Biology subjects

Prokopiou, P. C.

Publications and source records attributed to Prokopiou, P. C..

4 recordsLinked to original sources

bioRxiv2024

Does global signal regression alter fMRI connectivity patterns related to EEG activity? An EEG-fMRI study in humans

Functional brain connectivity measures extracted from resting-state functional magnetic resonance imaging (fMRI) scans have generated wide interest as potential noninvasive biomarkers. In this context, performing global signal regression (GSR) as a preprocessing step remains controversial. Specifically, while it has been shown that a considerable fraction of global signal variations is associated with physiological and motion sources, GSR may also result in removing neural activity. Here, we address this question by examining the fundamental sources of resting global signal fluctuations using simultaneous electroencephalography (EEG)-fMRI data combined with cardiac and breathing recordings. Our results suggest that systemic physiological fluctuations account for a significantly larger fraction of global signal variability compared to electrophysiological fluctuations. Furthermore, we show that GSR reduces artifactual connectivity due to heart rate and breathing fluctuations, but preserves connectivity patterns associated with electrophysiological activity within the alpha and beta frequency ranges. Overall, these results provide evidence that the neural component of resting-state fMRI-based connectivity is preserved after the global signal is regressed out.

neuroscience↗

bioRxiv2022

Modeling the carbon-dioxide response function in fMRI under task and resting-state conditions

Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli. While the CVR amplitude has established clinical utility, the temporal characteristics of CVR have been increasingly explored and may yield even more pathology-sensitive parameters. This work is motivated by the current need to evaluate the feasibility of dCVR modeling in various noise conditions. In this work, we present a comparison of several recently published model-based deconvolution approaches for estimating h(t), including maximum a posterior likelihood (MAP), inverse logit (IL), canonical correlation analysis (CCA), and basis expansion (using Gamma and Laguerre basis sets). To aid the comparison, we devised a novel simulation framework that allowed us to target a wide range of SNRs, ranging from 10 to -7 dB, representative of both task and resting-state CO2 changes. In addition, we built ground-truth h(t) into our simulation framework, overcoming the practical limitation that the true h(t) is unknown in methodological evaluations. Moreover, to best represent realistic noise found in fMRI scans, we extracted it from in-vivo resting-state scans. Furthermore, we introduce a simple optimization of the CCA method (CCAopt) and compare its performance to these existing methods. Our findings suggest that model-based methods can reasonably estimate dCVR even amidst high noise, and in a manner that is largely independent of the underlying model assumptions for each method. We also provide a quantitative basis for making methodological choices, based on the desired dCVR parameters, the estimation accuracy and computation time. The BEL method provided the highest accuracy and robustness, followed by the CCAopt and IL methods. Of the three, the CCAopt method required the lowest computational time. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping.

biophysics↗

bioRxiv2020

Modeling the hemodynamic response function using motor task and eyes-open resting-state EEG-fMRI

In this work, we investigated the regional characteristics of the dynamic interactions between oscillatory sources of ongoing neural activity obtained using electrophysiological recordings and the corresponding changes in the BOLD signal using simultaneous EEG-fMRI measurements acquired during a motor task, as well as under resting conditions. We casted this problem within a system-theoretic framework, where we initially performed distributed EEG source space reconstruction and subsequently employed block-structured linear and non-linear models to predict the BOLD signal from the instantaneous power in narrow frequency bands of the source local field potential (LFP) spectrum (<100 Hz). Our results suggest that the dynamics of the BOLD signal can be sufficiently described as the convolution between a linear combination of the power profile within individual frequency bands with a hemodynamic response function (HRF). During the motor task, BOLD signal variance was mainly explained by the EEG oscillations in the beta band. On the other hand, during resting-state all frequency bands of EEG exhibited significant contributions to BOLD signal variance. Moreover, the contribution of each band was found to be region specific. Our results also revealed considerable variability of the HRF across different brain regions. Specifically, sensory-motor cortices exhibited positive HRF shapes, whereas parietal and occipital cortices exhibited negative HRF shapes under both experimental conditions.

neuroscience↗

bioRxiv2017

Modeling of dynamic cerebrovascular reactivity to spontaneous and externally induced CO2 fluctuations in the brainstem respiratory control centers in the human brain using BOLD-fMRI

In this work, we investigate the regional characteristics of the dynamic interactions between arterial CO2 and BOLD (dynamic cerebrovascular reactivity - dCVR) during normal breathing and hypercapnic, externally induced step CO2 challenges. To obtain CVR curves at each voxel, we use a custom set of basis functions based on the Laguerre and gamma basis sets. This allows us to obtain robust dCVR estimates both in larger regions of interest (ROIs) as well as in individual voxels. We also implement classification schemes to identify brain regions with similar dCVR characteristics. Our results reveal considerable variability of dCVR across different brain regions, as well as during different experimental conditions (normal breathing and hypercapnia), suggesting a differential response of cerebral vasculature to spontaneous CO2 fluctuations and larger, externally induced CO2 changes. The clustering results suggest that anatomically distinct brain regions exhibit different dCVR curves that do not exhibit the standard, positive valued curves that have been previously reported using measurements of global cerebral blood flow or using larger ROIs. They also revealed a consistent set of dCVR cluster shapes for resting and forcing conditions that exhibited different distribution patterns across brain voxels.

neuroscience↗