Hostname: page-component-7479d7b7d-qs9v7 Total loading time: 0 Render date: 2024-07-13T01:09:59.401Z Has data issue: false hasContentIssue false

Linking genes, circuits, and behavior: network connectivity as a novel endophenotype of externalizing

Published online by Cambridge University Press:  12 September 2018

Naomi Sadeh*
Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, Boston, MA, USA
Jeffrey M. Spielberg
Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
Mark W. Logue
National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, Boston, MA, USA Department of Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA
Jasmeet P. Hayes
National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
Erika J. Wolf
National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
Regina E. McGlinchey
Translational Research Center for TBI and Stress Disorders and Geriatric Research, Educational and Clinical Center, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
William P. Milberg
Translational Research Center for TBI and Stress Disorders and Geriatric Research, Educational and Clinical Center, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
Steven A. Schichman
Pharmacogenomics Analysis Laboratory, Research Service, Central Arkansas Veterans Healthcare System, Little Rock, AR, USA
Annjanette Stone
Pharmacogenomics Analysis Laboratory, Research Service, Central Arkansas Veterans Healthcare System, Little Rock, AR, USA
Mark W. Miller
National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
Author for correspondence: Naomi Sadeh, E-mail:



Externalizing disorders are known to be partly heritable, but the biological pathways linking genetic risk to the manifestation of these costly behaviors remain under investigation. This study sought to identify neural phenotypes associated with genomic vulnerability for externalizing disorders.


One-hundred fifty-five White, non-Hispanic veterans were genotyped using a genome-wide array and underwent resting-state functional magnetic resonance imaging. Genetic susceptibility was assessed using an independently developed polygenic score (PS) for externalizing, and functional neural networks were identified using graph theory based network analysis. Tasks of inhibitory control and psychiatric diagnosis (alcohol/substance use disorders) were used to measure externalizing phenotypes.


A polygenic externalizing disorder score (PS) predicted connectivity in a brain circuit (10 nodes, nine links) centered on left amygdala that included several cortical [bilateral inferior frontal gyrus (IFG) pars triangularis, left rostral anterior cingulate cortex (rACC)] and subcortical (bilateral amygdala, hippocampus, and striatum) regions. Directional analyses revealed that bilateral amygdala influenced left prefrontal cortex (IFG) in participants scoring higher on the externalizing PS, whereas the opposite direction of influence was observed for those scoring lower on the PS. Polygenic variation was also associated with higher Participation Coefficient for bilateral amygdala and left rACC, suggesting that genes related to externalizing modulated the extent to which these nodes functioned as communication hubs.


Findings suggest that externalizing polygenic risk is associated with disrupted connectivity in a neural network implicated in emotion regulation, impulse control, and reinforcement learning. Results provide evidence that this network represents a genetically associated neurobiological vulnerability for externalizing disorders.

Original Articles
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


Aron, AR, Robbins, TW and Poldrack, RA (2004) Inhibition and the right inferior frontal cortex. Trends in Cognitive Sciences 8, 170177.Google Scholar
Baskin-Sommers, AR, Curtin, JJ, Larson, CL, Stout, D, Kiehl, KA and Newman, JP (2012) Characterizing the anomalous cognition–emotion interactions in externalizing. Biological Psychology 91, 4858.Google Scholar
Beckmann, CF, DeLuca, M, Devlin, JT and Smith, SM (2005) Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London B: Biological Sciences 360, 10011013.Google Scholar
Bender-deMoll, S and McFarland, DA (2006) The art and science of dynamic network visualization. Journal of Social Structure 7, 138.Google Scholar
Cardenas, VA, Durazzo, TC, Gazdzinski, S, Mon, A, Studholme, C and Meyerhoff, DJ (2011) Brain morphology at entry into treatment for alcohol dependence is related to relapse propensity. Biological Psychiatry 70, 561567.Google Scholar
Criaud, M and Boulinguez, P (2013) Have we been asking the right questions when assessing response inhibition in go/no-go tasks with fMRI? A meta-analysis and critical review. Neuroscience and Biobehavioral Reviews 37, 1123.Google Scholar
Delis, DC, Kaplan, E and Kramer, JH (2001) Delis-Kaplan Executive Function System (D-KEFS). New York: The Psychological Corporation.Google Scholar
DeVito, EE, Meda, SA, Jiantonio, R, Potenza, MN, Krystal, JH and Pearlson, GD (2013) Neural correlates of impulsivity in healthy males and females with family histories of alcoholism. Neuropsychopharmacology 38, 18541863.Google Scholar
Dom, G, Sabbe, BGCC, Hulstijn, W and Van Den Brink, W (2005) Substance use disorders and the orbitofrontal cortex. The British Journal of Psychiatry 187, 209220.Google Scholar
Douglas, KR, Chan, G, Gelernter, J, Arias, AJ, Anton, RF, Weiss, RD, Brady, K, Poling, J, Farrer, L and Kranzler, HR (2010) Adverse childhood events as risk factors for substance dependence: partial mediation by mood and anxiety disorders. Addictive Behaviors 35, 713.Google Scholar
Durazzo, TC, Tosun, D, Buckley, S, Gazdzinski, S, Mon, A, Fryer, SL and Meyerhoff, DJ (2011) Cortical thickness, surface area, and volume of the brain reward system in alcohol dependence: relationships to relapse and extended abstinence. Alcoholism: Clinical and Experimental Research 35, 11871200.Google Scholar
Eaton, WW, Roth, KB, Bruce, M, Cottler, L, Wu, L, Nestadt, G, Ford, D, Bienvenu, OJ, Crum, RM, Rebok, G and Anthony, JC (2013) The relationship of mental and behavioral disorders to all-cause mortality in a 27-year follow-up of 4 epidemiologic catchment area samples. American Journal of Epidemiology 178, 13661377.Google Scholar
Enoch, MA (2012) The influence of gene–environment interactions on the development of alcoholism and drug dependence. Current Psychiatry Reports 14, 150158.Google Scholar
Etkin, A, Egner, T, Peraza, DM, Kandel, ER and Hirsch, J (2006) Resolving emotional conflict: a role for the rostral anterior cingulate cortex in modulating activity in the amygdala. Neuron 51, 871882.Google Scholar
Farr, OM, Hu, S, Zhang, S and Chiang-shan, RL (2012) Decreased saliency processing as a neural measure of Barratt impulsivity in healthy adults. Neuroimage 63, 10701077.Google Scholar
First, MB, Spitzer, RL, Gibbon, M and Williams, JB (1994) Structured Clinical Interview for Axis I DSM-IV Disorders. New York: Biometrics Research.Google Scholar
Fischl, B, Salat, DH, Busa, E, Albert, M, Dieterich, M, Haselgrove, C, Van Der Kouwe, A, Killiany, R, Kennedy, D, Klaveness, S and Montillo, A (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341355.Google Scholar
Foell, J, Brislin, SJ, Strickland, CM, Seo, D, Sabatinelli, D and Patrick, CJ (2016) Externalizing proneness and brain response during pre-cuing and viewing of emotional pictures. Social Cognitive and Affective Neuroscience 11, 11021110.Google Scholar
Fornito, A and Bullmore, ET (2015) Connectomics: a new paradigm for understanding brain disease. European Neuropsychopharmacology 25, 733748.Google Scholar
Fortier, CB, Amick, MM, Grande, L, McGlynn, S, Kenna, A, Morra, L, Clark, A, Milberg, WP and McGlinchey, RE (2014) The Boston assessment of traumatic brain injury-lifetime (BAT-L) semistructured interview: evidence of research utility and validity. Journal of Head Trauma Rehabilitation 29, 8998.Google Scholar
Gilman, JM, Kuster, JK, Lee, S, Lee, MJ, Kim, BW, Makris, N, van der Kouwe, A, Blood, AJ and Breiter, HC (2014) Cannabis use is quantitatively associated with nucleus accumbens and amygdala abnormalities in young adult recreational users. The Journal of Neuroscience 34, 55295538.Google Scholar
Glahn, DC, Winkler, AM, Kochunov, P, Almasy, L, Duggirala, R, Carless, MA, Curran, JC, Olvera, RL, Laird, AR, Smith, SM and Beckmann, CF (2010) Genetic control over the resting brain. Proceedings of the National Academy of Sciences 107, 12231228.Google Scholar
Glenn, AL and Yang, Y (2012) The potential role of the striatum in antisocial behavior and psychopathy. Biological Psychiatry 72, 817822.Google Scholar
Han, K, Mac Donald, CL, Johnson, AM, Barnes, Y, Wierzechowski, L, Zonies, D, Oh, J, Flaherty, S, Fang, R, Raichle, ME and Brody, DL (2014) Disrupted modular organization of resting-state cortical functional connectivity in US military personnel following concussive ‘mild'blast-related traumatic brain injury. Neuroimage 84, 7696.Google Scholar
Heitzeg, MM, Villafuerte, S, Weiland, BJ, Enoch, MA, Burmeister, M, Zubieta, JK and Zucker, RA (2014) Effect of GABRA2 genotype on development of incentive-motivation circuitry in a sample enriched for alcoholism risk. Neuropsychopharmacology 39, 30773086.Google Scholar
Hyvärinen, A and Smith, SM (2013) Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. Journal of Machine Learning Research 14, 111152.Google Scholar
Hicks, BM, Krueger, RF, Iacono, WG, McGue, M and Patrick, CJ (2004) Family transmission and heritability of externalizing disorders: a twin-family study. Archives of General Psychiatry 61, 922928.Google Scholar
Karoly, HC, Harlaar, N and Hutchison, KE (2013) Substance use disorders: a theory-driven approach to the integration of genetics and neuroimaging. Annals of the New York Academy of Sciences 1282, 7191.Google Scholar
Korponay, C, Pujara, M, Deming, P, Philippi, C, Decety, J, Kosson, DS, Kiehl, KA and Koenigs, M (2017) Impulsive-antisocial dimension of psychopathy linked to enlargement and abnormal functional connectivity of the striatum. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2, 149157.Google Scholar
Krueger, RF and Markon, KE (2006) Reinterpreting comorbidity: a model-based approach to understanding and classifying psychopathology. Annual Review of Clinical Psychology 2, 111133.Google Scholar
Krueger, RF, Hicks, BM, Patrick, CJ, Carlson, SR, Iacono, WG and McGue, M (2002) Etiologic connections among substance dependence, antisocial behavior and personality: modeling the externalizing spectrum. Journal of Abnormal Psychology 111, 411424.Google Scholar
Little, K, Olsson, CA, Youssef, GJ, Whittle, S, Simmons, JG, Yücel, M, Sheeber, LB, Foley, DL and Allen, NB (2015) Linking the serotonin transporter gene, family environments, hippocampal volume and depression onset: A prospective imaging gene×environment analysis. Journal of Abnormal Psychology 124, 834849.Google Scholar
Luntz, BK and Widom, CS (1994) Antisocial personality disorder in abused and neglected children grown up. American Journal of Psychiatry 151, 670674.Google Scholar
Miller, GA and Rockstroh, B (2013) Endophenotypes in psychopathology research: where do we stand? Annual Review Clinical Psychology 9, 177213.Google Scholar
National Institute on Drug Abuse (2015) Trends and statistics. Available at Updated August, 2015. (Accessed 17 February 2016).Google Scholar
Nee, DE, Wager, TD and Jonides, J (2007) Interference resolution: insights from a meta-analysis of neuroimaging tasks. Cognitive, Affective, and Behavioral Neuroscience 7, 117.Google Scholar
Nikolova, YS, Knodt, AR, Radtke, SR and Hariri, AR (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. Molecular Psychiatry 21, 348356.Google Scholar
Odgers, CL, Caspi, A, Broadbent, JM, Dickson, N, Hancox, RJ, Harrington, HL, Poulton, R, Sears, MR, Thomson, WM and Moffitt, TE (2007) Conduct problem subtypes in males predict differential adult health burden. Archives of General Psychiatry 64, 476484.Google Scholar
Pagliaccio, D, Luby, JL, Bogdan, R, Agrawal, A, Gaffrey, MS, Belden, AC, Botteron, KN, Harms, MP and Barch, DM (2015) Amygdala functional connectivity, HPA axis genetic variation, and life stress in children and relations to anxiety and emotion regulation. Journal of Abnormal Psychology 124, 817833.Google Scholar
Power, JD, Schlaggar, BL, Lessov-Schlaggar, CN and Petersen, SE (2013) Evidence for hubs in human functional brain networks. Neuron 79, 798813.Google Scholar
Price, AL, Patterson, NJ, Plenge, RM, Weinblatt, ME, Shadick, NA and Reich, D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics 38, 904909.Google Scholar
Purcell, S, Neale, B, Todd-Brown, K, Thomas, L, Ferreira, MA, Bender, D, Maller, J, Sklar, P, De Bakker, PI, Daly, MJ and Sham, PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81, 559575.Google Scholar
Ramsey, JD, Sanchez-Romero, R and Glymour, C (2014) Non-Gaussian methods and high-pass filters in the estimation of effective connections. Neuroimage 84, 9861006.Google Scholar
Rubinov, M and Sporns, O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 10591069.Google Scholar
Sadeh, N, Spielberg, JM, Heller, W, Herrington, JD, Engels, AS, Warren, SL, Crocker, LD, Sutton, BP and Miller, GA (2013) Emotion disrupts neural activity during selective attention in psychopathy. Social Cognitive and Affective Neuroscience 8, 235246.Google Scholar
Sadeh, N, Spielberg, JM, Miller, MW, Milberg, WP, Salat, DH, Amick, MM, Fortier, CB and McGlinchey, RE (2015) Neurobiological indicators of disinhibition in posttraumatic stress disorder. Human Brain Mapping 36, 30763086.Google Scholar
Sadeh, N, Wolf, EJ, Logue, MW, Lusk, J, Hayes, JP, McGlinchey, RE, Milberg, WP, Stone, A, Schichman, SA and Miller, MW (2016) Polygenic risk for externalizing disorders and executive dysfunction in trauma-exposed veterans. Clinical Psychological Science 4, 545558.Google Scholar
Salvatore, JE, Aliev, F, Bucholz, K, Agrawal, A, Hesselbrock, V, Hesselbrock, M, Bauer, L, Kuperman, S, Schuckit, MA, Kramer, JR and Edenberg, HJ (2015) Polygenic risk for externalizing disorders gene-by-development and gene-by-environment effects in adolescents and young adults. Clinical Psychological Science 3, 189201.Google Scholar
Shannon, BJ, Raichle, ME, Snyder, AZ, Fair, DA, Mills, KL, Zhang, D, Bache, K, Calhoun, VD, Nigg, JT, Nagel, BJ and Stevens, AA (2011) Premotor functional connectivity predicts impulsivity in juvenile offenders. Proceedings of the National Academy of Sciences 108, 1124111245.Google Scholar
Shehzad, Z, DeYoung, CG, Kang, Y, Grigorenko, EL and Gray, JR (2012) Interaction of COMT val 158 met and externalizing behavior: relation to prefrontal brain activity and behavioral performance. Neuroimage 60, 21582168.Google Scholar
Smit, DJ, Stam, CJ, Posthuma, D, Boomsma, DI and De Geus, EJ (2008) Heritability of ‘small-world’ networks in the brain: a graph theoretical analysis of resting-state EEG functional connectivity. Human Brain Mapping 29, 13681378.Google Scholar
Spielberg, JM, McGlinchey, RE, Milberg, WP and Salat, DH (2015 a) Brain network disturbance related to posttraumatic stress and traumatic brain injury in veterans. Biological Psychiatry 78, 210216.Google Scholar
Spielberg, JM, Miller, GA, Heller, W and Banich, MT (2015 b) Flexible brain network reconfiguration supporting inhibitory control. Proceedings of the National Academy of Sciences 112, 1002010025.Google Scholar
Spielberg, JM, Sadeh, N, Leritz, EC, McGlinchey, RE, Milberg, WP, Hayes, JP and Salat, DH (2017) Higher serum cholesterol is associated with intensified age-related neural network decoupling and cognitive decline in early-to mid-life. Human Brain Mapping 38, 32493261.Google Scholar
Stam, CJ (2014) Modern network science of neurological disorders. Nature Reviews Neuroscience 15, 683695.Google Scholar
Tarter, RE, Kirisci, L, Mezzich, A, Cornelius, JR, Pajer, K, Vanyukov, M, Gardner, W, Blackson, T and Clark, D (2003) Neurobehavioral disinhibition in childhood predicts early age at onset of substance use disorder. American Journal of Psychiatry 160, 10781085.Google Scholar
Xia, M, Wang, J and He, Y (2013) Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8, e68910.Google Scholar
Yang, Y and Raine, A (2009) Prefrontal structural and functional brain imaging findings in antisocial, violent, and psychopathic individuals: a meta-analysis. Psychiatry Research: Neuroimaging 174, 8188.Google Scholar
Young, SE, Friedman, NP, Miyake, A, Willcutt, EG, Corley, RP, Haberstick, BC and Hewitt, JK (2009) Behavioral disinhibition: liability for externalizing spectrum disorders and its genetic and environmental relation to response inhibition across adolescence. Journal of Abnormal Psychology 118, 117130.Google Scholar
Zalesky, A, Fornito, A and Bullmore, ET (2010) Network-based statistic: identifying differences in brain networks. Neuroimage 53, 11971207.Google Scholar
Zuo, XN and Xing, XX (2014) Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective. Neuroscience and Biobehavioral Reviews 45, 100118.Google Scholar
Supplementary material: File

Sadeh et al. supplementary material

Sadeh et al. supplementary material 1

Download Sadeh et al. supplementary material(File)
File 1.3 MB