A mega-analysis of vertex and gyral cortical thickness differences in adults with and without PTSD

doi:10.21203/rs.3.rs-2085479/v1

Download PDF

Article

A mega-analysis of vertex and gyral cortical thickness differences in adults with and without PTSD

https://doi.org/10.21203/rs.3.rs-2085479/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

A number of studies of posttraumatic stress disorder (PTSD) report thinner cerebral cortical gyri using gyrus-based analysis or thinner foci within the gyri using vertex-based analysis. However, the locations of these findings are inconsistent across studies, and the spatial transformations required during vertex-based analysis may affect the focal findings. A mega-analysis using a large number of subjects from multiple PTSD studies could potentially identify more reproducible cortical thickness abnormalities. Investigating both the vertex and gyral thicknesses simultaneously may verify the vertex-based focal findings using gyral data without imposing any spatial transformation. Here we aggregated data from 24 international laboratories using ENIGMA standardized procedures for 949 adult PTSD patients and 1493 controls without PTSD (age 18 to 65 years). We examined whether gyral and vertex cortical thickness are (a) different between subjects with PTSD and controls and (b) associated with PTSD symptom severity in trauma-exposed subjects. Regions with overlapping thinner cortical gyri and thinner vertex clusters were located in frontal, temporal, parietal, and occipital cortices. Thinner right lateral orbitofrontal and right lingual gyri and concomitantly thinner vertex clusters in the anterior portions of both gyri were associated with PTSD symptom severity. Convergent findings in these locations suggest focally thinner cortex in these gyri, which may be involved in altered processing and regulation of emotion and sensory inputs underlying posttraumatic stress symptoms.

Traumatic experiences associated with war, disaster, assaults, or accidents can precipitate posttraumatic stress disorder (PTSD) ¹. Globally, millions of people suffer from debilitating PTSD symptoms, including intrusive memories of trauma, avoidance of trauma-related cues, hyperarousal, and negative alterations in mood and cognition. Existing treatments are time consuming, costly, and only partially efficacious in many patients ^{2, 3}. Therefore, the investigation of brain alterations that contribute to PTSD is critical for understanding the underlying pathophysiologic processes and, ultimately, for developing more effective treatment strategies.

Structural magnetic resonance imaging (sMRI) studies report widespread structural brain alterations in PTSD patients, but inconsistent results are common ⁴. Recent ENIGMA-PGC PTSD mega-analyses of anatomically defined cortical regional volumes in several thousand subjects from multiple cohorts identified PTSD associated differences. These studies, which are more reproducible and robust than individual studies with small samples, found that smaller left and right lateral orbitofrontal gyri (LOFG), right insular gyrus (IG), right superior parietal gyrus (SPG), right lingual gyrus (LING), left superior temporal gyrus (STG), and hippocampal volumes are associated with PTSD symptoms ^{5, 6}. Mega-analyses of PTSD subjects have used voxel-based morphometry (VBM) to identify lower grey matter density (GMD) in focal areas within the superior frontal gyrus (SFG) ^7–9, IG ^{7, 10}, anterior cingulate gyrus (ACG) ^7–11, middle temporal gyrus (MTG) ^7–9, LING ⁸, fusiform gyrus (FFG) ⁸, parahippocampal gyrus ¹⁰, and hippocampus ^{8, 11}. Lower cortical volumes and GMD in the prefrontal and limbic regions may contribute to functional impairments such as emotion dysregulation, abnormal fear extinction, and deficient context processing, as supported by current theories of PTSD pathophysiology ¹². Lower volumes in sensory processing and memory associated occipital, temporal, and parietal cortical regions raise the possibility that cortical sensory and memory abnormalities also contribute to exaggerated threat detection in PTSD ¹³. However, the gyral volume measures are insensitive to small-scale and focal changes within large multi-functional cortical gyri. Whereas VBM can potentially detect smaller scale changes at the voxel level, grey matter density differences mapped using VBM provide only indirect evidence of structural abnormalities ¹⁴. Furthermore, spatial registration of individual images to a common template and smoothing procedures used in between-group VBM comparisons require spatial transformation of individual data, which may confound the results ¹⁵. Thus, VBM findings require further confirmation.

Automated cortical thickness measurement reconstructs the cortical surface using tiny triangular meshes and then calculates high spatial resolution cortical thickness as the shortest distance between pial and white matter surfaces at the vertices of triangular meshes (approximately 1 mm apart on the cortical surface). After parcellation of gyri based on the curvature of cortical surface, the gyral mean cortical thickness is defined as the average cortical thickness of all vertices within a gyrus. Cortical gyral mean thickness can be compared between groups without spatial transformation for alignment across subjects, but the resolution is low. Vertex-based thickness comparison can detect focal differences to refine gyral mean thickness comparison, but it requires spatial transformation for surface-based spatial registration and smoothing, similar to VBM analysis. Parallel analyses of gyral and vertex thickness would be complementary in detecting the focal differences and verifying them in gyral data without spatial transformation. The cortical thickness measure avoids the structural ambiguity of VBM GMD measurements.

Cortical thickness measures have been analyzed in animal ¹⁶ and human cortical thickness studies have investigated the effects of childhood trauma ¹⁷, combat exposure ^18–20, and genetic factors ^21–23 on cortical structure. Single-cohort studies with small sample sizes have reported lower gyral mean thickness ^24–27 and thinner focal vertex clusters within a gyrus ^{19, 28–30}. Affected regions in adult PTSD patients include the prefrontal ^{17, 24–26, 28–31}, cingulate ^{17, 25, 27, 29, 32}, parietal ^{26, 28, 29, 31}, temporal ^{24, 27, 28, 30, 31, 33, 34}, occipital ^{17, 29, 30}, parahippocampal ²⁶ and fusiform gyri ^{19, 31}. Cortical thickness measures in PTSD patients have been reported to be negatively associated with PTSD symptom severity ^{20, 33, 35}. However, the locations of thinner cortical regions varied among these studies, and other studies reported no PTSD-related alterations ^36–39 or thicker cortex in PTSD ^{29, 40–42}. A large-scale collaborative analysis of cortical thickness across multiple studies may help identify reproducible gyral and focal alterations in cortical structure. The meta-analysis approaches, which aggregate summary statistics from different cohorts or publications, have been widely used, but they are not easily adaptable for analyzing thickness measurements at thousands of cortical vertices. For example, a meta-analysis that was limited to a few cortical vertex clusters, with demonstrated PTSD-related differences from 11 selected publications (438 patients and 396 controls), reported thicker vertex clusters in STG, SPG, and SFG ⁴³. This finding is inconsistent with the majority of individual studies. A mega-analysis approach centralizes whole-cortex data from individual subjects for analyzing every cortical location across all subjects together and cohort effects are modeled using mixed effects linear regression. This comprehensive approach has been increasingly used in prior studies ^{5, 44}, and it is suitable for both gyral and vertex cortical thickness analyses.

Until recently, mega-analyses of cortical vertex thickness data have been impeded technologically either because of the need to transfer large vertex-based thickness datasets generated in different laboratories (e.g., 432,000 kilobytes of data per subject after FreeSurfer processing) or due to the computational and human resources required at a central site to inspect and process several thousand MRI scans. Additionally, common imaging analytic toolboxes (e.g., FreeSurfer Group Analysis) are not designed for mega-analyses that use sophisticated mixed effects modeling or machine learning. The ENIGMA-PGC PTSD Working Group has overcome these difficulties by: (1) implementing a validated ENIGMA standardized image processing and quality control pipeline at participating laboratories, which minimizes variability in neuroimaging processing methods ^{44, 45}; (2) spatially registering subjects’ cortical thickness maps to a common atlas for efficient transfer of vertex-based thickness data in small registered vertex thickness maps (635 kilobytes of data per subject) and gyral mean thickness data without spatial transformation (0.2 kilobyte of data per subject), and (3) converting imaging data formats for analyses with validated analytic toolboxes while using FreeSurfer tools for imaging display and extraction. Using these analytical procedures and data from 24 laboratories, we aggregated thousands of subjects, allowing us to verify PTSD-related differences in thickness of prefrontal, cingulate, parietal, temporal, and occipital cortices reported by individual studies and to explore thickness abnormalities in other cortices.

Sample. Data from 24 participating laboratories worldwide were aggregated at the central site at the University of Toledo after approval by the Institutional Review Board. Subjects outside the age range of 18–65 years at the time of their scan were excluded to reduce potential age-related confounds in vertex spatial normalization. Individual studies that lost > 50% of subjects were excluded entirely. A total of 2,442 adult subjects from 34 studies was included (see Supplementary Table 1 for sample information and Supplementary Table 2 for subject inclusion and exclusion criteria for individual studies). PTSD was diagnosed in individual studies using the Clinician-Administered PTSD Scale CAPS-4 (19 cohorts, DSM-IV), CAPS-5 (6 cohorts, DSM-5), Structured Clinical Interview (SCID-4, 2 cohorts, DSM-IV), SCID-5 (1 cohort, DSM-IV), MINI International Neuropsychiatric Interview (MINI-6, 3 cohorts, DSM-IV), PTSD Checklist PCL-4 (3 cohorts, DSM-IV), PCL-5 (2 cohorts; DSM-5), or PTSD Symptom Scale (PSS,1 cohort, DSM-IV). A subset of 27 studies included trauma exposed subjects without PTSD as controls and assessed PTSD symptoms of these subjects. The remaining control subjects were not exposed to trauma.

Mega-analysis of cortical gyral thickness

The methods for gyral data analyses have been described in detail previously ⁵, and are briefly summarized below.

Imaging acquisition and processing. High resolution T1-weighted structural brain MRI scans acquired by individual studies were processed in participating laboratories using the standardized protocols developed by ENIGMA (http://enigma.ini.usc.edu/protocols/imaging-protocols). This involved: (1) use of the automated FreeSurfer processing stream (version 5.3 in 29 cohorts, and 6.0 in 5 cohorts) to create an individual cortical thickness map of each brain from ~ 150,000 vertex thickness measures in each hemisphere; (2) parcellation of 34 regions in each hemisphere using the Desikan–Killiany atlas to extract the gyral mean thickness of all vertices within each region, and (3) visual inspection of segmentations of grey and white matter and parcellations of regions using ENIGMA imaging quality control protocols (http://enigma.ini.usc.edu/protocols/imaging-protocols) to exclude regions with segmentation or parcellation errors.

Statistical Analyses. A linear mixed effects model mega-analysis of individual subject data from all studies was employed to assess associations between PTSD diagnosis and gyral mean thicknesses. A random intercept for a “study-by-scanner” variable was included to account for possible correlations for data of subjects from the same study and image data from the same scanner. Age and sex were included as fixed effects. We also tested possible two-way interactions of PTSD with age and sex, respectively. Spearman's rank-order correlation analysis of gyral mean thicknesses with PTSD symptom severity scores for the most commonly used instrument, CAPS-4, was performed for a subgroup of trauma-exposed PTSD and control subjects to examine associations between cortical thickness and posttraumatic stress symptom severity, while covarying for the “study-by-scanner” variable, age, and sex.

Group means, standard deviations, adjusted thickness means, and standard errors (SE) are reported in Supplementary Table 3. Effect size was calculated using a standardized coefficient, which was the coefficient from the model with all continuous variables standardized ⁴⁶. False discovery rate (FDR) was used for multiple comparisons correction across 68 cortical regions, and an FDR-corrected P-value < 0.05 was considered significant ⁴⁷. Heterogeneity of gyral data associations with diagnosis was evaluated using Higgins’s I², which indicates the percentage of variation across studies that is due to heterogeneity rather than chance. Statistical analyses of gyral data were performed in R v3.6.1 using the “lmer” function and the “lmerTest” function to test mixed effects.

Mega-analysis of cortical vertex thickness

Data aggregation and quality control. The vertex-based thickness map for each subject was spatially registered to the FreeSurfer atlas, FSaverage, using FreeSurfer nonlinear registration procedures to create a registered thickness map. The registered thickness map is a file containing sequential vertex thickness measures from the atlas. The same sequence of vertex measures across all subjects facilitates group analysis of the thickness at each vertex. Files for the registered thickness maps were transferred to our central site at the University of Toledo after all subject identifiable information was removed. For data quality control, if a region for a given subject had segmentation or parcellation errors/issues identified during visual inspection as described above, all vertices in that region for the given subject were excluded from the vertex analysis.

Statistical analysis. Registered cortical vertex thickness maps were smoothed by a 10 mm full-width/half-max kernel using FreeSurfer procedures and were then converted from the MGH format to a NUMPY array ⁴⁸ using the NIBABEL library ⁴⁹ at the central site. The linear mixed effects model used in gyral analysis, which included age and sex as fixed effects and the “study-by-scanner” variable as a random effect, was applied to the vertex thickness measures of both hemispheres (~ 300,000 vertices/brain) to test for differences between the PTSD and non-PTSD groups. This was performed using the Python library PANDAS ⁵⁰ and Python’s Statsmodels module ⁵¹. Vertex-wise p-values were obtained from the mixed effects model. The p-values were log transformed, converted back to an MGH file, and visualized using FreeSurfer/tksurfer. Multiple comparisons corrections were performed using the surface-based false positive rate and vertex-wise threshold of p < 0.005 and cluster-wise threshold of p < 0.05 ⁵².

Clusters of vertices that were significantly different between the PTSD and non-PTSD groups after multiple comparisons corrections were used to extract the cluster mean thickness from each subject’s registered cortical thickness maps using the FreeSurfer tool. Cluster mean thicknesses were computed after excluding vertices within regions that had parcellation issues/errors in individual subjects. Cluster mean thicknesses were tested for correlations with CAPS-4 scores in trauma-exposed subjects as in the gyral analyses.

Group characteristics

949 PTSD and 1,493 non-PTSD adult subjects were included in the gyral and vertex thickness mega-analyses. As summarized in Table 1, PTSD and non-PTSD groups did not differ in age, sex, hemisphere mean thicknesses, or intracranial volume. CAPS-4 scores were significantly higher in PTSD subjects compared to trauma-exposed non-PTSD controls.

Table 1

Characteristics of PTSD and control groups
	PTSD	non-PTSD	diff. ^b	p
N (%)	949 (38.8%)	1493 (61.1%)
female N (%)	423 (44.6%)	702 (47.0%)	χ² = 1.30	0.25
Age (yrs) ^a	36.5 ± 10.8	36.1 ± 11.5	T = 0.79	0.43
age range (yrs)	18–64	18–65
CAPS-4 scores (N) ^c	51.3 ± 17.0 (317)	10.9 ± 13.5 (414)	T = 57.42	< 1X10^− 4***
N of cohorts	33	32
N of scanners	38	32
left hemispheric mean thickness (mm)	2.44 ± 0.17	2.45 ± 0.15	T = -1.80	0.07
right hemispheric mean thickness (mm)	2.43 ± 0.17	2.44 ± 0.15	T = -1.36	0.17
ICV ^d (cm³)	1495.5 ± 192.1	1496.4 ± 201.7	T = -0.11	0.91
a: Data are reported as mean ± standard deviation.
b: Chi-square test (χ² scores) or independent T test (T scores).
c: CAPS-4 scores of trauma-exposed subjects with or without PTSD
d: intracranial volume (ICV)
***: significant at P < 0.0001 level

Vertex thicknesses in PTSD vs. non-PTSD groups

Compared to the non-PTSD group, thinner vertex clusters in the PTSD group were in the right hemisphere for (1) the anterior lateral orbitofrontal gyrus (aLOFG), (2) dorsal superior frontal gyrus (dSFG), (3) middle insular gyrus (mIG), (4) posterior middle temporal gyrus (pMTG), (5) temporoparietal junction (TPJ), (6) posterior intraparietal sulcus (pIPS), (7) anterior lingual gyrus (aLING), (8) occipital fusiform/inferior lateral occipital gyri (oFFG/iLOG), and the left hemisphere for (9) dorsal rostral middle frontal gyrus (dRMFG), (10) anterior intraparietal sulcus (aIPS), (11) posterior intraparietal sulcus (pIPS), (12) anterior supramarginal gyrus (aSMG), and (13) superior lateral occipital/inferior parietal gyri (sLOG/IPG) (Fig. 1A, Table 2, and Supplementary Fig. 1). No significantly thicker clusters were found in the PTSD vs. the non-PTSD group.

Table 2

Clusters of cortical vertices with significant thickness differences between PTSD and non-PTSD adults
cluster index			brain region ^a	size	peak vertex							corr. with CAPS-4 scores ^b
cluster index			brain region ^a	(mm²)	log₁₀P		Tal X		Tal Y		Tal Z	N ^c		corr. R ^d	FDR P
1	R	anterior lateral orbitofrontal gyrus (aLOFG)		188.90	-4.23	16.1		41.8		-17.0		715	-0.14			< 0.01**
2	R	dorsal superior frontal gyrus (dSFG)		301.19	-3.63	19.9		28.1		42.4		658	-0.15			< 0.01**
3	R	middle insular gyrus (mIG)		179.24	-3.09	33.9		-8.4		10.6		665	-0.07			0.08
4	R	posterior middle temporal gyrus (pMTG)		184.63	-3.71	50.8		-57.9		5.40		700	-0.13			< 0.01**
5	R	temporoparietal junction (TPJ)		374.51	-6.01	61.6		-36.7		21.7		704	-0.07			0.08
6	R	posterior intraparietal sulcus (pIPS)		450.89	-4.15	31.1		-62.5		26.3		694	-0.08			0.04*
7	R	anterior lingual gyrus (aLING)		887.25	-5.91	9.3		-62.8		3.5		617	-0.18			< 0.01**
8	R	occipital fusiform/inferior lateral occipital gyri (oFSFG/iLOG)		200.92	-3.41	41.1		-68.7		-9.0		719	-0.08			0.04*
9	L	dorsal rostral middle frontal gyrus (dRMFG)		203.99	-3.38	-38.5		40.5		21.3		715	-0.11			< 0.01**
10	L	anterior intraparietal sulcus (aIPS)		190.83	-3.36	-23.0		-72.2		28.6		691	-0.13			< 0.01**
11	L	posterior intraparietal sulcus (pIPS)		156.94	-3.36	-30.0		-59.1		39.7		606	-0.12			< 0.01**
12	L	anterior supramarginal gyrus (aSMG)		176.61	-3.31	-48.4		-27.3		18.3		677	-0.14			< 0.01**
13	L	superior lateral occipital/inferior parietal gyri (sLOG/IPG)		494.59	-4.72	-44.7		-75.3		12.6		716	-0.08			0.04*
a: left (L) and right (R) hemisphere
b: correlations between CAPS-4 scores and cluster mean thickness in trauma-exposed subjects
c: trauma-exposed subjects with and without PTSD
d: Spearman correlation coefficients of cluster mean thickness and CAPS-4 scores in partial correlation analyses adjusting for age, sex, and a combined variable of study and scanner.
: significant at FDR P < 0.05 () or P < 0.01 (**) level corrected for 13 clusters

Gyral thicknesses in PTSD vs. non-PTSD groups

Compared to the non-PTSD group, the PTSD group showed thinner gyri in the left frontal pole (FP), caudal middle frontal (CMFG), posterior cingulate gyrus (PCG), and SPG, right LOFG, FFG and LING, bilateral STG, IPG, supramarginal gyrus (SMG), and LOG (adjusted for age, sex, study-by-scanner; and cohort D: -0.12~-0.25, P: 0.00 ~ 0.04, FDR corrected;Table 3; Fig. 1B). Inverse relationships between age and gyral mean cortical thickness were significant for 65 of the 68 regions. Sex effects were significant for 24 of 68 regions where most (15 of 24) gyri were thicker in male than female subjects. However, neither interactions of age and PTSD diagnosis nor sex and PTSD diagnosis were significant for any region (Supplementary Table 4). Across all regions, I² from Higgins Heterogeneity tests ranged from 0 to 49.8, which indicated low to moderate heterogeneity across cohorts (Supplementary Table 4).

Table 3

Significant differences between PTSD and non-PTSD groups in gyral mean cortical thickness
Region ^a	PTSD N	non-PTSD N	effect size ^b	raw P	FDR P
frontal regions
L FP	886	1349	-0.15	< 0.01	0.03*
L CMFG	893	1432	-0.12	0.01	0.04*
R LOFG	922	1450	-0.16	< 0.01	0.02*
cingulate regions
L PCG	935	1467	-0.13	0.01	0.03*
temporal regions
L STG	866	1349	-0.14	< 0.01	0.03*
R STG	871	1401	-0.14	< 0.01	0.03*
R FFG	927	1453	-0.14	< 0.01	0.03*
parietal regions
L SPG	816	1338	-0.13	0.01	0.04*
L IPG	902	1432	-0.14	< 0.01	0.03*
L SMG	876	1405	-0.16	< 0.01	0.02*
R IPG	903	1438	-0.13	0.01	0.04*
R SMG	875	1382	-0.15	< 0.01	0.03*
occipital regions
L LOG	923	1454	-0.15	< 0.01	0.03*
R LING	838	1324	-0.25	< 0.01	0.01*
R LOG	926	1455	-0.12	0.01	0.04*
a: left (L) and right (R) hemisphere
b: standardized coefficients of linear mixed model are reported for effect sizes.
*: difference significant at FDR corrected P < 0.05 level

Comparison of PTSD-related gyral vs. vertex cortical thinning

The overlapping cluster and gyral thinning included: the right aLOFG (1) cluster and the LOFG, right aLING (7) cluster and the LING, right TPJ (5) cluster and the STG and SMG, right pIPS (6) cluster and IPG, right oFFG/iLOG (8) cluster and FFG and LOG, left aSMG (12) cluster and SMG, left aIPS (10) and pIPS (11) clusters and the SPG and IPG, and left sLOG/IPG (13) cluster and LOG and IPG (Fig. 1). The remaining four thinner vertex clusters, i.e., left dRMFG (9) and right dSFG (2), mIG (3), and pMTG (4) clusters, were located in regions where gyral mean thicknesses did not significantly differ between the PTSD and non-PTSD groups. Finally, PTSD-related thinner gyral mean thickness of the left FP, CMFG, STG, and PCG was not related to any thinner vertex cluster.

Correlations between CAPS-4 scores and PTSD-related vertex cluster and gyral thinning

With the exception of the right mIG (3) and TPJ (5) clusters, all other mean thicknesses of the vertex clusters that were thinner in the PTSD group were significantly negatively correlated with CAPS-4 scores in 731 trauma-exposed subjects with or without PTSD (adjusted for age, sex, and “study-by-scanner” variables; Table 2). CAPS-4 scores in these trauma-exposed subjects were also significantly negatively correlated with gyral mean thicknesses in right LOFG, RMFG, SFG, LING, and cuneus; and left CMFG, RMFG, and SFG (Table 4, Supplementary Table 5). Significant negative correlations between CAPS-4 scores and vertex clusters mean thicknesses were consistent with significant gyral mean thickness correlations for the right aLOFG (1) cluster with LOFG, right aLING (7) cluster with LING, right dSFG (2) cluster with SFG, and left dRMFG (9) cluster with RMFG.

Table 4

Significant correlations between CAPS-4 scores and cortical gyral mean thickness
regions ^a	N	corr. R ^b	FDR P
frontal regions
L CMFG	685	-0.14	< 0.01**
L RMFG	715	-0.10	0.048*
L SFG	665	-0.14	< 0.01**
R LOFG	715	-0.12	0.02*
R RMFG	715	-0.11	0.04*
R SFG	658	-0.14	< 0.01**
occipital regions
R LING	617	-0.17	< 0.01**
R cuneus	622	-0.14	0.01*
a: left (L) and right (R) hemisphere
b: Spearman correlation coefficients for partial correlation analyses adjusting for age, sex, and a combined variable of study and scanner.
: significant at FDR corrected P < 0.05 () or P < 0.01 (**) level

This is the first mega-analysis of PTSD-related cortical thickness to date. Thinner frontal, temporal, parietal, and occipital cortices were found in PTSD using both vertex-based and gyrus-based analyses. The results of both analyses were largely consistent, in that thinner vertex clusters fell within gyri with lower gyral mean thickness. Thinning of most vertex clusters was also associated with PTSD diagnosis and PTSD symptom severity in trauma-exposed subjects. Four clusters were colocalized with frontal gyri and right LING where mean gyral thickness was also negatively associated with PTSD symptom severity. Thinning and inverse relationships between thickness and PTSD symptom severity with vertex-based and gyral-based analyses were consistently found in right LOFG and LING.

Cortical thinning in PTSD

The findings of present mega-analysis across entire hemispheres are consistent with PTSD-related cortical thinning in individual studies ^{17, 19, 24, 25, 27, 28, 30, 31, 33, 34, 53}, with thinner cortex in right LING and left SMG that has not been reported previously. The present parallel mega-analyses of vertex and gyral cortical thicknesses in the same subjects identified substantial overlap between the two measurements, suggesting that the thinner vertex clusters do not stem from confounds of spatial transformations during vertex-based analysis. The observed thinner foci likely contribute to the thinning of these gyri. The consistent negative correlations of PTSD symptom severity, with gyral and vertex cluster thickness in the prefrontal gyri and LING, bolsters confidence in these associations ⁵⁴. Both the thinner foci in the right aLOFG and aLING and the colocalized thinner gyral mean thicknesses of LOFG and LING were negatively associated with PTSD symptom severity, implying that these findings are not attributable to spatial transformation. Besides the aLOFG and aLING, we also found thinner cortex in frontal, temporal, parietal, and occipital vertices that was also associated with PTSD symptom severity, except in the right mIG and TPJ clusters. However, lack of confirmation from gyral thickness made it impossible to rule out confounding factors, such as spatial transformation, as an explanation for these vertex findings. Thinner gyri and/or correlations with PTSD symptom severity in left CMFG, FP, STG, and PCG without vertex changes require further study to identify possible functional subdivisions within these large multi-functional gyri.

Potential functional implications of orbitofrontal and lingual cortical thinning in PTSD

Our findings suggest that thinner vertex clusters in the anterior portion of the right LOFG and LING could explain the lower gyral mean thickness, volume, and GMD of LOFG and LING observed in prior mega-analyses ^{5, 8}. The aLOFG is active during reward processing ⁵⁵ and has been linked to fear extinction recall in PTSD patients by recent meta-analysis ⁵⁶. Impairments in reward and fear extinction recall processes may contribute to the development of PTSD symptoms, such as negative mood and re-experiencing symptoms ^{57, 58}. The thinning of aLING is linked to visual target switching, i.e., evaluating different options in order to select a response ^{59, 60}. Altered aLING activation while viewing aversive images is negatively associated with CAPS scores ^{61, 62}, and right aLING activation while viewing trauma imagery is greater in PTSD patients than in trauma-exposed controls ⁶³. Thinning of the occipital cortex may reflect changes that underlie sensory processing of trauma-related cues and intrusive memories ⁶⁴. Taken together, the results provide insights into the relationship between cortical thinning and PTSD functional impairments, such as emotion processing and regulation, sensory processing, and memory.

Potential factors contributing to cortical thinning in PTSD

Cortical thickness is influenced by genetic, developmental, and environmental factors that may increase PTSD risk. For example, glucocorticoid signaling dysregulation is precipitated by methylation of the SKA2 gene (spindle and kinetochore-associated complex subunit 2), which increases PTSD symptoms and suicide risk and reduces prefrontal cortical thickness in the areas that overlap with the right aLOFG and other prefrontal vertex clusters and gyri, also found to be thinner in the present study ²². In the same vein, left pRMFG and right pSFG thinning is within a zone, where the interaction of PTSD severity and an oxidative stress-related gene ALOX 12, significantly affects cortical vertex thicknesses ²¹. The ALOX 12 risk genotype for PTSD is associated with increased cellular damage during stress, which may result in reduced cortical thickness ⁶⁵. RMFG and CMFG have been reported as bilaterally thinner in adult PTSD patients with childhood maltreatment histories ¹⁷, suggesting that this developmental risk factor for PTSD may affect cortical structure. Finally, trauma exposure itself may affect cortical thickness. For example, thickness of the right LOFG and the left RMFG and STG is negatively associated with combat exposure ^18–20. This suggests that presence of environmental risk factors of PTSD may reduce cortical thickness. In summary, cortical thickness alterations in the current findings may mediate effects of the various risk factors on PTSD.

Limitations

The present study has several limitations. Some of the studies that contributed data could not share information that may affect cortical thickness, such as medications, illness duration, trauma type, age at trauma exposure, and comorbidities, (e.g., depression, anxiety disorders, and substance abuse). Further work to examine these potentially moderating factors is needed. We also did not address age-dependent PTSD abnormalities in children or elderly adults. However smaller LOFG volumes were found in the pediatric PTSD patients, indicating that current results may be found at an early age ⁶⁶. Finally, the present analyses do not address differences in other brain regions, such as the hippocampus and subcortical structures.

The strength of the present study is that data were derived from multiple cohorts that were processed through a standard processing pipeline. Thus, our results are robust to the selection of image acquisition, clinical assessment instruments, and other site-specific parameters that are generally fixed for all subjects in conventional study designs. We adjusted for study-related variations in data sources statistically and had acceptable heterogeneities for gyral measures across cohorts. Present findings from a broad range of settings may be more generalizable than individual studies.

This is the largest mega-analysis of cortical thickness in PTSD patients to date. The combined gyral and vertex group comparisons and correlations with PTSD symptom severity in trauma exposed subjects suggest PTSD-related thinning in the prefrontal, temporal, occipital, and parietal regions, and consistent findings from these analyses strongly support the notion that PTSD-related thinner anterior portions of the right LOFG and LING might be involved in neural mechanisms linked to PTSD pathophysiology/symptom severity. Collectively, these regions contribute to cortical circuits involved in emotion, memory, and sensory processing. The alterations of cortical thicknesses may provide a basis to further examine potential neural substrates and mechanisms mediating genetic, developmental, and environmental risk factors for PTSD.

ENIGMA-PGC PTSD Working Group

Hong Xie ^1,90, Erin M. O’Leary ^2,90, Chiahao Shih ^3,90, Andrew S. Cotton ², John T. Wall ¹, Tian Chen ⁴, Rong Liu ⁴, Kevin S. Xu ^5,6, Chadi G. Abdallah ^10,11, Elpiniki Andrew ¹², C. Lexi Baird ⁷, Lee A. Baugh ^13,14,15, Jessica Bomyea ^16,17, Steven E. Bruce ¹⁸, Richard Bryant ¹⁹, Kyle Choi ²⁰, Judith K. Daniels ²¹, Nicholas D. Davenport ^22,23, Richard J. Davidson ^24,25,26, Michael D. De Bellis ²⁷, Emily L. Dennis ²⁸, Terri deRoon-Cassini ²⁹, Seth G. Disner ^22,23, Negar Fani ³⁰, Kelene A. Fercho ^31,13,14,15, Jacklynn Fitzgerald ³², Gina L. Forster ^13,33, Jessie L. Frijling ³⁴, Elbert Geuze ^35,36, Hassaan Gomaa ¹¹, Evan M. Gordon ³⁷, Dan Grupe ²⁵, Ilan Harpaz-Rotem ^38,11, Courtney C. Haswell ^7,8, Julia I. Herzog ³⁹, David Hofmann ⁴⁰, Michael Hollifield ⁴¹, Bobak Hosseini ⁴², Anna R. Hudson ⁴³, Jonathan Ipser ⁴⁴, Neda Jahanshad ⁴⁵, Tanja Jovanovic ⁴⁶, Milissa L. Kaufman ^47,48, Anthony P. King ⁴⁹, Saskia B.J. Koch ^34,50, Inga K. Koerte ^51,52, Sheri-Michelle Koopowitz ⁴⁴, Mayuresh S. Korgaonkar ⁵³, John H. Krystal ^38,11, Christine Larson ⁵⁴, Lauren A. M. Lebois ^55,48, Ifat Levy ^38,56, Gen Li ^57,58, Mark W. Logue ^59,60, Vincent A. Magnotta ^61,62, Antje Manthey ⁶³, Geoffrey May ^64,65,66,9, Katie A. McLaughlin ⁶⁷, Sven C. Mueller ⁴³, Laura Nawijn ^34,68, Steven M. Nelson ^64,65,66, Yuval Neria ^69,70, Jack B. Nitschke ²⁶, Miranda Olff ^34,71, Elizabeth A. Olson ^72,48, Matthew Peverill ⁷³, K. Luan Phan ^49,42,74, Faisal M. Rashid ⁴⁵, Kerry Ressler ^55,48, Isabelle M. Rosso ^72,48, Lauren E. Salminen ⁴⁵, Kelly Sambrook ⁷⁵, Freda Scheffler ⁴⁴, Christian Schmahl^39,76, Martha E. Shenton ^51,77,48,78, Anika Sierk ^63,79, Jeffrey S. Simons ^80,15, Raluca M. Simons ^80,14, Scott R. Sponheim ^22,23, Dan J. Stein ⁸¹, Murray B. Stein ^17,82, Jennifer S. Stevens ³⁰, Thomas Straube ⁴⁰, Benjamin Suarez-Jimenez ^69,70, Marijo B. Tamburrino ², Sophia I. Thomopoulos ⁴⁵, Nic J.A. van der Wee ^83,84, Steven J.A. van der Werff ^83,84, Theo G.M. van Erp ^85,86, Sanne J.H. van Rooij ³⁰, Mirjam van Zuiden ³⁴, Tim Varkevisser ^35,36, Dick J. Veltman⁶⁸, Robert R.J.M. Vermeiren ^87,88, Henrik Walter ⁶³, Li Wang ^89,58, Xi Zhu ^69,70, Ye Zhu ^57,58, Paul M. Thompson ⁴⁵, Xin Wang ^2,91, Rajendra A. Morey ^7,8,91, Israel Liberzon ^9,91

Affiliations

¹Department of Neurosciences, University of Toledo, Toledo, OH, USA

²Department of Psychiatry, University of Toledo, Toledo, OH, USA

³Department of Emergency Medicine, University of Toledo, Toledo, OH, USA

⁴Department of Mathematics and Statistics, University of Toledo, Toledo, OH, USA

⁵Department of Electrical Engineering and Computer Science, University of Toledo, Toledo, OH, USA

⁶Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA 44106

⁷Brain Imaging and Analysis Center, Duke University, Durham, NC, USA

⁸VISN 6 MIRECC, Durham VA Health Care System, Durham, NC, USA

⁹Department of Psychiatry and Behavioral Science, Texas A&M University College of Medicine, College Station, TX, USA

¹⁰Baylor College of Medicine; Yale University School of Medicine

¹¹Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

¹²University of Sydney, Westmead, NSW, Australia

¹³Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, USA

¹⁴Center for Brain and Behavior Research, University of South Dakota, Vermillion, SD, USA

¹⁵Sioux Falls VA Health Care System, Sioux Falls, SD, USA

¹⁶Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA

¹⁷Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA

¹⁸Center for Trauma Recovery, Department of Psychological Sciences, University of Missouri-St. Louis, St. Louis, MO, USA

¹⁹School of Psychology, University of New South Wales, Sydney, NSW, Australia

²⁰Health Services Research Center, University of California, San Diego, La Jolla, CA, USA

²¹Department of Clinical Psychology, University of Groningen, Groningen, The Netherlands

²²Minneapolis VA Health Care System, Minneapolis, MN, USA

²³Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA

²⁴Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA

²⁵Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA

²⁶Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA

²⁷Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA

²⁸Department of Neurology, University of Utah, Salt Lake City, UT, USA

²⁹Department of Surgery, Division of Trauma & Acute Care Surgery, Medical College of Wisconsin, Milwaukee, WI, USA

³⁰Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA

³¹Civil Aerospace Medical Institute, US Federal Aviation Administration, Oklahoma City, OK, USA

³²Department of Psychology, Marquette University, Milwaukee, WI, USA

³³Brain Health Research Centre, Department of Anatomy, University of Otago, Dunedin, New Zealand

³⁴Amsterdam UMC location University of Amsterdam, Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands

³⁵Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands

³⁶Brain Research and Innovation Centre, Ministry of Defence, Utrecht, The Netherlands

³⁷Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA

³⁸Clinical Neuroscience Division, National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA

³⁹Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany

⁴⁰Institute of Medical Psychology and Systems Neuroscience, University of Münster, Münster, Germany

⁴¹Program for Traumatic Stress, Tibor Rubin VA Medical Center, Long Beach, CA, USA

⁴²Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA

⁴³Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium

⁴⁴Department of Psychiatry, University of Cape Town, Cape Town, South Africa

⁴⁵Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA

⁴⁶Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA

⁴⁷Division of Women's Mental Health, McLean Hospital, Belmont, MA, USA

⁴⁸Department of Psychiatry, Harvard Medical School, Boston, MA, USA

⁴⁹The Ohio State University Wexner Medical Center, Columbus, OH, USA

⁵⁰Donders Institute for Brain, Cognition and Behavior, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, The Netherlands

⁵¹Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, USA

⁵²Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany

⁵³Brain Dynamics Centre, Westmead Institute of Medical Research, University of Sydney, Westmead, NSW, Australia

⁵⁴Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA

⁵⁵Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA

⁵⁶Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT, USA

⁵⁷Institute of Psychology, Chinese Academy of Sciences, Beijing, China

⁵⁸Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

⁵⁹National Center for PTSD, VA Boston Healthcare System, USA

⁶⁰Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA

⁶¹Departments of Radiology, Psychiatry, and Biomedical Engineering, University of Iowa, Iowa City, IA, USA

⁶²Deaprtment of Radiology, University of Iowa, Iowa City, IA 52242

⁶³Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany

⁶⁴Doris Miller VA Medical Center, VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX, USA

⁶⁵Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA

⁶⁶Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA

⁶⁷Department of Psychology, Harvard University, Boston, MA, USA

⁶⁸Amsterdam UMC location Vrije Universiteit Amsterdam, Psychiatry, Boelelaan 1117, Amsterdam, The Netherlands

⁶⁹Department of Psychiatry, Columbia University Medical Center, New York, NY, USA

⁷⁰New York State Psychiatric Institute, New York, NY, USA

⁷¹ARQ National Psychotrauma Centrum, Diemen, The Netherlands

⁷²Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA

⁷³Department of Psychology, University of Washington, Seattle, WA, USA

⁷⁴Mental Health Service Line, Jesse Brown VA Medical Center, Chicago, IL, USA

⁷⁵Department of Radiology, University of Washington, Seattle, WA, USA

⁷⁶Department of Psychiatry, University of Western Ontario, London, ON, Canada

⁷⁷Department of Psychiatry, VA Boston Healthcare System, Brockton, MA, USA

⁷⁸Department of Radiology, Harvard Medical School, Boston, MA, USA

⁷⁹Institute of Cognitive Neuroscience, University College London, London, United Kingdom

⁸⁰Department of Psychology, University of South Dakota, Vermillion, SD, USA

⁸¹SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa

⁸²Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA

⁸³Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands

⁸⁴Leiden Institute for Brain and Cognition, Leiden, The Netherlands

⁸⁵Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA

⁸⁶Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, USA

⁸⁷Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, The Netherlands

⁸⁸Youz-Parnassia Group, The Netherlands

⁸⁹Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China

⁹⁰These authors contributed equally: Hong Xie, Erin M. O’Leary, Chiahao Shih.

⁹¹ These authors jointly supervised this work: Xin Wang, Rajendra A. Morey, Israel Liberzon.

Acknowledgements

This work is supported by R21MH125277. The individual cohorts were supported by following grants: 1R21MH102634, 5U01AA021681-08, Academic Medical Center Research Council (110614), Anonymous Women’s Health Fund, Barlow Family Fund, BOF 01J05415, CDMRP W81XWH-08-2-0038, Center for Brain and Behavior Research Pilot Grant, CX001600 VA CDA, Dana Foundation, DoD W81XWH08-2-0159, DoD W81XWH-10-1-0925, DoD W81XWH-12-2-0012, German Research Foundation (DA 1222/4-1 and WA 1539/8-2), German Research Society (Deutsche Forschungsgemeinschaft, DFG; SFB/TRR 58: C06, C07), HD071982, HD085850, K01MH118428, K01MH118467, K23MH090366, K23MH112873, K24DA028773, K24MH71434, Kasparian Fund, L30MH114379, M01RR00039, MH071537, MH098212, MH101380, MJFF 14848, NARSAD- Young Investigator Grant, NCATS (CTSA)-Yale Center for Clinical Investigation, NHMRC Program Grant (1073041), NIAAA (P50)- Center for the Translational Neuroscience in Alcohol, P41EB015922, R01AA12479, R01AG059874, R01MH096987, R01MH103291, R01MH105535, R01MH106574, R01MH106574, R01MH110483, R01MH111671, R01MH116147, R01MH117601, R01MH125852, R01MH61744, R01MH63407, R21MH097196, R21MH098198, R21MH112956, R56AG058854, R61NS120249, South African Medical Research Council, South Dakota Governor’s Research Center Grant, Trauma Scholars Fund, U54EB020403, UCI-LBVA Biumvirate Grant, UL1TR000153, UL1TR000454, VA Merit Review Program, VA National Center for PTSD, VA RR&D1IK2RX000709, VA RR&D1K1RX002325, VA RR&D1K2RX002922, VA RR&DI01RX000622, VISN6 MIRECC.

Conflicts of interest

Dr. CGA 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. RJD is the founder and president of, and serves on the board of directors for, the non-profit organization Healthy Minds Innovations, Inc. Dr. NJ is MPI of a research grant from Biogen, Inc, for research unrelated to the contents of this manuscript. Dr JHK 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); filed a patent for Intranasal Administration of Ketamine to Treat Depression. U.S. Application No. 14/197,767 (filed on March 5, 2014); filed US application or Patent Cooperation Treaty international application No. 14/306,382 (filed on June 17, 2014); and filed a patent for using mTOR inhibitors to augment the effects of antidepressants (filed on August 20, 2018). Dr. LAML reports spousal IP payments from Vanderbilt University for technology licensed to Acadia Pharmaceuticals unrelated to the present work. Dr. PMT received partial research support from Biogen, Inc. (Boston, USA) for research unrelated to the topic of this paper. No other authors reported conflicts of interest.

Kessler RC, Aguilar-Gaxiola S, Alonso J, Benjet C, Bromet EJ, Cardoso G et al. Trauma and PTSD in the WHO World Mental Health Surveys. Eur J Psychotraumatol 2017; 8(sup5): 1353383–1353383.
Kessler RC. Posttraumatic stress disorder: the burden to the individual and to society. J Clin Psychiatry 2000; 61 Suppl 5: 4–12.
Andrews G, Issakidis C, Sanderson K, Corry J, Lapsley H. Utilising survey data to inform public policy: comparison of the cost-effectiveness of treatment of ten mental disorders. Br J Psychiatry 2004; 184: 526–533.
Liberzon I, Wang X, Xie H. Brain structural abnormalities in posttraumatic stress disorder and relations with sleeping problems. In: Vermetten E, Germain A, Neylan TC (eds). Sleep and combat-related post traumatic stress disorder, 1 edn. Springer-Verlag New York: New York, NY, USA, 2018, pp 145–168.
Wang X, Xie H, Chen T, Cotton AS, Salminen LE, Logue MW et al. Cortical volume abnormalities in posttraumatic stress disorder: an ENIGMA-psychiatric genomics consortium PTSD workgroup mega-analysis. Molecular psychiatry 2021; 8:4331–4343. doi: 10.1038/s41380-020-00967-1.
Logue MW, van Rooij SJH, Dennis EL, Davis SL, Hayes JP, Stevens JS et al. Smaller hippocampal volume in posttraumatic stress disorder: A multisite ENIGMA-PGC study: Subcortical volumetry results from posttraumatic stress disorder consortia. Biological psychiatry 2018; 83: 244–253.
Meng L, Jiang J, Jin C, Liu J, Zhao Y, Wang W et al. Trauma-specific grey matter alterations in PTSD. Sci Rep 2016; 6: DOI: 10.1038/srep33748.
Li L, Wu M, Liao Y, Ouyang L, Du M, Lei D et al. Grey matter reduction associated with posttraumatic stress disorder and traumatic stress. Neurosci Biobehav Rev 2014; 43: 163–172.
Kuhn S, Gallinat J. Gray matter correlates of posttraumatic stress disorder: a quantitative meta-analysis. Biological psychiatry 2013; 73: 70–74.
Meng Y, Qiu C, Zhu H, Lama S, Lui S, Gong Q et al. Anatomical deficits in adult posttraumatic stress disorder: A meta-analysis of voxel-based morphometry studies. Behav Brain Res 2014; 270: 307–315.
Bromis K, Calem M, Reinders A, Williams SCR, Kempton MJ. Meta-analysis of 89 structural MRI studies in posttraumatic stress disorder and comparison with major depressive disorder. Am J Psychiatry 2018; 175: 989–998.
Pitman RK, Rasmusson AM, Koenen KC, Shin LM, Orr SP, Gilbertson MW et al. Biological studies of post-traumatic stress disorder. Nat Rev Neurosci 2012; 13: 769–787.
Lanius RA, Rabellino D, Boyd JE, Harricharan S, Frewen PA, McKinnon MC. The innate alarm system in PTSD: conscious and subconscious processing of threat. Current Opinion in Psychology 2017; 14: 109–115.
Mechelli A, Price CJ, Friston KJ, Ashburner J. Voxel-based morphometry of the human brain: methods and applications. Curr Med Imaging Rev 2005; 1: 1–9.
Salmond CH, Ashburner J, Vargha-Khadem F, Connelly A, Gadian DG, Friston KJ. Distributional assumptions in voxel-based morphometry. NeuroImage 2002; 17: 1027–1030.
Pagani M, Damiano M, Galbusera A, Tsaftaris SA, Gozzi A. Semi-automated registration-based anatomical labelling, voxel based morphometry and cortical thickness mapping of the mouse brain. Journal of Neuroscience Methods 2016; 267: 62–73.
Bomyea J, Simmons AN, Shenton ME, Coleman MJ, Bouix S, Rathi Y et al. Neurocognitive markers of childhood abuse in individuals with PTSD: Findings from the INTRuST Clinical Consortium. Journal of psychiatric research 2020; 121: 108–117.
Averill LA, Abdallah CG, Pietrzak RH, Averill CL, Southwick SM, Krystal JH et al. Combat exposure severity is associated with reduced cortical thickness in combat veterans: A preliminary report. Chronic stress (Thousand Oaks, Calif) 2017; 1. doi: 10.1177/2470547017724714.
Clausen AN, Clarke E, Phillips RD, Haswell C, Morey RA. Combat exposure, posttraumatic stress disorder, and head injuries differentially relate to alterations in cortical thickness in military Veterans. Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology 2020; 45: 491–498.
Wrocklage KM, Averill LA, Cobb Scott J, Averill CL, Schweinsburg B, Trejo M et al. Cortical thickness reduction in combat exposed U.S. veterans with and without PTSD. Eur Neuropsychopharmacol 2017; 27: 515–525.
Miller MW, Wolf EJ, Sadeh N, Logue M, Spielberg JM, Hayes JP et al. A novel locus in the oxidative stress-related gene ALOX12 moderates the association between PTSD and thickness of the prefrontal cortex. Psychoneuroendocrinology 2015; 62: 359–365.
Sadeh N, Spielberg JM, Logue MW, Wolf EJ, Smith AK, Lusk J et al. SKA2 methylation is associated with decreased prefrontal cortical thickness and greater PTSD severity among trauma-exposed veterans. Molecular psychiatry 2016; 21: 357–363.
Grasby KL, Jahanshad N, Painter JN, Colodro-Conde L, Bralten J, Hibar DP et al. The genetic architecture of the human cerebral cortex. Science 2020; 367(6484): eaay6690. doi: 10.1126/science.aay6690.
Geuze E, Westenberg HG, Heinecke A, de Kloet CS, Goebel R, Vermetten E. Thinner prefrontal cortex in veterans with posttraumatic stress disorder. NeuroImage 2008; 41: 675–681.
Bing X, Ming-Guo Q, Ye Z, Jing-Na Z, Min L, Han C et al. Alterations in the cortical thickness and the amplitude of low-frequency fluctuation in patients with post-traumatic stress disorder. Brain Res 2013; 1490: 225–232.
Liu Y, Li YJ, Luo EP, Lu HB, Yin H. Cortical thinning in patients with recent onset post-traumatic stress disorder after a single prolonged trauma exposure. PloS one 2012; 7: e39025.
Woodward SH, Schaer M, Kaloupek DG, Cediel L, Eliez S. Smaller global and regional cortical volume in combat-related posttraumatic stress disorder. Archives of general psychiatry 2009; 66: 1373–1382.
Sussman D, Pang EW, Jetly R, Dunkley BT, Taylor MJ. Neuroanatomical features in soldiers with post-traumatic stress disorder. BMC neuroscience 2016; 17: 13.
Qi S, Mu Y, Liu K, Zhang J, Huan Y, Tan Q et al. Cortical inhibition deficits in recent onset PTSD after a single prolonged trauma exposure. NeuroImage Clinical 2013; 3: 226–233.
Kinzel P, Marx CE, Sollmann N, Hartl E, Guenette JP, Kaufmann D et al. Serum neurosteroid levels are associated with cortical thickness in individuals diagnosed with posttraumatic stress disorder and history of mild traumatic brain injury. Clinical EEG and neuroscience 2020; 51: 285–299.
Lindemer ER, Salat DH, Leritz EC, McGlinchey RE, Milberg WP. Reduced cortical thickness with increased lifetime burden of PTSD in OEF/OIF Veterans and the impact of comorbid TBI. NeuroImage Clinical 2013; 2: 601–611.
Helpman L, Papini S, Chhetry BT, Shvil E, Rubin M, Sullivan GM et al. Ptsd remission after prolonged exposure treatment Is associated with anterior cingulate cortex thinning and volume reduction. Depress Anxiety 2016; 33: 384–391.
Jin MJ, Jeon H, Hyun MH, Lee SH. Influence of childhood trauma and brain-derived neurotrophic factor Val66Met polymorphism on posttraumatic stress symptoms and cortical thickness. Sci Rep 2019; 9: 6028.
Hunter M, Villarreal G, McHaffie GR, Jimenez B, Smith AK, Calais LA et al. Lateralized abnormalities in auditory M50 sensory gating and cortical thickness of the superior temporal gyrus in post-traumatic stress disorder: preliminary results. Psychiatry research 2011; 191: 138–144.
Crombie KM, Ross MC, Letkiewicz AM, Sartin-Tarm A, Cisler JM. Differential relationships of PTSD symptom clusters with cortical thickness and grey matter volumes among women with PTSD. Scientific reports 2021; 11: 1825.
Landré L, Destrieux C, Baudry M, Barantin L, Cottier JP, Martineau J et al. Preserved subcortical volumes and cortical thickness in women with sexual abuse-related PTSD. Psychiatry research 2010; 183: 181–186.
Hu H, Sun Y, Su S, Wang Y, Qiu Y, Yang X et al. Cortical surface area reduction in identification of subjects at high risk for post-traumatic stress disorder: A pilot study. The Australian and New Zealand journal of psychiatry 2018; 52: 1084–1091.
Rinne-Albers MA, Boateng CP, van der Werff SJ, Lamers-Winkelman F, Rombouts SA, Vermeiren RR et al. Preserved cortical thickness, surface area and volume in adolescents with PTSD after childhood sexual abuse. Scientific reports 2020; 10: 3266.
Fogleman ND, Naaz F, Knight LK, Stoica T, Patton SC, Olson-Madden JH et al. Reduced lateral prefrontal cortical volume is associated with performance on the modified Iowa Gambling Task: A surface based morphometric analysis of previously deployed veterans. Psychiatry Research: Neuroimaging 2017; 267: 1–8.
Li S, Huang X, Li L, Du F, Li J, Bi F et al. Posttraumatic stress disorder: Structural characterization with 3-T MR imaging. Radiology 2016; 280: 537–544.
Proessl F, Dretsch MN, Connaboy C, Lovalekar M, Dunn-Lewis C, Canino MC et al. Structural connectome disruptions in military personnel with mild traumatic brain injury and post-traumatic stress disorder. Journal of neurotrauma 2020; 37: 2102–2112.
Lyoo IK, Kim JE, Yoon SJ, Hwang J, Bae S, Kim DJ. The neurobiological role of the dorsolateral prefrontal cortex in recovery from trauma. Longitudinal brain imaging study among survivors of the South Korean subway disaster. Archives of general psychiatry 2011; 68: 701–713.
Li L, Zhang Y, Zhao Y, Li Z, Kemp GJ, Wu M et al. Cortical thickness abnormalities in patients with post-traumatic stress disorder: A vertex-based meta-analysis. Neurosci Biobehav Rev 2022; 134: 104519.
Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain imaging and behavior 2014; 8: 153–182.
Sun D, Rakesh G, Haswell CC, Logue M, Baird CL, O'Leary EN et al. A comparison of methods to harmonize cortical thickness measurements across scanners and sites. NeuroImage 2022: 119509.
Lorah J. Effect size measures for multilevel models: definition, interpretation, and TIMSS example. Large-scale Assessments in Education 2018; 6: 8.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological) 1995; 57: 289–300.
Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D et al. Array programming with NumPy. Nature 2020; 585: 357–362.
Brett Ma, Markiewicz CJa, Hanke Ma, Côté M-Aa, Cipollini Ba, McCarthy Pa et al. nipy/nibabel: 3.2.1. vol. 3.2.1. Zenodo2020.
Reback J, McKinney W, jbrockmendel, Bossche JVd, Augspurger T, Cloud P et al. pandas-dev/pandas: Pandas 1.0.3. v1.0.3 edn. Zenodo2020.
Seabold S, and Josef Perktold. statsmodels: Econometric and statistical modeling with python. 9th Python in Science Conference2010.
Greve DN, Fischl B. False positive rates in surface-based anatomical analysis. NeuroImage 2018; 171: 6–14.
Qi S, Mu YF, Cui LB, Zhang J, Guo F, Tan QR et al. Anomalous gray matter structural networks in recent onset post-traumatic stress disorder. Brain imaging and behavior 2018; 12: 390–401.
Hengartner MP, Lehmann SN. Why psychiatric research must abandon traditional diagnostic classification and adopt a fully dimensional scope: Two solutions to a persistent problem. Frontiers in psychiatry 2017; 8: 101–101.
Li Y, Sescousse G, Amiez C, Dreher JC. Local morphology predicts functional organization of experienced value signals in the human orbitofrontal cortex. J Neurosci 2015; 35: 1648–1658.
Suarez-Jimenez B, Albajes-Eizagirre A, Lazarov A, Zhu X, Harrison BJ, Radua J et al. Neural signatures of conditioning, extinction learning, and extinction recall in posttraumatic stress disorder: a meta-analysis of functional magnetic resonance imaging studies. Psychological medicine 2020; 50: 1442–1451.
Nawijn L, van Zuiden M, Frijling JL, Koch SBJ, Veltman DJ, Olff M. Reward functioning in PTSD: A systematic review exploring the mechanisms underlying anhedonia. Neuroscience & Biobehavioral Reviews 2015; 51: 189–204.
Greco JA, Liberzon I. Neuroimaging of fear-associated learning. Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology 2016; 41: 320–334.
Ginther MR, Bonnie RJ, Hoffman MB, Shen FX, Simons KW, Jones OD et al. Parsing the behavioral and brain mechanisms of third-party punishment. J Neurosci 2016; 36: 9420–9434.
Lyu C, Hu S, Wei L, Zhang X, Talhelm T. Brain activation of identity switching in multiple identity tracking task. PloS one 2015; 10: e0145489. doi: 10.1371/journal.pone.0145489.
Mueller-Pfeiffer C, Schick M, Schulte-Vels T, O'Gorman R, Michels L, Martin-Soelch C et al. Atypical visual processing in posttraumatic stress disorder. NeuroImage: Clinical 2013; 3: 531–538.
Brunetti M, Sepede G, Ferretti A, Mingoia G, Romani GL, Babiloni C. Response inhibition failure to visual stimuli paired with a "single-type" stressor in PTSD patients: an fMRI pilot study. Brain Res Bull 2015; 114: 20–30.
Yang P, Wu MT, Hsu CC, Ker JH. Evidence of early neurobiological alternations in adolescents with posttraumatic stress disorder: a functional MRI study. Neurosci Lett 2004; 370: 13–18.
Brewin CR, Gregory JD, Lipton M, Burgess N. Intrusive images in psychological disorders: characteristics, neural mechanisms, and treatment implications. Psychol Rev 2010; 117: 210–232.
Miller MW, Sadeh N. Traumatic stress, oxidative stress and post-traumatic stress disorder: neurodegeneration and the accelerated-aging hypothesis. Molecular psychiatry 2014; 19: 1156–1162.
Morey RA, Haswell CC, Hooper SR, De Bellis MD. Amygdala, hippocampus, and ventral medial prefrontal cortex volumes differ in maltreated youth with and without chronic posttraumatic stress disorder. Neuropsychopharmacology 2016; 41: 791–801.

Dr. Chadi G. 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. Richard J. Davidson is the founder and president of, and serves on the board of directors for, the non-profit organization Healthy Minds Innovations, Inc. Dr. Neda Jahanshad is MPI of a research grant from Biogen, Inc, for research unrelated to the contents of this manuscript. Dr John H. 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); filed a patent for Intranasal Administration of Ketamine to Treat Depression. U.S. Application No. 14/197,767 (filed on March 5, 2014); filed US application or Patent Cooperation Treaty international application No. 14/306,382 (filed on June 17, 2014); and filed a patent for using mTOR inhibitors to augment the effects of antidepressants (filed on August 20, 2018). Dr. Lauren A. M. Lebois reports spousal IP payments from Vanderbilt University for technology licensed to Acadia Pharmaceuticals unrelated to the present work. Dr. Paul Thompson received partial research support from Biogen, Inc. (Boston, USA) for research unrelated to the topic of this paper. No other authors reported conflicts of interest.

PTSDcorticalthicknessmegaanalysissupplementaryXie.docx

Download PDF

Editorial decision: revise
28 Dec, 2022
Review #1 received at journal
22 Oct, 2022
Reviewer #1 agreed at journal
02 Oct, 2022
Reviewers invited by journal
02 Oct, 2022
Submission checks completed at journal
22 Sep, 2022
Unknown event
20 Sep, 2022
Editor assigned by journal
20 Sep, 2022
First submitted to journal
20 Sep, 2022

You are reading this latest preprint version

A mega-analysis of vertex and gyral cortical thickness differences in adults with and without PTSD

Status:

Version 1

Abstract

Figures

Introduction

Methods

Mega-analysis of cortical gyral thickness

Mega-analysis of cortical vertex thickness

Results

Group characteristics

Vertex thicknesses in PTSD vs. non-PTSD groups

Gyral thicknesses in PTSD vs. non-PTSD groups

Comparison of PTSD-related gyral vs. vertex cortical thinning

Correlations between CAPS-4 scores and PTSD-related vertex cluster and gyral thinning

Discussion

Cortical thinning in PTSD

Potential functional implications of orbitofrontal and lingual cortical thinning in PTSD

Potential factors contributing to cortical thinning in PTSD

Limitations

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1