Dynamic representation of time in brain states

Abstract

The ability to process time on the scale of milliseconds and seconds is essential for behaviour. A growing number of studies have started to focus on brain dynamics as a mechanism for temporal encoding. Although there is growing evidence in favour of this view from computational and in vitro studies, there is still a lack of results from experiments in humans. We show that high-dimensional brain states revealed by multivariate pattern analysis of human EEG are correlated to temporal judgements. First, we show that, as participants estimate temporal intervals, the spatiotemporal dynamics of their brain activity are consistent across trials. Second, we present evidence that these dynamics exhibit properties of temporal perception, such as scale invariance. Lastly, we show that it is possible to predict temporal judgements based on brain states. These results show how scalp recordings can reveal the spatiotemporal dynamics of human brain activity related to temporal processing.

A common representation of time across visual and auditory modalities

“…In a previous study, we have already shown that using multivariate pattern analysis on the EEG signal can reveal activity related to temporal processing (Bueno et al, 2017). However, in that experiment, MVPA was applied while participants performed a temporal categorisation task in which they had to judge whether a certain duration was shorter, equal or longer than a reference.…”

“…However, in that experiment, MVPA was applied while participants performed a temporal categorisation task in which they had to judge whether a certain duration was shorter, equal or longer than a reference. Thus, it was possible that the pattern analysis was picking up activity related to decisional processing or to particular strategies adopted by the participants (Bueno et al, 2017). Similarly, a study that also investigated whether the pattern of EEG activity during temporal judgements was consistent across vision and audition focused its analysis during the interval to be judged (N'Diaye et al, 2004).…”

“…To calculate the similarity across trials we used a bootstrap approach. For each participant and comparison of interest, data from all intervals that lasted at least 1.125 seconds were divided into two groups of trials and averaged across trials per group (Bueno et al, 2017). Then, for each time point, data from each split (two-row vectors with the averaged amplitude values of all 62 electrodes) were compared using a Pearson correlation.…”

“…In a recent study, we have shown that multivariate pattern analysis (MVPA) can reveal spatiotemporal dynamics of brain activity related to temporal processing (Bueno et al, 2017). Multivariate approaches can take advantage of small differences in the signal across electrodes that might not be detectable using classical EEG methods.…”

Metastable States of Multiscale Brain Networks Are Keys to Crack the Timing Problem

“…In this view, neural systems can benefit from the temporal evolution of their states, caused by the variation in neural and synaptic properties. The overall effect results in an adaptation of cerebral networks that could be tuned to discriminate temporal intervals (Bueno et al, 2017 ). State-dependent models can be extended to be consistent with the majority of timing models (Hass and Durstewitz, 2016 ), with the different models indicating specific constraints on what would collapse the state space.…”