Connectivity-based parcellation of the cortical surface using q-ball imaging

Guevara, Pamela; Perrin, M.; Cathier, Pascal; Yann, C.; Rivière, Denis; Poupon, Cyril; Mangin, J.-F.

doi:10.1109/isbi.2008.4541143

Cited by 3 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All the methods have to deal with the high inter-subject variability, especially in the brain cortex and superficial white matter (SWM). Therefore, to reduce the complexity of the problem, some methods are focused or have been tested on a few brain regions, or have used an anatomical parcellation for initial regions (Anwander et al, 2006 ; Klein et al, 2007 ; Guevara et al, 2008 ; Perrin et al, 2008 ; Roca et al, 2010 ; Li et al, 2017 ). In general, the similarity between the connectivity profiles of the voxels (or vertices) is estimated using some similarity measure and then, a method is applied to regroup elements with common connectivity patterns.…”

Section: Introductionmentioning

confidence: 99%

From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

López-López

Vázquez

Houenou

et al. 2020

Front. Neuroinform.

Self Cite

View full text Add to dashboard Cite

In this article, we present a hybrid method to create fine-grained parcellations of the cortical surface, from a coarse-grained parcellation according to an anatomical atlas, based on cortico-cortical connectivity. The connectivity information is obtained from segmented superficial and deep white matter bundles, according to bundle atlases, instead of the whole tractography. Thus, a direct matching between the fiber bundles and the cortical regions is obtained, avoiding the problem of finding the correspondence of the cortical parcels among subjects. Generating parcels from segmented fiber bundles can provide a good representation of the human brain connectome since they are based on bundle atlases that contain the most reproducible short and long connections found on a population of subjects. The method first processes the tractography of each subject and extracts the bundles of the atlas, based on a segmentation algorithm. Next, the intersection between the fiber bundles and the cortical mesh is calculated, to define the initial and final intersection points of each fiber. A fiber filtering is then applied to eliminate misclassified fibers, based on the anatomical definition of each bundle and the labels of Desikan-Killiany anatomical parcellation. A parcellation algorithm is then performed to create a subdivision of the anatomical regions of the cortex, which is reproducible across subjects. This step resolves the overlapping of the fiber bundle extremities over the cortical mesh within each anatomical region. For the analysis, the density of the connections and the degree of overlapping, is considered and represented with a graph. One of our parcellations, an atlas composed of 160 parcels, achieves a reproducibility across subjects of ≈0.74, based on the average Dice's coefficient between subject's connectivity matrices, rather than ≈0.73 obtained for a macro anatomical parcellation of 150 parcels. Moreover, we compared two of our parcellations with state-of-the-art atlases, finding a degree of similarity with dMRI, functional, anatomical, and multi-modal atlases. The higher similarity was found for our parcellation composed of 185 sub-parcels with another parcellation based on dMRI data from the same database, but created with a different approach, leading to 130 parcels in common based on a Dice's coefficient ≥0.5.

show abstract

Section: Introductionmentioning

confidence: 99%

From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

López-López

Vázquez

Houenou

et al. 2020

Front. Neuroinform.

Self Cite

View full text Add to dashboard Cite

show abstract

“…To overcome some of the limitations, a key idea is to collapse the connectivity profiles using a set of Regions Of Interest: connectivity weights are summed up across each ROI. Some approaches use a priori anatomical information such as lobar or gyri cortex segmentation [4,5] or regions of interest from invasive tracing primate studies [6]. When there is a correspondence across subjects between these segmentations, it provides a direct way to perform the clustering at the level of the group of subjects [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Some approaches use a priori anatomical information such as lobar or gyri cortex segmentation [4,5] or regions of interest from invasive tracing primate studies [6]. When there is a correspondence across subjects between these segmentations, it provides a direct way to perform the clustering at the level of the group of subjects [4,5]. The robustness is largely increased because the clustering is focusing on profiles reproducible across subjects.…”

Section: Introductionmentioning

confidence: 99%

Inter-subject Connectivity-Based Parcellation of a Patch of Cerebral Cortex

Roca

Tucholka

Rivière

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

Self Cite

View full text Add to dashboard Cite

This paper presents a connectivity-based parcellation of the human post-central gyrus, at the level of the group of subjects. The dimension of the clustering problem is reduced using a set of cortical regions of interest determined at the inter-subject level using a surfacebased coordinate system, and representing the regions with a strong connection to the post-central gyrus. This process allows a clustering based on criteria which are more reproducible across subjects than in an intra-subject approach. We obtained parcels relatively stable in localisation across subjects as well as homogenous and well-separated to each other in terms of connectivity profiles. To address the parcellation at the inter-subject level provides a direct matching between parcels across subjects. In addition, this method allows the identification of subject-specific parcels. This property could be useful for the study of pathologies.

show abstract

“…The first one is based on a segmentation of the brain to collapse the connectivity profiles: all the tracts reaching the same segment are summed up. This segmentation can be anatomical, for instance based on lobes or gyri [8,9,10], but the same idea could be applied with fMRI-based activation maps. In the papers cited above, the segmentation used for collapsing was based on individual data.…”

Section: Introductionmentioning

confidence: 99%

Tractography-Based Parcellation of the Cortex Using a Spatially-Informed Dimension Reduction of the Connectivity Matrix

Roca

Rivière

Guevara

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

Self Cite

View full text Add to dashboard Cite

Abstract. Determining cortical functional areas is an important goal for neurosciences and clinical neurosurgery. This paper presents a method for connectivity-based parcellation of the entire human cortical surface, exploiting the idea that each cortex region has a specific connection profile. The connectivity matrix of the cortex is computed using analytical Q-ball-based tractography. The parcellation is achieved independently for each subject and applied to the subset of the cortical surface endowed with enough connections to estimate safely a connectivity profile, namely the top of the cortical gyri. The key point of the method lies in a twofold reduction of the connectivity matrix dimension. First, parcellation amounts to iterating the clustering of Voronoï patches of the cortical surface into parcels endowed with homogeneous profiles. The parcels without intersection with the patch boundaries are selected for the final parcellation. Before clustering a patch, the complete profiles are collapsed into short profiles indicating connectivity with a set of putative cortical areas. These areas are supposed to correspond to the catchment basins of the watershed of the density of connection to the patch computed on the cortical surface. The results obtained for several brains are compared visually using a coordinate system.

show abstract

Connectivity-based parcellation of the cortical surface using q-ball imaging

Cited by 3 publications

References 19 publications

From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

Inter-subject Connectivity-Based Parcellation of a Patch of Cerebral Cortex

Tractography-Based Parcellation of the Cortex Using a Spatially-Informed Dimension Reduction of the Connectivity Matrix

Contact Info

Product

Resources

About