ArticleNetwork neuroscience (Cambridge, Mass.)2023
The arrow of time of brain signals in cognition: Potential intriguing role of parts of the default mode network.
Article in Network neuroscience (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
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- Data-driven ANN-based visual decoding enables unsupervised functional alignment.Communications biology · 2026Article
- TVB C++: A Fast and Flexible Back-End for The Virtual Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The turbulent brain: Modeling vortex interactions for understanding human cognition.Network neuroscience (Cambridge, Mass.) · 2026Article
- Disrupted Brain Hierarchical Organization in Alzheimer's Disease Progression.medRxiv : the preprint server for health sciences · 2025Article
- State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilibrium neuronal dynamics.Nature communications · 2025Article
- Brain metabolic-functional (de)coupling from health to glioma dysfunction.Communications biology · 2025Article
- Reconfiguration of functional brain hierarchy in schizophrenia.Translational psychiatry · 2025Article
- Disrupted hierarchical organization in disorders of consciousness revealed by fluctuation-dissipation deviations.bioRxiv : the preprint server for biology · 2025Article
- Large-scale cortical functional networks are organized in structured cycles.Nature neuroscience · 2025Article
- Multilevel irreversibility reveals higher-order organization of nonequilibrium interactions in human brain dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Broken time-reversal symmetry in visual motion detection.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Broken time reversal symmetry in visual motion detection.bioRxiv : the preprint server for biology · 2024Article
- Navigating Pubertal Goldilocks: The Optimal Pace for Hierarchical Brain Organization.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data.Network neuroscience (Cambridge, Mass.) · 2024Article
- The arrow-of-time in neuroimaging time series identifies causal triggers of brain function.Human brain mapping · 2023Article
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7 authors.
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Abstract
A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamics-inspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, 'arrow of time', in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.
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