Biology subjects

Jiricek, S.

Publications and source records attributed to Jiricek, S..

5 recordsLinked to original sources

bioRxiv2025

Spontaneous thought orientation tracked by fMRI networks and EEG alpha power dynamics

Understanding how spontaneous, rather than experimentally induced, thoughts relate to brain activity remains a major challenge. We combined simultaneous fMRI and EEG recordings with Descriptive Experience Sampling (DES) to link momentary, naturally occurring experiences to their neural signatures during rest. Using machine-learning classification of 240 time-locked samples from eight participants--each completing nine 25-minute resting-state sessions--we reliably distinguished internally from externally oriented experiences (fMRI accuracy = 65.4%, EEG = 62.5%). Externally oriented states showed greater fMRI activity in salience, auditory, and visuospatial networks and lower occipital alpha power in EEG, whereas internally oriented states exhibited the opposite pattern, extending prior DMN-focused accounts of internally directed states. Across modalities, integrated resting-state alpha power correlated negatively with BOLD fluctuations in parietal and occipital regions. These multimodal findings reveal distinct neural signatures of spontaneous experience and demonstrate that coordinated large-scale network dynamics and alpha-band oscillations track the natural alternation between inward and outward focus in the human mind.

neuroscience↗

bioRxiv2025

Microstate in rats EEG: a proof of concept study

The electroencephalogram (EEG) reflecting brain activity can be characterized through brief periods of stable neural activity patterns that recur over time and are referred to as microstates. Microstates are related to a range of cognitive processes, and their analysis has become an increasingly popular tool for studying human brain function. While microstates have been extensively studied in humans, their presence and characteristics in animal models have yet to be as thoroughly investigated. This study aims to address this gap by detecting and characterizing microstates in EEGs of rats collected using a superficial electrode system corresponding to homological areas of the human 10-20 system. Specifically, we demonstrate the presence of microstates in rats EEG; those can be captured by the same metrics as in humans. We define these microstates, describe them through topology and parameters, and identify the EEG frequency bands and intracranial sources that predominantly determine microstate topography. These findings have important implications for the use of microstates as a preclinical tool for investigating brain functions, detecting new biomarkers of brain diseases, and translating this knowledge to humans.

animal behavior and cognition↗

bioRxiv2025

Long-Range Input to Cortical Microcircuits Shapes EEG-BOLD Correlation

Electroencephalography (EEG) rhythms and blood-oxygen-level-dependent (BOLD) activity, though generated by different mechanisms, exhibit correlations. The level of correlation varies between EEG frequency bands, brain regions, and experimental paradigms, but the underpinning mechanisms of this correlation remain poorly understood. Here we create a mathematical, data-informed model of a cortical microcircuit that encompasses all major neuron types across cortical layers, and use it to generate EEG and BOLD under various external input conditions. The model exhibits noise-driven fluctuations giving rise to distinct EEG rhythms, with external inputs modulating EEG spectral characteristics. In line with experimental findings, we observe negative alpha-BOLD correlations and positive gamma-BOLD correlations across different input configurations. Temporal variability of the input is found to increase EEG-BOLD correlation and to improve the correspondence with experimental results. This study provides a mathematical framework to theoretically study the correlation of EEG and BOLD features in a comprehensive way.

neuroscience↗

bioRxiv2024

Nonlinear brain connectivity from neurons to networks: quantification, sources and localization.

Connectivity is a widespread tool for the study of complex systems dynamics. Since the first studies in functional connectivity, Pearsons correlation has been the primary tool to determine interdependencies in the activity at different brain locations. Over the years, concern over the information neglected by correlation has pushed toward using different measures accounting for non-linearity. However, one may pragmatically argue that, at the most common clinical observation scales, a linear description of the brain captures a vast majority of the information. Therefore, we measured the fraction of information disregarded using a linear description and which regions would be most affected. To assess how the spatial and temporal observation scale impacts the amount of non-linearity across multiple orders of magnitude, we considered fMRI, EEG, iEEG, and single-unit spikes. We observe that by treating the system as linear, the information loss is relatively mild for modalities with large temporal or spatial averaging (fMRI and EEG) and gains relevance on more fine descriptions of the activity (iEEG and single unit spikes). We conclude that Pearsons correlation coefficient adequately describes pairwise interactions in time series from current recording techniques for most non-invasive human applications. At the same time, microscale (typically invasive) measurements might be a more suitable field for mining information on nonlinear interactions. Significance StatementIn complex systems research, including neuroscience, the ubiquitous interest in network characterization by statistical dependencies (functional connectivity) invites increasingly sophisticated approaches. Various nonlinear measures, ultimately Mutual Information, emerge as alternatives to the conventional linear Pearsons correlation coefficient. To fundamentally inform such decisions, we systematically assess the amount and reliability of non-linearity of brain functional connectivity across imaging modalities and spatial and temporal scales. We demonstrate more pronounced non-linearity in microscale recordings, while it is limited and unreliable in more accessible, non-invasive, large-scale modalities: functional magnetic resonance imaging and scalp electrophysiology. This result fundamentally supports the use of robust and easily interpretable linear tools in large-scale neuroimaging and brings essential insights concerning the non-linearity of microscale connectivity, including the link to brain state dynamics.

neuroscience↗

bioRxiv2024

Spatial (Mis)match Between EEG and fMRI Signal Patterns Revealed by Spatio-Spectral Source-Space EEG Decomposition

In this work, we aimed to directly compare and integrate EEG whole-brain patterns of neural dynamics with concurrently measured fMRI BOLD data. For that purpose, we set out to derive EEG patterns based on a spatio-spectral decomposition of band-limited EEG power in the source-reconstructed space. On a large data set of 72 subjects resting-state hdEEG-fMRI we showed that the proposed approach is reliable both in terms of the extracted patterns as well as their spatial BOLD signatures. The five most robust EEG spatio-spectral patterns include, but go beyond, the well-known occipital alpha power dynamics. The EEG spatial-spectral patterns show relatively weak, yet statistically significant spatial similarity to their fMRI BOLD signatures, particularly the patterns that show stronger temporal synchronization with BOLD. However, we observed an insignificant relation between the temporal synchronization and spatial overlap of the EEG spatio-spectral patterns and the classical fMRI BOLD resting state networks (as obtained by independent component analysis). This provides evidence that both EEG (frequency-specific) power and BOLD signal capture reproducible spatiotemporal patterns of neural dynamics. Rather than being mutually redundant, these are only partially overlapping, carrying to a large extent complementary information concerning the underlying low-frequency dynamics. Finally, we report and interpret the most stable source space EEG-fMRI patterns, along with the corresponding EEG electrode space patterns better known from the literature.

neuroscience↗