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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Healthy Participants | Experimental | 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. |
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| PTSD Participants | Experimental | 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Computational Model - Real-time Support Vector Machine | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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) |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Keith Bush, PhD. | University of Arkansas | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Arkansas for Medical Sciences | Little Rock | Arkansas | 72205 | United States |
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| ID | Title | Description |
|---|---|---|
| FG000 | 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. 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. 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. |
| FG001 | 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. 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. 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. |
| Title | Milestones | Reasons Not Completed | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
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| ID | Title | Description |
|---|---|---|
| BG000 | 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. 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. 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. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Screen failure: Trauma history reported and subject age not recorded. |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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. | Completing subjects. | Posted | Mean | Standard Deviation | Standard score (decodings) | Real-time within the measurement of functional MRI (within 10 seconds of functional MRI volume acquisition and reconstruction) | Decodings | Decodings |
3 hours
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | 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. 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. 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. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr. Keith A. Bush | University of Arkansas for Medical Sciences | 501.526.8347 | kabush@uams.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Aug 24, 2017 | Jul 1, 2021 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| D013313 | Stress Disorders, Post-Traumatic |
| ID | Term |
|---|---|
| D040921 | Stress Disorders, Traumatic |
| D000068099 | Trauma and Stressor Related Disorders |
| D001523 | Mental Disorders |
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|
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| MRI data found to be unusable during analysis |
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| Did not meet criteria for PTSD |
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| Claustrophobia |
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| History of head trauma |
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| BG001 | 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. 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. 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. |
| BG002 | Total | Total of all reporting groups |
| Mean |
| Standard Deviation |
| years |
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| Sex: Female, Male | Count of Participants | Participants |
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| Ethnicity (NIH/OMB) | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
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| Region of Enrollment | Number | participants |
|
| ID |
|---|
| Title |
|---|
| Description |
|---|
| OG000 | 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. 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. 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. |
| OG001 | 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. 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. 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. |
|
|
|
| 0 |
| 11 |
| 0 |
| 11 |
| 0 |
| 11 |
| EG001 | 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. 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. 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. | 0 | 9 | 0 | 9 | 0 | 9 |
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| Unknown or Not Reported |
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| Native Hawaiian or Other Pacific Islander |
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| Black or African American |
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| White |
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| More than one race |
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| Unknown or Not Reported |
|