Attention training modulates resting-state neurophysiological abnormalities in posttraumatic stress disorder

Comparing resting-state connectivity of working memory networks in U.S. Service members with mild traumatic brain injury and posttraumatic stress disorder

Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) are prevalent among military populations, and both have been associated with working memory (WM) impairments. Previous resting-state functional connectivity (rsFC) research conducted separately in PTSD and mTBI populations suggests that there may be similar and distinct abnormalities in WM-related networks. However, no studies have compared rsFC of WM brain regions in participants with mTBI versus PTSD. We used resting-state fMRI to investigate rsFC of WM networks in U.S. Service Members (n = 127; ages 18-59) with mTBI only (n = 46), PTSD only (n = 24), and an orthopedically injured (OI) control group (n = 57). We conducted voxelwise rsFC analyses with WM brain regions to test for differences in WM network connectivity in mTBI versus PTSD. Results revealed reduced rsFC between ventrolateral prefrontal cortex (vlPFC), lateral premotor cortex, and dorsolateral prefrontal cortex (dlPFC) WM regions and brain regions in the dorsal attention and somatomotor networks in both mTBI and PTSD groups versus controls. When compared to those with mTBI, individuals with PTSD had lower rsFC between both the lateral premotor WM seed region and middle occipital gyrus as well as between the dlPFC WM seed region and paracentral lobule. Interestingly, only vlPFC connectivity was significantly associated with WM performance across the samples. In conclusion, we found primarily overlapping patterns of reduced rsFC in WM brain regions in both mTBI and PTSD groups. Our finding of decreased vlPFC connectivity associated with WM is consistent with previous clinical and neuroimaging studies. Overall, these results provide support for shared neural substrates of WM in individuals with either mTBI or PTSD.

Acute Posttraumatic Symptoms Are Associated With Multimodal Neuroimaging Structural Covariance Patterns: A Possible Role for the Neural Substrates of Visual Processing in Posttraumatic Stress Disorder

BACKGROUND

Although aspects of brain morphology have been associated with chronic posttraumatic stress disorder (PTSD), limited work has investigated multimodal patterns in brain morphology that are linked to acute posttraumatic stress severity. In the present study, we utilized multimodal magnetic resonance imaging to investigate if structural covariance networks (SCNs) assessed acutely following trauma were linked to acute posttraumatic stress severity.

METHODS

Structural magnetic resonance imaging data were collected around 1 month after civilian trauma exposure in 78 participants. Multimodal magnetic resonance imaging data fusion was completed to identify combinations of SCNs, termed structural covariance profiles (SCPs), related to acute posttraumatic stress severity collected at 1 month. Analyses assessed the relationship between participant SCP loadings, acute posttraumatic stress severity, the change in posttraumatic stress severity from 1 to 12 months, and depressive symptoms.

RESULTS

We identified an SCP that reflected greater gray matter properties of the anterior temporal lobe, fusiform face area, and visual cortex (i.e., the ventral visual stream) that varied curvilinearly with acute posttraumatic stress severity and the change in PTSD symptom severity from 1 to 12 months. The SCP was not associated with depressive symptoms.

CONCLUSIONS

We identified combinations of multimodal SCNs that are related to variability in PTSD symptoms in the early aftermath of trauma. The identified SCNs may reflect patterns of neuroanatomical organization that provide unique insight into acute posttraumatic stress. Furthermore, these multimodal SCNs may be potential candidates for neural markers of susceptibility to both acute posttraumatic stress and the future development of PTSD.

Neuromodulation of Visual Cortex Reduces the Intensity of Intrusive Memories

Aversive events can be reexperienced as involuntary and spontaneous mental images of the event. Given that the vividness of retrieved mental images is coupled with elevated visual activation, we tested whether neuromodulation of the visual cortex would reduce the frequency and negative emotional intensity of intrusive memories. Intrusive memories of a viewed trauma film and their accompanied emotional intensity were recorded throughout 5 days. Functional connectivity, measured with resting-state functional magnetic resonance imaging prior to film viewing, was used as predictive marker for intrusions-related negative emotional intensity. Results indicated that an interaction between the visual network and emotion processing areas predicted intrusions’ emotional intensity. To test the causal influence of early visual cortex activity on intrusions’ emotional intensity, participants’ memory of the film was reactivated by brief reminders 1 day following film viewing, followed by inhibitory 1 Hz repetitive transcranial magnetic stimulation (rTMS) over early visual cortex. Results showed that visual cortex inhibitory stimulation reduced the emotional intensity of later intrusions, while leaving intrusion frequency and explicit visual memory intact. Current findings suggest that early visual areas constitute a central node influencing the emotional intensity of intrusive memories for negative events. Potential neuroscience-driven intervention targets designed to downregulate the emotional intensity of intrusive memories are discussed.

Randomized-controlled trial of response inhibition training for individuals with PTSD and impaired response inhibition

Deficits in response inhibition, defined as an inability to stop a behavior that is no longer relevant, are characteristic of posttraumatic stress disorder (PTSD). Given that impaired response inhibition is associated with worse symptom recovery and accumulating evidence pointing to the effectiveness of cognitive control trainings in reducing PTSD symptoms, individuals with moderate to severe PTSD total severity (Posttraumatic Diagnostic Scale total score ≥ 21) and pre-training response inhibition deficits (M ≤ 75% successful inhibition on the Go/No-go) completed a 3-h, adaptive Go/No-go training designed to improve ability to withhold prepotent motor responses. Then forty-nine participants were randomized to an adaptive response inhibition training (n = 24, M = 19.27 years, SD = 0.70) or a waitlist condition (n = 25, M = 18.31 years, SD = 4.80). Behavioral response inhibition and self-reported trauma-related symptoms were assessed at pre- and post-training. Response inhibition training was associated with improved response inhibition on an untrained transfer Stop-Signal task and symptom reduction in PTSD compared to a waitlist group, at post-training. There was, however, reduced inhibition on a modified Go/No-go task from pre-to post-training. Overall, response inhibition deficits and PTSD symptoms are amenable to top-down remediation using response inhibition training. Our study provides preliminary evidence for the feasibility of response inhibition training in a PTSD sample characterized by response inhibition deficits.

Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning

Given the subjective nature of conventional diagnostic methods for post-traumatic stress disorder (PTSD), an objectively measurable biomarker is highly desirable; especially to clinicians and researchers. Macroscopic neural circuits measured using magnetoencephalography (MEG) has previously been shown to be indicative of the PTSD phenotype and severity. In the present study, we employed a machine learning-based classification framework using MEG neural synchrony to distinguish combat-related PTSD from trauma-exposed controls. Support vector machine (SVM) was used as the core classification algorithm. A recursive random forest feature selection step was directly incorporated in the nested SVM cross validation process (CV-SVM-rRF-FS) for identifying the most important features for PTSD classification. For the five frequency bands tested, the CV-SVM-rRF-FS analysis selected the minimum numbers of edges per frequency that could serve as a PTSD signature and be used as the basis for SVM modelling. Many of the selected edges have been reported previously to be core in PTSD pathophysiology, with frequency-specific patterns also observed. Furthermore, the independent partial least squares discriminant analysis suggested low bias in the machine learning process. The final SVM models built with selected features showed excellent PTSD classification performance (area-under-curve value up to 0.9). Testament to its robustness when distinguishing individuals from a heavily traumatised control group, these developments for a classification model for PTSD also provide a comprehensive machine learning-based computational framework for classifying other mental health challenges using MEG connectome profiles.

New perspectives on the neurobiology of PTSD: High-resolution imaging of neural circuit (dys)function with magnetoencephalography

Introduction: Combat-related posttraumatic stress disorder (PTSD) is increasingly conceptualized in psychiatry as a disorder of dysfunctional neural circuits. Advances in neuroimaging have enabled the study of those networks non-invasively. PTSD is currently assessed using subjective self-reporting to inform crucial decisions, such as fitness to deploy, but objective markers would aid in diagnosis and return-to-deployment decisions. Methods: Magnetoencephalography (MEG) allows investigation of neural circuit function via imaging of brain waves (known as neural oscillations) that index information processing in the brain and would prove a reliable, objective, biomarker. These measures of brain function establish how regions communicate to form brain circuits that support thinking and behaviour. Results: Studies into intrinsic brain function, both during rest and when engaged in a task designed to tap into cognitive dysfunction, have found these neurobiological mechanisms are disrupted in PTSD and are a reliable objective marker of illness. We now know that these alterations in brain function are directly related to core symptoms of PTSD and comorbid cognitive-behavioural challenges. Discussion: Continued characterization of neural function using MEG and related methods will advance understanding of the neurobiology underlying PTSD; allow for the identification of biomarkers that, coupled with machine learning, will aid in diagnoses; provide individualized therapeutic targets for neurostimulation; predict treatment outcomes; and track disorder remission in military personnel and Veterans who are disproportionately affected by this devastating illness.

PTSD as a Public Mental Health Priority

PURPOSE OF REVIEW

This article reviews recent evidence related to public health epidemiology and intervention for traumatic stress and PTSD. Recent evidence is presented regarding incidence of traumatic stress worldwide, as well as most frequent types of traumas, indicators of the public health burden of PTSD, and prevalence, predictors, and correlates of PTSD. Public health perspectives on intervention and treatment are delineated, and innovations in both psychosocial and psychopharmacological interventions are highlighted.

RECENT FINDINGS

PTSD has been associated with substantial medical and economic burden. Recent public health preventive innovations include integrated medical/behavioral health care, acute CBT and attention interventions, modifications to CBT protocols, use of novel and augmentative psychopharmacological agents, and use of technology. Recent research regarding the scope and impact of traumatic stress, as well as prevention strategies for PTSD, have resulted in an improved understanding of its impact and more effective public health interventions.

Functional reorganization of neural networks involved in emotion regulation following trauma therapy for complex trauma disorders

OBJECTIVES

We investigated whether patients with complex interpersonal trauma engage neural networks that are commonly activated during cognitive reappraisal and responding naturally to affect-laden images. In this naturalistic study, we examined whether trauma treatment not only reduces symptoms but also changes neural networks involved in emotional control.

METHODS

Before and after eight weeks of phase-oriented inpatient trauma treatment, patients (n = 28) with complex posttraumatic stress disorder (cPTSD) and complex dissociative disorders (CDD) performed a cognitive reappraisal task while electroencephalography (EEG) was registered. Patients were measured as a prototypical dissociative part that aims to fulfill daily life goals while avoiding traumatic memories and associated dissociative parts. Matched healthy controls (n = 38) were measured twice as well. We examined task-related functional connectivity and assessed self-reports of clinical symptoms and emotion regulation skills.

RESULTS

Prior to treatment and compared to controls, patients showed hypoconnectivity within neural networks involved in emotional downregulation while reappraising affect-eliciting pictures as well as viewing neutral and affect-eliciting pictures. Following treatment, connectivity became normalized in these networks comprising regions associated with cognitive control and memory. Additionally, patients showed a treatment-related reduction of negative but not of positive dissociative symptoms.

CONCLUSIONS

This is the first study demonstrating that trauma-focused treatment was associated with favorable changes in neural networks involved in emotional control. Emotional overregulation manifesting as negative dissociative symptoms was reduced but not emotional underregulation, manifesting as positive dissociative symptoms.