Biology subjects

Leger, D.

Publications and source records attributed to Leger, D..

6 recordsLinked to original sources

bioRxiv2025

AutoSTED: An automated workflow for STED super-resolution imaging of cell nuclei

The traditional microscopy workflow involves manual selection of objects one after the other. This is not only time consuming, but inevitably introduces human bias. Here, we describe a workflow, AutoSTED for identification of cell nuclei on wide-field fluorescence images and subsequent automated imaging of these nuclei at super-resolution by Stimulated Emission Depletion (STED) microscopy. We demonstrate that in an overnight run we are able to image hundreds of cells selected without bias. The resolution afforded by STED allowed straightforward identification and counting of individual nuclear pore complexes (NPCs), and enabled us to automatically extract parameters such as NPC density under different experimental conditions. Together, we illustrate how automation enables optimal use of instrument time to produce large unbiased datasets.

cell biology↗

bioRxiv2025

Listening to Your Own Brain Waves Sound Enhances Your Sleep Quality and Quantity

This pilot study examined the effects of relaxing personalized sound sequences (PSS), derived from individual slow-wave brain activity on sleep in adults with subjective insomnia complaints. Thirteen participants underwent one-night polysomnography to record delta wave activity (0.5-4 Hz), which was then transformed into individualized sound sequences. A randomized, single-blind, crossover protocol was then conducted at home, including two conditions of 3 to 5 consecutive nights: listening to the PSS and a non-personalized placebo sound sequence (PLA) for 30 minutes at bedtime. Objective sleep was assessed using a dry-electroencephalographic (EEG) headband and subjective sleep with a digital sleep diary. Compared to PLA, the PSS condition significantly increased total sleep time ({Delta} = +18.9 min, p = 0.05) and REM sleep proportion ({Delta} = +2.3%, p < 0.05), reduced REM latency ({Delta} = -16.6 min, p < 0.05) and improved overall sleep quality score ({Delta} = +1.4 A.U., p < 0.05). Participants with the shorter sleep duration (< 390 min, n = 5) and longer sleep onset latencies (> 20 min, n = 4) in PLA condition experienced greater improvements with PSS. These preliminary results suggest that listening to ones own slow brain waves converted into sound may improve both sleep quality and quantity in individuals with moderate insomnia, with potentially enhanced benefits for those with more severe sleep difficulties. HighlightsO_LIPersonalized soundtracks were created by transforming each participants N3 slow-wave activity into audio sequences to listen to before bedtime. C_LIO_LIParticipants reported significantly better subjective sleep quality when listening to their own brainwave-based soundtracks compared to placebo. C_LIO_LIObjective measures increased total sleep time and REM sleep during nights with personalized sound exposure. C_LI

neuroscience↗

bioRxiv2024

Sensorimotor restriction affects sleep-related motor memory consolidation through altered slow oscillation-spindle coupling.

Sleep benefits memory consolidation through periodic sleep spindle activity and associated memory reactivations. Recent evidence highlights that iterative spindle onsets follow two periodic rhythms: an infraslow periodicity ([~]0.02 Hz) and a mesoscale periodicity rhythm ([~]0.2-0.3 Hz). Consequently, spindles tend to cluster in "trains" on a low-frequency time scale every 50 seconds during which spindles iterate every 3 to 4 seconds. Such temporal organization of spindles in trains is considered a critical sleep mechanism for the timed and repeated reactivation of memories. Additionally, current trends indicate that a timely phase-locking between slow oscillations (SO) and spindles promotes learning-related synaptic plasticity. In this study, we explored how spindle clustering and coupling with SO contribute to motor memory consolidation by inducing a local reduction in synaptic efficacy over sensorimotor cortical regions through upper-limb immobilization after motor sequence learning. We also evaluated memory generalization using two transfer tests designed to assess the ability to transfer or generalize the newly acquired skill to another one (new sequence) or another effector (inter-limb transfer). Our results reveal that the temporal cluster-based organization of spindles is independent of daytime sensorimotor experience, while distinct overnight behavioral outcomes were elicited. Interestingly, immobilization induced a phase shift in the SO-spindle coupling for spindles grouped in trains, but not when isolated outside trains. In addition, the proportion of grouped spindles relative to isolated spindles was positively associated with skill consolidation and negatively correlated with skill generalization following sensorimotor restriction. These results suggest that spindle trains may promote skill-specific strengthening of motor memories, while isolated spindles may instead create memory-instability conditions that facilitate skill generalization.

neuroscience↗

bioRxiv2022

Sleep deprivation measured by voice analysis

Sleep deprivation has an ever-increasing impact on individuals and societies. Yet, to date, there is no quick and objective test for sleep deprivation. Here, we used automated acoustic analyses of the voice to detect sleep deprivation. Building on current machine-learning approaches, we focused on interpretability by introducing two novel ideas: the use of a fully generic auditory representation as input feature space, combined with an interpretation technique based on reverse correlation. The auditory representation consisted of a spectro-temporal modulation analysis derived from neurophysiology. The interpretation method aimed to reveal the regions of the auditory representation that supported the classifiers decisions. Results showed that generic auditory features could be used to detect sleep deprivation successfully, with an accuracy comparable to state-of-the-art speech features. Furthermore, the interpretation revealed two distinct effects of sleep deprivation on the voice: a change in prosody and a change in timbre. Importantly, the relative balance of the two effects varied widely across individuals, even though the amount of sleep deprivation was controlled, thus confirming the need to characterize sleep deprivation at the individual level. Moreover, while the prosody factor correlated with subjective sleepiness reports, the timbre factor did not, consistent with the presence of both explicit and implicit consequences of sleep deprivation. Overall, the findings show that individual effects of sleep deprivation may be observed in vocal biomarkers. Future investigations correlating such markers with objective physiological measures of sleep deprivation could enable "sleep stethoscopes" for the cost-effective diagnosis of the individual effects of sleep deprivation. Author summarySleep deprivation has an ever-increasing impact on individuals and societies, from accidents to chronic conditions costing billions to health systems. Yet, to date, there is no quick and objective test for sleep deprivation. We show that sleep deprivation can be detected at the individual level with voice recordings. Importantly, we focused on interpretability, which allowed us to identify two independent effects of sleep deprivation on the voice: a change in prosody and a change in timbre. The results also revealed a striking variability in individual reactions to the same deprivation, further confirming the need to consider the effects of sleep deprivation at the individual level. Vocal markers could be correlated to specific underlying physiological factors in future studies, outlining possible cost-effective and non-invasive "sleep stethoscopes".

neuroscience↗

bioRxiv2020

Probing machine-learning classifiers using noise, bubbles, and reverse correlation

BackgroundMany scientific fields now use machine-learning tools to assist with complex classification tasks. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. However, such tools often remain black-boxes: they lack interpretability. A lack of interpretability has obvious ethical implications for clinical applications, but it also limits the usefulness of these tools to formulate new theoretical hypotheses. New methodWe propose a simple and versatile method to help characterize the information used by a classifier to perform its task. Specifically, noisy versions of training samples or, when the training set is unavailable, custom-generated noisy samples, are fed to the classifier. Multiplicative noise, so-called "bubbles", or additive noise are applied to the input representation. Reverse correlation techniques are then adapted to extract either the discriminative information, defined as the parts of the input dataset that have the most weight in the classification decision, and represented information, which correspond to the input features most representative of each category. ResultsThe method is illustrated for the classification of written numbers by a convolutional deep neural network; for the classification of speech versus music by a support vector machine; and for the classification of sleep stages from neurophysiological recordings by a random forest classifier. In all cases, the features extracted are readily interpretable. Comparison with Existing MethodsQuantitative comparisons show that the present method can match state-of-the art interpretation methods for convolutional neural networks. Moreover, our method uses an intuitive and well-established framework in neuroscience, reverse correlation. It is also generic: it can be applied to any kind of classifier and any kind of input data. ConclusionsWe suggest that the method could provide an intuitive and versatile interface between neuroscientists and machine-learning tools. HighlightsO_LIThe heuristics of black-box classifiers can be probed with noisy inputs C_LIO_LIThe relevant features can be visualised in the input representation space C_LIO_LIThe method applies to any kind of data such as 2D images or 1D time series C_LIO_LIIt applies to any classifier such as deep neural networks, support vector machines, random forests C_LI

neuroscience↗

bioRxiv2017

PERFORMANCE OF AN AMBULATORY DRY-EEG DEVICE FOR AUDITORY CLOSED-LOOP STIMULATIONS IN THE HOME ENVIRONMENT

ObjectiveRecent research has shown that auditory closed-loop stimulations can enhance sleep slow oscillations (SO) to improve N3 sleep quality and cognition. Previous studies have been conducted in a lab environment and on a small sample size. The present study aimed at validating and assessing the performance of a novel ambulatory wireless dry-EEG device (WDD), for auditory closed-loop stimulations of SO during N3 sleep at home.\n\nMaterial and MethodsThe performance of the WDD to detect N3 sleep automatically and to send auditory closed-loop stimulations on SO were tested on 20 young healthy subjects who slept with both the WDD and a miniaturized polysomnography (part 1) in both stimulated and sham nights within a double blind, randomized and crossover design.\n\nThe electrophysiological effects of auditory closed-loop stimulation on delta power increase were assessed after 1 and 10 nights of stimulations on an observational pilot study in the home environment including 90 middle-aged subjects (part 2).\n\nResultsThe sensitivity and specificity of the WDD to automatically detect N3 sleep in real-time were 0.70 and 0.90, respectively. The stimulation accuracy of the SO ascending-phase targeting was 45{+/-}52{degrees}. The stimulation protocol induced an increase of 39.5 % of delta power after the stimulations. The increase of SO response to auditory stimulations remained at the same level after 10 consecutive nights.\n\nConclusionThe WDD shows good performances to automatically detect in real-time N3 sleep and to send auditory closed-loop stimulations on SO accurately. These stimulations increased the SO amplitude during N3 sleep without any adaptation effect after 10 consecutive nights. This tool provides new perspectives to figure out novel sleep EEG biomarkers in longitudinal studies and can be interesting to conduct broad studies on the effects of auditory stimulations during sleep.

bioengineering↗