Electrophysiological recordings of neuronal activity show spontaneous and task-dependent changes in their frequency-domain power spectra. These changes are conventionally interpreted as modulations in the amplitude of underlying oscillations. However, this overlooks the possibility of underlying transient spectral ‘bursts’ or events whose dynamics can map to changes in trial-average spectral power in numerous ways. Under this emerging perspective, a key challenge is to perform burst detection, i.e. to characterise single-trial transient spectral events, in a principled manner. Here, we describe how transient spectral events can be operationalised and estimated using Hidden Markov Models (HMMs). The HMM overcomes a number of the limitations of the standard amplitude-thresholding approach to burst detection; in that it is able to concurrently detect different types of bursts, each with distinct spectral content, without the need to predefine frequency bands of interest, and does so with less dependence on a priori threshold specification. We describe how the HMM can be used for burst detection and illustrate its benefits on simulated data. Finally, we apply this method to empirical data to detect multiple burst types in a task-MEG dataset, and illustrate how we can compute burst metrics, such as the task-evoked timecourse of burst duration.

Performance improves when participants respond to events that are structured in repeating sequences, suggesting that learning can lead to proactive anticipatory preparation. Whereas most sequence-learning studies have emphasised spatial structure, most sequences also contain a prominent temporal structure. We used MEG to investigate spatial and temporal anticipatory neural dynamics in a modified serial reaction time (SRT) task. Performance and brain activity were compared between blocks with learned spatial-temporal sequences and blocks with new sequences. After confirming a strong behavioural benefit of spatial-temporal predictability, we show lateralisation of beta oscillations in anticipation of the response associated with the upcoming target location and show that this also aligns to the expected timing of these forthcoming events. This effect was found both when comparing between repeated (learned) and new (unlearned) sequences, as well as when comparing targets that were expected after short vs. long intervals within the repeated (learned) sequence. Our findings suggest that learning of spatial-temporal structure leads to proactive and dynamic modulation of motor cortical excitability in anticipation of both the location and timing of events that are relevant to guide action.

Spatial and temporal expectations act synergistically to facilitate visual perception. In the current study, we sought to investigate the anticipatory oscillatory markers of combined spatial-temporal orienting and to test whether these decline with ageing. We examined anticipatory neural dynamics associated with joint spatial-temporal orienting of attention using magnetoencephalography (MEG) in both younger and older adults. Participants performed a cued covert spatial-temporal orienting task requiring the discrimination of a visual target. Cues indicated both where and when targets would appear. In both age groups, valid spatial-temporal cues significantly enhanced perceptual sensitivity and reduced reaction times. In the MEG data, the main effect of spatial orienting was the lateralised anticipatory modulation of posterior alpha and beta oscillations. In contrast to previous reports, this modulation was not attenuated in older adults; instead it was even more pronounced. The main effect of temporal orienting was a bilateral suppression of posterior alpha and beta oscillations. This effect was restricted to younger adults. Our results also revealed a striking interaction between anticipatory spatial and temporal orienting in the gamma-band (60–75 Hz). When considering both age groups separately, this effect was only clearly evident and only survived statistical evaluation in the older adults. Together, these observations provide several new insights into the neural dynamics supporting separate as well as combined effects of spatial and temporal orienting of attention, and suggest that different neural dynamics associated with attentional orienting appear differentially sensitive to ageing.