Intrusive Traumatic Re-Experiencing Domain: 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 were collected from 9 sites taking part in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) PTSD Consortium (n= 584) and included itemized PTSD symptom 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. A random forest classification model was built on a training set using cross-validation, and the averaged cross-validation model performance for classification was evaluated using the area under the curve. The model was tested using a fully independent portion of the data (test dataset), and the test area under the curve was evaluated. Results: rsFC signatures differentiated TE-only participants from PTSD and 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 ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontoparietal network, differentiated TE-only participants from one group (PTSD or ITRED-only) but to a lesser extent from the other group. Conclusions: Neural network connectivity supports ITRED as a novel neurobiologically based approach to classifying posttrauma psychopathology.

Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup.

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR < 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.

Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods.

Posttraumatic stress disorder (PTSD) is associated with lower cortical thickness (CT) in prefrontal, cingulate, and insular cortices in diverse trauma-affected samples. However, some studies have failed to detect differences between PTSD patients and healthy controls or reported that PTSD is associated with greater CT. Using data-driven dimensionality reduction, we sought to conduct a well-powered study to identify vulnerable networks without regard to neuroanatomic boundaries. Moreover, this approach enabled us to avoid the excessive burden of multiple comparison correction that plagues vertex-wise methods. We derived structural covariance networks (SCNs) by applying non-negative matrix factorization (NMF) to CT data from 961 PTSD patients and 1124 trauma-exposed controls without PTSD. We used regression analyses to investigate associations between CT within SCNs and PTSD diagnosis (with and without accounting for the potential confounding effect of trauma type) and symptom severity in the full sample. We performed additional regression analyses in subsets of the data to examine associations between SCNs and comorbid depression, childhood trauma severity, and alcohol abuse. NMF identified 20 unbiased SCNs, which aligned closely with functionally defined brain networks. PTSD diagnosis was most strongly associated with diminished CT in SCNs that encompassed the bilateral superior frontal cortex, motor cortex, insular cortex, orbitofrontal cortex, medial occipital cortex, anterior cingulate cortex, and posterior cingulate cortex. CT in these networks was significantly negatively correlated with PTSD symptom severity. Collectively, these findings suggest that PTSD diagnosis is associated with widespread reductions in CT, particularly within prefrontal regulatory regions and broader emotion and sensory processing cortical regions.

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

Underlying Hippocampal Mechanism of Posttraumatic Stress Disorder Treatment Outcome: Evidence From Two Clinical Trials.

Background: The hippocampus plays an important role in the pathophysiology of posttraumatic stress disorder (PTSD) and its prognosis. Accumulating findings suggest that individuals with larger pretreatment hippocampal volume are more likely to benefit from PTSD treatment, but the mechanism underlying this effect is unknown. We investigated whether further increase in hippocampal volume during treatment explains the better prognosis of individuals with greater pretreatment hippocampal volume. Methods: We collected structural magnetic resonance imagesfrom patients with PTSD before and after treatment. We examined whether larger hippocampal volume moderates the effect of increased hippocampal volume during treatment on symptom reduction. Given the relatively small sample sizes of treatment studies with pre- and posttreatment magnetic resonance imaging, we focused on effect sizes and sought to replicate findings in an external sample. We tested our hypothesis in study 1 (N = 38; prolonged exposure therapy) and then tested whether the results could be externally replicated in study 2 (N = 20; ketamine infusion followed by exposure therapy). Results: Findings from study 1 revealed that increased right hippocampal volume during treatment was associated with greater PTSD symptom reduction only in patients with greater pretreatment right hippocampal volume (p = .03; η2 = 0.13, a large effect). Findings were partially replicated in study 2 for depressive symptoms (p = .034; η2 = 0.25, a very large effect) and for PTSD symptoms (p = .15; η2 = 0.15, a large effect). Conclusions: Elucidating increased hippocampal volume as one of the neural mechanisms predictive of therapeutic outcome for individuals with larger pretreatment hippocampal volume may help identify clinical targets for this subgroup.

Eye-tracking indices of attention allocation and attention bias variability are differently related to trauma exposure and PTSD.

Amplified attention allocation to negative information in one's environment has been implicated in posttraumatic stress disorder (PTSD). Attention bias variability (ABV), the magnitude of attention fluctuation between negative and neutral cues, has also been found to be elevated in PTSD. While eye-tracking methodology has been used in research on attention allocation in PTSD, ABV was only explored using manual reaction-time-based indices. Thirty-seven participants with PTSD, 34 trauma-exposed healthy controls (TEHC), and 30 non-exposed healthy controls (HC) completed an eye-tracking free-viewing task in which matrices comprised of neutral and negatively-valenced faces were presented. Threat-related attention allocation was calculated as the proportion of dwell time (DT%) on negatively-valenced faces. Eye-tracking-based ABV was calculated as the standard deviation of DT% across matrices. DT% on negatively-valenced faces was greater in participants with PTSD compared to both TEHC (p = .036, d = 0.50) and HC (p < .001, d = 1.03), with TEHCs showing a greater attentional bias compared to HCs (p = .001, d = 0.84). Controlling for average fixation duration, ABV was higher in both the PTSD and TEHC groups relative to the HC group (p = .004, d = 0.40), with no difference between the two trauma-exposed groups. Biased attention allocation toward negative social information is related to PTSD pathology, whereas elevated ABV measured with eye-tracking appear to be related to trauma-exposure per-se.

Attention allocation to negatively-valenced stimuli in PTSD is associated with reward-related neural pathways.

BACKGROUND: In a recent eye-tracking study we found a differential dwell time pattern for negatively-valenced and neutral faces among patients with posttraumatic stress disorder (PTSD), trauma-exposed healthy control (TEHCs), and healthy control (HC) participants. Here, we explored whether these group differences relate to resting-state functional connectivity (rsFC) patterns of brain areas previously linked to both attention processes and PTSD. These encompass the amygdala, dorsal anterior cingulate cortex (dACC), dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), and nucleus accumbens (NAcc). METHODS: Ten minutes magnetic resonance imaging rsFC scans were recorded in 17 PTSD patients, 21 TEHCs, and 16 HCs. Participants then completed a free-viewing eye-tracking task assessing attention allocation outside the scanner. Dwell time on negatively-valenced stimuli (DT%) were assessed relative to functional connectivity in the aforementioned seed regions of interest (amygdala, dACC, dlPFC, vlPFC, and NAcc) to whole-brain voxel-wise rsFC. RESULTS: As previously reported, group differences occurred in attention allocation to negative-valence stimuli, with longer dwell time on negatively valence stimuli in the PTSD and TEHC groups than the HC group. Higher DT% correlated with weaker NAcc-orbitofrontal cortex (OFC) connectivity in patients with PTSD. Conversely, a positive association emerged in the HC group between DT% and NAcc-OFC connectivity. CONCLUSIONS: While exploratory in nature, present findings may suggest that reward-related brain areas are involved in disengaging attention from negative-valenced stimuli, and possibly in regulating ensuing negative emotions.

Sequential fear generalization and network connectivity in trauma exposed humans with and without psychopathology.

While impaired fear generalization is known to underlie a wide range of psychopathology, the extent to which exposure to trauma by itself results in deficient fear generalization and its neural abnormalities is yet to be studied. Similarly, the neural function of intact fear generalization in people who endured trauma and did not develop significant psychopathology is yet to be characterized. Here, we utilize a generalization fMRI task, and a network connectivity approach to clarify putative behavioral and neural markers of trauma and resilience. The generalization task enables longitudinal assessments of threat discrimination learning. Trauma-exposed participants (TE; N = 62), compared to healthy controls (HC; N = 26), show lower activity reduction in salience network (SN) and right executive control network (RECN) across the two sequential generalization stages, and worse discrimination learning in SN measured by linear deviation scores (LDS). Comparison of resilient, trauma-exposed healthy control participants (TEHC; N = 31), trauma exposed individuals presenting with psychopathology (TEPG; N = 31), and HC, reveals a resilience signature of network connectivity differences in the RECN during generalization learning measured by LDS. These findings may indicate a trauma exposure phenotype that has the potential to advance the development of innovative treatments by targeting and engaging specific neural dysfunction among trauma-exposed individuals, across different psychopathologies.

Elucidating behavioral and functional connectivity markers of aberrant threat discrimination in PTSD.

BACKGROUND: Patients with posttraumatic stress disorder (PTSD) tend to overgeneralize threat to safe stimuli, potentially reflecting aberrant stimuli discrimination. Yet, it is not clear whether threat overgeneralization reflects general discrimination deficits, or rather a specific bias related to aversive stimuli. Here we tested this question and characterized the neural correlates of threat discrimination. METHODS: One-hundred and eight participants (33 PTSD; 43 trauma-exposed controls; 32 healthy controls) completed an emotionally neutral complex shape discrimination task involving identifying in 42 similar pairs the previously observed shape; and an emotionally aversive discrimination task, involving providing risk ratings for an aversive conditioned stimulus (CS+), and for several stimuli gradually differing in size from the original CS+. Resting state functional connectivity (rsFC) was collected before completing the tasks. RESULTS: No group differences emerged on the emotionally neutral task. Conversely, on the emotionally aversive task, individuals with PTSD had steeper linear risk rating slopes as the stimuli more resembled the conditioned stimulus. Finally, lower rsFC of amygdala-default mode network (DMN) and DMN-salience network (SN) were associated with steeper risk slopes, while for hippocampus-SN, lower rsFC was found only among participants with PTSD. CONCLUSIONS: Individuals with PTSD show deficits in discrimination only when presented with aversive stimuli. Dysregulated discrimination pattern may relate to a lack of input from regulatory brain areas (e.g., DMN/hippocampus) to threat-related brain areas (e.g., SN/amygdala).

A comparison of methods to harmonize cortical thickness measurements across scanners and sites.

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants' demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

Structural brain features signaling trauma, PTSD, or resilience? A systematic exploration.

BACKGROUND: Studies have searched for neurobiological markers of trauma exposure, posttraumatic stress disorder (PTSD) diagnosis, and resilience to trauma to identify therapeutic targets for PTSD. Despite some promising results, findings are inconsistent. AIMS: The present study adopted a data-driven approach to systematically explore whether structural brain markers of trauma, PTSD, or resilience emerge when all are explored. MATERIALS & METHODS: Differences between clusters in the proportion of PTSD, healthy controls (HC), and trauma-exposed healthy controls (TEHC) served to indicate the presence of PTSD, trauma, and resilience markers, respectively. A total of 129 individuals, including 46 with PTSD, 49 TEHCs, and 34 HCs not exposed to trauma were scanned. Volumes, cortical thickness, and surface areas of interest were obtained from T1 structural MRI and used to identify data-driven clusters. RESULTS: Two clusters were identified, differing in the proportion of TEHCs but not of PTSDs or HCs. The cluster with the higher proportion of TEHCs, referred to as the resilience cluster, was characterized by higher volume in brain regions implicated in trauma exposure, especially the thalamus and rostral middle frontal gyrus. Cross-validation established the robustness and consistency of the identified clusters. DISCUSSION & CONCLUSION: Findings support the existence of structural brain markers of resilience.

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

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.

Treatment dropout among veterans and their families: Quantitative and qualitative findings.

BACKGROUND: Psychotherapy noncompletion rates for veterans and their families are high. This study sought to (a) measure noncompletion rates of such patients at a university-based treatment center, (b) compare veteran and family member attrition rates, (c) identify dropout predictors, and (d) explore clinicians' perspectives on treatment noncompletion. METHOD: Using quantitative and qualitative approaches, we analyzed demographic and clinical characteristics of 141 patients (90 military veterans; 51 family members) in a university treatment center. We defined dropout as not completing the time-limited therapy contract. Reviewing semistructured interview data assessing clinicians' perspectives on their patients' dropout, three independent raters agreed on key themes, with interrater coefficient kappa range .74 to 1. RESULTS: Patient attrition was 24%, not differing significantly between veterans and family members. Diagnosis of major depression (MDD) and exposure-based therapies predicted noncompletion, as did higher baseline Hamilton Depression Rating Scale (HDRS) total scores, severe depression (HDRS > 20), lack of Beck Depression Inventory weekly improvement, and history of military sexual trauma. Clinicians mostly attributed noncompletion to patient difficulties coping with intense emotions, especially in exposure-based therapies. CONCLUSION: Noncompletion rate at this study appeared relatively low compared to other veteran-based treatment centers, if still unfortunately substantial. Patients with comorbid MDD/PTSD and exposure-based therapies carried greater noncompletion risk due to the MDD component, and this should be considered in treatment planning. Ongoing discussion of dissatisfaction and patient discontinuation, in the context of a strong therapeutic alliance, might reduce noncompletion in this at-risk population. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups.

BACKGROUND: Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. METHOD: Adult subjects (N = 2229; 56.2% male) aged 18-69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age - chronological age) controlling for chronological age, sex, and scan site. RESULTS: BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. DISCUSSION: Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan.

Location-dependent threat and associated neural abnormalities in clinical anxiety.

Anxiety disorders are characterized by maladaptive defensive responses to distal or uncertain threats. Elucidating neural mechanisms of anxiety is essential to understand the development and maintenance of anxiety disorders. In fMRI, patients with pathological anxiety (ANX, n = 23) and healthy controls (HC, n = 28) completed a contextual threat learning paradigm in which they picked flowers in a virtual environment comprising a danger zone in which flowers were paired with shock and a safe zone (no shock). ANX compared with HC showed 1) decreased ventromedial prefrontal cortex and anterior hippocampus activation during the task, particularly in the safe zone, 2) increased insula and dorsomedial prefrontal cortex activation during the task, particularly in the danger zone, and 3) increased amygdala and midbrain/periaqueductal gray activation in the danger zone prior to potential shock delivery. Findings suggest that ANX engage brain areas differently to modulate context-appropriate emotional responses when learning to discriminate cues within an environment.

Reversed patterns of resting state functional connectivity for females vs. males in posttraumatic stress disorder.

BACKGROUND: Posttraumatic stress disorder (PTSD) is twice as prevalent among females as compared to males following potentially traumatic events. While there is evidence for aberrant functional connectivity between hubs of the central executive network (CEN), salience network (SN), and the default mode network (DMN) in PTSD, little is known regarding sex-specificity of this connectivity. The current study aims to directly examine sex-specific resting-state functional connectivity (rs-FC) in trauma exposed males and females, with and without PTSD. METHODS: One hundred and seventy-eight individuals underwent functional magnetic resonance imaging (fMRI) at rest, of them 85 females (45 with PTSD) and 93 males (57 with PTSD). We conducted whole-brain seed-based analysis using CEN (lateral prefrontal cortex [lPFC]), SN (anterior cingulate cortex [ACC], insula, amygdala [AMG]), and DMN (medial prefrontal cortex [mPFC], posterior parietal cortex [PCC], and hippocampus [HIP]) hubs as seed regions. Group-by-Sex ANOVA was conducted. RESULTS: The amygdala-precuneus, ACC-precuneus, and hippocampus-precuneus pathways exhibited significant group-by-sex interaction effects, with females with PTSD consistently differing in connectivity patterns from males with PTSD and from trauma-exposed healthy females. CONCLUSIONS: Sex-specific neural connectivity patterns were found within and between key nodes of the CEN, DMN, and the SN, suggesting opposite patterns of connectivity in PTSD and trauma-exposed controls as a function of sex as a biological variable (SABV). This may point to mechanistic sex differences in adaptation following trauma and may inform differential neural targets for treatment of females and males with PTSD.

Equine-Assisted Therapy for Posttraumatic Stress Disorder Among Military Veterans: An Open Trial.

Objective: As veterans have high rates of posttraumatic stress disorder (PTSD) and historically poor treatment outcomes and high attrition, alternative treatments have gained much popularity despite lack of rigorous research. In this study, a recently developed and manualized 8-session group Equine-Assisted Therapy for PTSD (EAT-PTSD) was tested in an open trial to assess its preliminary feasibility, acceptability, and outcomes for military veterans.Methods: The study was conducted from July 2016 to July 2019. Sixty-three treatment-seeking veterans with PTSD enrolled. PTSD diagnosis was ascertained using the Structured Clinical Interview for DSM-5, Research Version (SCID-5-RV) and confirmed using the Clinician-Administered PTSD Scale (CAPS-5). Mean age was 50 years, and 23 patients (37%) were women. Clinician and self-report measures of PTSD and depression were assessed at pretreatment, midtreatment, and posttreatment and at a 3-month follow-up. An intent-to-treat analysis and a secondary analysis of those who completed all 4 clinical assessments were utilized.Results: Only 5 patients (8%) withdrew from treatment, 4 before midtreatment and 1 afterward. Posttreatment assessment revealed marked reductions in both clinician-rated and self-reported PTSD and depression symptoms, which persisted at 3-month follow-up. Specifically, mean (SD) CAPS-5 scores fell from 38.6 (8.1) to 26.9 (12.4) at termination. Thirty-two patients (50.8%) showed clinically significant change (≥ 30% decrease in CAPS-5 score) at posttreatment and 34 (54.0%) at follow-up.Conclusions: Manualized EAT-PTSD shows promise as a potential new intervention for veterans with PTSD. It appears safe, feasible, and clinically viable. These preliminary results encourage examination of EAT-PTSD in larger, randomized controlled trials.Trial Registration: ClinicalTrials.gov identifier: NCT03068325.

Attention allocation in posttraumatic stress disorder: an eye-tracking study.

BACKGROUND: Eye-tracking-based attentional research implicates sustained attention to threat in posttraumatic stress disorder (PTSD). However, most of this research employed small stimuli set-sizes, small samples that did not include both trauma-exposed healthy participants and non-trauma-exposed participants, and generally failed to report the reliability of used tasks and attention indices. Here, using an established eye-tracking paradigm, we explore attention processes to different negatively-valenced cues in PTSD while addressing these limitations. METHODS: PTSD patients (n = 37), trauma-exposed healthy controls (TEHC; n = 34), and healthy controls (HC; n = 30) freely viewed three blocks of 30 different matrices of faces, each presented for 6 s. Each block consisted of matrices depicting eight negatively-valenced faces (anger, fear, or sadness) and eight neutral faces. Gaze patterns on negative and neural areas of interest were compared. Internal consistency and test-retest reliability were evaluated for the entire sample and within groups. RESULTS: The two trauma-exposed groups dwelled longer on negatively-valenced faces over neutral faces, while HC participants showed the opposite pattern. This attentional bias was more prominent in the PTSD than the TEHC group. Similar results emerged for first-fixation dwell time, but with no differences between the two trauma-exposed groups. No group differences emerged for first-fixation latency or location. Internal consistency and 1-week test-retest reliability were adequate, across and within groups. CONCLUSIONS: Sustained attention on negatively-valenced stimuli emerges as a potential target for therapeutic intervention in PTSD designed to divert attention away from negatively-valenced stimuli and toward neutral ones.

Neural changes following equine-assisted therapy for posttraumatic stress disorder: A longitudinal multimodal imaging study.

BACKGROUND: While effective treatments for posttraumatic stress disorder (PTSD) exist, many individuals, including military personnel and veterans fail to respond to them. Equine-assisted therapy (EAT), a novel PTSD treatment, may complement existing PTSD interventions. This study employs longitudinal neuro-imaging, including structural magnetic resonance imaging (sMRI), resting state-fMRI (rs-fMRI), and diffusion tensor imaging (DTI), to determine mechanisms and predictors of EAT outcomes for PTSD. METHOD: Nineteen veterans with PTSD completed eight weekly group sessions of EAT undergoing multimodal MRI assessments before and after treatment. Clinical assessments were conducted at baseline, post-treatment and at 3-month follow-up. RESULTS: At post-treatment patients showed a significant increase in caudate functional connectivity (FC) and reduction in the gray matter density of the thalamus and the caudate. The increase of caudate FC was positively associated with clinical improvement seen immediately at post-treatment and at 3-month follow-up. In addition, higher baseline caudate FC was associated with greater PTSD symptom reduction post-treatment. CONCLUSIONS: This exploratory study is the first to demonstrate that EAT can affect functional and structural changes in the brains of patients with PTSD. The findings suggest that EAT may target reward circuitry responsiveness and produce a caudate pruning effect from pre- to post-treatment.

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology.

BACKGROUND: In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis). METHODS: Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement). RESULTS: Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses. CONCLUSIONS: The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.