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W1 S9: Exploring relevant spatiotemporal scales for analyses of brain dynamics
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**Speakers**
## Xenia Kobeleva

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Workshop on Methods of Information Theory in Computational Neuroscience

**Xenia Kobeleva**

University Hospital Bonn

Exploring relevant spatiotemporal scales for analyses of brain dynamics

Introduction: The brain switches between cognitive states at a high speed by rearranging interactions between distant brain regions. Using analyses of brain dynamics neuroimaging researchers were able to further describe this dynamical brain- behavior relationship. However, the diversity of methodological choices for the brain dynamics analyses impedes comparisons between studies of brain dynamics, reducing their reproducibility and generalizability. A key choice constitutes deciding on the spatiotemporal scale of the analysis, which includes both the number of regions (spatial scale) as well as the sampling rate (temporal scale). Choosing a suboptimal scale might either lead to loss of information or inefficient analyses with increase of noise. Therefore, the aim of this study was to assess the effect of different spatiotemporal scales on analyses of brain dynamics and to determine which spatiotemporal scale would retrieve the most relevant information on dynamic spatiotemporal patterns of brain regions.

Methods: We compared the effect of different spatiotemporal scales on the information content of the evolution of spatiotemporal patterns using empirical as well as simulated timeseries. Empirical timeseries were extracted from the Human connectome project [Van Essen et al., 2013]. We then created a whole-brain mean-field model of neural activity [Deco et al., 2013] resembling the key properties of the empirical data by fitting the global synchronization level and measures of dynamical functional connectivity. This resulted in different spatiotemporal with spatial scales from 100 to 900 regions and varying temporal scales from milliseconds to seconds. With a variation of an eigenvalue analysis [Deco et al., 2019], we estimated the number of spatiotemporal patterns over time and then extracted these patterns with an independent component analysis. The evolution of these patterns was then compared between scales in regard to the richness of switching activity (corrected for the number of patterns in total) using the measure of entropy. Given the probability of the occurrence of a pattern over time, we defined the entropy as a function of the probability of patterns.

Results: Using the entropy measure, we were able to specify both optimal and temporal scales for the evolution of spatiotemporal patterns (fig. 1). The entropy followed an inverted U-shaped function with the highest value at an intermediate parcellation of n = 300. The entropy was highest at a temporal scale of around 200 ms.

Conclusions and discussion: We have investigated which spatiotemporal scale contained the highest information content for brain dynamics analyses. By combining whole-brain computational modelling with an estimation of the number of resulting patterns, we were able to analyze whole-brain dynamics in different spatial and temporal scales. From a probabilistic perspective, we explored the entropy of the probability of resulting brain patterns, which was highest at a parcellation of n = 300. Our results indicate that although more spatiotemporal patterns with increased heterogeneity are found with higher parcellations, the most relevant information on brain dynamics is captured when using a spatial scale of n = 200 and a temporal scale of 200 ms. Our results therefore provide guidance for researchers on choosing the optimal spatiotemporal scale in studies of brain dynamics.

University Hospital Bonn

Exploring relevant spatiotemporal scales for analyses of brain dynamics

Introduction: The brain switches between cognitive states at a high speed by rearranging interactions between distant brain regions. Using analyses of brain dynamics neuroimaging researchers were able to further describe this dynamical brain- behavior relationship. However, the diversity of methodological choices for the brain dynamics analyses impedes comparisons between studies of brain dynamics, reducing their reproducibility and generalizability. A key choice constitutes deciding on the spatiotemporal scale of the analysis, which includes both the number of regions (spatial scale) as well as the sampling rate (temporal scale). Choosing a suboptimal scale might either lead to loss of information or inefficient analyses with increase of noise. Therefore, the aim of this study was to assess the effect of different spatiotemporal scales on analyses of brain dynamics and to determine which spatiotemporal scale would retrieve the most relevant information on dynamic spatiotemporal patterns of brain regions.

Methods: We compared the effect of different spatiotemporal scales on the information content of the evolution of spatiotemporal patterns using empirical as well as simulated timeseries. Empirical timeseries were extracted from the Human connectome project [Van Essen et al., 2013]. We then created a whole-brain mean-field model of neural activity [Deco et al., 2013] resembling the key properties of the empirical data by fitting the global synchronization level and measures of dynamical functional connectivity. This resulted in different spatiotemporal with spatial scales from 100 to 900 regions and varying temporal scales from milliseconds to seconds. With a variation of an eigenvalue analysis [Deco et al., 2019], we estimated the number of spatiotemporal patterns over time and then extracted these patterns with an independent component analysis. The evolution of these patterns was then compared between scales in regard to the richness of switching activity (corrected for the number of patterns in total) using the measure of entropy. Given the probability of the occurrence of a pattern over time, we defined the entropy as a function of the probability of patterns.

Results: Using the entropy measure, we were able to specify both optimal and temporal scales for the evolution of spatiotemporal patterns (fig. 1). The entropy followed an inverted U-shaped function with the highest value at an intermediate parcellation of n = 300. The entropy was highest at a temporal scale of around 200 ms.

Conclusions and discussion: We have investigated which spatiotemporal scale contained the highest information content for brain dynamics analyses. By combining whole-brain computational modelling with an estimation of the number of resulting patterns, we were able to analyze whole-brain dynamics in different spatial and temporal scales. From a probabilistic perspective, we explored the entropy of the probability of resulting brain patterns, which was highest at a parcellation of n = 300. Our results indicate that although more spatiotemporal patterns with increased heterogeneity are found with higher parcellations, the most relevant information on brain dynamics is captured when using a spatial scale of n = 200 and a temporal scale of 200 ms. Our results therefore provide guidance for researchers on choosing the optimal spatiotemporal scale in studies of brain dynamics.

Department of Neurology, University of Bonn

Interested in clinical computational neuroscience. Happy to talk about project ideas!

Wednesday July 22, 2020 11:30am - 12:00pm CEST

Crowdcast (W01)

Crowdcast (W01)