Dissociative subtype of posttraumatic stress disorder or PTSD with comorbid dissociative disorders: Comparative evaluation of clinical profiles.

2020 ◽  
Vol 12 (1) ◽  
pp. 38-45 ◽  
Author(s):  
Sanne Swart ◽  
Marleen Wildschut ◽  
Nel Draijer ◽  
Willemien Langeland ◽  
Jan H. Smit
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Comparative Evaluation ◽  
Stress Disorder ◽  
Dissociative Disorders ◽  
Clinical Profiles ◽  
Dissociative Subtype

scholarly journals Dissociative subtype of posttraumatic stress disorder in women in partial and residential levels of psychiatric care

2019 ◽  
Vol 21 (3) ◽  
pp. 305-318
Author(s):  
Sarah B. Hill ◽  
Jonathan D. Wolff ◽  
Cara E. Bigony ◽  
Sherry R. Winternitz ◽  
Kerry J. Ressler ◽  
...  
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Psychiatric Care ◽  
Stress Disorder ◽  
Dissociative Subtype

Social and Environmental Pedagogue, 15+ years’ experience, Germany

2020 ◽  
pp. 149-149
Author(s):  
Markus Reuber ◽  
Gregg H. Rawlings ◽  
Steven C. Schachter
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Epileptic Seizures ◽  
Dissociative Disorders ◽  
Traumatic Experiences ◽  
Epilepsy Center ◽  
Dissociative Seizures

This chapter assesses the experiences of a social and environmental pedagogue with patients with Psychogenic Non-Epileptic Seizures (PNES) while working on a psychotherapeutic ward in an epilepsy center. These experiences mostly involve women with serious traumatic experiences, with a diagnosis of Posttraumatic Stress Disorder (PTSD) and resulting Dissociative Disorders, with dissociative attacks, and even with dissociative fugue states. These women often experience long-lasting seizure states, with dramatic dynamic manifestations, including screams, beating, and defensive or protective movements, in which contents of the traumatic experiences are re-enacted. As such, it is important to adopt an inner attitude that conveys safety and makes it possible to establish a connection with the person concerned. This makes it possible to talk about the seizures/states with the person affected after the seizure has passed, so that the person concerned manages to gain more control over his or her dissociative seizures/states.

Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach

2018 ◽  
Vol 49 (12) ◽  
pp. 2049-2059 ◽  
Author(s):  
Andrew A. Nicholson ◽  
Maria Densmore ◽  
Margaret C. McKinnon ◽  
Richard W.J. Neufeld ◽  
Paul A. Frewen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Mean Amplitude ◽  
Machine Learning Algorithms ◽  
Group Differences ◽  
Significant Group ◽  
Dissociative Subtype

AbstractBackgroundThe field of psychiatry would benefit significantly from developing objective biomarkers that could facilitate the early identification of heterogeneous subtypes of illness. Critically, although machine learning pattern recognition methods have been applied recently to predict many psychiatric disorders, these techniques have not been utilized to predict subtypes of posttraumatic stress disorder (PTSD), including the dissociative subtype of PTSD (PTSD + DS).MethodsUsing Multiclass Gaussian Process Classification within PRoNTo, we examined the classification accuracy of: (i) the mean amplitude of low-frequency fluctuations (mALFF; reflecting spontaneous neural activity during rest); and (ii) seed-based amygdala complex functional connectivity within 181 participants [PTSD (n = 81); PTSD + DS (n = 49); and age-matched healthy trauma-unexposed controls (n = 51)]. We also computed mass-univariate analyses in order to observe regional group differences [false-discovery-rate (FDR)-cluster corrected p < 0.05, k = 20].ResultsWe found that extracted features could predict accurately the classification of PTSD, PTSD + DS, and healthy controls, using both resting-state mALFF (91.63% balanced accuracy, p < 0.001) and amygdala complex connectivity maps (85.00% balanced accuracy, p < 0.001). These results were replicated using independent machine learning algorithms/cross-validation procedures. Moreover, areas weighted as being most important for group classification also displayed significant group differences at the univariate level. Here, whereas the PTSD + DS group displayed increased activation within emotion regulation regions, the PTSD group showed increased activation within the amygdala, globus pallidus, and motor/somatosensory regions.ConclusionThe current study has significant implications for advancing machine learning applications within the field of psychiatry, as well as for developing objective biomarkers indicative of diagnostic heterogeneity.

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