Biology subjects

Roshchupkina, L.

Publications and source records attributed to Roshchupkina, L..

4 recordsLinked to original sources

bioRxiv2023

Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep

Learning and consolidation of motor skills dynamically evolve both online during practice and offline after training. We investigated, using magnetoencephalography, the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods shortly after the end of learning (short-term boost window, within 30 min) and at more delayed time scales (silent 4h and next day 24h windows) with an intermediate nap or wakefulness after the boost window. Resting-state neural dynamics in brain networks were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and resting-state functional connectivity (rsFC), respectively, as well as their relationship with the evolution of motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance achievements, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training re-exposure to the task modulated fast and slow network characteristics during the boost but not in the silent window. These short practice-related induction effects were observed again on the next day, to a reduced extent as compared to the boost window. Daytime naps did not significantly modulate memory consolidation both at behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and subsequent memory consolidation processes and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.

neuroscience↗

bioRxiv2021

Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales

State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. Two widely-used techniques are the microstate analysis of EEG signals and hidden Markov modeling (HMM) of MEG power envelopes. Both reportedly lead to similar state lifetimes on the 100 ms timescale, suggesting a common neural basis. We addressed this issue by using simultaneous MEG/EEG recordings at rest and comparing the spatial signature and temporal activation dynamics of microstates and power envelope HMM states obtained separately from EEG and MEG. Results showed that microstates and power envelope HMM states differed both spatially and temporally. Microstates tend to exhibit spatio-temporal locality, whereas power envelope HMM states disclose network-level activity with 100-200 ms lifetimes. Further, MEG microstates do not correspond to the canonical EEG microstates but are better interpreted as split HMM states. On the other hand, both MEG and EEG HMM states involve the (de)activation of similar functional networks. Microstate analysis and power envelope HMM thus appear sensitive to neural events occurring over different spatial and temporal scales. As such, they represent complementary approaches to explore the fast, sub-second scale bursting electrophysiological dynamics in spontaneous human brain activity.

neuroscience↗

bioRxiv2020

Beta-tACS does not impact the dynamics of motor memory consolidation

The consolidation of motor memory is a non-linear temporal dynamic. There are critical time points at which post-training performance can improve (e.g., 30 min and 24 h) or merely stabilize (e.g., 4 h). Besides, neuronal plasticity is supported by synchronized oscillatory activity in and between brain areas at play during the acquisition and consolidation of motor skills. Transcranial alternating current stimulation (tACS) can entrain cortical oscillatory activity, which may eventually modulate brain plasticity-related processes. Previous reports suggest that 20 Hz electrical stimulation over the primary motor cortex (M1) following training facilitates the consolidation of motor memories. To the best of our knowledge, the effect of tACS was not investigated when applied at critical post-training time points, nor its impact at longer time scales. In the present study, we investigated the effect of 20 Hz tACS applied over M1 within critical time periods (25min vs. 4h) on motor memory consolidation at immediate and delayed (24h) retrieval. Performance similarly evolved over time in all conditions, independently from the stimulation type (20 Hz tACS vs. Sham tACS) or the stimulation time point. As it stands, our results do not support the proposal that 20 Hz tACS exerts a positive, specific effect on the consolidation of motor memories.

neuroscience↗

bioRxiv2020

Intrinsic/extrinsic duality of large-scale neural functional integration in the human brain

Human brain activity is not merely responsive to environmental context but includes intrinsic dynamics, as suggested by the discovery of functionally meaningful neural networks at rest, i.e., even without explicit engagement of the corresponding function. Yet, the neurophysiological coupling mechanisms distinguishing intrinsic (i.e., task-invariant) from extrinsic (i.e., task-dependent) brain networks remain indeterminate. Here, we investigated functional brain integration using magnetoencephalography throughout rest and various tasks recruiting different functional systems and modulating perceptual/cognitive loads. We demonstrated that two distinct modes of neural communication continually operate in parallel: extrinsic coupling supported by phase synchronization and intrinsic integration encoded in amplitude correlation. Intrinsic integration also contributes to phase synchronization, especially over short (second-long) timescales, through modulatory effects of amplitude correlation. Our study establishes the foundations of a novel conceptual framework for human brain function that fundamentally relies on electrophysiological features of functional integration. This framework blurs the boundary between resting-state and task-related neuroimaging.

neuroscience↗