Visualizing cortical laminar architecture in the living human brain using next-generation ultra-high-gradient diffusion MRI

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Authors: Hansol Lee, Hansol Lee, Yixin Ma, Kwok‐Shing Chan, Eva A. Krijnen, Laleh Eskandarian, Aneri Bhatt, Julianna Gerold, Mirsad Mahmutović, Oula Puonti, Xiangrui Zeng, LI Binder
+7 more Bruce Fischl, Boris Keil, Gabriel Ramos-Llordén, Eric C. Klawiter, Hong‐Hsi Lee, Hong‐Hsi Lee, Susie Y. Huang
Cited by: 3
References: 104

Abstract

Characterizing cortical laminar microstructure is essential for understanding the organization of the human brain. Leveraging the next-generation Connectome MRI scanner (maximum gradient strength=500mT/m, slew rate=600 T/m/s), we characterized in vivo cortical laminar cytoarchitecture and myeloarchitecture through cortical depth-dependent analyses of soma and neurite density imaging (SANDI) metrics derived from 1 mm diffusion MRI generated using a super-resolution technique. SANDI revealed distinct laminar profiles: intra-soma signal fraction peaked at ~55% cortical depth, while the intra-neurite signal fraction increased toward deeper cortical layers, consistent with known histological patterns. The visual cortex showed higher intra-soma signal fraction than the motor cortex, particularly in deeper layers. Intra-soma signal fraction correlated positively with cortical curvature in superficial layers and negatively in deeper layers, indicating layer-specific relationships between cortical microstructure and geometry. These findings demonstrate the feasibility of noninvasive mapping of laminar architecture, offering a potential in vivo surrogate for histology and enabling future studies of cortical laminar organization using high-performance gradient MRI.

Fields of Study & Subjects

Advanced Neuroimaging Techniques and Applications Functional Brain Connectivity Studies Advanced MRI Techniques and Applications

Publication details

Journal / source
Communications Biology
Publisher
Springer Science and Business Media LLC
Volume
9
Issue
1
Publication date
23 March 2026
Language
EN