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

NCT ID: NCT02500719

Last Updated: 2021-08-20

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 30 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2015-08-15
• Study Completion Date: 2019-04-30

Brief 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.

Detailed 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.

Conditions

Stress Disorders, Post-Traumatic

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

Healthy Participants

A group of healthy participants will be enrolled first in the pilot phase of the study. This phase allows for the refinement (prior to the implementing in our PTSD participant group) the application of our support vector machine based real-time functional magnetic resonance imaging (rt-fMRI) algorithm, which evaluates brain networks thought to mediate emotional arousal and presents them (in real time) to subjects to aide in volitional manipulation of arousal.

Group Type: EXPERIMENTAL

Computational Model - Real-time Support Vector Machine

Intervention Type: DEVICE

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.

PTSD Participants

A group of participants with symptoms of PTSD will be enrolled in the implementation phase of the study. This phase allows for the evaluation of rt-fMRI guidance of brain networks thought to mediate emotional arousal, specifically whether participants can learn volitional control of these networks.

Group Type: EXPERIMENTAL

Computational Model - Real-time Support Vector Machine

Intervention Type: DEVICE

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.

Interventions

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.

Intervention Type: DEVICE

Other Intervention Names

Machine Learning Algorithm; Real-time Neurofeedback

Eligibility Criteria

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

• Minimum Eligible Age: 21 Years
• Maximum Eligible Age: 50 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

University of Arkansas

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Keith Bush, PhD.

Role: PRINCIPAL_INVESTIGATOR

University of Arkansas

Locations

University of Arkansas for Medical Sciences

Little Rock, Arkansas, United States

Countries

United States

Provided Documents

Document Type: Study Protocol and Statistical Analysis Plan

View Document

Other Identifiers

203600

Identifier Type: -

Identifier Source: org_study_id