Biology subjects

Champetier, P.

Publications and source records attributed to Champetier, P..

4 recordsLinked to original sources

bioRxiv2025

CARACAS, a novel automated tool for Cardiac Artifact Removal in Absence of CArdiac Signal

BackgroundEEG recordings can contain cardiac related artifacts. Independent Component Analysis (ICA) followed by removal of cardiac Independent Components (ICs) is a powerful and widely used strategy for artifact correction. Most existing methods for automatic labeling of cardiac ICs require a simultaneously recorded ECG (e.g., to compute correlation with the IC time course). However, ECG is not always available. To address this limitation, we developed CARACAS (Cardiac Artifact Removal in Absence of CArdiac Signal), a novel tool that identifies cardiac ICs using only the IC time courses. New methodBecause cardiac ICs exhibit temporal profiles highly similar to ECG signals, we used an existing tool designed to detect cardiac events (R waves) in ECG signals and applied it to each IC time course. Analysis of the detected events enabled the differentiation of cardiac ICs from non-cardiac ICs, where unrelated signal variations are incorrectly identified as cardiac events. Using the 375 EEG-ECG recordings of the open-source dataset OpenNeuro ds003690, we compared the performances of three algorithms: CARACAS, IClabel (a generic IC classifier which does not require ECG), and correlation with ECG channel. Results (comparison with existing methods)A total of 21,375 ICs were manually and automatically classified. CARACAS achieved high performance (sensitivity = 0.960, specificity = 0.976), substantially outperforming ICLabel (sensitivity = 0.210, specificity = 0.999) and approaching the performance of ECG correlation method (sensitivity = 0.975, specificity = 0.998). ConclusionWe present a reliable ECG-free algorithm for cardiac IC detection in EEG. CARACAS provides a practical solution when ECG is unavailable, and is implemented in the SASICA toolbox. HighlightsO_LICardiac independent component (IC) removal after ICA corrects cardiac EEG artifacts. C_LIO_LIMost automatic cardiac IC detectors require a simultaneously recorded ECG. C_LIO_LIWe developed CARACAS, a novel ECG-free method for automatic cardiac IC labeling. C_LIO_LICARACAS achieved a sensitivity of 0.960 and a specificity of 0.976 on 21,375 ICs. C_LIO_LICARACAS outperforms ICLabel, and is available in SASICA toolbox (command line & GUI). C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/673728v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@178bd49org.highwire.dtl.DTLVardef@1d34500org.highwire.dtl.DTLVardef@1572bf1org.highwire.dtl.DTLVardef@5f799_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Live access to the emotional dynamics of REM sleep dreams in lucid dreamers with narcolepsy

Sleep helps regulate emotions, but it is still unclear whether -and how- the emotions we experience in dreams contribute to this regulation. To uncover the potential function of dream emotions, we must first understand what they are and how they unfold in dreams. The emotional content of dreams has mostly been studied using post-sleep dream reports, which provide a biased and static snapshot of a complex and dynamic experience. In this study, we took a more direct approach, accessing dream emotions in real-time. We asked twenty-four lucid dreamers with narcolepsy to report the emotional valence of their dreams, - positive, negative or neutral-, while still asleep, using predefined facial codes during daytime naps monitored with polysomnography. Of the 126 naps recorded, 62 contained at least one emotional code during REM sleep, yielding 191 codes in total. The ratios of positive and negative codes were evenly balanced per nap. The 33 naps with at least two codes allowed us to track the dream emotional dynamics. Over half of these naps showed opposite emotional valences (positive and negative). By measuring the time elapsed between codes, we estimated the average duration of a given dreams emotional valence in REM sleep to be about one minute. Positive emotions emerged on average earlier than negative ones during lucid REM sleep. These findings confirm the highly emotional nature of dreams and, more importantly, highlight that emotions in REM sleep dreams are fluid and fast-changing. Such emotional dynamics during REM sleep dreams may help us to better understand the mechanisms of the emotional regulatory function of dreams.

neuroscience↗

bioRxiv2025

EEG Signature of Idiopathic Hypersomnia: Insights from Sleep Microarchitecture and Hypnodensity Metrics

Background and ObjectivesPatients with idiopathic hypersomnia with long sleep time (IH) report daytime hypersomnolence despite prolonged sleep time and normal sleep macrostructure. As they often have non-restorative sleep, we investigated whether the structure of their sleep is abnormal. MethodsIn polysomnography recordings from 80 IH participants and 48 controls, we quantified hypnodensity metrics across the night (macro level), periodic and aperiodic spectral properties, infraslow fluctuations of sigma power within the night (meso level), slow waves, sleep spindles and their clustering (microstructure). Multivariate machine-learning models were used to classify IH vs. control sleep. ResultsHypnodensity metrics were comparable between IH and controls, apart from more mixed wake/N1 sleep epochs in IH, and greater divergence between consecutive epochs of the same stage during NREM sleep in IH. Sigma power was increased in N2 sleep in IH and sleep spindles were more frequent and clustered. Slow wave density was higher in IH. Higher mean spindle cluster size correlated with higher Epworth Sleepiness Scale scores. Multivariate machine learning models incorporating these features achieved a balanced accuracy of 74% in distinguishing IH from controls. DiscussionWhile spindles and slow waves are typically associated with good sleep quality, they are increased in IH patients. This could reflect greater need for sleep and increased difficulty waking up in IH, which is also characterized by more mixed wake/N1 stages.

neuroscience↗

bioRxiv2024

Resting-state functional connectivity and fast spindle temporal organization contribute to episodic memory consolidation in healthy aging

Episodic memory consolidation relies on the functional specialization of brain networks and sleep quality, both of which are affected by aging. Functional connectivity during wakefulness is crucial to support the integration of newly acquired information into memory networks. Additionally, the temporal dynamics of sleep spindles facilitates overnight memory consolidation by promoting hippocampal replay and integration of memories within neocortical structures. This study aimed at exploring how resting-state functional connectivity during wakefulness contributes to sleep-dependent memory consolidation in aging, and whether spindles clustered in trains modulates this relationship. Forty-two healthy older adults (68.82 {+/-} 3.03 years), enrolled in the Age-Well clinical trial, were included. Sleep-dependent memory consolidation was assessed using a visuo-spatial memory task performed before and after a polysomnography night. Resting-state functional connectivity data were analyzed using graph theory applied to the whole brain, specific brain networks and the hippocampus. Lower limbic network integration and higher centrality of the anterior hippocampus were associated with better memory consolidation. Spindle trains modulated these effects, such that older participants with longer spindle trains exhibited a stronger negative association between limbic network integration and memory consolidation. These results indicate that lower functional specialization at rest is associated with weaker memory consolidation during sleep. This aligns with the dedifferentiation hypothesis, which posits that aging is associated with reduced brain specificity, leading to less efficient cognitive functioning. These findings reveal a novel mechanism linking daytime brain network organization and sleep-dependent memory consolidation, and suggest that targeting spindle dynamics could help preserve cognitive functioning in aging.

neuroscience↗