scholarly journals Auditory Mismatch Negativity Under Predictive Coding Framework and Its Role in Psychotic Disorders

2020 ◽  
Vol 11 ◽  
Author(s):  
Chun Yuen Fong ◽  
Wai Him Crystal Law ◽  
Takanori Uka ◽  
Shinsuke Koike
Keyword(s):  
Mismatch Negativity ◽  
Psychotic Disorders ◽  
Predictive Coding

Mismatch negativity amplitude in first-degree relatives of individuals with psychotic disorders: Links with cognition and schizotypy

2021 ◽  
Vol 238 ◽  
pp. 161-169
Author(s):  
Kayla R. Donaldson ◽  
Emmett M. Larsen ◽  
Katherine Jonas ◽  
Sara Tramazzo ◽  
Greg Perlman ◽  
...  
Keyword(s):  
Mismatch Negativity ◽  
Psychotic Disorders ◽  
First Degree Relatives

scholarly journals Smoking as a Common Modulator of Sensory Gating and Reward Learning in Individuals with Psychotic Disorders

2021 ◽  
Vol 11 (12) ◽  
pp. 1581
Author(s):  
Alexis E. Whitton ◽  
Kathryn E. Lewandowski ◽  
Mei-Hua Hall
Keyword(s):  
Bipolar Disorder ◽  
Prediction Error ◽  
Psychotic Disorders ◽  
Predictive Coding ◽  
Sensory Gating ◽  
Event Related Potential ◽  
Reward Learning ◽  
Smoking History ◽  
Impaired Performance ◽  
Psychotic Bipolar Disorder

Motivational and perceptual disturbances co-occur in psychosis and have been linked to aberrations in reward learning and sensory gating, respectively. Although traditionally studied independently, when viewed through a predictive coding framework, these processes can both be linked to dysfunction in striatal dopaminergic prediction error signaling. This study examined whether reward learning and sensory gating are correlated in individuals with psychotic disorders, and whether nicotine—a psychostimulant that amplifies phasic striatal dopamine firing—is a common modulator of these two processes. We recruited 183 patients with psychotic disorders (79 schizophrenia, 104 psychotic bipolar disorder) and 129 controls and assessed reward learning (behavioral probabilistic reward task), sensory gating (P50 event-related potential), and smoking history. Reward learning and sensory gating were correlated across the sample. Smoking influenced reward learning and sensory gating in both patient groups; however, the effects were in opposite directions. Specifically, smoking was associated with improved performance in individuals with schizophrenia but impaired performance in individuals with psychotic bipolar disorder. These findings suggest that reward learning and sensory gating are linked and modulated by smoking. However, disorder-specific associations with smoking suggest that nicotine may expose pathophysiological differences in the architecture and function of prediction error circuitry in these overlapping yet distinct psychotic disorders.

scholarly journals A Predictive Coding Perspective on Mismatch Negativity Impairment in Schizophrenia

2020 ◽  
Vol 11 ◽  
Author(s):  
Kenji Kirihara ◽  
Mariko Tada ◽  
Daisuke Koshiyama ◽  
Mao Fujioka ◽  
Kaori Usui ◽  
...  
Keyword(s):  
Mismatch Negativity ◽  
Predictive Coding

scholarly journals Associations of mismatch negativity with psychotic symptoms and functioning transdiagnostically across psychotic disorders.

2020 ◽  
Vol 129 (6) ◽  
pp. 570-580 ◽  
Author(s):  
Kayla R. Donaldson ◽  
Keisha D. Novak ◽  
Dan Foti ◽  
Maya Marder ◽  
Greg Perlman ◽  
...  
Keyword(s):  
Mismatch Negativity ◽  
Psychotic Disorders ◽  
Psychotic Symptoms

scholarly journals Weighting of neural prediction error by rhythmic complexity: A predictive coding account using mismatch negativity

2019 ◽  
Vol 49 (12) ◽  
pp. 1597-1609 ◽  
Author(s):  
Massimo Lumaca ◽  
Niels Trusbak Haumann ◽  
Elvira Brattico ◽  
Manon Grube ◽  
Peter Vuust
Keyword(s):  
Mismatch Negativity ◽  
Prediction Error ◽  
Predictive Coding

scholarly journals 49. The Predictive Coding Account of Psychosis: A Meta-Analysis of the Relationship Between Mismatch Negativity and Symptom Severity

2017 ◽  
Vol 43 (suppl_1) ◽  
pp. S26-S26 ◽  
Author(s):  
Molly Erickson ◽  
Abigail Ruffle ◽  
Leah Fleming ◽  
Philip Corlett ◽  
James Gold
Keyword(s):  
Symptom Severity ◽  
Mismatch Negativity ◽  
Meta Analysis ◽  
Predictive Coding ◽  
The Relationship

scholarly journals A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

2012 ◽  
Vol 32 (11) ◽  
pp. 3665-3678 ◽  
Author(s):  
C. Wacongne ◽  
J.-P. Changeux ◽  
S. Dehaene
Keyword(s):  
Mismatch Negativity ◽  
Predictive Coding ◽  
Neuronal Model

scholarly journals Mismatch Negativity and Stimulus-Preceding Negativity in Paradigms of Increasing Auditory Complexity: A Possible Role in Predictive Coding

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 346
Author(s):  
Francisco J. Ruiz-Martínez ◽  
Antonio Arjona ◽  
Carlos M. Gómez
Keyword(s):  
Auditory Processing ◽  
Mismatch Negativity ◽  
Prediction Error ◽  
Intertrial Interval ◽  
Predictive Coding ◽  
Auditory Stimuli ◽  
Stimulus Preceding Negativity ◽  
Complex Sequences ◽  
Transfer Of Information ◽  
Error Computation

The auditory mismatch negativity (MMN) has been considered a preattentive index of auditory processing and/or a signature of prediction error computation. This study tries to demonstrate the presence of an MMN to deviant trials included in complex auditory stimuli sequences, and its possible relationship to predictive coding. Additionally, the transfer of information between trials is expected to be represented by stimulus-preceding negativity (SPN), which would possibly fit the predictive coding framework. To accomplish these objectives, the EEG of 31 subjects was recorded during an auditory paradigm in which trials composed of stimulus sequences with increasing or decreasing frequencies were intermingled with deviant trials presenting an unexpected ending. Our results showed the presence of an MMN in response to deviant trials. An SPN appeared during the intertrial interval and its amplitude was reduced in response to deviant trials. The presence of an MMN in complex sequences of sounds and the generation of an SPN component, with different amplitudes in deviant and standard trials, would support the predictive coding framework.

scholarly journals Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

2019 ◽  
Author(s):  
Lilian A. Weber ◽  
Andreea O. Diaconescu ◽  
Christoph Mathys ◽  
André Schmidt ◽  
Michael Kometer ◽  
...  
Keyword(s):  
Mismatch Negativity ◽  
Transition Probability ◽  
Predictive Coding ◽  
Prediction Errors ◽  
Hierarchical Bayesian ◽  
Single Trial ◽  
Perceptual Inference ◽  
Trial Analysis ◽  
Statistical Regularities ◽  
High Level

AbstractThe auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the “Bayesian brain” notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, predictive coding views perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs). Disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia.Here, we provide empirical evidence for this clinical theory, demonstrating the existence of multiple, hierarchically related PEs in a “roving MMN” paradigm. We applied a computational model, the Hierarchical Gaussian Filter (HGF), to single-trial EEG data from healthy volunteers that received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our computational trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207ms post-stimulus), while high-level PEs (about transition probability) are reflected by later components (152-199ms, 215-277ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts inference on abstract statistical regularities.Our findings are consistent with long-standing notions that NMDAR dysfunction may cause positive symptoms in schizophrenia by impairing hierarchical Bayesian inference about the world’s statistical structure. Beyond their relevance for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential disease mechanisms.

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