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Brain mapping of auditory steady‐state responses: A broad view of cortical and subcortical sources

Auditory steady‐state responses (ASSRs) are evoked brain responses to modulated or repetitive acoustic stimuli. Investigating the underlying neural generators of ASSRs is important to gain in‐depth insight into the mechanisms of auditory temporal processing. The aim of this study is to reconstruct a...

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Detalles Bibliográficos
Autores principales: Farahani, Ehsan Darestani, Wouters, Jan, van Wieringen, Astrid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814770/
https://www.ncbi.nlm.nih.gov/pubmed/33166050
http://dx.doi.org/10.1002/hbm.25262
Descripción
Sumario:Auditory steady‐state responses (ASSRs) are evoked brain responses to modulated or repetitive acoustic stimuli. Investigating the underlying neural generators of ASSRs is important to gain in‐depth insight into the mechanisms of auditory temporal processing. The aim of this study is to reconstruct an extensive range of neural generators, that is, cortical and subcortical, as well as primary and non‐primary ones. This extensive overview of neural generators provides an appropriate basis for studying functional connectivity. To this end, a minimum‐norm imaging (MNI) technique is employed. We also present a novel extension to MNI which facilitates source analysis by quantifying the ASSR for each dipole. Results demonstrate that the proposed MNI approach is successful in reconstructing sources located both within (primary) and outside (non‐primary) of the auditory cortex (AC). Primary sources are detected in different stimulation conditions (four modulation frequencies and two sides of stimulation), thereby demonstrating the robustness of the approach. This study is one of the first investigations to identify non‐primary sources. Moreover, we show that the MNI approach is also capable of reconstructing the subcortical activities of ASSRs. Finally, the results obtained using the MNI approach outperform the group‐independent component analysis method on the same data, in terms of detection of sources in the AC, reconstructing the subcortical activities and reducing computational load.