scholarly journals A resting-state network comparison of combat-related PTSD with combat-exposed and civilian controls

2019 ◽  
Vol 14 (9) ◽  
pp. 933-945
Author(s):  
Thomas J Vanasse ◽  
Crystal Franklin ◽  
Felipe S Salinas ◽  
Amy E Ramage ◽  
Vince D Calhoun ◽  
...  
Keyword(s):  
Resting State ◽  
Anterior Cingulate ◽  
Post Traumatic Stress ◽  
Resting State Functional Connectivity ◽  
Intrinsic Connectivity ◽  
Post Traumatic ◽  
Ptsd Symptom Severity ◽  
Intrinsic Connectivity Networks ◽  
Group Independent Component Analysis ◽  
Left Insula

Abstract Resting-state functional connectivity (rsFC) is an emerging means of understanding the neurobiology of combat-related post-traumatic stress disorder (PTSD). However, most rsFC studies to date have limited focus to cognitively related intrinsic connectivity networks (ICNs), have not applied data-driven methodologies or have disregarded the effect of combat exposure. In this study, we predicted that group independent component analysis (GICA) would reveal group-wise differences in rsFC across 50 active duty service members with PTSD, 28 combat-exposed controls (CEC), and 25 civilian controls without trauma exposure (CC). Intranetwork connectivity differences were identified across 11 ICNs, yet combat-exposed groups were indistinguishable in PTSD vs CEC contrasts. Both PTSD and CEC demonstrated anatomically diffuse differences in the Auditory Vigilance and Sensorimotor networks compared to CC. However, intranetwork connectivity in a subset of three regions was associated with PTSD symptom severity among executive (left insula; ventral anterior cingulate) and right Fronto-Parietal (perigenual cingulate) networks. Furthermore, we found that increased temporal synchronization among visuospatial and sensorimotor networks was associated with worse avoidance symptoms in PTSD. Longitudinal neuroimaging studies in combat-exposed cohorts can further parse PTSD-related, combat stress-related or adaptive rsFC changes ensuing from combat.

scholarly journals Similarities between Borderline Personality Disorder and Post traumatic Stress Disorder: evidence from Resting-State Meta-Analysis

2019 ◽  
Author(s):  
Ali Amad ◽  
Joaquim Radua ◽  
Guillaume Vaiva ◽  
SCR Williams ◽  
Thomas Fovet
Keyword(s):  
Personality Disorder ◽  
Post Traumatic Stress Disorder ◽  
Traumatic Stress ◽  
Resting State ◽  
Stress Disorder ◽  
Meta Analysis ◽  
Borderline Personality ◽  
Anterior Cingulate ◽  
Post Traumatic Stress ◽  
Post Traumatic

AbstractBorderline personality disorder (BPD) and post-traumatic stress disorder (PTSD) are common psychiatric disorders. The nature of the relationship between BPD and PTSD remains controversial, but it has been suggested that these disorders should brought closer because of their many similarities. We thus performed a quantitative meta-analysis of resting-state functional imaging to assess similarities in the brain activation across BPD and PTSD diagnostic groups.Overlap analyses revealed decreased activation in the left and right precuneus of both BPD and PTSD groups when compared to control subjects. BPD showed significant increased, but PTSD showed decreased activation, relative to control subjects, in the anterior cingulate/paracingulate gyri and in the left superior frontal gyrus. Complementary overlap analyses on a subgroup of studies with similar sex and age distribution partially confirmed the main results as the same pattern of functional activation in the anterior cingulate and in the left superior frontal gyrus were found.Our findings are in agreement with the hypothesis that BPD and PTSD share common neuropathological pathways.

scholarly journals Tracking the Brain's Intrinsic Connectivity Networks in EEG

2021 ◽  
Author(s):  
Saurabh Bhaskar Shaw ◽  
Margaret C. McKinnon ◽  
Jennifer J Heisz ◽  
Amabilis H. Harrison ◽  
John F. Connolly ◽  
...  
Keyword(s):  
Imaging Modality ◽  
Brain Networks ◽  
Network Dynamics ◽  
Post Traumatic Stress ◽  
Non Invasive ◽  
Clinical Biomarkers ◽  
Intrinsic Connectivity ◽  
Post Traumatic ◽  
Treatment Responses ◽  
Intrinsic Connectivity Networks

Functional magnetic resonance imaging (fMRI) has identified dysfunctional network dynamics underlying a number of psychopathologies, including post-traumatic stress disorder, depression and schizophrenia. There is tremendous potential for the development of network-based clinical biomarkers to better characterize these disorders. However, to realize this potential requires the ability to track brain networks using a more affordable imaging modality, such as Electroencephalography (EEG). Here we present a novel analysis pipeline capable of tracking brain networks from EEG alone, after training on supervisory signals derived from data simultaneously recorded in EEG and fMRI, while people engaged in various cognitive tasks. EEG-based features were then used to classify three cognitively-relevant brain networks with up to 75\% accuracy. These findings could lead to affordable and non-invasive methods to objectively diagnose brain disorders involving dysfunctional network dynamics, and to track and even predict treatment responses.

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