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An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan.

ABSTRACT: This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.

SUBMITTER: Zhang F

PROVIDER: S-EPMC6080311 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan.

Zhang Fan F Wu Ye Y Norton Isaiah I Rigolo Laura L Rathi Yogesh Y Makris Nikos N O'Donnell Lauren J LJ

NeuroImage 20180618

This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A ...[more]

PMID: 29920375

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Project description:White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. While widely used fiber clustering approaches have shown good performance using classical unsupervised machine learning techniques, recent advances in deep learning reveal a promising direction toward fast and effective fiber clustering. In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This process learns a high-dimensional embedding feature representation for each fiber, regardless of the order of fiber points reconstructed during tractography. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus, DFC makes use of combined information about white matter fiber geometry and gray matter anatomy to improve the anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal naturally by rejecting fibers with low cluster assignment probability. We evaluate DFC on three independently acquired cohorts, including data from 220 individuals across genders, ages (young and elderly adults), and different health conditions (healthy control and multiple neuropsychiatric disorders). We compare DFC to several state-of-the-art white matter fiber clustering algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.

Project description: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.

Project description:White matter hyperintensities of presumed vascular origin are frequently observed on magnetic resonance imaging in normal aging. They are typically found in cerebral small vessel disease and suspected culprits in the etiology of complex age- and small vessel disease-related conditions, such as late-onset depression. White matter hyperintensities may interfere with surrounding white matter metabolic demands by disrupting fiber tract integrity. Meanwhile, risk factors for small vessel disease are thought to reduce tissue oxygenation, not only by reducing regional blood supply, but also by impairing capillary function. To address white matter oxygen supply-demand balance, we estimated voxel-wise capillary density as an index of resting white matter metabolism, and combined estimates of blood supply and capillary function to calculate white matter oxygen availability. We conducted a cross-sectional study with structural, perfusion- and diffusion-weighted magnetic resonance imaging in 21 patients with late-onset depression and 21 controls. We outlined white matter hyperintensities and used tractography to identify the tracts they intersect. Perfusion data comprised cerebral blood flow, blood volume, mean transit time and relative transit time heterogeneity-the latter a marker of capillary dysfunction. Based on these, white matter oxygenation was calculated as the steady state cerebral metabolic rate of oxygen under the assumption of normal tissue oxygen tension and vice versa. The number, volume and perfusion characteristics of white matter hyperintensities did not differ significantly between groups. Hemodynamic data showed white matter hyperintensities to have lower blood flow and blood volume, but higher relative transit time heterogeneity, than normal-appearing white matter, resulting in either reduced capillary metabolic rate of oxygen or oxygen tension. Intersected tracts showed significantly lower blood flow, blood volume and capillary metabolic rate of oxygen than normal-appearing white matter. Across groups, lower lesion oxygen tension was associated with higher lesion number and volume. Compared with normal-appearing white matter, tissue oxygenation is significantly reduced in white matter hyperintensities as well as the fiber tracts they intersect, independent of parallel late-onset depression. In white matter hyperintensities, reduced microvascular blood volume and concomitant capillary dysfunction indicate a severe oxygen supply-demand imbalance with hypoxic tissue injury. In intersected fiber tracts, parallel reductions in oxygenation and microvascular blood volume are consistent with adaptations to reduced metabolic demands. We speculate, that aging and vascular risk factors impair white matter hyperintensity perfusion and capillary function to create hypoxic tissue injury, which in turn affect the function and metabolic demands of the white matter tracts they disrupt.

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An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan.

Publications

An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan.

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