Abstract
Processing of sensory information is embedded into ongoing neural processes which contribute to brain states. Electroencephalographic microstates are semi-stable short-lived power distributions which have been associated with subsystem activity such as auditory, visual and attention networks. Here we explore changes in electrical brain states in response to an audiovisual perception and memorization task under conditions of auditory distraction. We discovered changes in brain microstates reflecting a weakening of states representing activity of the auditory system and strengthening of salience networks, supporting the idea that salience networks are active after audiovisual encoding and during memorization to protect memories and concentrate on upcoming behavioural response.
Highlights
Background noise is a ubiquitous phenomenon which increasingly infiltrates processes of our daily life, e.g., background music in supermarkets, street noise and traffic noise
We developed a paradigm in which subjects were instructed to watch a video and memorise as many auditory and visual items as possible in order to recall content later in a questionnaire, not knowing that the effect of auditory distraction would be the target of the project
Distracting music during active audiovisual perceptions had a significant impact on microstates
Summary
Background noise is a ubiquitous phenomenon which increasingly infiltrates processes of our daily life, e.g., background music in supermarkets, street noise and traffic noise. Speaking, resting state (RS) activity in the electroencephalography (EEG) can be described as a EEG Microstates Reflect Auditory Distraction short time period of semi-stable electric field topographies, called microstates (MS) (Lehmann et al, 1987; Lehmann, 1990). These small numbers of topographies are stable for tens of milliseconds and transition into another semi-stable pattern (Koenig et al, 2002). Some studies have established relations between MS dynamics and various RS brain networks as revealed by functional magnetic resonance imaging (fMRI) (Britz et al, 2010) or in electrophysiological RS networks (Custo et al, 2017, for Review see Michel and Koenig, 2018)
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