Real-time fMRI and Neurofeedback of Brain Networks Mediating Trauma Memory Recall in PTSD

Stress Disorders, Post-Traumatic Clinical Trial

Official title:

Real-time Functional MRI and Neurofeedback of Brain Networks Mediating Trauma Memory Recall in PTSD

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT02500719
• Other study ID #: 203600
• Status: Completed
• Phase: N/A
• Start date: August 15, 2015
• Est. completion date: April 2019

Study information

• Verified date: April 2021
• Source: University of Arkansas
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The purpose of the current study is to develop a better understanding of the brain mechanisms involved in psychological treatments for posttraumatic stress disorder (PTSD). This project will build on past research using script-driven imagery in our lab by investigating brain activity in areas activated during exposure to trauma-related cues. This project will also develop new knowledge concerning volitional control of those areas. The ultimate goal of this study is a better understanding of whether volitional control of these brain areas will improve therapeutic outcomes. This process will first be piloted in a sample of healthy controls. This will allow investigators to refine the methodology prior to recruiting a sample with PTSD.

Description:

Post-traumatic stress disorder (PTSD) is characterized by intense emotional distress upon exposure to trauma reminders and avoidance of people and places that can trigger the trauma memory. Neurocircuitry models of PTSD that seek to explain symptoms of heightened emotional reactivity, hypervigilance for threat, and avoidance suggest abnormal activity of neural regions involved in emotional reactivity (e.g., amygdala) and cognitive control of emotional responding (e.g., ventral medial prefrontal cortex, anterior cingulate cortex). While knowledge exists about neurobiological abnormalities associated with PTSD, these data are cross-sectional in nature and ignore individual differences in both neural encoding and subjective aspects of the trauma itself (e.g., whether it elicits fear vs guilt vs disgust). Additionally, the manner by which existing psychological treatments alter these neural mechanisms mediating core PTSD symptoms is unknown. This is problematic, given that state-of-the-art treatment for PTSD is only effective ~60% of the time.

Here, the investigator proposes to utilize a novel computational modeling approach combined with state-of-the-art functional magnetic resonance imaging (fMRI)-based neurofeedback to directly identify and modulate the idiosyncratic neural network encoding the trauma memory. Successful pursuit of these aims would 1) provide scientific support for the hypothesis that a distributed network including the amygdala, hippocampus, medial prefrontal cortex (PFC), lateral PFC, and anterior insula mediates emotional responding upon trauma memory recall, and 2) provide proof-of-concept evidence that neurofeedback modulation of this network can boost existing therapy efficacy.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 30
• Est. completion date: April 2019
• Est. primary completion date: April 2019
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 21 Years to 50 Years

Eligibility

Inclusion Criteria:

• Female

• Aged 21-50

• Medically healthy

Exclusion Criteria:

• Claustrophobia, or the inability to lie still in a confined space

• Major medical disorders (e.g., HIV, cancer)

• Magnetic metallic implants (such as screws, pins, shrapnel remnants, aneurysm clips, artificial heart valves, inner ear (cochlear) implants, artificial joints, and vascular stents)

• Electronic or magnetic implants, such as pacemakers

• Permanent makeup or tattoos with metallic dyes

• Currently pregnant

• A self-reported history of loss of consciousness (greater than 10 minutes)

• Physical disabilities that prohibit task performance (such as blindness or deafness)

• Psychotic disorders (e.g., schizophrenia)

• Any other condition that the investigator believes might put the participant at risk

Related Conditions & MeSH terms

• Stress Disorders, Post-Traumatic
• Stress Disorders, Traumatic

Intervention

Device:
• Computational Model - Real-time Support Vector Machine
A support vector machine algorithm will be applied in real-time to fMRI data to identify distributed patterns of co-activated brain regions that specifically encode high emotional arousal (i.e,. high SCR) to the stress/trauma memory (note, this is equivalent to predictions of fitted Q-iteration in which the all actions are specified as zero, reward is equal to the support vector machine predicted arousal, and the discount factor of 0). The resulting idiosyncratic brain map would inform the neurofeedback phase in the next stage of fMRI data collection. This approach will first be piloted in the healthy participant group, then implemented in the PTSD participant group.

Locations

Country	Name	City	State
United States	University of Arkansas for Medical Sciences	Little Rock	Arkansas

Sponsors (1)

Lead Sponsor	Collaborator
University of Arkansas

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Patient Emotional Response to Volitional Engagement and Disengagement of Emotional Arousal as Measured Using Support Vector Machine Decodings When the Decoding is Provided as Real-time Neurofeedback Guidance or Not.	Support vector machine decodings of functional MRI data acquired during volitional engagement or disengagement of emotional arousal. Each decoding represents the Euclidean distance and direction (either positive or negative) of the functional MRI data volume with respect to the patient's support vector machine decision hyperplane. Positive distances denote engagement of emotional arousal and negative distances denote disengagement of emotion arousal. Distance represents the magnitude of volitional engagement or disengagement. Decodings can either be provided to patients as real-time neurofeedback (via visual representation of the distance) or hidden from view. When hidden, the visual representation of neurofeedback remains stationary.	Real-time within the measurement of functional MRI (within 10 seconds of functional MRI volume acquisition and reconstruction)