Autonomic Regulation and Auditory Hallucinations in Individuals With Schizophrenia: An Experience Sampling Study

Abstract

Auditory Hallucinations (AH) cause substantial suffering and dysfunction, yet remain poorly understood and modeled. Previous reports have linked AH to increases in negative emotions, suggesting a role for the autonomic nervous system (ANS) in underlying this link. Employing an Experience Sampling Method (ESM) approach, 40 individuals with schizophrenia completed a 36-hour ambulatory assessment of AH and cardiac autonomic regulation. Participants carried mobile electronic devices that prompted them to report 10 times/d the severity of their momentary AH, along with a Holter monitor that continuously recorded their cardiac autonomic regulation. The clocks of the devices and monitors were synchronized, allowing for high time-resolution temporal linking of the AH and concurrent autonomic data. Power spectral analysis was used to determine the relative vagal (parasympathetic) contribution to autonomic regulation during 5 minutes prior to each experience sample. The participants also completed interview-based measures of AH (SAPS; PSYRATS). The ESM-measured severity of AH was significantly correlated with the overall SAPS-indexed AH severity, along with the PSYRATS-indexed AH frequency, duration, loudness, degree of negative content, and associated distress. A mixed-effect regression model indicated that momentary increases in autonomic arousal, characterized by decreases in vagal input, significantly predicted increases in ESM-measured AH severity. Vagal input averaged over the 36-hour assessment displayed a small but significant inverse correlation with the SAPS-indexed AH. The results provide preliminary support for a link between ANS regulation and AH. The findings also underscore the highly dynamic nature of AH and the need to utilize high time-resolution methodologies to investigate AH.

Keywords: arousal; auditory hallucinations; autonomic regulation; cardiac; experience sampling method; heart; heart rate variability; mobile devices; negative emotions; psychosis; schizophrenia; stress; vagal.

© The Author 2017. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Figures

Fig. 1.

Sample screenshot of the mobile device and a schematic diagram of sampling of momentary auditory hallucinations and concurrent autonomic regulation. (A) A screenshot of an ESM-based question presented on the mobile device assessing severity of momentary auditory hallucinations; (B) Schematic diagram of ambulatory sampling of momentary auditory hallucinations and epochs of cardiac autonomic regulation during daily functioning; ESM—Experience Sampling Method.

Fig. 2.

Distribution of ESM-based auditory hallucinations severity ratings. Distribution of frequency ESM-based ratings of auditory hallucinations severity during the course of daily functioning—range from 0 (“not at all”) to 100 (“very much”); ESM—Experience Sampling Method.

Fig. 3.

Individuals’ ESM-based auditory hallucinations grouped by interview-based auditory hallucination severity ratings. Each line represent one individual’s ESM-based severity of auditory hallucinations across time. Ratings of no Hallucinations as well as moderate, marked, and severe hallucinations correspond with SAPS item #1 rating of 0, 3, 4, and 5 (respectively). No individuals had questionable or mild hallucinations ratings (SAPS item #1 ratings of 1 or 2, respectively); ESM—Experience Sampling Method; SAPS—Scale for Assessment of Positive Symptoms.

Fig. 4.

The association of momentary auditory hallucinations and concurrent cardiac vagal input. Severity of auditory hallucinations—ambulatory ESM-based assessment of momentary auditory hallucinations; ESM—Experience Sampling Method; Cardiac Vagal Input—parasympathetic contribution to cardiac functioning during 5-min epoch prior to each experience sample (as indexed by log transformed High-Frequency data analyzed using power spectral analysis).