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Meta-Analysis
. 2022 Sep;7(9):935-948.
doi: 10.1016/j.bpsc.2022.02.008. Epub 2022 Mar 15.

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium

Delin Sun  1 Gopalkumar Rakesh  1 Emily K Clarke-Rubright  1 Courtney C Haswell  1 Mark W Logue  2 Erin N O'Leary  3 Andrew S Cotton  3 Hong Xie  3 Emily L Dennis  4 Neda Jahanshad  5 Lauren E Salminen  5 Sophia I Thomopoulos  5 Faisal M Rashid  5 Christopher R K Ching  5 Saskia B J Koch  6 Jessie L Frijling  7 Laura Nawijn  8 Mirjam van Zuiden  7 Xi Zhu  9 Benjamin Suarez-Jimenez  10 Anika Sierk  11 Henrik Walter  11 Antje Manthey  11 Jennifer S Stevens  12 Negar Fani  12 Sanne J H van Rooij  12 Murray B Stein  13 Jessica Bomyea  13 Inga Koerte  14 Kyle Choi  15 Steven J A van der Werff  16 Robert R J M Vermeiren  17 Julia I Herzog  18 Lauren A M Lebois  19 Justin T Baker  20 Kerry J Ressler  21 Elizabeth A Olson  22 Thomas Straube  23 Mayuresh S Korgaonkar  24 Elpiniki Andrew  25 Ye Zhu  26 Gen Li  26 Jonathan Ipser  27 Anna R Hudson  28 Matthew Peverill  29 Kelly Sambrook  30 Evan Gordon  31 Lee A Baugh  32 Gina Forster  33 Raluca M Simons  34 Jeffrey S Simons  34 Vincent A Magnotta  35 Adi Maron-Katz  36 Stefan du Plessis  37 Seth G Disner  38 Nicholas D Davenport  38 Dan Grupe  39 Jack B Nitschke  40 Terri A deRoon-Cassini  41 Jacklynn Fitzgerald  42 John H Krystal  43 Ifat Levy  43 Miranda Olff  44 Dick J Veltman  7 Li Wang  26 Yuval Neria  9 Michael D De Bellis  45 Tanja Jovanovic  46 Judith K Daniels  47 Martha E Shenton  48 Nic J A van de Wee  16 Christian Schmahl  18 Milissa L Kaufman  49 Isabelle M Rosso  22 Scott R Sponheim  38 David Bernd Hofmann  23 Richard A Bryant  50 Kelene A Fercho  51 Dan J Stein  27 Sven C Mueller  52 K Luan Phan  53 Katie A McLaughlin  54 Richard J Davidson  55 Christine Larson  56 Geoffrey May  57 Steven M Nelson  57 Chadi G Abdallah  43 Hassaan Gomaa  58 Amit Etkin  59 Soraya Seedat  37 Ilan Harpaz-Rotem  43 Israel Liberzon  60 Xin Wang  3 Paul M Thompson  5 Rajendra A Morey  61
Affiliations
Meta-Analysis

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium

Delin Sun et al. Biol Psychiatry Cogn Neurosci Neuroimaging. 2022 Sep.

Abstract

Background: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA).

Methods: Neuroimaging and clinical data were aggregated from 29 research sites in >1300 PTSD cases and >2000 trauma-exposed control subjects (ages 6.2-85.2 years) by the ENIGMA-PGC (Enhancing Neuro Imaging Genetics through Meta Analysis-Psychiatric Genomics Consortium) PTSD working group. Cortical regions in the network were rank ordered by the effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2-148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared with the mean SC of 5000 randomly generated n-region networks.

Results: Patients with PTSD, relative to non-PTSD control subjects, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex, and age modulated covariance differences of PTSD-related structural networks.

Conclusions: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The SC networks that are perturbed in PTSD comport with converging evidence from resting-state functional connectivity networks and networks affected by inflammatory processes and stress hormones in PTSD.

Keywords: Brain network; Cortical thickness; Depression; PTSD; Structural covariance; Surface area.

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Conflict of interest statement

Conflicts of Interest

Dr. Abdallah has served as a consultant, speaker and/or on advisory boards for FSV7, Lundbeck, Psilocybin Labs, Genentech and Janssen, and editor of Chronic Stress for Sage Publications, Inc.; he has filed a patent for using mTOR inhibitors to augment the effects of antidepressants (filed on August 20, 2018). Dr. Davidson is the founder and president of, and serves on the board of directors for, the non-profit organization Healthy Minds Innovations, Inc. Dr. Jahanshad received partial research support from Biogen, Inc. (Boston, USA) for research unrelated to the content of this manuscript. Dr. Krystal is a consultant for AbbVie, Inc., Amgen, Astellas Pharma Global Development, Inc., AstraZeneca Pharmaceuticals, Biomedisyn Corporation, Bristol-Myers Squibb, Eli Lilly and Company, Euthymics Bioscience, Inc., Neurovance, Inc., FORUM Pharmaceuticals, Janssen Research & Development, Lundbeck Research USA, Novartis Pharma AG, Otsuka America Pharmaceutical, Inc., Sage Therapeutics, Inc., Sunovion Pharmaceuticals, Inc., and Takeda Industries; is on the Scientific Advisory Board for Lohocla Research Corporation, Mnemosyne Pharmaceuticals, Inc., Naurex, Inc., and Pfizer; is a stockholder in Biohaven Pharmaceuticals; holds stock options in Mnemosyne Pharmaceuticals, Inc.; holds patents for Dopamine and Noradrenergic Reuptake Inhibitors in Treatment of Schizophrenia, US Patent No. 5,447,948 (issued September 5, 1995), and Glutamate Modulating Agents in the Treatment of Mental Disorders, U.S. Patent No. 8,778,979 (issued July 15, 2014); and filed a patent for Intranasal Administration of Ketamine to Treat Depression. U.S. Application No. 14/197,767 (filed on March 5, 2014); US application or Patent Cooperation Treaty international application No. 14/306,382 (filed on June 17, 2014). Filed a patent for using mTOR inhibitors to augment the effects of antidepressants (filed on August 20, 2018). Dr. Schmahl is consultant for Boehringer Ingelheim International GmbH. Dr. Stein has received research grants and/or consultancy honoraria from Lundbeck and Sun. Dr. Thompson received partial research support from Biogen, Inc. (Boston, USA) for research unrelated to the topic of this manuscript. All other authors have no conflicts of interest to declare.

Figures

Figure 1.
Figure 1.. Analyses pipelines.
(A) Anatomical neuroimaging data from 29 research sites was aggregated by the ENIGMA PGC PTSD working group. Regional estimates of cortical thickness (CT) and surface area (SA) extracted from 148 cortical regions based on the Destrieux atlas (Destrieux, Fischl, Dale, & Halgren, 2010) were harmonized to remove site effects with ComBat approach and entered into a linear model to adjust for effects of age, age2, sex, and whole-brain mean CT (or SA). The residuals were used to compute Pearson correlation coefficients for each pair of cortical regions across subjects within groups. The correlation coefficients were r-to-z transformed to improve normality and yielded a structural covariance (SC) matrix for each participant group. The cortical regions were rank ordered according to the magnitude of effect size when contrasting CT (or SA) between PTSD and non-PTSD groups. The top-n (n = 2 to 148) regions with the largest effect size of differences for PTSD > non-PTSD constituted atrophic networks, PTSD < non-PTSD constituted hypertrophic networks, while the smallest effect size stable networks. The mean SC of a given n-region network measured by the mean of positive correlations between all possible pairs of regions were compared to 5,000 randomly generated n-region networks matched for hemisphere and distance. Both global and individual tests were employed to compute statistical significance based on the proportion of mean SC values from randomly chosen sets of n regions that exceeded or equaled the mean SC of the actual top-n network. As illustrated in (B), the top-n (n = 5, 10, and 20) regions showed (i) the largest effect size in CT (or SA) for PTSD < non-PTSD (atrophic networks); (ii) the largest effect size of PTSD > non-PTSD (hypertrophic networks); or (iii) the smallest effect size of PTSD vs. non-PTSD (stable networks). (C) CT-based hypertrophic networks for top-3, top-10 and top-50 regions.
Figure 2.
Figure 2.. The top-20 regions showing PTSD-related differences.
The top-20 regions that (A) PTSD < non-PTSD and (B) PTSD > non-PTSD in cortical thickness. The top-20 regions that (C) PTSD < non-PTSD and (D) PTSD > non-PTSD in surface area. Node size represents the magnitude of effect size for between-group differences per region. Warm color denotes PTSD > non-PTSD, and cool color denotes PTSD < non-PTSD. Regions names are listed in Supplementary Table S4. Two examples are shown on the right to denote the node size and the corresponding effect size (Cohen’s d). The directions of the brain maps (axial view) are also shown.
Figure 3.
Figure 3.. Mean SC of patients with PTSD.
Global tests showed that PTSD patients have higher mean SC in both CT- (p < 0.001) and SA-based (p = 0.017) atrophic networks, both CT- (p = 0.029) and SA-based (p = 0.017) hypertrophic networks, and CT-based (p < 0.001) but not SA-based (p > 0.5) stable networks than the corresponding random networks. The curves of networks with up to 50 nodes are shown for illustrative purposes, given that the mean SC of actual networks and the mean SC of the average of random networks were very similar for large network sizes. Red curve, mean SC of the actual networks; Blue curve, mean SC of the average of 5,000 random networks; light blue ribbon, 95% confidence interval (CI) of the 5,000 random networks.
Figure 4.
Figure 4.. Mean SC of trauma-exposed participants without PTSD.
Global tests showed that participants without PTSD had higher mean SC in both CT- (p < 0.001) and SA-based (p < 0.001) atrophic networks, SA-based (p = 0.014) but not CT-based (p = 0.139) hypertrophic networks, and neither CT- (p = 0.264) nor SA-based (p = 0.732) stable networks than in corresponding random networks. The curves for networks with up to 50 nodes are shown for illustrative purpose, given that the mean SC of actual networks and the mean SC of the average of random networks were very similar for large network sizes. Red curve, mean SC of the actual networks; Blue curve, mean SC of the average of 5,000 random networks; light blue ribbon, 95% confidence interval (CI) of the 5,000 random networks.
Figure 5.
Figure 5.. Mean SC of PTSD vs. non-PTSD.
Global tests showed that patients with PTSD versus non-PTSD participants had lower mean SC in both CT- (p = 0.014) and SA-based (p = 0.024) atrophic networks, but no significant difference in CT- (p = 0.098) and SA-based (p > 0.5) hypertrophic networks as well as CT- (p > 0.5) and SA-based (p > 0.5) stable networks. The curves of networks with up to 50 nodes are shown for illustrative purpose, given that the mean SC of actual networks and the mean SC of the average of random networks were very similar for large network sizes. Red curve, mean SC of the actual networks; Blue curve, mean SC of the average of 5,000 random networks; light blue ribbon, 95% confidence interval (CI) of the 5,000 random networks.
Figure 6.
Figure 6.. Replication analyses results.
The global-tests results shown in Figures 3, 4, and 5 are reliable as underscored by the area under curve (AUC) of mean SC for the results based on all 29 sites (represented by the red vertical line) was always located within the 95% confidence interval (represented by two blue vertical dashed lines) of the AUC of mean SC from 5,000 iterations leaving out 3 sites at each iteration across all types of networks.
Figure 7.
Figure 7.. Interaction effects of PTSD and depression.
Global tests showed that patients with depression alone had higher mean SC in (A) CT-based (p < 0.001) and (B) SA-based (p < 0.001) atrophic networks, and lower mean SC in (D) SA-based hypertrophic networks (p = 0.029), than patients with both PTSD and depression. Patients with depression alone also showed higher mean SC in both (A) CT-based (p < 0.001) and (B) SA-based (p < 0.001) atrophic networks, and lower mean SC in (D) SA-based hypertrophic networks (p < 0.001), than patients with neither PTSD nor depression. Patients with PTSD alone showed lower mean SC in (A) CT-based atrophic networks than patients with depression alone (p < 0.001), and higher mean SC in (B) SA-based atrophic networks than patients with both PTSD and depression (p < 0.001) as well as participants with neither PTSD nor depression (p = 0.014). No significant PTSD x depression interaction effect (global p-values > 0.2) was found in the other types of networks shown in (C), (E) and (F). The curves of networks with up to 30 nodes were shown for illustrative purposes. Error bar denotes 95% confidence interval of 5,000 random networks. * represents p < 0.05; *** represents p < 0.001.

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