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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.


Left: Example of Cohesiveness curve and polynomial fitting. Right: Strategy to remove outliers from intra-subject clustering using gaussian curve and statical test. All positive peaks and negative above green line are considered as significant for multi-subject clustering.
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Figure 3: Left: Example of Cohesiveness curve and polynomial fitting. Right: Strategy to remove outliers from intra-subject clustering using gaussian curve and statical test. All positive peaks and negative above green line are considered as significant for multi-subject clustering.

Mentions: DS clustering was first applied to single subject tractography volume to extract the WM bundles (intra-subject clustering). To reduce data dimensionality and thus computational complexity, we split the whole brain into three smaller datasets: left hemisphere, right hemisphere, and inter-hemispheric fibers, resulting in approximately 15,000 fibers per sub-datasets. The quality of retrieved bundles was then evaluated measuring the cohesiveness, which is a quantitative index measuring the internal coherence of each cluster δ as follows:(12)C(δ)=xTAxwhere x is the characteristic vector corresponding to δ and A is the adjacency matrix. High values of cohesiveness are related to clusters with high internal similarity between elements while clusters with low cohesiveness aggregates fibers with little structural significance. Hence, we used the cohesiveness index to remove the less significant clusters. Figure 3-Left shows an example of cohesiveness determined for all iteratively generated clusters. Since the last generated clusters are generally not significant (Pavan and Pelillo, 2007), we removed the last 5% clusters which are mostly the cluster with very low internal cohesivity.


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)

Left: Example of Cohesiveness curve and polynomial fitting. Right: Strategy to remove outliers from intra-subject clustering using gaussian curve and statical test. All positive peaks and negative above green line are considered as significant for multi-subject clustering.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Left: Example of Cohesiveness curve and polynomial fitting. Right: Strategy to remove outliers from intra-subject clustering using gaussian curve and statical test. All positive peaks and negative above green line are considered as significant for multi-subject clustering.
Mentions: DS clustering was first applied to single subject tractography volume to extract the WM bundles (intra-subject clustering). To reduce data dimensionality and thus computational complexity, we split the whole brain into three smaller datasets: left hemisphere, right hemisphere, and inter-hemispheric fibers, resulting in approximately 15,000 fibers per sub-datasets. The quality of retrieved bundles was then evaluated measuring the cohesiveness, which is a quantitative index measuring the internal coherence of each cluster δ as follows:(12)C(δ)=xTAxwhere x is the characteristic vector corresponding to δ and A is the adjacency matrix. High values of cohesiveness are related to clusters with high internal similarity between elements while clusters with low cohesiveness aggregates fibers with little structural significance. Hence, we used the cohesiveness index to remove the less significant clusters. Figure 3-Left shows an example of cohesiveness determined for all iteratively generated clusters. Since the last generated clusters are generally not significant (Pavan and Pelillo, 2007), we removed the last 5% clusters which are mostly the cluster with very low internal cohesivity.

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.