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Brain structural covariance network centrality in maltreated youth with PTSD and in maltreated youth resilient to PTSD

Published online by Cambridge University Press:  10 April 2018

Delin Sun ,
Courtney C. Haswell ,
Rajendra A. Morey  and
Michael D. De Bellis
Delin Sun
Affiliation:
Duke University Mid-Atlantic Mental Illness Research and Clinical Center
Courtney C. Haswell
Affiliation:
Duke University Mid-Atlantic Mental Illness Research and Clinical Center
Rajendra A. Morey
Affiliation:
Duke University Mid-Atlantic Mental Illness Research and Clinical Center Duke University School of Medicine
Michael D. De Bellis*
Affiliation:
Duke University Duke University School of Medicine
*
Address correspondence and reprint requests to: Michael D. De Bellis, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Box 104360, Durham, NC 27710; E-mail: [email protected].
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Abstract

Child maltreatment is a major cause of pediatric posttraumatic stress disorder (PTSD). Previous studies have not investigated potential differences in network architecture in maltreated youth with PTSD and those resilient to PTSD. High-resolution magnetic resonance imaging brain scans at 3 T were completed in maltreated youth with PTSD (n = 31), without PTSD (n = 32), and nonmaltreated controls (n = 57). Structural covariance network architecture was derived from between-subject intraregional correlations in measures of cortical thickness in 148 cortical regions (nodes). Interregional positive partial correlations controlling for demographic variables were assessed, and those correlations that exceeded specified thresholds constituted connections in cortical brain networks. Four measures of network centrality characterized topology, and the importance of cortical regions (nodes) within the network architecture were calculated for each group. Permutation testing and principle component analysis method were employed to calculate between-group differences. Principle component analysis is a methodological improvement to methods used in previous brain structural covariance network studies. Differences in centrality were observed between groups. Larger centrality was found in maltreated youth with PTSD in the right posterior cingulate cortex; smaller centrality was detected in the right inferior frontal cortex compared to youth resilient to PTSD and controls, demonstrating network characteristics unique to pediatric maltreatment-related PTSD. Larger centrality was detected in right frontal pole in maltreated youth resilient to PTSD compared to youth with PTSD and controls, demonstrating structural covariance network differences in youth resilience to PTSD following maltreatment. Smaller centrality was found in the left posterior cingulate cortex and in the right inferior frontal cortex in maltreated youth compared to controls, demonstrating attributes of structural covariance network topology that is unique to experiencing maltreatment. This work is the first to identify cortical thickness-based structural covariance network differences between maltreated youth with and without PTSD. We demonstrated network differences in both networks unique to maltreated youth with PTSD and those resilient to PTSD. The networks identified are important for the successful attainment of age-appropriate social cognition, attention, emotional processing, and inhibitory control. Our findings in maltreated youth with PTSD versus those without PTSD suggest vulnerability mechanisms for developing PTSD.

Keywords

cortical networkscortical thicknessmaltreatmentnetwork centralitypediatric PTSDresilience
Type
Regular Articles
Information
Development and Psychopathology , Volume 31 , Issue 2 , May 2019 , pp. 557 - 571
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

This work has been funded by NIH Grants K24MH71434, K24 DA028773, R01 MH63407, R01 AA12479, and R01 MH61744 (to M.D.D.B.); and VA and NIH Grants VHA VISN 6 MIRECC, VHA CSR&D 5I01CX000120-03, VHA CSR&D 5I01CX000748-03, and NINDS 5R01NS086885-02 (to R.A.M.). This study was presented in poster and abstract form at the American College of Neuropsychopharmacology's 55th Annual Meeting, in Hollywood, Florida, December 4–8, 2016. The authors of this study would like to thank the staff of the Healthy Childhood Brain Development Research Program, and the individuals who participated in this study.

References

Boersma, M., Smit, D. J. A., de Bie, H. M. A., Van Baal, G. C. M., Boomsma, D. I., de Geus, E. J. C., … Stam, C. J. (2011). Network analysis of resting state EEG in the developing young brain: Structure comes with maturation. Human Brain Mapping, 32, 413–425.Google Scholar
Bremner, J. D., Narayan, M., Staib, L., Southwick, S. M., McGlashan, T., & Charney, D. S. (1999). Neural correlates of memories of childhood sexual abuse in women with and without posttraumatic stress disorder. American Journal of Psychiatry, 156, 1787–1795.Google Scholar
Cabeza, R., & Nyberg, L. (2000). Imaging cognition II: An empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 12, 1–47.Google Scholar
Carrion, V. G., Weems, C. F., Watson, C., Eliez, S., Menon, V., & Reiss, A. L. (2009). Converging evidence for abnormalities of the prefrontal cortex and evaluation of midsagittal structures in pediatric posttraumatic stress disorder: An MRI study. Psychiatry Research: Neuroimaging, 172, 226–234.Google Scholar
Cowell, R. A., Cicchetti, D., Rogosch, F. A., & Toth, S. L. (2015). Childhood maltreatment and its effect on neurocognitive functioning: Timing and chronicity matter. Development and Psychopathology, 27, 521–533.Google Scholar
Crozier, J. C., Wang, L., Huettel, S. A., & De Bellis, M. D. (2014). Neural correlates of cognitive and affective processing in maltreated youth with posttraumatic stress symptoms: Does gender matter? Development and Psychopathology, 26, 491–513.Google Scholar
De Bellis, M. D. (2001). Developmental traumatology: The psychobiological development of maltreated children and its implications for research, treatment, and policy. Development and Psychopathology, 13, 539–564. doi:10.1017/S0954579401003078.Google Scholar
De Bellis, M. D., Hooper, S. R., Chen, S. D., Provenzale, J. M., Boyd, B. D., Glessner, C. E., … Woolley, D. P. (2015). Posterior structural brain volumes differ in maltreated youth with and without chronic posttraumatic stress disorder. Development and Psychopathology, 27, 1555–1576. doi:10.1017/S0954579415000942.Google Scholar
De Bellis, M. D., Hooper, S., Spratt, E. G., & Woolley, D. W. (2009). Neuropsychological findings in childhood neglect and their relationships to pediatric PTSD. Journal of the International Neuropsychological Society, 15, 868–878.Google Scholar
De Bellis, M., & Kuchibhatla, M. (2006). Cerebellar volumes in pediatric maltreatment-related posttraumatic stress disorder. Biological Psychiatry, 60, 697–703.Google Scholar
De Bellis, M. D., Keshavan, M. S., Clark, D. B., Casey, B. J., Giedd, J. N., Boring, A. M., … Ryan, N. D. (1999). A.E. Bennett Research Award. Developmental traumatology. Part II: Brain development. Biological Psychiatry, 45, 1271–1284.Google Scholar
De Bellis, M. D., Woolley, D. P., & Hooper, S. R. (2013). Neuropsychological findings in pediatric maltreatment: Relationship of PTSD, dissociative symptoms, and abuse/neglect indices to neurocognitive outcomes. Child Maltreatment, 18, 171–183. doi:10.1177/1077559513497420.Google Scholar
De Bellis, M. D., & Zisk, A. (2014). The biological effects of childhood trauma. Child and Adolescent Psychiatric Clinics of North America, 23, 185–222. doi:10.1016/j.chc.2014.01.002.Google Scholar
Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010). Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage, 53, 1–15. doi:10.1016/j.neuroimage.2010.06.010.Google Scholar
Di Martino, A., Fair, D. A., Kelly, C., Satterthwaite, T. D., Castellanos, F. X., Thomason, M. E., … Milham, M. P. (2014). Unraveling the miswired connectome: A developmental perspective. Neuron, 83, 1335–1353. doi:10.1016/j.neuron.2014.08.050.Google Scholar
Dube, S. R., Felitti, V. J., Dong, M., Giles, W. H., & Anda, R. F. (2003). The impact of adverse childhood experiences on health problems: Evidence from four birth cohorts dating back to 1900. Pediatrics, 37, 268–277.Google Scholar
Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap. New York: Chapman & Hall.Google Scholar
Etkin, A., Buchel, C., & Gross, J. J. (2015). The neural bases of emotion regulation. Nature Reviews Neuroscience, 16, 693–700.Google Scholar
Fair, D. A., Cohen, A. L. Dosenbach, N. U. F., Church, J. A., Miezin, F. M., Barch, D. M., … Schlaggar, B. L. (2008). The maturing architecture of the brain's default network. Proceedings of the National Academy of Sciences USA, 105, 4028–4032.Google Scholar
Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., … Marks, J. S. (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes if death in adults. American Journal of Preventive Medicine, 14, 245–258.Google Scholar
Fleischman, D. A., Leurgans, S., Arfanakis, K., Arvanitakis, Z., Barnes, L. L., Boyle, P. A., … Bennett, D. A. (2014). Gray-matter macrostructure in cognitively healthy older persons: Associations with age and cognition. Brain Structure and Function, 219, 2029–2049.Google Scholar
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the USA, 102, 9673–9678.Google Scholar
Gold, A. L., Sheridan, M. A., Peverill, M., Busso, D. S., Lambert, H. K., Alves, S., … K. A. (2016). Childhood abuse and reduced cortical thickness in brain regions involved in emotional processing. Journal of Child Psychology and Psychiatry, 57, 1154–1164. doi:10.1111/jcpp.12630.Google Scholar
Gong, G. L., He, Y., Chen, Z. J., & Evans, A. C. (2012). Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex. Neuroimage, 59, 1239–1248. doi:10.1016/j.neuroimage.2011.08.017.Google Scholar
Gong, Q. Y., Li, L. J., Du, M. Y., Pettersson-Yeo, W., Crossley, N., Yang, X., … Mechelli, A. (2014). Quantitative prediction of individual psychopathology in trauma survivors using resting-state fMRI. Neuropsychopharmacology, 39, 681–687. doi:10.1038/npp.2013.251.Google Scholar
Hampshire, A., Chamberlain, S. R., Monti, M. M., Duncan, J., & Owen, A. M. (2010). The role of the right inferior frontal gyrus: Inhibition and attentional control. Neuroimage, 50, 1313–1319.Google Scholar
He, Y., Chen, Z. J., & Evans, A. C. (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral Cortex, 17, 2407–2419. doi:10.1093/cercor/bhl149.Google Scholar
He, Y., Chen, Z., & Evans, A. (2008). Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's Disease. Journal of Neuroscience, 28, 4756–4766. doi:10.1523/Jneurosci.0141-08.2008.Google Scholar
He, Y., Dagher, A., Chen, Z., Charil, A., Zijdenbos, A., Worsley, K., & Evans, A. (2009). Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain, 132, 3366–3379.Google Scholar
He, Y., & Evans, A. (2010). Graph theoretical modeling of brain connectivity. Current Opinion in Neurology, 23, 341–350. doi:10.1097/WCO.0b013e32833aa567.Google Scholar
Hofer, S., & Frahm, J. (2006). Topography of the human corpus callosum revisited—Comprehensive fiber tractography using diffusion tensor magnetic resonance imaging. Neuroimage, 32, 989–994.Google Scholar
Hughes, K. C., & Shin, L. M. (2011). Functional neuroimaging studies of post-traumatic stress disorder. Expert Review of Neurotherapeutics, 11, 275–285.Google Scholar
Hussey, J. M., Chang, J. J., & Kotch, J. B. (2006). Child maltreatment in the United States: Prevalence, risk factors, and adolescent health consequences. Pediatrics, 118, 933–942.Google Scholar
Jovanovic, T., Norrholm, S. D., Blanding, N. Q., Davis, M., Duncan, E., Bradley, B., & Ressler, K. J. (2010). Impaired fear inhibition is a biomarker of PTSD but not depression. Depression and Anxiety, 27, 244–251.Google Scholar
Kaufman, J., Birmaher, B., Brent, D., Rao, U., Flynn, C., Moreci, P., … Ryan, N. (1997). Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (K-SADS-PL): Initial reliability and validity data. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 980–988.Google Scholar
Kaufman, J., Cook, A., Arny, L., Jones, B., & Pittinsky, T. (1994). Problems defining resiliency: Illustrations from the study of maltreated children. Development and Psychopathology, 6, 215–229.Google Scholar
Kaufman, J., Jones, B., Stieglitz, E., Vitulano, L., & Mannarino, A. (1994). The use of multiple informants to assess children's maltreatment experiences. Journal of Family Violence, 9, 227–248.Google Scholar
Kim, J., & Cicchetti, D. (2010). Longitudinal pathways linking child maltreatment, emotion regulation, peer relations, and psychopathology. Journal of Child Psychology and Psychiatry, 51, 706–716.Google Scholar
Leech, R., & Sharp, D. J. (2014). The role of the posterior cingulate cortex in cognition and disease. Brain, 137, 12–32. doi:10.1093/brain/awt162.Google Scholar
Leslie, L. K., Gordon, J. N., Meneken, L., Premji, K., Michaelmore, K. L., & Ganger, W. (2005). The physical, developmental, and mental health needs of young children in child welfare by initial placement type. Developmental and Behavioral Pediatrics, 26, 177–185.Google Scholar
Li, C.-S. R., Yan, P., Bergquist, K. L., & Sinha, R. (2007). Greater activation of the “default” brain regions predicts stop signal errors. Neuroimage, 38, 640–648.Google Scholar
Liu, H. G., Qin, W., Li, W., Fan, L. Z., Wang, J. J., Jiang, T. Z., & Yu, C. S. (2013). Connectivity-based parcellation of the human frontal pole with diffusion tensor imaging. Journal of Neuroscience, 33, 6782–6790. doi:10.1523/Jneurosci.4882-12.2013.Google Scholar
Maddock, R. J., Garrett, A. S., & Buonocore, M. H. (2008). Remembering familiar people: The posterior cingulate cortex and autobiographical memory retrieval. Neuroscience, 104, 667–676.Google Scholar
Miller, G. A., & Chapman, J. P. (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology, 110, 40–48. doi:10.1037//0021-843x.110.1.40.Google Scholar
Morey, R. A., Haswell, C. C., Hooper, S. R., & De Bellis, M. D. (2016). Amygdala, hippocampus, and ventral medial prefrontal cortex volumes differ in maltreated youth with and without chronic posttraumatic stress disorder. Neuropsychopharmacology, 41, 791–801. doi:10.1038/npp.2015.205.Google Scholar
Mueller, S. G., Ng, P., Neylan, T., Mackin, S., Wolkowitz, O., Mellon, S., … Weiner, M. W. (2015). Evidence for disrupted gray matter structural connectivity in posttraumatic stress disorder. Psychiatry Research: Neuroimaging, 234, 194–201. doi:10.1016/j.pscychresns.2015.09.006.Google Scholar
Perez, C. M., & Widom, C. S. (1994). Childhood victimization and long-term intellectual and academic outcomes. Child Abuse & Neglect, 18, 617–633. doi:10.1016/0145-2134(94)90012-4.Google Scholar
Pfefferbaum, A., Rohlfing, T., Pohl, K. M., Lane, B., Chu, W., Kwon, D., … Sullivan, E. V. (2015). Adolescent development of cortical and white matter structure in the NCANDA sample: Role of sex, ethnicity, puberty, and alcohol drinking. Cerebral Cortex, 26, 4101–4121. doi:10.1093/cercor/bhv205.Google Scholar
Raffelt, D. A., Tournier, J. D., Smith, R. E., Vaughan, D. N., Jackson, G., Ridgway, G. R., & Connelly, A. (2017). Investigating white matter fibre density and morphology using fixel-based analysis. Neuroimage, 144, 58–73.Google Scholar
Raghavan, R., Zima, B. T., Andersen, R. M., Leibowitz, A. A., Schuster, M. A., & Landsverk, J. (2005). Psychotropic medication use in a national probability sample of children in the child welfare system. Journal of Child and Adolescent Psychopharmacology, 15, 97–106.Google Scholar
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52, 1059–1069. doi:10.1016/j.neuroimage.2009.10.003.Google Scholar
Russell, J. D., Neill, E. L., Carrion, V. G., & Weems, C. F. (2017). The network structure of posttraumatic stress symptoms in children and adolescents exposed to disasters. Journal of the American Academy of Child & Adolescent Psychiatry, 56, 669–677.Google Scholar
Sadeh, N., Spielberg, J. M., Logue, M. W., Wolf, E. J., Smith, A. K., Lusk, J., … Miller, M. W. (2016). SKA2 methylation is associated with decreased prefrontal cortical thickness and greater PTSD severity among trauma-exposed veterans. Molecular Psychiatry, 21, 357–363. doi:10.1038/mp.2015.134.Google Scholar
Sadeh, N., Spielberg, J. M., Miller, M. W., Milberg, W. P., Salat, D. H., Amick, M. M., … McGlinchey, R. E. (2015). Neurobiological indicators of disinhibition in posttraumatic stress disorder. Human Brain Mapping, 36, 3076–3086. doi:10.1002/hbm.22829.Google Scholar
Shaffer, D., Gould, M. S., Brasic, J., Ambrosini, P., Fisher, P., Bird, H., & Aluwahlia, S. (1983). A Children's Global Assessment Scale. Archives of General Psychiatry, 40, 1228–1231.Google Scholar
Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., … Wise, S. P. (2008). Neurodevelopmental trajectories of the human cerebral cortex. Journal of Neuroscience, 28, 3586–3594. doi:10.1523/Jneurosci.5309-07.2008.Google Scholar
Shepherd, L., & Wild, J. (2014). Emotion regulation, physiological arousal and PTSD symptoms in trauma-exposed individuals. Journal of Behavior Therapy and Experimental Psychiatry, 45, 360–367.Google Scholar
Shin, L. M., Whalen, P. J., Pitman, R. K., Bush, G., Macklin, M. L., Lasko, N. B., … Rauch, S. L. (2001). An fMRI study of anterior cingulate function in posttraumatic stress disorder. Biological Psychiatry, 50, 932–942. doi:10.1016/S0006-3223(01)01215-X.Google Scholar
Smith, D. K., Johnson, A. B., Pears, K. C., Fisher, P. A., & DeGarmo, D. S. (2007). Child maltreatment and foster care: Unpacking the effects of prenatal and postnatal parental substance use. Child Maltreatment, 12, 150–160. doi:10.1177/1077559507300129.Google Scholar
Sripada, R. K., King, A. P., Welsh, R. C., Garfinkel, S. N., Wang, X., Sripada, C. S., & Liberzon, I. (2012). Neural dysregulation in posttraumatic stress disorder: Evidence for disrupted equilibrium between salience and default mode brain networks. Psychosomatic Medicine, 74, 904–911. doi:10.1097/PSY.0b013e318273bf33.Google Scholar
Supekar, K., Uddin, L. Q., Prater, K., Amin, H., Greicius, M. D., & Menon, V. (2010). Development of functional and structural connectivity within the default mode network in young children. Neuroimage, 52, 290–301.Google Scholar
Teicher, M. H., Anderson, C. M., Ohashi, K., & Polcari, A. (2014). Childhood maltreatment: Altered network centrality of cingulate, precuneus, temporal pole and insula. Biological Psychiatry, 76, 297–305. doi:10.1016/j.biopsych.2013.09.016.Google Scholar
Teicher, M. H., Samson, J. A., Anderson, C. M., & Ohashi, K. (2016). The effects of childhood maltreatment on brain structure, function and connectivity. Nature, 17, 652–666.Google Scholar
Utevsky, A. V., Smith, D. V., & Huettel, S. A. (2014). Precuneus is a functional core of the default-mode network. Journal of Neuroscience, 34, 932–940.Google Scholar
van Rooij, S. J. H., Rademaker, A. R., Kennis, M., Vink, M., Kahn, R. S., & Geuze, E. (2014). Impaired right inferior frontal gyrus response to contextual cues in male veterans with PTSD during response inhibition. Journal of Psychiatry and Neuroscience, 39, 330–338. doi:10.1503/jpn.130223.Google Scholar
van Straaten, E. C. W., & Stam, C. J. (2012). Structure out of chaos: Functional brain network analysis with EEG, MEG, and functional MRI. European Neuropsychopharmacology, 23, 7–18.Google Scholar
Wager, T. D., Davidson, M. L., Hughes, B. L., Lindquist, M. A., & Ochsner, K. N. (2008). Prefrontal-subcortical pathways mediating successful emotion regulation. Neuron, 59, 1037–1050.Google Scholar
Wechsler, D. (1991). Wechsler Intelligence Scale for Children (3rd ed.). San Antonio: Psychological Corporation.Google Scholar
Winkler, A. M., Webster, M. A., Vidaurre, D., Nichols, T. E., & Smith, S. M. (2015). Multi-level block permutation. Neuroimage, 123, 253–268. doi:10.1016/j.neuroimage.2015.05.092.Google Scholar
Yamasue, H., Kasai, K., Iwanami, A., Ohtani, T., Yamada, H., Abe, O., … Kato, N. (2003). Voxel-based analysis of MRI reveals anterior cingulate gray-matter volume reduction in posttraumatic stress disorder due to terrorism. Proceedings of the National Academy of Sciences of the USA, 100, 9039–9043.Google Scholar
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: Identifying differences in brain networks. Neuroimage, 53, 1197–1207. doi:10.1016/j.neuroimage.2010.06.041.Google Scholar
Zhou, Y., Wang, Z., Qin, L. D., Wan, J. Q., Sun, Y. W., Su, S. S., … Xu, J. R. (2012). Early altered resting-state functional connectivity predicts the severity of post-traumatic stress disorder symptoms in acutely traumatized subjects. PLOS ONE, 7, e46833.Google Scholar