Mapping the individual human cortex using multidimensional MRI and unsupervised learning

Shinjini Kundu; Stephanie Barsoum; Jeanelle Ariza; Amber L Nolan; Caitlin S Latimer; C Dirk Keene; Peter J Basser; Dan Benjamini

doi:10.1093/braincomms/fcad258

Mapping the individual human cortex using multidimensional MRI and unsupervised learning

Brain Commun. 2023 Oct 6;5(6):fcad258. doi: 10.1093/braincomms/fcad258. eCollection 2023.

Authors

Shinjini Kundu^{1

2}, Stephanie Barsoum³, Jeanelle Ariza⁴, Amber L Nolan⁴, Caitlin S Latimer⁴, C Dirk Keene⁴, Peter J Basser², Dan Benjamini³

Affiliations

¹ Department of Radiology, The Johns Hopkins Hospital, Baltimore, MD 21287, USA.
² Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20892, USA.
³ Multiscale Imaging and Integrative Biophysics Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD 21224, USA.
⁴ Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA.

Abstract

Human evolution has seen the development of higher-order cognitive and social capabilities in conjunction with the unique laminar cytoarchitecture of the human cortex. Moreover, early-life cortical maldevelopment has been associated with various neurodevelopmental diseases. Despite these connections, there is currently no noninvasive technique available for imaging the detailed cortical laminar structure. This study aims to address this scientific and clinical gap by introducing an approach for imaging human cortical lamina. This method combines diffusion-relaxation multidimensional MRI with a tailored unsupervised machine learning approach that introduces enhanced microstructural sensitivity. This new imaging method simultaneously encodes the microstructure, the local chemical composition and importantly their correlation within complex and heterogenous tissue. To validate our approach, we compared the intra-cortical layers obtained using our ex vivo MRI-based method with those derived from Nissl staining of postmortem human brain specimens. The integration of unsupervised learning with diffusion-relaxation correlation MRI generated maps that demonstrate sensitivity to areal differences in cytoarchitectonic features observed in histology. Significantly, our observations revealed layer-specific diffusion-relaxation signatures, showing reductions in both relaxation times and diffusivities at the deeper cortical levels. These findings suggest a radial decrease in myelin content and changes in cell size and anisotropy, reflecting variations in both cytoarchitecture and myeloarchitecture. Additionally, we demonstrated that 1D relaxation and high-order diffusion MRI scalar indices, even when aggregated and used jointly in a multimodal fashion, cannot disentangle the cortical layers. Looking ahead, our technique holds the potential to open new avenues of research in human neurodevelopment and the vast array of disorders caused by disruptions in neurodevelopment.

Keywords: cortical parcellation; diffusion–relaxation; machine learning; microstructure imaging; multidimensional MRI.

Published by Oxford University Press on behalf of the Guarantors of Brain 2023.

Grants and funding

P30 AG066509/AG/NIA NIH HHS/United States