Aberrant connectivity in auditory precision encoding in schizophrenia spectrum disorder and across the continuum of psychotic-like experiences

Abstract

BackgroundThe ability to generate a precise internal model of statistical regularities is impaired in schizophrenia. Predictive coding accounts of schizophrenia suggest that psychotic symptoms may be explained by a failure to build precise beliefs or a model of the world. The precision of this model may vary with context. For example, in a noisy environment the model will be more imprecise compared to a model built in an environment with lower noise. However compelling, this idea has not yet been empirically studied in schizophrenia. Methods: In this study, 62 participants engaged in a stochastic mismatch negativity paradigm with high and low precision. We included inpatients with a schizophrenia spectrum disorder (N=20), inpatients with a psychiatric disorder but without psychosis (N=20), and healthy controls (N=22), with comparable sex ratio and age distribution. Bayesian mapping and dynamic causal modelling were employed to investigate the underlying microcircuitry of precision encoding of auditory stimuli. Results: We found strong evidence (exceedance p > 0.99) for differences in the underlying connectivity associated with precision encoding between the three groups as well as on the continuum of psychotic-like experiences assessed across all participants. Critically, we show changes in interhemispheric connectivity between the two inpatient groups, with some connections further aligning on the continuum of psychotic-like experiences. Conclusions: While our results suggest continuity in backward connectivity alterations with psychotic-like experiences regardless of diagnosis, they also point to specificity for the schizophrenia spectrum disorder group in interhemispheric connectivity alterations.

Standard Tone Stability as a Manipulation of Precision in the Oddball Paradigm: Modulation of Prediction Error Responses to Fixed-Probability Deviants

“…Instead, previous studies have shown that neuronal responses scale with the spectral distribution of auditory stimulation, a finding showing a dynamic variation in stimulus-specific adaptation, interpreted as adaptation to stimulus statistics ( Herrmann et al, 2013 , 2014 ). Indeed, several findings indicate that alphabet size ( Winkler et al, 1992 ; Barascud et al, 2016 ; Auksztulewicz et al, 2017 ; Quiroga-Martinez et al, 2019 , 2020 ) or the width of the distribution ( Garrido et al, 2013 ; Larsen et al, 2020 ) of the stimulation sequence are reflected on neural signals, supporting the idea that variability in the stimulation (inverse precision) plays a role in the modulation of deviance detection. In our study, decreasing the confidence (from oddball to random) increases the spectral variability of the stimulation (i.e., tones of different frequencies become more probable), without broadening the spectral range of the sequence.…”

RDoC Framework Through the Lens of Predictive Processing: Focusing on Cognitive System Domain

“…They used the community assessment of psychic experience (CAPE) in all participants to assess psychotic-like experiences across the continuum and their association with connectivity parameters related to precision encoding. They found a negative correlation between CAPE+ and lateral connections in right and left IFG and top-down connections from right IFG to STG, which is important in encoding precision (Larsen et al, 2020). This suggests that aberrant predictive activity and precision encoding in these connections is associated with increased CAPE+ scores.…”

“…This process could begin with identifying an interpretable physiological component that could explained in terms of the PP framework (e.g., MMN as a prediction error) that demonstrates variability across a spectrum of psychiatric conditions (Randeniya et al, 2018) (Fig 2A). This spectrum includes individuals at high risk of a condition as well as those with established psychiatric disorders (Larsen et al, 2020;Randeniya et al, 2018).…”

“…Moreover, the generation of frequency and duration MMN in oddball paradigms is linked to neural populations in the A1, STG, and IFG (Garrido et al, 2008;Schall et al, 2003). In a study by Larsen et al (2020), EEG data were collected from 20 schizophrenic patients, 20 non-psychotic controls, and 22 healthy controls during an auditory stochastic mismatch negativity paradigm with precision manipulation (high vs. low). This was done to examine PP in the auditory MMN circuit using Bayesian mapping and DCM (Larsen et al, 2020).…”

“…In a study by Larsen et al (2020), EEG data were collected from 20 schizophrenic patients, 20 non-psychotic controls, and 22 healthy controls during an auditory stochastic mismatch negativity paradigm with precision manipulation (high vs. low). This was done to examine PP in the auditory MMN circuit using Bayesian mapping and DCM (Larsen et al, 2020). They used the community assessment of psychic experience (CAPE) in all participants to assess psychotic-like experiences across the continuum and their association with connectivity parameters related to precision encoding.…”