ArticleNational science review2024
Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture.
Article in National science review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Competitive interactions shape mammalian brain network dynamics and computation.Nature neuroscience · 2026Article
- Lifespan development of EEG alpha and aperiodic component sources is shaped by the connectome and axonal delays.National science review · 2026Article
- Digital Twin Brain simulation and manipulation of a functional brain network underlying mental illness.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- A scoping review of human digital twins in healthcare applications and usage patterns.NPJ digital medicine · 2025Article
- Building an Egocentric-to-Allocentric Travelling Direction Transformation Model for Enhanced Navigation in Intelligent Agents.Sensors (Basel, Switzerland) · 2025Article
- Human brain computing and brain-inspired intelligence.National science review · 2024Article
Corrections and comments
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Authors and funding
8 authors.
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Abstract
A computational human brain model with the voxel-wise assimilation method was established based on individual structural and functional imaging data. We found that the more similar the brain model is to the biological counterpart in both scale and architecture, the more similarity was found between the assimilated model and the biological brain both in resting states and during tasks by quantitative metrics. The hypothesis that resting state activity reflects internal body states was validated by the interoceptive circuit's capability to enhance the similarity between the simulation model and the biological brain. We identified that the removal of connections from the primary visual cortex (V1) to downstream visual pathways significantly decreased the similarity at the hippocampus between the model and its biological counterpart, despite a slight influence on the whole brain. In conclusion, the model and methodology present a solid quantitative framework for a digital twin brain for discovering the relationship between brain architecture and functions, and for digitally trying and testing diverse cognitive, medical and lesioning approaches that would otherwise be unfeasible in real subjects.
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Registered trials
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