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

Abstract

Objective: Although lower brain volume has been routinely observed in individuals with substance dependence compared with nondependent control subjects, the brain regions exhibiting lower volume have not been consistent across studies. In addition, it is not clear whether a common set of regions are involved in substance dependence regardless of the substance used or whether some brain volume effects are substance specific. Resolution of these issues may contribute to the identification of clinically relevant imaging biomarkers. Using pooled data from 14 countries, the authors sought to identify general and substance-specific associations between dependence and regional brain volumes. Method: Brain structure was examined in a mega-analysis of previously published data pooled from 23 laboratories, including 3,240 individuals, 2,140 of whom had substance dependence on one of five substances: alcohol, nicotine, cocaine, methamphetamine, or cannabis. Subcortical volume and cortical thickness in regions defined by FreeSurfer were compared with nondependent control subjects when all sampled substance categories were combined, as well as separately, while controlling for age, sex, imaging site, and total intracranial volume. Because of extensive associations with alcohol dependence, a secondary contrast was also performed for dependence on all substances except alcohol. An optimized split-half strategy was used to assess the reliability of the findings. Results: Lower volume or thickness was observed in many brain regions in individuals with substance dependence. The greatest effects were associated with alcohol use disorder. A set of affected regions related to dependence in general, regardless of the substance, included the insula and the medial orbitofrontal cortex. Furthermore, a support vector machine multivariate classification of regional brain volumes successfully classified individuals with substance dependence on alcohol or nicotine relative to nondependent control subjects. Conclusions: The results indicate that dependence on a range of different substances shares a common neural substrate and that differential patterns of regional volume could serve as useful biomarkers of dependence on alcohol and nicotine.

Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

“…Note that, as in this example, the sources do not have to be spatially clustered for ICA to extract them [Color figure can be viewed at wileyonlinelibrary.com] Attention Deficit Hyperactivity disorder (ADHD) for classification (Hart et al, 2014). More recently, in a large analysis study which involves over 3,000 subjects, an SVM-based classification of regional brain volumes from MRI data successfully classified individuals with substance dependence to nondependent control subjects (Mackey et al, 2019).…”

Dimensional neuroimaging: (Internet) Gaming Disorder symptoms according to the WHO and APA frameworks associate with lower striatal volume

“…Neuroplastic changes in the striatum and associated circuitries engaged in reward processing and habit formation have been demonstrated extensively in animal models of substance-addiction (Everitt & Robbins, 2016). Human neuroimaging studies have also repeatedly reported striatal gray matter alterations in addicted populations (Mackey et al, 2019), with the extent of volumetric reductions being associated with escalating use and clinical symptom severity (Garza-Villarreal et al, 2018;Becker et al, 2015) Provisional evidence from a growing number of human imaging studies in pathological gambling -an established behavioral addiction -indicates that striatal circuits play an important role in the initial rewarding effects of gambling (Breiter, Aharon, Kahneman, Dale, & Shizgal, 2001) as well as the formation of compulsive gambling during later stages of the disorder (Clark, Boileau, & Zack, 2019).…”

Addiction in focus: molecular mechanisms, model systems, circuit maps, risk prediction and the quest for effective interventions

“…The latter results accord well with those of prior studies in drug‐addicted individuals (Potenza et al ., ) although the former do not (Hester & Garavan, ), perhaps due to the inclusion of participants at different stages in the addiction process or because of inherent difficulties with human neuroimaging studies, which often rely on modest sample sizes and may not always have the power to correct for multiple comparisons or to conduct whole‐brain analyses. Importantly, to circumvent these difficulties, large consortium data sets are now available including the IMAGEN sample, a longitudinal study of adolescence (Ewald et al ., ; Spechler et al ., ) and the ENIGMA sample, a cross‐sectional multisite aggregation of adult addiction data (Mackey et al ., ), efforts that allow for highly powered studies of potential neural phenotypes in addiction. In the current issue, machine‐learning techniques were applied to data provided by 1581 cannabis‐naïve 14‐year‐olds (some who used cannabis at age 16, N = 365) to derive a sex‐specific risk profile comprised of both psychosocial and brain prognostic markers likely to have preceded and influenced cannabis initiation in adolescence (Spechler et al ., ).…”

“…Importantly, to circumvent these difficulties, large consortium data sets are now available including the IMAGEN sample, a longitudinal study of adolescence (Ewald et al, 2016;Spechler et al, 2018) and the ENIGMA sample, a cross-sectional multisite aggregation of adult addiction data (Mackey et al, 2019), efforts that allow for highly powered studies of potential neural phenotypes in addiction. In the current issue, machine-learning techniques were applied to data provided by 1581 cannabis-na€ ıve 14-year-olds (some who used cannabis at age 16, N = 365) to derive a sex-specific risk profile comprised of both psychosocial and brain prognostic markers likely to have preceded and influenced cannabis initiation in adolescence (Spechler et al, 2018).…”