Resting-state functional connectivity in combat veterans suffering from impulsive aggression

Impulsive aggression is common among military personnel after deployment and may arise because of impaired top-down regulation of the amygdala by prefrontal regions.This study sought to further explore this hypothesis via resting-state functional connectivity analyses in impulsively aggressive combat veterans. Male combat veterans with (n¼28) and without (n¼30) impulsive aggression problems underwent resting-state functional magnetic resonance imaging. Functional connectivity analyses were conducted with the following seed-regions: basolateral amygdala (BLA), centromedial amygdala, anterior cingulate cortex (ACC), and anterior insular cortex (AIC). Regions-of-interest analyses focused on the orbitofrontal cortex and periaqueductal gray, and yielded no significant results. In exploratory cluster analyses, we observed reduced functional connectivity between the (bilateral) BLA and left dorsolateral prefrontal cortex in the impulsive aggression group, relative to combat controls. This finding indicates that combat-related impulsive aggression may be marked by weakened functional connectivity between the amygdala and prefrontal regions, already in the absence of explicit emotional stimuli. Group differences in functional connectivity were also observed between the (bilateral) ACC and left cuneus, which may be related to heightened vigilance to potentially threatening visual cues, as well as between the left AIC and right temporal pole, possibly related to negative memory association in impulsive aggression

Pattern classification based on the amygdala does not predict an individual's response to emotional stimuli

Functional magnetic resonance imaging (fMRI) studies have often recorded robust univariate group effects in the amygdala of subjects exposed to emotional stimuli. Yet it is unclear to what extent this effect also holds true when multi-voxel pattern analysis (MVPA) is applied at the level of the individual participant. Here we sought to answer this question. To this end, we combined fMRI data from two prior studies (N = 112). For each participant, a linear support vector machine was trained to decode the valence of emotional pictures (negative, neutral, positive) based on brain activity patterns in either the amygdala (primary region-of-interest analysis) or the whole-brain (secondary exploratory analysis). The accuracy score of the amygdala-based pattern classifications was statistically significant for only a handful of participants (4.5%) with a mean and standard deviation of 37% ± 5% across all subjects (range: 28–58%; chance-level: 33%). In contrast, the accuracy score of the whole-brain pattern classifications was statistically significant in roughly half of the participants (50.9%), and had an across-subjects mean and standard deviation of 49% ± 6% (range: 33–62%). The current results suggest that the information conveyed by the emotional pictures was encoded by spatially distributed parts of the brain, rather than by the amygdala alone, and may be of particular relevance to studies that seek to target the amygdala in the treatment of emotion regulation problems, for example via real-time fMRI neurofeedback training.publishedVersio

Intrusive Traumatic Re-Experiencing Domain (ITRED) – Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

Background Intrusive Traumatic Re-Experiencing Domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective. Methods Data was collected from nine sites taking part in the ENIGMA-PTSD Consortium (n=584) and included itemized PTSD symptoms scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and Trauma-exposed (TE)-only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. Random forest classification model was built on a training set using cross-validation (CV), and the averaged CV model performance for classification was evaluated using area-under-the-curve (AUC). The model was tested using a fully independent portion of the data (test dataset), and the test AUC was evaluated. Results RsFC signatures differentiated TE-only participants from PTSD and from ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and from ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontal-parietal network, differentiated TE-only participants from one group (PTSD or ITRED-only), but to a lesser extent from the other. Conclusion Neural network connectivity supports ITRED as a novel neurobiologically-based approach to classifying post-trauma psychopathology

Predicting Trauma-Focused Therapy Outcome From Resting-State Functional Magnetic Resonance Imaging in Veterans With Posttraumatic Stress Disorder

Background Trauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30-50% of patients do not benefit sufficiently. Here, we tested whether resting-state functional magnetic imaging (rs-fMRI) can predict treatment response for individual patients. Methods 44 male veterans with PTSD underwent baseline rs-fMRI scanning followed by trauma-focused therapy (EMDR or TF-CBT). Resting-state networks (RSN) were obtained using independent component analysis with 70 components on the basis of 28 trauma-exposed healthy controls, matched for age and gender. Dual regression was used to obtain subject-specific RSNs for the PTSD patients. All RSNs were individually included in a machine learning classification analysis using Gaussian process classifiers. Classifier performance was assessed using 10 times repeated 10-fold cross-validation. Results Patients were grouped into treatment responders (n = 24) and non-responders (n = 20), based on a 30% decrease in total clinician-administered PTSD scale for the DSM-IV (CAPS) score from pre- to post-treatment assessment. A network centered around the pre-supplementary motor area achieved an average accuracy of 81% (p < 0.001, based on a permutation test, corrected for multiple comparisons across 44 signal components), with a sensitivity of 84.5%, specificity of 77.5%, and area under receiver-operator curve (AUC) of 0.93. Conclusions Rs-fMRI recordings are capable of providing personalized predictions of treatment response in a sample of veterans with PTSD. It therefore has the potential to be useful as a biomarker of treatment response and should be validated in larger independent studies. Supported By ZonMw; AMC; Dutch Ministry of Defens

Trauma and posttraumatic stress disorder modulate polygenic predictors of hippocampal and amygdala volume

The volume of subcortical structures represents a reliable, quantitative, and objective phenotype that captures genetic effects, environmental effects such as trauma, and disease effects such as posttraumatic stress disorder (PTSD). Trauma and PTSD represent potent exposures that may interact with genetic markers to influence brain structure and function. Genetic variants, associated with subcortical volumes in two large normative discovery samples, were used to compute polygenic scores (PGS) for the volume of seven subcortical structures. These were applied to a target sample enriched for childhood trauma and PTSD. Subcortical volume PGS from the discovery sample were strongly associated in our trauma/PTSD enriched sample (n = 7580) with respective subcortical volumes of the hippocampus (p = 1.10 × 10(−20)), thalamus (p = 7.46 × 10(−10)), caudate (p = 1.97 × 10(−18)), putamen (p = 1.7 × 10(−12)), and nucleus accumbens (p = 1.99 × 10(−7)). We found a significant association between the hippocampal volume PGS and hippocampal volume in control subjects from our sample, but was absent in individuals with PTSD (GxE; (beta = −0.10, p = 0.027)). This significant GxE (PGS × PTSD) relationship persisted (p < 1 × 10(−19)) in four out of five threshold peaks (0.024, 0.133, 0.487, 0.730, and 0.889) used to calculate hippocampal volume PGSs. We detected similar GxE (G × ChildTrauma) relationships in the amygdala for exposure to childhood trauma (rs4702973; p = 2.16 × 10(−7)) or PTSD (rs10861272; p = 1.78 × 10(−6)) in the CHST11 gene. The hippocampus and amygdala are pivotal brain structures in mediating PTSD symptomatology. Trauma exposure and PTSD modulate the effect of polygenic markers on hippocampal volume (GxE) and the amygdala volume PGS is associated with PTSD risk, which supports the role of amygdala volume as a risk factor for PTSD

Trauma and posttraumatic stress disorder modulate polygenic predictors of hippocampal and amygdala volume.

The volume of subcortical structures represents a reliable, quantitative, and objective phenotype that captures genetic effects, environmental effects such as trauma, and disease effects such as posttraumatic stress disorder (PTSD). Trauma and PTSD represent potent exposures that may interact with genetic markers to influence brain structure and function. Genetic variants, associated with subcortical volumes in two large normative discovery samples, were used to compute polygenic scores (PGS) for the volume of seven subcortical structures. These were applied to a target sample enriched for childhood trauma and PTSD. Subcortical volume PGS from the discovery sample were strongly associated in our trauma/PTSD enriched sample (n = 7580) with respective subcortical volumes of the hippocampus (p = 1.10 × 10-20), thalamus (p = 7.46 × 10-10), caudate (p = 1.97 × 10-18), putamen (p = 1.7 × 10-12), and nucleus accumbens (p = 1.99 × 10-7). We found a significant association between the hippocampal volume PGS and hippocampal volume in control subjects from our sample, but was absent in individuals with PTSD (GxE; (beta = -0.10, p = 0.027)). This significant GxE (PGS × PTSD) relationship persisted (p &lt; 1 × 10-19) in four out of five threshold peaks (0.024, 0.133, 0.487, 0.730, and 0.889) used to calculate hippocampal volume PGSs. We detected similar GxE (G × ChildTrauma) relationships in the amygdala for exposure to childhood trauma (rs4702973; p = 2.16 × 10-7) or PTSD (rs10861272; p = 1.78 × 10-6) in the CHST11 gene. The hippocampus and amygdala are pivotal brain structures in mediating PTSD symptomatology. Trauma exposure and PTSD modulate the effect of polygenic markers on hippocampal volume (GxE) and the amygdala volume PGS is associated with PTSD risk, which supports the role of amygdala volume as a risk factor for PTSD