ArticleFrontiers in neuroinformatics2014
Multi-scale integration and predictability in resting state brain activity.
Article in Frontiers in neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain.Scientific data · 2024Article
- Functional Connectivity's Degenerate View of Brain Computation.PLoS computational biology · 2016Article
- Dynamic Multiscale Modes of Resting State Brain Activity Detected by Entropy Field Decomposition.Neural computation · 2016Article
- The Structural and Functional Connectome and Prediction of Risk for Cognitive Impairment in Older Adults.Current behavioral neuroscience reports · 2015Article
- A novel brain partition highlights the modular skeleton shared by structure and function.Scientific reports · 2015Article
- A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables.PloS one · 2015Article
- Lagged and instantaneous dynamical influences related to brain structural connectivity.Frontiers in psychology · 2015Article
- Editorial for the research topic: information-based methods for neuroimaging: analyzing structure, function and dynamics.Frontiers in neuroinformatics · 2014Article
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Authors and funding
7 authors.
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Abstract
The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales.
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