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

paper Cited Public

Authors: Mackey, Scott; Allgaier, Nicholas; Chaarani, Bader; Spechler, Philip; Orr, Catherine; Bunn, Janice; Allen, Nicholas B; Alia-Klein, Nelly; Batalla, Albert; Blaine, Sara; Brooks, Samantha; Caparelli, Elisabeth; Chye, Yann Ying; Cousijn, Janna; Dagher, Alain; Desrivieres, Sylvane; Feldstein-Ewing, Sarah; Foxe, John J; Goldstein, Rita Z; Goudriaan, Anna E; Heitzeg, Mary M; Hester, Robert; Hutchison, Kent; Korucuoglu, Ozlem; Li, Chiang-Shan R; London, Edythe; Lorenzetti, Valentina; Luijten, Maartje; Martin-Santos, Rocio; May, April; Momenan, Reza; Morales, Angelica; Paulus, Martin P; Pearlson, Godfrey; Rousseau, Marc-Etienne; Salmeron, Betty Jo; Schluter, Renée; Schmaal, Lianne; Schumann, Gunter; Sjoerds, Zsuzsika; Stein, Dan J; Stein, Elliot A; Sinha, Rajita; Solowij, Nadia; Tapert, Susan; Uhlmann, Anne; Veltman, Dick; van Holst, Ruth; Whittle, Sarah; Wright, Margaret J; Yücel, Murat; Zhang, Sheng; Yurgelun-Todd, Deborah; Hibar, Derrek P; Jahanshad, Neda; Evans, Alan; Thompson, Paul M; Glahn, David C; Conrod, Patricia; Garavan, Hugh; ENIGMA Addiction Working Group
Year: 2019
Journal: The American journal of psychiatry
PMID: 30336705
DOI: 10.1176/appi.ajp.2018.17040415
PMCID: PMC6427822

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.

FIGURE 1.

Cortical Regions of Interest Exhibiting Substance- Specific or Shared Substance-General Effects Displayed on the Surface of Partially Inflated Average Brainsaa Substance specific: alcohol alone (green), alcohol and cocaine (purple); substance general: pattern 2 (yellow), pattern 3 (orange).

FIGURE 2.

Different Contributions of Dependence on the Five Substances Studied to the Association of Lower Volume or Thickness With Substance Dependenceaa For illustration purposes, both halves of the data (serving as the discovery and replication datasets) have been combined in the bar graphs. Three different patterns are illustrated. In pattern 1 (substance-specific effect), lower volume in the right nucleus accumbens was largely accounted for by dependence on alcohol alone. In pattern 2 (substance-general effect), volume in the left supramarginal gyrus was significantly lower in dependent compared with nondependent individuals (model 1) but was not significantly lower in any one particular substance group (model 2) compared with control subjects. In pattern 3 (substance-general effect), volume in the left insula was lower when either the alcohol-dependent group or the linear contrast of all substance groups except alcohol was compared with nondependent control subjects. Bars represent estimated marginal means expressed as percent difference from mean volume or thickness in nondependent control subjects. Error bars represent standard error. Meth=methamphetamine.

FIGURE 3.

Plot of Receiver Operating Characteristic Curves for the Support Vector Machine Classification of Individuals Dependent on One of Five Substances Relative to Nondependent Control SubjectsaaThe area under the curve (AUC) is significant for alcohol or nicotine dependence when trained on the first half of the data and tested on the second half (left) as well as when trained on the second half and tested on the first half (right). Meth=methamphetamine.

#	Section	Preview
0	METHOD — Behavioral Phenotyping	All procedures were performed in accordance with the Declaration of Helsinki. Data sets from the…
1	METHOD — Preparation of Structural MRI Data	Structural T1-weighted MRI brain scans were acquired from all participants. Scanner and acquisition…
2	METHOD — Linear Mixed-Effects Models With Cross-Validation	Differences in region-of-interest thickness or volume between substance-dependent participants and…
3	METHOD — Linear Mixed-Effects Models With Cross-Validation	“nicotine,” “cocaine,” “methamphetamine,” or “cannabis.” Model 2 did not permit…
4	METHOD — Linear Mixed-Effects Models With Cross-Validation	The replicability of neuroimaging results has recently been brought into question (22, 23). The…
5	METHOD — General Versus Substance-Specific Dependence Effects	Model 2 permitted a comparison of the estimated marginal mean region-of-interest volume or thickness…
6	METHOD — Past-30-Day Use	Linear mixed-effects models were used to determine whether past-30-day nicotine or alcohol use was…
7	METHOD — Support Vector Machine Classification	Support vector machine classification was implemented in MATLAB (MathWorks, Natick, Mass.) with a…
8	METHOD — Support Vector Machine Classification	half of the data for each of two train-test scenarios (i.e., train on the first half, test on the…
9	RESULTS	Basic demographic information (sex distribution and mean age) is provided in Table 1 and by site in…
10	RESULTS — Model 1: Dependent Versus Nondependent Subjects	Subcortical volume in dependent individuals was significantly lower in the left and right…
11	RESULTS — Model 2: Substance Dependence Groups Compared Separately to Nondependent Control Subjects	All subcortical regions of interest identified in model 1 plus the right thalamus, the left and…
12	RESULTS — Substance-Specific Versus Shared Substance-General Effects	Three distinct patterns of results emerged, which are illustrated in Figure 2.
13	RESULTS — Substance-Specific Versus Shared Substance-General Effects — Pattern 1 (substance specific).	In most regions of interest where a significant difference was observed, the effect was demonstrated…
14	RESULTS — Substance-Specific Versus Shared Substance-General Effects — Pattern 2 (substance general).	Six cortical regions of interest (e.g., the left supramarginal gyrus and the right medial…
15	RESULTS — Substance-Specific Versus Shared Substance-General Effects — Pattern 3 (substance general).	Three cortical regions of interest (the left inferior parietal cortex and the right and left insula)…
16	RESULTS — Past-30-Day Use	The volume of several subcortical regions of interest were negatively associated with past-30-day…
17	RESULTS — Support Vector Machine	The support vector machine produced a significant classification of alcohol- and nicotine-dependent…
18	DISCUSSION	Subcortical volume or cortical thickness was significantly lower on average in substance-dependent…
19	DISCUSSION	and right middle temporal gyrus, and the left supramarginal gyrus. No region of interest was…

Name	Type
Addiction Working Group local	cohort
age	phenotype
alcohol	phenotype
Alcohol and cocaine dependence local	phenotype
alcohol- and nicotine-dependent individuals local	cohort
alcohol dependence	phenotype
Alcohol_dependence	phenotype
Alcohol dependence effects local	phenotype
alcohol-dependent group local	cohort
Alcohol Use	phenotype
amygdala	anatomy
anxiety	phenotype
behavioral phenotypes	phenotype
brain	anatomy
brain region thickness local	phenotype
brain region volume local	phenotype
brain volume	anatomy
cannabis dependence	phenotype
cannabis use	phenotype
cannabis use disorder	phenotype
cocaine	phenotype
cocaine-dependent participants local	cohort
control participants	cohort
control subjects	cohort
cortex	anatomy
cortical thinning local	phenotype
craving	phenotype
dependence	phenotype
dependent groups local	cohort
dependent individuals local	cohort
Dependent individuals (non-alcohol) local	cohort
depression	phenotype
Discovery Data Set local	cohort
ENIGMA	cohort
ENIGMA project local	cohort
ENIGMA Project local	cohort
hippocampus	anatomy
insula	anatomy
intracranial volume	anatomy
left inferior parietal lobule	anatomy
left middle temporal gyrus	anatomy
left supramarginal gyrus local	anatomy
Left supramarginal gyrus local	anatomy
mean age	phenotype
Medial orbital cortex local	anatomy
medial orbitofrontal cortex	anatomy
Medial orbitofrontal region local	anatomy
methamphetamine	drug
methamphetamine dependence	phenotype
methamphetamine-dependent participants local	cohort
Neuroimaging biomarker local	phenotype
nicotine	drug
nicotine dependence	phenotype
nicotine-dependent participants local	cohort
nicotine use	phenotype
nondependence local	phenotype
nondependent control subjects local	cohort
Nondependent control subjects local	cohort
Nondependent control subjects local	phenotype
nucleus accumbens	anatomy
other substances	phenotype
pallidum	anatomy
parietal cortex	anatomy
past-30-day alcohol use	phenotype
past-30-day nicotine use	phenotype
PhenX Toolkit	cohort
posterior cingulate cortex	anatomy
precentral gyrus	anatomy
putamen	anatomy
region of interest	anatomy
region-of-interest volume local	phenotype
relapse	phenotype
replication sample	cohort
right medial orbitofrontal cortex	anatomy
right middle temporal gyrus	anatomy
Right posterior cingulate local	anatomy
right thalamus	anatomy
sex	phenotype
sex distribution local	phenotype
smoking	phenotype
study cohort	cohort
subcortical regions	anatomy
Substance-dependent participants local	phenotype
substance use	phenotype
superior frontal cortex	anatomy
supramarginal gyrus	anatomy
thickness	phenotype
Total lifetime substance use local	phenotype
Urge to smoke local	phenotype

Citation	PMID	DOI	Status
BauldL, BellK, McCulloughL, : The effectiveness of NHS smoking cessation services: a systematic review. J Public Health (Oxf) 2010; 32:71–821963839710.1093/pubmed/fdp074	—	—	—
BecharaA, TranelD, DamasioH: Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain 2000; 123:2189–22021105002010.1093/brain/123.11.2189	—	—	—
BoedhoePS, SchmaalL, AbeY, : Distinct subcortical volume alterations in pediatric and adult OCD: a worldwide meta- and mega-analysis. Am J Psychiatry 2017; 174:60–692760924110.1176/appi.ajp.2016.16020201PMC5344782	—	—	—
BrewerJA, WorhunskyPD, CarrollKM, : Pretreatment brain activation during Stroop task is associated with outcomes in cocaine-dependent patients. Biol Psychiatry 2008; 64:998–10041863515710.1016/j.biopsych.2008.05.024PMC2601637	—	—	—
ButtonKS, IoannidisJP, MokryszC, : Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 2013; 14:365–3762357184510.1038/nrn3475	—	—	—
BühlerM, MannK: Alcohol and the human brain: a systematic review of different neuroimaging methods. Alcohol Clin Exp Res 2011; 35:1771–17932177726010.1111/j.1530-0277.2011.01540.x	—	—	—
CraigAD: How do you feel? Interoception: the sense of the physiological condition of the body. Nat Rev Neurosci 2002; 3:655–6661215436610.1038/nrn894	—	—	—
DaleAM, FischlB, SerenoMI: Cortical surface-based analysis, I: segmentation and surface reconstruction. Neuroimage 1999; 9: 179–194993126810.1006/nimg.1998.0395	—	—	—
DesikanRS, SégonneF, FischlB, : An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006; 31:968–9801653043010.1016/j.neuroimage.2006.01.021	—	—	—
EverittBJ, RobbinsTW: Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat Neurosci 2005; 8:1481–14891625199110.1038/nn1579	—	—	—
GalbraithS, DanielJA, VisselB: A study of clustered data and approaches to its analysis. J Neurosci 2010; 30:10601–106082070269210.1523/JNEUROSCI.0362-10.2010PMC6634702	—	—	—
GarrisonKA, PotenzaMN: Neuroimaging and biomarkers in addiction treatment. Curr Psychiatry Rep 2014; 16:5132530838510.1007/s11920-014-0513-5PMC4427893	—	—	—
GoldsteinRZ, CraigAD, BecharaA, : The neurocircuitry of impaired insight in drug addiction. Trends Cogn Sci 2009; 13:372–3801971675110.1016/j.tics.2009.06.004PMC2844118	—	—	—
GuyonI, WestonJ, BarnhillS, : Gene selection for cancer classification using support vector machines. Mach Learn 2002; 46: 389–422	—	—	—
HibarDP, SteinJL, RenteriaME, : Common genetic variants influence human subcortical brain structures. Nature 2015; 520: 224–2292560735810.1038/nature14101PMC4393366	—	—	—
IoannidisJP: Excess significance bias in the literature on brain volume abnormalities. Arch Gen Psychiatry 2011; 68:773–7802146434210.1001/archgenpsychiatry.2011.28	—	—	—
KoenigsM, BarbeyAK, PostleBR, : Superior parietal cortex is critical for the manipulation of information in working memory. J Neurosci 2009; 29:14980–149861994019310.1523/JNEUROSCI.3706-09.2009PMC2799248	—	—	—
KoobGF, VolkowND: Neurocircuitry of addiction. Neuro-psychopharmacology 2010; 35:217–23810.1038/npp.2009.110PMC280556019710631	—	—	—
Krishnan-SarinS, BalodisIM, KoberH, : An exploratory pilot study of the relationship between neural correlates of cognitive control and reduction in cigarette use among treatment-seeking adolescent smokers. Psychol Addict Behav 2013; 27:526–5322358645810.1037/a0032479PMC4106014	—	—	—
MackeyS, KanKJ, ChaaraniB, : Genetic imaging consortium for addiction medicine: from neuroimaging to genes. Prog Brain Res 2016; 224:203–2232682236010.1016/bs.pbr.2015.07.026PMC4820288	—	—	—
MackeyS, OlafssonV, AupperleRL, : Greater preference consistency during the willingness-to-pay task is related to higher resting state connectivity between the ventromedial prefrontal cortex and the ventral striatum. Brain Imaging Behav 2016; 10: 730–7382627120610.1007/s11682-015-9435-zPMC4753147	—	—	—
MackeyS, PaulusM: Are there volumetric brain differences associated with the use of cocaine and amphetamine-type stimulants? Neurosci Biobehav Rev 2013; 37:300–3162325394510.1016/j.neubiorev.2012.12.003PMC3604030	—	—	—
Martin-SantosR, FagundoAB, CrippaJA, : Neuroimaging in cannabis use: a systematic review of the literature. Psychol Med 2010; 40:383–3981962764710.1017/S0033291709990729	—	—	—
McLellanAT, LewisDC, O’BrienCP, : Drug dependence, a chronic medical illness: implications for treatment, insurance, and outcomes evaluation. JAMA 2000; 284:1689–16951101580010.1001/jama.284.13.1689	—	—	—
MoosRH, MoosBS: Rates and predictors of relapse after natural and treated remission from alcohol use disorders. Addiction 2006; 101:212–2221644555010.1111/j.1360-0443.2006.01310.xPMC1976118	—	—	—
NaqviNH, RudraufD, DamasioH, : Damage to the insula disrupts addiction to cigarette smoking. Science 2007; 315:531–5341725551510.1126/science.1135926PMC3698854	—	—	—
National Drug Intelligence Center: National Drug Threat Assessment. Washington, DC, US Department of Justice, 2011	—	—	—
National Institute on Drug Abuse: Trends and Statistics. 2016 https://www.drugabuse.gov/related-topics/trends-statistics	—	—	—
PaulusMP, TapertSF, SchulteisG: The role of interoception and alliesthesia in addiction. Pharmacol Biochem Behav 2009; 94:1–71969873910.1016/j.pbb.2009.08.005PMC2753707	—	—	—
PaulusMP: Pragmatism instead of mechanism: a call for impactful biological psychiatry. JAMA Psychiatry 2015; 72:631–6322599254010.1001/jamapsychiatry.2015.0497	—	—	—
PtakR: The frontoparietal attention network of the human brain: action, saliency, and a priority map of the environment. Neuroscientist 2012; 18:502–5152163684910.1177/1073858411409051	—	—	—
RayR, SchnollRA, LermanC: Nicotine dependence: biology, behavior, and treatment. Annu Rev Med 2009; 60:247–2601963057210.1146/annurev.med.60.041707.160511	—	—	—
RehmJ, LachenmeierDW, RoomR: Why does society accept a higher risk for alcohol than for other voluntary or involuntary risks? BMC Med 2014; 12:1892542464810.1186/s12916-014-0189-zPMC4203927	—	—	—
SacksJJ, GonzalesKR, BoucheryEE, : 2010 national and state costs of excessive alcohol consumption. Am J Prev Med 2015; 49: e73–e792647780710.1016/j.amepre.2015.05.031	—	—	—
SanchezVD: Advanced support vector machines and kernel methods. Neurocomputing 2003; 55:5–20	—	—	—
SeoD, LacadieCM, TuitK, : Disrupted ventromedial prefrontal function, alcohol craving, and subsequent relapse risk. JAMA Psychiatry 2013; 70:727–7392363684210.1001/jamapsychiatry.2013.762PMC3788824	—	—	—
SherifT, RiouxP, RousseauME, : CBRAIN: a web-based distributed computing platform for collaborative neuroimaging research. Front Neuroinform 2014; 8:542490440010.3389/fninf.2014.00054PMC4033081	—	—	—
SteinJL, MedlandSE, VasquezAA, : Identification of common variants associated with human hippocampal and intracranial volumes. Nat Genet 2012; 44:552–5612250441710.1038/ng.2250PMC3635491	—	—	—
SutherlandMT, RayKL, RiedelMC, : Neurobiological impact of nicotinic acetylcholine receptor agonists: an activation likelihood estimation meta-analysis of pharmacologic neuroimaging studies. Biol Psychiatry 2015; 78:711–7202566210410.1016/j.biopsych.2014.12.021PMC4494985	—	—	—
ThompsonPM, SteinJL, MedlandSE, : The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav 2014; 8:153–1822439935810.1007/s11682-013-9269-5PMC4008818	—	—	—
TsuangMT, LyonsMJ, MeyerJM, : Co-occurrence of abuse of different drugs in men: the role of drug-specific and shared vulnerabilities. Arch Gen Psychiatry 1998; 55:967–972981906410.1001/archpsyc.55.11.967	—	—	—
US Department of Health and Human Services: The Health Consequences of Smoking: 50 Years of Progress: A Report of the Surgeon General. Atlanta, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2014	—	—	—
van ErpTG, HibarDP, RasmussenJM, : Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry 2016; 21:5852628364110.1038/mp.2015.118PMC5751698	—	—	—
WhitefordHA, DegenhardtL, RehmJ, : Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 2013; 382: 1575–15862399328010.1016/S0140-6736(13)61611-6	—	—	—
World Health Organization (WHO): WHO Report on the Global Tobacco Epidemic 2011. Geneva, WHO, 2011	—	—	—

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Structural Cerebellar and Lateral Frontoparietal Networks are altered in CUD: An SBM Analysis.	Lacomba-Arnau E et al.	—	2025	→
The effects of alcohol use severity and polygenic risk on gray matter volumes in young adults.	Chen Y et al.	—	2025	→
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[Cannabis use and cannabis use disorders].	Hoch E et al.	—	2024	→
Compensation patterns and altered functional connectivity in alcohol use disorder with and without Korsakoff's syndrome.	Morand A et al.	—	2024	→
Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer's Disease: A Data-Driven Meta-Analysis with N = 3,118.	Kang X et al.	—	2024	→
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Large-Scale Neuroimaging of Mental Illness.	Ching CRK et al.	—	2024	→
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Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders.	Zhu T et al.	—	2024	→
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Altered brain structural and functional connectivity in cannabis users.	Soleimani N et al.	—	2023	→
Association of smoking with brain gray and white matter volume: a Mendelian randomization study.	Lin W et al.	—	2023	→
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Brain volumes in alcohol use disorder: Do females and males differ? A whole-brain magnetic resonance imaging mega-analysis.	Maggioni E et al.	—	2023	→
Common and distinct fronto-striatal volumetric changes in heroin and cocaine use disorders.	Ceceli AO et al.	—	2023	→
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Functional and structural brain abnormalities in substance use disorder: A multimodal meta-analysis of neuroimaging studies.	Yan H et al.	—	2023	→
Impact of Adolescent Cannabis Use on Neurocognitive and Brain Development.	Scott JC	—	2023	→
Impact of Adolescent Cannabis Use on Neurocognitive and Brain Development.	Scott JC	—	2023	→
Integrative Analysis of Proteome-wide Association Studies and Functional Enrichment Analysis to Identify Genes and Chemicals Associated with Alcohol Dependence.	Zhang J et al.	—	2023	→
Lifestyle-related risk factors and their cumulative associations with hippocampal and total grey matter volume across the adult lifespan: A pooled analysis in the European Lifebrain consortium.	Binnewies J et al.	—	2023	→
Machine learning with neuroimaging biomarkers: Application in the diagnosis and prediction of drug addiction.	Yang L et al.	—	2023	→
Measuring white matter microstructure in 1,457 cannabis users and 1,441 controls: A systematic review of diffusion-weighted MRI studies.	Robinson EA et al.	—	2023	→
Methylphenidate as a treatment option for substance use disorder: a transdiagnostic perspective.	van Ruitenbeek P et al.	—	2023	→
Mini-review: The neurobiology of treating substance use disorders with classical psychedelics.	Urban MM et al.	—	2023	→
Misadventures in Toxicology: Concentration Matters for Testosterone-Induced Neurotoxicity.	Willson C	—	2023	→
Neuroimaging and immunological features of neurocognitive function related to substance use in people with HIV.	Murdoch DM et al.	—	2023	→
Pathways to the persistence of drug use despite its adverse consequences.	McNally GP et al.	—	2023	→
Recalibrating single-study effect sizes using hierarchical Bayesian models.	Cao Z et al.	—	2023	→
Residual deficits in functional brain activity after chronic cocaine self-administration in rhesus monkeys.	Porrino LJ et al.	—	2023	→
Sex differences in externalizing and internalizing traits and ventral striatal responses to monetary loss.	Li G et al.	—	2023	→
Testing the consequences of alcohol, cannabis, and nicotine use on hippocampal volume: a quasi-experimental cotwin control analysis of young adult twins.	Harper J et al.	—	2023	→
The evolution of Big Data in neuroscience and neurology.	Dipietro L et al.	—	2023	→
The Genetically Informed Neurobiology of Addiction (GINA) model.	Bogdan R et al.	—	2023	→
The role of the orbitofrontal cortex in exercise addiction and exercise motivation: A brain imaging study based on multimodal magnetic resonance imaging.	Zhang F et al.	—	2023	→
Translating the hierarchical taxonomy of psychopathology (HiTOP) from potential to practice: Ten research questions.	Conway CC et al.	—	2023	→
Alcohol and cannabis co-use and longitudinal gray matter volumetric changes in early and late adolescence.	Luo X et al.	—	2022	→
Alcohol consumption and MRI markers of brain structure and function: Cohort study of 25,378 UK Biobank participants.	Topiwala A et al.	—	2022	→
Association Between Brain Structure and Alcohol Use Behaviors in Adults: A Mendelian Randomization and Multiomics Study.	Mavromatis LA et al.	—	2022	→
Brain Magnetic Resonance Imaging Features of Nicotine-Dependent Individuals and Its Correlation with Polymorphisms of Dopamine D Receptor Gene.	Liu H et al.	—	2022	→
Brain morphometric abnormalities and their associations with affective symptoms in males with methamphetamine use disorder during abstinence.	Hu X et al.	—	2022	→
Brain structural covariance network differences in adults with alcohol dependence and heavy-drinking adolescents.	Ottino-González J et al.	—	2022	→
Brain tract structure predicts relapse to stimulant drug use.	Tisdall L et al.	—	2022	→
Brain volume in chronic ketamine users - relationship to sub-threshold psychotic symptoms and relevance to schizophrenia.	Chesters RA et al.	—	2022	→
Common and gender-specific associations with cocaine use on gray matter volume: Data from the ENIGMA addiction working group.	Rabin RA et al.	—	2022	→
Common Gray Matter Reductions in Alcohol Use and Obsessive-Compulsive Disorders: A Meta-analysis.	Stevens BW et al.	—	2022	→
Consensus paper of the WFSBP task force on cannabis, cannabinoids and psychosis.	D'Souza DC et al.	—	2022	→
Consortium neuroscience of attention deficit/hyperactivity disorder and autism spectrum disorder: The ENIGMA adventure.	Hoogman M et al.	—	2022	→
Convergent gray matter alterations across drugs of abuse and network-level implications: A meta-analysis of structural MRI studies.	Hill-Bowen LD et al.	—	2022	→
Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges.	Dennison JB et al.	—	2022	→
Diminished Structural Brain Integrity in Long-term Cannabis Users Reflects a History of Polysubstance Use.	Knodt AR et al.	—	2022	→
Driving under the influence of drugs: Correlation between blood psychoactive drug concentrations and cognitive impairment. A narrative review taking into account forensic issues.	Blandino A et al.	—	2022	→
Effects of Persistent Binge Drinking on Brain Structure in Emerging Adults: A Longitudinal Study.	Pérez-García JM et al.	—	2022	→
ENIGMA brain injury: Framework, challenges, and opportunities.	Dennis EL et al.	—	2022	→
Gray matter volumes of the insula and anterior cingulate cortex and their dysfunctional roles in cigarette smoking.	Chen Y et al.	—	2022	→
How do substance use disorders compare to other psychiatric conditions on structural brain abnormalities? A cross-disorder meta-analytic comparison using the ENIGMA consortium findings.	Navarri X et al.	—	2022	→
Identifying alcohol misuse biotypes from neural connectivity markers and concurrent genetic associations.	Zhu T et al.	—	2022	→
Intracortical myelin in individuals with alcohol use disorder: An initial proof-of-concept study.	Morris V et al.	—	2022	→
Morphometric similarity deviations in stimulant use disorder point towards abnormal brain ageing.	Zhukovsky P et al.	—	2022	→
Neuroimaging studies within Cognitive Genetics Collaborative Research Organization aiming to replicate and extend works of ENIGMA.	Koshiyama D et al.	—	2022	→
Neuroplasticity, the Prefrontal Cortex, and Psychopathology-Related Deviations in Cognitive Control.	Luciana M et al.	—	2022	→
Predicting alcohol dependence from multi-site brain structural measures.	Hahn S et al.	—	2022	→
Resting state functional connectivity in alcohol users and co-users of other substances.	Morris V et al.	—	2022	→
Sex-different interrelationships of rs945270, cerebral gray matter volumes, and attention deficit hyperactivity disorder: a region-wide study across brain.	Luo X et al.	—	2022	→
Spatial frequency processing and its modulation by emotional content in severe alcohol use disorder.	Creupelandt C et al.	—	2022	→
Structural and functional brain recovery in individuals with substance use disorders during abstinence: A review of longitudinal neuroimaging studies.	Parvaz MA et al.	—	2022	→
The Mexican magnetic resonance imaging dataset of patients with cocaine use disorder: SUDMEX CONN.	Angeles-Valdez D et al.	—	2022	→
The neurobiology of drug addiction: cross-species insights into the dysfunction and recovery of the prefrontal cortex.	Ceceli AO et al.	—	2022	→
What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.	Ching CRK et al.	—	2022	→
White matter microstructure differences in individuals with dependence on cocaine, methamphetamine, and nicotine: Findings from the ENIGMA-Addiction working group.	Ottino-González J et al.	—	2022	→
Whole-brain morphometry in Canadian soldiers with posttraumatic stress disorder.	Roy O et al.	—	2022	→
Accelerated Aging of the Amygdala in Alcohol Use Disorders: Relevance to the Dark Side of Addiction.	Tomasi D et al.	—	2021	→
Alcohol and the Brain.	Nutt D et al.	—	2021	→
Alcohol use and interoception - A narrative review.	Wiśniewski P et al.	—	2021	→
Beyond the tip of the iceberg: A narrative review to identify research gaps on comorbid psychiatric disorders in adolescents with methamphetamine use disorder or chronic methamphetamine use.	Kuitunen-Paul S et al.	—	2021	→
Brain morphometry in adults with gambling disorder.	Bouchard AE et al.	—	2021	→
Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders : Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group.	Opel N et al.	—	2021	→
Cognitive dysfunction and cerebral volumetric deficits in individuals with Alzheimer's disease, alcohol use disorder, and dual diagnosis.	Zhornitsky S et al.	—	2021	→
Common abnormality of gray matter integrity in substance use disorder and obsessive-compulsive disorder: A comparative voxel-based meta-analysis.	Klugah-Brown B et al.	—	2021	→
Contribution of sleep disturbances to the heterogeneity of cognitive and brain alterations in alcohol use disorder.	Laniepce A et al.	—	2021	→
Coordinating Global Multi-Site Studies of Military-Relevant Traumatic Brain Injury: Opportunities, Challenges, and Harmonization Guidelines.	Tate DF et al.	—	2021	→
Cortical thickness and subcortical volume abnormalities in male crack-cocaine users.	Bittencourt AML et al.	—	2021	→
Effect of alcohol, tobacco, and cannabis co-use on gray matter volume in heavy drinkers.	Grodin EN et al.	—	2021	→
Effects of weather and season on human brain volume.	Book GA et al.	—	2021	→
Frontocerebellar gray matter plasticity in alcohol use disorder linked to abstinence.	Muller AM et al.	—	2021	→
Gender-related neuroanatomical differences in alcohol dependence: findings from the ENIGMA Addiction Working Group.	Rossetti MG et al.	—	2021	→
Internet gaming disorder impacts gray matter structural covariance organization in the default mode network.	Chen S et al.	—	2021	→
Long-term Anabolic-Androgenic Steroid Use Is Associated With Deviant Brain Aging.	Bjørnebekk A et al.	—	2021	→
Long-Term Cocaine Self-administration Produces Structural Brain Changes That Correlate With Altered Cognition.	Jedema HP et al.	—	2021	→
Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer.	Shane MS et al.	—	2021	→
Mapping cortical and subcortical asymmetries in substance dependence: Findings from the ENIGMA Addiction Working Group.	Cao Z et al.	—	2021	→
Neural correlates of visuoperceptive changes in severe alcohol use disorder: A critical review of neuroimaging and electrophysiological findings.	Creupelandt C et al.	—	2021	→
Neuroimaging Association Scores: reliability and validity of aggregate measures of brain structural features linked to mental disorders in youth.	Axelrud LK et al.	—	2021	→
Neuropsychosocial markers of binge drinking in young adults.	Gowin JL et al.	—	2021	→
Orbitofrontal cortex thickness and substance use disorders in emerging adulthood: causal inferences from a co-twin control/discordant twin study.	Harper J et al.	—	2021	→
Parietal P3 and midfrontal theta prospectively predict the development of adolescent alcohol use.	Harper J et al.	—	2021	→
Perceived stress, self-efficacy, and the cerebral morphometric markers in binge-drinking young adults.	Li G et al.	—	2021	→
Polygenic risk scores for alcohol involvement relate to brain structure in substance-naïve children: Results from the ABCD study.	Hatoum AS et al.	—	2021	→
Polysubstance use in the U.S. opioid crisis.	Compton WM et al.	—	2021	→
Psychopathy.	De Brito SA et al.	—	2021	→
Recent cannabis use is associated with smaller hippocampus volume: High-resolution segmentation of structural subfields in a large non-clinical sample.	Owens MM et al.	—	2021	→
Regional gray matter volume associated with exercise dependence: A voxel-based morphometry study.	Zhang F et al.	—	2021	→
Role of inflammation in alcohol-related brain abnormalities: a translational study.	Lanquetin A et al.	—	2021	→
Sex and dependence related neuroanatomical differences in regular cannabis users: findings from the ENIGMA Addiction Working Group.	Rossetti MG et al.	—	2021	→
Sex Differences in Substance Use Disorders: A Neurobiological Perspective.	Cornish JL et al.	—	2021	→
Sex/gender differences in brain function and structure in alcohol use: A narrative review of neuroimaging findings over the last 10 years.	Verplaetse TL et al.	—	2021	→
Significant, replicable, and functional associations between KTN1 variants and alcohol and drug codependence.	Luo X et al.	—	2021	→
Sleep disturbances are associated with cortical and subcortical atrophy in alcohol use disorder.	Zhang R et al.	—	2021	→
Substance-Specific and Shared Gray Matter Signatures in Alcohol, Opioid, and Polysubstance Use Disorder.	Muller AM et al.	—	2021	→
The Effects of Alcohol and Cannabis Use on the Cortical Thickness of Cognitive Control and Salience Brain Networks in Emerging Adulthood: A Co-twin Control Study.	Harper J et al.	—	2021	→
The ENIGMA Brain Injury working group: approach, challenges, and potential benefits.	Wilde EA et al.	—	2021	→
Addictions NeuroImaging Assessment (ANIA): Towards an integrative framework for alcohol use disorder.	Voon V et al.	—	2020	→
A large-scale genome-wide association study meta-analysis of cannabis use disorder.	Johnson EC et al.	—	2020	→
Association of Gray Matter and Personality Development With Increased Drunkenness Frequency During Adolescence.	Robert GH et al.	—	2020	→
Associations of cigarette smoking with gray and white matter in the UK Biobank.	Gray JC et al.	—	2020	→
Cognitive deficits in methamphetamine addiction: Independent contributions of dependence and intelligence.	Fitzpatrick RE et al.	—	2020	→
Convergent Evidence for Predispositional Effects of Brain Gray Matter Volume on Alcohol Consumption.	Baranger DAA et al.	—	2020	→
Cortical Thickness in Alcohol Dependent Patients With Apathy.	Yang K et al.	—	2020	→
Deconstructing the neurobiology of cannabis use disorder.	Ferland JN et al.	—	2020	→
Different aspects of impulsivity in chronic alcohol use disorder with and without comorbid problem gambling.	Kovács I et al.	—	2020	→
Diurnal Preference and Grey Matter Volume in a Large Population of Older Adults: Data from the UK Biobank.	Norbury R	—	2020	→
ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.	Thompson PM et al.	—	2020	→
ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing.	Schmaal L et al.	—	2020	→
Ethical issues in global neuroimaging genetics collaborations.	Palk A et al.	—	2020	→
Higher impulsivity and lower grey matter volume in the bilateral prefrontal cortex in long-term abstinent individuals with severe methamphetamine use disorder.	Huang S et al.	—	2020	→
Leptin predicts cortical and subcortical gray matter volume recovery in alcohol dependent patients: A longitudinal structural magnetic resonance imaging study.	Bach P et al.	—	2020	→
Neuroanatomical correlates of impulsive traits in children aged 9 to 10.	Owens MM et al.	—	2020	→
Subcortical surface morphometry in substance dependence: An ENIGMA addiction working group study.	Chye Y et al.	—	2020	→
The Anabolic Androgenic Steroid Treatment Gap: A National Study of Substance Use Disorder Treatment.	Havnes IA et al.	—	2020	→
γ-aminobutyric acid and glutamate/glutamine alterations of the left prefrontal cortex in individuals with methamphetamine use disorder: a combined transcranial magnetic stimulation-magnetic resonance spectroscopy study.	Su H et al.	—	2020	→
Addiction in focus: molecular mechanisms, model systems, circuit maps, risk prediction and the quest for effective interventions.	Goldstein RZ et al.	—	2019	→
Associations Between Drinking and Cortical Thickness in Younger Adult Drinkers: Findings From the Human Connectome Project.	Morris VL et al.	—	2019	→
Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples.	Hoogman M et al.	—	2019	→
Classification of cocaine-dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data.	Sakoglu U et al.	—	2019	→
Impulsivities and addictions: a multidimensional integrative framework informing assessment and interventions for substance use disorders.	Vassileva J et al.	—	2019	→
Posterior Cingulate Cortical Response to Active Avoidance Mediates the Relationship between Punishment Sensitivity and Problem Drinking.	Le TM et al.	—	2019	→
Regional differences in white matter integrity in stimulant use disorders: A meta-analysis of diffusion tensor imaging studies.	Beard CL et al.	—	2019	→
Structural brain characteristics of anabolic-androgenic steroid dependence in men.	Hauger LE et al.	—	2019	→