Improving perception of brain structure using fiber clustering

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2011

Fiber clustering is a prerequisite step towards tract-based analysis of white mater integrity via diffusion tensor imaging (DTI) in various clinical neuroscience applications. Many methods reported in the literature used geometric or anatomic information for fiber clustering. This paper proposes a novel method that uses functional coherence as the criterion to guide the clustering of fibers derived from DTI tractography. Specifically, we represent the functional identity of a white matter fiber by two resting state fMRI (rsfMRI) time series extracted from the two gray matter voxels to which the fiber connects. Then, the functional coherence or similarity between two white matter fibers is defined as their rsfMRI time series' correlations, and the data-driven affinity propagation (AP) algorithm is used to cluster fibers into bundles. At current stage, we use the corpus callosum (CC) fibers that are the largest fiber bundle in the brain as an example. Experimental results show tha...

White matter fiber bundles of the human brain form a spatial pattern defined by the anatomical and functional architecture. Tractography applied to the tensor field in diffusion tensor imaging (DTI) results in sets of streamlines which can be associated with major fiber tracts. Comparison of fiber tract properties across subjects requires comparison at corresponding anatomical locations. Clinical analysis studying fiber tract disruption and connectivity requires analysis along tracts and within cross-sections, which is hard to accomplish by conventional region of interest and voxel-based analysis. We propose a new framework via tractography, fiber clustering and parametrization. The resulting fiber tract models are parametrized with arc length and carry local properties derived from diffusion tensors, e.g. fractional anisotropy FA and apparent diffusion coefficient ADC, local shape characteristics derived from the Frénet frame (curvature, torsion) and area and shape of cross-sections. Comparison of transversal tracts and projection tracts between adults and neonates are shown. This extended set of features might lead to an improved understanding of diffusion properties and its association to brain development and eventually changes due to pathology. ⋆

2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

We present a hybrid method that performs the complete parcellation of the cerebral cortex of an individual, based on the connectivity information of the white matter fibers from a whole-brain tractography dataset. The method consists of five steps, first intra-subject clustering is performed on the brain tractography. The fibers that make up each cluster are then intersected with the cortical mesh and then filtered to discard outliers. In addition, the method resolves the overlapping between the different intersection regions (sub-parcels) throughout the cortex efficiently. Finally, a post-processing is done to achieve more uniform sub-parcels. The output is the complete labeling of cortical mesh vertices, representing the different cortex sub-parcels, with strong connections to other sub-parcels. We evaluated our method with measures of brain connectivity such as functional segregation (clustering coefficient), functional integration (characteristic path length) and small-world. Results in five subjects from ARCHI database show a good individual cortical parcellation for each one, composed of about 200 sub-parcels per hemisphere and complying with these connectivity measures.

NeuroImage, 2011

Signal transmission between different brain regions requires connecting fiber tracts, the structural basis of the human connectome. In contrast to animal brains, where a multitude of tract tracing methods can be used, magnetic resonance (MR)-based diffusion imaging is presently the only promising approach to study fiber tracts between specific human brain regions. However, this procedure has various inherent restrictions caused by its relatively low spatial resolution. Here, we introduce 3D-polarized light imaging (3D-PLI) to map the three-dimensional course of fiber tracts in the human brain with a resolution at a submillimeter scale based on a voxel size of 100 μm isotropic or less. 3D-PLI demonstrates nerve fibers by utilizing their intrinsic birefringence of myelin sheaths surrounding axons. This optical method enables the demonstration of 3D fiber orientations in serial microtome sections of entire human brains. Examples for the feasibility of this novel approach are given here. 3D-PLI enables the study of brain regions of intense fiber crossing in unprecedented detail, and provides an independent evaluation of fiber tracts derived from diffusion imaging data.

Frontiers in Neuroinformatics, 2015

† These authors have contributed equally to this work.

IEEE Transactions on Visualization and Computer Graphics, 2008

We present a method for clustering diffusion tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical accuracy of the bundles. We also evaluated the method by applying an experimental cross-subject labeling method to the clustering results. Our approach is guided by assumptions about the proximity of fibers comprising discrete white-matter bundles, and proceeds as follows. We first employ a sampling and culling strategy for generating DTI integral curves and then constrain the curves so that they terminate in gray matter. This approach seems likely to retain anatomically plausible fibers. We then employ a clustering method based on a proximity measure calculated between every pair of curves. We interactively selected a proximity threshold to achieve visually optimal clustering in models from four DTI datasets. An expert rater then assigned a confidence rating about bundle presence and accuracy for each of 12 target fiber bundles of varying calibers and type (i.e., commissural, association, projection) in each dataset. The interactive clustering and evaluation information was incorporated to create a fiberbundle template. We then used the template to cluster and label the fiber bundles automatically in new datasets. According to expert evaluation, the automated proximity-based clustering and labeling algorithm consistently yields anatomically plausible fiber bundles, although fiber bundles with smaller calibers and those that are not highly directionally coherent are identified with lower confidence. This work has the potential to provide an automatic and robust way to find and study neural fiber bundles within DTI.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2005

A new framework is presented for clustering fiber tracts into anatomically known bundles. This work is motivated by medical applications in which variation analysis of known bundles of fiber tracts in the human brain is desired. To include the anatomical knowledge in the clustering, we invoke an atlas of fiber tracts, labeled by the number of bundles of interest. In this work, we construct such an atlas and use it to cluster all fiber tracts in the white matter. To build the atlas, we start with a set of labeled ROIs specified by an expert and extract the fiber tracts initiating from each ROI. Affine registration is used to project the extracted fiber tracts of each subject to the atlas, whereas their B-spline representation is used to efficiently compare them to the fiber tracts in the atlas and assign cluster labels. Expert visual inspection of the result confirms that the proposed method is very promising and efficient in clustering of the known bundles of fiber tracts.

2017

ABSTRACTThe brain’s white matter fiber tracts are impaired in a range of common and devastating conditions, from Alzheimer’s disease to brain trauma, and in developmental disorders such as autism and neurogenetic syndromes. Many studies now examine the connectivity and microstructure of the brain’s neural pathways, spurring the development of algorithms to extract and measure tracts and fiber bundles. Clustering white matter (WM) fibers, from whole-brain tractography, into anatomically meaningful bundles is still a challenging problem. Existing tract segmentation methods use atlases or regions of interest (ROI) or unsupervised spectral clustering. Even so, atlas-based segmentation does not always partition the brain into a set of recognizable fiber bundles. Deep learning techniques can be applied to automatically segment and cluster white matter fibers. Here we propose a robust approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which we...

Cerebral Cortex

To assess normal organization of frontostriatal brain wiring, we analyzed diffusion magnetic resonance imaging (dMRI) scans in 100 young adult healthy subjects (HSs). We identified fiber clusters intersecting the frontal cortex and caudate, a core component of associative striatum, and quantified their degree of deviation from a strictly topographic pattern. Using whole brain dMRI tractography and an automated tract parcellation clustering method, we extracted 17 white matter fiber clusters per hemisphere connecting the frontal cortex and caudate. In a novel approach to quantify the geometric relationship among clusters, we measured intercluster endpoint distances between corresponding cluster pairs in the frontal cortex and caudate. We show first, the overall frontal cortex wiring pattern of the caudate deviates from a strictly topographic organization due to significantly greater convergence in regionally specific clusters; second, these significantly convergent clusters originate...

Journal of Neuroscience Methods, 2019

Background: MR tractography from diffusion tensor imaging provides a non-invasive way to explore white matter pathways in the human brain. However, a challenge to extracting reliable anatomical information from these data is the use of reliable and effective clustering methodologies. In this paper, we implemented a new version of a robust unsupervised clustering method from MR tractography data using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. New method: Conventional DBSCAN clustering methods for MR tractography data use each fiber's start and end point as well as the distance between start and end points. Instead, in this study, we extracted and used a fiberdistance matrix generated for all fiber combinations from the tractography dataset in DBSCAN clustering. The two DBSCAN parameters-minimum point number and maximum radius of the neighborhood-were selected according to the value generated with the cluster stability index (CSI). Results: Performing the proposed CSI-optimized DBSCAN-based clustering method on MR tractography data of the superior longitudinal fasciculus generated 6 robust, non-overlapping, clusters that are neuroanatomically related. Comparison with existing methods: Conventional DBSCAN-based clustering methods have intrinsic error potential in the clustering results due to deviations in fiber shape and fiber location. The proposed method did not exhibit clustering error caused by deviation in fiber trajectory or fiber location. Conclusions: We implemented a new, robust DBSCAN-based fiber clustering method for MR tractography data. The CSI-optimized DBSCAN-based unsupervised clustering is applicable to investigation of the neuroconnectome and the fiber structure of the brain.