Literature DB >> 15722203

What is novel in the novelty oddball paradigm? Functional significance of the novelty P3 event-related potential as revealed by independent component analysis.

Stefan Debener1, Scott Makeig, Arnaud Delorme, Andreas K Engel.   

Abstract

To better understand whether voluntary attention affects how the brain processes novel events, variants of the auditory novelty oddball paradigm were presented to two different groups of human volunteers. One group of subjects (n=16) silently counted rarely presented 'infrequent' tones (p=0.10), interspersed with 'novel' task-irrelevant unique environmental sounds (p=0.10) and frequently presented 'standard' tones (p=0.80). A second group of subjects (n=17) silently counted the 'novel' environmental sounds, the 'infrequent' tones now serving as the task-irrelevant deviant events. Analysis of event-related potentials (ERPs) recorded from 63 scalp channels suggested a spatiotemporal overlap of fronto-central novelty P3 and centro-parietal P3 (P3b) ERP features in both groups. Application of independent component analysis (ICA) to concatenated single trials revealed two independent component clusters that accounted for portions of the novelty P3 and P3b response features, respectively. The P3b-related ICA cluster contributed to the novelty P3 amplitude response to novel environmental sounds. In contrast to the scalp ERPs, the amplitude of the novelty P3 related cluster was not affected by voluntary attention, that is, by the target/nontarget distinction. This result demonstrates the usefulness of ICA for disentangling spatiotemporally overlapping ERP processes and provides evidence that task irrelevance is not a necessary feature of novelty processing.

Entities:  

Mesh:

Year:  2005        PMID: 15722203     DOI: 10.1016/j.cogbrainres.2004.09.006

Source DB:  PubMed          Journal:  Brain Res Cogn Brain Res        ISSN: 0926-6410


  78 in total

1.  Salience in a social landscape: electrophysiological effects of task-irrelevant and infrequent vocal change.

Authors:  Ana P Pinheiro; Carla Barros; João Pedrosa
Journal:  Soc Cogn Affect Neurosci       Date:  2015-10-13       Impact factor: 3.436

Review 2.  Updating P300: an integrative theory of P3a and P3b.

Authors:  John Polich
Journal:  Clin Neurophysiol       Date:  2007-06-18       Impact factor: 3.708

Review 3.  Influence of cognitive control and mismatch on the N2 component of the ERP: a review.

Authors:  Jonathan R Folstein; Cyma Van Petten
Journal:  Psychophysiology       Date:  2007-09-10       Impact factor: 4.016

4.  Gamma-band activity reflects multisensory matching in working memory.

Authors:  Daniel Senkowski; Till R Schneider; Frithjof Tandler; Andreas K Engel
Journal:  Exp Brain Res       Date:  2009-05-21       Impact factor: 1.972

5.  Contribution of subregions of human frontal cortex to novelty processing.

Authors:  Marianne Løvstad; Ingrid Funderud; Magnus Lindgren; Tor Endestad; Paulina Due-Tønnessen; Torstein Meling; Bradley Voytek; Robert T Knight; Anne-Kristin Solbakk
Journal:  J Cogn Neurosci       Date:  2011-08-03       Impact factor: 3.225

6.  Inferential revision in narrative texts: An ERP study.

Authors:  Ana Pérez; Kate Cain; María C Castellanos; Teresa Bajo
Journal:  Mem Cognit       Date:  2015-11

7.  Session Frequency Matters in Neurofeedback Training of Athletes.

Authors:  Christophe Domingos; Miguel Peralta; Pedro Prazeres; Wenya Nan; Agostinho Rosa; José G Pereira
Journal:  Appl Psychophysiol Biofeedback       Date:  2021-02-02

8.  Expectancy effects in feedback processing are explained primarily by time-frequency delta not theta.

Authors:  Adreanna T M Watts; Matthew D Bachman; Edward M Bernat
Journal:  Biol Psychol       Date:  2017-09-01       Impact factor: 3.251

9.  Very low frequency EEG oscillations and the resting brain in young adults: a preliminary study of localisation, stability and association with symptoms of inattention.

Authors:  S Helps; C James; S Debener; A Karl; E J S Sonuga-Barke
Journal:  J Neural Transm (Vienna)       Date:  2007-11-12       Impact factor: 3.575

10.  Classification of ADHD patients on the basis of independent ERP components using a machine learning system.

Authors:  Gian Candrian; Juri D Kropotov; Valery A Ponomarev; Gian-Marco Baschera; Andreas Mueller
Journal:  Nonlinear Biomed Phys       Date:  2010-06-03
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.