Literature DB >> 27174389

Visual mismatch negativity (vMMN): A review and meta-analysis of studies in psychiatric and neurological disorders.

Jan Kremláček1, Kairi Kreegipuu2, Andrea Tales3, Piia Astikainen4, Nele Põldver5, Risto Näätänen6, Gábor Stefanics7.   

Abstract

The visual mismatch negativity (vMMN) response is an event-related potential (ERP) component, which is automatically elicited by events that violate predictions based on prior events. VMMN experiments use visual stimulus repetition to induce predictions, and vMMN is obtained by subtracting the response to rare unpredicted stimuli from those to frequent stimuli. One increasingly popular interpretation of the mismatch response postulates that vMMN, similar to its auditory counterpart (aMMN), represents a prediction error response generated by cortical mechanisms forming probabilistic representations of sensory signals. Here we discuss the physiological and theoretical basis of vMMN and review thirty-three studies from the emerging field of its clinical applications, presenting a meta-analysis of findings in schizophrenia, mood disorders, substance abuse, neurodegenerative disorders, developmental disorders, deafness, panic disorder and hypertension. Furthermore, we include reports on aging and maturation as they bear upon many clinically relevant conditions. Surveying the literature we found that vMMN is altered in several clinical populations which is in line with aMMN findings. An important potential advantage of vMMN however is that it allows the investigation of deficits in predictive processing in cognitive domains which rely primarily on visual information; a principal sensory modality and thus of vital importance in environmental information processing and response, and a modality which arguably may be more sensitive to some pathological changes. However, due to the relative infancy of research in vMMN compared to aMMN in clinical populations its potential for clinical application is not yet fully appreciated. The aim of this review and meta-analysis therefore is to present, in a detailed systematic manner, the findings from clinically-based vMMN studies, to discuss their potential impact and application, to raise awareness of this measure and to improve our understanding of disease upon fundamental aspects of visual information processing.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Repetition suppression (RS); Stimulus-specific adaptation (SSA); Visual mismatch negativity (vMMN); effect size; schizophrenia

Mesh:

Year:  2016        PMID: 27174389     DOI: 10.1016/j.cortex.2016.03.017

Source DB:  PubMed          Journal:  Cortex        ISSN: 0010-9452            Impact factor:   4.027


  26 in total

1.  Sex-specific alterations of cortical morphometry in treatment-naïve patients with major depressive disorder.

Authors:  Xinyue Hu; Lianqing Zhang; Kaili Liang; Lingxiao Cao; Jing Liu; Hailong Li; Yingxue Gao; Xinyu Hu; Yongbo Hu; Weihong Kuang; John A Sweeney; Qiyong Gong; Xiaoqi Huang
Journal:  Neuropsychopharmacology       Date:  2022-01-03       Impact factor: 8.294

2.  Visual Mismatch and Predictive Coding: A Computational Single-Trial ERP Study.

Authors:  Gabor Stefanics; Jakob Heinzle; András Attila Horváth; Klaas Enno Stephan
Journal:  J Neurosci       Date:  2018-03-26       Impact factor: 6.167

3.  Cortical ensembles selective for context.

Authors:  Jordan P Hamm; Yuriy Shymkiv; Shuting Han; Weijian Yang; Rafael Yuste
Journal:  Proc Natl Acad Sci U S A       Date:  2021-04-06       Impact factor: 11.205

4.  When Elderly Outperform Young Adults-Integration in Vision Revealed by the Visual Mismatch Negativity.

Authors:  Zsófia Anna Gaál; Flóra Bodnár; István Czigler
Journal:  Front Aging Neurosci       Date:  2017-01-31       Impact factor: 5.750

5.  Visual mismatch negativity to vanishing parts of objects in younger and older adults.

Authors:  István Sulykos; Zsófia Anna Gaál; István Czigler
Journal:  PLoS One       Date:  2017-12-11       Impact factor: 3.240

6.  Revealing the Dysfunction of Schematic Facial-Expression Processing in Schizophrenia: A Comparative Study of Different References.

Authors:  Shenglin She; Haijing Li; Yuping Ning; Jianjuan Ren; Zhangying Wu; Rongcheng Huang; Jingping Zhao; Qian Wang; Yingjun Zheng
Journal:  Front Neurosci       Date:  2017-05-31       Impact factor: 4.677

7.  Facial Expression Related vMMN: Disentangling Emotional from Neutral Change Detection.

Authors:  Klara Kovarski; Marianne Latinus; Judith Charpentier; Helen Cléry; Sylvie Roux; Emmanuelle Houy-Durand; Agathe Saby; Frédérique Bonnet-Brilhault; Magali Batty; Marie Gomot
Journal:  Front Hum Neurosci       Date:  2017-01-30       Impact factor: 3.169

8.  Automatic Processing of Changes in Facial Emotions in Dysphoria: A Magnetoencephalography Study.

Authors:  Qianru Xu; Elisa M Ruohonen; Chaoxiong Ye; Xueqiao Li; Kairi Kreegipuu; Gabor Stefanics; Wenbo Luo; Piia Astikainen
Journal:  Front Hum Neurosci       Date:  2018-05-04       Impact factor: 3.169

9.  The dysfunction of processing emotional faces in schizophrenia revealed by expression-related visual mismatch negativity.

Authors:  Guimei Yin; Shenglin She; Lun Zhao; Yingjun Zheng
Journal:  Neuroreport       Date:  2018-07-04       Impact factor: 1.837

10.  Automatic auditory and somatosensory brain responses in relation to cognitive abilities and physical fitness in older adults.

Authors:  Juho M Strömmer; Nele Põldver; Tomi Waselius; Ville Kirjavainen; Saara Järveläinen; Sanni Björksten; Ina M Tarkka; Piia Astikainen
Journal:  Sci Rep       Date:  2017-10-20       Impact factor: 4.379

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