Convergent Evidence for Predispositional Effects of Brain Gray Matter Volume on Alcohol Consumption.
- Authors
- Baranger, David A A; Demers, Catherine H; Elsayed, Nourhan M; Knodt, Annchen R; Radtke, Spenser R; Desmarais, Aline; Few, Lauren R; Agrawal, Arpana; Heath, Andrew C; Barch, Deanna M; Squeglia, Lindsay M; Williamson, Douglas E; Hariri, Ahmad R; Bogdan, Ryan
- Year
- 2020
- Journal
- Biological psychiatry
- PMID
- 31699293
- DOI
- 10.1016/j.biopsych.2019.08.029
- PMCID
- PMC7412715
BACKGROUND: Alcohol use has been reliably associated with smaller subcortical and cortical regional gray matter volumes (GMVs). Whether these associations reflect shared predisposing risk factors or causal consequences of alcohol use remains poorly understood. METHODS: Data came from 3 neuroimaging samples (NΒ = 2423), spanning childhood or adolescence to middle age, with prospective or family-based data. First, we identified replicable GMV correlates of alcohol use. Next, we used family-based and longitudinal data to test whether these associations may plausibly reflect a predispositional liability for alcohol use or a causal consequence of alcohol use. Finally, we used heritability, gene-set enrichment, and transcriptome-wide association study approaches to evaluate whether genome-wide association study-defined genomic risk for alcohol consumption is enriched for genes that are preferentially expressed in regions that were identified in our neuroimaging analyses. RESULTS: Smaller right dorsolateral prefrontal cortex (DLPFC) (i.e., middle and superior frontal gyri) and insula GMVs were associated with increased alcohol use across samples. Family-based and prospective longitudinal data suggest that these associations are genetically conferred and that DLPFC GMV prospectively predicts future use and initiation. Genomic risk for alcohol use was enriched in gene sets that were preferentially expressed in the DLPFC and was associated with replicable differential gene expression in the DLPFC. CONCLUSIONS: These data suggest that smaller DLPFC and insula GMV plausibly represent genetically conferred predispositional risk factors for, as opposed to consequences of, alcohol use. DLPFC and insula GMV represent promising biomarkers for alcohol-consumption liability and related psychiatric and behavioral phenotypes.
Identification of replicable volumetric associations with alcohol consumption. This statistical parametric map illustrates regions of reduced brain volume associated with increased alcohol consumption (Table S1 in Supplement 1), which are overlaid onto a canonical structural brain image Montreal Neurological Institute coordinates and statistics (Duke Neurogenetics Study [DNS]: p < .05, familywise error whole-brain corrected, β₯10 contiguous voxels; Human Connectome Project [HCP]: p < .05, familywise error region-of-interest corrected, β₯10 contiguous voxels). Alcohol consumption was not associated with increased volume in any region. Notably, in the HCP dataset, the superior frontal gyrus cluster extended into the right middle frontal gyrus and was located relatively far (34 mm dorsal) from the original right superior frontal cluster identified in the DNS. In contrast, this peak in the HCP was located 11.6 mm away from the right middle frontal peak identified in the DNS. Thus, for the purposes of post hoc analyses, the combined volume of both the right middle and superior frontal gyri cortices was extracted from both samples. Cluster overlap at an uncorrected threshold and comparison of effect sizes are shown in Figures S2 and S3 in Supplement 1.
LLM interpretation
This figure consists of five axial brain slices (labeled X = -1, 25, 30, 45, and 50) showing a statistical parametric map overlaid on a canonical structural brain image. Red clusters represent regions of reduced brain volume associated with alcohol consumption from the discovery dataset (DNS), while blue clusters represent the replication dataset (HCP). The color scales indicate T-values ranging from 0 to 5 for both the DNS and HCP groups.
Shared genetic predisposition between alcohol consumption and brain volume. In the Human Connectome Project (HCP) sample, (A) alcohol consumption scores (approximated Alcohol Use Disorders Identification Test consumption subscale scores [aAUDIT-C]) and gray matter volume of the right insula and right middle and superior frontal cortices were all observed to be heritable (aAUDIT-C: 51.79%, p < 2.2 Γ 10β16; insula: 68.83%, p < 2.2 Γ 10β16; frontal: 74.46%, p < 2.2 Γ 10β16) (Table S1 in Supplement 1). (B) Significant phenotypic correlations between aAUDIT-C scores and volumes of the right insula and middle and superior frontal gyri are attributable to shared genetic factors (insula: β0.2314, p = .0022; frontal: β0.2192, p = .0054) but not unique environmental factors (Table S1 in Supplement 1). Distribution of (C) right insula and (D) right middle and superior frontal volumes by alcohol exposure group. High = aAUDIT-C score > sample mean + 0.5 SD (i.e., > 4.67); Low = aAUDIT-C score < sample mean β 0.5 SD (i.e., < 1.54); Concordant = both siblings are in the same alcohol exposure group; Discordant = one sibling is in the high group, while the other is in the low group. Contrast comparisons found evidence for predispositional effects of brain volume on alcohol consumption in both cases (insula: graded liability: Ξ² = β0.0037, p = .049, predispositional: Ξ² = 0.0037, p = .0006; frontal: predispositional: Ξ² = 0.0019, p = .029) (Table S2 in Supplement 1).
LLM interpretation
This figure consists of two bar charts and two violin plots analyzing the genetic relationship between alcohol consumption (aAUDIT-C) and brain volume. Panel A shows the heritability of aAUDIT-C, frontal, and insula volumes, while Panel B displays the correlation of frontal and insula volumes with aAUDIT-C across phenotypic, genetic, and environmental factors. Panels C and D are violin plots showing frontal and insula volumes across four sibling groups (Concordant Low, Discordant Low, Discordant High, and Concordant High), with significant differences indicated by p-values (p < 0.05 and p < 0.001).
Frontal volume prospectively predicts alcohol use and initiation of consumption. (A) In the Duke Neurogenetics Study, participants with reduced volume of the right middle and superior frontal cortices reported elevated alcohol consumption before 20.85 years of age following the neuroimaging scan, and after accounting for baseline drinking (frontal Γ age interaction: Ξ² = 0.150, false discovery rateβcorrected p = .008) (Table S3 in Supplement 1). (B, C) In the Teen Alcohol Outcomes Study, participants with increased volume of the right middle and superior frontal cortices report initiation of alcohol consumption at an older age (midfrontal Γ age interaction: Ξ² = β 57.042, false discovery rateβcorrected p = .036; superior frontal Γ age interaction: Ξ² = β60.74, false discovery rateβcorrected p = .036) (Table S4 in Supplement 1). Analyses were conducted with continuous data; the partition into 3 equally sized groups according to volume was done for display purposes only. AUDIT-C, Alcohol Use Disorders Identification Test consumption subscale.
LLM interpretation
This figure consists of three panels (A, B, and C) showing the relationship between frontal brain volume and alcohol use. Panel A is a line plot showing AUDIT-C scores over age (18β24), where the "Low" relative frontal volume group (dark blue line) shows a downward trend compared to "Medium" and "High" groups. Panels B and C are logistic regression plots showing the probability of initiating alcohol consumption by age, indicating that participants with "High" relative middle-frontal and superior-frontal volumes (yellow lines) have a lower probability of initiation at younger ages compared to the "Low" and "Medium" groups.
Tissue-specific enrichment of alcohol-consumption genomic risk. Enrichment of alcohol-consumption genome-wide association study (UK Biobank, N = 112,117) (A) associations and (B, C) heritability, in gene sets defined by the relative expression of genes (A, B) across all tissues and (C) within the brain, in the Genotype-Tissue Expression project dataset (Supplemental Data). The x-axis and color scale represent the significance of the enrichment (negative logarithmic scale of the p value). Solid, dashed, and dotted lines represent Bonferroni-corrected, false discovery rateβcorrected, and nominally significant p values, respectively. BA, Brodmann area; EBV, Epstein-Barr virus.
LLM interpretation
This figure consists of three horizontal bar charts (A, B, and C) showing the tissue-specific enrichment of alcohol-consumption genomic risk. The x-axis for all plots represents the significance of enrichment as $-\log(p)$, with color scales transitioning from dark blue to yellow to indicate higher significance. Vertical lines mark thresholds for Bonferroni-corrected (solid), false discovery rate-corrected (dashed), and nominally significant (dotted) p-values, with various brain regions showing the highest enrichment across all three panels.
Transcriptome-wide association study of alcohol consumption predicting gene expression. Genetic risk for alcohol consumption according to the UK Biobank genome-wide association study (n = 112,117) is associated with differences in human postmortem gene expression (Genotype-Tissue Expression project; ns = 81β103), including frontal cortex Brodmann area (BA) 9 (Supplemental Data). Notably, associations in the liver (far-right panel) do not survive Bonferroni correction for multiple comparisons, though 4 are significant at a less-stringent false discovery rateβbased correction. The y-axis represents the significance of the association. Solid, dashed, and dotted lines represent Bonferroni-corrected, false discovery rateβcorrected, and nominally significant p values, respectively.
LLM interpretation
This is a Manhattan plot showing the association between genetic risk for alcohol consumption and gene expression across various human tissues. The y-axis represents the significance of the association as $-\log_{10}(p)$, with horizontal lines indicating Bonferroni-corrected (solid), false discovery rate-corrected (dashed), and nominally significant (dotted) thresholds. Several genes, including *RFC1* and *C16orf93*, exceed the Bonferroni threshold in brain regions, while no genes in the liver panel reach this level of significance.
No entities extracted from this document yet.
No uploaded files.
| Citation | PMID | DOI | Status |
|---|---|---|---|
| AgrawalA, LynskeyMT (2008): Are there genetic influences on addiction: Evidence from family, adoption and twin studies. Addiction 103:1069β1081.1849484310.1111/j.1360-0443.2008.02213.x | β | β | β |
| BaborTF, Higgins-BiddleJC, SaundersJB, MonteiroMG (2001): The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Care. Geneva, Switzerland: World Health Organization. | β | β | β |
| BarangerDAA, IfrahC, PratherAA, CareyCE, Corral-FrΓasNS, Drabant ConleyE, (2016): PER1 rs3027172 genotype interacts with early life stress to predict problematic alcohol use, but not reward-related ventral striatum activity. Front Psychol 7:464.2706592910.3389/fpsyg.2016.00464PMC4814479 | β | β | β |
| BatesD, MaechlerMartin, WalkerS (2019): Package βlme4β: Linear Mixed-Effects Models Using βEigenβ and S4. Version 1.1β19. Available at: https://cran.r-project.org/package=lme4. | β | β | β |
| BatesD, MΓ€chlerM, BolkerBM, WalkerSC, MaechlerMartin, WalkerSC (2015): Fitting linear mixed-effects models using lme4. J Stat Softw 67:1β48. | β | β | β |
| BucholzKK, CadoretR, CloningerCR, DinwiddieSH, HesselbrockVM, NurnbergerJI, (1994): A new, semi-structured psychiatric interview for use in genetic linkage studies: A report on the reliability of the SSAGA. J Stud Alcohol 55:149β158.818973510.15288/jsa.1994.55.149 | β | β | β |
| Bulik-SullivanB, LohPR, FinucaneHK, RipkeS, YangJ, PattersonN, (2015): LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47:291β295.2564263010.1038/ng.3211PMC4495769 | β | β | β |
| CacciolaEET, NevidJS (2014): Alcohol consumption in relation to residence status and ethnicity in college students. Psychol Addict Behav 28:1278β1283.2543715410.1037/a0038362 | β | β | β |
| CareyCE, AgrawalA, BucholzKK, HartzSM, LynskeyMT, NelsonEC, (2016): Associations between polygenic risk for psychiatric disorders and substance involvement. Front Genet 7:149.2757452710.3389/fgene.2016.00149PMC4983546 | β | β | β |
| ClarkeT-K, AdamsMJ, DaviesG, HowardDM, HallLS, PadmanabhanS, (2017): Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N = 112 117). Mol Psychiatry 22:1376β1384.2893769310.1038/mp.2017.153PMC5622124 | β | β | β |
| CollinsSE (2016): Associations between socioeconomic factors and alcohol outcomes. Alcohol Res 38:83β94.2715981510.35946/arcr.v38.1.11PMC4872618 | β | β | β |
| DagerAD, McKayDR, KentJW, CurranJE, KnowlesE, SprootenE, (2015): Shared genetic factors influence amygdala volumes and risk for alcoholism. Neuropsychopharmacology 40:412β420.2507928910.1038/npp.2014.187PMC4443955 | β | β | β |
| de LeeuwCA, MooijJM, HeskesT, PosthumaD (2015): MAGMA: Generalized gene-set analysis of GWAS data. PLoS Comput Biol 11: e1004219.10.1371/journal.pcbi.1004219PMC440165725885710 | β | β | β |
| DelkerE, BrownQ, HasinDS (2016): Alcohol consumption in demographic subpopulations: An epidemiologic overview. Alcohol Res 38:7β15.2715980710.35946/arcr.v38.1.02PMC4872616 | β | β | β |
| DickDM, AgrawalA (2008): The genetics of alcohol and other drug dependence. Alcohol Res Health 31:111β118.23584813PMC3860452 | β | β | β |
| DroutmanV, ReadSJ, BecharaA (2015): Revisiting the role of the insula in addiction. Trends Cogn Sci 19:414β420.2606658810.1016/j.tics.2015.05.005PMC4486609 | β | β | β |
| EnochM-A (2011): The role of early life stress as a predictor for alcohol and drug dependence. Psychopharmacology (Berl) 214:17β31.2059685710.1007/s00213-010-1916-6PMC3005022 | β | β | β |
| FelsonJ (2014): What can we learn from twin studies? A comprehensive evaluation of the equal environments assumption. Soc Sci Res 43:184β199.2426776110.1016/j.ssresearch.2013.10.004 | β | β | β |
| FinucaneHK, Bulik-SullivanB, GusevA, TrynkaG, ReshefY, LohPR, (2015): Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47:1228β1235.2641467810.1038/ng.3404PMC4626285 | β | β | β |
| FinucaneHK, ReshefY, AnttilaV, SlowikowskiK, GusevA, ByrnesA, (2018): Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat Genet 50:621β629.2963238010.1038/s41588-018-0081-4PMC5896795 | β | β | β |
| FromerM, RoussosP, SiebertsSK, JohnsonJS, KavanaghDH, PerumalTM, (2016): Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci 19:1442β1453.2766838910.1038/nn.4399PMC5083142 | β | β | β |
| GrantBF, GoldsteinRB, SahaTD, ChouSP, JungJ, ZhangH, (2015): Epidemiology of DSM-5 alcohol use disorder results from the National Epidemiologic Survey on Alcohol and Related Conditions III. JAMA Psychiatry 72:757β766.2603907010.1001/jamapsychiatry.2015.0584PMC5240584 | β | β | β |
| GrittnerU, KuntscheS, GmelG, BloomfieldK (2013): Alcohol consumption and social inequality at the individual and country levelsβResults from an international study. Eur J Public Health 23:332β339.2256271210.1093/eurpub/cks044PMC3610336 | β | β | β |
| GusevA, KoA, ShiH, BhatiaG, ChungW, PenninxBWJH, (2016): Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48:245β252.2685491710.1038/ng.3506PMC4767558 | β | β | β |
| HahslerM, PiekenbrockM, AryaS, MountD (2017): dbscan: Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms. Version 1.1β3. Available at: https://cran.r-project.org/package=dbscan. | β | β | β |
| HendersonKE, VaidyaJG, KramerJR, KupermanS, LangbehnDR, OβLearyDS (2018): Cortical thickness in adolescents with a family history of alcohol use disorder. Alcohol Clin Exp Res 42:89β99.2910511410.1111/acer.13543PMC7219278 | β | β | β |
| HolmesAJ, HollinsheadMO, RoffmanJL, SmollerJW, BucknerRL (2016): Individual differences in cognitive control circuit anatomy link sensation seeking, impulsivity, and substance use. J Neurosci 36:4038β4049.2705321010.1523/JNEUROSCI.3206-15.2016PMC4821913 | β | β | β |
| KapoorM, WangJ, FarrisSP, LiuY, McclintickJ, GuptaI, (2019): Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism. Transl Psychiatry 9:89.3076568810.1038/s41398-019-0384-yPMC6376002 | β | β | β |
| KellerMC (2014): Gene Γ environment interaction studies have not properly controlled for potential confounders: The problem and the (simple) solution. Biol Psychiatry 75:18β24.2413571110.1016/j.biopsych.2013.09.006PMC3859520 | β | β | β |
| KendlerKS, GardnerCO, HickmanM, HeronJ, MacleodJ, LewisG, DickDM (2014): Socioeconomic status and alcohol-related behaviors in mid- to late adolescence in the Avon Longitudinal Study of Parents and Children. J Stud Alcohol Drugs 75:541β545.2498825210.15288/jsad.2014.75.541PMC4108596 | β | β | β |
| KeyesKM, HatzenbuehlerML, GrantBF, HasinDS (2012): Stress and alcohol: Epidemiologic evidence. Alcohol Res 34:391β400.2358410510.35946/arcr.v34.4.03PMC3797525 | β | β | β |
| KochunovP, JahanshadN, MarcusD, WinklerA, SprootenE, NicholsTE, (2015): Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data. Neuroimage 111:300β301.2574791710.1016/j.neuroimage.2015.02.050PMC4387079 | β | β | β |
| KΓΌhnS, GallinatJ (2013): Gray matter correlates of posttraumatic stress disorder: A quantitative meta-analysis. Biol Psychiatry 73:70β74.2284076010.1016/j.biopsych.2012.06.029 | β | β | β |
| LaBudaMC, SvikisDS, PickensRW (1997): Twin closeness and co-twin risk for substance use disorders: Assessing the impact of the equal environment assumption. Psychiatry Res 70:155β164.921157710.1016/s0165-1781(97)03045-x | β | β | β |
| LangeEHH, NerlandS, JΓΈrgensenKNN, MΓΈrch-JohnsenL, NesvΓ₯gR, HartbergCBB, (2017): Alcohol use is associated with thinner cerebral cortex and larger ventricles in schizophrenia, bipolar disorder and healthy controls. Psychol Med 4:55β668.10.1017/S003329171600292027830632 | β | β | β |
| LiuJ, LewohlJM, HarrisRA, IyerVR, DoddPR, RandallPK, MayfieldRD (2006): Patterns of gene expression in the frontal cortex discriminate alcoholic from nonalcoholic individuals. Neuropsychopharmacology 31:1574β1582.1629232610.1038/sj.npp.1300947 | β | β | β |
| LucianaM, CollinsPF, MuetzelRL, LimKO (2013): Effects of alcohol use initiation on brain structure in typically developing adolescents. Am J Drug Alcohol Abuse 39:345β355.2420020410.3109/00952990.2013.837057PMC4076828 | β | β | β |
| MackeyS, AllgaierN, ChaaraniB, SpechlerP, OrrC, BunnJ, (2018): Mega-analysis of gray matter volume in substance dependence: general and substance-specific regional effects. Am J Psychiatry 17:19β128.10.1176/appi.ajp.2018.17040415PMC642782230336705 | β | β | β |
| MclaughlinKA, GreenJG, GruberMJ, SampsonNA, ZaslavskyAM, KesslerRC (2013): Childhood adversities and adult psychiatric disorders in the National Comorbidity Survey Replication II. 67:124β132.10.1001/archgenpsychiatry.2009.187PMC284735920124112 | β | β | β |
| MengY, HolmesJ, Hill-McmanusD, BrennanA, MeierPS (2014): Trend analysis and modelling of gender-specific age, period and birth cohort effects on alcohol abstention and consumption level for drinkers in Great Britain using the General Lifestyle Survey 1984β2009. Addiction 109:206β215.2394136310.1111/add.12330PMC4016750 | β | β | β |
| MolinaBSG, FloryK, HinshawSP, GreinerAR, ArnoldLE, SwansonJM, (2007): Delinquent behavior and emerging substance use in the MTA at 36 months: Prevalence, course, and treatment effects. J Am Acad Child Adolesc Psychiatry 46:1028β1040.1766748110.1097/chi.0b013e3180686d96 | β | β | β |
| MunafΓ²MR, Davey SmithG (2018): Robust research needs many lines of evidence. Nature 553:399β401.10.1038/d41586-018-01023-329368721 | β | β | β |
| NikolovaYS, KnodtAR, RadtkeSR, HaririAR (2016): Divergent responses of the amygdala and ventral striatum predict stress-related problem drinking in young adults: possible differential markers of affective and impulsive pathways of risk for alcohol use disorder. Mol Psychiatry 21:348β356.2612258410.1038/mp.2015.85PMC4696925 | β | β | β |
| PfefferbaumA, KwonD, BrumbackT, ThompsonWK, CumminsK, TapertSF, (2017): Altered brain developmental trajectories in adolescents after initiating drinking. Am J Psychiatry 175:370β380.2908445410.1176/appi.ajp.2017.17040469PMC6504929 | β | β | β |
| PinheiroJ, DebRoyS, BatesD, SarkarD, R Core Team (2017): nlme: Linear and Nonlinear Mixed Effects Models. Version 3.1β137. Available at: https://cran.r-project.org/package=nlme. | β | β | β |
| R Core Team (2014): R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing Version 3.5.1. Available at: http://www.r-project.org/. | β | β | β |
| RevelleW (2019): Package βpsychβ: Procedures for Psychological, Psychometric and Personality Research. Version 1.8.10. Available at: https://cran.r-project.org/package=psych. | β | β | β |
| RijsdijkFV, ShamPC (2002): Analytic approaches to twin data using structural equation models. Brief Bioinform 3:119β133.1213943210.1093/bib/3.2.119 | β | β | β |
| SaundersJB, AaslandOG, BaborTF, de la FuenteJR, GrantM (1993): Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons With Harmful Alcohol ConsumptionβII. Addiction 88:791β804.832997010.1111/j.1360-0443.1993.tb02093.x | β | β | β |
| SchumannG, LiuC, OβReillyP, GaoH, SongP, XuB, (2016): KLB is associated with alcohol drinking, and its gene product Ξ²-Klotho is necessary for FGF21 regulation of alcohol preference. Proc Natl Acad Sci U S A 113:14372β14377.2791179510.1073/pnas.1611243113PMC5167198 | β | β | β |
| SeoS, BeckA, MatthisC, GenauckA, BanaschewskiT, BokdeALW, (2018): Risk profiles for heavy drinking in adolescence: Differential effects of gender. Addict Biol 21:348β356.10.1111/adb.1263629847018 | β | β | β |
| SharmaVK, HillSY (2017): Differentiating the effects of familial risk for alcohol dependence and prenatal exposure to alcohol on offspring brain morphology. Alcohol Clin Exp Res 41:312β322.2808463110.1111/acer.13289PMC5272865 | β | β | β |
| ShnitkoTA, LiuZ, WangX, GrantKA, ChristopherD (2019): Chronic alcohol drinking slows brain development in adolescent and young adult nonhuman primates. eNeuro 6:1β11.10.1523/ENEURO.0044-19.2019PMC646451130993181 | β | β | β |
| SmollerJW, CraddockN, KendlerK, LeePH, NealeBM, NurnbergerJI, (2013): Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381:1371β1379.2345388510.1016/S0140-6736(12)62129-1PMC3714010 | β | β | β |
| SquegliaLM, GrayKM (2016): Alcohol and drug use and the developing brain. Curr Psychiatry Rep 18:46.2698468410.1007/s11920-016-0689-yPMC4883014 | β | β | β |
| SquegliaLM, TapertSF, SullivanEV, JacobusJ, MeloyMJ, RohlfingT, PfefferbaumA (2015): Brain development in heavy-drinking adolescents. Am J Psychiatry 172:531β542.2598266010.1176/appi.ajp.2015.14101249PMC4451385 | β | β | β |
| Substance Abuse and Mental Health Services Administration (2015): Results from the 2015 National Survey on Drug Use and Health: Detailed Tables. Available at: https://www.samhsa.gov/data/report/results-2015-national-survey-drug-use-and-health-detailed-tables. Accessed February 22, 2018. | β | β | β |
| Substance Abuse and Mental Health Services Administration (2018): Key Substance Use and Mental Health Indicators in the United States: Results from the 2017 National Survey on Drug Use and Health. Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration Available at: https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHFFR2017/NSDUHFFR2017.pdf. Accessed February 22, 2018. | β | β | β |
| SwartzJR, WilliamsonDE, HaririAR (2015): Developmental change in amygdala reactivity during adolescence: Effects of family history of depression and stressful life events. Am J Psychiatry 172:276β283.2552659910.1176/appi.ajp.2014.14020195PMC4452289 | β | β | β |
| TaffeMA, KotzebueRW, CreanRD, CrawfordEF, EdwardsS, MandyamCD (2010): Long-lasting reduction in hippocampal neurogenesis by alcohol consumption in adolescent nonhuman primates. Proc Natl Acad Sci U S A 107:11104β11109.10.1073/pnas.0912810107PMC289075520534463 | β | β | β |
| ThayerRE, YorkWilliamsS, KarolyHC, SabbineniA, EwingSF, BryanAD, HutchisonKE (2017): Structural neuroimaging correlates of alcohol and cannabis use in adolescents and adults. Addiction 112:2144β2154.2864656610.1111/add.13923PMC5673530 | β | β | β |
| The GTEx Consortium, WelterD, MacArthurJ, MoralesJ, BurdettT, HallP, (2015): The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science 348:648β660.2595400110.1126/science.1262110PMC4547484 | β | β | β |
| Tzourio-MazoyerN, LandeauB, PapathanassiouD, CrivelloF, EtardO, DelcroixN, (2002): Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273β289.1177199510.1006/nimg.2001.0978 | β | β | β |
| Van EssenDC, SmithSM, BarchDM, BehrensTEJ, YacoubE, UgurbilK (2013): The WU-Minn Human Connectome Project: An overview. Neuroimage 80:62β79.2368488010.1016/j.neuroimage.2013.05.041PMC3724347 | β | β | β |
| VolkowND, KoobGF, CroyleRT, BianchiDW, GordonJA, KoroshetzWJ, (2018): The conception of the ABCD study: From substance use to a broad NIH collaboration. Dev Cogn Neurosci 32:4β7.2905102710.1016/j.dcn.2017.10.002PMC5893417 | β | β | β |
| VulE, HarrisC, WinkielmanP, PashlerH (2009): Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition1. Perspect Psychol Sci 4:274β290.2615896410.1111/j.1745-6924.2009.01125.x | β | β | β |
| WatanabeK, StringerS, FreiO, UmiΔeviΔ MirkovM, de LeeuwC, PoldermanTJC, (2018): A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet 51:1339β1348.10.1038/s41588-019-0481-031427789 | β | β | β |
| WatanabeK, TaskesenE, Van BochovenA, PosthumaD (2017): Functional mapping and annotation of genetic associations with FUMA. Nat Commun 8:1826.2918405610.1038/s41467-017-01261-5PMC5705698 | β | β | β |
| WhelanR, WattsR, OrrCA, AlthoffRR, ArtigesE, BanaschewskiT, (2014): Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 512:185β189.2504304110.1038/nature13402PMC4486207 | β | β | β |
| WilsonS, MaloneSM, ThomasKM, IaconoWG (2015): Adolescent drinking and brain morphometry: A co-twin control analysis. Dev Cogn Neurosci 16:130β138.2627868210.1016/j.dcn.2015.07.005PMC4691358 | β | β | β |
| WindleM, GrayJC, MankitK, BartonAW, BrodyG, BeachSRH, (2018): Age sensitive associations of adolescent substance use with amygdalar, ventral striatum, and frontal volumes in young adulthood. Drug Alcohol Depend 186:94β101.2955867410.1016/j.drugalcdep.2018.02.007PMC5911233 | β | β | β |
| WinklerAM, RidgwayGR, DouaudG, NicholsTE, SmithSM (2016): Faster permutation inference in brain imaging. Neuroimage 141:502β516.2728832210.1016/j.neuroimage.2016.05.068PMC5035139 | β | β | β |
| WinklerAM, RidgwayGR, WebsterMA, SmithSM, NicholsTE (2014): Permutation inference for the general linear model. Neuroimage 92:381β397.2453083910.1016/j.neuroimage.2014.01.060PMC4010955 | β | β | β |
| WinklerAM, WebsterMA, VidaurreD, NicholsTE, SmithSM (2015): Multi-level block permutation. Neuroimage 123:253β268.2607420010.1016/j.neuroimage.2015.05.092PMC4644991 | β | β | β |
| World Health Organization (2014): Global status report on alcohol and health. Geneva, Switzerland: World Health Organization Press. | β | β | β |
| YangX, TianF, ZhangH, ZengJ, ChenT, WangS, (2016): Cortical and subcortical gray matter shrinkage in alcohol-use disorders: A voxel-based meta-analysis. Neurosci Biobehav Rev 66:92β103.2710821610.1016/j.neubiorev.2016.03.034 | β | β | β |
| Young-WolffKC, EnochMA, PrescottCA (2011): The influence of gene-environment interactions on alcohol consumption and alcohol use disorders: A comprehensive review. Clin Psychol Rev 31:800β816.2153047610.1016/j.cpr.2011.03.005PMC3192029 | β | β | β |
| ZiyatdinovA, BrunelH, Martinez-PerezA, BuilA, PereraA, SoriaJM (2016): Solarius: An R interface to SOLAR for variance component analysis in pedigrees. Bioinformatics 32:1901β1902.2715368410.1093/bioinformatics/btw080 | β | β | β |
| ZouX, DurazzoTC, MeyerhoffDJ (2018): Regional brain volume changes in alcohol-dependent individuals during short-term and long-term abstinence. Alcohol Clin Exp Res 42:1062β1072.2967287610.1111/acer.13757PMC5984169 | β | β | β |
In this knowledge base
| Title | Year | PMID |
|---|---|---|
| Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases. | 2021 | 34417470 |
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Lower cortical thickness and accelerated brain aging in individuals engaging in at-risk alcohol use. | Hermesdorf M et al. | β | 2026 | β |
| Altered effective connectivity of emotion perception and regulation networks during an emotional face perception task in adults with alcohol use disorder. | Hammond CJ et al. | β | 2025 | β |
| Genetic, psychological, and environmental factors are uniquely associated with onset of alcohol use in the adolescent brain cognitive development (ABCD) study. | Choi M et al. | β | 2025 | β |
| Grey Matter Volume in Substance Use: A Preregistered, Dimensional Approach to Disentangle Substance Use and Disorder Severity. | Schwarz K et al. | β | 2025 | β |
| Integrating multilevel, multidomain and multimodal neuroimaging factors to predict early alcohol exposure trajectories using explainable AI. | Ferariu A et al. | β | 2025 | β |
| Mechanisms Underlying Hazardous Alcohol Use After Mild Traumatic Brain Injury. | Patarino M et al. | β | 2025 | β |
| Morphologic characteristics of distal intracranial arteries in relation to structural changes in the brain after chronic alcohol consumption. | Wang C et al. | β | 2025 | β |
| Neural correlates associated with a family history of alcohol use disorder: A narrative review of recent findings. | Cservenka A et al. | β | 2025 | β |
| Sex-specific differences in brain activity dynamics of youth with a family history of substance use disorder. | Schilling L et al. | β | 2025 | β |
| The effects of alcohol use severity and polygenic risk on gray matter volumes in young adults | Chen Y et al. | β | 2025 | β |
| The effects of alcohol use severity and polygenic risk on gray matter volumes in young adults. | Chen Y et al. | β | 2025 | β |
| The functional connectivity between the dorsolateral prefrontal cortex and the medial prefrontal cortex underlying the association between self-control and delay discounting. | Gong W et al. | β | 2025 | β |
| Alcohol and brain structure across the lifespan: A systematic review of large-scale neuroimaging studies. | Karoly HC et al. | β | 2024 | β |
| Characteristics of women concordant and discordant for urine drug screens for cannabis exposure and self-reported cannabis use during pregnancy. | Bogdan R et al. | β | 2024 | β |
| Investigating the Relationship Between Smoking Behavior and Global Brain Volume. | Chang Y et al. | β | 2024 | β |
| Let's focus on the insula in addiction: A refined anatomical exploration of insula in severe alcohol and cocaine use disorders. | Billaux P et al. | β | 2024 | β |
| Neural responses to reward, threat, and emotion regulation and transition to hazardous alcohol use. | Kirk-Provencher KT et al. | β | 2024 | β |
| Neuroanatomical Variability and Substance Use Initiation in Late Childhood and Early Adolescence. | Miller AP et al. | β | 2024 | β |
| Neuron enriched extracellular vesicles' MicroRNA expression profiles as a marker of early life alcohol consumption. | Yakovlev V et al. | β | 2024 | β |
| Adolescent Neurodevelopment Within the Context of Impulsivity and Substance Use. | Green R et al. | β | 2023 | β |
| Alcohol use and grey matter structure: Disentangling predispositional and causal contributions in human studies. | Baranger DAA et al. | β | 2023 | β |
| Insular volumetry in severe alcohol use disorder and Korsakoff's syndrome through an anatomical parcellation: Let us go back to basics. | Billaux P et al. | β | 2023 | β |
| Neuroanatomical predictors of problematic alcohol consumption in adolescents: a systematic review of longitudinal studies. | Honarvar F et al. | β | 2023 | β |
| Structured tracking of alcohol reinforcement (STAR) for basic and translational alcohol research. | Brown AR et al. | β | 2023 | β |
| The Genetically Informed Neurobiology of Addiction (GINA) model. | Bogdan R et al. | β | 2023 | β |
| Association Between Brain Structure and Alcohol Use Behaviors in Adults: A Mendelian Randomization and Multiomics Study. | Mavromatis LA et al. | β | 2022 | β |
| Brain morphology predictors of alcohol, tobacco, and cannabis use in adolescence: A systematic review. | Boer OD et al. | β | 2022 | β |
| Mesial Prefrontal Cortex and Alcohol Misuse: Dissociating Cross-sectional and Longitudinal Relationships in UK Biobank. | Zhao Y et al. | β | 2022 | β |
| Neuroplasticity, the Prefrontal Cortex, and Psychopathology-Related Deviations in Cognitive Control. | Luciana M et al. | β | 2022 | β |
| Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. | Madan CR | β | 2022 | β |
| Alcohol use and interoception - A narrative review. | WiΕniewski P et al. | β | 2021 | β |
| Associations Between Prenatal Cannabis Exposure and Childhood Outcomes: Results From the ABCD Study. | Paul SE et al. | β | 2021 | β |
| Bidirectional causality between addiction and cognitive deficits. | Melugin PR et al. | β | 2021 | β |
| Brain anatomical covariation patterns linked to binge drinking and age at first full drink. | Zhao Y et al. | β | 2021 | β |
| Brain structure and problematic alcohol use: a test of plausible causation using latent causal variable analysis. | Hatoum AS et al. | β | 2021 | β |
| Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases. | Kapoor M et al. | β | 2021 | β |
| Polygenic risk scores for alcohol involvement relate to brain structure in substance-naΓ―ve children: Results from the ABCD study. | Hatoum AS et al. | β | 2021 | β |
| Promising vulnerability markers of substance use and misuse: A review of human neurobehavioral studies. | Lees B et al. | β | 2021 | β |
| 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. | β | 2021 | β |
| Alcohol Induced Brain and Liver Damage: Advantages of a Porcine Alcohol Use Disorder Model. | Shin SK et al. | β | 2020 | β |
| Borderline Personality Traits Are Not Correlated With Brain Structure in Two Large Samples. | Baranger DAA et al. | β | 2020 | β |
| Decreases in retinal nerve fiber layer thickness correlates with cumulative alcohol intake. | Orum MH et al. | β | 2020 | β |