Biology subjects

Chaumon, M.

Publications and source records attributed to Chaumon, M..

3 recordsLinked to original sources

bioRxiv2026

Episodic memory ERPs reflect preclinical Alzheimers disease progression

Structured AbstractO_ST_ABSBackgroundC_ST_ABSAlzheimers disease (AD) neuropathology emerges decades before symptoms, requiring sensitive, non-invasive markers for early diagnosis. We investigated behavioural and EEG markers of episodic memory in cognitively normal older adults at-risk for AD. MethodsForty-five INSIGHT-preAD cohort participants (15 progressors to prodromal AD, 15 stable {beta}-amyloid positive [A{beta}+] participants, 15 controls [A{beta}-]) completed an Old/New word recognition task during high-density EEG. Behavioural performance and memory-related evoked-related potentials (ERPs; P3, FN400, P600, post-retrieval monitoring) were analysed longitudinally over 5 years. ResultsProgressors showed significantly reduced accuracy, sensitivity, and slower responses. They showed attenuated FN400, P600, and monitoring amplitudes over left parieto-occipital regions. Source analysis revealed hypoactivation in the left inferior frontal and right posterior cingulate cortex, across components with additional disruption during P600. Stable A{beta}+ individuals showed milder P600 hypoactivations. DiscussionBehavioural and ERP alterations preceded clinical decline, supporting EEG-based markers of episodic memory decline for preclinical AD risk stratification.

neuroscience↗

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↗

bioRxiv2019

Statistical power: implications for planning MEG studies

Statistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated "experiments" using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the "signal condition", but not in the "noise condition", and detected significant differences at sensor level with classical paired t-tests across subjects, using amplitude, squared amplitude, and global field power (GFP) measures. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not. Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies and should be adapted according to the brain regions under study.

neuroscience↗