Dataset Information

Evaluating the Contribution of EEG Power Profiles to Characterize and Discriminate Posttraumatic Stress Symptom Factors in a Combat-Exposed Population.

ABSTRACT: This study investigated the use of resting-state electroencephalography (EEG) data to help differentiate posttraumatic stress disorder (PTSD) symptom factors. The sample, 147 combat-exposed OIF/OEF (Operation Iraqi Freedom/Operation Enduring Freedom) Veterans and service members, was a polytrauma population with variable PTSD and mild traumatic brain injury (mTBI) diagnoses. Participants completed the PTSD Checklist (PCL) and resting-state EEG was assessed for 10 minutes, with eyes closed. Regional averages of absolute power in alpha, beta, delta, and theta frequency bands were computed to estimate a single EEG common factor per band. An oblique 4 common-factor model was then fit to the 17 PCL items that included a residual EEG factor as an exogenous predictor with the group mean effect of mTBI on the EEG factor removed. Separate comparative model testing sequences for the alpha, beta, delta, and theta EEG factor frequency bands were conducted. An inverse relationship of delta and theta frequency bands on avoidance and numbing symptom factors (but not re-experiencing and hyperarousal) was found. Results provide evidence for possible neurobiological basis for the 4 PTSD symptom factors.

SUBMITTER: Sheerin CM

PROVIDER: S-EPMC6119519 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Evaluating the Contribution of EEG Power Profiles to Characterize and Discriminate Posttraumatic Stress Symptom Factors in a Combat-Exposed Population.

Sheerin Christina M CM Franke Laura M LM Aggen Steven H SH Amstadter Ananda B AB Walker William C WC

Clinical EEG and neuroscience 20180403 6

This study investigated the use of resting-state electroencephalography (EEG) data to help differentiate posttraumatic stress disorder (PTSD) symptom factors. The sample, 147 combat-exposed OIF/OEF (Operation Iraqi Freedom/Operation Enduring Freedom) Veterans and service members, was a polytrauma population with variable PTSD and mild traumatic brain injury (mTBI) diagnoses. Participants completed the PTSD Checklist (PCL) and resting-state EEG was assessed for 10 minutes, with eyes closed. Regio ...[more]

PMID: 29611429

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Project description:Recent work inspired by graph theory has begun to conceptualize mental disorders as networks of interacting symptoms. Posttraumatic stress disorder (PTSD) symptom networks have been investigated in clinical samples meeting full diagnostic criteria, including military veterans, natural disaster survivors, civilian survivors of war, and child sexual abuse survivors. Despite reliable associations across reported networks, more work is needed to compare central symptoms across trauma types. Additionally, individuals without a diagnosis who still experience symptoms, also referred to as subthreshold cases, have not been explored with network analysis in veterans. A sample of 1,050 Iraq/Afghanistan-era U.S. military veterans (851 males, mean age = 36.3, SD = 9.53) meeting current full-criteria PTSD (n = 912) and subthreshold PTSD (n = 138) were assessed with the Structured Clinical Interview for DSM-IV Disorders (SCID). Combat Exposure Scale (CES) scores were used to group the sample meeting full-criteria into high (n = 639) and low (n = 273) combat exposure subgroups. Networks were estimated using regularized partial correlation models in the R-package qgraph, and robustness tests were performed with bootnet. Frequently co-occurring symptom pairs (strong network connections) emerged between two avoidance symptoms, hypervigilance and startle response, loss of interest and detachment, as well as, detachment and restricted affect. These associations replicate findings reported across PTSD trauma types. A symptom network analysis of PTSD in a veteran population found significantly greater overall connectivity in the full-criteria PTSD group as compared to the subthreshold PTSD group. Additionally, novel findings indicate that the association between intrusive thoughts and irritability is a feature of the symptom network of veterans with high levels of combat exposure. Mean node predictability is high for PTSD symptom networks, averaging 51.5% shared variance. With the tools described here and by others, researchers can help refine diagnostic criteria for PTSD, develop more accurate measures for assessing PTSD, and eventually inform therapies that target symptoms with strong network connections to interrupt interconnected symptom complexes and promote functional recovery.

Project description:Two decades of human neuroimaging research have associated volume reductions in the hippocampus with posttraumatic stress disorder. However, little is known about the distribution of volume loss across hippocampal subfields. Recent advances in neuroimaging methods have made it possible to accurately delineate 10 gray matter hippocampal subfields. Here, we apply a volumetric analysis of hippocampal subfields to data from a group of combat-exposed Veterans. Veterans (total, n = 68, posttraumatic stress disorder, n = 36; combat control, n = 32) completed high-resolution structural magnetic resonance imaging. Based on previously validated methods, hippocampal subfield volume measurements were conducted using FreeSurfer 6.0. The Clinician-Administered PTSD Scale assessed posttraumatic stress disorder symptom severity; Beck Depression Inventory assessed depressive symptom severity. Controlling for age and intracranial volume, partial correlation analysis examined the relationship between hippocampal subfields and symptom severity. Correction for multiple comparisons was performed using false discovery rate. Gender, intelligence, combat severity, comorbid anxiety, alcohol/substance use disorder, and medication status were investigated as potential confounds. In the whole sample, total hippocampal volume negatively correlated with Clinician-Administered PTSD Scale and Beck Depression Inventory scores. Of the 10 hippocampal subfields, Clinician-Administered PTSD Scale symptom severity negatively correlated with the hippocampus-amygdala transition area (HATA). Beck Depression Inventory scores negatively correlated with dentate gyrus, cornu ammonis 4 (CA4), HATA, CA2/3, molecular layer, and CA1. Follow-up analysis limited to the posttraumatic stress disorder group showed a negative correlation between Clinician-Administered PTSD Scale symptom severity and each of HATA, CA2/3, molecular layer, and CA4. This study provides the first evidence relating posttraumatic stress disorder and depression symptoms to abnormalities in the HATA, an anterior hippocampal region highly connected to prefrontal-amygdala circuitry. Notably, dentate gyrus abnormalities were associated with depression severity but not posttraumatic stress disorder symptoms. Future confirmatory studies should determine the extent to which dentate gyrus volume can differentiate between posttraumatic stress disorder- and depression-related pathophysiology.

Project description:Objectives A core assumption of meditation training is that cognitive capacities developed during formal practice will transfer to other contexts or activities as expertise develops over time. This implies that meditation training might influence domain-general neurocognitive systems, the spontaneous activity of which should be reflected in the dynamics of the resting brain. Previous research has demonstrated that 3 months of meditation training led to reductions in EEG beta band power during mindfulness of breathing practice. The current study extends these findings to ask whether concomitant shifts in power are observed during 2 min of eyes closed rest, when participants are not explicitly engaged in formal meditation. Methods Experienced meditation practitioners were randomly assigned to practice 3 months of focused attention meditation in a residential retreat, or to serve as waitlist controls. The waitlist controls later completed their own 3-month retreat. Permutation-based cluster analysis of 88-channel resting EEG data was used to test for spectral changes in spontaneous brain activity over the course of the retreats. Results Longitudinal reductions in EEG power in the beta frequency range were identified and replicated across the two independent training periods. Less robust reductions were also observed in the high alpha frequency range, and in individual peak alpha frequency. These changes closely mirror those previously observed during formal mindfulness of breathing meditation practice. Conclusions These findings suggest that the neurocognitive effects of meditation training can extend beyond the bounds of formal practice, influencing the spontaneous activity of the resting brain. Rather than serving as an invariant baseline, resting states might carry meaningful training-related effects, blurring the line between state and trait change. Supplementary Information The online version contains supplementary material available at 10.1007/s12671-022-01974-9.

Project description:Background: Supporting wellbeing beyond symptom reduction is necessary in trauma care. Research suggests increased posttraumatic growth (PTG) may promote wellbeing more effectively than posttraumatic stress disorder (PTSD) symptom reduction alone. Understanding neurobiological mechanisms of PTG would support PTG intervention development. However, most PTG research to-date has been cross-sectional data self-reported through surveys or interviews.Objective: Neural evidence of PTG and its coexistence with resilience and PTSD is limited. To advance neural PTG literature and contribute translational neuroscientific knowledge necessary to develop future objectively measurable neural-based PTG interventions.Method: Alpha frequency EEG and validated psychological inventories measuring PTG, resilience, and PTSD symptoms were collected from 30 trauma-exposed healthy adults amidst the COVID-19 pandemic. EEG data were collected using custom MNE-Python software, and a wireless OpenBCI 16-channel dry electrode EEG headset. Psychological inventory scores were analysed in SPSS Statistics and used to categorise the EEG data. Power spectral density analyses, t-tests and ANOVAs were conducted within EEGLab to identify brain activity differentiating high and low PTG, resilience, and PTSD symptoms.Results: Higher PTG was significantly differentiated from low PTG by higher alpha power in the left centro-temporal brain area around EEG electrode C3. A trend differentiating high PTG from PTSD was also indicated in this same location. Whole-scalp spectral topographies revealed alpha power EEG correlates of PTG, resilience and PTSD symptoms shared limited, but potentially meaningful similarities.Conclusion: This research provides the first comparative neural topographies of PTG, resilience and PTSD symptoms in the known literature. Results provide objective neural evidence supporting existing theory depicting PTG, resilience and PTSD as independent, yet co-occurring constructs. PTG neuromarker alpha C3 significantly delineated high from low PTG and warrants further investigation for potential clinical application. Findings provide foundation for future neural-based interventions and research for enhancing PTG in trauma-exposed individuals.

Project description:IntroductionElectroencephalographic (EEG) data quality is severely compromised when recorded inside the magnetic resonance (MR) environment. Here we characterized the impact of the ballistocardiographic (BCG) artifact on resting-state EEG spectral properties and compared the effectiveness of seven common BCG correction methods to preserve EEG spectral features. We also assessed if these methods retained posterior alpha power reactivity to an eyes closure-opening (EC-EO) task and compared the results from EEG-informed fMRI analysis using different BCG correction approaches.MethodElectroencephalographic data from 20 healthy young adults were recorded outside the MR environment and during simultaneous fMRI acquisition. The gradient artifact was effectively removed from EEG-fMRI acquisitions using Average Artifact Subtraction (AAS). The BCG artifact was corrected with seven methods: AAS, Optimal Basis Set (OBS), Independent Component Analysis (ICA), OBS followed by ICA, AAS followed by ICA, PROJIC-AAS and PROJIC-OBS. EEG signal preservation was assessed by comparing the spectral power of traditional frequency bands from the corrected rs-EEG-fMRI data with the data recorded outside the scanner. We then assessed the preservation of posterior alpha functional reactivity by computing the ratio between the EC and EO conditions during the EC-EO task. EEG-informed fMRI analysis of the EC-EO task was performed using alpha power-derived BOLD signal predictors obtained from the EEG signals corrected with different methods.ResultsThe BCG artifact caused significant distortions (increased absolute power, altered relative power) across all frequency bands. Artifact residuals/signal losses were present after applying all correction methods. The EEG reactivity to the EC-EO task was better preserved with ICA-based correction approaches, particularly when using ICA feature extraction to isolate alpha power fluctuations, which allowed to accurately predict hemodynamic signal fluctuations during the EEG-informed fMRI analysis.DiscussionCurrent software solutions for the BCG artifact problem offer limited efficiency to preserve the EEG spectral power properties using this particular EEG setup. The state-of-the-art approaches tested here can be further refined and should be combined with hardware implementations to better preserve EEG signal properties during simultaneous EEG-fMRI. Existing and novel BCG artifact correction methods should be validated by evaluating signal preservation of both ERPs and spontaneous EEG spectral power.

Dataset Information

Evaluating the Contribution of EEG Power Profiles to Characterize and Discriminate Posttraumatic Stress Symptom Factors in a Combat-Exposed Population.

Publications

Evaluating the Contribution of EEG Power Profiles to Characterize and Discriminate Posttraumatic Stress Symptom Factors in a Combat-Exposed Population.

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