The temporal dynamics of symptoms during exposure therapies of PTSD: a network approach.
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ABSTRACT:
SUBMITTER: Hoffart A
PROVIDER: S-EPMC6566586 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Understanding whether a history of psychological trauma is associated with perpetrating aggressive and violent behavior is of critical importance to public health. This relationship is especially important to study within urban areas where violence is prevalent. In this paper we examined whether a history of trauma or Post Traumatic Stress Disorder (PTSD) in inner city civilians was associated with violent behavior. Data were collected from over 1900 primary care patients at Grady Memorial Hospital in Atlanta, Georgia. Childhood trauma history was assessed with the Childhood Trauma Questionnaire (CTQ) and adult trauma history with the Traumatic Events Inventory (TEI). PTSD symptoms were measured with the PTSD Symptom Scale (PSS) and violent behaviors were measured with the Behavior Questionnaire (BQ). Using these measures we studied violent behavior in the inner city and its association with childhood or adult trauma history or PTSD. Trauma, PTSD and violence were all prevalent in this at-risk urban cohort. Perpetrating interpersonal violence was associated with a history childhood and adult trauma history, and with PTSD symptoms and diagnosis. An association between violent behavior and PTSD diagnosis was maintained after controlling for other pertinent variables such as demographics and presence of depression. Our findings point to a dysregulation of aggressive and violent behavior that may be a consequence of trauma and PTSD. These data indicate that more effective PTSD screening and treatment may help to reduce urban violence.
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Project description:BACKGROUND:Emerging data suggests PM2.5 (particulate matter with aerodynamic diameter <2.5??m) may be associated with both earlier declines in episodic memory (EM) and increased depressive symptoms in older adults. Although late-life depressive symptoms are associated with EM, no longitudinal studies have examined the inter-relationship among PM2.5, depressive symptoms and EM. METHODS:Older women (n?=?2,202; aged 67-83 in 1999) enrolled in the Women's Health Initiative Study of Cognitive Aging completed up to eight annual assessments of depressive symptoms (15-item Geriatric Depression Scale) and EM (California Verbal Learning Test). A nationwide spatiotemporal model (1999-2010) was used to estimate ambient PM2.5 exposure at residential locations. Univariate and bivariate structural equation models (SEMs) for latent-change scores were used to examine how 3-year average PM2.5 preceding each assessment affects the temporal dynamics and bidirectional relations of annual changes in depressive symptoms and EM. RESULTS:In univariate SEMs, one inter-quartile (4.04??g/m3) increment of 3-year PM2.5 was significantly (p?<?0.05) associated with accelerated declines in verbal learning (List A trials 1-3: ??=?-1.48) and free-recall memory (short-delay: ??=?-1.43; long-delay: ??=?-1.11), but not with change in depressive symptoms (??=?0.12; p?=?0.71). In bivariate SEMs, significant associations were observed between PM2.5 and accelerated declines in EM measures (??=?-1.44 to -0.99; p?<?0.05) and between EM performance and changes in depressive symptoms (??=?-0.08 to -0.05; p?<?0.05), with significant indirect PM2.5 effects on changes in depressive symptoms (??=?0.08-0.10; p?<?0.05). These findings were robust with adjustment for multiple demographic, lifestyle, and clinical factors, and remained after excluding subjects with dementia or mild cognitive impairment. No associations were found between PM2.5 and change in depressive symptoms or depressive symptoms and subsequent EM decline. CONCLUSIONS:Findings suggest that PM2.5 neurotoxicity may damage brain areas implicated in EM, followed by manifestation of depressive symptoms. Our data did not support depressive symptoms as the neuropsychological mediator of accelerated brain aging associated with PM2.5 exposure.
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Project description:Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19. Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers. Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels. Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms. Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging.
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Project description:In temporal lobe epilepsy (TLE), the variation of chemical receptor expression underlies the basis of neural network activity shifts, resulting in neuronal hyperexcitability and epileptiform discharges. However, dynamical mechanisms involved in the transitions of TLE are not fully understood, because of the neuronal diversity and the indeterminacy of network connection. Hence, based on Hodgkin-Huxley (HH) type neurons and Pinsky-Rinzel (PR) type neurons coupling with glutamatergic and GABAergic synaptic connections respectively, we propose a computational framework which contains dentate gyrus (DG) region and CA3 region. By regulating the concentration range of N-methyl-D-aspartate-type glutamate receptor (NMDAR), we demonstrate the pyramidal neuron can generate transitions from interictal to seizure discharges. This suggests that enhanced endogenous activity of NMDAR contributes to excitability in pyramidal neuron. Moreover, we conclude that excitatory discharges in CA3 region vary considerably on account of the excitatory currents produced by the excitatory pyramidal neuron. Interestingly, by changing the backprojection connection, we find that glutamatergic type backprojection can promote the dominant frequency of firings and further motivate excitatory counterpropagation from CA3 region to DG region. However, GABAergic type backprojection can reduce firing rate and block morbid counterpropagation, which may be factored into the terminations of TLE. In addition, neuronal diversity dominated network shows weak correlation with different backprojections. Our modeling and simulation studies provide new insights into the mechanisms of seizures generation and connectionism in local hippocampus, along with the synaptic mechanisms of this disease.
| S-EPMC5504536 | biostudies-literature
Project description:BackgroundCombat exposure is associated with elevated risk for post-traumatic stress disorder (PTSD). Despite considerable research on PTSD symptom clustering, it remains unknown how symptoms of PTSD re-organize following combat. Network analysis provides a powerful tool to examine such changes.MethodsA network analysis approach was taken to examine how symptom networks change from pre- to post-combat using longitudinal prospective data from a cohort of infantry male soldiers (Mage = 18.8 years). PTSD symptoms measured using the PTSD Checklist (PCL) were assessed after 6 months of combat training but before deployment and again after 6 months of combat (Ns = 910 and 725 at pre-deployment and post-combat, respectively).ResultsStronger connectivity between PTSD symptoms was observed post-combat relative to pre-deployment (global strength values of the networks were 7.54 pre v. 7.92 post; S = .38, p < 0.05). Both the re-experiencing symptoms cluster (1.92 v. 2.12; S = .20, p < 0.03) and the avoidance symptoms cluster (2.61 v. 2.96; S = .35, p < 0.005) became more strongly inter-correlated post-combat. Centrality estimation analyses revealed that psychological reaction to triggers was central and linked the intrusion and avoidance sub-clusters at post-combat. The strength of associations between the arousal and reactivity symptoms cluster remained stable over time (1.85 v. 1.83; S = .02, p = .92).ConclusionsFollowing combat, PTSD symptoms and particularly the re-experiencing and avoidance clusters become more strongly inter-correlated, indicating high centrality of trigger-reactivity symptoms.
| S-EPMC7690054 | biostudies-literature