Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2023 Dec 22;7(4):1363–1388. doi: 10.1162/netn_a_00330

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© 2023 Massachusetts Institute of Technology

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/.

PMC Copyright notice

Figure 1. — Edge weight spatial distribution. Connectivity matrices of group-level edge weights for FC (functional connectivity), NoS (number of streamlines), SIFT2 (spherical-deconvolution informed filtering of tractograms), COMMIT (convex optimization modeling for microstructure informed tractography), R₁ (longitudinal relaxation rate), ICVF (intracellular volume fraction), FA (fractional anisotropy), RD (radial diffusivity), and LoS (mean length of streamlines). Each network is composed of 414 nodes as defined by the Schaefer-400 cortical parcellation and 14 subcortical ROIs. Nodes are grouped into the canonical resting-state modules (Yeo et al., 2011) plus the subcortex: SUB (subcortex), VIS (visual), SMN (somatomotor), DAN (dorsal attention), SVAN (salience ventral attention), LIMB (limbic), CONT (control), and DMN (default mode). 3D cortical surfaces (shown below) of group-level edge weights in the Schaefer-100 parcellation generated with BrainNet Viewer (Xia et al., 2013). Edge diameter and color indicate weight magnitude. The edge weights in NoS, SIFT2, and COMMIT networks were log₁₀ transformed for visualization.