Functional neural dynamics underlying auditory event-related N1 and N1 suppression response

Abstract

Presenting tone triplets of identical stimuli preceded by silent intervals of 30 s produces a series of three N1 averaged event-related potentials (ERPs), the first being of greater amplitude (non-suppressed N1) than the second and third ones (suppressed N1). Maximal statistically independent components (ICs) of single-trial multi-electrode scalp EEG responses to triplets were obtained by ICA algorithm, and then each IC was searched for underlying brain structures by LORETA inverse solution, and for oscillatory contributions by time–frequency analysis. Non-suppressed N1 cortical mechanisms were broken down into five ICs, grouped in two time-windows (early-onset and late-onset) involving the participation of temporal, frontal and parietal structures, and sub-serving EEG oscillatory contributions of power enhancement and putative phase concentration of mainly theta, alpha and low beta bands. Suppressed N1 was due to the modulation of two above-mentioned early-onset ICs, involving temporal structures only, and mainly sub-serving oscillatory contributions of phase concentration of theta and alpha. Present results, showing quantifiable changes of IC descriptors – i.e. time window of activation, implied structures and oscillatory contributions – extracted from two distinct brain functional situations (non-suppressed versus suppressed N1), give support to the view that ICA is not merely a statistical “latent variables” model when applied to ERPs, but could help to capture underlying specific function subunits of brain dynamics.

Introduction

Brain electrical responses time-locked to events (stimulus, response or behavioural task) have been studied for decades by averaging the event-locked single trials to obtain event-related potentials (ERPs). Although averaged ERPs resemble basic waves, they undergo complex neural dynamic activity that is likely to arise from multiple temporarily and spatially distributed specialized brain sources (Bressler and Kelso, 2001). Hence, it has been suggested that large-scale interaction of these neural contributions to ERPs gives rise to their functional integration, which might lead to support of specific function activities (Friston, 2005). In addition, the relevance of oscillatory activity as a framework for the functional integration of a distributed neural activity has been pointed out (Fries, 2005). Indeed, converging recent evidence showed that a distributed functionally conformed network of neurons might be transiently linked by reciprocal dynamic connections (Varela et al., 2001), thus resulting in multiple feedback loops that give rise to oscillatory neural-based behaviour (Bressler, 2002). Therefore, based on these arguments, underlying neural functional properties given by an ERP are unlikely to be captured solely by studying the representation of the temporal course and scalp features of the averaged electrical activity, but might require identification of their related temporal evolution, cortical topography and oscillatory behaviour (Fogelson et al., 2006), which can only be captured at the single-trial level.

The concept of ERPs as being composed of orchestrated clusters of distributed neural contributions, which transiently integrate to support specific functional activities, has been recently enlarged on the basis of ICA (Independent Component Analysis) results on multi-electrode EEG data (Makeig et al., 2002, Penny et al., 2002). Accordingly, ERPs could be broken down into maximal statistically independent contributions (independent components, ICs), which might be characterised by specific temporo-structural (Makeig et al., 2002, Marco-Pallarés et al., 2005) and frequency domain features (Makeig et al., 2004a). This leads to the proposition that the properties of maximal statistical independence and the multifaceted physiological characterization (in the time-, space- and frequency-domains) of each IC strongly suggest that they might also pick up specific functional brain subunits. However, at present, there is no direct evidence that neural activity identified by an IC intrinsically modulate to functional experimental manipulations, though this would, unequivocally, transfer meaningful functional entity to the given neural activity.

The first objective of this study was to provide evidence that separate specific functional activity of brain computing captured by ICs and their descriptors (i.e. time–structures–frequency) show quantifiable changes in distinct functional situations. To this end, we selected auditory N1, which in front of identical external repeated stimulation shows reduced amplitude (attenuation or repetitive suppression) (Näätänen, 1992, Friston, 2005). This reduced amplitude is associated with the prevention of the inflow of redundant information from the environment into daily life (Näätänen, 1992). The advantage of this experimental paradigm resides in that, as the stimuli of the sequence are physically identical, functional changes sub-serving N1 amplitude reduction other than the ones experienced by its intrinsic neural substrate can be discarded.

The second objective of this work is to advance on the characterization of functional neural dynamics underlying auditory event-related N1 and N1 suppression response. Auditory N1 ERP involves a complex neural network of several dynamic properties described by temporal, spatial and spectral contributions. Hence, in the time domain, scalp electromagnetic (MEG) (Loveless et al., 1996, McEvoy et al., 1997) studies showed distinct sequential N1 contribution activations, referred to as “early” (∼ 80 ms) and “late” N1 (∼ 130 ms) (Näätänen, 1992). Further, in the spatial domain, N1 are sub-served by a multi-generator process (Naatanen and Picton, 1987), involving temporal (Hari et al., 1980), frontal (Knight et al., 1980), cingulate (Tzourio et al., 1997) and parietal regions (Knight et al., 1980). And, in the frequency domain, the study of the underlying auditory N1 neural oscillatory activity showed the involvement of a multi-frequency phenomenon enclosing enhanced spectral power and phase-resetting of theta and alpha frequency bands at scalp EEG (Jansen et al., 2003, Fuentemilla et al., 2006). In addition, it has been argued that suppressed N1 might be due to the functional modulation of basic N1 processes, such as distinct EEG recovery cycles of N1 “early” and “late” contributions (Loveless et al., 1996), to decreased auditory cortical neuron activity (Rosburg et al., 2006) or to a reduction in scalp theta and alpha phase synchrony together with a disappearance of a concomitant spectral power modulation (Fuentemilla et al., 2006). However, to date, no integrated approach has made a comparative study of auditory N1 and N1 suppressed addressing the behaviour of time–structure–frequency IC descriptors relating to N1 and N1 repetitive suppression, though it could provide new insights in the study of N1 functional neural dynamics.

Here, we tackle these objectives by applying a 3-step approach analysis. The first two steps were based on the combined Independent Component Analysis (ICA) (Bell and Sejnowski, 1995, Jung et al., 2001) + Low-Resolution Tomography (LORETA) (Pascual-Marqui et al., 1994, Pascual-Marqui, 1999) proposed by Marco-Pallarés et al. (2005). ICA has been used to “blindly” identify neural modes describing activity in concurrent electromagnetic activity that is spatially fixed and temporally independent (Makeig et al., 1996), while LORETA analysis of the spatial maps associated with each IC provided solutions to the inverse problem of the neural source location of each component (Marco-Pallarés et al., 2005). In addition, the spectral content of each IC will be studied by computing a single-trial time–frequency analysis (Makeig et al., 2004b).