skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Attention:

The NSF Public Access Repository (PAR) system and access will be unavailable from 10:00 PM to 12:00 PM ET on Tuesday, March 25 due to maintenance. We apologize for the inconvenience.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Computer-Based PTSD Assessment in VR Exposure Therapy
Post-traumatic stress disorder (PTSD) is a mental health condition affecting people who experienced a traumatic event. In addition to the clinical diagnostic criteria for PTSD, behavioral changes in voice, language, facial expression and head movement may occur. In this paper, we demonstrate how a machine learning model trained on a general population with self-reported PTSD scores can be used to provide behavioral metrics that could enhance the accuracy of the clinical diagnosis with patients. Both datasets were collected from a clinical interview conducted by a virtual agent (SimSensei) [10]. The clinical data was recorded from PTSD patients, who were victims of sexual assault, undergoing a VR exposure therapy. A recurrent neural network was trained on verbal, visual and vocal features to recognize PTSD, according to self-reported PCL-C scores [4]. We then performed decision fusion to fuse three modalities to recognize PTSD in patients with a clinical diagnosis, achieving an F1-score of 0.85. Our analysis demonstrates that machine-based PTSD assessment with self-reported PTSD scores can generalize across different groups and be deployed to assist diagnosis of PTSD.  more » « less
Award ID(s):
1852583
PAR ID:
10209032
Author(s) / Creator(s):
; ; ;
Editor(s):
Stephanidis, Constantine; Chen, Jessie Y.; Fragomeni, Gino
Date Published:
Journal Name:
HCI International 2020 – Late Breaking Papers: Virtual and Augmented Reality
Page Range / eLocation ID:
440-449
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, Frontiers in Neuroscience)
    It is well-known that morphological features in the brain undergo changes due to traumatic events and associated disorders such as post-traumatic stress disorder (PTSD). However, existing approaches typically offer group-level comparisons, and there are limited predictive approaches for modeling behavioral outcomes based on brain shape features that can account for heterogeneity in PTSD, which is of paramount interest. We propose a comprehensive shape analysis framework representing brain sub-structures, such as the hippocampus, amygdala, and putamen, as parameterized surfaces and quantifying their shape differences using an elastic shape metric. Under this metric, we compute shape summaries (mean, covariance, PCA) of brain sub-structures and represent individual brain shapes by their principal scores under a shape-PCA basis. These representations are rich enough to allow visualizations of full 3D structures and help understand localized changes. In order to validate the elastic shape analysis, we use the principal components (PCs) to reconstruct the brain structures and perform further evaluation by performing a regression analysis to model PTSD and trauma severity using the brain shapes representedviaPCs and in conjunction with auxiliary exposure variables. We apply our method to data from the Grady Trauma Project (GTP), where the goal is to predict clinical measures of PTSD. The framework seamlessly integrates accurate morphological features and other clinical covariates to yield superior predictive performance when modeling PTSD outcomes. Compared to vertex-wise analysis and other widely applied shape analysis methods, the elastic shape analysis approach results in considerably higher reconstruction accuracy for the brain shape and reveals significantly greater predictive power. It also helps identify local deformations in brain shapes associated with PTSD severity. 
    more » « less
  2. ; ; ; ; ; ; (, CHI '20: CHI Conference on Human Factors in Computing Systems)
    Post-traumatic stress disorder (PTSD) disproportionately affects United States veterans, yet they may be reluctant to seek or engage in care. We interview 21 participants, including veterans with PTSD, clinicians who treat veterans and friends and family that support veterans through mental health ordeals. We investigate the military identity these veterans share. We explore how this may add to their reluctance in care-seeking behaviors. We also explore the roles of human and non-human intermediaries in ecologies of care and the potential for enhancing patient empowerment in current clinical treatment contexts. We discuss how military culture can be utilized in clinical care, how multiple perspectives can be leveraged to create a more holistic view of the patient, and finally, how veterans can be empowered during treatment. We conclude with recommendations for the design of sociotechnical systems that prioritize the above in support of the mental well-being of veterans with PTSD. 
    more » « less
  3. ; ; ; ; (, ACM)
    Post-traumatic stress disorder (PTSD) is a serious public health issue. Approximately 8 million adults in the United States suffer from PTSD in any given year, and 7–8% of the U.S. population will have PTSD at some point in their lives. Recent studies have explored eHealth technologies to support persons living with PTSD. However, current approaches are often unable to sustain adherence, leading to sub-optimal clinical outcomes. Conversational agents (CAs) can help to improve longitudinal adherence by interactively engaging users and maintaining social presence. In this work, we present prototypes of PTSDialogue — a finite-state CA to deliver evidence-based strategies and support self-management for individuals living with PTSD. We also discuss the design requirements and process of adapting existing eHealth content to interactive dialogues. Furthermore, we detail design decisions to address safety and ethical concerns to develop a CA for a vulnerable population. 
    more » « less
  4. (, Lecture notes in computer science)
    Post-traumatic stress disorder (PTSD) is a serious mental health condition. Prolonged Exposure (PE) therapy has been shown to have the greatest therapeutic efficacy in treating PTSD. However, clinicians face significant challenges in delivering PE therapy successfully. We conducted a two-part study with 17 clinicians where we (1) gathered requirements for a PE therapy dashboard and (2) had clinicians assess the relevance and usability of our dashboard prototypes. The first part of our study showed that clinicians wanted (1)improved workflow for their PE sessions (2)improved methods of monitoring their patients’ self-report data and (3)a way to assess their patients’ progress outside of sessions. The second part showed that our dashboard prototypes were highly usable. Our study provides preliminary steps in designing user-centered tools that assist clinicians in the delivery of PE therapy by improving data access and workflow efficiency. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al (, Nature Communications)
    Abstract The neurophysiological mechanisms in the human amygdala that underlie post-traumatic stress disorder (PTSD) remain poorly understood. In a first-of-its-kind pilot study, we recorded intracranial electroencephalographic data longitudinally (over one year) in two male individuals with amygdala electrodes implanted for the management of treatment-resistant PTSD (TR-PTSD) under clinical trial NCT04152993. To determine electrophysiological signatures related to emotionally aversive and clinically relevant states (trial primary endpoint), we characterized neural activity during unpleasant portions of three separate paradigms (negative emotional image viewing, listening to recordings of participant-specific trauma-related memories, and at-home-periods of symptom exacerbation). We found selective increases in amygdala theta (5–9 Hz) bandpower across all three negative experiences. Subsequent use of elevations in low-frequency amygdala bandpower as a trigger for closed-loop neuromodulation led to significant reductions in TR-PTSD symptoms (trial secondary endpoint) following one year of treatment as well as reductions in aversive-related amygdala theta activity. Altogether, our findings provide early evidence that elevated amygdala theta activity across a range of negative-related behavioral states may be a promising target for future closed-loop neuromodulation therapies in PTSD. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email