PASTA: Pointwise assessment of streamline tractography attributes

2005 ◽  
Vol 53 (6) ◽  
pp. 1462-1467 ◽  
Author(s):  
Derek K. Jones ◽  
Adam R. Travis ◽  
Greg Eden ◽  
Carlo Pierpaoli ◽  
Peter J. Basser
Keyword(s):  
Streamline Tractography

scholarly journals White matter disconnectivity fingerprints causally linked to dissociated forms of alexia

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sam Ng ◽  
Sylvie Moritz-Gasser ◽  
Anne-Laure Lemaitre ◽  
Hugues Duffau ◽  
Guillaume Herbet
Keyword(s):  
Word Reading ◽  
Population Based ◽  
Neural System ◽  
Word Form ◽  
Pure Alexia ◽  
Visual Word Form Area ◽  
Neurosurgical Patients ◽  
Streamline Tractography ◽  
Models Of Reading

AbstractFor over 150 years, the study of patients with acquired alexia has fueled research aimed at disentangling the neural system critical for reading. An unreached goal, however, relates to the determination of the fiber pathways that root the different visual and linguistic processes needed for accurate word reading. In a unique series of neurosurgical patients with a tumor close to the visual word form area, we combine direct electrostimulation and population-based streamline tractography to map the disconnectivity fingerprints characterizing dissociated forms of alexia. Comprehensive analyses of disconnectivity matrices establish similarities and dissimilarities in the disconnection patterns associated with pure, phonological and lexical-semantic alexia. While disconnections of the inferior longitudinal and posterior arcuate fasciculi are common to all alexia subtypes, disconnections of the long arcuate and vertical occipital fasciculi are specific to phonological and pure alexia, respectively. These findings provide a strong anatomical background for cognitive and neurocomputational models of reading.

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

scholarly journals Resolving crossings in the corticospinal tract by two-tensor streamline tractography: Method and clinical assessment using fMRI

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. T98-T106 ◽  
Author(s):  
Arish A. Qazi ◽  
Alireza Radmanesh ◽  
Lauren O'Donnell ◽  
Gordon Kindlmann ◽  
Sharon Peled ◽  
...  
Keyword(s):  
Clinical Assessment ◽  
Corticospinal Tract ◽  
Streamline Tractography

scholarly journals Quantification of Structural Brain Connectivity via a Conductance Model

2018 ◽  
Author(s):  
Aina Frau-Pascual ◽  
Morgan Fogarty ◽  
Bruce Fischl ◽  
Anastasia Yendiki ◽  
Iman Aganj ◽  
...  
Keyword(s):  
Brain Structure ◽  
Structural Information ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Normal Brain ◽  
Human Connectome Project ◽  
The Brain ◽  
Brain Connections ◽  
Streamline Tractography ◽  
Standard Techniques

AbstaractConnectomics has proved promising in quantifying and understanding the effects of development, aging and an array of diseases on the brain. In this work, we propose a new structural connectivity measure from diffusion MRI that allows us to incorporate direct brain connections, as well as indirect ones that would not be otherwise accounted for by standard techniques and that may be key for the better understanding of function from structure. From our experiments on the Human Connectome Project dataset, we find that our measure of structural connectivity better correlates with functional connectivity than streamline tractography does, meaning that it provides new structural information related to function. Through additional experiments on the ADNI-2 dataset, we demonstrate the ability of this new measure to better discriminate different stages of Alzheimer’s disease. Our findings suggest that this measure is useful in the study of the normal brain structure, and for quantifying the effects of disease on the brain structure.

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