Skip to main content

Main menu

  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Blog
    • Collections
    • Podcast
  • TOPICS
    • Cognition and Behavior
    • Development
    • Disorders of the Nervous System
    • History, Teaching and Public Awareness
    • Integrative Systems
    • Neuronal Excitability
    • Novel Tools and Methods
    • Sensory and Motor Systems
  • ALERTS
  • FOR AUTHORS
  • ABOUT
    • Overview
    • Editorial Board
    • For the Media
    • Privacy Policy
    • Contact Us
    • Feedback
  • SUBMIT

User menu

Search

  • Advanced search
eNeuro
eNeuro

Advanced Search

 

  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Blog
    • Collections
    • Podcast
  • TOPICS
    • Cognition and Behavior
    • Development
    • Disorders of the Nervous System
    • History, Teaching and Public Awareness
    • Integrative Systems
    • Neuronal Excitability
    • Novel Tools and Methods
    • Sensory and Motor Systems
  • ALERTS
  • FOR AUTHORS
  • ABOUT
    • Overview
    • Editorial Board
    • For the Media
    • Privacy Policy
    • Contact Us
    • Feedback
  • SUBMIT
PreviousNext
Research ArticleResearch Article: New Research, Sensory and Motor Systems

Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities

Yiyuan Teresa Huang, Chien-Te Wu, Shinsuke Koike and Zenas C. Chao
eNeuro 3 May 2024, 11 (5) ENEURO.0050-24.2024; https://doi.org/10.1523/ENEURO.0050-24.2024
Yiyuan Teresa Huang
1International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo 113-0033, Japan
2School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei 100, Taiwan
3Department of Multidisciplinary Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Chien-Te Wu
1International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo 113-0033, Japan
2School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei 100, Taiwan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shinsuke Koike
1International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo 113-0033, Japan
3Department of Multidisciplinary Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
4University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo 113-0033, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zenas C. Chao
1International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo 113-0033, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF
Loading

Article Figures & Data

Figures

  • Extended Data
  • Figure 1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 1.

    Task design. The configuration of sequence types, number of trials, transition probabilities, and sequence probabilities in four blocks. The tone icons colored in black and red represent tone x and y in different pitches, while the tone icon with the dashed outline represents omission (“o”, no tone delivered). The stimulus onset asynchrony (denoted as SOA) represents time interval between the onsets of a tone and the next. The inter-trial interval (ITI) represents time interval between the offset of one sequence's last tone and the onset of the next sequence's first tone. The probabilities were rounded to two decimal places.

  • Figure 2.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 2.

    ERPs and deviant responses from contrasts. A, The group mean ERPs of sequences xxx, xxy, and xxo in Block 1 at channel Cz. Solid vertical lines represent onsets of the stimuli of a sequence. Time zero was set to be the onset of the last stimulus. B, Contrast responses obtained by contrasting ERPs of sequences xxy and xxx in 4 blocks. The gray shade represents the time range of the MMN. C and D, Peak amplitudes and latencies of the MMN at Cz in 4 blocks. The block order was sorted from low to high probability in terms of TP(y|x) and SP(xxy). The 30 dots in each block correspond to participants. The box plot represents the median (red horizontal line), quartiles (the bottom and top edges), 95% confidence interval (notches), and the maximum and minimum (black horizontal lines). Results from repeated measures ANOVA and Spearman's rank correlation analyses are shown above the plots. The red line represents a significant difference between two blocks (*<0.05; ***<0.001).

  • Figure 3.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 3.

    Neural signatures of prediction-error signals. We resampled 100 datasets, each containing contrast responses of 4 blocks, and then performed PARAFAC decomposition for each dataset. For the two extracted subcomponents, activations are shown in (A) Channel, (B) Timecourse, and (C) Block dimensions. The Block dimension represents activation of the subcomponents to the responses (the blue line). The error bar represents the standard deviation. The model values of the local and global prediction errors are denoted as PE1 and PE2 (the green line).

  • Figure 4.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 4.

    Reconstruction with functional assignments and their differences from the actual ERP. A, Examples of reconstructed signals at Cz in Block 1. The black line represents the actual contrast response. The blue line represents the reconstructed response with Subcomponent 1 as PE1 and Subcomponent 2 as PE2. The orange line represents the reconstructed response with Subcomponent 1 as PE2 and Subcomponent 2 as PE1. The yellow line represents the reconstructed response with Subcomponent 1 and 2 both as PE1. The purple line represents the reconstructed response with Subcomponent 1 and 2 both as PE2. Mean squared differences in panel B and Pearson correlation coefficients in panel C were calculated then averaged across channels and blocks. The 100 dots for each assignment represent resamples. Results from repeated measures ANOVA and pairwise comparisons are shown above the plots.

Extended Data

  • Figures
  • Figure 2-1

    Download Figure 2-1, TIF file.

  • Figure 2-2

    Download Figure 2-2, TIF file.

  • Figure 2-3

    Download Figure 2-3, DOCX file.

  • Figure 3-1

    Download Figure 3-1, TIF file.

  • Figure 3-2

    Download Figure 3-2, TIF file.

Back to top

In this issue

eneuro: 11 (5)
eNeuro
Vol. 11, Issue 5
May 2024
  • Table of Contents
  • Index by author
  • Masthead (PDF)
Email

Thank you for sharing this eNeuro article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities
(Your Name) has forwarded a page to you from eNeuro
(Your Name) thought you would be interested in this article in eNeuro.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Print
View Full Page PDF
Citation Tools
Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities
Yiyuan Teresa Huang, Chien-Te Wu, Shinsuke Koike, Zenas C. Chao
eNeuro 3 May 2024, 11 (5) ENEURO.0050-24.2024; DOI: 10.1523/ENEURO.0050-24.2024

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Respond to this article
Share
Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities
Yiyuan Teresa Huang, Chien-Te Wu, Shinsuke Koike, Zenas C. Chao
eNeuro 3 May 2024, 11 (5) ENEURO.0050-24.2024; DOI: 10.1523/ENEURO.0050-24.2024
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Significance Statement
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Data Availability
    • Footnotes
    • References
    • Synthesis
    • Author Response
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF

Keywords

  • EEG
  • hierarchy
  • mismatch negativity
  • predictive coding
  • subcomponents

Responses to this article

Respond to this article

Jump to comment:

No eLetters have been published for this article.

Related Articles

Cited By...

More in this TOC Section

Research Article: New Research

  • Early Development of Hypothalamic Neurons Expressing Proopiomelanocortin Peptides, Neuropeptide Y and Kisspeptin in Fetal Rhesus Macaques
  • Experience-dependent neuroplasticity in the hippocampus of bilingual young adults
  • Characterisation of transgenic lines labelling reticulospinal neurons in larval zebrafish
Show more Research Article: New Research

Sensory and Motor Systems

  • Characterisation of transgenic lines labelling reticulospinal neurons in larval zebrafish
  • Task Modulation of Resting-State Functional Gradient Stability in Lifelong Premature Ejaculation: An fMRI Study
  • Synaptic Drive onto Inhibitory and Excitatory Principal Neurons of the Mouse Lateral Superior Olive
Show more Sensory and Motor Systems

Subjects

  • Sensory and Motor Systems
  • Home
  • Alerts
  • Follow SFN on BlueSky
  • Visit Society for Neuroscience on Facebook
  • Follow Society for Neuroscience on Twitter
  • Follow Society for Neuroscience on LinkedIn
  • Visit Society for Neuroscience on Youtube
  • Follow our RSS feeds

Content

  • Early Release
  • Current Issue
  • Latest Articles
  • Issue Archive
  • Blog
  • Browse by Topic

Information

  • For Authors
  • For the Media

About

  • About the Journal
  • Editorial Board
  • Privacy Notice
  • Contact
  • Feedback
(eNeuro logo)
(SfN logo)

Copyright © 2025 by the Society for Neuroscience.
eNeuro eISSN: 2373-2822

The ideas and opinions expressed in eNeuro do not necessarily reflect those of SfN or the eNeuro Editorial Board. Publication of an advertisement or other product mention in eNeuro should not be construed as an endorsement of the manufacturer’s claims. SfN does not assume any responsibility for any injury and/or damage to persons or property arising from or related to any use of any material contained in eNeuro.