ArticleNetwork neuroscience (Cambridge, Mass.)2021
A morphospace of functional configuration to assess configural breadth based on brain functional networks.
Article in Network neuroscience (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Gene-Modulated Network Diffusion for Improved Modeling of Amyloid-bioRxiv : the preprint server for biology · 2026Article
- Divergence unveils further distinct phenotypic traits of human brain connectomics fingerprint.iScience · 2026Article
- Reconstructing brain causal dynamics for subject and task fingerprints using fMRI time-series data.Health information science and systems · 2025Article
- Tangent space functional reconfigurations in individuals at risk for alcohol use disorder.Network neuroscience (Cambridge, Mass.) · 2025Article
- H-VIP: quantifying regional topological contributions of the brain network to cognition.Frontiers in radiology · 2025Article
- Characterizing Amyloid Pathogenic Spread in Alzheimer's Disease Through A Network Diffusion Model.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025Article
- Causality-based Subject and Task Fingerprints using fMRI Time-series Data.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2024Article
- Caudal and Thalamic Segregation in White Matter Brain Network Communities in Alzheimer's Disease Population.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2024Article
- Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- A Principled Framework to Assess the Information-Theoretic Fitness of Brain Functional Sub-Circuits.Mathematics (Basel, Switzerland) · 2024Article
- A principled framework to assess the information-theoretic fitness of brain functional sub-circuits.ArXiv · 2024Article
- Article
- PRESERVING HUMAN LARGE-SCALE BRAIN CONNECTIVITY FINGERPRINT IDENTIFIABILITY WITH RANDOM PROJECTIONS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Homological landscape of human brain functional sub-circuits.Mathematics (Basel, Switzerland) · 2024Article
- Mining Correlation between Fluid Intelligence and Whole-brain Large Scale Structural Connectivity.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Multitask brain network reconfiguration is inversely associated with human intelligence.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Evolution of Brains and Computers: The Roads Not Taken.Entropy (Basel, Switzerland) · 2022Article
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8 authors.
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Abstract
The quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.
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