0673 Script-Driven Imagery in PTSD: Comparing Reactivity to Imagery of Trauma Memories to Imagery of Trauma-Nightmare Memories
Sleep(2022)SCI 2区
Massachusetts Gen Hosp | Harvard Med Sch | Boston Vet Affairs
Abstract
Abstract Introduction Prolonged Exposure (PE) therapy produces therapeutic fear extinction via imaginal exposure to trauma memories. However, traumatic events that occurred in the distant past and the associated memories may become distorted or habituated. Posttraumatic nightmares are more recent, potentially salient, and may better support extinction learning. Physiological responses to imagery of a trauma and nightmare related to this trauma were compared to each other and to neutral imagery. Methods Twelve participants (mean age=26.16, 11 female) with PTSD (mean CAPS-5=27.83) and frequent trauma-related nightmares wrote accounts of their trauma. Participants then completed a 14-day sleep-monitoring period with diaries, actigraphy and two nights of ambulatory PSG. Participants narrated a nightmare report into an audio recorder when awoken by a nightmare or when recalled upon awakening. Two pairs of short narratives were created from the written account of the trauma and recording of a nightmare most similar to the trauma. These narratives (scripts) were audio-recorded by an investigator. Participants then underwent two script-driven imagery (SDI) sessions, one hour apart, during which they listened to either their two trauma-memory or their two nightmare-memory scripts (counterbalanced across participants) with 3 interspersed neutral scripts. Each script in an SDI session included baseline, listening, and imagery periods (approximately 30 sec apiece). Skin conductance (SC), heart rate (HR), and corrugator electromyography (EMG) biosignals were continuously recorded throughout each SDI session. For each script, HR, SC, and EMG means during the baseline period were subtracted from their respective imagery-period means. These difference scores were square-root transformed and analyzed by ANOVA with Type (trauma vs. nightmare) and Valence (trauma/nightmare vs. neutral) factors. Results Biosignals from scripts of both Types (trauma and nightmare) significantly exceeded those from their respective neutral scripts [HR:F(1,11)=23.42, p=0.0005; SC:F(1,11)=9.53, p=0.01; EMG:F(1,10)=8.0, p=0.018]. However, biosignals from trauma and nightmare scripts did not differ (p’s>0.39) nor did the Type x Valence interactions (p’s>0.10). Conclusion Physiological reactivity during imagery of a trauma memory and a trauma-related nightmare both significantly exceeded reactivity to neutral scenarios. Nightmare-memory and trauma-memory imagery produced similar reactivity. Thus, imagery of nightmares have potential utility as alternative PE stimuli. Support (If Any) This project was supported by NIMH grant 1R21MH121832-01A1 to E.P.S.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper