Biology subjects

Mitsis, G. D.

Publications and source records attributed to Mitsis, G. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Denoising the central autonomic network: characterizing processes jointly associated with arousal and non-neuronal physiological artefacts in dynamic fMRI analyses

Using a multimodal dataset including fMRI, EEG-fMRI and concurrent physiological recordings, we investigated the effect of denoising systemic low frequency oscillations (sLFOs) on the characterization of dynamic signatures of central autonomic regulation and their relation to ongoing physiological states. We demonstrated that the time-frequency profiles of couplings between BOLD time series and cardiac and respiratory processes were statistically comparable between regions of the brain associated with autonomic function and non-autonomic motor regions, suggesting that these couplings largely do not reflect neuronal activation related to autonomic activity. We further showed that model-based (via physiological response functions) and data-driven (via CompCor nuisance regressors extracted from cerebrospinal fluid) methods of sLFO denoising had a statistically similar effect on these frequency profiles. We novelly applied co-activation pattern analysis to assess state dynamics of autonomic-associated regions of the brain, finding that such brain states interrelate decreases in vigilance and increases in heart rate, respiratory flow, and head motion, thus providing evidence for global arousal processes affecting BOLD signal in autonomic-associated regions. Lastly, we modelled sliding-window dynamic functional connectivity within the central autonomic network (CAN) as modulated by heart rate variability, showing significant differences in model outcomes linked to each denoising pipeline. With these findings, we provide a comprehensive discussion of the implications for the application of denoising techniques to the CAN and comment on the origins of dynamic components of the BOLD signal linked to autonomic regulation, highlighting the global role played by arousal.

neuroscience↗

bioRxiv2026

When Can Brain Connectivity Track the Working Mind? A Large-Scale Benchmark of Dynamic Functional Connectivity Across Cognitive Paradigms

Dynamic functional connectivity (dFC) -- the time-varying re-configuration of brain network interactions -- has become a widely adopted method for studying how neural dynamics reflect ongoing cognition. Yet a fundamental question remains unresolved: can dFC reliably track when a person is cognitively engaged, and if not, why does it fail? Here, we address this question through a large-scale benchmark of seven widely used dFC methods, evaluating how well each predicts task presence across 16 fMRI datasets encompassing over 1,500 participants and 28 distinct experimental settings, complemented by realistic simulated data. Across experimental data, dFC-based tracking of cognitive engagement was unreliable in many cases: most method-experiment combinations performed near chance, and no single method succeeded across all contexts. This failure, however, was not uniform. Both experimental and simulated data showed that decoding performance varied systematically with three interacting factors -- experimental design, data quality, and the choice of dFC method -- rather than depending on dFC features alone. Critically, we identify specific experimental design conditions associated with more reliable tracking: paradigms with longer, more regular task blocks and fewer task-rest transitions were substantially more decodable, while data quality independently influenced performance across methods. These findings offer actionable principles for when dFC can -- and cannot -- be expected to serve as a reliable marker of underlying cognitive states.

neuroscience↗

bioRxiv2026

Spatiotemporal Propagation of Sensorimotor Beta Bursts Across Adulthood

Beta activity (13-30 Hz) is a prominent feature of motor control, widely distributed across the cerebral cortex. However, how beta bursts (transient high-amplitude events) propagate along cortical organizational axes, such as the posterior-anterior gradient that coordinates cortical structure and function from sensory to association regions, remains poorly understood. Using magnetoencephalography (MEG) data from the Cambridge Centre for Ageing and Neuroscience (CamCAN) dataset, beta burst propagation was characterized using burst onset timing and optical flow analysis during motor tasks and rest in 573 participants (ages 18-88). Beta bursts propagated systematically along the posterior-anterior cortical axis during motor tasks, with propagation direction reversing between movement phases and exhibiting hemispheric asymmetry. In contrast, the resting state exhibited no consistent spatial organization of beta burst propagation. Propagation patterns in motor tasks significantly correlated with cortical distributions of GABAA, cholinergic, and mu-opioid receptors in a hemisphere-specific and phase-dependent manner. Propagation energy was highest in sensorimotor regions and decreased towards more peripheral areas of the cortex. Older adults exhibited significant temporal expansion of beta activity (earlier pre-movement, later post-movement), suggesting a mediation effect of age on reaction time. These results suggest that the propagation of beta bursts is influenced by cortical architecture and may provide a mechanistic explanation for the age-related slowing of motor function.

neuroscience↗

bioRxiv2024

Towards the implementation and interpretation of masked ICA for identifying signatures of autonomic activation in the brainstem with resting-state BOLD fMRI

The brainstem is the site of key exchanges between the autonomic and central nervous systems but has historically presented a challenging target for study with BOLD fMRI. A potentially powerful although under-characterized approach to identifying nucleic activation within the brainstem is masked independent component analysis (mICA), which restricts signal decomposition to the brainstem itself, thus aiming to reduce the strong effect of physiological noise in nearby regions such as ventricles and large arteries. In this study, we systematically investigate the use of mICA to uncover signatures of autonomic activation in the brainstem at rest. We apply mICA on 40 subjects in a high-resolution resting state 7T dataset following different strategies for dimensionality selection, denoising, and component classification. We show that among the noise mitigation techniques investigated, cerebrospinal fluid denoising makes the largest impact in terms of mICA outcomes. We further demonstrate that across preprocessing pipelines and previously reported results the majority of components are spatially reproducible, but temporal outcomes differ widely depending on denoising strategy. Evaluating both hand-labelling and whole-brain specificity criteria, we develop an intuitive framework for mICA classifications. Finally, we make a comparison between mICA and atlas-based segmentations of brainstem nuclei, finding little consistency between these two approaches. Based on our evaluation of the effects of methodology on mICA and its relationship to other signals of interest in the brainstem, we provide recommendations for future uses of mICA to identify autonomically-relevant BOLD fluctuations in subcortical structures.

neuroscience↗

bioRxiv2024

Investigation of the effect of physiological factors on resting-state and task-based functional connectivity

Understanding the brains functional network through functional connectivity (FC) is crucial for gaining deeper insights into brain functional mechanism and identifying a potential biomarker for diagnosing neurological disorders. Despite the development of various FC measures, their reliability under different conditions remains under-explored. Moreover, physiological noise can obscure true neural activity, and accordingly, introduce errors into FC patterns. This issue necessitates further investigation. In this study, we evaluate and compare the performance of various methods using Local Field Potential and Blood-Oxygen-Level-Dependent signals across different conditions. We also examine the impact of physiological artifacts on BOLD-FC results. Our comprehensive assessment covers multiple modalities of brain signals, diverse task paradigms, and varying noise levels. Our findings reveal that while Granger Causality-based methods exhibit significant limitations, particularly with BOLD data, multivariate techniques (e.g. partial correlation) demonstrate greater robustness in distinguishing between different types of connections within the network. Notably, our results indicate that physiological artifacts substantially affect FC values, leading to erroneous connectivity estimates, especially with bivariate methods. This research offers a foundational analysis of the effects of physiological artifacts on FC results and provides valuable insights for future studies.

neuroscience↗

bioRxiv2024

Investigating the variability of physiological response functions across individuals and brain regions in functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) is a valuable neuroimaging tool for studying brain function and functional connectivity between brain regions. However, the blood oxygen level dependent (BOLD) signal used to generate the fMRI images can be influenced by various physiological factors, such as cardiac and respiratory activity. These physiological effects, in turn, influence the resulting functional connectivity patterns, making physiological noise correction a crucial step in the preprocessing of fMRI data. When concurrent physiological recordings are available, researchers often generate nuisance regressors to account for the effect of heart rate and respiratory variations by convolving physiological response functions (PRF) with the corresponding physiological signals. However, it has been suggested that the PRF characteristics may vary across subjects and different regions of the brain, as well as across scans of the same subject. To investigate the dependence of PRFs on these factors, we examine the performance of several different PRF models, in terms of BOLD variance explained, using resting-state fMRI data from the Human Connectome Project (N=100). We examined both one-input (heart rate or respiration) and two-input (heart rate and respiration) PRF models and show that allowing PRF curves to vary across subjects and brain regions generally improves PRF model performance. For one-input models, the improvement in model performance gained by allowing spatial variability was most prominent for respiration, particularly for a subset of the subjects (about a third) examined. Allowing for subject-specific or regional variability in the cardiac response function resulted in a significant model performance improvement only when using a two-input PRF model. Overall, our results highlight the importance of considering spatial and subject-specific variability in PRFs when analyzing fMRI data, particularly regarding respiratory-related fluctuations.

bioengineering↗

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↗

bioRxiv2023

On the variability of dynamic functional connectivity assessment methods

Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly-used dFC methods. We implemented seven dFC assessment methods in Python and used them to analyze fMRI data of 395 subjects from the Human Connectome Project. We measured the pairwise similarity of dFC results using several similarity metrics in terms of overall, temporal, spatial, and inter-subject similarity. Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Surprisingly, the observed variability in dFC estimates was comparable to the expected natural variation over time, emphasizing the impact of methodological choices on the results. Our findings revealed three distinct groups of methods with significant inter-group variability, each exhibiting distinct assumptions and advantages. These findings highlight the need for multi-analysis approaches to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds, and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox that enables multi-analysis dFC assessment. This study sheds light on the impact of dFC assessment analytical flexibility, emphasizing the need for careful method selection and validation, and promoting the use of multi-analysis approaches to enhance reliability and interpretability of dFC studies.

neuroscience↗

bioRxiv2023

A calibration and uncertainty quantification analysis of classical, fractional and multiscale logistic models of tumour growth

The validation of mathematical models of tumour growth is frequently hampered by the lack of sufficient experimental data, resulting in qualitative rather than quantitative studies. Recent approaches to this problem have attempted to extract information about tumour growth by integrating multiscale experimental measurements, such as longitudinal cell counts and gene expression data. In the present study, we investigated the performance of several mathematical models of tumour growth, including classical logistic, fractional and novel multiscale models, in terms of quantifying in-vitro tumour growth in the presence and absence of therapy. We further examined the effect of genes associated with changes in chemosensitivity in cell death rates. State-of-the-art Bayesian inference, likelihood maximisation and uncertainty quantification techniques allowed a thorough evaluation of model performance. The results suggest that the classical single-cell population model (SCPM) was the best fit for the untreated and low-dose treatment conditions, while the multiscale model with a cell death rate symmetric with the expression profile of OCT4 (SymSCPM) yielded the best fit for the high-dose treatment data. Further identifiability analysis showed that the multiscale model was both structurally and practically identifiable under the condition of known OCT4 expression profiles. Overall, the present study demonstrates that model performance can be improved by incorporating multiscale measurements of tumour growth.

systems biology↗

bioRxiv2023

Cancer cell sedimentation in 3D cultures reveals active migration regulated by self-generated gradients and adhesion sites

Cell sedimentation in 3D hydrogel cultures refers to the vertical migration of cells towards the bottom of the space. To explain this poorly understood phenomenon, we conducted a multiscale experimental and mathematical examination of 3D cancer growth in triple negative breast cancer cells. Migration was examined in the presence and absence of Paclitaxel, in high and low adhesion environments and in the presence of fibroblasts. The observed behaviour was modeled by hypothesizing active migration due to self-generated chemotactic gradients. Our results confirmed this hypothesis, whereby migration was regulated by the MAPK and TGF-{beta} pathways. The mathematical model enabled us to describe the experimental data in absence (normalized error< 40%) and presence of Paclitaxel (normalized error< 10%), suggesting inhibition of random motion and advection in the latter case. Inhibition of sedimentation in low adhesion and co-culture experiments further supported the conclusion that cells actively migrated downwards due to the presence of signals produced by cells already attached to the adhesive glass surface.

bioengineering↗

bioRxiv2022

Bringing language to dynamic brain states: the default network dominates neural responses to evolving movie stories

Naturalistic neuroscience opened the door to new insights into neural circuits that serve real-world dynamic perception. Such studies have often neglected the rich texture of the movie narrative itself, but semantic content can be used to contextualize the induced neural responses. Here, we translated natural language processing tools from machine learning to characterize brain states estimated from hidden Markov models. Our analytical strategy allowed pitting shallow unimodal against the deep associative brain network layers in explaining how semantic content of the movie links to observed neural activity. Pooling information across >53,000 brain image time points watching Forrest Gump, we could show that distinct dynamic brain states capture unique semantic facets along the unfolding movie narrative. The spatiotemporal dynamics of brain states explicitly captured subject-level responses throughout the brain network hierarchy. Across all analyses, the default network was most intimately linked to semantic information integration, and this neural system switched online for longest durations during movie watching. Further, we identified and described two mechanisms of how the default network liaises dynamically with microanatomically defined subregion partners: the amygdala and the hippocampus. Our study thus unlocks the potential of natural language processing to explore neural processes in everyday life situations that engage key aspects of conscious awareness.

neuroscience↗

bioRxiv2021

Quantifying the Morphology and Mechanisms of Cancer Progression in 3D in-vitro environments: Integrating Experiments, Multiscale Models, and Spatial Validation

Mathematical models of cancer growth have become increasingly more accurate both in the space and time domains. However, the limited amount of data typically available has resulted in a larger number of qualitative rather than quantitative studies. In the present study, we provide an integrated experimental-computational framework for the quantification of the morphological characteristics and the mechanistic modelling of cancer progression in 3D environments. The proposed framework allows for the calibration of multiscale, spatiotemporal models of cancer growth using state-of-the-art 3D cell culture data, and their validation based on the resulting experimental morphological patterns using spatial point-pattern analysis techniques. We applied this framework to the study of the development of Triple Negative Breast Cancer cells cultured in Matrigel scaffolds, and validated the hypothesis of chemotactic migration using a multiscale, hybrid Keller-Segel model. The results revealed transient, non-random spatial distributions of cancer cells that consist of clustered, and dispersion patterns. The proposed model was able to describe the general characteristics of the experimental observations and suggests that cancer cells exhibited chemotactic migration and accumulation, as well as random motion during the examined time period of development. The developed framework enabled us to pursue two goals; first, the quantitative description of the morphology of cancer growth in 3D cultures using point-pattern analysis, and second, the relation of tumour morphology with underlying biophysical mechanisms that govern cancer growth and migration.

bioengineering↗

bioRxiv2021

Detection and Spatiotemporal analysis of in-vitro 3D migratory Triple-Negative Breast cancer cells

The invasion of cancer cells into the surrounding tissues is one of the hallmarks of cancer. However, a precise quantitative understanding of the spatiotemporal patterns of cancer cell migration and invasion still remains elusive. A promising approach to investigate these patterns are 3D cell cultures, which provide more realistic models of cancer growth compared to conventional 2D monolayers. Quantifying the spatial distribution of cells in these 3D cultures yields great promise for understanding the spatiotemporal progression of cancer. In the present study, we present an image processing and segmentation pipeline for the detection of 3D GFP-fluorescent Triple-Negative Breast Cancer cell nuclei, and we perform quantitative analysis of the formed spatial patterns and their temporal evolution. The performance of the proposed pipeline was evaluated using experimental 3D cell culture data, and was found to be comparable to manual segmentation, outperforming four alternative automated methods. The spatiotemporal statistical analysis of the detected distributions of nuclei revealed transient, non-random spatial distributions that consisted of clustered patterns across a wide range of neighbourhood distances, as well as dispersion for larger distances. Overall, the implementation of the proposed framework revealed the spatial organization of cellular nuclei with improved accuracy, providing insights into the 3 dimensional inter-cellular organization and its progression through time.

bioengineering↗

bioRxiv2020

Modeling the hemodynamic response function using simultaneous EEG-fMRI data and convolutional sparse coding analysis with rank-1 constraints

Over the last few years, an increasing body of evidence points to the hemodynamic response function as an important confound of resting-state functional connectivity. Several studies in the literature proposed using blind deconvolution of resting-state fMRI data to retrieve the HRF, which can be subsequently used for hemodynamic deblurring. A basic hypothesis in these studies is that relevant information of the resting-state brain dynamics is condensed in discrete events resulting in large amplitude peaks in the BOLD signal. In this work, we showed that important information of resting-state activity, in addition to the larger amplitude peaks, is also concentrated in lower amplitude peaks. Moreover, due to the strong effect of physiological noise and head motion on the BOLD signal, which in many cases may not be completely removed after preprocessing, the neurophysiological origin of the large amplitude BOLD signal peaks is questionable. Hence, focusing on the large amplitude BOLD signal peaks may yield biased HRF estimates. To define discrete events of neuronal origins, we proposed using simultaneous EEG-fMRI along with convolutional sparse coding analysis. Our results suggested that events detected in the EEG are able to describe the slow oscillations of the BOLD signal and to obtain consistent HRF shapes across subjects under both task-based and resting-state conditions.

neuroscience↗

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↗

bioRxiv2020

Phase dynamics of cerebral blood flow in subarachnoid haemorrhage in response to sodium nitrite infusion

Aneurysmal subarachnoid haemorrhage (SAH) is a devastating subset of stroke. One of the major determinates of morbidity is the development of delayed cerebral ischemia (DCI). Disruption of the nitric oxide (NO) pathway and consequently the control of cerebral blood flow (CBF), known as cerebral autoregulation, is believed to play a role in its pathophysiology. Through the pharmacological manipulation of in vivo NO levels using an exogenous NO donor we sought to explore this relationship. Phase synchronisation index (PSI), an expression of the interdependence between CBF and arterial blood pressure (ABP) and thus cerebral autoregulation, was calculated before and during sodium nitrite administration in 10 high-grade SAH patients acutely postrupture. In patients that did not develop DCI, there was a significant increase in PSI around 0.1 Hz during the administration of sodium nitrite (33%; p-value 0.006). In patients that developed DCI, PSI did not change significantly. Synchronisation between ABP and CBF at 0.1 Hz has been proposed as a mechanism by which organ perfusion is maintained, during periods of physiological stress. These findings suggest that functional NO depletion plays a role in impaired cerebral autoregulation following SAH, but the development of DCI may have a distinct pathophysiological aetiology.

neuroscience↗

bioRxiv2020

Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph

The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS - defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.

neuroscience↗

bioRxiv2020

Physiological and head motion signatures in static and time-varying functional connectivity and their subject discriminability

Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

neuroscience↗