Biology subjects

Ossadtchi, A.

Publications and source records attributed to Ossadtchi, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Tracking propagating cortical activity in MEG/EEG with a bilinear state-space model

Magnetoencephalography (MEG) and electroencephalography (EEG) are ideal for studying macroscopic neural dynamics, but non-invasive tracking of cortical traveling waves remains a major methodological challenge. Traditional inverse solutions assume spatiotemporal separability, restricting sources to fixed spatial topographies. Consequently, they struggle to capture the continuous spatial migration of cortical traveling waves and often misinterpret phase-locked static sources as spurious propagation. To address this fundamental limitation, we propose a dynamic state-space framework that explicitly accommodates the spatiotemporal inseparability of propagating neural activity. Our approach models the sensor signal as a bilinear combination of two states that evolve together: a fast, narrowband stochastic oscillator carrying the electrical time course, and a slowly evolving spatial topography that drifts through a data-driven singular value decomposition subspace. Both the rhythmic electrical time series and the migrating source trajectory track jointly via an Unscented Kalman Filter. We evaluated the method on realistically simulated MEG data and empirical resting-state MEG and EEG recordings targeting the occipital alpha rhythm. In simulations, the approach accurately recovered electrical time courses and spatial trajectories across varying signal-to-noise ratios, spatial envelope velocities up to 0.1 m/s, and distinct cortical geometries (calcarine and central sulci), significantly outperforming traditional minimum norm estimation and dipole fitting. Crucially, the model resists fabricating spurious propagation trajectories when presented with stationary, coherent dipoles. Application to empirical MEG and EEG recordings of the occipital alpha rhythm yields anatomically plausible, temporally cohesive propagation paths that explain significantly more sensor-level variance than static baselines. By embedding the evolving source geometry directly into the inverse solution, this framework provides a robust, proof-of-concept tool for the non-invasive investigation of macroscopic propagating brain dynamics.

neuroscience↗

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

Interoceptive training enhances emotional awareness and body image perception: evidence from improved heartbeat detection and psychological outcomes

The conscious integration of interoceptive signals, such as heartbeats and breathing, is essential for emotional regulation, self-awareness, and stress resilience. We conducted a controlled trial implementing a 5-day biofeedback-guided interoceptive training program to assess its impact on interoceptive accuracy (IAc) and psychological dimensions of bodily and mental perception. The experimental group showed significant improvements in IAc compared to controls, reflecting enhanced detection of heartbeats. Training increased scores on two MAIA subscales: "Awareness of Negative Emotional States" and "Not Distracting". These outcomes suggest the intervention enhanced participants capacity to acknowledge and engage with unpleasant bodily sensations without resorting to avoidance or distraction strategies. A trend toward improved body image perception emerged in the experimental group, with gains correlating with reductions in depressive symptoms, underscoring the interplay between bodily awareness and emotional well-being. These findings underscore that targeted interoceptive training represents a powerful tool for refining awareness of internal states, enhancing attentional control, and optimizing emotional processing. By bridging bodily signals and affective experiences, this approach offers a pathway for innovative, evidence-based therapies to bolster emotional resilience and address conditions rooted in dysregulated emotional states, such as anxiety, depression, and stress-related disorders.

physiology↗

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

SIGNAL: Dataset for Semantic and Inferred Grammar Neurological Analysis of Language

Recently, the idea of comparison of models representations and human brain signals has been a topic of several works. Consequently, several datasets with text data and EEG representations have been published. However, most of the datasets are based on normal reading task with grammatical sentences. At the same time, in the interpretability studies of LLMs, more and more attention is paid to thoroughly designed linguistic tasks based on acceptability measures. In this paper, we present SIGNAL, a dataset for Semantic and Inferred Grammar Neurological Analysis of Language. Our dataset contains a group of sentences with a combination of a fully acceptable sentence and a grammatically or/and semantically incongruent sentences. The dataset has been approved by native speakers and later used for an EEG experiment. In total, our dataset contains recordings of 21 participants, each of whom read 600 sentences. In addition, we present a pilot study where we compare EEG analysis with simple probing experiments.

neuroscience↗

bioRxiv2024

Beyond the Surface: Revealing the Depths of Brain Activity by Predicting fMRI from EEG with Deep Learning

AO_SCPLOWBSTRACTC_SCPLOWElectroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the two most commonly used non-invasive methods for studying brain function, having different but complementary strengths: high temporal resolution of the former and high spatial resolution of the latter. Crucially, fMRI is vital for studying subcortical areas, as those are practically out of reach for conventional EEG. At the same time, EEG is cost-effective and, thus, often preferable to fMRI if comparable information could be extracted which is not the case then the deep subcortical brain activity is of interest. Here we explore the possibility of recovering subcortical hemodynamics from the non-invasively recorded scalp EEG signals. To this end, we have developed a lightweight EEG-to-fMRI neural network and using an extended and publicly available dataset with concurrently recorded EEG-fMRI data show that our model allows for the prediction of the detailed Blood Oxygenation Level Dependent (BOLD) activity of 7 bilaterally symmetric subcortical structures solely from multichannel EEG data. We report the performance significantly above chance and exceeding the scores achieved for a single subcortical structure and obtained on the proprietary datasets. In contrast to the studies focusing on a single subcortical region in our approach we were able to decode multichannel EEG into 14 + 4 region-specific variations of BOLD signals measured relative to their mean hemodynamic activity. The use of relative BOLD signals allowed us to exert control over the artificial inflation of decoding accuracy scores when the decoder predicts the common mode component that is likely to have a non-neuronal origin (heartbeat, movement, etc). Finally, we interpreted our model. The electrical activity of the sensorimotor cortex appeared to contribute most to the prediction of the subcortical hemodynamics. Also, the hemodynamics of the thalamus has the smallest delay with respect to the EEG signals. Both observations are physiologically plausible and ensure the potential reliability of the decoder. Taken together, these findings pave the road towards the creation of low-cost AI-powered EEG-based fMRI digital twin technology capable of tracking subcortical activity in an ecological setting. The technology, once mature, will find numerous applications from fundamental neuroscience through diagnostics to neurorehabilitation and affective neurointerfaces.12

neuroscience↗

bioRxiv2024

Representational dissimilarity component analysis (ReDisCA)

The principle of Representational Similarity Analysis (RSA) posits that neural representations reflect the structure of encoded information, allowing exploration of spatial and temporal organization of brain information processing. Traditional RSA when applied to EEG or MEG data faces challenges in accessing activation time series at the brain source level due to modeling complexities and insufficient geometric/anatomical data. To address this, we introduce Representational Dissimilarity Component Analysis (ReDisCA), a method for estimating spatial-temporal components in EEG or MEG responses aligned with a target representational dissimilarity matrix (RDM). ReDisCA yields informative spatial filters and associated topographies, offering insights into the location of "representationally relevant" sources. Applied to evoked response time series, ReDisCA produces temporal source activation profiles with the desired RDM. Importantly, while ReDisCA does not require inverse modeling its output is consistent with EEG and MEG observation equation and can be used as an input to rigorous source localization procedures. Demonstrating ReDisCAs efficacy through simulations and comparison with conventional methods, we show superior source localization accuracy and apply the method to real EEG and MEG datasets, revealing physiologically plausible representational structures without inverse modeling. ReDisCA adds to the family of inverse modeling free methods such as independent component analysis [34], Spatial spectral decomposition [41], and Source power comodulation [9] designed for extraction sources with desired properties from EEG or MEG data. Extending its utility beyond EEG and MEG analysis, ReDisCA is likely to find application in fMRI data analysis and exploration of representational structures emerging in multilayered artificial neural networks.

neuroscience↗

bioRxiv2023

Short-Term Meditation Training Alters Brain Activity and Sympathetic Responses at Rest, but not during the meditation

Numerous studies have shown that meditation has a number of positive effects on the physical and psychological well-being of practitioners. As a result, meditation has become widely practiced not only as a religious practice but also as a self-regulation technique to achieve specific measurable goals. This raises the question of how quickly physiological changes can be noticed in individuals for whom meditation is not the main focus of their lives but rather a wellbeing keeping technique. Another question is whether it is possible to observe changes occurring directly during meditation and use bio- or neuro-feedback to enhance such meditation training and achieve tangible results. In our study, the experimental group of individuals with no previous meditation experience underwent eight weeks of training in Taoist meditation (2 sessions lasting 1 hour each week), under the guidance of a certified instructor. Participants in the control group attended offline group meetings during the same period, where they listened to audio books. All participants performed meditation testing before and after the intervention, following audio instructions. During the meditation practice, participants EEG, photoplethysmogram, respiratory rate, and skin conductance were recorded. The meditation training, but not the control group activity, resulted in topically organized changes of the resting state brain activity and heart rate variability. Specifically, we observed an increase in EEG power in multiple frequency bands (delta, theta, alpha, beta) and changes in the heart rate variability indicators associated with sympathetic system activation. However, no significant changes were observed when we compared the physiological indicators during the actual meditation process performed prior and post the 8-week training. We interpret these changes as signs of increased alertness and possibly accelerated resting metabolic rate. Importantly, these changes were observed after only 16 hours of meditation training performed during the 8-week period of time. The absence of difference in the band-specific power profiles between the experimental and control groups during the process of meditation conceptually complicates the development of assistive devices aimed at "guiding" the novice meditators during the actual meditation. Our results suggest that the focus in creating such digital assistants should rather be shifted towards monitoring neurophysiological activity during the time intervals outside of the actual meditation. The apparent changes occur not only in the EEG derived parameters but are also detectable based on the markers of autonomous nervous system activity that can be readily registered with a range of wearable gadgets which renders hope for a rapid translation of our results into practical applications.

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

Speech decoding from a small set of spatially segregated minimally invasive intracranial EEG electrodes with a compact and interpretable neural network

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSSpeech decoding, one of the most intriguing BCI applications, opens up plentiful opportunities from rehabilitation of patients to direct and seamless communication between human species. Typical solutions rely on invasive recordings with a large number of distributed electrodes implanted through craniotomy. Here we explored the possibility of creating speech prosthesis in a minimally invasive setting with a small number of spatially segregated intracranial electrodes. MethodsWe collected one hour of data (from two sessions) in two patients implanted with invasive electrodes. We then used only the contacts that pertained to a single sEEG shaft or an ECoG stripe to decode neural activity into 26 words and one silence class. We employed a compact convolutional network-based architecture whose spatial and temporal filter weights allow for a physiologically plausible interpretation. ResultsWe achieved on average 55% accuracy using only 6 channels of data recorded with a single minimally invasive sEEG electrode in the first patient and 70% accuracy using only 8 channels of data recorded for a single ECoG strip in the second patient in classifying 26+1 overtly pronounced words. Our compact architecture did not require the use of pre-engineered features, learned fast and resulted in a stable, interpretable and physiologically meaningful decision rule successfully operating over a contiguous dataset collected during a different time interval than that used for training. Spatial characteristics of the pivotal neuronal populations corroborate with active and passive speech mapping results and exhibit the inverse space-frequency relationship characteristic of neural activity. Compared to other architectures our compact solution performed on par or better than those recently featured in neural speech decoding literature. ConclusionsWe showcase the possibility of building a speech prosthesis with a small number of electrodes and based on a compact feature engineering free decoder derived from a small amount of training data.

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↗

bioRxiv2021

Cortical and autonomic responses during staged Taoist meditation: two distinct meditation strategies.

Meditation is a consciousness state associated with specific physiological and neural correlates. Numerous investigations of these correlates reported controversial results which prevented a consistent depiction of the underlying neurophysiological processes. Here we investigated the dynamics of multiple neurophysiological indicators during a staged meditation session. We measured the physiological changes at rest and during the guided Taoist meditation in experienced meditators and naive subjects. We recorded EEG, respiration, galvanic skin response, and photoplethysmography. All subjects followed the same instructions split into 16 stages. In the experienced meditators group we identified two subgroups with different physiological markers dynamics. One subgroup showed several signs of general relaxation evident from the changes in heart rate variability, respiratory rate, and EEG rhythmic activity. The other subgroup exhibited mind concentration patterns primarily noticeable in the EEG recordings while no autonomic responses occurred. The duration and type of previous meditation experience or any baseline indicators we measured did not explain the segregation of the meditators into these two groups. These results suggest that two distinct meditation strategies could be used by experienced meditators, which partly explains the inconsistent results reported in the earlier studies evaluating meditation effects. Our findings are also relevant to the development of the high-end biofeedback systems.

physiology↗

bioRxiv2021

Evolution of MEG: a first MEG-feasible fluxgate magnetometer

In the current article we present a first solid-state sensor feasible for magnetoencephalography (MEG), and working at room temperature. The sensor is a fluxgate magnetometer based on yttrium-iron garnet films (YIGM). In this feasibility study we prove the concept of usage the YIGM in terms of MEG by registering a simple brain induced field: the human alpha rhythm 1. All the experiments and results are validated with usage of another kind of high-sensitive magnetometers - optically pumped magnetometer (OPM), which currently appears to be well-established in terms of MEG.

neuroscience↗

bioRxiv2020

A P300 brain-computer interface with a reduced visual field

A P300 brain-computer interface (BCI) is a paradigm, where text characters are decoded from visual evoked potentials (VEPs). In a popular implementation, called P300 speller, a subject looks at a display where characters are flashing and selects one character by attending to it. The selection is recognized by the strongest VEP. The speller performs well when cortical responses to target and non-target stimuli are sufficiently different. Although many strategies have been proposed for improving the spelling, a relatively simple one received insufficient attention in the literature: reduction of the visual field to diminish the contribution from non-target stimuli. Previously, this idea was implemented in a single-stimulus switch that issued an urgent command. To tackle this approach further, we ran a pilot experiment where ten subjects first operated a traditional P300 speller and then wore a binocular aperture that confined their sight to the central visual field. Visual field restriction resulted in a reduction of non-target responses in all subjects. Moreover, in four subjects, target-related VEPs became more distinct. We suggest that this approach could speed up BCI operations and reduce user fatigue. Additionally, instead of wearing an aperture, non-targets could be removed algorithmically or with a hybrid interface that utilizes an eye tracker. We further discuss how a P300 speller could be improved by taking advantage of the different physiological properties of the central and peripheral vision. Finally, we suggest that the proposed experimental approach could be used in basic research on the mechanisms of visual processing.

neuroscience↗

bioRxiv2020

The importance of being on time: delayed neurofeedback impedes training of the parietal alpha-band power

The therapeutic effects of neurofeedback (NFB) remain controversial. Here we show that visual NFB of parietal electroencephalographic (EEG) alpha-activity is efficient only when delivered to human subjects at short latency, which guarantees that NFB arrives when an alpha spindle is still ongoing. NFB was displayed either as soon as EEG envelope was processed, or with an extra 250 or 500-ms delay. The time course of NFB-induced changes in the alpha rhythm clearly depended on NFB latency, as shown with the adaptive Neyman test. NFB had a strong effect on the alpha-spindle incidence rate, but not on their duration or amplitude. The sustained changes in alpha activity measured after the completion of NFB training were negatively correlated to latency, with the maximum change for the shortest tested latency and no change for the longest. Such a considerable effect of NFB latency on the alpha-activity temporal structure could explain some of the previous inconsistent results, where latency was neither controlled nor documented. Clinical practitioners and manufacturers of NFB equipment should add latency to their specifications while enabling latency monitoring and supporting short-latency operations.

neuroscience↗

bioRxiv2020

Decoding neural signals with a compact and interpretable convolutional neural network

AO_SCPLOWBSTRACTC_SCPLOWBrain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. In recent years, we have seen an emergence of new algorithms for BCI decoding including those based on the deep-learning principles. Here we describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time, such as the one described here. In these architectures the weights are optimized not only to align with the target sources but also to tune away from the interfering ones, in both the spatial and the frequency domains. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models. We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI IV competition dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Moreover, using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from another ECoG dataset with known sensor positions. As such, the proposed solution offers a good decoder and a tool for investigating neural mechanisms of motor control.

bioengineering↗

bioRxiv2020

Local propagation dynamics of MEG interictal spikes: source reconstruction with traveling-wave priors

AO_SCPLOWBSTRACTC_SCPLOWEpilepsy is one of the most common neurological disorders, with about 30% of cases being drug-resistant and requiring surgical intervention. To localize the epileptogenic zone (EZ), the pathological area that has to be surgically removed, brain regions are inspected for the presence of spikes during the interictal periods. This procedure maps irritative zones where spikes are present, but it is still challenging to determine which of the irritative zones generate seizures. To localize the source of seizures more precisely, a large-scale approach could be applied where the causal relationship is assessed between the signals recorded in a finite number of irritative zones [27]. This method however, does not reveal the fine-grained spatiotemporal patterns of spikes, which could provide valuable information regarding EZ location and increase the likelihood of surgery success [33]. Here we present a framework to noninvasively investigate the fine patterns of interictal spikes present in magnetoencephalographic (MEG) data. We use a traveling wave model, previously employed in the analysis of cortical alpha oscillations [16], to regularize the MEG inverse problem and to determine the cortical paths of spike traveling waves. Our algorithm represents spike propagation patterns as a superposition of local waves traveling along radial paths stemming from a single origin. With the help of the positively constrained LASSO technique we scan over wave onset moment and propagation velocity parameters to determine their combination that yields the best fit to the MEG sensor data of each spike. We first used realistically simulated MEG data to validate the algorithm ability to successfully track interictal activity on a millimeter-millisecond scale. Next, we examined MEG data from three patients with drug-resistant epilepsy. Wave-like spike patterns with clear propagation dynamics were found in a fraction of spikes, whereas the other fraction could not be explained by the wave propagation model with a small number of propagation directions. Moreover, in agreement with the previous work [33], the spike waves with clear propagation dynamics exhibited spatial segregation and matched the clinical records on seizure onset zones (SOZs) available for two patients out of three.

neuroscience↗