1,139 research outputs found
BundleSeg: A versatile, reliable and reproducible approach to white matter bundle segmentation
This work presents BundleSeg, a reliable, reproducible, and fast method for
extracting white matter pathways. The proposed method combines an iterative
registration procedure with a recently developed precise streamline search
algorithm that enables efficient segmentation of streamlines without the need
for tractogram clustering or simplifying assumptions. We show that BundleSeg
achieves improved repeatability and reproducibility than state-of-the-art
segmentation methods, with significant speed improvements. The enhanced
precision and reduced variability in extracting white matter connections offer
a valuable tool for neuroinformatic studies, increasing the sensitivity and
specificity of tractography-based studies of white matter pathways
The non-Abelian dual Meissner effect as color-alignment in SU(2) lattice gauge theory
A new gauge (m-gauge) condition is proposed by means of a generalization of
the Maximal Abelian gauge (MAG). The new gauge admits a space time dependent
embedding of the residual U(1) into the SU(2) gauge group. This embedding is
characterized by a color vector . It turns out that this vector
only depends of gauge invariant parts of the link configurations. Our numerical
results show color ferromagnetic correlations of the field in
space-time. The correlation length scales towards the continuum limit. For
comparison with the MAG, we introduce a class of gauges which smoothly
interpolates between the MAG and the m-gauge. For a wide range of the gauge
parameter, the vacuum decomposes into regions of aligned vectors . The
''neutral particle problem'' of MAG is addressed in the context of the new
gauge class.Comment: 15 pages, 6 figures, LaTeX using eps
Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow
When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods
Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites
Connectivity matrices derived from diffusion MRI (dMRI) provide an
interpretable and generalizable way of understanding the human brain
connectome. However, dMRI suffers from inter-site and between-scanner
variation, which impedes analysis across datasets to improve robustness and
reproducibility of results. To evaluate different harmonization approaches on
connectivity matrices, we compared graph measures derived from these matrices
before and after applying three harmonization techniques: mean shift, ComBat,
and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects
from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the
Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we
plotted the graph measures and used coefficient of variation (CoV) and the
Mann-Whitney U test to evaluate different methods' effectiveness in removing
site effects on the matrices and the derived graph measures. ComBat effectively
eliminated site effects for global efficiency and modularity and outperformed
the other two methods. However, all methods exhibited poor performance when
harmonizing average betweenness centrality. Second, we tested whether our
harmonization methods preserved correlations between age and graph measures.
All methods except for CycleGAN in one direction improved correlations between
age and global efficiency and between age and modularity from insignificant to
significant with p-values less than 0.05.Comment: 11 pages, 5 figures, to be published in SPIE Medical Imaging 2024:
Image Processin
Predicting Age from White Matter Diffusivity with Residual Learning
Imaging findings inconsistent with those expected at specific chronological
age ranges may serve as early indicators of neurological disorders and
increased mortality risk. Estimation of chronological age, and deviations from
expected results, from structural MRI data has become an important task for
developing biomarkers that are sensitive to such deviations. Complementary to
structural analysis, diffusion tensor imaging (DTI) has proven effective in
identifying age-related microstructural changes within the brain white matter,
thereby presenting itself as a promising additional modality for brain age
prediction. Although early studies have sought to harness DTI's advantages for
age estimation, there is no evidence that the success of this prediction is
owed to the unique microstructural and diffusivity features that DTI provides,
rather than the macrostructural features that are also available in DTI data.
Therefore, we seek to develop white-matter-specific age estimation to capture
deviations from normal white matter aging. Specifically, we deliberately
disregard the macrostructural information when predicting age from DTI scalar
images, using two distinct methods. The first method relies on extracting only
microstructural features from regions of interest. The second applies 3D
residual neural networks (ResNets) to learn features directly from the images,
which are non-linearly registered and warped to a template to minimize
macrostructural variations. When tested on unseen data, the first method yields
mean absolute error (MAE) of 6.11 years for cognitively normal participants and
MAE of 6.62 years for cognitively impaired participants, while the second
method achieves MAE of 4.69 years for cognitively normal participants and MAE
of 4.96 years for cognitively impaired participants. We find that the ResNet
model captures subtler, non-macrostructural features for brain age prediction.Comment: SPIE Medical Imaging: Image Processing. San Diego, CA. February 2024
(accepted as poster presentation
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