Posttraumatic stress disorder (PTSD) is associated with regional alterations in brain structure and function that are hypothesized to contribute to symptoms and cognitive deficits associated with the disorder. We present here the first systematic meta-analysis of neurocognitive outcomes associated with PTSD to examine a broad range of cognitive domains and describe the profile of cognitive deficits, as well as modifying clinical factors and study characteristics. This report is based on data from 60 studies totaling 4,108 participants, including 1,779with PTSD, 1,446 trauma-exposed comparison participants, and 895 healthy comparison participants without trauma exposure. Effect size estimates were calculated using a mixed-effects meta-analysis for nine cognitive domains: attention/working memory, executive functions, verbal learning, verbal memory, visual learning, visual memory, language, speed of information processing, and visuospatial abilities. Analyses revealed significant neurocognitive effects associated with PTSD, although these ranged widely in magnitude, with the largest effect sizes in verbal learning (d =−.62), speed of information processing (d =−.59), attention/working memory (d =−.50), and verbal memory (d =−.46). Effect size estimates were significantly larger in treatment-seeking than community samples and in studies that did not exclude participants with attention-deficit hyperactivity disorder, and effect sizes were affected by between-group IQ discrepancies and the gender composition of the PTSD groups. Our findings indicate that consideration of neuropsychological functioning in attention, verbal memory, and speed of information processing may have important implications for the effective clinical management of persons with PTSD. Results are further discussed in the context of cognitive models of PTSD and the limitations of this literature.

BACKGROUND Many studies report smaller hippocampal and amygdala volumes in posttraumatic stress disorder (PTSD), but findings have not always been consistent. Here, we present the results of a large-scale neuroimaging consortium study on PTSD conducted by the Psychiatric Genomics Consortium (PGC)–Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) PTSD Working Group. METHODS We analyzed neuroimaging and clinical data from 1868 subjects (794 PTSD patients) contributed by 16 cohorts, representing the largest neuroimaging study of PTSD to date. We assessed the volumes of eight subcortical structures (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, and lateral ventricle). We used a standardized image-analysis and quality-control pipeline established by the ENIGMA consortium. RESULTS In a meta-analysis of all samples, we found significantly smaller hippocampi in subjects with current PTSD compared with trauma-exposed control subjects (Cohen’s d = −0.17, p = .00054), and smaller amygdalae (d = −0.11, p = .025), although the amygdala finding did not survive a significance level that was Bonferroni corrected for multiple subcortical region comparisons (p < .0063). CONCLUSIONS Our study is not subject to the biases of meta-analyses of published data, and it represents an important milestone in an ongoing collaborative effort to examine the neurobiological underpinnings of PTSD and the brain’s response to trauma.

Disruption in the default mode network (DMN) has been implicated in numerous neuropsychiatric disorders, including posttraumatic stress disorder (PTSD). However, studies have largely been limited to seed-based methods and involved inconsistent definitions of the DMN. Recent advances in neuroimaging and graph theory now permit the systematic exploration of intrinsic brain networks. In this study, we used resting-state functional magnetic resonance imaging (fMRI), diffusion MRI, and graph theoretical analyses to systematically examine the DMN connectivity and its relationship with PTSD symptom severity in a cohort of 65 combat-exposed US Veterans. We employed metrics that index overall connectivity strength, network integration (global efficiency), and network segregation (clustering coefficient). Then, we conducted a modularity and network-based statistical analysis to identify DMN regions of particular importance in PTSD. Finally, structural connectivity analyses were used to probe whether white matter abnormalities are associated with the identified functional DMN changes. We found decreased DMN functional connectivity strength to be associated with increased PTSD symptom severity. Further topological characterization suggests decreased functional integration and increased segregation in subjects with severe PTSD. Modularity analyses suggest a spared connectivity in the posterior DMN community (posterior cingulate, precuneus, angular gyrus) despite overall DMN weakened connections with increasing PTSD severity. Edge-wise network-based statistical analyses revealed a prefrontal dysconnectivity. Analysis of the diffusion networks revealed no alterations in overall strength or prefrontal structural connectivity. DMN abnormalities in patients with severe PTSD symptoms are characterized by decreased overall interconnections. On a finer scale, we found a pattern of prefrontal dysconnectivity, but increased cohesiveness in the posterior DMN community and relative sparing of connectivity in this region. The DMN measures established in this study may serve as a biomarker of disease severity and could have potential utility in developing circuit-based therapeutics.