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ENIGMA PTSD Work Group Proposal: Structural Covariance Between Regions with Cortical
Thickness Reduction in PTSD
Delin Sun a,b
a Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham NC b VA Mid-Atlantic Mental Illness Research, Education and Clinical Center
(MIRECC), Durham, NC
PTSD is a mental health problem that some people develop after experiencing or witnessing a
life-threatening event, like combat, a natural disaster, a car accident, or sexual assault 1.
Trauma and chronic stress elicit synaptic dysconnectivity 2,3 and synaptic density reduction 4
that are associated with brain morphometric changes. PTSD is accompanied with cortical
thickness reduction in various brain regions 5,6 (however, see Li et al.7
).Structural covariance measures of cortico-to-cortical connectivity corresponding to
transcriptional brain networks 8 and anatomical connectivity based on white matter fiber
tractography 9. It may index mutually trophic factors between distant regions that are
anatomically connected. Structural covariance is sensitive to aberrant connectivity and brain
network organization in PTSD 10-12. It is speculated that neural connections can propagate
pathological processes to distant cortical regions 13.
A study recently published in American Journal of Psychiatry 13 reported that Structural covariance was significantly increased among regions with the most extensive thickness
reductions, irrespective of whether it was measured in patients or healthy control subjects. The
findings suggest that cortico-cortical connectivity can provide an explanation for the irregular
topographic distribution of thickness reduction, and the regions that are affected in patients
are part of networks that are present in healthy individuals.
Little is known about the association between cortical thickness reduction and structural
covariance in PTSD. Few studies have reported structural covariance between all pairs of
regions in PTSD, which may be due to the difficulty of detecting significance after correcting
multiple comparisons. Moreover, studies of PTSD based on small samples are hard to fully
understand the effects of variables such as age, gender, trauma experience, and are hard to
reveal robust and generalizable results. We aimed to investigate the structural covariance
between regions with cortical thickness reduction in PTSD. We specifically hypothesized that
the structural covariance between regions with cortical thickness reduction in PTSD patients
would be stronger compared with the structural covariance between randomly selected regions
in both PTSD and controls, would be stronger in patients with PTSD than in controls, would be
stronger in PTSD patients with more severe symptoms, and would be modulated by factors
such as age and gender.
The ENIMGA-PGC PTSD Working Group aggregated data from 29 cohorts in five countries. PTSD
patients and control subjects had varying levels of trauma exposure. We will analyze cortical
thickness data from 3,505 subjects, including 1,344 PTSD patients and 2,131 control subjects.
Harmonized scales of childhood trauma and alcohol use disorder (AUD) were obtained from the
sites in addition to SCID diagnoses and PTSD severity scale scores. All participating sites
obtained approval from local institutional review boards and ethics committees. All study
participants provided written informed consent.
Imaging and Statistical Analysis
Raw structural imaging (T1) data obtained from previously conducted cross-sectional casecontrol studies were analyzed at Duke University through a standardized neuroimaging and QC
pipeline developed by the ENIGMA Consortium (need citation). Cortical parcellation was
performed with FreeSurfer (http://surfer.nmr.mgh.harvard.edu/) that calculates regional mean
cortical thickness and surface area measures for 148 regions (74 outputs in each hemisphere
according to the Destrieux Atlas 14.
Structural Covariance Analysis
The pipeline of the structural covariance analysis is shown in Fig. 1. The influence of age, sex,
and study site were regressed out from the association with cortical thickness values. Age was
not regressed out when investigating the interaction of diagnosis and age, and sex was not
regressed out when investigating the interaction of diagnosis and sex. After this step, groupspecific matrices of structural covariance between all possible pairs of 148 were calculated
based on Pearson’s correlation coefficients. The r-to-z transformation was applied to all
correlation coefficients to improve normality 13. Cortical regions were ranked from highest to
lowest according to the effect size of cortical thickness reductions.
To investigate whether structural covariance is significantly stronger between regions with
cortical thickness reductions compared with randomly selected sets of regions, we will employ
permutation testing for structural covariance measured in each subject group separately and
for the between-group difference. The mean structural covariance between the top-n regions
with the most significant cortical thickness reductions will be computed. To generate a null
distribution for this mean value, the mean structural covariance will be computed between
5,000 randomly chosen sets of n regions. The proportion of random region sets for which the
mean structural covariance exceeds or equals the mean structural covariance in the actual
(nonrandom) data will provide a p value for the null hypothesis of equality in structural
covariance among regions of cortical thickness reductions and randomly chosen pairs of
regions. This procedure will be repeated independently for n=2,…,148, and the mean structural
covariance between the top-n regions with the most significant cortical thickness reductions
will be plotted as a function of n. The area under this plot of mean structural covariance as a
function of n will be used to compute a global p value for all values of n.
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