Group-Wise Consistent Fiber Clustering Based on Multimodal Connectional and Functional Profiles

Abstract

Abstract. Fiber clustering is an essential step towards brain connectivity modeling and tract-based analysis of white matter integrity via diffusion tensor imaging (DTI) in many clinical neuroscience applications. A variety of methods have been developed to cluster fibers based on various types of features such as geometry, anatomy, connection, or function. However, identification of groupwise consistent fiber bundles that are harmonious across multi-modalities is rarely explored yet. This paper proposes a novel hybrid two-stage approach that incorporates connectional and functional features, and identifies group-wise consistent fiber bundles across subjects. In the first stage, based on our recently developed 358 dense and consistent cortical landmarks, we identified consistent backbone bundles with representative fibers. In the second stage, other remaining fibers are then classified into the existing backbone bundles using their correlations of resting state fMRI signals at the two ends of fibers. Our experimental results show that the proposed methods can achieve group-wise consistent fiber bundles with similar shapes and anatomic profiles, as well as strong functional coherences.

Fiber clustering versus the parcellation-based connectome

“…A multimodality approach to parcellation and clustering was proposed that used a novel fiber bundle model based on tangent vectors in order to identify corresponding cortical landmarks across subjects [92]. These landmarks were used to do an initial clustering of fibers [25]. The same group also proposed to cluster fibers based on the correlation of their resting state fMRI data (a measure of functional connectivity) epge2012group,ge2012resting.…”

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

“…While multiple studies have assessed test–retest reproducibility of white matter parcellations using cortical‐parcellation‐based strategies, for example, on the brain connectome network (Besson, Lopes, Leclerc, Derambure, & Tyvaert, ; Bonilha et al, ; Buchanan, Pernet, Gorgolewski, Storkey, & Bastin, ; Dennis et al, ; Duda, Cook, & Gee, ; Schumacher et al, ; Smith et al, ; Vaessen et al, ; Zhao et al, ; Zhang, Descoteaux, et al, ) and on anatomical fiber tracts (Besseling et al, ; Cousineau et al, ; Heiervang, Behrens, Mackay, Robson, & Johansen‐Berg, ; Kristo et al, ; Lin et al, ; Papinutto et al, ; Tensaouti, Lahlou, Clarisse, Lotterie, & Berry, ; Wang et al, ; Yendiki et al, ), there are no existing studies of fiber clustering, to our knowledge. Studies have suggested that fiber clustering approaches have advantages in parcellating the white matter in a highly consistent way, aiming to reconstruct fiber parcels/tracts corresponding to the white matter anatomy (Ge et al, ; Sydnor et al, ; Zhang et al, ; Zhang, Wu, Norton, et al, ; Ziyan, Sabuncu, Eric, Grimson, & Westin, ), while cortical‐parcellation‐based methods could be less consistent considering factors such as the variability of intersubject cortical anatomy, the dependence on registration between dMRI and structural images, and the presence of false positive/negative connections (Amunts et al, ; Fischl, Sereno, Tootell, & Dale, ; Maier‐Hein et al, ; Sinke et al, ; Zhang, Descoteaux, et al, ). However, to our knowledge, test–retest reproducibility of the cortical‐parcellation‐based and fiber clustering white matter parcellation strategies has not yet been quantitatively compared.…”

Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder

“…Compared to CPB, FC relies on a different WM connectivity modeling assumption, aiming to group neighboring fibers with similar trajectories into clusters, which reconstruct fiber tracts according to the WM anatomy (Guevara et al, 2012; Maddah et al, 2008; O’Donnell and Westin, 2007). A variety of methods have been developed for unsupervised clustering of whole brain tractography in individual subjects based on various types of features such as geometry, anatomy, connection, or function (Garyfallidis et al, 2012; Ge et al, 2012, 2013; Guevara et al, 2011; Wassermann et al, 2010). Our work in groupwise fiber clustering (O’Donnell et al, 2012; O’Donnell and Westin, 2007) has demonstrated that white matter regions can be automatically clustered, correspond across subjects, and be augmented with anatomical annotations.…”

“…Unlike the connections defined by a CPB method that are easily interpreted because their cortical terminations are known, in the FC approach this interpretation requires additional expert analyses to identify anatomically meaningful tracts by manually assigning an anatomical annotation to each fiber cluster (Guevara et al, 2012; O’Donnell and Westin, 2007). The combination of the two methods, representing a hybrid strategy, has been suggested to have advantages over their individual usages (Xia et al, 2005; Li et al, 2010; Ros et al, 2013; Wassermann et al, 2016; Ge et al, 2012; Siless et al, 2018; Tunç et al, 2013; Wang et al, 2013a; Guevara et al, 2017; Román et al, 2017).…”