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失配负波范式分析中的头皮电流密度映射

Scalp current density mapping in the analysis of mismatch negativity paradigms.

作者信息

Giard Marie-Hélène, Besle Julien, Aguera Pierre-Emmanuel, Gomot Marie, Bertrand Olivier

机构信息

Brain Dynamics and Cognition Team, INSERM, U1028, CNRS, UMR5292, CRNL, Lyon Neuroscience Research Center, Lyon, France,

出版信息

Brain Topogr. 2014 Jul;27(4):428-37. doi: 10.1007/s10548-013-0324-8. Epub 2013 Oct 29.

Abstract

MMN oddball paradigms are frequently used to assess auditory (dys)functions in clinical populations, or the influence of various factors (such as drugs and alcohol) on auditory processing. A widely used procedure is to compare the MMN responses between two groups of subjects (e.g. patients vs controls), or between experimental conditions in the same group. To correctly interpret these comparisons, it is important to take into account the multiple brain generators that produce the MMN response. To disentangle the different components of the MMN, we describe the advantages of scalp current density (SCD)-or surface Laplacian-computation for ERP analysis. We provide a short conceptual and mathematical description of SCDs, describe their properties, and illustrate with examples from published studies how they can benefit MMN analysis. We conclude with practical tips on how to correctly use and interpret SCDs in this context.

摘要

失匹配负波(MMN)异常范式常用于评估临床人群的听觉(功能)障碍,或各种因素(如药物和酒精)对听觉处理的影响。一种广泛使用的方法是比较两组受试者(如患者与对照组)之间或同一组实验条件下的MMN反应。为了正确解释这些比较结果,考虑产生MMN反应的多个脑电发生器非常重要。为了区分MMN的不同成分,我们描述了头皮电流密度(SCD)或表面拉普拉斯算子计算在事件相关电位(ERP)分析中的优势。我们对SCD进行了简短的概念和数学描述,阐述了它们的特性,并通过已发表研究中的实例说明它们如何有助于MMN分析。最后,我们给出了在这种情况下正确使用和解释SCD的实用建议。

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