28,159 research outputs found
Analyzing Heterogeneity In Neuroimaging With Probabilistic Multivariate Clustering Approaches
Automated quantitative neuroimaging analysis methods have been crucial in elucidating normal and pathological brain structure and function, and in building in vivo markers of disease and its progression. Commonly used methods can identify and precisely quantify
subtle and spatially complex imaging patterns of brain change associated with brain diseases. However, the overarching premise of these methods is that the disease group is a homogeneous entity resulting from a single, unifying pathophysiological process that has
a single imaging signature. This assumption ignores ample evidence for the heterogeneous nature of neurodegenerative diseases and neuropsychiatric disorders, resulting in incomplete or misleading descriptions. Accurate characterization of heterogeneity is important
for deepening our understanding of neurobiological processes, thus leading to improved disease diagnosis and prognosis.
In this thesis, we leveraged machine learning techniques to develop novel tools that can analyze the heterogeneity in both cross-sectional and longitudinal neuroimaging studies. Specifically, we developed a semi-supervised clustering method for characterizing
heterogeneity in cross-sectional group comparison studies, where normal and patient populations are modeled as high-dimensional point distributions, and heterogeneous disease effects are captured by estimating multiple transformations that align the two distributions, while accounting for the effect of nuisance covariates. Moreover, toward dissecting the heterogeneity in longitudinal cohorts, we proposed a method which simultaneously fits multiple population longitudinal multivariate trajectories and clusters subjects into subgroups. Longitudinal trajectories are modeled using spatiotemporally regularized cubic splines, while clustering is performed by assigning subjects to the subgroup whose population trajectory best fits their data.
The proposed tools were extensively validated using synthetic data. Importantly, they were applied to study the heterogeneity in large clinical neuroimaging cohorts. We identified four disease subtypes with distinct imaging signatures using data from Alzheimerâs
Disease Neuroimaging Initiative, and revealed two subgroups with different longitudinal patterns using data from Baltimore Longitudinal Study on Aging. Critically, we were able to further characterize the subgroups in each of the studies by performing statistical analyses
evaluating subgroup differences with additional information such as neurocognitive data. Our results demonstrate the strength of the developed methods, and may pave the road for a broader understanding of the complexity of brain aging and Alzheimerâs disease
Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples
Laser-generated ultrasound is a modern non-destructive testing technique. It has been investigated over recent years as an alternative to classical ultrasonic methods, mainly in industrial maintenance and quality control procedures. In this study, the detection and reconstruction of internal defects in a metallic sample is performed by means of a time-frequency analysis of ultrasonic waves generated by a laser-induced thermal mechanism. In the proposed methodology, we used wavelet transform due to its multi-resolution time frequency characteristics. In order to isolate and estimate the corresponding time of flight of eventual ultrasonic echoes related to internal defects, a density-based spatial clustering was applied to the resulting time frequency maps. Using the laser scan beamâs position, the ultrasonic transducerâs location and the echoesâ arrival times were determined, the estimation of the defectâs position was carried out afterwards. Finally, clustering algorithms were applied to the resulting geometric solutions from the set of the laser scan points which was proposed to obtain a two-dimensional projection of the defect outline over the scan plane. The study demonstrates that the proposed method of wavelet transform ultrasonic imaging can be effectively applied to detect and size internal defects without any reference information, which represents a valuable outcome for various applications in the industry. View Full-TextPeer ReviewedPostprint (published version
KL Estimation of the Power Spectrum Parameters from the Angular Distribution of Galaxies in Early SDSS Data
We present measurements of parameters of the 3-dimensional power spectrum of
galaxy clustering from 222 square degrees of early imaging data in the Sloan
Digital Sky Survey. The projected galaxy distribution on the sky is expanded
over a set of Karhunen-Loeve eigenfunctions, which optimize the signal-to-noise
ratio in our analysis. A maximum likelihood analysis is used to estimate
parameters that set the shape and amplitude of the 3-dimensional power
spectrum. Our best estimates are Gamma=0.188 +/- 0.04 and sigma_8L = 0.915 +/-
0.06 (statistical errors only), for a flat Universe with a cosmological
constant. We demonstrate that our measurements contain signal from scales at or
beyond the peak of the 3D power spectrum. We discuss how the results scale with
systematic uncertainties, like the radial selection function. We find that the
central values satisfy the analytically estimated scaling relation. We have
also explored the effects of evolutionary corrections, various truncations of
the KL basis, seeing, sample size and limiting magnitude. We find that the
impact of most of these uncertainties stay within the 2-sigma uncertainties of
our fiducial result.Comment: Fig 1 postscript problem correcte
Mapping Topographic Structure in White Matter Pathways with Level Set Trees
Fiber tractography on diffusion imaging data offers rich potential for
describing white matter pathways in the human brain, but characterizing the
spatial organization in these large and complex data sets remains a challenge.
We show that level set trees---which provide a concise representation of the
hierarchical mode structure of probability density functions---offer a
statistically-principled framework for visualizing and analyzing topography in
fiber streamlines. Using diffusion spectrum imaging data collected on
neurologically healthy controls (N=30), we mapped white matter pathways from
the cortex into the striatum using a deterministic tractography algorithm that
estimates fiber bundles as dimensionless streamlines. Level set trees were used
for interactive exploration of patterns in the endpoint distributions of the
mapped fiber tracks and an efficient segmentation of the tracks that has
empirical accuracy comparable to standard nonparametric clustering methods. We
show that level set trees can also be generalized to model pseudo-density
functions in order to analyze a broader array of data types, including entire
fiber streamlines. Finally, resampling methods show the reliability of the
level set tree as a descriptive measure of topographic structure, illustrating
its potential as a statistical descriptor in brain imaging analysis. These
results highlight the broad applicability of level set trees for visualizing
and analyzing high-dimensional data like fiber tractography output
Role of thermal friction in relaxation of turbulent Bose-Einstein condensates
In recent experiments, the relaxation dynamics of highly oblate, turbulent
Bose-Einstein condensates (BECs) was investigated by measuring the vortex decay
rates in various sample conditions [Phys. Rev. A , 063627 (2014)] and,
separately, the thermal friction coefficient for vortex motion was
measured from the long-time evolution of a corotating vortex pair in a BEC
[Phys. Rev. A , 051601(R) (2015)]. We present a comparative analysis of
the experimental results, and find that the vortex decay rate is
almost linearly proportional to . We perform numerical simulations of
the time evolution of a turbulent BEC using a point-vortex model equipped with
longitudinal friction and vortex-antivortex pair annihilation, and observe that
the linear dependence of on is quantitatively accounted for
in the dissipative point-vortex model. The numerical simulations reveal that
thermal friction in the experiment was too strong to allow for the emergence of
a vortex-clustered state out of decaying turbulence.Comment: 7 pages, 5 figure
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