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Dynamic representation of time in brain states

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Spatiotemporal dynamics and temporal perception (a) Similarity index of the spatiotemporal dynamics across trials (mean ± s.e.m.). Red line at the bottom represents the temporal cluster where similarity was significant. (b) Pairwise multivariate distance matrix between all time points. There is a strong similarity for time points closer in time, suggesting a sequence of activation states. (c) Multidimensional distance between time points for the period between 0.65 s and 2.2 s visualised in two dimensions using multidimensional scaling (MDS). The colour of each point represents its physical interval. The trajectory represents the path linking the sequence of activation states.
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f2: Spatiotemporal dynamics and temporal perception (a) Similarity index of the spatiotemporal dynamics across trials (mean ± s.e.m.). Red line at the bottom represents the temporal cluster where similarity was significant. (b) Pairwise multivariate distance matrix between all time points. There is a strong similarity for time points closer in time, suggesting a sequence of activation states. (c) Multidimensional distance between time points for the period between 0.65 s and 2.2 s visualised in two dimensions using multidimensional scaling (MDS). The colour of each point represents its physical interval. The trajectory represents the path linking the sequence of activation states.

Mentions: For a neural system to be able to read time by its trajectory through state space, the trajectory of the elicited activity must be consistent across activations. Thus, we checked if the recorded dynamics were consistent across trials. Indeed, the pattern of EEG signals across the scalp followed a structured sequence in time across different trials (one-sample t-test, cluster – p < 0.001, Fig. 2a). Next, we used the Mahalanobis distance1922 to perform pairwise comparisons across time points to determine whether the pattern of the EEG signal contained information about the interval between events. As shown in Fig. 2b, multivariate distances between time points followed a diagonal-shaped pattern (i.e., a stronger similarity across points closer in time), suggesting a sequential activation of overlapping states.


Dynamic representation of time in brain states
Spatiotemporal dynamics and temporal perception (a) Similarity index of the spatiotemporal dynamics across trials (mean ± s.e.m.). Red line at the bottom represents the temporal cluster where similarity was significant. (b) Pairwise multivariate distance matrix between all time points. There is a strong similarity for time points closer in time, suggesting a sequence of activation states. (c) Multidimensional distance between time points for the period between 0.65 s and 2.2 s visualised in two dimensions using multidimensional scaling (MDS). The colour of each point represents its physical interval. The trajectory represents the path linking the sequence of activation states.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5385543&req=5

f2: Spatiotemporal dynamics and temporal perception (a) Similarity index of the spatiotemporal dynamics across trials (mean ± s.e.m.). Red line at the bottom represents the temporal cluster where similarity was significant. (b) Pairwise multivariate distance matrix between all time points. There is a strong similarity for time points closer in time, suggesting a sequence of activation states. (c) Multidimensional distance between time points for the period between 0.65 s and 2.2 s visualised in two dimensions using multidimensional scaling (MDS). The colour of each point represents its physical interval. The trajectory represents the path linking the sequence of activation states.
Mentions: For a neural system to be able to read time by its trajectory through state space, the trajectory of the elicited activity must be consistent across activations. Thus, we checked if the recorded dynamics were consistent across trials. Indeed, the pattern of EEG signals across the scalp followed a structured sequence in time across different trials (one-sample t-test, cluster – p < 0.001, Fig. 2a). Next, we used the Mahalanobis distance1922 to perform pairwise comparisons across time points to determine whether the pattern of the EEG signal contained information about the interval between events. As shown in Fig. 2b, multivariate distances between time points followed a diagonal-shaped pattern (i.e., a stronger similarity across points closer in time), suggesting a sequential activation of overlapping states.

View Article: PubMed Central - PubMed

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.

No MeSH data available.