Numerous choline-containing compounds contribute to the tCho signal measured in this study,complicating the interpretation of this sex difference.For example,phosphatidylcholine plays an important role in the phospholipid bilayer in cell membranes,and choline is essential in the formation of the neurotransmitter acetylcholine.Generally speaking,increases in choline signal in the brain have been demonstrated in cases with pathology.While this study has numerous strengths,it is not without limitations.Given time constraints on the scanning protocol,glutamate and glutamine could not be resolved separately from the acquired spectra.Even though this is a common problem,especially at lower field strengths,it poses limitations on the interpretation of the data because of the different biochemical functions of these metabolites.After release of glutamate into the synapse,cycling between glutamate and glutamine occurs in glial support cells in order to maintain high SNR in glutamatergic neurons,and to protect against adverse excitotoxic effects.Resolution of the glutamate versus glutamine signals would allow stronger interpretations to be offered regarding the meaning of the low levels observed in female users.Given that more extensive spectroscopy scanning is time intensive and requires higher field strengths to be conducted most efficiently,these findings together with other recent studies suggest that a more in-depth examination of neurochemical metabolism within front ostriatal circuits in heavy marijuana users is warranted.Another limitation of the study is the constrained spatial resolution of the spectra.It would be beneficial to examine additional brain structures,however spectral resolution was chosen over spatial resolution for the current study.Moreover,while the sample sizes are small in relation to the reported group by sex interactions,numerous reports exist which demonstrate a similar a pattern of sex-effects,where females who use or are exposed to illicit substances are differentially affected.Finally,we did not measure urine or hair concentrations of THC,so it is possible that participants in the study used less marijuana than they reported.
We find this to be unlikely given the level of detail that was provided about habits surrounding use in our direct interviews,vertical grow participants’ consistent reporting regarding their symptoms of DSM-IV marijuana dependence,and concomitant evidence of neurocognitive impairment consistent with marijuana exposure.Further,the majority of previous studies that collected urine/hair data and quantified cannabinoid concentrations did not show significant associations between these concentrations and brain metabolite data,suggesting such data are perhaps not necessary for this type of analysis in the presence of detailed clinical assessments.Nonetheless,the study would be strengthened by the ability to compare brain metabolic data with cannabinoid levels as obtained by blood,hair or urine analysis.Previous epidemiological studies have revealed strong negative impacts of marijuana use, suggesting that marijuana has similar potential for abuse as other illicit substances , is associated with respiratory illnesses, and leads to cognitive impairment . However, several focused empirical studies have countered these results, finding instead no significant effect of marijuana use on sub-cortical brain morphometry and only an uncertain effect on cognition . 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 ; moreover, perceptions of the risk of regular marijuana use have decreased, even amongst adolescents, particularly in Colorado, recreational marijuana is now legal . As increases in the potency of marijuana have accompanied these shifts in attitudes , it is becoming increasingly important to understand the precise neural effects of long-term marijuana use and the impact of the age of first use. 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 . While preliminary studies of the effects of marijuana use on white matter integrity showed no significant effects in adolescents or adults , a growing body of research suggests that an adolescent onset of heavy marijuana use can have neurotoxic effects on developing white matter, reflected in decreased white matter coherence as assessed by measures of diffusivity, e.g., fractional anisotropy and radial diffusivity. Importantly, these effects have been observed longitudinally, suggesting a causation between marijuana use and white matter changes .
However, most of these studies have relied on small sample sizes , so their ability to generalize to a broader population is limited. Moreover, the majority of these studies all examined the effects of heavy use , and much less is known about the effects of casual marijuana use on white matter integrity. As many white matter tracts continue to develop in adolescence and young adulthood , with maximal change in such development during this time frame , it is important to understand how the age of onset of marijuana use impacts neuro development not only in heavy users but more casual users, especially considering that adolescence is often a time of experimentation with substances of abuse . Studies of the effects of marijuana use on cortical and sub-cortical morphometrics in humans have typically focused on the amygdala and hippocampus and, to a lesser extent, the nucleus accumbens and orbitofrontal cortex. These structures are known to have important roles in reward processing and their function/structure is known to be disrupted by drugs of abuse . 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 , and amygdala volume reductions have been correlated with increased levels of self-reported craving and relapse in consumption after 6-months from detoxification from alcohol dependence . 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 hippo campal volume . In addition, a large number of studies with animals and humans have shown that marijuana affects the structure of the nucleus accumbens . 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 . Nonetheless, a recent well-controlled study by Weiland et al. found no evidence of an effect of marijuana on the morphometry of these structures. They compared morphometry in a sample of adult and adolescent daily users of marijuana to nonusers , 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 neuro imaging data from 466 participants in the Human Connectome Project who reported using marijuana at least once during their lives . 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 . However, this study did not investigate the effects of marijuana use on white matter integrity or the shape of sub-cortical regions, which was the focus of the current study. Rather than investigating extremes of marijuana use like most previous studies, we leveraged the large sample size to take a parametric approach, examining marijuana use along a spectrum, so as to search more specifically for dose-dependent effects. Nevertheless, a comparison of users and nonusers was also performed as a replication of prior work.Data analyzed in the current study came from the most recent S900 Release from the WU-Minn HCP Consortium . Data were only considered if they had structural and diffusion imaging scans, and had complete SSAGA and family information , resulting in 857 possible participants. We further restricted analyses to individuals who had reported using marijuana at least once in their lifetime, resulting in 466 participants in the final sample. In brief, the HCP aims to “recruit a sample of relatively healthy individuals free of a prior history of significant psychiatric or neurological illnesses. Our goal is to capture a broad range of variability in healthy individuals with respect to behavioral, ethnic,indoor growers and socioeconomic diversity .” The sample is meant to be representative of the population at large and includes individuals who smoke, are overweight, have sub-clinical psychiatric symptoms, and—critical for the current study—use recreational drugs. HCP participants are human adult twins and their non-twin siblings, aged 22–35 years. The data included in this study consisted of individuals from 270 different families, ranging from 1 to 4 members per family, with a mean number of 1.7 members per family. Sibships with individuals having severe neuro developmental 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.
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 .MRI scans were collected using a HCP-customized Siemens 3T Connectome Skyra magnet, as described in detail elsewhere . Structural MRI scans were acquired at 0.7 mm isotropic resolution and include a pair of T1-weighted and a pair of T2-weighted images. Diffusion images were acquired at very-high spatial resolution with a high-angular resolution diffusion imaging approach, incorporating 3 shells of b = 1000, 2000, and 3000 s/mm2 with 270 q-points distributed over the 3 shells. Not all participants had complete diffusion images, with the total number of q-points in the b = 1000 shell ranging from 30 to 90 with a mean of 87.3. Data downloaded from the HCP for the present study had undergone a minimal preprocessing pipeline, described in detail elsewhere . 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 , brain extracted, and corrected for readout distortion. 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 b0 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 , the shared variance between family members violates assumptions of independence of observations within the sample. 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 . More recently, it has been demonstrated that multi-level exchange ability blocks can be defined to allow for permutation tests on voxel-wise brain data with complex sib-ships such as those contained in the HCP . The exchange ability block file used in the current study consisted of separate columns that coded the family type , family ID , or sibling type . Rather than allowing for permutations amongst all individuals, permutations were constrained at both the whole-block level or the within-block level . 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 , an extension of permutation methods for the General Linear Model . Nested exchange ability 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.