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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.


Related in: MedlinePlus

Distance in state space is correlated to distance in time (a) Multidimensional distance between the activity in the different possible intervals visualised in two dimensions using multidimensional scaling (MDS). The colour of each point represents its physical interval. (b) Mean distances in state space (Mahalanobis distance) as a function of temporal separation (log10 scale). Blue (red) markers shows pairwise multivariate distances (mean ± s.e.m.) between the 0.8 s (2.27 s) and all other intervals. The slope of the fitted linear functions indicated that the rate of change in state space as a function of time is faster for the first than for the last interval.
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f3: Distance in state space is correlated to distance in time (a) Multidimensional distance between the activity in the different possible intervals visualised in two dimensions using multidimensional scaling (MDS). The colour of each point represents its physical interval. (b) Mean distances in state space (Mahalanobis distance) as a function of temporal separation (log10 scale). Blue (red) markers shows pairwise multivariate distances (mean ± s.e.m.) between the 0.8 s (2.27 s) and all other intervals. The slope of the fitted linear functions indicated that the rate of change in state space as a function of time is faster for the first than for the last interval.

Mentions: To quantify the relation between state space and behaviour, we focused our analysis at time points when the interval could have ended. We performed multivariate pairwise comparisons (using Mahalanobis distances) on data for the six first intervals (0.8, 0.98, 1.22, 1.5, 1.85, 2.27 seconds) and used multidimensional scaling to represent them in a two dimensional plot (Fig. 3a).


Dynamic representation of time in brain states
Distance in state space is correlated to distance in time (a) Multidimensional distance between the activity in the different possible intervals visualised in two dimensions using multidimensional scaling (MDS). The colour of each point represents its physical interval. (b) Mean distances in state space (Mahalanobis distance) as a function of temporal separation (log10 scale). Blue (red) markers shows pairwise multivariate distances (mean ± s.e.m.) between the 0.8 s (2.27 s) and all other intervals. The slope of the fitted linear functions indicated that the rate of change in state space as a function of time is faster for the first than for the last interval.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: Distance in state space is correlated to distance in time (a) Multidimensional distance between the activity in the different possible intervals visualised in two dimensions using multidimensional scaling (MDS). The colour of each point represents its physical interval. (b) Mean distances in state space (Mahalanobis distance) as a function of temporal separation (log10 scale). Blue (red) markers shows pairwise multivariate distances (mean ± s.e.m.) between the 0.8 s (2.27 s) and all other intervals. The slope of the fitted linear functions indicated that the rate of change in state space as a function of time is faster for the first than for the last interval.
Mentions: To quantify the relation between state space and behaviour, we focused our analysis at time points when the interval could have ended. We performed multivariate pairwise comparisons (using Mahalanobis distances) on data for the six first intervals (0.8, 0.98, 1.22, 1.5, 1.85, 2.27 seconds) and used multidimensional scaling to represent them in a two dimensional plot (Fig. 3a).

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.


Related in: MedlinePlus