Biology subjects

Kleeva, D.

Publications and source records attributed to Kleeva, D..

7 recordsLinked to original sources

bioRxiv2026

Desync and connect: noninvasive MEG evidence of zero-phase coupling between locally desynchronized regions

A prevailing assumption in systems neuroscience is that long-range phase coupling between cortical regions requires locally synchronized oscillatory activity within each interacting area. Here we test whether this assumption holds at the macroscopic level by examining whether distal phase coupling can be detected from MEG data when both interacting regions undergo concurrent event-related desynchronization. Using controlled simulations with progressively disrupted local phase coherence, we first establish that the Context-Dependent PSIICOS (CD-PSIICOS) framework - a projection-based connectivity estimator that separates power-dominated spatial leakage from interaction-specific cross-spectral structure - can recover distal coupling under increasing local phase dispersion. We then apply this framework to empirical MEG recordings from a center-out reaching task in healthy participants. In both the alpha (8-13 Hz) and beta (15-25 Hz) bands, we identify time windows in which robust distal phase coupling emerges between regions exhibiting pronounced movement-related desynchronization. A rank-dependent subpopulation analysis, in which the projection rank is systematically varied to progressively suppress power-related confounds, classifies local networks into leakage-resistant and leakage-driven categories. In the alpha band, no local subpopulation exceeds baseline coupling levels at any projection rank, indicating that interhemispheric coordination operates without detectable local oscillatory support. In the beta band, a more graded pattern is observed: the earliest post-movement window shows above-baseline leakage-resistant local pairs consistent with residual subpopulation-level phase structure, while later windows show reduced decoupling without genuine local synchronization. Phase concentration analysis independently confirms that the detected distal interactions reflect genuine phase-locked coupling in both bands. These results demonstrate that local synchrony is not a necessary prerequisite for long-range phase coupling at the macroscopic level and that this dissociation is accessible to noninvasive MEG when leakage-aware connectivity estimation is employed.

neuroscience↗

bioRxiv2025

Qualitative and Quantitative Comparative Analysis of Common Normal Variants and Physiological Artifacts in MEG and EEG

AbstractMagnetoencephalography (MEG) and electroencephalography (EEG) provide complementary insights into brain activity, yet their distinct biophysical principles influence how normal neurophysiological patterns and artifacts are represented. This study presents a comprehensive qualitative and quantitative analysis of common physiological variants and artifacts in simultaneously recorded MEG and EEG data. We systematically examined patterns such as alpha spindles, sensorimotor rhythms, sleep-related waveforms (vertex waves, K-complexes, sleep spindles, and posterior slow waves of youth), as well as common artifacts including eye blinks, chewing, and movement-related interferences. By applying time-domain, time-frequency, and source- space analyses, we identified modality-specific differences in signal representation, source localization, and artifact susceptibility. Our results demonstrate that MEG provides a more spatially focal representation of physiological patterns, whereas EEG captures broader, radially oriented cortical activity. Mutual information analysis indicated that MEG-derived independent components exhibited greater topographical variability and higher information content for neurophysiological activity, while EEG components were more homogeneous. Signal-to-noise ratio (SNR) analysis confirmed that MEG gradiometers capture the highest total information, followed by magnetometers and then EEG. Notably, physiological signals such as vertex waves and K-complexes exhibited significantly higher total information in MEG, whereas EEG was more sensitive to high-amplitude artifacts, including swallowing and muscle activity. These findings highlight the distinct strengths and limitations of MEG and EEG, reinforcing the necessity of multimodal approaches in clinical and research applications to improve the accuracy of neurophysiological assessments.

neuroscience↗

bioRxiv2024

Context-Dependent PSIICOS: A Novel Framework for Functional Connectivity Estimation Accounting for Task-Related Power Leakage

Functional connectivity (FC) analysis using non-invasive neuroimaging methods, such as MEG and EEG, is often confounded by artifacts from spatial leakage and task-related power modulations. To address these limitations, we present Context-Dependent PSIICOS (CD-PSIICOS), a novel framework that improves the estimation of FC by incorporating task-specific cortical power distributions into the projection operator applied to the vectorized sensor-space cross-spectrum. Unlike the original PSIICOS (Phase Shift Invariant Imaging of Coherent Sources) approach, designed to suppress spatial leakage from all the sources, CD-PSIICOS dynamically adjusts the projection based on the active source distribution, enabling more accurate suppression of spatial leakage while preserving true zero-phase interactions. We validated CD-PSIICOS using realistic simulations and a multi-subject MEG dataset. The results demonstrate that CD-PSIICOS outperforms the original PSIICOS in suppressing artifacts at the lower projection ranks, maintaining robust detection of functional networks across theta and gamma frequency bands. By requiring lower projection ranks for optimal performance, CD-PSIICOS facilitates the reconstruction of physiologically relevant networks with improved sensitivity and stability.

neuroscience↗

bioRxiv2024

Scents Modulate Anxiety Levels, but Electroencephalographic and Electrocardiographic Assessments Could Diverge from Subjective Reports

Scents could modulate anxiety levels, such as anxiety in a medical office. Here we investigated the impact of two scents on the subjective and physiological anxiety markers in the dental office environment, utilizing self-reported anxiety assessments alongside physiological assessment with electroencephalographic (EEG) and electrocardiographic (ECG) measurements. Lavender was the first tested scent with the previously reported calming effect. African stone was the second stimulus with a musky scent. Twenty healthy participants took part in scent exposure sessions. Anxiety levels were assessed using the State-Trait Anxiety Inventory (STAI), EEG-based theta, alpha, and beta power ratios, and heart rate variability (HRV) indices derived from ECG data. Lavender exposure significantly decreased self-reported anxiety whereas African stone reduced physiological indicators of anxiety. Namely, African stone exposure led to decreased theta and increased alpha power in the parietal-occipital EEG signals. Additionally, decreases were observed in low-frequency (LF) HRV power and total HRV power, reflecting lowered autonomic arousal. These findings support the potential effectiveness for olfactory interventions to aid in anxiety management within clinical environments, but draw attention to the issue of proper evaluation of anxiety. In particular, the difference between the subjective reports and traditional EEG and HRV markers indicates that anxiety involves a complexity of factors, which makes its treatment by scents challenging.

neuroscience↗

bioRxiv2023

Resting-state EEG recorded with gel-based versus consumer dry electrodes: spectral characteristics and across-device correlations

Recordings of electroencephalographic (EEG) rhythms and their analyses have been instrumental in basic Neuroscience, clinical diagnostics, and the field of brain-computer interfaces (BCIs). While in the past such measurements have been conducted mostly in laboratory settings, recent advancements in dry electrode technology pave way to a broader range of consumer and medical application because of their greater convenience compared to gel-based electrodes. Here we conducted resting-state EEG recordings in two groups of healthy participants using three dry-electrode devices, the Neiry Headband, the Neiry Headphones and the Muse Headband, and one standard gel electrode-based system, the NVX. We examined signal quality for various spatial and spectral ranges which are essential for cognitive monitoring and consumer applications. Distinctive characteristics of signal quality were found, with the Neiry Headband showing sensitivity in low-frequency ranges and replicating the modulations of delta, theta and alpha power corresponding to the eyes-open and eyes-closed conditions, and the NVX system performing well in capturing high-frequency oscillations. The Neiry Headphones were more prone to low-frequency artifacts compared to the Neiry Headband, yet recorded modulations in alpha power and had a strong alignment with the NVX at higher frequencies. The Muse Headband had several limitations in signal quality. We suggest that while dry-electrode technology appears to be appropriate for the EEG rhythm-based applications the potential benefits of these technologies in terms of ease of use and accessibility should be carefully weighted against the capacity of each concrete system.

neuroscience↗

bioRxiv2023

PSIICOS projection optimality for EEG and MEG based functional coupling detection

Functional connectivity is crucial for cognitive processes in the healthy brain and serves as a marker for a range of neuropathological conditions. Non-invasive exploration of functional coupling using temporally resolved techniques such as MEG allows for a unique opportunity of exploring this fundamental brain mechanism in a reasonably ecological setting. The indirect nature of MEG measurements complicates the the estimation of functional coupling due to the spatial leakage effects. In previous work (Ossadtchi et al., 2018), we introduced PSIICOS, a method that for the first time allowed us to suppress the spatial leakage and yet retain information about functional networks whose nodes are coupled with close to zero or zero mutual phase lag. In this paper, we demonstrate analytically that the PSIICOS projection is optimal in achieving a controllable trade-off between suppressing mutual spatial leakage and retaining information about zero-phase coupled networks. We also derive an alternative solution using the regularization-based inverse of the mutual spatial leakage matrix and show its equivalence to the original PSIICOS. This approach allows us to incorporate the PSIICOS solution into the conventional source estimation framework. Instead of sources, the unknowns are the elementary networks and their activation timeseries are formalized by the corresponding source-space cross-spectral coefficients. Additionally, we outline potential avenues for future research to enhance functional coupling estimation and discuss alternative estimators that parallel the established source estimation approaches. Finally, we propose that the PSIICOS framework is well-suited for Bayesian techniques and offers a principled way to incorporate priors derived from structural connectivity.

neuroscience↗

bioRxiv2022

Fast parametric curve matching (FPCM) for automatic spike detection

Epilepsy is a widely spread neurological disease, whose treatment often requires resection of the pathological cortical tissue. Interictal spike analysis observed in the non-invasively collected EEG or MEG data offers an attractive way to localize epileptogenic cortical structures for surgery planning purposes. Interictal spike detection in lengthy multichannel data is a daunting task that is still often performed manually. This frequently limits such an analysis to a small portion of the data which renders the appropriate risks of missing the potentially epileptogenic region. While a plethora of automatic spike detection techniques have been developed each with its own assumptions and limitations, non of them is ideal and the best results are achieved when the output of several automatic spike detectors are combined. This is especially true in the low signal-to-noise ratio conditions. To this end we propose a novel biomimetic approach for automatic spike detection based on a constrained mixed spline machinery that we dub as fast parametric curve matching (FPCM). Using the peak-wave shape parametrization, the constrained parametric morphological model is constructed and convolved with the observed multichannel data to efficiently determine mixed spline parameters corresponding to each time-point in the dataset. Then the logical predicates that directly map to verbalized text-book like descriptions of the expected interictal event morphology allow us to accomplish the spike detection task. The results of simulations mimicking typical low SNR scenario show the robustness and high ROC AUC values of the FPCM method as compared to the spike detection performed using more conventional approaches such as wavelet decomposition, template matching or simple amplitude thresholding. Applied to the real MEG and EEG data from the human patients and to rat ECoG data, the FPCM technique demonstrates reliable detection of the interictal events and localization of epileptogenic zones concordant with independent conclusions made by the epileptologist. Since the FPCM is computationally light, tolerant to high amplitude artifacts and flexible to accommodate verbalized descriptions of the arbitrary target morphology, it may complement the existing arsenal of means for analysis of noisy interictal datasets.

neuroscience↗