures, those shaded in green represent other substance use covariates, those shaded in blue represent demographic covariates, and the gray row represents the diffusion Total Motion Index TMI covariate that was only used for white matter analyses. First, structural images were brain-extracted and gray matter-segmented before being registered to the MNI standard space using non-linear registration Andersson et al. The resulting images were averaged and flipped along the x-axis to create a left-right symmetric, study-specific gray matter template. The modulated gray matter images were then smoothed with an isotropic Gaussian kernel with a sigma of 3 mm. Following Weiland et al. Rather than analyzing whether marijuana showed a multivariate effect across all 35 cortical regions contained in this table as did Weiland et al. Regions of interest included 15 prefrontal cortical regions in each the left and right hemispheres : medial and lateral orbitofrontal cortex, caudal anterior cingulate, caudal middle frontal, inferior frontal gyrus 3 subregions: pars orbitalis, pars opercularis, pars triangularis , rostral middle frontal, superior frontal, and frontal pole. Subcortical regions included nucleus accumbens, hippocampus, cerebellum cortex and white matter, thalamus, and amygdala. White matter volumes were included for the anterior and mid anterior corpus callosum. FIRST allows for a model-based segmentation and registration of anatomical images, where volumetric labels are parameterized as surface meshes. Models for each subcortical structure are based on a training set of manually traced images. Vertex locations from each participant were projected onto the surface of the average shape transformed to MNI space , resulting in scalar projection values. This approach normalizes for brain size. The same models of marijuana use generated for the white matter analyses were used here. Again, PALM was used to perform multi-level block permutation analyses. Because FIRST only reports changes at the surface of the structure and does not contain information about which if any deep levels of a structure are impacted, these subregion labels represent approximations. Descriptive statistics for age, gender, race, and education level are reported in Table 2. Histograms of the distribution of marijuana use measures are shown in Fig. Summarizing these data, non-twins were more likely to have used marijuana than twins and men were more likely to have used marijuana than women, but marijuana use did not differ by race, ethnicity, or education level. Table of demographic information from the full sample on participants with imaging data in the HCP S data release. The group comparison showed no significant effects, possibly suggesting that the frequency of marijuana use in the HCP sample is not severe enough to replicate previous studies, which largely focused on comparisons of non-users and daily marijuana users. In line with this possibility, there were no linear effects of the number of times used on white matter coherence in users. As shown in Fig. The SLF connects the prefrontal cortex and parietal cortex and is involved in executive functions Depue et al. The Forceps Major and Minor are extensions of the corpus callosum connecting the left and right occipital and frontal lobes, respectively. Thus, even though most of the effects on FA and RD were found in the right hemisphere, communication between the left and right hemispheres may be impacted by marijuana age of onset. Effects of age of first use of marijuana on Tract-Based Spatial Statistics from the full sample of marijuana users. Hot colors depict a positive relationship between age of first use and FA, i. Earlier marijuana use was associated with decreased white matter coherence in the right Superior Longitudinal Fasciculus, lateral prefrontal white matter, anterior and posterior corpus callosum extending to the Forceps Minor and Major , as well as the right Inferior Longitudinal Fasciculus. When examining the subset of unrelated marijuana users, we confirmed the negative effect of an earlier age of first use on FA and RD in the SLF, as shown in Fig. Increased RD has been related to demyelination in neurodegenerative diseases such as multiple sclerosis Klawiter et al. These findings taken together, and in line with previous studies Becker et al. Moreover, these results suggest that marijuana use has an impact on the development of these white matter tracts during adolescence. Effects of age of first use of marijuana on Tract-Based Spatial Statistics from the restricted sample of unrelated marijuana users. Earlier marijuana use was associated with decreased white matter coherence in the right Superior Longitudinal Fasciculus, lateral prefrontal white matter, and the anterior corpus callosum extending to the Forceps Minor. The VBM analysis in the full sample and the subsample of unrelated users showed no significant results, nor in the group comparison. These findings are in line with those presented by Weiland et al. We then investigated whether the shape of the hippocampus, amygdala, or accumbens was impacted by marijuana use. For the full sample of users, the results are shown in Fig. Participants with a higher number of times used showed more inward deflection in the superficial group of the left amygdala. In the right nucleus accumbens, there was a significant positive relationship between age of first use and shape, such that participants with an earlier age of onset of use showed greater outward deflection Fig. This finding suggests that a later age of first use is related to more outward deflection but not overall volume of the right anterior nucleus accumbens. In the left hippocampus cornu ammonis and posterior hippocampus subiculum, a greater number of times used was associated with greater inward deflection Fig. Effects of marijuana use on subcortical shape in the full sample of users. A Trend for significant negative relationship between number of times used and scalar values in the left amygdala. Saggital slice left , Coronal slice middle , Axial slice right B Significant positive relationship between age of first use and scalar values in the right nucleus accumbens. Participants with a earlier age of first use showed more inward deflection in the right posterior ventral accumbens. Saggital slice left , Coronal slice middle , Axial slice right C Significant negative association between times used marijuana and scalar values in the left hippocampus. Participants with greater number of lifetime uses showed more inward deflection in the anterior hippocampus cornu ammonius and the posterior hippocampus subiculum. The effects of the number of times used marijuana in the restricted sample closely mirrored those in the full sample. In the left superficial amygdala there was a trend for participants with a higher number of times used to show more inward deflection Fig 5 B. Likewise, the same pattern was observed in the left hippocampus cornu ammonis Fig. This latter finding is similar to that observed by Gilman et al. Effects of marijuana use on subcortical shape in the restricted sample of unrelated users and the group comparison of users and non-users. A Trend for a significant negative relationship between number of times used and scalar values in the left amygdala. B Significant negative relationship between age of first use and scalar values in the left nucleus accumbens. C Significant negative association between times used marijuana and scalar values in the left hippocampus. Participants with greater number of lifetime uses showed more inward deflection in the anterior hippocampus cornu ammonius. D Significant group effect between users and non-users on shape in the right amygdala. Users showed greater outward deflection of the superficial group of the amygdala left image and the laterobasal amygdala right image compared to non-users. E Significant group effect between users and non-users on shape in the left hippocampus view of the medial surface, with right posterior hemisphere cropped. Users showed greater outward deflection of the hippocampus subiculum relative to non-users. When comparing users and non-users, significant effects on shape were discovered in the right amygdala and left hippocampus. There were two clusters in the right amygdala, one in the dorsal superficial group and another in the ventral laterobasal group, that showed more outward deflection in users compared to non-users. Similarly, in the left ventral anterior hippocampus subiculum , users showed more outward deflection compared to non-users. This region was more ventral and anterior to the cluster in the subiculum that showed a negative association with times used in users. These group results are somewhat surprising, as the correlational analyses within users showed inward deflections in more heavy users. However, the left and right nucleus accumbens also showed opposite patterns of inward and outward deflections with greater use, respectively. Thus, one possibility is that marijuana has differential effects in the left and right hemispheres. Nevertheless, Gilman et al. Despite a trend of de-criminalization and softening societal views, the scientific literature on the effects of marijuana on the brain has not yet reached a consensus. Work with adolescents has consistently shown that heavy marijuana use leads to disruptions in the integrity of white matter e. However, work investigating the effects of marijuana on the morphometry of gray matter and subcortical regions has yielded inconsistent findings, with several papers even providing strong evidence that heavy marijuana has no effects on brain morphometry Weiland et al. The current study took a broad look at this question using a multi-modal neuroimaging approach on data from almost participants reporting varying levels of recreational marijuana use. These data from the HCP include the highest quality neuroimaging data publically available, with almost twice the resolution for structural images Van Essen et al. This large dataset enabled us to investigate parametric associations between marijuana use and cortical and subcortical brain morphometry and white matter integrity. An advantage of this dataset is that it represents a representative community sample, with great variability in race, ethnicity, gender, education level, mental health symptoms, and drug use marijuana and otherwise. Importantly, this data set provided information on age of first use providing information on whether or not use occurred during early adolescence. Adolescence is a time of critical brain development for white matter tracts, with white matter volume increasing into young adulthood Lebel and Beaulieu, Heavy marijuana use during this critical period may impact development Bava et al. Recently, Becker et al. The current findings are largely consistent with those reports. We found that an earlier age of first use was associated with decreased FA and increased RD in long-range tracts including the forceps minor, Superior Longitudinal Fasciculus, and Inferior Longitudinal Fasciculus. Given that the average age of participants in the current study was Future, studies should examine the effects of duration of use on the trajectory of brain development. With regards to shape, a recent meta-analysis has suggested that the subcortical structure most consistently implicated in showing an effect of marijuana use is the hippocampus Rocchetti et al. Consistent with that finding, we observed that anterior aspects of the hippocampus showed shape differences due to the number of times marijuana has been used. This finding is interesting as anterior and posterior subregions of the hippocampus have dissociable roles in memory, with the anterior portion in particular the cornu ammonis being involved more in memory encoding and the posterior portion being involved more in memory retrieval Bartsch et al. Gilman et al. 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. When matched for alcohol use, marijuana users and controls showed no significant effects in terms of cortical or subcortical morphometry. Moreover, when Weiland et al. 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 S release examined how genetic vs. They found that marijuana use was associated with smaller volume of the left amygdala and right ventral striatum Pagliaccio et al. 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. With the upcoming complete HCP dataset consisting of participants, it will be important to update the analyses of Pagliaccio and colleagues, as well as adding subcortical shape as a measure, to see if a causal relationship arises with a larger sample potentially three-times larger. 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. 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 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. 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 i. 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 Sartor et al. 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 Mori and Zhang, For example, while shape changes of the accumbens and hippocampus might reflect inflammation, marijuana has been found to have anti-inflammatory properties Burstein and Zurier, 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 Kubicki et al. As such, morphometry studies can strongly inform where such changes are occurring, but cannot pinpoint the microscopic causes of these structural changes. 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 for a similar argument, see Weiland et al. Examined from multiple perspectives including white matter integrity, subcortical shape, and brain volume, our parametric analyses suggest that an early onset of marijuana use may be associated with subtle changes in brain regions implicated as being altered in substance abuse. These findings provide for the possibility that marijuana use during adolescence, which is a time of rapid brain development, might, at least in some individuals, have long-lasting effects, independent of the genetic effects suggested by a recent analysis of cortical volume on a somewhat overlapping sample Pagliaccio et al. The results of the current study show the utility of analyzing data from data sets with large Ns, as they can reveal subtle effects that might otherwise appear to be contradictory with smaller sample sizes. As a library, NLM provides access to scientific literature. Neuroimage Clin. Find articles by Joseph M Orr. Find articles by Courtnie J Paschall. Find articles by Marie T Banich. 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