Expanding connectomics to the laminar level: A perspective

• Published in: Network neuroscience (Cambridge, Mass.) Vol. 7; no. 2; pp. 377 - 388
• Main Authors: Shamir, Ittai, Assaf, Yaniv
• Format: Journal Article
• Language: English
• Published: One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA MIT Press 2023; MIT Press Journals, The; The MIT Press
• Subjects: Age; Algorithms; Anatomical mapping; Automation; Brain; Brain network analysis; Cerebral cortex; Composition; Computational models; Connectomics; Cortical layers; Epilepsy; Graph representations; Magnetic resonance imaging; Medical imaging; Nervous system; Network hubs; Neural networks; Neuroimaging; Neuronal structures; Neurosciences; Substantia grisea; Brain network analysis; Computational models; Cortical layers; Anatomical mapping; Connectomics; Neuronal structures
• ISSN: 2472-1751; 2472-1751
• DOI: 10.1162/netn_a_00304

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.