scholarly journals Diffusion time dependence of the apparent diffusion tensor in healthy human brain and white matter disease

2001 ◽  
Vol 45 (6) ◽  
pp. 1126-1129 ◽  
Author(s):  
Chris A. Clark ◽  
Maj Hedehus ◽  
Michael E. Moseley
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Time Dependence ◽  
Diffusion Tensor ◽  
Diffusion Time ◽  
White Matter Disease ◽  
Healthy Human ◽  
Apparent Diffusion

Hierarchical Density-Based Clustering of White Matter Tracts in the Human Brain

2010 ◽  
Vol 1 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Junming Shao ◽  
Klaus Hahn ◽  
Qinli Yang ◽  
Afra Wohlschläeger ◽  
Christian Boehm ◽  
...  
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Similarity Measure ◽  
Multiple Scales ◽  
Diffusion Tensor ◽  
Clustering Algorithms ◽  
Vast Number ◽  
Lower Bounding ◽  
Density Based Clustering ◽  
Neurosurgical Planning

Diffusion tensor magnetic resonance imaging (DTI) provides a promising way of estimating the neural fiber pathways in the human brain non-invasively via white matter tractography. However, it is difficult to analyze the vast number of resulting tracts quantitatively. Automatic tract clustering would be useful for the neuroscience community, as it can contribute to accurate neurosurgical planning, tract-based analysis, or white matter atlas creation. In this paper, the authors propose a new framework for automatic white matter tract clustering using a hierarchical density-based approach. A novel fiber similarity measure based on dynamic time warping allows for an effective and efficient evaluation of fiber similarity. A lower bounding technique is used to further speed up the computation. Then the algorithm OPTICS is applied, to sort the data into a reachability plot, visualizing the clustering structure of the data. Interactive and automatic clustering algorithms are finally introduced to obtain the clusters. Extensive experiments on synthetic data and real data demonstrate the effectiveness and efficiency of our fiber similarity measure and show that the hierarchical density-based clustering method can group these tracts into meaningful bundles on multiple scales as well as eliminating noisy fibers.

Study of the effect of CSF suppression on white matter diffusion anisotropy mapping of healthy human brain

2002 ◽  
Vol 48 (2) ◽  
pp. 394-398 ◽  
Author(s):  
Nikos G. Papadakis ◽  
Kay M. Martin ◽  
Mohammed H. Mustafa ◽  
Iain D. Wilkinson ◽  
Paul D. Griffiths ◽  
...  
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Diffusion Anisotropy ◽  
Healthy Human

scholarly journals Functional Diffusion Tensor Imaging: Measuring Task-Related Fractional Anisotropy Changes in the Human Brain along White Matter Tracts

PLoS ONE ◽  
2008 ◽  
Vol 3 (11) ◽  
pp. e3631 ◽  
Author(s):  
René C. W. Mandl ◽  
Hugo G. Schnack ◽  
Marcel P. Zwiers ◽  
Arjen van der Schaaf ◽  
René S. Kahn ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Human Brain ◽  
Fractional Anisotropy ◽  
Diffusion Tensor ◽  
White Matter Tracts

scholarly journals Euclidean distance as a measure to distinguish ventral and dorsal white matter connectivity in the human brain

2016 ◽  
Author(s):  
Philipp Kellmeyer ◽  
Magnus-Sebastian Vry
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Cognitive Processing ◽  
Euclidean Distance ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Large Body ◽  
Coordinate Space ◽  
Fiber Tractography

AbstractFiber tractography based on diffusion tensor imaging (DTI) has become an important research tool for investigating the anatomical connectivity between brain regions in vivo. Combining DTI with functional magnetic resonance imaging (fMRI) allows for the mapping of structural and functional architecture of large-scale networks for cognitive processing. This line of research has shown that ventral and dorsal fiber pathways subserve different aspects of bottom-up- and top-down processing in the human brain.Here, we investigate the feasibility and applicability of Euclidean distance as a simple geometric measure to differentiate ventral and dorsal long-range white matter fiber pathways tween parietal and inferior frontal cortical regions, employing a body of studies that used probabilistic tractography.We show that ventral pathways between parietal and inferior frontal cortex have on average a significantly longer Euclidean distance in 3D-coordinate space than dorsal pathways. We argue that Euclidean distance could provide a simple measure and potentially a boundary value to assess patterns of connectivity in fMRI studies. This would allow for a much broader assessment of general patterns of ventral and dorsal large-scale fiber connectivity for different cognitive operations in the large body of existing fMRI studies lacking additional DTI data.

scholarly journals Do Candidate Genes Affect the Brain’s White Matter Microstructure? Large-Scale Evaluation of 6,165 Diffusion MRI Scans

2017 ◽  
Author(s):  
Neda Jahanshad ◽  
Habib Ganjgahi ◽  
Janita Bralten ◽  
Anouk den Braber ◽  
Joshua Faskowitz ◽  
...  
Keyword(s):  
White Matter ◽  
Multiple Testing ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Candidate Snps ◽  
Nucleotide Polymorphisms ◽  
Multiple Testing Correction ◽  
Healthy Human ◽  
Multiple Granularity ◽  
Single Cohort

Abstract:Susceptibility genes for psychiatric and neurological disorders - including APOE, BDNF, CLU,CNTNAP2, COMT, DISC1, DTNBP1, ErbB4, HFE, NRG1, NTKR3, and ZNF804A - have been reported to affect white matter (WM) microstructure in the healthy human brain, as assessed through diffusion tensor imaging (DTI). However, effects of single nucleotide polymorphisms (SNPs) in these genes explain only a small fraction of the overall variance and are challenging to detect reliably in single cohort studies. To date, few studies have evaluated the reproducibility of these results. As part of the ENIGMA-DTI consortium, we pooled regional fractional anisotropy (FA) measures for 6,165 subjects (CEU ancestry N=4,458) from 11 cohorts worldwide to evaluate effects of 15 candidate SNPs by examining their associations with WM microstructure. Additive association tests were conducted for each SNP. We used several meta-analytic and mega-analytic designs, and we evaluated regions of interest at multiple granularity levels. The ENIGMA-DTI protocol was able to detect single-cohort findings as originally reported. Even so, in this very large sample, no significant associations remained after multiple-testing correction for the 15 SNPs investigated. Suggestive associations (1.3×10-4 < p < 0.05, uncorrected) were found for BDNF, COMT, and ZNF804A in specific tracts. Meta-and mega-analyses revealed similar findings. Regardless of the approach, the previously reported candidate SNPs did not show significant associations with WM microstructure in this largest genetic study of DTI to date; the negative findings are likely not due to insufficient power. Genome-wide studies, involving large-scale meta-analyses, may help to discover SNPs robustly influencing WM microstructure.

A Novel Method for Quantifying White Matter Disease Burden Using Diffusion Tensor Imaging (P03.094)

Neurology ◽  
2012 ◽  
Vol 78 (Meeting Abstracts 1) ◽  
pp. P03.094-P03.094
Author(s):  
K. Womack ◽  
J. Strain ◽  
L. Hynan ◽  
M. Devous ◽  
J. Hart ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Disease Burden ◽  
Diffusion Tensor ◽  
White Matter Disease ◽  
Novel Method
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