Biology subjects

Lefevre, J.

Publications and source records attributed to Lefevre, J..

7 recordsLinked to original sources

bioRxiv2026

Characterization of Fetal Cortical Development Using Spectral Analysis of Gyrification (SPANGY)

The prenatal period of human brain development is critical for mental health and cognition across the entire lifespan. During this period, the cortex undergoes a dramatic transformation from a smooth lissencephalic surface into an elaborately folded structure, a process whose precise characterization is essential for understanding neurodevelopmental trajectories. This study represents the first application of Spectral Analysis of Gyrification (SPANGY) to a large multi-centric fetal brain MRI dataset (635 subjects, 20-38 weeks gestational age). SPANGY characterizes geometric variations on a surface based on the wavelength of folds, hence, providing a quantitative local description of gyrification at the individual level. Using rigorous normative modeling (GAMLSS) and statistical harmonization (ComBat-GAM), we established age-specific reference trajectories for multi-scale gyrification features (spectral frequency bands). We provide the first ever quantification of the temporally-ordered emergence of cortical folding in successive waves: the earliest-emerging low frequency, deep fissures are progressively superseded by the accelerating expansion of higher frequency folds. The normative curves provide the first step in taking prenatal neurodevelopmental assessment from qualitative inspection into a rigorous statistical inference, creating an objective reference against which deviations from healthy brain growth can be caught earlier, and with greater precision.

neuroscience↗

bioRxiv2026

Improved Ensemble Performance by Weight Optimisation for the Genomic Prediction of Maize Flowering Time Traits

Ensembles of multiple genomic prediction models have demonstrated improved prediction performance over the individual models contributing to the ensemble. The outperformance of ensemble models is expected from the Diversity Prediction Theorem, which states that for ensembles constructed with diverse prediction models, the ensemble prediction error becomes lower than the mean prediction error of the individual models. While a naive ensemble-average model provides baseline performance improvement by aggregating all individual prediction models with equal weights, optimising weights for each individual model could further enhance ensemble prediction performance. The weights can be optimised based on their level of informativeness regarding prediction error and diversity. Here, we evaluated weighted ensemble-average models with three possible weight optimisation approaches (linear transformation, Nelder-Mead and Bayesian) using flowering time and tillering traits from two maize nested associated mapping (NAM) datasets; TeoNAM and MaizeNAM. The three proposed weighted ensemble-average approaches improved prediction performance in several of the prediction scenarios investigated. In particular, the weighted ensemble models enhanced prediction performance when the adjusted weights differed substantially from the equal weights used by the naive ensemble models. For performance comparisons among the weighted ensembles, there was no clear superiority among the proposed approaches in both prediction accuracy and error across the prediction scenarios. Weight optimisation for ensembles warrants further investigation to explore the opportunities to improve their prediction performance; for example, integration of a weighted ensemble with a simultaneous hyperparameter tuning process may offer a promising direction for further research.

bioinformatics↗

bioRxiv2024

Faecalibacterium prausnitzii induces an anti-inflammatory response and a metabolic reprogramming in human monocytes

Background and aimsFaecalibacterium prausnitzii, a highly abundant bacterium in the human gut microbiota, has been linked to overall health and is decreased in several pathological conditions, such as Inflammatory Bowel Disease (IBD). F. prausnitzii has shown anti-inflammatory properties in human and mouse models, notably through the induction of IL-10 signaling. Here, we investigated which cell types from human blood and intestinal tissue are responsible for producing IL-10 induced by F. prausnitzii, and providing the first mechanistic insights. MethodsImmune cells isolated from human blood and intestinal lamina propria of patients with IBD and non-inflamed controls, were stimulated with either F. prausnitzii EXL01 strain or Escherichia coli lipopolysaccharide (LPS) and analysed by Legendplex, ELISA, flow cytometry, RNA-sequencing (RNAseq), and Seahorse technology. ResultsF. prausnitzii EXL01 strain induced the direct and dose-dependent production of IL-10 in CD14+ monocytes from the systemic circulation and intestinal tissue of IBD patients and non-inflamed controls, without inducing a pro-inflammatory response as compared to LPS stimulation. RNAseq analysis corroborated these results and revealed that F. prausnitzii EXL01 strain differentially affects cell energy metabolism compared to LPS. The anti-inflammatory response induced by F. prausnitzii in monocytes was dependent on mitochondrial respiration. ConclusionF. prausnitzii induces an anti-inflammatory response and rewires energy metabolism in human monocytes, which might explain its beneficial impact on intestinal inflammation and human health in general. These results provide new insight into the mechanisms underlying the anti-inflammatory effects of F. prausnitzii and are crucial for a better understanding of its potential use in the treatment of IBD.

immunology↗

bioRxiv2024

Optimal control of Multiple Myeloma assuming drug evasion and off-target effects

Multiple Myeloma (MM) is a plasma cell cancer that occurs in the bone marrow. A leading treatment for MM is the monoclonal antibody Daratumumab, targeting the CD38 receptor, which is highly overexpressed in myeloma cells. In this work we model drug evasion via loss of CD38 expression, which is a proposed mechanism of resistance to Daratumumab treatment. We develop an ODE model that includes drug evasion via two mechanisms: a direct effect in which CD38 expression is lost without cell death in response to Daratumumab, and an indirect effect in which CD38 expression switches on and off in the cancer cells; myeloma cells that do not express CD38 have lower fitness but are shielded from the drug action. The model also incorporates competition with healthy cells, death of healthy cells due to off-target drug effects, and a Michaelis-Menten type immune response. Using optimal control theory, we study the effect of the drug evasion mechanisms and the off-target drug effect on the optimal treatment regime. We identify a general increase in treatment duration and costs, with varying patterns of response for the different controlling parameters. Several distinct optimal treatment regimes are identified within the parameter space. Author summaryIn this work we investigate a model of Multiple Myeloma, a cancer of the bone marrow, and its treatment with the drug Daratumumab. The model incorporates proposed mechanisms by which the cancer evades Daratumumab by reduced expression of the receptor CD38, which is the drug target and normally abundent in the cancer cells. The model includes an off-target effect, meaning that the drug treatment destroys some healthy cells alongside the targeted cancer cells. Both mechanisms can reasonably be expected to reduce the efficacy of the drug. We investigate the model using optimal control methods, which are used to find the drug dose over time which best balances the financial and health costs of treatment against cancer persistence, according to a specified cost function. We show that this drug resistence and off-target effect prolongs the optimal treatment and increase the burden of both the disease and drug. We analyse the distinct effects of the controlling parameters on each of these costs factors as well as the time course, and identify conditions under which extended treatment is required, with either intermittant treatment or a steady reduced dose. Extended treatment may be indefinite or for a fixed period.

systems biology↗

bioRxiv2024

Normative models combining fetal and postnatal MRI data to characterize neurodevelopmental trajectories during the transition from in- to ex-utero

The perinatal period involves transitioning from an intra- to an extrauterine environment, which requires a complex adaptation of the brain. This period is marked with dynamic and multifaceted cortical changes in both structure and function. Most studies to date have focused either on the fetal or postnatal period, independently. To the best of our knowledge, this is the first neurodevelopmental study targeting the cortical trajectory of typically developing perinatal subjects, combining MRIs from both fetal and postnatal participants. Prior to analysis, preprocessing and segmentation parameters were harmonized across all subjects in order to overcome methodological limitations that arise when studying such different populations. We conducted a normative modeling analysis on a sample of 607 subjects, age ranged 24 to 45 weeks post-conception, to observe changes that arise as participants traverse the birth barrier. We observed that the trajectories of global surface area and several volumetric features, including total gray matter, white matter, brainstem, cerebellum and hippocampi, follow distinct but continuous patterns during this transition. We further report three features presenting a discontinuity in their neurodevelopmental trajectories as participants traverse from a fetal to a postnatal environment: the extra-cerebrospinal fluid volume, the ventricular volume and global gyrification. The current study demonstrates the presence of unique neurodevelopmental patterns for several structural features during the perinatal period, and confirms that not all features are affected in the same way as they cross the birth barrier. SIGNIFICANCE STATEMENTThe perinatal phase comprises the fetal and immediate postnatal period, and is generally described as the time surrounding birth. Comprehensively understanding this period is crucial due to the presence of dynamic and multifaceted brain changes. What makes this investigation unique is that it is the first neurodevelopmental study, to the best of our knowledge, focused on the cortical trajectory of typically developing perinatal subjects through the combination of both fetal and postnatal participants into one analysis. We report that certain brain feature trajectories change drastically as fetuses become newborns, while other features remain continuous. These observations are relevant in both the isolation of biomarkers for later cognitive and physiological disorders and in the understanding of typical cerebral development.

neuroscience↗

bioRxiv2020

A Connectome-Based Approach to Assess Motor Outcome after Neonatal Arterial Ischemic Stroke

Objectivestudies of motor outcome after Neonatal Arterial Ischemic Stroke (NAIS) often rely on lesion mapping using MRI. However, clinical measurements indicate that motor deficit can be different than what would solely be anticipated by the lesion extent and location. Because this may be explained by the cortical disconnections between motor areas due to necrosis following the stroke, the investigation of the motor network can help in the understanding of visual inspection and outcome discrepancy. In this study, we propose to examine the structural connectivity between motor areas in NAIS patients compared to healthy controls in order to define the cortical and subcortical connections that can reflect the motor outcome Methods30 healthy controls and 32 NAIS patients with and without Cerebral Palsy (CP) underwent MRI acquisition and manual assessment. The connectome of all participants was obtained from T1-weighted and diffusion-weighted imaging. Resultssignificant disconnections in the lesioned and contra-lesioned hemispheres of patients were found. Furthermore, significant correlations were detected between the structural connectivity metric of specific motor areas and manuality assessed by the Box and Block Test (BBT) scores in patients. Interpretationusing the connectivity measures of these links the BBT score can be estimated using a multiple linear regression model. In addition, the presence or not of CP can also be predicted using the KNN classification algorithm. According to our results, the structural connectome can be an asset in the estimation of gross manual dexterity and can help uncover structural changes between brain regions related to NAIS.

bioengineering↗

bioRxiv2017

SPANOL (SPectral ANalysis Of Lobes): A spectral clustering framework for individual and group parcellation of cortical surfaces in lobes

Understanding the link between structure, function and development in the brain is a key topic in neuroimaging that benefits from the tremendous progress of multi-modal MRI and its computational analysis. It implies, inter alia, to be able to parcellate the brain volume or cortical surface into biologically relevant regions. These parcellations may be inferred from existing atlases (e.g. Desikan) or sets of rules, as would do a neuroanatomist for lobes, but also directly driven from the data (e.g. functional or structural connectivity) with minimum a priori. In the present work, we aimed at using the intrinsic geometric information contained in the eigenfunctions of Laplace-Beltrami Operator to obtain parcellations of the cortical surface based only on its description by triangular meshes. We proposed a framework adapted from spectral clustering, general in scope and suitable for the co-parcellation of a group of subjects. We applied it to a dataset of 62 adults, optimized it and revealed a striking agreement between parcels produced by this unsupervised clustering and Freesurfer lobes (Desikan atlas), which cannot be explained by chance. Already suitable by itself, this spectral analysis of lobes (Spanol) could conveniently be fitted into a multimodal pipeline for optimized and fast lobar segmentation. Eventually, we showed promising results of Spanol on smoother brains and notably on a dataset of 15 fetuses, with an interest for both the understanding of cortical ontogeny and the applicative field of perinatal computational neuroanatomy.

neuroscience↗