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Automated multi-subject fiber clustering of mouse brain using dominant sets.

Dodero L, Vascon S, Murino V, Bifone A, Gozzi A, Sona D - Front Neuroinform (2015)

Bottom Line: The intra-subject step allows us to reduce the complexity of the raw tractography data, thus obtaining homogeneous neuroanatomically-plausible bundles in each diffusion space.We also performed qualitative analysis on mouse brain tractography retrieving significant WM structures.The approach serves the final goal of detecting WM bundles at a population level, thus paving the way to the study of the WM organization across groups.

View Article: PubMed Central - PubMed

Affiliation: Pattern Analysis and Computer Vision Department (PAVIS), Istituto Italiano di Tecnologia Genova, Italy.

ABSTRACT
Mapping of structural and functional connectivity may provide deeper understanding of brain function and disfunction. Diffusion Magnetic Resonance Imaging (DMRI) is a powerful technique to non-invasively delineate white matter (WM) tracts and to obtain a three-dimensional description of the structural architecture of the brain. However, DMRI tractography methods produce highly multi-dimensional datasets whose interpretation requires advanced analytical tools. Indeed, manual identification of specific neuroanatomical tracts based on prior anatomical knowledge is time-consuming and prone to operator-induced bias. Here we propose an automatic multi-subject fiber clustering method that enables retrieval of group-wise WM fiber bundles. In order to account for variance across subjects, we developed a multi-subject approach based on a method known as Dominant Sets algorithm, via an intra- and cross-subject clustering. The intra-subject step allows us to reduce the complexity of the raw tractography data, thus obtaining homogeneous neuroanatomically-plausible bundles in each diffusion space. The cross-subject step, characterized by a proper space-invariant metric in the original diffusion space, enables the identification of the same WM bundles across multiple subjects without any prior neuroanatomical knowledge. Quantitative analysis was conducted comparing our algorithm with spectral clustering and affinity propagation methods on synthetic dataset. We also performed qualitative analysis on mouse brain tractography retrieving significant WM structures. The approach serves the final goal of detecting WM bundles at a population level, thus paving the way to the study of the WM organization across groups.

No MeSH data available.


2-D Histograms of affinity matrices using different similarity measure. Top-Left symmetrized minimum landmark distance vs. symmetrized point to point distance. Top-Right symmetrized minimum landmark distance vs. symmetrized mean closest point distance. Bottom-Left landmark distance vs. symmetrized point to point distance. Bottom-Right: landmark distance vs. symmetrized mean closest point distance.
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Figure 2: 2-D Histograms of affinity matrices using different similarity measure. Top-Left symmetrized minimum landmark distance vs. symmetrized point to point distance. Top-Right symmetrized minimum landmark distance vs. symmetrized mean closest point distance. Bottom-Left landmark distance vs. symmetrized point to point distance. Bottom-Right: landmark distance vs. symmetrized mean closest point distance.

Mentions: The above two groups of similarity measures were defined for the two clustering steps in the light of their different requirements. Since the choice of the similarity measure can greatly affect the clustering algorithms we compared the measures aiming at selecting the two that produce most similar results. The landmark measure is almost mandatory in order to avoid the tractography alignment. However, being the landmarks-based representation an approximation of the real fiber location, we have to choose the similarity between elements able to preserve the geometry and the shape of the subject bundles. We thus pairwise compared all proposed measures computing each similarity measure between each pair of fibers of a random subject. In Figure 2 are depicted the distributions of all pairwise comparisons. Comparing the the similarities with Pearson correlation we found that symmetrized point to point distance and landmark distance are the most correlated presenting the closest correspondence (see Figure 2-Bottom Left). Based on these results, we adopted the symmetrized point to point distance for intra-subject clustering and the landmark distance for cross-subject clustering.


Automated multi-subject fiber clustering of mouse brain using dominant sets.

Dodero L, Vascon S, Murino V, Bifone A, Gozzi A, Sona D - Front Neuroinform (2015)

2-D Histograms of affinity matrices using different similarity measure. Top-Left symmetrized minimum landmark distance vs. symmetrized point to point distance. Top-Right symmetrized minimum landmark distance vs. symmetrized mean closest point distance. Bottom-Left landmark distance vs. symmetrized point to point distance. Bottom-Right: landmark distance vs. symmetrized mean closest point distance.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4290731&req=5

Figure 2: 2-D Histograms of affinity matrices using different similarity measure. Top-Left symmetrized minimum landmark distance vs. symmetrized point to point distance. Top-Right symmetrized minimum landmark distance vs. symmetrized mean closest point distance. Bottom-Left landmark distance vs. symmetrized point to point distance. Bottom-Right: landmark distance vs. symmetrized mean closest point distance.
Mentions: The above two groups of similarity measures were defined for the two clustering steps in the light of their different requirements. Since the choice of the similarity measure can greatly affect the clustering algorithms we compared the measures aiming at selecting the two that produce most similar results. The landmark measure is almost mandatory in order to avoid the tractography alignment. However, being the landmarks-based representation an approximation of the real fiber location, we have to choose the similarity between elements able to preserve the geometry and the shape of the subject bundles. We thus pairwise compared all proposed measures computing each similarity measure between each pair of fibers of a random subject. In Figure 2 are depicted the distributions of all pairwise comparisons. Comparing the the similarities with Pearson correlation we found that symmetrized point to point distance and landmark distance are the most correlated presenting the closest correspondence (see Figure 2-Bottom Left). Based on these results, we adopted the symmetrized point to point distance for intra-subject clustering and the landmark distance for cross-subject clustering.

Bottom Line: The intra-subject step allows us to reduce the complexity of the raw tractography data, thus obtaining homogeneous neuroanatomically-plausible bundles in each diffusion space.We also performed qualitative analysis on mouse brain tractography retrieving significant WM structures.The approach serves the final goal of detecting WM bundles at a population level, thus paving the way to the study of the WM organization across groups.

View Article: PubMed Central - PubMed

Affiliation: Pattern Analysis and Computer Vision Department (PAVIS), Istituto Italiano di Tecnologia Genova, Italy.

ABSTRACT
Mapping of structural and functional connectivity may provide deeper understanding of brain function and disfunction. Diffusion Magnetic Resonance Imaging (DMRI) is a powerful technique to non-invasively delineate white matter (WM) tracts and to obtain a three-dimensional description of the structural architecture of the brain. However, DMRI tractography methods produce highly multi-dimensional datasets whose interpretation requires advanced analytical tools. Indeed, manual identification of specific neuroanatomical tracts based on prior anatomical knowledge is time-consuming and prone to operator-induced bias. Here we propose an automatic multi-subject fiber clustering method that enables retrieval of group-wise WM fiber bundles. In order to account for variance across subjects, we developed a multi-subject approach based on a method known as Dominant Sets algorithm, via an intra- and cross-subject clustering. The intra-subject step allows us to reduce the complexity of the raw tractography data, thus obtaining homogeneous neuroanatomically-plausible bundles in each diffusion space. The cross-subject step, characterized by a proper space-invariant metric in the original diffusion space, enables the identification of the same WM bundles across multiple subjects without any prior neuroanatomical knowledge. Quantitative analysis was conducted comparing our algorithm with spectral clustering and affinity propagation methods on synthetic dataset. We also performed qualitative analysis on mouse brain tractography retrieving significant WM structures. The approach serves the final goal of detecting WM bundles at a population level, thus paving the way to the study of the WM organization across groups.

No MeSH data available.