Biology subjects

Legouhy, A.

Publications and source records attributed to Legouhy, A..

6 recordsLinked to original sources

bioRxiv2026

Modulating Sleep Oscillations Through Targeted Memory Reactivation: Linking Functional Dynamics to Brain Microstructure

Background: Sleep-dependent memory consolidation reputedly rely on the coordinated activity of sleep oscillations, which may facilitate the reactivation and redistribution of newly acquired memories. Targeted memory reactivation (TMR) during sleep can experimentally modulate these processes, although effects remain variable. Moreover, how learning-related microstructural plasticity relates to sleep oscillatory activity and spindle-related network dynamics remains poorly understood. We investigated whether auditory TMR modulates sleep spindles, slow oscillations (SOs) and their co-occurrence, whether these changes relate to motor memory consolidation and diffusion MRI (dMRI) measures of brain microstructure, and whether learning and TMR alter the evolution of cortico-cortical connectivity during spindle development. Methods: Sixty healthy young adults underwent five dMRI sessions before and after motor sequence learning (Day 1), after a post-training night of regular sleep (RS) or TMR (Day 2), and before and after relearning (Day 5). Baseline and experimental nights were recorded with 256-channel high-density EEG. Oscillation densities were analyzed using mixed-design ANOVAs and related to behavioral and dMRI changes using Pearson or Spearman correlations. Intra-spindle connectivity between 148 source-reconstructed cortical regions was compared between spindle onset and end, across nights and between groups using Network-Based Statistics. Results: Spindle density, SO density and SO-spindle co-occurrence remained stable across nights in the RS group but decreased after TMR. Higher spindle density and co-occurrence were associated with greater overnight performance gains, and all oscillatory measures were associated with learning-related dMRI changes in subcortical regions, notably striatum and thalamus. Connectivity increased from the beginning to the end of the spindle duration during the baseline night in both groups, but during the experimental night only in the RS group. Conclusions: Motor learning is associated with coordinated changes in sleep oscillatory activity, within-spindle cortical connectivity and brain microstructure, supporting a dynamic interaction between learning-induced plasticity and sleep-dependent network reorganization. Under the present stimulation conditions, TMR did not enhance these processes but instead reduced sleep oscillatory activity and attenuated spindle-related connectivity dynamics, underscoring the importance of precisely targeting endogenous sleep rhythms when using TMR.

neuroscience↗

bioRxiv2026

Cortical folding patterns are encoded in the geometry of the unfolded neocortex.

Cortical folding patterns are conserved across individuals of gyrencephalic species and are closely related to cytoarchitectural organisation, connectivity, and function. Early morphogen gradients have been proposed as the molecular source of positional information encoding these patterns - a gyral molecular protomap - but the contribution of neocortical geometry to this encoding has not been examined. Here we show that the geometry of the unfolded ferret brain guides the adult folding pattern before any folding has occurred. Using high-resolution MRI surface reconstructions from postnatal day (P) 0 to adults, we demonstrate that newborn neocortical curvature predicts mature curvature maps, sulcal-gyral fate, and fold orientation. Pre-folding curvature correlates with expression of key patterning genes, and a mediation analysis indicates that geometry at P0 is the principal predictor of sulcal-gyral fate at P6. Mechanical simulations show that regions of high curvature act as autonomous anchors that organise the folding pattern. These results suggest that neocortical geometry constitutes a form of positional information that complements molecular patterns in regulating cortical organisation, and that a complete account of cortical development requires the integration of geometric and mechanical processes alongside molecular signalling.

neuroscience↗

bioRxiv2024

Shaping the structural dynamics of motor learning through cueing during sleep

Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted Memory Reactivation (TMR), involving cueing learned material during post-training sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and post-training sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during post-training sleep affects performance gains and delayed microstructural remodeling, using both standard Diffusion Tensor Imaging (DTI) and advanced Neurite Orientation Dispersion & Density Imaging (NODDI). Sixty healthy young adults participated in a five-day protocol, undergoing five Diffusion-Weighted Imaging (DWI) sessions, pre- and post-two motor sequence training sessions, and after a post-training night of either regular sleep (RS) or TMR. Results demonstrated rapid skill acquisition on Day 1, followed by performance stabilization on Day 2, and improvement on Day 5, in both RS and TMR groups. (Re)training induced widespread microstructural changes in motor-related areas, initially involving the hippocampus, followed by a delayed engagement of the caudate nucleus. Mean Diffusivity (MD) changes were accompanied by increased Neurite Density Index (NDI) in the putamen, suggesting increased neurite density, while Free Water Fraction (FWF) reduction indicated glial reorganization. TMR-related structural differences emerged in the dorsolateral prefrontal cortex (DLPFC) on Day 2 and the right cuneus on Day 5, suggesting unique sleep TMR-related neural reorganization patterns. Persistence of practice-related structural changes, although moderated over time, suggest a lasting neural network reorganization, partially mediated by sleep TMR. Statement of SignificanceThis study demonstrates how motor practice triggers short-term and long-term microstructural reorganization in (sub)cortical grey matter, with a focus on how sleep Targeted Memory Reactivation (TMR) influences these changes, particularly in motor-related areas. Combining standard Diffusion Tensor Imaging (DTI) with advanced Neurite Orientation Dispersion & Density Imaging (NODDI) imaging techniques, we highlight distinct patterns of neural reorganization, suggesting glial and/or synaptic changes, in response to motor practice, possibly partially mediated by TMR. This research enhances our understanding of motor practice-related structural plasticity mechanisms and highlights the potential of targeted sleep interventions to modulate brain reorganization, suggesting new avenues for optimizing motor skill acquisition.

neuroscience↗

bioRxiv2023

Microstructural dynamics of motor learning and sleep-dependent consolidation: a diffusion imaging study

Diffusion-weighted magnetic resonance imaging (DWI) allows the observation of (micro)structural brain remodelling in cortical and subcortical regions after time-limited motor learning. Post-learning sleep consolidation should lead to long-term microstructural brain changes, but concrete evidence remains limited. Here, we used both conventional diffusion tensor imaging (DTI) and Neurite Orientation Dispersion & Density Imaging (NODDI), that estimates dendritic and axonal complexity in white and grey matter, to investigate the microstructural brain mechanisms underlying time- and sleep-dependent motor memory consolidation. Sixty-one young healthy adults underwent 2 DWI sessions, with sequential motor training in between, on day 1 followed by the experimental night of total sleep deprivation or regular sleep. After 3 recovery nights at home, they underwent 2 further DWI sessions separated by a motor retraining session. Sequential motor learning resulted in immediate modifications in structural parameters in occipitoparietal and temporal regions, as well as in subcortical regions of interest. Similar changes were observed following relearning but at a smaller magnitude. Regarding delayed consolidation effects, learning-related changes only partially persisted 3 days after initial learning, and no post-learning sleep effect was detected. Our results show rapid motor learning-related remodelling, reflecting temporary processes in learning-related neuronal brain plasticity. Post-learning sleep-related cellular changes remain to be evidenced, possibly using more sophisticated brain imaging measures and spanning more extended timescales to allow the expression of structural changes.

neuroscience↗

bioRxiv2022

Correction of susceptibility distortion in EPI: a semi-supervised approach with deep learning.

Echo planar imaging (EPI) is the most common approach for acquiring diffusion and functional MRI data due to its high temporal resolution. However, this comes at the cost of higher sensitivity to susceptibility-induced B0 field inhomogeneities around air/tissue inter-faces. This leads to severe geometric distortions along the phase encoding direction (PED). To correct this distortion, the standard approach involves an analogous acquisition using an opposite PED leading to images with inverted distortions and then non-linear image registration, with a transformation model constrained along the PED, to estimate the voxelwise shift that undistorts the image pair and generates a distortion-free image. With conventional image registration approaches, this type of processing is computationally intensive. Recent advances in unsupervised deep learning-based approaches to image registration have been proposed to drastically reduce the computational cost of this task. However, they rely on maximizing an intensity-based similarity measure, known to be suboptimal surrogate measures of image alignment. To address this limitation, we propose a semi-supervised deep learning algorithm that directly leverages ground truth spatial transformations during training. Simulated and real data experiments demonstrate improvement to distortion field recovery compared to the unsupervised approach, improvement image similarity compared to supervised approach and precision similar to TOPUP but with much faster processing.

neuroscience↗

bioRxiv2019

Regional brain development analysis through registration using anisotropic similarity, a constrained affine transformation

We propose a novel method to quantify brain growth in 3 arbitrary orthogonal directions of the brain or its sub-regions through linear registration. This is achieved by introducing a 9 degrees of freedom (dof) transformation called anisotropic similarity which is an affine transformation with constrained scaling directions along arbitrarily chosen orthogonal vectors. This gives the opportunity to extract scaling factors describing brain growth along those directions by registering a database of subjects onto a common reference. This information about directional growth brings insights that are not usually available in longitudinal volumetry analysis. The interest of this method is illustrated by studying the anisotropic regional and global brain development of 308 healthy subjects betwen 0 and 19 years old. A gender comparison of those scaling factors is also performed for 4 classes of age. We demonstrate through these applications the stability of the method to the chosen reference and its ability to highlight growth differences accros regions and gender.

neuroscience↗