Biology subjects

Lefebvre, A.

Publications and source records attributed to Lefebvre, A..

9 recordsLinked to original sources

bioRxiv2026

Heterogeneity of Brain Dynamics in Genetic and Psychiatric Conditions

Whether the heterogeneity of psychiatric conditions converges on shared neurophysiological alterations or translates into distinct signatures remains unclear. We assembled high-density electroencephalogram (hd-EEG) resting-state recordings from 4,812 individuals aged 5 months to 66 years across 11 psychiatric conditions, a broad spectrum of rare genetic variants, and typically developing (TD) individuals. We established normative developmental trajectories of source-space EEG across spectral organization, connectivity, and signal complexity. Psychiatric conditions showed small deviations, revealing a shared transdiagnostic profile. In contrast, single rare variants showed substantially larger, distinct and sometimes mirror-opposite signatures that collapsed toward the psychiatric profile when pooled. Autism Spectrum Disorder showed some of the smallest group-level effects yet the largest individual deviations, indicating substantial but directionally inconsistent alterations. EEG deviations followed a cortical gradient, with larger effects in sensorimotor regions. We demonstrate that sample sizes in the hundreds are required for robust associations with psychiatric diagnoses. This interactive open resource provides normative scores to benchmark future results.

neuroscience↗

bioRxiv2025

Characterization of aperiodic and theta activity in preterm infants using EEG: Insights into cerebral maturation and inter-individual variability

Disrupting critical processes of brain development, preterm birth interferes with the maturation of brain networks and functional activity, including theta oscillations that are thought to play a key role in early network formation. Traditional EEG spectral analyses have indicated marked development of theta power in early infancy, but these approaches mix oscillatory and non-oscillatory activity, limiting insights into the mechanisms underlying the neural changes. Using spectral parameterization, we aimed to evaluate developmental changes in aperiodic activity and periodic theta power in infants born very preterm compared to full-terms, and to further explore whether clinical factors and brain microstructure could explain the inter-individual variability within preterms. High-density EEG was acquired during active/REM sleep at term-equivalent age (TEA) and 2 months corrected age (2mCA) in 41 very preterm infants (born <32 weeks gestational age [GA]; mean {+/-} standard deviation: 26.9{+/-}1.7 weeks) and 13 full-term controls (born [≥]37 weeks GA; 40.1{+/-}1 weeks). Spectral parameterization was used to extract aperiodic components (offset, exponent) and periodic theta power globally and across spatial clusters of electrodes (anterior, central, posterior). From TEA to 2mCA, offset, exponent, and theta power increased with no differences between preterm and full-term infants. At TEA, metrics of aperiodic activity were stronger in anterior compared with posterior areas, but this regional landscape shifted by 2mCA with pronounced increases in aperiodic offset and exponent in posterior areas from TEA to 2mCA. Within preterms, inter-individual variability in aperiodic and periodic activity at TEA was partly explained by clinical risk factors: male sex, lower gestational-age at birth, small weight at birth, and invasive ventilation were linked to alterations of aperiodic offset, exponent and theta power. Additionally, higher theta power at TEA correlated with lower cortical fractional anisotropy assessed with diffusion MRI at the same age, consistently with more advanced maturation of the brain. Collectively, these findings indicate that EEG spectral parameterization combined with spatial analysis provides a sensitive framework for characterizing the postnatal maturation of brain activity in infants, as well as early vulnerabilities associated with prematurity and perinatal adversity.

neuroscience↗

bioRxiv2025

Transdiagnostic electrophysiological subtypes reveal brain-behavior dimensions in youth psychiatry

Conventional psychiatric diagnoses often fail to reflect the underlying neurobiological and behavioral complexity of mental health conditions. Here, we propose a transdiagnostic, data-driven framework for stratifying youth based on large-scale multisite electroencephalography (EEG) data from 1,707 individuals aged 5-18 years, including healthy controls and individuals diagnosed with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), anxiety disorders (ANX), and learning disabilities (LD), along with their common comorbidities. By applying normative modeling to quantify individual deviations from typical brain functional maturation, and integrating multidimensional EEG features across spectral, temporal, complexity, and dynamical domains via similarity network fusion clustering, we identified three robust neurophysiological biotypes. These biotypes showed distinct electrophysiological and behavioral profiles, and captured meaningful brain-behavior relationships. Our findings suggest that biologically informed subtypes capture meaningful neuropsychiatric heterogeneity in youth, challenging conventional diagnostic boundaries in psychiatric nosology.

neuroscience↗

bioRxiv2024

Temporal dynamics and maturation of resting-state activity in preterm infants

By interfering with the normal sequence of mechanisms serving the brain maturation, premature birth and related stress can alter perinatal experiences, with potential long-term consequences on a childs neurodevelopment. The early characterization of brain functioning and maturational changes is thus of critical interest in premature infants who are at high risk of atypical outcomes and could benefit from early diagnosis and dedicated interventions. Using high-density electroencephalography (HD-EEG), we recorded brain activity in extreme and very preterm infants at the equivalent age of pregnancy term (n=43), and longitudinally 2-months later (n=33), compared with full-term born infants (n=14). We characterized the maturation of brain activity by using a dedicated microstate analysis to quantify the spatio-temporal dynamics of the spontaneous transient network activity while controlling for vigilance states. The comparison of premature and full-term infants first showed slower dynamics as well as altered spatio-temporal properties of brain activity in preterm infants. Maturation of functional networks between term-equivalent age and 2 months later in preterms was linked to the emergence of faster dynamics, manifested in part by shorter duration of microstates, as well as an evolution in the spatial organization of the dominant microstates. The inter-individual differences in the temporal dynamics of brain activity at term-equivalent age were further impacted by sex (with slower microstate dynamics in boys) and by gestational age at birth for some microstate dynamics but not by other considered risk factors. This study highlights the potential of the microstate approach to reveal maturational properties of the emerging brain network activity in premature infants.

neuroscience↗

bioRxiv2024

Beyond homogeneity: Charting the landscape of heterogeneity in psychiatric electroencephalography

Electroencephalography (EEG) has been thoroughly studied for decades in psychiatry research. Yet its integration into clinical practice as a diagnostic/prognostic tool remains unachieved. We hypothesize that a key reason is the underlying patients heterogeneity, overlooked in psychiatric EEG research relying on a case-control approach. We combine HD-EEG with normative modeling to quantify this heterogeneity using two well-established and extensively investigated EEG characteristics -spectral power and functional connectivity-across a cohort of 1674 patients with attention-deficit/hyperactivity disorder, autism spectrum disorder, learning disorder, or anxiety, and 560 matched controls. Normative models showed that deviations from population norms among patients were highly heterogeneous and frequency-dependent. Deviation spatial overlap across patients did not exceed 40% and 24% for spectral and connectivity, respectively. Considering individual deviations in patients has significantly enhanced comparative analysis, and the identification of patient-specific markers has demonstrated a correlation with clinical assessments, representing a crucial step towards attaining precision psychiatry through EEG.

neuroscience↗

bioRxiv2020

Long-read error correction: a survey and qualitative comparison

Third generation sequencing technologies Pacific Biosciences and Oxford Nanopore Technologies were respectively made available in 2011 and 2014. In contrast with second generation sequencing technologies such as Illumina, these new technologies allow the sequencing of long reads of tens to hundreds of kbp. These so called long reads are particularly promising, and are especially expected to solve various problems such as contig and haplotype assembly or scaffolding, for instance. However, these reads are also much more error prone than second generation reads, and display error rates reaching 10 to 30%, according to the sequencing technology and to the version of the chemistry. Moreover, these errors are mainly composed of insertions and deletions, whereas most errors are substitutions in Illumina reads. As a result, long reads require efficient error correction, and a plethora of error correction tools, directly targeted at these reads, were developed in the past ten years. These methods can adopt a hybrid approach, using complementary short reads to perform correction, or a self-correction approach, only making use of the information contained in the long reads sequences. Both these approaches make use of various strategies such as multiple sequence alignment, de Bruijn graphs, Hidden Markov Models, or even combine different strategies. In this paper, we describe a complete survey of long-read error correction, reviewing all the different methodologies and tools existing up to date, for both hybrid and self-correction. Moreover, the long reads characteristics, such as sequencing depth, length, error rate, or even sequencing technology, have huge impacts on how well a given tool or strategy performs, and can thus drastically reduce the correction quality. We thus also present an in-depth benchmark of available long-read error correction tools, on a wide variety of datasets, composed of both simulated and real data, with various error rates, coverages, and read lengths, ranging from small bacterial to large mammal genomes.

bioinformatics↗

bioRxiv2019

CONSENT: Scalable self-correction of long reads with multiple sequence alignment

MotivationThird-generation sequencing technologies Pacific Biosciences and Oxford Nanopore allow the sequencing of long reads of tens of kbp, that are expected to solve various problems, such as contig and haplotype assembly, scaffolding, and structural variant calling. However, they also display high error rates that can reach 10 to 30%, for basic ONT and non-CCS PacBio reads. As a result, error correction is often the first step of projects dealing with long reads. As first long reads sequencing experiments produced reads displaying error rates higher than 15% on average, most methods relied on the complementary use of short reads data to perform correction, in a hybrid approach. However, these sequencing technologies evolve fast, and the error rate of the long reads now reaches 10 to 12%. As a result, self-correction is now frequently used as the first step of third-generation sequencing data analysis projects. As of today, efficient tools allowing to perform self-correction of the long reads are available, and recent observations suggest that avoiding the use of second-generation sequencing reads could bypass their inherent bias. ResultsWe introduce CONSENT, a new method for the self-correction of long reads that combines different strategies from the state-of-the-art. More precisely, we combine a multiple sequence alignment strategy with the use of local de Bruijn graphs. Moreover, the multiple sequence alignment benefits from an efficient segmentation strategy based on k-mer chaining, which allows a considerable speed improvement. Our experiments show that CONSENT compares well to the latest state-of-the-art self-correction methods, and even outperforms them on real Oxford Nanopore datasets. In particular, they show that CONSENT is the only method able to efficiently scale to the correction of Oxford Nanopore ultra-long reads, and is able to process a full human dataset, containing reads reaching lengths up to 1.5 Mbp, in 15 days. Additionally, CONSENT also implements an assembly polishing feature, and is thus able to correct errors directly from raw long read assemblies. Our experiments show that CONSENT outperforms state-of-the-art polishing tools in terms of resource consumption, and provides comparable results. Moreover, we also show that, for a full human dataset, assembling the raw data and polishing the assembly afterwards is less time consuming than assembling the corrected reads, while providing better quality results. Availability and implementationCONSENT is implemented in C++, supported on Linux platforms and freely available at https://github.com/morispi/CONSENT. Contactpierre.morisse2@univ-rouen.fr

bioinformatics↗

bioRxiv2019

ELECTOR: Evaluator for long reads correction methods

MotivationIn the last few years, the error rates of third generation sequencing data have been capped above 5%, including many insertions and deletions. Thereby, an increasing number of long reads correction methods have been proposed to reduce the noise in these sequences. Whether hybrid or self-correction methods, there exist multiple approaches to correct long reads. As the quality of the error correction has huge impacts on downstream processes, developing methods allowing to evaluate error correction tools with precise and reliable statistics is therefore a crucial need. Since error correction is often a resource bottleneck in long reads pipelines, a key feature of assessment methods is therefore to be efficient, in order to allow the fast comparison of different tools. ResultsWe propose ELECTOR, a reliable and efficient tool to evaluate long reads correction, that enables the evaluation of hybrid and self-correction methods. Our tool provides a complete and relevant set of metrics to assess the read quality improvement after correction and scales to large datasets. ELECTOR is directly compatible with a wide range of state-of-the-art error correction tools, using whether simulated or real long reads. We show that ELECTOR displays a wider range of metrics than the state-of-the-art tool, LRCstats, and additionally importantly decreases the runtime needed for assessment on all the studied datasets. AvailabilityELECTOR is available at https://github.com/kamimrcht/ELECTOR. Contactcamille.marchet@univ-lille.fr or pierre.morisse2@univ-rouen.fr

bioinformatics↗

bioRxiv2017

Hybrid correction of highly noisy Oxford Nanoporelong reads using a variable-order de Bruijn graph

MotivationThe recent rise of long read sequencing technologies such as Pacific Biosciences and Oxford Nanopore allows to solve assembly problems for larger and more complex genomes than what allowed short reads technologies. However, these long reads are very noisy, reaching an error rate of around 10 to 15% for Pacific Biosciences, and up to 30% for Oxford Nanopore. The error correction problem has been tackled by either self-correcting the long reads, or using complementary short reads in a hybrid approach, but most methods only focus on Pacific Biosciences data, and do not apply to Oxford Nanopore reads. Moreover, even though recent chemistries from Oxford Nanopore promise to lower the error rate below 15%, it is still higher in practice, and correcting such noisy long reads remains an issue.\n\nResultsWe present HG-CoLoR, a hybrid error correction method that focuses on a seed-and-extend approach based on the alignment of the short reads to the long reads, followed by the traversal of a variable-order de Bruijn graph, built from the short reads. Our experiments show that HG-CoLoR manages to efficiently correct Oxford Nanopore long reads that display an error rate as high as 44%. When compared to other state-of-the-art long read error correction methods able to deal with Oxford Nanopore data, our experiments also show that HG-CoLoR provides the best trade-off between runtime and quality of the results, and is the only method able to efficiently scale to eukaryotic genomes.\n\nAvailability and implementationHG-CoLoR is implemented is C++, supported on Linux platforms and freely available at https://github.com/morispi/HG-CoLoR\n\nContact: pierre.morisse2@univ-rouen.fr\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗