arXiv subjects

Daniil Vlasenko

Publications and source records attributed to Daniil Vlasenko.

6 recordsLinked to original sources

arXiv2026

Hypergraphs from multivariate connectivity: caCOH-based EEG/MEG representation

Hypergraphs provide a natural framework for representing neurophysiological interactions distributed across sets of sensors. A key methodological question is how hyperedges should be defined from frequency-resolved electroencephalography/magnetoencephalography (EEG/MEG) data. We demonstrate a construction strategy in which hyperedges are obtained from canonical coherence (caCOH), an extension of coherence that estimates coupling between multidimensional signal spaces. To our knowledge, this is the first work to construct hypergraphs directly from a multivariate connectivity measure specifically designed for frequency-resolved neurophysiological analysis. We propose two caCOH-based representations: a one-to-space hypergraph, where each external signal defines a hyperedge over the EEG/MEG sensor space, and a space-to-space hypergraph, where two multidimensional signal spaces are represented by a single hyperedge. We evaluate the approach in controlled simulations with known coupling frequencies and varying signal-to-noise ratio (SNR). Compared with graphs based on magnitude-squared coherence (MSC), caCOH-based hypergraphs showed statistically higher target-baseline contrasts at almost all SNR levels, indicating stronger recovery of coupling frequencies. They also recovered sensor-level spatial patterns associated with the simulated sources. In addition, one-to-space and space-to-space representations reduced 610 MSC edges per frequency to 10 and 1 hyperedges, respectively. These results establish multivariate spectral connectivity as a natural methodological basis for EEG/MEG hypergraphs.

q-bio.QM↗

arXiv2025

Dynamic Functional Connectivity Features for Brain State Classification: Insights from the Human Connectome Project

We analyze functional magnetic resonance imaging (fMRI) data from the Human Connectome Project (HCP) to match brain activities during a range of cognitive tasks. Our findings demonstrate that even basic linear machine learning models can effectively classify brain states and achieve state-of-the-art accuracy, particularly for tasks related to motor functions and language processing. Feature importance ranking allows to identify distinct sets of brain regions whose activation patterns are uniquely associated with specific cognitive functions. These discriminative features provide strong support for the hypothesis of functional specialization across cortical and subcortical areas of the human brain. Additionally, we investigate the temporal dynamics of the identified brain regions, demonstrating that the time-dependent structure of fMRI signals are essential for shaping functional connectivity between regions: uncorrelated areas are least important for classification. This temporal perspective provides deeper insights into the formation and modulation of brain neural networks involved in cognitive processing.

q-bio.NC↗

arXiv2025

Ensemble-based graph representation of fMRI data for cognitive brain state classification

fMRI is a non-invasive technique for investigating brain activity, offering high-resolution insights into neural processes. Understanding and decoding cognitive brain states from fMRI depends on how functional interactions are represented. We propose an ensemble-based graph representation in which each edge weight encodes state evidence as the difference between posterior probabilities of two states, estimated by an ensemble of edge-wise probabilistic classifiers from simple pairwise time-series features. We evaluate the method on seven task-fMRI paradigms from the Human Connectome Project, performing binary classification within each paradigm. Using compact node summaries (mean incident edge weights) and logistic regression, we obtain average accuracies of 97.07-99.74 %. We further compare ensemble graphs with conventional correlation graphs using the same graph neural network classifier; ensemble graphs consistently yield higher accuracy (88.00-99.42 % vs 61.86-97.94 % across tasks). Because edge weights have a probabilistic, state-oriented interpretation, the representation supports connection- and region-level interpretability and can be extended to multiclass decoding, regression, other neuroimaging modalities, and clinical classification.

q-bio.NC↗

arXiv2021

Black Holes LIGO/Virgo Domination and Single-lined Binaries with a Black Hole Candidate Component

In this letter, we note that the observed in the LIGO / Virgo experiment ratio of the detection rate of black holes to the rate of detection of binary neutron stars requires the assumption of a "conservative" collapse of massive stars into a black hole: almost all the mass of the collapsing star goes under the horizon. This is consistent with the large masses of black holes detected by LIGO/Virgo. On the other hand, the assumption of a small loss of matter during the collapse into a black hole is in good agreement with the small eccentricity of Single-lined Binaries. At the same time, the absence of X-rays from most black holes in binary systems with blue stars is explained. We argue that three sets of LIGO / Virgo observations and data on the Single-lined Binary with a Candidate Black Hole Component confirm the scenario of the evolution of massive field binaries.

astro-ph.HE↗

arXiv2017

Discovery of the neutron stars merger GW170817/GRB170817A and Binary Stellar Evolution

The Multimessenger discovery of the merger of two neutron stars on August 17, 2017, GW170817 / GRB170817A, accompanied by a gamma-ray burst and an optical kilonova, is a triumph of the ideas about the evolution of the baryon component in the Universe. Despite the current uniqueness of this observation, the obtained variety of experimental data makes it possible right now to draw important theoretical conclusions about the origin of the double neutron star, their merger, and the subsequent flare-up of the electromagnetic radiation. We present that the discovery of the merger at a distance of 40 Mpc is in full agreement with the very first calculations of the Scenario Machine (Lipunov et al. 1987). In modern terms, the predicted rate is ~ 10 000 Gpc-3.

astro-ph.HE↗

arXiv2017

Smooth optical self similar emission of gamma-ray bursts

We offer the new type of calibration for gamma-ray bursts (GRB), in which some class of GRB can be marked and has common behavior. We name this behavior Smooth Optical Self Similar Emission (SOS Similar Emission) and identify this subclass of gamma-ray bursts with optical light curves described by a universal scaling function.

astro-ph.HE↗