Biology subjects

Urunuela, E.

Publications and source records attributed to Urunuela, E..

5 recordsLinked to original sources

bioRxiv2024

Uncovering the Neural Correlates of the Urge-to-Blink: A Study Utilising Subjective Urge Ratings and Paradigm Free Mapping

Neuroimaging plays a significant role in understanding the neurophysiology of Tourette syndrome (TS), in particular the main symptom, tics, and the urges associated with them. Premonitory urge is thought to be a negative reinforcer of tic expression in TS. Tic expression during neuroimaging is most often required as an overt marker of increased urge-to-tic, which can lead to considerable head movement, and thus data loss. This study aims to identify the brain regions involved in urge in healthy subjects using multi-echo functional MRI and a timing- free approach to localise the BOLD response associated with the urge-to-act without information of when these events occur. Blink suppression is an analogous behaviour that can be expressed overtly in the MRI scanner which gives rise to an urge like those described by individuals with TS. We examined the urge-to-blink in 20 healthy volunteers with an experimental paradigm including two conditions, "Okay to blink" and "Suppress blinking", to identify brain regions involved in blink suppression. Multi-echo functional MRI data was analysed using a novel approach to investigate the BOLD signal correlated with the build-up of the urge-to-blink that participants continuously reported using a rollerball device. In addition, we used the method of multi-echo paradigm free mapping (MESPFM) to identify these regions without prior specification of task timings. Subjective urge scores were correlated with activity in the right posterior and ventral-anterior insula as well as the mid-cingulate and occipital cortices. Furthermore, blink suppression was associated with activation in the dorsolateral prefrontal cortex, cerebellum, right dorsal-anterior insula, mid-cingulate cortex and thalamus. These findings illustrate that different insula subregions contribute to the urge-for-action and suppression networks. The MESPFM approach showed co-activation of the right insula and cingulate cortex. The MESPFM activation maps showed the highest overlap with activation associated with blink suppression, as identified using general linear model analysis, demonstrating that activity associated with suppression can be determined without prior knowledge of task timings.

neuroscience↗

bioRxiv2024

Individual variability in the relationship between physiological and resting-state fMRI metrics

Cerebrovascular Reactivity (CVR), the brains vascular response to a vasodilatory stimulus, can be measured using fMRI during breathing challenges that modulate arterial CO2 levels. CVR is an important indicator of cerebrovascular health, although its estimation can be challenging due to the extra experimental setup and/or the subject compliance required. To overcome these limitations, summary metrics based on resting state fluctuations (RSF), such as the (fractional) amplitude of low frequency fluctuations (f/ALFF) and the resting state fluctuation amplitude (RSFA), have been proposed as alternative estimates of CVR, as they are frequently associated with vascular and physiological factors. Previous studies have reported a significant relationship between CVR estimates obtained by means of respiratory paradigms and RSF metrics. However, the total sample sizes, considering both subjects and sessions, are typically small, and not all studies agree on the degree of the relationship. Furthermore, to our knowledge, these studies have only reported cross-sectional analyses, whereas intra-subject longitudinal relationships between CVR estimates and RSF metrics are unknown. Leveraging a unique dense sampling dataset in which resting state and breath-hold multi-echo fMRI were collected in 7 subjects with 10 sessions each, we provide evidence of high individual variability in the inter-session (i.e. intra-subject) relationship between RSF metrics and CVR. These results indicate that RSF metrics might not be a suitable proxy of CVR in clinical settings or for BOLD signal calibration as they may not properly account for intra-individual physiological variations in BOLD fMRI data.

neuroscience↗

bioRxiv2024

Bayesian Modelling Approaches for Breath-Hold Induced Cerebrovascular Reactivity

Cerebrovascular reactivity (CVR) reflects the ability of blood vessels to dilate and constrict in response to a vasoactive stimulus and is an important indicator of cerebrovascular health. CVR can be mapped non-invasively with functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) contrast in combination with a breath-hold (BH) task. There are several ways to analyse this type of data and retrieve individual CVR amplitude and timing information. The most common approach involves employing a time-shifted general linear model with the measured end-tidal carbon dioxide signal as a regressor of interest. In this work, we introduce a novel method for CVR mapping based on a variational Bayesian approach. We analysed BOLD fMRI data from six participants that performed a BH task in ten different sessions each, and computed the corresponding CVR amplitude and delay maps for each session/subject. No statistically significant differences were observed between the modelling approaches in the CVR delay and amplitude maps in grey matter. Notably, the largest difference between methods was apparent in the case of low CVR amplitude, attributed to how each method addressed noisy voxels, particularly in white matter and cerebral spinal fluid. Both approaches showed highly reproducible CVR amplitude maps where between-subject variability was significantly larger than between-session variability. Furthermore, our results illustrated that the Bayesian approach is more computationally efficient, and future implementations could incorporate more complex noise models, non-linear fitting, and physiologically meaningful information into the model in the form of priors. This work demonstrates the utility of variational Bayesian modelling for CVR mapping and highlights its potential for characterising BOLD fMRI dynamics in the study of cerebrovascular health and its application to clinical settings.

neuroscience↗

bioRxiv2022

Improved whole-brain multivariate hemodynamic deconvolution for multi-echo fMRI with stability selection

Conventionally, analysis of functional MRI (fMRI) data relies on available information about the experimental paradigm to establish hypothesized models of brain activity. However, this information can be inaccurate, incomplete or unavailable in multiple scenarios such as resting-state, naturalistic paradigms or clinical conditions. In these cases, blind estimates of neuronal-related activity can be obtained with paradigm-free analysis methods such as hemodynamic deconvolution. Yet, current formulations of the hemodynamic deconvolution problem have three important limitations: 1) their efficacy strongly depends on the appropriate selection of regularization parameters, 2) being univariate, they do not take advantage of the information present across the brain, and 3) they do not provide any measure of statistical certainty associated with each detected event. Here we propose a novel approach that addresses all these limitations. Specifically, we introduce MvME-SPFM (multivariate multi-echo sparse paradigm free mapping), a novel hemodynamic deconvolution algorithm that operates at the whole brain level and adds spatial information via a mixed-norm regularization term over all voxels. Additionally, MvME-SPFM employs a stability selection procedure that removes the need to select regularization parameters and also lets us obtain an estimate of the true probability of having a neuronal-related BOLD event at each voxel and time-point based on the area under the curve (AUC) of the stability paths. Besides, the formulation is tailored for multi-echo fMRI acquisitions, which allows us to better isolate fluctuations of BOLD origin on the basis of their linear dependence with Echo Time (TE) and to assign physiologically interpretable units (i.e., changes in the apparent transverse relaxation [Formula]) to the resulting deconvolved events. We demonstrate that this algorithm outperforms existing state-of-the-art deconvolution approaches, and shows higher spatial and temporal agreement with the activation maps and BOLD signals obtained with a standard model-based linear regression approach, even at the level of individual neuronal events. Consequently, the proposed algorithm provides more reliable estimates of neuronal-related activity, here in terms of [Formula], for the study of the dynamics of brain activity when no information about the timings of the BOLD events is available. This algorithm will be made publicly available as part of the splora Python package.

neuroscience↗

bioRxiv2020

ICA-based Denoising Strategies in Breath-Hold Induced Cerebrovascular Reactivity Mapping with Multi Echo BOLD fMRI

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 subjectspecific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models including drifts and motion timecourses as nuisance regressors applied on singleecho 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 demonstrate 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.

neuroscience↗