Biology subjects

Moiseev, A.

Publications and source records attributed to Moiseev, A..

6 recordsLinked to original sources

bioRxiv2025

Source Reconstruction of Resting-State MEG and EEG Activity: A Technical Note on the Choice of Noise Covariance

Minimum variance beamforming is widely used to reconstruct neural sources from MEG and EEG data, but results critically depend on the choice of noise covariance. In task-based studies, this is often defined from pre-stimulus baselines, but for resting-state data the problem presents a fundamental challenge. Conventional solutions, such as empty-room recordings or diagonal white sensor noise, are not optimal. They either ignore brain-generated noise or yield artificial, non-uniform source-level baselines that can distort results. Our approach is to define a baseline at the source level as a uniform distribution of uncorrelated, randomly oriented neural dipoles, representing a maximum-entropy "ground state" of brain activity. Projecting this source model through the electromagnetic lead fields yields a sensor-level covariance that captures realistic spatial correlations. A data-driven constraint scales the model to match measured data, ensuring a physically admissible solution. Applied to real human resting-state data, the method produces a structured, non-uniform sensor covariance dictated by participants anatomy, source reconstructions that are smooth and plausible, and free from the artificial peaks induced by diagonal noise models. This source-level approach provides a principled and physiologically grounded baseline for beamforming and improves the reliability of resting-state analyses and interpretation.

bioengineering↗

bioRxiv2021

Simulating brain signals with predefined mutual correlations

ObjectiveWhen modeling task-related human brain activity it is often necessary to simulate brain signals with specific mutual correlations between them. The signals should resemble those observed in practice, and consist of an "evoked" ("phase-locked") component and a random oscillatory part. To be neurophysiologically plausible their waveforms must be shaped in a certain way or exhibit specific global features; in technical terms - they should be modulated by a certain envelope function. The goal of this technical note is to describe a simple way of how such signal sets can be obtained. MethodsWe derive a procedure which allows generating multi-epoch signals with the above properties. This is done by mixing a "seed" set of waveforms typically reflecting particular qualities of the target brain activity. As an example, the seed set can consist of realizations of colored noise with desired power spectrum, or can be obtained from real brain measurements. ResultsThe algorithm yields a set of n multi-epoch signals with specified mutual correlations. Evoked parts, oscillatory parts and global envelopes of the signals can be controlled independently in order to obtain desired properties of the generated time courses. ConclusionThe procedure provides versatile sets of mutually correlated signals suitable for modeling task-related brain activity. SignificanceIn contrast to other methods often relying on complicated computations, the suggested approach is straightforward and easy to apply in everyday practical work, yet yielding realistic "functionally connected" simulated brain signals.

neuroscience↗

bioRxiv2021

Subspace based Multiple Constrained Minimum Variance (SMCMV) Beamformers

In MEG and EEG brain imaging research two popular approaches are often used for spatial localization of focal task- or stimuli-related brain activations. One is a so called MUSIC approach applied in the form of RAP or TRAP MUSIC algorithms. Another one is the beamformer approach, specifically multiple constrained minimum variance (MCMV) beamformer when dealing with significantly correlated activations. Either method is using its own source localizer functions. Considering simplicity, accuracy and computational efficiency both approaches have their advantages and disadvantages. In this study we introduce a novel set of so called Subspace MCMV (or SMCMV) beamformers whose localizer functions combine MUSIC and MCMV localizers. We show that in ideal situations where forward modeling, data recording and noise measurements are error-free, SMCMV localizers allow precise identification of n arbitrarily correlated sources irrespective to their strength in just n scans of the brain volume using RAP MUSIC type algorithm. We also demonstrate by extensive computer simulations that with respect to source localization errors and the total number of identified sources SMCMV outperforms both the TRAP MUSIC and MIA MCMV (which is the most accurate MCMV algorithm to our knowledge) in non-ideal practical situations, specifically when the noise covariance cannot be estimated precisely, signal to noise ratios are small, source correlations are significant and larger numbers of sources are involved.

neuroscience↗

bioRxiv2020

Determination and correction of aberrations in full field OCT using phase gradient autofocus by maximizing the likelihood function

A method for numerical estimation and correction of aberrations of the eye in fundus imaging with optical coherence tomography (OCT) is presented. Aberrations are determined statistically by using the estimate based on likelihood function maximization. The method can be considered as an extension of the phase gradient autofocusing algorithm in synthetic aperture radar imaging to 2D optical aberrations correction. The efficiency of the proposed method has been demonstrated in OCT fundus imaging with 6{lambda} aberrations. After correction, single photoreceptors were resolved. It is also shown that wavefront distortions with high spatial frequencies can be determined and corrected. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY Graphical Abstract for Table of Contents[Text. This work is dedicated to development a method for numerical estimation and correction of aberrations of the eye in fundus imaging with OCT. Aberration evaluation is performed statistically by using estimate based on likelihood function maximization. The efficiency of the proposed method has been demonstrated in OCT fundus imaging with 6{lambda} aberrations. It has been shown that spatial high-frequency wavefront distortions can be determined] O_FIG_DISPLAY_R [Figure 2] M_FIG_DISPLAY C_FIG_DISPLAY

biophysics↗

bioRxiv2019

Neuromagnetic activation dynamics of stimulus-locked processing during a naturalistic viewing

Naturalistic stimuli such as watching a movie while in the scanner provide an ecologically valid paradigm that has the potential of extracting valuable information on how the brain processes complex stimuli in a short period of time. Naturalistic viewing is also easier to conduct with challenging participant groups including patients and children. Given the high temporal resolution of MEG, in the present study, we demonstrate how a short movie clip can be used to map distinguishable activation dynamics underlying the processing of specific classes of visual stimuli such as face and hand manipulations, as well as auditory stimuli with words and non-words.\n\nMEG data were collected from 22 healthy volunteers (6 females, 3 left handed, mean age - 27.7 {+/-} 5.28 years) during the presentation of naturalistic audiovisual stimuli. The MEG data were split into trials with the onset of the stimuli belonging to classes of interest (words, non-words, faces, hand manipulations). Based on the components of the averaged sensor ERFs time-locked to the visual and auditory stimulus onset, four and three time-windows, respectively, were defined to explore brain activation dynamics. Pseudo-Z, defined as the ratio of the source-projected time-locked power to the projected noise power for each vertex, was computed and used as a proxy of time-locked brain activation. Statistical testing using the mean-centered Partial Least Squares analysis indicated periods where a given visual or auditory stimuli had higher activation. Based on peak pseudo-Z differences between the visual conditions, time-frequency resolved analyses were carried to assess beta band desynchronization in motor-related areas, and inter-trial phase synchronization between face processing areas. Our results provide the first evidence that activation dynamics in canonical brain regions associated with the processing of particular classes of visual and auditory stimuli (words, faces, etc.) can be reliably mapped using MEG during presentation of naturalistic stimuli. Given the strength of MEG for brain mapping in temporal and frequency domains, the use of naturalistic stimuli may open new techniques in analyzing brain dynamics during ecologically valid sensation and perception.\n\nHighlightsO_LIA time-locking analysis was employed in naturalistic stimuli paradigm.\nC_LIO_LISpecific visual and auditory stimuli from the movie were mapped in brain space.\nC_LIO_LIMotor {beta}-suppression was evident in periods of watching hand manipulation.\nC_LIO_LIIncreased synchronization between core face-processing areas was found around 200 and 300ms in the face condition.\nC_LIO_LINaturalistic viewing paradigms provide a reliable approach for investigating brain dynamics.\nC_LI

neuroscience↗

bioRxiv2019

Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses

Functional brain connectivity is increasingly being seen as critical for cognition, perception and motor control.Magnetoencephalography and electroencephalography are modalities that offer noninvasive mapping of electrophysiological interactions among brain regions, yet suffer from signal leakage and signal cancellation when estimating brain activity. This leads to biased connectivity values which complicate interpretation. In this study, we test the hypothesis that a Multiple Constrained Minimum Variance beamformer (MCMV) outperforms the more traditional Linearly Constrained Minimum Variance beamformer (LCMV) for estimation of electrophysiological connectivity. To this end, MCMV and LCMV performance is compared in task related analyses with both simulated data and human MEG recordings of visual steady state signals, and in resting state analyses with simulated data and human MEG data of 89 subjects. In task related scenarios connectivity was estimated using coherence and phase locking values, whereas envelope correlations were used for the resting state data. We also introduce a novel Augmented Pairwise MCMV (APW-MCMV) approach for signal leakage suppression in resting state analyses and assess its performance against LCMV and more conventional MCMV approaches. We demonstrate that with MCMV effects of signal mixing and coherent source cancellation are greatly reduced in both task related and resting state conditions, while in contrast to other approaches 0-and short time lag interactions are preserved. In addition, we demonstrate that in resting state analyses, APW-MCMV strongly reduces spurious connections while better controlling for false negatives compared to more conservative measures such as symmetrical orthogonalization.

neuroscience↗