Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis

Abstract

Understanding the laminar brain structure is of great help in further developing our knowledge of the functions of the brain. However, since most layer segmentation methods are invasive, it is difficult to apply them to the human brain in vivo. To systematically explore the human brain's laminar structure noninvasively, the K-means clustering algorithm was used to automatically segment the left hemisphere into two layers, the superficial and deep layers, using a 7 Tesla (T) diffusion magnetic resonance imaging (dMRI)open dataset. The obtained layer thickness was then compared with the layer thickness of the BigBrain reference dataset, which segmented the neocortex into six layers based on the von Economo atlas. The results show a significant correlation not only between our automatically segmented superficial layer thickness and the thickness of layers 1-3 from the reference histological data, but also between our automatically segmented deep layer thickness and the thickness of layers 4-6 from the reference histological data. Second, we constructed the laminar connections between two pairs of unidirectional connected regions, which is consistent with prior research. Finally, we conducted the laminar analysis of the working memory, which was challenging to do in the past, and explained the conclusions of the functional analysis. Our work successfully demonstrates that it is possible to segment the human cortex noninvasively into layers using dMRI data and further explores the mechanisms of the human brain.

Layer-dependent effect of Aβ-pathology on cortical microstructure with ex-vivo human brain diffusion MRI at 7 Tesla

“…Also, the variation of FA in the cortical lamina is determined directly by the organizational structure of myelinated bers [39], and the cortical depth pro les of diffusivity re ect similar laminar structures correlated with cell density [40]. Moreover, our ndings are consistent with recent in-vivo studies [17,41], revealed that both myeloarchitectonic and cytoarchitectonic structures in uence diffusion properties in the cortex. We also found that diffusional anisotropy nonlinearly increased and diffusivity linearly decreased with layer increase in cortical regions, especially in prefrontal (IFC) and visual cortexes.…”

A Deep Learning-Based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images

“…To summarize the existing literature, previous attempts using machine learning and deep learning techniques have shown the possibilities to segment the cortical layers (Bastiani et al, 2016;Zhang et al, 2022). Thus, we decided to take it one step further by using a more advanced network, such as nnU-Net (Isensee, Jaeger, et al, 2021), which has been developed and showed great segmentation results specifically targeting the medical image processing field.…”