DOI: 10.5061/dryad.1c59zw45n
title: Data from: Direct segmentation of cortical cytoarchitectonic domains using ultra-high-resolution whole-brain diffusion MRI

description: We acquired whole-brain MAP-MRI data with 200μm resolution in a fixed
 macaque monkey brain and processed the cortical voxels with a local
 anisotropic Gaussian denoising filter determined voxel-wise by the plane
 tangent to the cortical surface. We computed DTI-derived parameters,
 (i.e., fractional anisotropy – FA; mean, axial, and radial diffusivities -
 MD, AD, and RD, respectively) and MAP-MRI-derived microstructural
 parameters (i.e., propagator anisotropy – PA, non-Gaussianity – NG,
 return-to-origin probability – RTOP, return-to-axis probability – RTAP,
 and return-to-plane probability – RTPP). We directly clustered all
 cortical voxels using only the MAP-derived microstructural imaging
 biomarkers using a Gaussian mixture model (GMM) analysis. MAP-based 3D
 cytoarchitectonic segmentation revealed laminar patterns similar to those
 observed in the corresponding histological images. Moreover, transition
 regions between these laminar patterns agreed more accurately with
 histology than the borders between cortical areas estimated using
 conventional atlas/template-warping cortical parcellation. By
 cross-tabulating all cortical labels in the atlas- and MAP-based
 segmentations, we automatically matched the corresponding MAP-derived
 clusters (i.e., cytoarchitectonic domains) across the left and right
 hemispheres. High-resolution MAP-MRI biomarkers can effectively delineate
 three-dimensional cortical cytoarchitectonic domains in single
 individuals.
description: We acquired diffusion MRI data brain with 200μm isotropic
 resolution in a fixed macaque monkey brain using a 3D diffusion-weighted
 spin-echo (SE) echo-planar imaging (EPI) sequence with 50 ms echo time
 (TE) and 650 ms repetition time (TR), imaging matrix 375x320x230 on a
 7.5x6.4x4.6 cm field-of-view (FOV) with 17 segments per k-z plane and 1.5
 partial Fourier acceleration. The diffusion encoding table contained
 multiple b-value shells: 0.1, 1.0, 2.5, 4.5, 7.0, and 10.0
 ms/μm<sup>2</sup> with diffusion-encoding gradient
 orientations (3, 9, 15, 21, 28, and 36, respectively) uniformly sampling
 the unit sphere on each shell and across shells. The diffusion gradient
 pulse duration and separation were 6 ms and 28 ms, respectively.
 We processed the diffusion MRI data using the TORTOISE software
 package (https://tortoise.nibib.nih.gov/). From a structural scan, we
 derived the brain tissue segmentation using FSL-FAST
 (https://fsl.fmrib.ox.ac.uk) and computed the directions parallel and
 perpendicular with respect to the cortical surface at each voxel (i.e.,
 the cortical reference frame) using LAYNII
 (https://layerfmri.com/category/laynii/). Next, we denoised the signals in
 gray matter voxels using a spatially varying anisotropic Gaussian filter
 whose orientation was determined by the local cortical reference frame. In
 each voxel we estimated the diffusion propagator using a MAP-MRI series
 expansion truncated at order 4 and computed DTI (i.e., fractional
 anisotropy – FA; mean, axial, and radial diffusivities - MD, AD, and RD,
 respectively) and MAP-MRI microstructural parameters (i.e., propagator
 anisotropy – PA, non-Gaussianity – NG, return-to-origin probability –
 RTOP, return-to-axis probability – RTAP, and return-to-plane probability –
 RTPP). We directly clustered individual cortical voxels
 in each hemisphere based on their values of scalar-valued MAP/DTI
 parameters (FA, RD, AD, PA, NG, RTOP, RTAP, and RTPP) using a Gaussian
 mixture model (GMM) clustering algorithm. The best segmentation
 consistency across the left and right hemispheres was obtained when the
 MAP parameters PA, NG, RTAP, and RTPP were used as features and the number
 of clusters was set to 14. We used 3D morphological
 processing to separate disjoint (i.e., disconnected) components larger
 than 100 voxels initially assigned to the same GMM cluster and relabeled
 them as new clusters. Isolated cluster components with volumes smaller
 than 100 voxels were likely due to noise and were merged with the
 neighboring cluster with the largest shared boundary. This step reduced
 the dependence of the final segmentation on the number of clusters chosen
 in the initial GMM clustering analysis.  We
 automatically matched and labeled the cytoarchitectonic domains segmented
 using MAP-based voxelwise GMM clustering in the left and right
 hemispheres. First, we derived a canonical layer segmentation of each
 label in the symmetric D99 atlas using six layers with equivolumetric
 spacing obtained with LayNii and computed the cross-tabulation matrix
 between the MAP- and D99 layer segmentations and applied the Kuhn-Munkres
 assignment algorithm. Because the D99 layer labels are symmetric across
 the left and right hemispheres, we directly match the MAP labels by
 multiplying the MAP-D99 cross-tabulation matrices of the left and right
 hemispheres.  Finally, we compared the segmented MAP
 cytoarchitectonic regions with cortical area estimates from the warped D99
 cortical atlas using matched histological tissue sections obtained from
 the same brain.
description: # Direct segmentation of cortical cytoarchitectonic domains using
 ultra-high-resolution whole-brain diffusion MRI
 [https://doi.org/10.5061/dryad.1c59zw45n](https://doi.org/10.5061/dryad.1c59zw45n) ## Description of the data and file structure Microstructural parameters derived from MAP-MRI data acquired with 200μm spatial resolution in a fixed macaque brain. DWI signals in the cortical gray matter were filtered using an anisotropic Gaussian filter with local orientation determined by the cortical reference frame in each voxel. MAP-MRI and DTI parameters were used for direct segmentation using Gaussian mixture model clustering. The code for processing the data can be found at the link in the Related Works section.  ### Files and variables #### File: ad.nii.gz **Description:** Axial diffusivity derived from CRF-filtered MAP-MRI DWIs #### File: am.nii.gz **Description:** Non-diffusion weighted image (.i.e., T2-weighted b=0 image) #### File: fa.nii.gz **Description:** Fractional Anisotropy derived from CRF-filtered MAP-MRI DWIs #### File: ng.nii.gz **Description:** Non-Gaussianity derived from CRF-filtered MAP-MRI DWIs #### File: d99.nii.gz **Description:** Warped d99 cortical atlas labels #### File: pa.nii.gz **Description:** Propagator anisotropy derived from CRF-filtered MAP-MRI DWIs #### File: rtap.nii.gz **Description:** Return-to-axis probability derived from CRF-filtered MAP-MRI DWIs #### File: d99layers.nii.gz **Description:** Canonical segmentation of cortical layers derived from the warped d99 atlas labels (d99.nii.gz) and distance the rim.nii.gz files #### File: rim.nii.gz **Description:** rim file used for LayNii processing, derived from MTR-based GM/WM segmentation with FSL-FAST #### File: rtop.nii.gz **Description:** Return-to-origin probability derived from CRF-filtered MAP-MRI DWIs #### File: rd.nii.gz **Description:** Radial diffusivity derived from CRF-filtered MAP-MRI DWIs #### File: rtpp.nii.gz **Description:** Return-to-plane probability derived from CRF-filtered MAP-MRI DWIs #### File: mtr.nii.gz **Description:** Magnetization Transfer Ratio (MTR) volume used for tissue segmentation and as a structural template for processing the MAP-MRI data ## Access information NA