6,234 research outputs found
Information flow between resting state networks
The resting brain dynamics self-organizes into a finite number of correlated
patterns known as resting state networks (RSNs). It is well known that
techniques like independent component analysis can separate the brain activity
at rest to provide such RSNs, but the specific pattern of interaction between
RSNs is not yet fully understood. To this aim, we propose here a novel method
to compute the information flow (IF) between different RSNs from resting state
magnetic resonance imaging. After haemodynamic response function blind
deconvolution of all voxel signals, and under the hypothesis that RSNs define
regions of interest, our method first uses principal component analysis to
reduce dimensionality in each RSN to next compute IF (estimated here in terms
of Transfer Entropy) between the different RSNs by systematically increasing k
(the number of principal components used in the calculation). When k = 1, this
method is equivalent to computing IF using the average of all voxel activities
in each RSN. For k greater than one our method calculates the k-multivariate IF
between the different RSNs. We find that the average IF among RSNs is
dimension-dependent, increasing from k =1 (i.e., the average voxels activity)
up to a maximum occurring at k =5 to finally decay to zero for k greater than
10. This suggests that a small number of components (close to 5) is sufficient
to describe the IF pattern between RSNs. Our method - addressing differences in
IF between RSNs for any generic data - can be used for group comparison in
health or disease. To illustrate this, we have calculated the interRSNs IF in a
dataset of Alzheimer's Disease (AD) to find that the most significant
differences between AD and controls occurred for k =2, in addition to AD
showing increased IF w.r.t. controls.Comment: 47 pages, 5 figures, 4 tables, 3 supplementary figures. Accepted for
publication in Brain Connectivity in its current for
Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data
In systems biomedicine, an experimenter encounters different potential
sources of variation in data such as individual samples, multiple experimental
conditions, and multi-variable network-level responses. In multiparametric
cytometry, which is often used for analyzing patient samples, such issues are
critical. While computational methods can identify cell populations in
individual samples, without the ability to automatically match them across
samples, it is difficult to compare and characterize the populations in typical
experiments, such as those responding to various stimulations or distinctive of
particular patients or time-points, especially when there are many samples.
Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous
modeling and registration of populations across a cohort. JCM models every
population with a robust multivariate probability distribution. Simultaneously,
JCM fits a random-effects model to construct an overall batch template -- used
for registering populations across samples, and classifying new samples. By
tackling systems-level variation, JCM supports practical biomedical
applications involving large cohorts
Compressive Sensing of Signals Generated in Plastic Scintillators in a Novel J-PET Instrument
The J-PET scanner, which allows for single bed imaging of the whole human
body, is currently under development at the Jagiellonian University. The dis-
cussed detector offers improvement of the Time of Flight (TOF) resolution due
to the use of fast plastic scintillators and dedicated electronics allowing for
sam- pling in the voltage domain of signals with durations of few nanoseconds.
In this paper we show that recovery of the whole signal, based on only a few
samples, is possible. In order to do that, we incorporate the training signals
into the Tikhonov regularization framework and we perform the Principal
Component Analysis decomposition, which is well known for its compaction
properties. The method yields a simple closed form analytical solution that
does not require iter- ative processing. Moreover, from the Bayes theory the
properties of regularized solution, especially its covariance matrix, may be
easily derived. This is the key to introduce and prove the formula for
calculations of the signal recovery error. In this paper we show that an
average recovery error is approximately inversely proportional to the number of
acquired samples
Structured penalties for functional linear models---partially empirical eigenvectors for regression
One of the challenges with functional data is incorporating spatial
structure, or local correlation, into the analysis. This structure is inherent
in the output from an increasing number of biomedical technologies, and a
functional linear model is often used to estimate the relationship between the
predictor functions and scalar responses. Common approaches to the ill-posed
problem of estimating a coefficient function typically involve two stages:
regularization and estimation. Regularization is usually done via dimension
reduction, projecting onto a predefined span of basis functions or a reduced
set of eigenvectors (principal components). In contrast, we present a unified
approach that directly incorporates spatial structure into the estimation
process by exploiting the joint eigenproperties of the predictors and a linear
penalty operator. In this sense, the components in the regression are
`partially empirical' and the framework is provided by the generalized singular
value decomposition (GSVD). The GSVD clarifies the penalized estimation process
and informs the choice of penalty by making explicit the joint influence of the
penalty and predictors on the bias, variance, and performance of the estimated
coefficient function. Laboratory spectroscopy data and simulations are used to
illustrate the concepts.Comment: 29 pages, 3 figures, 5 tables; typo/notational errors edited and
intro revised per journal review proces
Spatial Efficiency Analysis of Arable Crops in Greece
This paper aims at the analysis of determinants of efficiency of arable crops in a spatial context in Greece. Moreover it suggests policy interventions in order to diminish regional inequalities in efficiency and to raise the average level of efficiency, so as Greek arable crops will follow the new CAP framework which imposes single area payment scheme (SAPS). Efficiency will be estimated within the production function framework using a quasi-production function. In empirical analysis production functions are specified as spatially seemingly unrelated regression equations (spatial SURE). In the paper spatial lag and spatial error specifications as well as common SURE estimations are tested. Data come from National Statistical Service.
Effects of seawalls on the adjacent beach
This study was carried out to examine the effects of seawalls on the adjacent
beach by three dimensional model test. The results obtained from model test were
analyzed in terms of volumetric changes and shoreline and hydrographic change to
quantify the effects of seawalls.
The experiments were carried out in the wave basin of Coastal and Oceanographic
Engineering department, University of Florida. A model seawall was installed
on the test beach (19mxl4m) which was initially molded into equilibrium
shapes. During the test, hydrographic surveys were conducted at regular time intervals.
The main variable in the experiment is the wave angle. Cases both with
and without seawall were tested. (141pp.
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