Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional Effects

Basic demographic information (sex distribution and mean age) is provided in Table 1 and by site in Table S1 in the online supplement.

Subcortical volume or cortical thickness was significantly lower on average in substance-dependent individuals compared with nondependent control subjects across widespread parts of the brain (i.e., 22 distinct regions of interest out of a total of 82) (see Table 2; see also Table S2 in the online supplement). Some of these differences were substance specific, and others appear to constitute a shared neural substrate associated with dependence regardless of the substance used (see Table 3 and Figure 1). A majority of the identified regions of interest were smaller or thinner specifically in the brains of alcohol-dependent individuals (e.g., the left and right posterior cingulate and superior frontal cortex). A more limited set of seven regions with lower cortical thickness across substance dependence groups included the left and right insula, the left inferior parietal cortex, the right medial orbitofrontal cortex, the left and right middle temporal gyrus, and the left supramarginal gyrus. No region of interest was significantly larger or thicker in substance-dependent individuals relative to control subjects. An unexpected finding of the study was the absence of substance-specific linear effects on brain volume related to nicotine, methamphetamine, or cannabis dependence despite the collection of large pooled samples. Also, the successful classification of individuals dependent on nicotine, alcohol, or cocaine using the support vector machine approach suggests that the development of clinically useful neuroimaging biomarkers of substance dependence may be more productive if based on broader patterns of brain function or structure rather than differences in unique brain regions considered alone.

The set of brain regions identified with substance dependence in general is supported by previous evidence. The insula performs a central role in the perception of the internal state of the body (34). Disruption of the insula could alter regulation of the intense positive and negative bodily states associated with drug taking and withdrawal, biasing the individual toward relapse as a maladaptive response to anticipated challenges to physiological homeostasis (35). It has been reported that smokers who have suffered brain damage involving the insula have subsequently lost the urge to smoke (36). The parietal cortex has been associated with attention and working memory (37, 38). Disruption of these processes could interfere with self-awareness about a substance use problem and the management of stressful situations. The medial orbitofrontal region of interest defined by FreeSurfer (also known as the ventromedial prefrontal cortex) encodes the subjective value of future rewards during decision making (39). Lesions of this region produce disadvantageous choices on gambling tasks that model real-life decisions (40). Altered neural activity in the insula and the medial orbital and parietal cortex has frequently been linked to substance dependence and may predict greater craving and risk of relapse (41–44). The present results support the idea that substance dependence is mediated by a shared set of mechanisms across substance groups. Indeed, twin studies suggest that vulnerability to substance dependence is accounted for principally by a shared set of genetic variations regardless of the substance used, with proportionately smaller substance-specific effects (16).

Although subtle in magnitude, the wide spatial distribution of alcohol-specific effects is a striking finding of the study. Alcohol consumption enjoys greater cultural acceptance in the countries from which the data for this study were sampled relative to the other substances examined (45). Alcohol is legal to buy and consume, and widely publicized government-sanctioned guidelines exist for “safe” low-dose use of alcohol. This tolerance of alcohol-related health risks is unlike the cultural views toward any of the other substances investigated here, whose use even in small amounts is discouraged (45). It should be noted that lifetime exposure to each substance could not be uniformly assessed in the data sets used here. As a consequence, the scope of the alcohol dependence effects may in part be related to greater absolute consumption of alcohol relative to the other substances. It was possible to assess past-30-day use of nicotine and alcohol, a limited proxy of level of exposure, in a sizable minority of the data sets. Several subcortical regions of interest, such as the amygdala and the nucleus accumbens, were significantly smaller in individuals who reported the highest numbers of alcoholic drinks consumed in the past 30 days, consistent with the notion that greater exposure could be responsible for the magnitude of the observed alcohol effects. Further studies will be required to clarify whether the greater number of observed alcohol-specific effects relative to the other substances is related to differences in toxicity or total exposure.

It is also notable that, besides the seven brain regions associated with dependence in general, there were no drug-specific effects for dependence on nicotine, methamphetamine, and cannabis. Although cross-validation demonstrated that the volumetric differences observed were reliable, the effect sizes were uniformly small (see Tables 2 and 3). This suggests that the lack of consistency in the literature (13–15) may be related to the insufficient power of most studies to detect true effects. Other imaging modalities, such as task-based functional MRI (41–44) and higher-resolution structural imaging, may be required to detect reliable substance-specific nicotine, methamphetamine, or cannabis effects if they exist. It is also possible that substance dependence has multiple, heterogeneous interactions with brain volume that are not well assessed by simple linear analyses. Evidence for this is provided by the support vector machine classification.

The support vector machine classification found that the pattern of regional volume differences could be used successfully to distinguish between nondependent control subjects and individuals dependent on alcohol and nicotine. The transformation of the data with a radial basis function kernel prior to classification facilitated the detection of nonlinear patterns that cannot be detected by models 1 and 2. Additionally, the support vector machine can identify a multivariate pattern of effects across numerous regions of interest, each of which, in isolation, may not pass statistical threshold. Thus, the support vector machine detected useful information in the pattern of results that was not apparent from the linear analysis. The significant classifications suggest that the overall pattern of volumetric effects may contain useful clinical information that would not be apparent if only traditional univariate linear analyses were performed. While influential features in the classification partly overlapped with the regions of interest identified by the univariate analyses—for example, brain regions associated with alcohol dependence, such as the hippocampus and amygdala—additional regions not identified by the linear mixed effects analyses (i.e., model 1 and model 2) were also involved (see Table S3 in the online supplement). Future efforts of the Addiction Working Group will include the incorporation of other imaging modalities with which it may be possible to distinguish individuals with dependence on additional substances, such as methamphetamine and cannabis, from nondependent control subjects. It would also be clinically useful to examine whether the support vector machine classifications developed in this study offer an index of the strength of substance dependence in individuals who go on to recover or relapse. It is worth noting that current blood and urine tests do not identify dependence, as the machine learning classifier in the present study does, but rather detect, and to an extent quantify, recent substance use. While the present findings are preliminary and the support vector machine classifications should be tested on other independent samples, if brain volume is confirmed as a viable biomarker of dependence, or of biological risk of dependence, it could be used to plan how prevention and treatment resources are allocated to individual patients as well as, potentially, to track intervention success. A structural MRI scan in combination with other factors known to be related to substance use problems (e.g., change in employment or marital status, health issues) could be used to assess risk of transition to problematic patterns of use or to quantify the current degree of dependence, which would influence the intervention strategy.

Several factors limit the interpretation of the study findings. Different diagnostic instruments were used to assess substance dependence (see Table S1 in the online supplement). Although the validity of each of these instruments has been well established, variation between instruments could add noise to the measured behavioral phenotype. This, however, could be an advantage because the extrapolation of significant findings to the general population is also likely to be more robust by virtue of generalizing across different methods of assessment. The absence of nutrition and education information, which are potential confounders, also limits the interpretability of the results. A perennial concern with multisite studies is variation attributable to different scanners and acquisition protocols. This issue was mitigated by using a standard data extraction protocol developed by the ENIGMA project that has been validated in previous multisite reports (20, 28–30) and by the formal consideration of potential site differences in all statistical analyses. As discussed above, the degree of exposure to the various substances was not characterized uniformly across studies, which limits, for instance, the interpretation of the widespread alcohol effects and whether alcohol represents a greater source of toxicity than the other substances examined. It should be emphasized, however, that this study examined brain volumetric associations with dependence and not with total lifetime substance use. A beneficial outcome of this first study of the Addiction Working Group will be to raise awareness of the data needed to estimate the relation between brain volume and total exposure and, more generally, of the utility of uniform phenotypic data for data pooling. Greater consideration of how data may be used in international collaborations may influence the collection of data in future studies, which will increase their impact beyond their primary research focus. The PhenX Toolkit (https://www.phenxtoolkit.org/), for example, provides an extensive catalog of standardized measures expressly intended to facilitate secondary cross-study comparisons. Finally, co-occurring substance use limits the interpretation of the findings. Pervasive recreational substance use is a general issue for all studies of human substance dependence. For example, it is likely epidemiologically that a methamphetamine user will be exposed to alcohol. Methamphetamine users who do not use any other addictive substance would be an unusual group who, in practice, would be difficult to identify but, more importantly, would not be characteristic of the real-world population of methamphetamine users—that is, there would be a selection bias. Unlike studies in animal models, it is not possible to randomly assign humans to groups with restricted exposure to one substance alone. The typical strategy, which was used in the data sets included in this study, is to screen subjects for dependence on other substances but not to exclude for nondependent use of other substances.

The field of neuroimaging faces a crisis of relevance if published studies cannot be replicated, as noted in a series of reviews (22, 23). The authors of the present study joined to form a working group within the preexisting framework of the ENIGMA project to assemble a sufficiently large sample to overcome issues related to low statistical power that affect most individual neuroimaging studies. Using a rigorous cross-validation method, several brain regions were found to have a reliable association with substance dependence, including a shared set of regions across substances, such as the insula and the medial orbitofrontal cortex. Although the univariate analyses failed to identify linear effects in relation to dependence on nicotine, methamphetamine, and cannabis specifically, a machine learning algorithm, which was also able to detect nonlinear patterns in the data, successfully classified individuals dependent on alcohol or nicotine relative to nondependent control subjects. This suggests that the overall pattern of volumetric effects may contain more useful information with regard to the development of a neuroimaging biomarker of substance dependence than is revealed by the magnitude of single brain regions examined in isolation.

The authors thank Alex Wonnell, Alexandra Ivanciu, Noah Markowitz, Michael Lawler, Styles Crawford, and Mitchell Snowe at the University of Vermont for excellent technical assistance in tabulating the behavioral data and performing final quality control checks on the neuroimaging data.