Biology subjects

Lajous, H.

Publications and source records attributed to Lajous, H..

3 recordsLinked to original sources

bioRxiv2025

Non-invasive prediction of conduction velocities in the human brain from MRI-derived microstructure features at 7 Tesla

The conduction velocity of neuronal signals along axons is a key neurophysiological property that can be altered in various disease processes. While cortico-cortical evoked potentials (CCEPs) can be measured in presurgical assessment to provide information about conduction delay between a subset of brain regions, it is currently not possible to efficiently and systematically estimate conduction velocity in vivo across the whole brain. Given the established link between conduction velocity and axon morphology (most notably axon diameter but also myelination), mapping a reliable and quantitative metric linked to axon properties could fill the gap of inferring conduction velocity across the entire human brain. By integrating multiple MRI-derived microstructural measures - including axon radius, axonal water fraction, extra-axonal perpendicular diffusivity, and longitudinal relaxation time - and conduction velocity estimates obtained from a large database of CCEPs, we developed a whole-brain prediction model of conduction velocity. Our multivariate MRI-based model explained 29% of variance in neurophysiological conduction velocity, making it possible to partially predict whole-brain conduction velocity and delay matrices along connections for which no direct measurement is commonly available from epilepsy surgery investigations. This integrative MRI-based approach could provide a non-invasive framework for comprehensively characterising conduction delays in vivo across the human brain white matter.

neuroscience↗

bioRxiv2024

Maturation-informed synthetic Magnetic Resonance Images of the Developing Human Fetal Brain

Magnetic resonance imaging is a powerful modality to investigate abnormal developmental patterns in utero. However, since it is not the first-line diagnostic tool in this sensitive population, data remain scarce and heterogeneous between scanners and centers. In addressing the data scarcity issue while generating data representative of real fetal brain MRI, we proposed FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom. Here, we present a novel synthetic dataset of 594 two-dimensional, low-resolution series of T2-weighted images corresponding to 78 developing human fetal brains between 20.0 and 34.8 weeks of gestational age. Data are generated with substantive improvements from the original FaBiAN to account for local heterogeneities within white matter tissues throughout maturation. These synthetic-yet-highly-realistic images cover both healthy and pathological development trajectories simulated with standard clinical settings and anatomically informed by the Fetal Tissue Annotations (FeTA) dataset. Two independent radiologists qualitatively assessed the realism of the simulated images. We also quantitatively demonstrate the simulated datas increased fidelity to real data compared to the previous FaBiAN version. The reuse potential of the proposed dataset was also evaluated in the context of automated fetal brain tissue segmentation. Besides, our dataset that combines images generated from various clinical scenarios has been made publicly available to support the continuous endeavor of the community to develop advanced post-processing methods aswell as cutting-edge artificial intelligence models.

bioinformatics↗

bioRxiv2021

Through-plane super-resolution with autoencoders in diffusion magnetic resonance imaging of the developing human brain

Fetal brain diffusion magnetic resonance images are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ an unsupervised learning algorithm using an autoencoder neural network to enhance the through-plane resolution by leveraging a large amount of data. Our framework, which can also be used for slice outliers replacement, overperformed conventional interpolations quantitatively and qualitatively on pre-term newborns of the developing Human Connectome Project. The evaluation was performed on both the original diffusion-weighted signal and on the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser. The network was able to generalize to fetal data with different levels of motion and we qualitatively showed its consistency, hence supporting the relevance of pre-term datasets to improve the processing of fetal brain images.

bioinformatics↗