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

  • Naomi Sadeh (a1) (a2), Jeffrey M. Spielberg (a1) (a3), Mark W. Logue (a2) (a4), Jasmeet P. Hayes (a2) (a5), Erika J. Wolf (a2) (a5), Regina E. McGlinchey (a6) (a7), William P. Milberg (a6) (a7), Steven A. Schichman (a8), Annjanette Stone (a8) and Mark W. Miller (a2) (a5)...

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Copyright

Corresponding author

Author for correspondence: Naomi Sadeh, E-mail: nsadeh@udel.edu

References

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Aron, AR, Robbins, TW and Poldrack, RA (2004) Inhibition and the right inferior frontal cortex. Trends in Cognitive Sciences 8, 170177.
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.
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.
Bender-deMoll, S and McFarland, DA (2006) The art and science of dynamic network visualization. Journal of Social Structure 7, 138.
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.
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.
Delis, DC, Kaplan, E and Kramer, JH (2001) Delis-Kaplan Executive Function System (D-KEFS). New York: The Psychological Corporation.
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.
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.
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.
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.
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.
Enoch, MA (2012) The influence of gene–environment interactions on the development of alcoholism and drug dependence. Current Psychiatry Reports 14, 150158.
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.
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.
First, MB, Spitzer, RL, Gibbon, M and Williams, JB (1994) Structured Clinical Interview for Axis I DSM-IV Disorders. New York: Biometrics Research.
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.
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.
Fornito, A and Bullmore, ET (2015) Connectomics: a new paradigm for understanding brain disease. European Neuropsychopharmacology 25, 733748.
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.
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.
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.
Glenn, AL and Yang, Y (2012) The potential role of the striatum in antisocial behavior and psychopathy. Biological Psychiatry 72, 817822.
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.
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.
Hyvärinen, A and Smith, SM (2013) Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. Journal of Machine Learning Research 14, 111152.
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.
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.
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.
Krueger, RF and Markon, KE (2006) Reinterpreting comorbidity: a model-based approach to understanding and classifying psychopathology. Annual Review of Clinical Psychology 2, 111133.
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.
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.
Luntz, BK and Widom, CS (1994) Antisocial personality disorder in abused and neglected children grown up. American Journal of Psychiatry 151, 670674.
Miller, GA and Rockstroh, B (2013) Endophenotypes in psychopathology research: where do we stand? Annual Review Clinical Psychology 9, 177213.
National Institute on Drug Abuse (2015) Trends and statistics. Available at http://www.drugabuse.gov/related-topics/trends-statistics. Updated August, 2015. (Accessed 17 February 2016).
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.
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.
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.
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.
Power, JD, Schlaggar, BL, Lessov-Schlaggar, CN and Petersen, SE (2013) Evidence for hubs in human functional brain networks. Neuron 79, 798813.
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.
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.
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.
Rubinov, M and Sporns, O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 10591069.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Stam, CJ (2014) Modern network science of neurological disorders. Nature Reviews Neuroscience 15, 683695.
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.
Xia, M, Wang, J and He, Y (2013) Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8, e68910.
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.
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.
Zalesky, A, Fornito, A and Bullmore, ET (2010) Network-based statistic: identifying differences in brain networks. Neuroimage 53, 11971207.
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.

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