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Common N1 and mismatch negativity neural evoked components are revealed by independent component model-based clustering analysis

Authors

  • Diego Lozano-Soldevilla,

    1. Psychiatry and Clinical Psychobiology Department, University of Barcelona, Barcelona, Spain
    2. Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
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  • Josep Marco-Pallarés,

    Corresponding author
    1. Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
    • Basic Psychology Department, University of Barcelona, Barcelona, Spain
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  • Lluís Fuentemilla,

    1. Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
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  • Carles Grau

    1. Psychiatry and Clinical Psychobiology Department, University of Barcelona, Barcelona, Spain
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  • The authors would like to thank David M. Groppe for sharing the code of the split-half reliability analysis and for his help on script assessment and data analysis. We also thank Filipa Campos-Viola for solving our software bugs with the CORRMAP plug-in and Arnaud Delorme, Scott Makeig, and Wendy Martinez for their generous gift of software (EEGLAB and EDA Toolbox). This study was supported by grants from the Generalitat de Catalunya (SGR2009-00093), the Ministerio de Ciencia y Tecnología (SEJ2006-13998 to CG, PSI2009-09101 to JMP, and PSI2010-15024 to LlF), the Ramón y Cajal program (RYC-2007-01614 to JMP and RYC-2009-05471 to LlF), and the Fundació la Marató (2006-061632 to CG and JMP).

Address correspondence to: Josep Marco-Pallarés, Ph.D., Basic Psychology Department, Campus Bellvitge, University of Barcelona, 4a planta del Pavelló de Govern, Feixa Llarga s/n,08907 L'Hospitalet (Barcelona), Spain. E-mail: josepmarco@gmail.com

Abstract

Mismatch negativity (MMN) is an event-related brain potential that appears when an auditory regularity is violated. Two main hypotheses have been proposed to explain it: the adaptation hypothesis and the memory-based hypothesis. Critically, they differ in whether the MMN can be distinguished from the N1. In this study, we assessed the differential contribution of the N1 and the MMN using independent component analysis (ICA) combined with model-based clustering. Our results show that the neural responses associated with the standard and deviant tones are explained by three clusters of reliable ICs with frontocentral scalp distribution. Two of these clusters exhibited a common N1 for both the standard and deviant tones and one cluster showed an enhancement of the anterior N1 at the MMN time range. These results support the adaptation hypothesis, which proposes that MMN is generated by neural mechanisms similar to those associated with auditory N1.

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