scholarly journals Simultaneous Manifold Learning and Clustering: Grouping White Matter Fiber Tracts Using a Volumetric White Matter Atlas

2008 ◽  
Author(s):  
Demian Wassermann ◽  
Rachid Deriche
Keyword(s):  
White Matter ◽  
Manifold Learning ◽  
Clustering Algorithm ◽  
A Priori ◽  
Anatomical Structures ◽  
Fiber Tracts ◽  
Anatomical Knowledge ◽  
Graph Theoretical Approach ◽  
White Matter Fiber Tracts ◽  
Streamline Tractography

We propose a new clustering algorithm. This algorithm performs clustering and manifold learning simultaneously by using a graph-theoretical approach to manifold learning. We apply this algorithm in order to cluster white matter fiber tracts obtained fromDiffusion TensorMRI (DT-MRI) through streamline tractography. Our algorithm is able perform clustering of these fiber tracts incorporating information about the shape of the fiber and a priori knowledge as the probability of the fiber belonging to known anatomical structures. This anatomical knowledge is incorporated as a volumetric white matter atlas, in this case LONI’s ICBM DTI-81

The Structural Language Network

2017 ◽  
Author(s):  
Angela D. Friederici ◽  
Noam Chomsky
Keyword(s):  
White Matter ◽  
Language Processing ◽  
Sentence Processing ◽  
Information Transfer ◽  
Right Hemisphere ◽  
Brain Regions ◽  
Neural Basis ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
The Right

An adequate description of the neural basis of language processing must consider the entire network both with respect to its structural white matter connections and the functional connectivities between the different brain regions as the information has to be sent between different language-related regions distributed across the temporal and frontal cortex. This chapter discusses the white matter fiber bundles that connect the language-relevant regions. The chapter is broken into three sections. In the first, we look at the white matter fiber tracts connecting the language-relevant regions in the frontal and temporal cortices; in the second, the ventral and dorsal pathways in the right hemisphere that connect temporal and frontal regions; and finally in the third, the two syntax-relevant and (at least) one semantic-relevant neuroanatomically-defined networks that sentence processing is based on. From this discussion, it becomes clear that online language processing requires information transfer via the long-range white matter fiber pathways that connect the language-relevant brain regions within each hemisphere and between hemispheres.

scholarly journals Motor cortex gliomas induces microstructural changes of large fiber tracts revealed by TBSS

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiangdong Wang ◽  
Chunyao Zhou ◽  
Lei Wang ◽  
Yinyan Wang ◽  
Tao Jiang
Keyword(s):  
White Matter ◽  
Motor Cortex ◽  
Diffusion Tensor ◽  
Early Stage ◽  
Mean Diffusivity ◽  
Cerebral White Matter ◽  
Control Group ◽  
Fiber Tracts ◽  
3.0 T ◽  
White Matter Fiber Tracts

Abstract Gliomas grow and invade along white matter fiber tracts. This study assessed the effects of motor cortex gliomas on the cerebral white matter fiber bundle skeleton. The motor cortex glioma group included 21 patients, and the control group comprised 14 healthy volunteers. Both groups underwent magnetic resonance imaging-based 3.0 T diffusion tensor imaging. We used tract-based spatial statistics to analyze the characteristics of white matter fiber bundles. The left and right motor cortex glioma groups were analyzed separately from the control group. Results were statistically corrected by the family-wise error rate. Compared with the controls, patients with left motor cortex gliomas exhibited significantly reduced fractional anisotropy and an increased radial diffusivity in the corpus callosum. The alterations in mean diffusivity (MD) and the axial diffusivity (AD) were widely distributed throughout the brain. Furthermore, atlas-based analysis showed elevated MD and AD in the contralateral superior fronto-occipital fasciculus. Motor cortex gliomas significantly affect white matter fiber microstructure proximal to the tumor. The range of affected white matter fibers may extend beyond the tumor-affected area. These changes are primarily related to early stage tumor invasion.

scholarly journals The impact of isolated lesions on white-matter fiber tracts in multiple sclerosis patients

2015 ◽  
Vol 8 ◽  
pp. 110-116 ◽  
Author(s):  
Amgad Droby ◽  
Vinzenz Fleischer ◽  
Marco Carnini ◽  
Hilga Zimmermann ◽  
Volker Siffrin ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
Multiple Sclerosis Patients ◽  
The Impact

Measuringin VivoMyelination of Human White Matter Fiber Tracts with Magnetization Transfer MR

NeuroImage ◽  
1999 ◽  
Vol 9 (4) ◽  
pp. 393-406 ◽  
Author(s):  
J. Rademacher ◽  
V. Engelbrecht ◽  
U. Bürgel ◽  
H.-J. Freund ◽  
K. Zilles
Keyword(s):  
White Matter ◽  
Magnetization Transfer ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts

scholarly journals Dynamic Responses of Selective Brain White Matter Fiber Tracts to Binge Alcohol and Recovery in the Rat

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0124885 ◽  
Author(s):  
Adolf Pfefferbaum ◽  
Natalie M. Zahr ◽  
Dirk Mayer ◽  
Torsten Rohlfing ◽  
Edith V. Sullivan
Keyword(s):  
White Matter ◽  
Dynamic Responses ◽  
Fiber Tracts ◽  
Brain White Matter ◽  
White Matter Fiber Tracts

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

2017 ◽  
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.

scholarly journals QUANTITATIVE EXAMINATION OF A NOVEL CLUSTERING METHOD USING MAGNETIC RESONANCE DIFFUSION TENSOR TRACTOGRAPHY

2008 ◽  
Vol 31 (4) ◽  
pp. 24
Author(s):  
Aristotle N Voineskos ◽  
L J O’Donnell ◽  
N J Lobaugh ◽  
D Markant ◽  
M Niethammer ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
A Priori ◽  
Region Of Interest ◽  
Clustering Method ◽  
Fiber Tracts ◽  
Clustering Approach ◽  
White Matter Fiber Tracts ◽  
Highly Correlated ◽  
High Level

Introduction: MR diffusion tensor imaging (DTI) is the most powerful and currentlythe only way to visualize the organization of white matter fiber tracts in vivo. As this is a relatively newimaging technique, new tools are developed for quantifying fiber tracts, andrequire evaluation. We examined scalar indices of the diffusion tensor with two different tractography methods. We compared a novel clustering approach with a multiple region of interest (MROI) approach in a healthy and disease (schizophrenia) population. Methods: DTI images were acquired in 12 participants (n=6 patients withschizophrenia: 58 ± 12 years; n=6 controls: 57 ± 21 years) on a 1.5 Tesla GE system with diffusion gradients applied in 23 non-collinear directions, repeated three times. Tractography andfiber tract creation was performed using 3D Slicer software. Interraterreliability of the clustering approach and its similarity to the MROI methodwere evaluated. Results: The clustering approach was reliable both quantitatively and spatially (k > 0.8 for all tracts). There was high spatial(voxel-based) agreement between the clustering and MROI methods. Fractionalan isotropy and trace values were highly correlated between the clustering and MROI methods (p < 0.001 for all tracts). Discussion: Our clustering method has excellent interrater reliability and thereis a high level of agreement between our clustering method and the MROI method, both quantitatively and spatially. The clustering method is less susceptible touser bias. Moreover, not limited by a priori predictions, our clustering method may be a more robust and efficient way to identify and measure fiber tracts of interest. (colour figure available in PDF version)

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