ArticleNetwork neuroscience (Cambridge, Mass.)2023
Expanding connectomics to the laminar level: A perspective.
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 10 papers.
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10 citing papers in PubMed.
- A phantom mimicking layered biological microenvironments investigated with 7T diffusion weighted MRI.Scientific reports · 2026Article
- Visualizing cortical laminar architecture in the living human brain using next-generation ultra-high-gradient diffusion MRI.Communications biology · 2026Article
- Visualizing cortical laminar architecture in the living human brain using next-generation ultra-high-gradient diffusion MRI.Research square · 2025Article
- Tutorial: a guide to diffusion MRI and structural connectomics.Nature protocols · 2025Review
- Multilayer network analysis across cortical depths in 7-T resting-state fMRI.Network neuroscience (Cambridge, Mass.) · 2025Article
- Localization of the epileptogenic network from scalp EEG using a patient-specific whole-brain model.Network neuroscience (Cambridge, Mass.) · 2025Article
- Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI.bioRxiv : the preprint server for biology · 2024Article
- Clustering the cortical laminae: in vivo parcellation.Brain structure & function · 2024Article
- Topographic organization across foveal visual areas in macaques.Frontiers in neuroanatomy · 2024Article
- Cellular and laminar architecture: A short history and commentary.The Journal of comparative neurology · 2023Article
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2 authors.
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Abstract
Despite great progress in uncovering the complex connectivity patterns of the human brain over the last two decades, the field of connectomics still experiences a bias in its viewpoint of the cerebral cortex. Due to a lack of information regarding exact end points of fiber tracts inside cortical gray matter, the cortex is commonly reduced to a single homogenous unit. Concurrently, substantial developments have been made over the past decade in the use of relaxometry and particularly inversion recovery imaging for exploring the laminar microstructure of cortical gray matter. In recent years, these developments have culminated in an automated framework for cortical laminar composition analysis and visualization, followed by studies of cortical dyslamination in epilepsy patients and age-related differences in laminar composition in healthy subjects. This perspective summarizes the developments and remaining challenges of multi-T1 weighted imaging of cortical laminar substructure, the current limitations in structural connectomics, and the recent progress in integrating these fields into a new model-based subfield termed 'laminar connectomics'. In the coming years, we predict an increased use of similar generalizable, data-driven models in connectomics with the purpose of integrating multimodal MRI datasets and providing a more nuanced and detailed characterization of brain connectivity.
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