Studying the human brain anatomical network via diffusion-weighted MRI and graph theory
Abstract
Our goal is to study the human brain anatomical network. For this, the anatomical connection probabilities (ACP) between 90 cortical and subcortical brain gray matter areas are estimated from diffusion-weighted Magnetic Resonance Imaging (DW-MRI) techniques. The ACP between any two areas gives the probability that those areas are connected at least by a single nervous fiber. Then, the brain is modeled as a non-directed weighted graph with continuous arc weights given by the ACP matrix. Based on this approach, complex networks properties such as small-world attributes, efficiency, degree distribution, vulnerability, betweenness centrality and motifs composition are studied. The analysis was carried out for 20 right-handed healthy subjects (mean age: 31.10, S.D.: 7.43). According to the results, all networks have small-world and broad-scale characteristics. Additionally, human brain anatomical networks present bigger local efficiency and smaller global efficiency than the corresponding random networks. In a vulnerability and betweenness centrality analysis, the most indispensable and critical anatomical areas were identified: putamens, precuneus, insulas, superior parietals and superior frontals. Interestingly, some areas have a negative vulnerability (e.g. superior temporal poles, pallidums, supramarginals and hechls), which suggest that even at the cost of losing in global anatomical efficiency, these structures were maintained through the evolutionary processes due to their…
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A large number of graph theoretical measures have been proposed to study network properties of the brain's connectome.
“A daunting number of graph theoretical measures have been proposed to study the network properties of the brain’s connectome (Bullmore and Sporns, 2009b; Iturria-Medina et al., 2008; Rubinov and Sporns, 2010).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Iturria-Medina et al., 2008
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- Authors
- Iturria-Medina, Y., Sotero, R. C., Canales-Rodríguez, E. J., Alemán-Gómez, Y., & Melie-García, L.
- Affiliations
- Cuban Neuroscience Center (CU)
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2008-04
- Volume
- 40
- Issue
- 3
- Pages
- 1064-1076
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2064125324
- Cited by
- 506
- References
- 3
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 10 · 2023: 21 · 2022: 23 · 2021: 22 · 2020: 27 · 2019: 20 · 2018: 24 · 2017: 36 · 2016: 34 · 2015: 34 · 2014: 40 · 2013: 43 · 2012: 47
- FWCI
- 9.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- diffusion-weighted imaging, weighted complex networks, small-world property, local efficiency, global efficiency, network vulnerability, betweenness centrality, network motifs, putamen, precuneus, insula
- MeSH terms
- Adult, Algorithms, Brain, Humans, Image Processing, Computer-Assisted, Male, Nerve Fibers, Nerve Net, Neural Pathways, Diffusion Magnetic Resonance Imaging
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