Evidence map›Paper›PMID 32733298›Full record

ReviewFrontiers in psychiatry2020

A Predictive Coding Perspective on Mismatch Negativity Impairment in Schizophrenia.

Kenji Kirihara, Mariko Tada, Daisuke Koshiyama, Mao Fujioka, Kaori Usui, Tsuyoshi Araki, Kiyoto Kasai

Abstract readReview
In one paragraph

Review in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  17. Memory-Based Prediction Deficits and Dorsolateral Prefrontal Dysfunction in Schizophrenia.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kenji KiriharaDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Mariko TadaDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Daisuke KoshiyamaDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Mao FujiokaDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Kaori UsuiDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Tsuyoshi ArakiDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Kiyoto KasaiDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mismatch negativity (MMN) is a widely used biological marker for schizophrenia research. Previous studies reported that MMN amplitude was reduced in schizophrenia and that reduced MMN amplitude was associated with cognitive impairments and poor functional outcome in schizophrenia. However, the neurobiological mechanisms underlying the reduced MMN amplitude remain unclear. Recent studies suggest that reduced MMN amplitude may reflect altered predictive coding in schizophrenia. In this paper, we reviewed MMN studies that used new paradigms and computational modeling to investigate altered predictive coding in schizophrenia. Studies using the roving oddball paradigm and modified oddball paradigm revealed that the effects of conditional probability were impaired in schizophrenia. Studies using omission paradigms and many-standards paradigms revealed that prediction error, but not adaptation, was impaired in schizophrenia. A study using a local-global paradigm revealed that hierarchical structures were impaired at both local and global levels in schizophrenia. Furthermore, studies using dynamic causal modeling revealed that neural networks with hierarchical structures were impaired in schizophrenia. These findings indicate that altered predictive coding underlies the reduced MMN amplitude in schizophrenia. However, there are several unsolved questions about optimal procedures, association among paradigms, and heterogeneity of schizophrenia. Future studies using several paradigms and computational modeling may solve these questions, and may lead to clarifying the pathophysiology of schizophrenia and to the development of individualized treatments for schizophrenia.

Indexed as

computational modelingelectroencephalographymismatch negativitypredictive codingschizophrenia

Identifiers

PMID32733298
PMCPMC7360815

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.