23 research outputs found
A LOW RANK AND SPARSE PARADIGM FREE MAPPING ALGORITHM FOR DECONVOLUTION OF FMRI DATA
Date Added to IEEE Xplore: 25 May 2021Current deconvolution algorithms for functional magnetic resonance
imaging (fMRI) data are hindered by widespread signal changes
arising from motion or physiological processes (e.g. deep breaths)
that can be interpreted incorrectly as neuronal-related hemodynamic
events. This work proposes a novel deconvolution approach that
simultaneously estimates global signal fluctuations and neuronalrelated
activity with no prior information about the timings of the
blood oxygenation level-dependent (BOLD) events by means of a
low rank plus sparse decomposition algorithm. The performance
of the proposed method is evaluated on simulated and experimental
fMRI data, and compared with state-of-the-art sparsity-based deconvolution
approaches and with a conventional analysis that is aware of
the temporal model of the neuronal-related activity. We demonstrate
that the novel low-rank and sparse paradigm free mapping algorithm
can estimate global signal fluctuations related to motion in our task,
while estimating the neuronal-related activity with high fidelity
Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work
Deconvolution of the hemodynamic response is an important step to access
short timescales of brain activity recorded by functional magnetic resonance
imaging (fMRI). Albeit conventional deconvolution algorithms have been around
for a long time (e.g., Wiener deconvolution), recent state-of-the-art methods
based on sparsity-pursuing regularization are attracting increasing interest to
investigate brain dynamics and connectivity with fMRI. This technical note
revisits the main concepts underlying two main methods, Paradigm Free Mapping
and Total Activation, in the most accessible way. Despite their apparent
differences in the formulation, these methods are theoretically equivalent as
they represent the synthesis and analysis sides of the same problem,
respectively. We demonstrate this equivalence in practice with their
best-available implementations using both simulations, with different
signal-to-noise ratios, and experimental fMRI data acquired during a motor task
and resting-state. We evaluate the parameter settings that lead to equivalent
results, and showcase the potential of these algorithms compared to other
common approaches. This note is useful for practitioners interested in gaining
a better understanding of state-of-the-art hemodynamic deconvolution, and aims
to answer questions that practitioners often have regarding the differences
between the two methods.Comment: 18 pages, 6 figures, submitted to Apertur
ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI
Available online 6 March 2021.Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi- echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.This research was supported by the European Union’s Horizon 2020 research and innovation program ( Marie Sk ł odowska-Curie grant agreement No. 713673 ), a fellowship from La Caixa Foundation (ID 100010434 , fellowship code LCF/BQ/IN17/11620063 ), the Spanish Ministry of Economy and Competitiveness ( Ramon y Cajal Fellowship, RYC-2017- 21845 ), the Spanish State Research Agency (BCBL “Severo Ochoa ”excellence accreditation, SEV- 2015-490 ), the Basque Govern- ment ( BERC 2018-2021 and PIBA_2019_104 ), the Spanish Ministry of Science, Innovation and Universities (MICINN; PID2019-105520GB-100 and FJCI-2017-31814 ), and the Eunice Kennedy Shriver National Insti- tute of Child Health and Human Development of the National Institutes of Health under award number K12HD073945
Unraveling Hidden Patterns of Brain Activity: A Journey Through Hemodynamic Deconvolution in Functional MRI
177 p.Functional magnetic resonance imaging data analysis is often directed to identify and disentangle the neural processes that occur in different brain regions during task or at rest, and employs the blood oxygenation-level dependent (BOLD) signal of fMRI as a proxy for neuronal activity mediated through neurovascular coupling. The goal of this thesis is to enhance and expand techniques for identifying and analyzing individual trial event-related BOLD responses based on the Paradigm Free Mapping (PFM) algorithm, which utilizes a linear hemodynamic response model and relies on regularized least squares estimators to deconvolve the neuronal-related signal that drives the BOLD effect. Notably, these techniques estimate neuronal-related activity without relying on prior paradigm information
Hemodynamic Deconvolution Demystified: Sparsity-DrivenRegularization at Work
Repository containing the data used in the following paper: Hemodynamic Deconvolution Demystified: Sparsity-DrivenRegularization at Wor
A multivariate sparse deconvolution algorithm for multi echo fMRI.
This thesis presents a novel algorithm for the deconvolution of multi echo fMRI data with no prior
information on the timings of the neuronal events. Based on previous work on the field, a new signal
model is proposed in order to take the processing from a voxelwise analysis to an entire brain one.
Different proximal operators have been studied for solving the optimisation problem present in the
deconvolution, since it is an ill-posed inverse problem, and a novel method based on the stability selection
procedure has been suggested to answer to the choice of the regularization parameter dilemma. The
method takes advantage of the area under the curve (AUC) of the stability paths to avoid the selection of
a single regularization parameter. An optimal approach for the thresholding of AUC timeseries is studied
and different debiasing methods for removing the sparsity in prolonged events are presented.
The results demonstrate that the MvMESPFM algorithm provides promising results when estimating
neuronal-related events even on noisy data. Subject to being thoroughly tested on experimental data,
testing conducted on simulated signals suggests that the tool could eventually be introduced to the
processing pipelines of different research lines regarding fMRI data analysis