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Stimulus-specific adaptation is the phenomenon whereby neural response magnitude decreases with repeated stimulation. Inconsistencies between recent nonhuman animal recordings and computational modeling suggest dynamic influences on stimulus-specific adaptation. The present human electroencephalography (EEG) study investigates the potential role of statistical context in dynamically modulating stimulus-specific adaptation by examining the auditory cortex-generated N1 and P2 components. As in previous studies of stimulus-specific adaptation, listeners were presented with oddball sequences in which the presentation of a repeated tone was infrequently interrupted by rare spectral changes taking on three different magnitudes. Critically, the statistical context varied with respect to the probability of small versus large spectral changes within oddball sequences (half of the time a small change was most probable; in the other half a large change was most probable). We observed larger N1 and P2 amplitudes (i.e., release from adaptation) for all spectral changes in the small-change compared with the large-change statistical context. The increase in response magnitude also held for responses to tones presented with high probability, indicating that statistical adaptation can overrule stimulus probability per se in its influence on neural responses. Computational modeling showed that the degree of coadaptation in auditory cortex changed depending on the statistical context, which in turn affected stimulus-specific adaptation. Thus the present data demonstrate that stimulus-specific adaptation in human auditory cortex critically depends on statistical context. Finally, the present results challenge the implicit assumption of stationarity of neural response magnitudes that governs the practice of isolating established deviant-detection responses such as the mismatch negativity.

Transcranial magnetic stimulation (TMS) influences cortical processes. Recent findings indicate, however, that, in turn, the efficacy of TMS depends on the state of ongoing cortical oscillations. Whereas power and phase of electromyographic (EMG) activity recorded from the hand muscles as well as neural synchrony between cortex and hand muscles are known to influence the effect of TMS, to date, no study has shown an influence of the phase of cortical oscillations during wakefulness. We applied single-pulse TMS over the motor cortex and recorded motor-evoked potentials along with the electroencephalogram (EEG) and EMG. We correlated phase and power of ongoing EEG and EMG signals with the motor-evoked potential (MEP) amplitude. We also investigated the functional connectivity between cortical and hand muscle activity (corticomuscular coherence) with the MEP amplitude. EEG and EMG power and phase in a frequency band around 18 Hz correlated with the MEP amplitude. High beta-band (∼34 Hz) corticomuscular coherence exhibited a positive linear relationship with the MEP amplitude, indicating that strong synchrony between cortex and hand muscles at the moment when TMS is applied entails large MEPs. Improving upon previous studies, we demonstrate a clear dependence of TMS-induced motor effects on the state of ongoing EEG phase and power fluctuations. We conclude that not only the sampling of incoming information but also the susceptibility of cortical communication flow depends cyclically on neural phase.

In auditory cortex, activation and subsequent adaptation is strongest for regions responding best to a stimulated tone frequency and less for regions responding best to other frequencies. Previous attempts to characterize the spread of neural adaptation in humans investigated the auditory cortex N1 component of the event-related potentials. Importantly, however, more recent studies in animals show that neural response properties are not independent of the stimulation context. To link these findings in animals to human scalp potentials, we investigated whether contextual factors of the acoustic stimulation, namely, spectral variance, affect the spread of neural adaptation. Electroencephalograms were recorded while human participants listened to random tone sequences varying in spectral variance (narrow vs. wide). Spread of adaptation was investigated by modeling single-trial neural adaptation and subsequent recovery based on the spectro-temporal stimulation history. Frequency-specific neural responses were largest on the N1 component, and the modeled neural adaptation indices were strongly predictive of trial-by-trial amplitude variations. Yet the spread of adaption varied depending on the spectral variance in the stimulation, such that adaptation spread was broadened for tone sequences with wide spectral variance. Thus the present findings reveal context-dependent auditory cortex adaptation and point toward a flexibly adjusting auditory system that changes its response properties with the spectral requirements of the acoustic environment.