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Kobeleva, X.

Publications and source records attributed to Kobeleva, X..

6 recordsLinked to original sources

Rhythmic replay of short-term memory neural patterns revealed by time-resolved error prediction

Theta oscillations are hypothesized to provide a temporal scaffold for short-term memory (STM). In this model, memory representations are organized into successive theta phases, reducing conflict between competing representations during encoding and maintenance. Previous studies have shown that sensory representations of memorized information are rhythmically reactivated at theta frequency. Whether this theta-rhythmic mechanism is shared across encoding and maintenance, traditionally viewed as temporally distinct processes, remains unclear. It is also unknown whether this rhythmic reactivation predicts subsequent memory fidelity rather than merely reflecting the reinstatement of sensory representations. We recorded EEG from 21 participants performing STM with colored, oriented items under low and high memory load. Using time-resolved multivariate pattern analysis, we predicted subsequent STM error from encoding- and maintenance-period activity. We found that distributed neural patterns predicted STM error and that this prediction fluctuated at theta frequency, independently of memory load and feature. Cross-temporal generalization indicated that a shared neural pattern recurred across encoding and into maintenance, consistent with periodic reactivation of a common neural code rather than a succession of distinct states. Prediction was temporally offset across spatial positions and item features, indicating that the features of a multi-feature object are encoded by separate, rhythmically interleaved processes. These findings characterize STM encoding and maintenance as a rhythmic, recurrent process and link theta-rhythmic fluctuations in memory fidelity to distributed cortical activity spanning multiple brain regions and frequency bands.

neuroscience↗

Bayesian Nonparametric Identification of Frequency-Selective Neural Oscillatory States

Identifying neural oscillations is essential for linking fast brain dynamics to underlying cognitive processes. However, this is challenging because oscillatory events can be brief, embedded in 1/f-like background activity, and may comprise an unknown number of spectrally distinct states. Conventional approaches often apply narrowband band-pass filters to one or a few predefined frequency bands and then use amplitude thresholding to identify oscillatory events, but detection outcomes can be highly sensitive to these choices. Although recent unsupervised alternatives based on hidden Markov models (HMMs) address these limitations, they still require the number of states to be specified in advance and can underfit or overfit when this number is misspecified. We propose a Bayesian nonparametric method that identifies distinct oscillatory states while inferring an appropriate number of states directly from the data. This method combines time-delay embedding (TDE) with the Dirichlet-process Gaussian mixture model (DP-GMM). TDE augments the signal with time-shifted copies, enabling the DP-GMM to capture frequency-specific local autocovariance structures, while the Dirichlet-process prior adapts model complexity by pruning inactive components. We benchmarked the approach against a filter-based thresholding method and the time-delay embedded HMM using single-channel synthetic data designed to mimic neural time series (e.g., EEG, MEG, and local field potentials), with multiple frequency components masked by 1/f-like noise. In this setting, the proposed model reliably recovered multiple distinct frequency components under noisy conditions while also inferring the number of oscillatory states. Applied to a resting-state motor-cortex MEG dataset, the model identified multiple frequency-selective, short-lived oscillatory states alongside distinct aperiodic states with different spectral profiles. These states exhibited substantial inter-individual heterogeneity in peak frequency, occurrence rate, and power. Overall, this provides an unsupervised framework for discovering frequency-selective oscillatory states without predefining frequency bands or fixing the number of states.

neuroscience↗

The global communication architecture of the human brain transcends the subcortical - cortical - cerebellar subdivisions

The white matter is made of anatomical fibres that constitute the highway of long-range connections between different parts of the brain. This network is referred to as the brains structural connectivity and lays the foundation of network interaction between brain areas. When analysing the architectural principles of this global network most studies have mainly focused on cortico-cortical and partly on cortico-subcortical connections. Here we show, for the first time, how the integrated cortical, subcortical, and cerebellar brain areas shape the structural architecture of the whole brain. We find that dense clusters vertically transverse cortical, subcortical, and cerebellar brain areas, which are themselves centralised by a global rich-club consisting similarly of cortical and subcortical brain areas. Notably, the most prominent hubs can be found in subcortical brain regions, and their targeted in-silico lesions proved to be most harmful for global signal propagation. Individually, the cortical, subcortical, and cerebellar sub-networks manifest distinct network features despite some similarities, which underline their unique structural fingerprints. Our results, exposing the heterogeneity of internal organisation across cortex, subcortex, and cerebellum, and the crucial role of the subcortex for the integration of the global anatomical pathways, highlight the need to overcome the prevalent cortex-centric focus towards a global consideration of the structural connectivity.

neuroscience↗

Whole-brain modeling of the differential influences of Amyloid-Beta and Tau in Alzheimer's Disease

BackgroundAlzheimers Disease is a neurodegenerative condition associated with the accumulation of two misfolded proteins, amyloid-beta (A{beta}) and tau. We study their effect on neuronal activity, with the aim of assessing their individual and combined impact. MethodsWe use a whole-brain dynamic model to find the optimal parameters that best describe the effects of A{beta} and tau on the excitation-inhibition balance of the local nodes. ResultsWe found a clear dominance of A{beta} over tau in the early disease stages (MCI), while tau dominates over A{beta} in the latest stages (AD). We identify crucial roles for A{beta} and tau in complex neuronal dynamics and demonstrate the viability of using regional distributions to define models of large-scale brain function in AD. ConclusionsOur study provides further insight into the dynamics and complex interplay between these two proteins, opening the path for further investigations on biomarkers and candidate therapeutic targets in-silico.

neuroscience↗

Spatiotemporal scales of dynamical functional networks - using whole-brain modelling to identify the optimal resolution

The brain rapidly processes and adapts to new information by dynamically switching between activity in whole-brain functional networks. In this whole-brain modelling study we investigate the relevance of spatiotemporal scale in whole-brain functional networks. This is achieved through estimating brain parcellations at different spatial scales (100-900 regions) and time series at different temporal scales (from milliseconds to seconds) generated by a whole-brain model fitted to fMRI data. We quantify a fingerprint of healthy dynamics quantifying the richness of the dynamical repertoire at each spatiotemporal scale by computing the entropy of switching activity between whole-brain functional networks. The results show that the optimal relevant spatial scale is around 300 regions and a temporal scale of around 150 milliseconds. Overall, this study provides much needed evidence for the relevant spatiotemporal scales needed to make sense of neuroimaging data.

neuroscience↗

Evidence of compensation in the brain networks of Lewy body dementia and Alzheimer’s disease patients

Dementia with Lewy bodies (DLB) and Alzheimers disease (AD) require differential management despite presenting with symptomatic overlap. A human electrophysiological difference is a decrease of dominant frequency (DF) -the highest power frequency between 4-15Hz- in DLB; a characteristic of Parkinsonian diseases. We analysed electroencephalographic (EEG) recordings from old adults: healthy controls (HCs), AD, DLB and Parkinsons disease dementia (PDD) patients. Brain networks were assessed with the minimum spanning tree (MST) within six EEG bands: delta, theta, high-theta, alpha, beta and DF. Patients showed lower alpha band connectivity and lower DF than HCs. Lewy body dementias showed a randomised MST compared with HCs and AD in high-theta and alpha but not within the DF. The MST randomisation in DLB and PDD reflects decreased brain efficiency as well as impaired neural synchronisation. However, the lack of network topology differences at the DF indicates a compensatory response of the brain to the neuropathology.

neuroscience↗