Biology subjects

Koudelka, V.

Publications and source records attributed to Koudelka, V..

6 recordsLinked to original sources

bioRxiv2026

The impact of behavioural activity on the EEG power spectrum, its source localisation, and global functional connectivity in rats

The behavioural activity of freely moving animals is a confounding factor that affects the recording, analysis, and final results of animal EEG experiments. Along with the lack of standardisation in animal in vivo electrophysiology experiments, this could lead to huge inconsistencies, especially in the analysis of centrally acting drugs. Therefore, the main aim of this paper is to investigate the effects of behavioural activity versus inactivity on the multichannel EEG in freely moving rats. In a large sample (n = 116) of waking recordings from 12 cortical electrodes (ECoG) in Wistar rats, we evaluated behavioural activity-related changes in the power spectrum, current source density, and power-based global functional connectivity (GFC) in a 3D rat brain model, according to the TOHOKU Rat Brain Atlas. The main findings were that behavioural activity induced 1) a robust power increase in 6-8 Hz, peaking at 7 Hz with maximum changes over the parietal and temporal cortex, 2) an increase in gamma power (30-80 Hz) across the whole brain, 3) a decrease in delta (1-4 Hz) and beta (12-30 Hz) power across the whole cortex. Changes were also localised in subcortical regions, particularly in the diencephalon/thalamus. The GFC analysis showed a similar pattern of power changes across the 6-8 Hz, delta, and beta bands; however, GFC in the gamma band decreased. Again, the GFC analysis revealed changes in connectivity within subcortical structures, primarily in the thalamus. None of the measures was affected in the alpha band (8-12 Hz). These findings emphasise behavioural state as a critical factor influencing EEG outcomes, with important implications for the standardisation and translational validity of preclinical neurophysiological studies.

neuroscience↗

bioRxiv2026

Resting-state EEG alpha-BOLD coupling spatially follows cortical cell-type and receptor gradients

The coupling between electroencephalography (EEG) and blood-oxygen-level-dependent (BOLD) signals has been investigated across numerous studies, but its neurobiological underpinnings remain poorly understood. Resting-state EEG alpha-BOLD coupling follows a characteristic spatial pattern, shifting from negative correlations in sensory regions to positive correlations in association cortices. In this study, we examined neurobiological correlates of resting-state alpha-BOLD coupling. We compared the spatial pattern of the alpha-BOLD coupling map to 82 cortical feature maps, including gene expression profiles of different cell types and receptor subunits as well as structural MRI measures. We identified three statistically significant (q < 0.05 FDR-corrected) maps: the layer 6 VIP interneuron marker, excitatory layer-5 marker, and NMDA receptor subunit GRIN2C. The three significant gene maps, combined in a multiple linear regression model, explained R2 = 0.312 of the spatial variance in alpha-BOLD coupling. Analysis of the spatial mismatch between cortical maps and the alpha-BOLD coupling map revealed that the early auditory cortex is the region that consistently diverges from predictions across gene expression and T1/T2 maps. The spatial correspondence between alpha-BOLD coupling and gene expression profiles of specific receptor subunits, neuronal types, and layer-specific populations identifies these as concrete candidates for future computational and experimental studies of alpha-BOLD coupling. Author SummaryThe brains electrical rhythms and metabolic activity are coupled, yet why this coupling differs across brain regions remains poorly understood. This study shows that resting-state alpha-BOLD coupling, a well-established link between EEG alpha oscillations and fMRI signals, maps onto the brains cellular landscape: regions enriched in specific inhibitory interneurons and NMDA receptor subunits show systematically different coupling strengths. These findings suggest that regional differences in cell-type composition and receptor expression, rather than purely anatomical features, could shape the spatial organization of alpha-BOLD coupling. By identifying candidate cortical features, this work can guide future experimental and computational studies, ultimately helping to establish alpha-BOLD coupling as a relevant biomarker for psychiatric and neurological disorders.

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

Unsupervised Approach for Electric Signal Separation in Gnathonemus petersii: Linking Behavior and Electrocommunication

The transfer of information between individuals is fundamental to living systems. Therefore, communication should be studied in various species. Weakly electric fish, Gnathonemus petersii, provides a unique model organism for such investigations due to its advanced electrocommunication capabilities, using electric organ discharges (EODs). Separating EODs from multiple individuals is crucial yet challenging. To remediate it, we developed an unsupervised algorithm for EOD separation in two free-swimming individuals. Using continuous wavelet transform, t-distributed Stochastic Neighbor Embedding, and hierarchical clustering, we achieved accurate discrimination of EODs without the necessity of any training data. This approach overcomes the supervised algorithms based on previously published methods in accuracy and computational efficiency, simplifies experimental procedures, and supports animal well-being by reducing the number of required measurements. Additionally, we introduced a novel technique to map electric signals onto auditory representations, facilitating intuitive analysis of EOD sequences. These advancements lay the groundwork for future studies of EOD-based communication, highlighting the potential of Gnathonemus petersii in neuroethological, psychopharmacological, and translational research.

animal behavior and cognition↗

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↗

bioRxiv2024

Unveiling Stimulus Transduction Artifacts in Auditory Steady-State Response Experiments: Characterization, Risks, and Mitigation Strategies

This scientific paper addresses the potential risk of spurious responses in neuroscientific auditory steady-state response (ASSR) experiments attributed to transduction artifacts. The focus is particularly on click train stimuli, given their spectral content in the frequency range of interest (e.g., 40 Hz). Building upon a pilot experiment demonstrating the existence of the artifact in a phantom head, this study focuses on the characterization of stimulus artifacts in extended measurements with phantoms and the evaluation of associated risks in experiments involving human subjects. The investigation is divided into two parts: the first part scrutinizes stimulus artifact properties crucial for mitigation, while the second part assesses risks in ASSR experiments with human subjects based on the characterized artifact. The discussion covers stimulus characterization, experimental setups with phantoms, and experiments with human subjects, exploring potential sources of the artifact, its spatial properties, and the influence of re-referencing. The results reveal the role of headphone cables as a source of stimulus artifacts, along with the surprising impact of headphone transducers. The study emphasizes the need for careful experimental design and data analysis to avoid misinterpretations of stimulus artifacts as genuine brain responses in ASSR experiments.

neuroscience↗