In the predictive coding framework, mismatch negativity (MMN) is regarded a correlate of the prediction error that occurs when top-down predictions conflict with bottom-up sensory inputs. Expression-related MMN is a relatively novel construct thought to reflect a prediction error specific to emotional processing, which, however, has not yet been tested directly. Our paradigm includes both neutral and emotional deviants, thereby allowing for investigating whether expression-related MMN is emotion-specific or unspecifically arises from violations of a given sequence. Twenty healthy participants completed a visual sequence oddball task where they were presented with (1) sequence deviants, (2) emotional sequence deviants, and (3) emotional deviants. Mismatch components were assessed at ventral occipitotemporal scalp sites and analyzed regarding their amplitudes, spatiotemporal profiles, and neuronal sources. Expression-related MMN could be clearly separated from its neutral counterpart in all investigated aspects. Specifically, expression-related MMN showed enhanced amplitude, shorter latency, and different neuronal sources. Our results, therefore, provide converging evidence for a quantitative specificity of expression-related MMN and seems to provide an opportunity to study prediction error during preattentive emotional processing. Our neurophysiological evidence ultimately suggests that a basic cognitive operator, the prediction error, is enhanced at the cortical level by processing of emotionally salient stimuli.

BackgroundThe predictive coding model is rapidly gaining attention in schizophrenia research. It posits the neuronal computation of residual variance (‘prediction error’) between sensory information and top-down expectation through multiple hierarchical levels. Event-related potentials (ERP) reflect cortical processing stages that are increasingly interpreted in the light of the predictive coding hypothesis. Both mismatch negativity (MMN) and repetition suppression (RS) measures are considered a prediction error correlates based on error detection and error minimization, respectively.MethodsTwenty-five schizophrenia patients and 25 healthy controls completed auditory tasks designed to elicit MMN and RS responses that were investigated using repeated measures models and strong spatio-temporal a priori hypothesis based on previous research. Separate correlations were performed for controls and schizophrenia patients, using age and clinical variables as covariates.ResultsMMN and RS deficits were largely replicated in our sample of schizophrenia patients. Moreover, MMN and RS measures were strongly correlated in healthy controls, while no correlation was found in schizophrenia patients. Single-trial analyses indicated significantly lower signal-to-noise ratio during prediction error computation in schizophrenia.ConclusionsThis study provides evidence that auditory ERP components relevant for schizophrenia research can be reconciled in the light of the predictive coding framework. The lack of any correlation between the investigated measures in schizophrenia patients suggests a disruption of predictive coding mechanisms in general. More specifically, these results suggest that schizophrenia is associated with an irregular computation of residual variance between sensory input and top-down models, i.e. prediction error.

No abstract

Attention deficits, among other cognitive deficits, are frequently observed in schizophrenia. Although valid and reliable neurocognitive tasks have been established to assess attention deficits in schizophrenia, the hierarchical value of those tests as diagnostic discriminants on a single-subject level remains unclear. Thus, much research is devoted to attention deficits that are unlikely to be translated into clinical practice. On the other hand, a clear hierarchy of attention deficits in schizophrenia could considerably aid diagnostic decisions and may prove beneficial for longitudinal monitoring of therapeutic advances. To propose a diagnostic hierarchy of attention deficits in schizophrenia, we investigated several facets of attention in 86 schizophrenia patients and 86 healthy controls using a set of established attention tests. We applied state-of-the-art machine learning algorithms to determine attentive test variables that enable an automated differentiation between schizophrenia patients and healthy controls. After feature preranking, hypothesis building, and hypothesis validation, the polynomial support vector machine classifier achieved a classification accuracy of 90.70% ± 2.9% using psychomotor speed and 3 different attention parameters derived from sustained and divided attention tasks. Our study proposes, to the best of our knowledge, the first hierarchy of attention deficits in schizophrenia by identifying the most discriminative attention parameters among a variety of attention deficits found in schizophrenia patients. Our results offer a starting point for hierarchy building of schizophrenia-associated attention deficits and contribute to translating these concepts into diagnostic and therapeutic practice on a single-subject level.

No abstract