Biology subjects

Urbain, C.

Publications and source records attributed to Urbain, C..

6 recordsLinked to original sources

bioRxiv2026

Theta-band brain synchronization supports the immediate and post-sleep dynamics of memory recall in children

Memory engrams emerge from dynamic, coordinated interactions among synchronized functional brain networks. Yet, how these networks are selectively reactivated upon cued recall and gradually (re)organized over time, especially through sleep, remains poorly understood. Using magnetoencephalography (MEG), sleep electroencephalography (EEG) and behavioral measures, we investigated the spatiotemporal neural dynamics of cued recall memory during immediate and post-sleep (i.e., 90-minute post-learning nap) recall sessions in school-aged children. Results showed that immediate recall engaged a temporally ordered sequence of theta-band phase synchronization across two transiently synchronized networks: an early (150-350 ms) network involving the ventral visual pathway and left medial temporal lobe (MTL), followed by a later (550-750 ms) network encompassing the bilateral MTL and widespread neocortical associative regions. Post-sleep recall was associated, relative to wakefulness, with strengthened theta-band phase synchronization between the left MTL and widespread bilateral neocortical regions in a similar late window (450-650 ms). Post-sleep theta-band synchronization and memory gains in performance positively correlated with slow oscillation-spindle coupling during the post-learning nap. Altogether, these findings highlight oscillatory and spatiotemporal dynamics of memory recall networks and suggest that sleep, possibly driven by slow-oscillation-spindle coupling mechanisms, supports the efficient reinstatement of memory traces in the developing brain.

neuroscience↗

bioRxiv2025

Association between theta-band resting-state functional connectivity and declarative memory abilities in children

Declarative memory formation critically relies on the synchronization of brain oscillations in the theta (4-8 Hz) frequency band within specific brain networks. The development of this capacity is closely linked to the functional organization of these networks already at rest. However, the relationship between theta-band resting-state functional connectivity and declarative memory abilities remains unexplored in children. Here, using magnetoencephalography, we examined the association between declarative memory performance and pre-learning resting-state functional connectivity across frequency bands in 32 school-aged children. Declarative memory was assessed as the percentage of correct retrieval of 50 new associations between non-objects and magical functions, while resting-state functional connectivity was measured through power envelope correlation of the theta, alpha, low and high beta frequency bands. We found that stronger theta-band resting-state functional connectivity within occipito-temporo-frontal networks correlated with better declarative memory retrieval, while no correlation was observed in the alpha and beta frequency bands. These findings suggest that the functional brain architecture at rest, specifically involving theta-band oscillations, supports declarative memory in children. This mechanism may facilitate the subsequent rapid transformation of sensory input into visuo-semantic representations, highlighting the critical role of theta-band connectivity in early cognitive development.

neuroscience↗

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↗

bioRxiv2023

Towards the automated detection of interictal epileptiform discharges with magnetoencephalography

The analysis of clinical magnetoencephalography (MEG) in patients with epilepsy traditionally relies on the visual identification of interictal epileptiform discharges (IEDs), which is time consuming and dependent on (subjective) human criteria. Data-driven approaches enabling both spatial and temporal localization of epileptic spikes would represent a major leap forward in clinical MEG practice. Here, we explore the ability of Independent Components Analysis (ICA) and Hidden Markov Modeling (HMM) to automatically detect and localize IEDs. Combined with kurtosis mapping, we developed a fully automated identification of epileptiform independent components (ICs) or HMM states. We tested our pipeline on MEG recordings at rest from 10 school-age children with either focal or multifocal epilepsy and compared results with the traditional MEG analysis performed by an experienced clinical magnetoencephalographer. In patients with focal epilepsy, both ICA- and HMM-based pipelines successfully detected visually identified IEDs with high sensitivity, but also revealed low-amplitude IEDs unidentified by the visual detection. Success was more mitigated in patients with multifocal epilepsy, as our automated pipeline missed IED activity associated with some foci--an issue that could be alleviated by post-hoc manual selection of epileptiform ICs or HMM states. Therefore, IED detection based on ICA or HMM represents an efficient way to identify spike localization and timing, with heightened sensitivity to IEDs compared to visual MEG signal inspection and requiring minimal input from clinical practitioners.

neuroscience↗

bioRxiv2021

Spectro-temporal neural dynamics during sentence completion

This magnetoencephalography (MEG) study aimed at characterizing the spectro-temporal dynamics of brain oscillatory activity elicited by sentence completion (SC). For that purpose, we adapted a version of the SC experimental paradigm typically used in functional magnetic resonance imaging to MEG investigation constraints. Twenty right-handed healthy young adults underwent MEG recordings while they were sequentially presented with short sentences divided in three parts: the first two giving context and the last requiring completion. MEG data were then analysed using a prior-free, non-parametric statistical approach with stringent control of the family-wise error rate. We identified three successive significant neural response patterns associated with distinct spatial and spectro-temporal characteristics: (i) an early (<300 ms) bioccipital 4-10-Hz event-related synchronization (ERS); (ii) an intermediate (at about 400 ms) 8-30-Hz event-related desynchronization (ERD) in an extended semantic network involving the ventral language stream as well as bilateral posterior nodes of the default mode network (DMN) in both hemispheres; (iii) a late (>800 ms) 8-30 Hz ERD involving the left dorsal language stream. Furthermore, the left component of the ventral language stream displayed prolonged ERD after 800 ms compared to the right which showed signs of inhibition in the form of ERS. Overall, this study elucidates the dynamics of the recruitment of the language network that accompany SC and the spectro-temporal signature of an extended semantic network. This MEG adaptation of an SC paradigm also paves the way for novel approaches in presurgical language mapping and may help to understand the neural underpinnings of the alterations of sentence completion in various neurologic disorders affecting language.

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↗