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Neural correlates of heart rate variability in PTSD during sub- and supraliminal processing of trauma-related cues.

ABSTRACT: BACKGROUND:Posttraumatic stress disorder (PTSD) is characterized by dysregulated arousal and altered cardiac autonomic response as evidenced by decreased high-frequency heart rate variability (HF-HRV), an indirect measure of parasympathetic modulation of the heart. Indeed, subtle threatening cues can cause autonomic dysregulation, even without explicit awareness of the triggering stimulus. Accordingly, examining the neural underpinnings associated with HF-HRV during both sub- and supraliminal exposure to trauma-related cues is critical to an enhanced understanding of autonomic nervous system dysfunction in PTSD. METHODS:We compared neural activity in brain regions associated with HF-HRV in PTSD (n?=?18) and healthy controls (n?=?18) during exposure to sub- and supraliminal processing of personalized trauma-related words. RESULTS:As compared to controls, PTSD exhibited decreased HF-HRV reactivity in response to sub- and supraliminal cues. Notably, during subliminal processing of trauma-related versus neutral words, as compared to controls, PTSD showed decreased neural response associated with HF-HRV within the left dorsal anterior insula. By contrast, during supraliminal processing of trauma-related versus neutral words, decreased neural activity associated with HF-HRV within the posterior insula/superior temporal cortex, and increased neural activity associated with HF-HRV within the left centromedial amygdala was observed in PTSD as compared to controls. CONCLUSIONS:Impaired parasympathetic modulation of autonomic arousal in PTSD appears related to altered activation of cortical and subcortical regions involved in the central autonomic network. Interestingly, both sub- and supraliminal trauma-related cues appear to elicit dysregulated arousal and may contribute to the maintenance of hyperarousal in PTSD. Hum Brain Mapp 38:4898-4907, 2017. © 2017 Wiley Periodicals, Inc.

SUBMITTER: Rabellino D

PROVIDER: S-EPMC6867110 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Neural correlates of heart rate variability in PTSD during sub- and supraliminal processing of trauma-related cues.

Rabellino Daniela D D'Andrea Wendy W Siegle Greg G Frewen Paul A PA Minshew Reese R Densmore Maria M Neufeld Richard W RW Théberge Jean J McKinnon Margaret C MC Lanius Ruth A RA

Human brain mapping 20170717 10

<h4>Background</h4>Posttraumatic stress disorder (PTSD) is characterized by dysregulated arousal and altered cardiac autonomic response as evidenced by decreased high-frequency heart rate variability (HF-HRV), an indirect measure of parasympathetic modulation of the heart. Indeed, subtle threatening cues can cause autonomic dysregulation, even without explicit awareness of the triggering stimulus. Accordingly, examining the neural underpinnings associated with HF-HRV during both sub- and suprali ...[more]

PMID: 28714594

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Project description:Background: Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective of our research was to improve assessment of sleep quality, relative to existing IAs, through development of a novel IA using deep learning methods, utilizing as input activity count and heart rate variability (HRV) metrics of different window length (number of epochs of data). Methods: Simultaneously recorded polysomnography (PSG) and wrist actigraphy data of 222 participants were utilized. Classic deep learning models were applied to: (a) activity count alone (without HRV), (b) activity count + HRV (30-s window), (c) activity count + HRV (3-min window), and (d) activity count + HRV (5-min window) to ascertain the best set of inputs. A novel deep learning model (Haghayegh Algorithm, HA), founded on best set of inputs, was developed, and its sleep scoring performance was then compared with the most popular University of California San Diego (UCSD) and Actiwatch proprietary IAs. Results: Activity count combined with HRV metrics calculated per 5-min window produced highest agreement with PSG. HA showed 84.5% accuracy (5.3-6.2% higher than comparator IAs), 89.5% sensitivity (6.2% higher than UCSD IA and 6% lower than Actiwatch proprietary IA), 70.0% specificity (8.2-34.3% higher than comparator IAs), and 58.7% Kappa agreement (16-23% higher than comparator IAs) in detecting sleep epochs. HA did not differ significantly from PSG in deriving sleep parameters-sleep efficiency, total sleep time, sleep onset latency, and wake after sleep onset; moreover, bias and mean absolute error of the HA model in estimating them was less than the comparator IAs. HA showed, respectively, 40.9% and 54.0% Kappa agreement with PSG in detecting rapid and non-rapid eye movement (REM and NREM) epochs. Conclusions: The HA model simultaneously incorporating activity count and HRV metrics calculated per 5-min window demonstrates significantly better sleep scoring performance than existing popular IAs.

Dataset Information

Neural correlates of heart rate variability in PTSD during sub- and supraliminal processing of trauma-related cues.

Publications

Neural correlates of heart rate variability in PTSD during sub- and supraliminal processing of trauma-related cues.

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