Framework for shape analysis of white matter fiber bundles

Abstract Background Autism spectrum disorder (ASD) is prevalent in tuberous sclerosis complex (TSC), occurring in approximately 50% of patients, and is hypothesized to be caused by disruption of neural circuits early in life. Tubers, or benign hamartomas distributed stochastically throughout the brain, are the most conspicuous of TSC neuropathology, but have not been consistently associated with ASD. Widespread neuropathology of the white matter, including deficits in myelination, neuronal migration, and axon formation, exist and may underlie ASD in TSC. We sought to identify the neural circuits associated with ASD in TSC by identifying white matter microstructural deficits in a prospectively recruited, longitudinally studied cohort of TSC infants. Methods TSC infants were recruited within their first year of life and longitudinally imaged at time of recruitment, 12 months of age, and at 24 months of age. Autism was diagnosed at 24 months of age with the ADOS-2. There were 108 subjects (62 TSC-ASD, 55% male; 46 TSC+ASD, 52% male) with at least one MRI and a 24-month ADOS, for a total of 187 MRI scans analyzed (109 TSC-ASD; 78 TSC+ASD). Diffusion tensor imaging properties of multiple white matter fiber bundles were sampled using a region of interest approach. Linear mixed effects modeling was performed to test the hypothesis that infants who develop ASD exhibit poor white matter microstructural integrity over the first 2 years of life compared to those who do not develop ASD. Results Subjects with TSC and ASD exhibited reduced fractional anisotropy in 9 of 17 white matter regions, sampled from the arcuate fasciculus, cingulum, corpus callosum, anterior limbs of the internal capsule, and the sagittal stratum, over the first 2 years of life compared to TSC subjects without ASD. Mean diffusivity trajectories did not differ between groups. Conclusions Underconnectivity across multiple white matter fiber bundles develops over the first 2 years of life in subjects with TSC and ASD. Future studies examining brain-behavior relationships are needed to determine how variation in the brain structure is associated with ASD symptoms.

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Test-Retest Reliability of Diffusion Measures Extracted Along White Matter Language Fiber Bundles Using HARDI-Based Tractography

Frontiers in Neuroscience ◽

10.3389/fnins.2018.01055 ◽

2019 ◽

Vol 12 ◽

Cited By ~ 1

Author(s):

Mariem Boukadi ◽

Karine Marcotte ◽

Christophe Bedetti ◽

Jean-Christophe Houde ◽

Alex Desautels ◽

...

Keyword(s):

White Matter ◽

Fiber Bundles ◽

Retest Reliability ◽

Test Retest Reliability

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Fibernet 2.0: An Automatic Neural Network Based Tool for Clustering White Matter Fibers in the Brain

10.1101/210781 ◽

2017 ◽

Cited By ~ 1

Author(s):

Vikash Gupta ◽

Sophia I. Thomopoulos ◽

Conor K. Corbin ◽

Faisal Rashid ◽

Paul M. Thompson

Keyword(s):

White Matter ◽

Spectral Clustering ◽

Developmental Disorders ◽

Fiber Bundles ◽

Neural Pathways ◽

Fiber Tracts ◽

Learning Techniques ◽

Segmentation Methods ◽

White Matter Fiber Tracts ◽

The Brain

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 then exploit to cluster WM fibers into bundles. In a range of tests across diverse fiber bundles, we illustrate the accuracy of our method, and its ability to suppress false positive fibers.

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FiberNET: An ensemble deep learning framework for clustering white matter fibers

10.1101/141036 ◽

2017 ◽

Author(s):

Vikash Gupta ◽

Sophia I. Thomopoulos ◽

Faisal M. Rashid ◽

Paul M. Thompson

Keyword(s):

Deep Learning ◽

White Matter ◽

Structural Integrity ◽

Region Of Interest ◽

Fiber Bundles ◽

Anatomical Connectivity ◽

Learning Framework ◽

Fiber Clustering ◽

Learning Techniques ◽

The Brain

AbstractWhite matter tracts are commonly analyzed in studies of micro-structural integrity and anatomical connectivity in the brain. Over the last decade, it has been an open problem as to how best to cluster white matter fibers, extracted from whole-brain tractography, into anatomically meaningful groups. Some existing techniques use region of interest (ROI) based clustering, atlas-based labeling, or unsupervised spectral clustering. ROI-based clustering is popular for analyzing anatomical connectivity among a set of ROIs, but it does not always partition the brain into recognizable fiber bundles. Here we propose an approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which are then exploited to cluster white matter fibers. To achieve such clustering, we first need to re-parameterize the fibers in an intrinsic space. The clustering is performed in induced parameterized coordinates. To our knowledge, this is one of the first approaches for fiber clustering using deep learning techniques. The results show strong accuracy - on a par with or better than other state-of-the-art methods.

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TU-CD-BRB-05: Radiation Damage Signature of White Matter Fiber Bundles Using Diffusion Tensor Imaging (DTI)

Medical Physics ◽

10.1118/1.4925590 ◽

2015 ◽

Vol 42 (6Part32) ◽

pp. 3603-3603

Author(s):

T Zhu ◽

C Chapman ◽

C Tsien ◽

T Lawrence ◽

Y Cao

Keyword(s):

Diffusion Tensor Imaging ◽

White Matter ◽

Radiation Damage ◽

Diffusion Tensor ◽

Fiber Bundles ◽

Diffusion Tensor Imaging Dti

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Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging Segmentation

International Journal of Biomedical Imaging ◽

10.1155/2008/526906 ◽

2008 ◽

Vol 2008 ◽

pp. 1-12 ◽

Cited By ~ 20

Author(s):

Demian Wassermann ◽

Maxime Descoteaux ◽

Rachid Deriche

Keyword(s):

White Matter ◽

Diffusion Tensor ◽

Diffusion Imaging ◽

Orientation Distribution ◽

Fiber Bundles ◽

High Angular Resolution ◽

Complex Data ◽

Diffusion Maps ◽

Clustering Method ◽

Number Of Clusters

White matter fiber clustering aims to get insight about anatomical structures in order to generate atlases, perform clear visualizations, and compute statistics across subjects, all important and current neuroimaging problems. In this work, we present a diffusion maps clustering method applied to diffusion MRI in order to segment complex white matter fiber bundles. It is well known that diffusion tensor imaging (DTI) is restricted in complex fiber regions with crossings and this is why recent high-angular resolution diffusion imaging (HARDI) such as Q-Ball imaging (QBI) has been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maxima agreeing with the underlying fiber populations. In this paper, we use a spherical harmonic ODF representation as input to the diffusion maps clustering method. We first show the advantage of using diffusion maps clustering over classical methods such as N-Cuts and Laplacian eigenmaps. In particular, our ODF diffusion maps requires a smaller number of hypothesis from the input data, reduces the number of artifacts in the segmentation, and automatically exhibits the number of clusters segmenting the Q-Ball image by using an adaptive scale-space parameter. We also show that our ODF diffusion maps clustering can reproduce published results using the diffusion tensor (DT) clustering with N-Cuts on simple synthetic images without crossings. On more complex data with crossings, we show that our ODF-based method succeeds to separate fiber bundles and crossing regions whereas the DT-based methods generate artifacts and exhibit wrong number of clusters. Finally, we show results on a real-brain dataset where we segment well-known fiber bundles.

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