# Autonomic and fronto-cortical correlates of script-driven imagery of trauma-related nightmares compared with such imagery of index trauma in PTSD using ambulatory physiological and fNIRS recordings.

> **NIH NIH R21** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $462,000

## Abstract

Trauma-related nightmares (TRNs) are a hallmark re-experiencing symptom of Posttraumatic Stress Disorder
(PTSD) and a severe, re-traumatizing source of distress to those with this condition. Proposed research will
investigate whether such nightmares might serve as targets for imaginal exposure during prolonged exposure
therapy (PE), a first-line treatment for PTSD. Learning and memory of fear extinction are the neurocognitive
underpinnings of PE and increased physiological arousal during PE can aid in extinction learning. Thus, PE
using TRNs might enhance treatment outcomes because of the greater immediacy and salience of nightmares
relative to the more temporally distant traumatic event. Our research group has developed standardized script-
driven imagery (SDI) procedures whereby the degree of psychophysiological arousal induced by recollection of
the traumatic event that precipitated posttraumatic symptoms (index trauma) can be assessed. Our team has
also developed a novel ambulatory device, the NINscan, that can record skin conductance (SC), facial
electromyography (EMG) and electrocardiography (ECG) along with simultaneous functional near-infrared
spectroscopic (fNIRS) imaging of the lateral prefrontal cortex (LPFC). The LPFC encompasses regions that
activate and/or deactivate during SDI in persons with PTSD and includes the right inferior frontal gyrus (rIFG),
an area that plays an important role in inhibiting unwanted behavior and cognition and shows reduced
inhibitory performance in PTSD. Following screenings that include a structured clinical evaluation establishing
PTSD diagnosis and self-report of a least 2 TRNs per week, qualified participants will record an account of
their index trauma. They will then complete 2 weeks of sleep diaries and wrist actigraphy with 4 nights of
ambulatory polysomnography (PSG), during which they will audio-record, on a time-stamped digital recorder, a
detailed nightmare report following any nightmare-induced awakening. A TRN report having sufficient length,
clarity and resemblance to the index trauma will be selected and, along with the index-trauma report, will be re-
recorded as a script for SDI. Only participants who generated a TRN suitable for SDI (N=40) will then undergo
2 SDI sessions on a single day while wearing the NINscan. One script will be derived from their index trauma
and the other from their TRN, the order of which will be counterbalanced across participants.
Psychophysiological response and fNIRS LPFC activation will be compared between scripts using 2 primary
outcome measures: (1) a validated canonical psychophysiological variable combining SC, EMG and ECG, and
(2) script-related activation of the rIFC. We hypothesize that both activations will be greater for the TRN than
for the index-trauma script. Exploratory aims will include (1) examination of the sleep stage and
electroencephalographic characteristics of sleep preceding awakenings when a nightmare occurred on a night
with PSG, and ...

## Key facts

- **NIH application ID:** 10057868
- **Project number:** 1R21MH121832-01A1
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Edward F. Pace-Schott
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $462,000
- **Award type:** 1
- **Project period:** 2020-06-01 → 2022-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10057868

## Citation

> US National Institutes of Health, RePORTER application 10057868, Autonomic and fronto-cortical correlates of script-driven imagery of trauma-related nightmares compared with such imagery of index trauma in PTSD using ambulatory physiological and fNIRS recordings. (1R21MH121832-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10057868. Licensed CC0.

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