### Abstract

A new methodology based on Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) and Graph Theory is presented for characterizing the anatomical connections between brain gray matter areas. In a first step, brain voxels are modeled as nodes of a non-directed graph in which the weight of an arc linking two neighbor nodes is assumed to be proportional to the probability of being connected by nervous fibers. This probability is estimated by means of probabilistic tissue segmentation and intravoxel white matter orientational distribution function, obtained from anatomical MRI and DW-MRI, respectively. A new tractography algorithm for finding white matter routes is also introduced. This algorithm solves the most probable path problem between any two nodes, leading to the assessment of probabilistic brain anatomical connection maps. In a second step, for assessing anatomical connectivity between K gray matter structures, the previous graph is redefined as a K + 1 partite graph by partitioning the initial nodes set in K non-overlapped gray matter subsets and one subset clustering the remaining nodes. Three different measures are proposed for quantifying anatomical connections between any pair of gray matter subsets: Anatomical Connection Strength (ACS), Anatomical Connection Density (ACD) and Anatomical Connection Probability (ACP). This methodology was applied to both artificial and actual human data. Results show that nervous fiber pathways between some regions of interest were reconstructed correctly. Additionally, mean connectivity maps of ACS, ACD and ACP between 71 gray matter structures for five healthy subjects are presented.

Language | English |
---|---|

Pages | 645-660 |

Number of pages | 16 |

Journal | NeuroImage |

Volume | 36 |

Issue number | 3 |

DOIs | |

Publication status | Published - 1 Jul 2007 |

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### Keywords

- Brain connectivity
- Diffusion weighted magnetic resonance imaging
- Graph model
- Tractography

### Cite this

*36*(3), 645-660. https://doi.org/10.1016/j.neuroimage.2007.02.012

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**Characterizing brain anatomical connections using diffusion weighted MRI and graph theory.** / Iturria-Medina, Y.; Canales-Rodríguez, E. J.; Melie-García, L.; Valdés-Hernández, P. A.; Martínez-Montes, E.; Alemán-Gómez, Y.; Sánchez-Bornot, J. M.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Characterizing brain anatomical connections using diffusion weighted MRI and graph theory

AU - Iturria-Medina, Y.

AU - Canales-Rodríguez, E. J.

AU - Melie-García, L.

AU - Valdés-Hernández, P. A.

AU - Martínez-Montes, E.

AU - Alemán-Gómez, Y.

AU - Sánchez-Bornot, J. M.

PY - 2007/7/1

Y1 - 2007/7/1

N2 - A new methodology based on Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) and Graph Theory is presented for characterizing the anatomical connections between brain gray matter areas. In a first step, brain voxels are modeled as nodes of a non-directed graph in which the weight of an arc linking two neighbor nodes is assumed to be proportional to the probability of being connected by nervous fibers. This probability is estimated by means of probabilistic tissue segmentation and intravoxel white matter orientational distribution function, obtained from anatomical MRI and DW-MRI, respectively. A new tractography algorithm for finding white matter routes is also introduced. This algorithm solves the most probable path problem between any two nodes, leading to the assessment of probabilistic brain anatomical connection maps. In a second step, for assessing anatomical connectivity between K gray matter structures, the previous graph is redefined as a K + 1 partite graph by partitioning the initial nodes set in K non-overlapped gray matter subsets and one subset clustering the remaining nodes. Three different measures are proposed for quantifying anatomical connections between any pair of gray matter subsets: Anatomical Connection Strength (ACS), Anatomical Connection Density (ACD) and Anatomical Connection Probability (ACP). This methodology was applied to both artificial and actual human data. Results show that nervous fiber pathways between some regions of interest were reconstructed correctly. Additionally, mean connectivity maps of ACS, ACD and ACP between 71 gray matter structures for five healthy subjects are presented.

AB - A new methodology based on Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) and Graph Theory is presented for characterizing the anatomical connections between brain gray matter areas. In a first step, brain voxels are modeled as nodes of a non-directed graph in which the weight of an arc linking two neighbor nodes is assumed to be proportional to the probability of being connected by nervous fibers. This probability is estimated by means of probabilistic tissue segmentation and intravoxel white matter orientational distribution function, obtained from anatomical MRI and DW-MRI, respectively. A new tractography algorithm for finding white matter routes is also introduced. This algorithm solves the most probable path problem between any two nodes, leading to the assessment of probabilistic brain anatomical connection maps. In a second step, for assessing anatomical connectivity between K gray matter structures, the previous graph is redefined as a K + 1 partite graph by partitioning the initial nodes set in K non-overlapped gray matter subsets and one subset clustering the remaining nodes. Three different measures are proposed for quantifying anatomical connections between any pair of gray matter subsets: Anatomical Connection Strength (ACS), Anatomical Connection Density (ACD) and Anatomical Connection Probability (ACP). This methodology was applied to both artificial and actual human data. Results show that nervous fiber pathways between some regions of interest were reconstructed correctly. Additionally, mean connectivity maps of ACS, ACD and ACP between 71 gray matter structures for five healthy subjects are presented.

KW - Brain connectivity

KW - Diffusion weighted magnetic resonance imaging

KW - Graph model

KW - Tractography

UR - http://www.scopus.com/inward/record.url?scp=34250336119&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2007.02.012

DO - 10.1016/j.neuroimage.2007.02.012

M3 - Article

VL - 36

SP - 645

EP - 660

IS - 3

ER -