1,798 research outputs found
Disambiguating the role of blood flow and global signal with partial information decomposition
Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas
Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis
We propose a generic multivariate extension of detrended fluctuation analysis
(DFA) that incorporates interchannel dependencies within input multichannel
data to perform its long-range correlation analysis. We next demonstrate the
utility of the proposed method within multivariate signal denoising problem.
Particularly, our denosing approach first obtains data driven multiscale signal
representation via multivariate variational mode decomposition (MVMD) method.
Then, proposed multivariate extension of DFA (MDFA) is used to reject the
predominantly noisy modes based on their randomness scores. The denoised signal
is reconstructed using the remaining multichannel modes albeit after removal of
the noise traces using the principal component analysis (PCA). The utility of
our denoising method is demonstrated on a wide range of synthetic and real life
signals
A Hierarchical Bayesian Model for Frame Representation
In many signal processing problems, it may be fruitful to represent the
signal under study in a frame. If a probabilistic approach is adopted, it
becomes then necessary to estimate the hyper-parameters characterizing the
probability distribution of the frame coefficients. This problem is difficult
since in general the frame synthesis operator is not bijective. Consequently,
the frame coefficients are not directly observable. This paper introduces a
hierarchical Bayesian model for frame representation. The posterior
distribution of the frame coefficients and model hyper-parameters is derived.
Hybrid Markov Chain Monte Carlo algorithms are subsequently proposed to sample
from this posterior distribution. The generated samples are then exploited to
estimate the hyper-parameters and the frame coefficients of the target signal.
Validation experiments show that the proposed algorithms provide an accurate
estimation of the frame coefficients and hyper-parameters. Application to
practical problems of image denoising show the impact of the resulting Bayesian
estimation on the recovered signal quality
Data-driven Signal Decomposition Approaches: A Comparative Analysis
Signal decomposition (SD) approaches aim to decompose non-stationary signals
into their constituent amplitude- and frequency-modulated components. This
represents an important preprocessing step in many practical signal processing
pipelines, providing useful knowledge and insight into the data and relevant
underlying system(s) while also facilitating tasks such as noise or artefact
removal and feature extraction. The popular SD methods are mostly data-driven,
striving to obtain inherent well-behaved signal components without making many
prior assumptions on input data. Among those methods include empirical mode
decomposition (EMD) and variants, variational mode decomposition (VMD) and
variants, synchrosqueezed transform (SST) and variants and sliding singular
spectrum analysis (SSA). With the increasing popularity and utility of these
methods in wide-ranging application, it is imperative to gain a better
understanding and insight into the operation of these algorithms, evaluate
their accuracy with and without noise in input data and gauge their sensitivity
against algorithmic parameter changes. In this work, we achieve those tasks
through extensive experiments involving carefully designed synthetic and
real-life signals. Based on our experimental observations, we comment on the
pros and cons of the considered SD algorithms as well as highlighting the best
practices, in terms of parameter selection, for the their successful operation.
The SD algorithms for both single- and multi-channel (multivariate) data fall
within the scope of our work. For multivariate signals, we evaluate the
performance of the popular algorithms in terms of fulfilling the mode-alignment
property, especially in the presence of noise.Comment: Resubmission with changes in the reference lis
Multivariate time-frequency analysis
Recent advances in time-frequency theory have led to the development of high resolution time-frequency algorithms, such as the empirical mode decomposition (EMD) and
the synchrosqueezing transform (SST). These algorithms provide enhanced localization in representing time varying oscillatory components over conventional linear and quadratic time-frequency algorithms. However, with the emergence of low cost multichannel sensor technology, multivariate extensions of time-frequency algorithms are needed in order to
exploit the inter-channel dependencies that may arise for multivariate data. Applications
of this framework range from filtering to the analysis of oscillatory components.
To this end, this thesis first seeks to introduce a multivariate extension of the synchrosqueezing transform, so as to identify a set of oscillations common to the multivariate
data. Furthermore, a new framework for multivariate time-frequency representations is developed using the proposed multivariate extension of the SST. The performance of the proposed algorithms are demonstrated on a wide variety of both simulated and real world
data sets, such as in phase synchrony spectrograms and multivariate signal denoising.
Finally, multivariate extensions of the EMD have been developed that capture the
inter-channel dependencies in multivariate data. This is achieved by processing such data directly in higher dimensional spaces where they reside, and by accounting for the power
imbalance across multivariate data channels that are recorded from real world sensors, thereby preserving the multivariate structure of the data. These optimized performance
of such data driven algorithms when processing multivariate data with power imbalances and inter-channel correlations, and is demonstrated on the real world examples of Doppler radar processing.Open Acces
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