Brain structural covariance network differences in adults with alcohol dependence and heavy-drinking adolescents

The ENIGMA-Addiction and IMAGEN consortiums

doi:10.1111/add.15772

Brain structural covariance network differences in adults with alcohol dependence and heavy-drinking adolescents

The ENIGMA-Addiction and IMAGEN consortiums

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Background and aims: Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol. Design: Cross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics. Setting and participants: A total of 745 adults with AD and 979 non-dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)–Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe. Measurements: Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency). Findings: The younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area-under-the-curve (AUC) difference = −0.0142, 95% confidence interval (CI) = −0.1333, 0.0092; P-value = 0.017], clustering coefficient (AUC difference = −0.0164, 95% CI = −0.1456, 0.0043; P-value = 0.008) and local efficiency (AUC difference = −0.0141, 95% CI = −0.0097, 0.0034; P-value = 0.010), as well as lower average shortest path length (AUC difference = −0.0405, 95% CI = −0.0392, 0.0096; P-value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = −0.0011, 0.0043; P-value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = −0.0131, 95% CI = −0.1304, 0.0033; P-value = 0.024), lower average shortest path length (AUC difference = −0.0362, 95% CI = −0.0334, 0.0118; P-value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = −0.0011, 0.0038; P-value = 0.048). Conclusions: Cross-sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy-drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking.

Original language	English
Journal	Addiction
DOIs	https://doi.org/10.1111/add.15772
Publication status	Accepted/In press - 2021

Keywords

Alcohol
cortical thickness
graph theory
neurodevelopment
structural covariance networks

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1111/add.15772

Cite this

@article{ec612fd5aa914da6935398892dd9900a,

title = "Brain structural covariance network differences in adults with alcohol dependence and heavy-drinking adolescents",

abstract = "Background and aims: Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol. Design: Cross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics. Setting and participants: A total of 745 adults with AD and 979 non-dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)–Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe. Measurements: Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency). Findings: The younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area-under-the-curve (AUC) difference = −0.0142, 95% confidence interval (CI) = −0.1333, 0.0092; P-value = 0.017], clustering coefficient (AUC difference = −0.0164, 95% CI = −0.1456, 0.0043; P-value = 0.008) and local efficiency (AUC difference = −0.0141, 95% CI = −0.0097, 0.0034; P-value = 0.010), as well as lower average shortest path length (AUC difference = −0.0405, 95% CI = −0.0392, 0.0096; P-value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = −0.0011, 0.0043; P-value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = −0.0131, 95% CI = −0.1304, 0.0033; P-value = 0.024), lower average shortest path length (AUC difference = −0.0362, 95% CI = −0.0334, 0.0118; P-value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = −0.0011, 0.0038; P-value = 0.048). Conclusions: Cross-sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy-drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking.",

keywords = "Alcohol, cortical thickness, graph theory, neurodevelopment, structural covariance networks",

author = "{The ENIGMA-Addiction and IMAGEN consortiums} and Jonatan Ottino-Gonz{\'a}lez and Hugh Garavan and Albaugh, {Matthew D.} and Zhipeng Cao and Cupertino, {Renata B.} and Nathan Schwab and Spechler, {Philip A.} and Nicholas Allen and Eric Artiges and Tobias Banaschewski and Bokde, {Arun L.W.} and {Burke Quinlan}, Erin and R{\"u}diger Br{\"u}hl and Catherine Orr and Janna Cousijn and Sylvane Desrivi{\`e}res and Herta Flor and Foxe, {John J.} and Fr{\"o}hner, {Juliane H.} and Goudriaan, {Anna E.} and Penny Gowland and Antoine Grigis and Andreas Heinz and Robert Hester and Kent Hutchison and Li, {Chiang Shan R.} and London, {Edythe D.} and Valentina Lorenzetti and Maartje Luijten and Frauke Nees and Rocio Mart{\'i}n-Santos and Martinot, {Jean Luc} and Sabina Millenet and Reza Momenan and {Paill{\`e}re Martinot}, {Marie Laure} and {Papadopoulos Orfanos}, Dimitri and Paulus, {Martin P.} and Luise Poustka and Lianne Schmaal and Gunter Schumann and Rajita Sinha and Smolka, {Michael N.} and Nadia Solowij and Stein, {Dan J.} and Stein, {Elliot A.} and Anne Uhlmann and {van Holst}, {Ruth J.} and Veltman, {Dick J.} and Henrik Walter and Patricia Conrod",

note = "Funding Information: Supported by NIDA grants 1R21DA038381 and R01DA047119 to Dr. Garavan and by NIH grant U54EB020403 with funds provided for the trans‐NIH Big Data to Knowledge (BD2K) initiative. M.D.A. is supported by K08MH12165401A1 and a NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation. R.W.W. received support for the Neuro‐ADAPT study from VICI grant 4530801 from the Netherlands Organization for Scientific Research (NWO); L.S. and D.J.V. received funding from the Netherlands Organization for Health Research and Development (ZON‐Mw) grant 31160003 from NWO. Z.S. and D.J.V. received funding from ZON‐Mw grant 31160004 from NWO. A.E.G. and R.J.v.H. received funding from ZON‐Mw grant 91676084 from NWO. M.L. and D.J.V. received funding for the DABIS study from VIDI grant 01608322 from NWO awarded to and A.E.G. received funding from ZON‐Mw grant 31180002, awarded to A.E.G. H.G. and J.J.F. received funds from NIDA grant R01DA014100. C.‐S.R.L. received funding from NIDA grants R01AA021449, R01DA023248 and K25DA040032. E.D.L. was supported by NIDA grant R01DA020726, the Thomas P. and Katherine K. Pike Chair in Addiction Studies, the Endowment from the Marjorie Greene Family Trust and UCLA contract 20063287 with Philip Morris USA. R.M. received funding from the Intramural Research Program of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institutes of Health, ZAIAA000123 funding. M.P.P. received funding from NIMH grant R01DA018307. E.A.S. was supported by the Intramural Research Program of NIDA and NIH. R.S. received funds from NIDA (PL301DA024859‐01), the NIH National Center for Research Resources (UL1RR2492501) and NIAAA (R01AA013892). N.S. received funding from the Clive and Vera Ramaciotti Foundation for Biomedical Research National and Health and Medical Research Council Project grant 459111 and was supported by Australian Research Council Future Fellowship FT110100752. M.Y. was supported by National Health and Medical Research Council Fellowship 1117188 and the David Winston Turner Endowment Fund. The IMAGEN study received support from the following sources: the European Union‐funded FP6 Integrated Project IMAGEN (Reinforcement‐related behaviour in normal brain function and psychopathology) (LSHM‐CT‐2007‐037286), the Horizon 2020 funded ERC Advanced Grant {\textquoteleft}STRATIFY{\textquoteright} (Brain network based stratification of reinforcement‐related disorders) (695313), Human Brain Project (HBP SGA 2, 785907 and HBP SGA 3, 945539), the Medical Research Council Grant {\textquoteleft}c‐VEDA{\textquoteright} (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the NIH (R01DA049238, a decentralized macro and micro gene‐by‐environment interaction analysis of substance use behavior and its brain biomarkers), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministeriumf{\"u}r Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC‐Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7‐2, SFB 940, TRR 265, NE 1383/14‐1), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1) and the NIH‐funded ENIGMA (grants 5U54EB02040305 and 1R56AG05885401). Further support was provided by grants from: the ANR (ANR‐12‐SAMA‐0004, AAPG2019‐GeBra), the Eranet Neuron (AF12‐NEUR0008‐01‐WM2NA and ANR‐18‐NEUR00002‐01‐ADORe), the Fondation de France (00081242), the Fondation pour la Recherche M{\'e}dicale (DPA20140629802), the Mission Interminist{\'e}rielle de Lutte‐contre‐les‐Drogues‐et‐les‐Conduites‐Addictives (MILDECA), the Assistance‐Publique‐H{\^o}pitaux‐de‐Paris and INSERM (interface grant), Paris Sud University IDEX2012, the Fondation de l'Avenir (grant AP‐RM‐17‐013), the F{\'e}d{\'e}ration pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; R01MH085772‐01A1), by NIH Consortium grant U54EB020403, supported by a cross‐NIH alliance that funds Big Data to Knowledge Centres of Excellence and by ImagenPathways {\textquoteleft}Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways{\textquoteright} is a collaborative project supported by the European Research Area Network on Illicit Drugs (ERANID). this paper is based on independent research commissioned and funded in England by the National Institute for Health Research (NIHR) Policy Research Programme (Project ref. PR‐ST‐0416‐10001). The views expressed in this article are those of the authors and not necessarily those of the national funding agencies or ERANID. Funding Information: Supported by NIDA grants 1R21DA038381 and R01DA047119 to Dr. Garavan and by NIH grant U54EB020403 with funds provided for the trans-NIH Big Data to Knowledge (BD2K) initiative. M.D.A. is supported by K08MH12165401A1 and a NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation. R.W.W. received support for the Neuro-ADAPT study from VICI grant 4530801 from the Netherlands Organization for Scientific Research (NWO); L.S. and D.J.V. received funding from the Netherlands Organization for Health Research and Development (ZON-Mw) grant 31160003 from NWO. Z.S. and D.J.V. received funding from ZON-Mw grant 31160004 from NWO. A.E.G. and R.J.v.H. received funding from ZON-Mw grant 91676084 from NWO. M.L. and D.J.V. received funding for the DABIS study from VIDI grant 01608322 from NWO awarded to and A.E.G. received funding from ZON-Mw grant 31180002, awarded to A.E.G. H.G. and J.J.F. received funds from NIDA grant R01DA014100. C.-S.R.L. received funding from NIDA grants R01AA021449, R01DA023248 and K25DA040032. E.D.L. was supported by NIDA grant R01DA020726, the Thomas P. and Katherine K. Pike Chair in Addiction Studies, the Endowment from the Marjorie Greene Family Trust and UCLA contract 20063287 with Philip Morris USA. R.M. received funding from the Intramural Research Program of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institutes of Health, ZAIAA000123 funding. M.P.P. received funding from NIMH grant R01DA018307. E.A.S. was supported by the Intramural Research Program of NIDA and NIH. R.S. received funds from NIDA (PL301DA024859-01), the NIH National Center for Research Resources (UL1RR2492501) and NIAAA (R01AA013892). N.S. received funding from the Clive and Vera Ramaciotti Foundation for Biomedical Research National and Health and Medical Research Council Project grant 459111 and was supported by Australian Research Council Future Fellowship FT110100752. M.Y. was supported by National Health and Medical Research Council Fellowship 1117188 and the David Winston Turner Endowment Fund. The IMAGEN study received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT-2007-037286), the Horizon 2020 funded ERC Advanced Grant {\textquoteleft}STRATIFY{\textquoteright} (Brain network based stratification of reinforcement-related disorders) (695313), Human Brain Project (HBP SGA 2, 785907 and HBP SGA 3, 945539), the Medical Research Council Grant {\textquoteleft}c-VEDA{\textquoteright} (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the NIH (R01DA049238, a decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministeriumf{\"u}r Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC-Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1) and the NIH-funded ENIGMA (grants 5U54EB02040305 and 1R56AG05885401). Further support was provided by grants from: the ANR (ANR-12-SAMA-0004, AAPG2019-GeBra), the Eranet Neuron (AF12-NEUR0008-01-WM2NA and ANR-18-NEUR00002-01-ADORe), the Fondation de France (00081242), the Fondation pour la Recherche M{\'e}dicale (DPA20140629802), the Mission Interminist{\'e}rielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-H{\^o}pitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX2012, the Fondation de l'Avenir (grant AP-RM-17-013), the F{\'e}d{\'e}ration pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; R01MH085772-01A1), by NIH Consortium grant U54EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence and by ImagenPathways {\textquoteleft}Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways{\textquoteright} is a collaborative project supported by the European Research Area Network on Illicit Drugs (ERANID). this paper is based on independent research commissioned and funded in England by the National Institute for Health Research (NIHR) Policy Research Programme (Project ref. PR-ST-0416-10001). The views expressed in this article are those of the authors and not necessarily those of the national funding agencies or ERANID. Publisher Copyright: {\textcopyright} 2021 Society for the Study of Addiction",

year = "2021",

doi = "10.1111/add.15772",

language = "English",

journal = "Addiction",

issn = "0965-2140",

publisher = "Wiley-Blackwell Publishing Ltd",

}

TY - JOUR

T1 - Brain structural covariance network differences in adults with alcohol dependence and heavy-drinking adolescents

AU - The ENIGMA-Addiction and IMAGEN consortiums

AU - Ottino-González, Jonatan

AU - Garavan, Hugh

AU - Albaugh, Matthew D.

AU - Cao, Zhipeng

AU - Cupertino, Renata B.

AU - Schwab, Nathan

AU - Spechler, Philip A.

AU - Allen, Nicholas

AU - Artiges, Eric

AU - Banaschewski, Tobias

AU - Bokde, Arun L.W.

AU - Burke Quinlan, Erin

AU - Brühl, Rüdiger

AU - Orr, Catherine

AU - Cousijn, Janna

AU - Desrivières, Sylvane

AU - Flor, Herta

AU - Foxe, John J.

AU - Fröhner, Juliane H.

AU - Goudriaan, Anna E.

AU - Gowland, Penny

AU - Grigis, Antoine

AU - Heinz, Andreas

AU - Hester, Robert

AU - Hutchison, Kent

AU - Li, Chiang Shan R.

AU - London, Edythe D.

AU - Lorenzetti, Valentina

AU - Luijten, Maartje

AU - Nees, Frauke

AU - Martín-Santos, Rocio

AU - Martinot, Jean Luc

AU - Millenet, Sabina

AU - Momenan, Reza

AU - Paillère Martinot, Marie Laure

AU - Papadopoulos Orfanos, Dimitri

AU - Paulus, Martin P.

AU - Poustka, Luise

AU - Schmaal, Lianne

AU - Schumann, Gunter

AU - Sinha, Rajita

AU - Smolka, Michael N.

AU - Solowij, Nadia

AU - Stein, Dan J.

AU - Stein, Elliot A.

AU - Uhlmann, Anne

AU - van Holst, Ruth J.

AU - Veltman, Dick J.

AU - Walter, Henrik

AU - Conrod, Patricia

N1 - Funding Information: Supported by NIDA grants 1R21DA038381 and R01DA047119 to Dr. Garavan and by NIH grant U54EB020403 with funds provided for the trans‐NIH Big Data to Knowledge (BD2K) initiative. M.D.A. is supported by K08MH12165401A1 and a NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation. R.W.W. received support for the Neuro‐ADAPT study from VICI grant 4530801 from the Netherlands Organization for Scientific Research (NWO); L.S. and D.J.V. received funding from the Netherlands Organization for Health Research and Development (ZON‐Mw) grant 31160003 from NWO. Z.S. and D.J.V. received funding from ZON‐Mw grant 31160004 from NWO. A.E.G. and R.J.v.H. received funding from ZON‐Mw grant 91676084 from NWO. M.L. and D.J.V. received funding for the DABIS study from VIDI grant 01608322 from NWO awarded to and A.E.G. received funding from ZON‐Mw grant 31180002, awarded to A.E.G. H.G. and J.J.F. received funds from NIDA grant R01DA014100. C.‐S.R.L. received funding from NIDA grants R01AA021449, R01DA023248 and K25DA040032. E.D.L. was supported by NIDA grant R01DA020726, the Thomas P. and Katherine K. Pike Chair in Addiction Studies, the Endowment from the Marjorie Greene Family Trust and UCLA contract 20063287 with Philip Morris USA. R.M. received funding from the Intramural Research Program of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institutes of Health, ZAIAA000123 funding. M.P.P. received funding from NIMH grant R01DA018307. E.A.S. was supported by the Intramural Research Program of NIDA and NIH. R.S. received funds from NIDA (PL301DA024859‐01), the NIH National Center for Research Resources (UL1RR2492501) and NIAAA (R01AA013892). N.S. received funding from the Clive and Vera Ramaciotti Foundation for Biomedical Research National and Health and Medical Research Council Project grant 459111 and was supported by Australian Research Council Future Fellowship FT110100752. M.Y. was supported by National Health and Medical Research Council Fellowship 1117188 and the David Winston Turner Endowment Fund. The IMAGEN study received support from the following sources: the European Union‐funded FP6 Integrated Project IMAGEN (Reinforcement‐related behaviour in normal brain function and psychopathology) (LSHM‐CT‐2007‐037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement‐related disorders) (695313), Human Brain Project (HBP SGA 2, 785907 and HBP SGA 3, 945539), the Medical Research Council Grant ‘c‐VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the NIH (R01DA049238, a decentralized macro and micro gene‐by‐environment interaction analysis of substance use behavior and its brain biomarkers), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC‐Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7‐2, SFB 940, TRR 265, NE 1383/14‐1), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1) and the NIH‐funded ENIGMA (grants 5U54EB02040305 and 1R56AG05885401). Further support was provided by grants from: the ANR (ANR‐12‐SAMA‐0004, AAPG2019‐GeBra), the Eranet Neuron (AF12‐NEUR0008‐01‐WM2NA and ANR‐18‐NEUR00002‐01‐ADORe), the Fondation de France (00081242), the Fondation pour la Recherche Médicale (DPA20140629802), the Mission Interministérielle de Lutte‐contre‐les‐Drogues‐et‐les‐Conduites‐Addictives (MILDECA), the Assistance‐Publique‐Hôpitaux‐de‐Paris and INSERM (interface grant), Paris Sud University IDEX2012, the Fondation de l'Avenir (grant AP‐RM‐17‐013), the Fédération pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; R01MH085772‐01A1), by NIH Consortium grant U54EB020403, supported by a cross‐NIH alliance that funds Big Data to Knowledge Centres of Excellence and by ImagenPathways ‘Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways’ is a collaborative project supported by the European Research Area Network on Illicit Drugs (ERANID). this paper is based on independent research commissioned and funded in England by the National Institute for Health Research (NIHR) Policy Research Programme (Project ref. PR‐ST‐0416‐10001). The views expressed in this article are those of the authors and not necessarily those of the national funding agencies or ERANID. Funding Information: Supported by NIDA grants 1R21DA038381 and R01DA047119 to Dr. Garavan and by NIH grant U54EB020403 with funds provided for the trans-NIH Big Data to Knowledge (BD2K) initiative. M.D.A. is supported by K08MH12165401A1 and a NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation. R.W.W. received support for the Neuro-ADAPT study from VICI grant 4530801 from the Netherlands Organization for Scientific Research (NWO); L.S. and D.J.V. received funding from the Netherlands Organization for Health Research and Development (ZON-Mw) grant 31160003 from NWO. Z.S. and D.J.V. received funding from ZON-Mw grant 31160004 from NWO. A.E.G. and R.J.v.H. received funding from ZON-Mw grant 91676084 from NWO. M.L. and D.J.V. received funding for the DABIS study from VIDI grant 01608322 from NWO awarded to and A.E.G. received funding from ZON-Mw grant 31180002, awarded to A.E.G. H.G. and J.J.F. received funds from NIDA grant R01DA014100. C.-S.R.L. received funding from NIDA grants R01AA021449, R01DA023248 and K25DA040032. E.D.L. was supported by NIDA grant R01DA020726, the Thomas P. and Katherine K. Pike Chair in Addiction Studies, the Endowment from the Marjorie Greene Family Trust and UCLA contract 20063287 with Philip Morris USA. R.M. received funding from the Intramural Research Program of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institutes of Health, ZAIAA000123 funding. M.P.P. received funding from NIMH grant R01DA018307. E.A.S. was supported by the Intramural Research Program of NIDA and NIH. R.S. received funds from NIDA (PL301DA024859-01), the NIH National Center for Research Resources (UL1RR2492501) and NIAAA (R01AA013892). N.S. received funding from the Clive and Vera Ramaciotti Foundation for Biomedical Research National and Health and Medical Research Council Project grant 459111 and was supported by Australian Research Council Future Fellowship FT110100752. M.Y. was supported by National Health and Medical Research Council Fellowship 1117188 and the David Winston Turner Endowment Fund. The IMAGEN study received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT-2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313), Human Brain Project (HBP SGA 2, 785907 and HBP SGA 3, 945539), the Medical Research Council Grant ‘c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the NIH (R01DA049238, a decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC-Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1) and the NIH-funded ENIGMA (grants 5U54EB02040305 and 1R56AG05885401). Further support was provided by grants from: the ANR (ANR-12-SAMA-0004, AAPG2019-GeBra), the Eranet Neuron (AF12-NEUR0008-01-WM2NA and ANR-18-NEUR00002-01-ADORe), the Fondation de France (00081242), the Fondation pour la Recherche Médicale (DPA20140629802), the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX2012, the Fondation de l'Avenir (grant AP-RM-17-013), the Fédération pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; R01MH085772-01A1), by NIH Consortium grant U54EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence and by ImagenPathways ‘Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways’ is a collaborative project supported by the European Research Area Network on Illicit Drugs (ERANID). this paper is based on independent research commissioned and funded in England by the National Institute for Health Research (NIHR) Policy Research Programme (Project ref. PR-ST-0416-10001). The views expressed in this article are those of the authors and not necessarily those of the national funding agencies or ERANID. Publisher Copyright: © 2021 Society for the Study of Addiction

PY - 2021

Y1 - 2021

N2 - Background and aims: Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol. Design: Cross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics. Setting and participants: A total of 745 adults with AD and 979 non-dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)–Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe. Measurements: Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency). Findings: The younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area-under-the-curve (AUC) difference = −0.0142, 95% confidence interval (CI) = −0.1333, 0.0092; P-value = 0.017], clustering coefficient (AUC difference = −0.0164, 95% CI = −0.1456, 0.0043; P-value = 0.008) and local efficiency (AUC difference = −0.0141, 95% CI = −0.0097, 0.0034; P-value = 0.010), as well as lower average shortest path length (AUC difference = −0.0405, 95% CI = −0.0392, 0.0096; P-value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = −0.0011, 0.0043; P-value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = −0.0131, 95% CI = −0.1304, 0.0033; P-value = 0.024), lower average shortest path length (AUC difference = −0.0362, 95% CI = −0.0334, 0.0118; P-value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = −0.0011, 0.0038; P-value = 0.048). Conclusions: Cross-sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy-drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking.

AB - Background and aims: Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol. Design: Cross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics. Setting and participants: A total of 745 adults with AD and 979 non-dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)–Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe. Measurements: Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency). Findings: The younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area-under-the-curve (AUC) difference = −0.0142, 95% confidence interval (CI) = −0.1333, 0.0092; P-value = 0.017], clustering coefficient (AUC difference = −0.0164, 95% CI = −0.1456, 0.0043; P-value = 0.008) and local efficiency (AUC difference = −0.0141, 95% CI = −0.0097, 0.0034; P-value = 0.010), as well as lower average shortest path length (AUC difference = −0.0405, 95% CI = −0.0392, 0.0096; P-value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = −0.0011, 0.0043; P-value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = −0.0131, 95% CI = −0.1304, 0.0033; P-value = 0.024), lower average shortest path length (AUC difference = −0.0362, 95% CI = −0.0334, 0.0118; P-value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = −0.0011, 0.0038; P-value = 0.048). Conclusions: Cross-sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy-drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking.

KW - Alcohol

KW - cortical thickness

KW - graph theory

KW - neurodevelopment

KW - structural covariance networks

UR - http://www.scopus.com/inward/record.url?scp=85128161016&partnerID=8YFLogxK

U2 - 10.1111/add.15772

DO - 10.1111/add.15772

M3 - Article

C2 - 34907616

AN - SCOPUS:85128161016

SN - 0965-2140

JO - Addiction

JF - Addiction

ER -

Brain structural covariance network differences in adults with alcohol dependence and heavy-drinking adolescents

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