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

Roca, Pauline; Rivière, Denis; Guevara, Pamela; Poupon, Cyril; Mangin, Jean‐François

doi:10.1007/978-3-642-04268-3_115

Cited by 22 publications

(26 citation statements)

References 22 publications

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“…This can have a strong impact on the obtained parcellation. This issue is often accounted for by thresholding the shortest fibres (Roca et al, 2009). However, the value of this threshold is typically decided heuristically and it is very difficult to estimate what threshold value yields an appropriate representation of the connectivity between vertices of the mesh.…”

Section: Methodsmentioning

confidence: 99%

Group-wise parcellation of the cortex through multi-scale spectral clustering

et al. 2016

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The delineation of functionally and structurally distinct regions as well as their connectivity can provide key knowledge towards understanding the brain’s behaviour and function. Cytoarchitecture has long been the gold standard for such parcellation tasks, but has poor scalability and cannot be mapped in vivo. Functional and diffusion magnetic resonance imaging allow in vivo mapping of brain’s connectivity and the parcellation of the brain based on local connectivity information. Several methods have been developed for single subject connectivity driven parcellation, but very few have tackled the task of group-wise parcellation, which is essential for uncovering group specific behaviours. In this paper, we propose a group-wise connectivity-driven parcellation method based on spectral clustering that captures local connectivity information at multiple scales and directly enforces correspondences between subjects. The method is applied to diffusion Magnetic Resonance Imaging driven parcellation on two independent groups of 50 subjects from the Human Connectome Project. Promising quantitative and qualitative results in terms of information loss, modality comparisons, group consistency and inter-group similarities demonstrate the potential of the method.

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Section: Methodsmentioning

confidence: 99%

Group-wise parcellation of the cortex through multi-scale spectral clustering

et al. 2016

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“…For that, the individual connectivity matrix of the gyrus was calculated using the tractography results and surface data [15]. As shown in Figure 1a., for each subject, we obtained a matrix of size (m, n) where m and n are the number of vertices of the patch (2000 on average) and of the whole cortical surface (around 80000) respectively.…”

Section: Individual Connectivity Profilesmentioning

confidence: 99%

Validation of consistent inter-subject connectivity-based parcellation

Lefranc¹,

Roca²,

Perrot³

et al. 2014

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

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Splitting the cortical surface into regions with homogeneous dMRI-based connectivity profiles is a promising but challenging topic. This paper extends the inter-subject connectivity-based cortex parcellation framework proposed by Roca [1]. In a first step, we implement the state-of-the-art algorithm with tuned parameters and, then propose a refined algorithm validated on the large high quality ARCHI database. This algorithm consists in clustering and subdividing each gyrus, in a reasonable time. Cross-validation shows that the resulting patterns are reproducible across groups. The stability is illustrated for the post-central gyrus of three different groups of subjects. Finally, the method has successfully been made robust to initial conditions: tracking type, cortical mesh characteristics and boundaries of gyri.

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“…In contrast, with respect to functional connectivity, several research groups have used clustering algorthms to generate fMR-based networks (e.g., Beckmann et al 2005; Zang et al 2004). Although there have been attempts to generate connectivity-based atlases based on anatomical connectivity (e.g., Roca et al 2009, 2010), these attempts have been limited to a small subset of brain voxels or small numbers of subjects, due to the computational burden of clustering these data. For these reasons, this data-driven atlas-generation approach has been rarely used despite its great potential.…”

Section: Introductionmentioning

confidence: 99%

Connectivity-Based Brain Parcellation

et al. 2015

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Defining brain structures of interest is an important preliminary step in brain-connectivity analysis. Researchers interested in connectivity patterns among brain structures typically employ manually delineated volumes of interest, or regions in a readily available atlas, to limit the scope of connectivity analysis to relevant regions. However, most structural brain atlases, and manually delineated volumes of interest, do not take voxel-wise connectivity patterns into consideration, and therefore may not be ideal for anatomic connectivity analysis. We herein propose a method to parcellate the brain into regions of interest based on connectivity. We formulate connectivity-based parcellation as a graph-cut problem, which we solve approximately using a novel multi-class Hopfield network algorithm. We demonstrate the application of this approach using diffusion tensor imaging data from an ongoing study of schizophrenia. Compared to a standard anatomic atlas, the connectivity-based atlas supports better classification performance when distinguishing schizophrenic from normal subjects. Comparing connectivity patterns averaged across the normal and schizophrenic subjects, we note significant systematic differences between the two atlases.

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

Cited by 22 publications

References 22 publications

Group-wise parcellation of the cortex through multi-scale spectral clustering

Group-wise parcellation of the cortex through multi-scale spectral clustering

Validation of consistent inter-subject connectivity-based parcellation

Connectivity-Based Brain Parcellation

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