Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

Kumar, Kuldeep; Desrosiers, Christian; Siddiqi, Kaleem; Colliot, Olivier; Toews, Matthew

doi:10.1016/j.neuroimage.2017.06.083

Cited by 41 publications

(35 citation statements)

References 81 publications

(107 reference statements)

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“…There are two popular strategies for automated white matter parcellation (O'Donnell, Golby, & Westin, ): (a) fiber clustering that groups white matter fibers according to their geometric trajectories, aiming to reconstruct tracts corresponding to the white matter anatomy (Ding, Gore, & Anderson, ; Garyfallidis et al, ; Garyfallidis, Brett, Correia, Williams, & Nimmo‐Smith, ; Guevara et al, ; Jin et al, ; Kumar, Desrosiers, Siddiqi, Colliot, & Toews, ; O'Donnell & Westin, ; Prasad et al, ; Siless, Chang, Fischl, & Yendiki, ; Visser, Nijhuis, Buitelaar, & Zwiers, ; Wassermann, Bloy, Kanterakis, Verma, & Deriche, ; Zhang, Wu, Norton, ) and (b) cortical‐parcellation‐based that parcellates tractography according to a cortical parcellation, focusing on the structural connectivity among different brain regions of interest (ROIs) (Bassett & Bullmore, ; Bastiani, Shah, Goebel, & Roebroeck, ; Bullmore & Sporns, ; Gong et al, ; Ingalhalikar et al, ; Sporns et al, ; Wakana et al, ; Wassermann et al, ; Yeh, Badre, & Verstynen, ; Zalesky et al, ; Zhang et al, ).…”

Section: Introductionmentioning

confidence: 99%

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Zhang

Norton

et al. 2019

Human Brain Mapping

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There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical‐parcellation‐based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test–retest reproducibility of automated white matter parcellations using cortical‐parcellation‐based strategies, there are no existing studies of test–retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test–retest reproducibility. The assessment is performed on three test–retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5–82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical‐parcellation‐based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test–retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical‐parcellation‐based method.

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Section: Introductionmentioning

confidence: 99%

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Zhang

Norton

et al. 2019

Human Brain Mapping

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“…M. Smith et al, 2015). Moreover, SC (Kumar, Desrosiers, Siddiqi, Colliot, & Toews, 2017;Munsell, 2017;Yeh et al, 2016) as well as FC (E. Amico, Goñi, J., 2017;Finn et al, 2015) can be used to identify individual connectome fingerprints. Nonetheless, the extent of this individual variability has been called into question (Marrelec, Messe, Giron, & Rudrauf, 2016;Waller et al, 2017), particularly for smaller sample sizes (Waller et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Subject-specificity of the correlation between large-scale structural and functional connectivity

Zimmermann

Griffiths

Schirner

et al. 2018

Preprint

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Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal co-activations, are known to be tightly linked.However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individual's SC and FC is not subject-specific for five of the datasets. Subject-specificity of SC-FC fit is achieved only for one of the six datasets, the multi-modal Glasser HCP parcellated dataset. We show that subjectspecificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in the SC compared to the larger variability in FC.

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“…The present study extends our preliminary work in [28,47,48,49] by providing an in-depth analysis that compares different sparsity priors and evaluates the impact of various parameters. As algorithmic contributions, we present two extensions of the model in [48], based on group sparsity and manifold regularization, that provide more meaningful bundles and can incorporate information on streamline geometry, such as the proximity of streamline endpoints, to constrain the clustering process.…”

Section: Sparse Coding For Neuroimagingmentioning

confidence: 68%

“…b) shows the mean of average SI obtained for the 10 unrelated subjects, using a varying number m of clusters and 3 runs for each m value. This plot was generated by sampling 5000 streamlines uniformly over the full tractography ([13,49]) and computing their pairwise MCP distance. We observe that clustering quality decreases with higher values of m, and that this quality varies across subjects.…”

mentioning

confidence: 99%

White matter fiber analysis using kernel dictionary learning and sparsity priors

Kumar

Siddiqi

Desrosiers

2019

Pattern Recognition

Self Cite

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Diffusion magnetic resonance imaging, a non-invasive tool to infer white matter fiber connections, produces a large number of streamlines containing a wealth of information on structural connectivity. The size of these tractography outputs makes further analyses complex, creating a need for methods to group streamlines into meaningful bundles. In this work, we address this by proposing a set of kernel dictionary learning and sparsity priors based methods. Proposed frameworks include L 0 norm, group sparsity, as well as manifold regularization prior. The proposed methods allow streamlines to be assigned to more than one bundle, making it more robust to overlapping bundles and inter-subject variations. We evaluate the performance of our method on a labeled set and data from Human Connectome Project. Results highlight the ability of our method to group streamlines into plausible bundles and illustrate the impact of sparsity priors on the performance of the proposed methods.

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Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

Cited by 41 publications

References 81 publications

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Subject-specificity of the correlation between large-scale structural and functional connectivity

White matter fiber analysis using kernel dictionary learning and sparsity priors

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