Biology subjects

Maurel, P.

Publications and source records attributed to Maurel, P..

3 recordsLinked to original sources

bioRxiv2026

CTL1 (SLC44A1) regulates myelin lipid composition and architecture in Schwann cells

Myelin formation requires extensive membrane lipid synthesis, yet how myelinating glia acquire the choline needed for this process remains incompletely understood. Choline transporter-like protein 1 (CTL1; SLC44A1) is highly expressed in Schwann cells, and CTL1 deficiency in oligodendrocytes impairs CNS myelination. Here, we examined the role of CTL1 in peripheral nerve myelination using Schwann cell-specific Ctl1 knockout mice. Unexpectedly, loss of Ctl1 did not alter Schwann cell differentiation, the number of myelinated axons, or myelin thickness, but increased myelin abnormalities, including in-foldings and out-foldings. Lipidomic analysis revealed selective alterations in myelin lipid composition, particularly among long-chain lipid species, together with triglyceride accumulation in whole nerves. CTL1 deficiency also increased mTORC1-associated S6 and mTORC2-associated AKT S473 phosphorylation. Transcriptomic analysis revealed downregulation of gene programs associated with fatty acid {beta}-oxidation, triglyceride catabolism, and oxidative phosphorylation. Following peripheral nerve injury, Ctl1-deficient Schwann cells generated a normal repair response and efficiently remyelinated regenerated axons despite altered mTOR signaling. Together, these findings demonstrate that CTL1 is not required for overall peripheral myelin production but contributes to maintaining normal myelin lipid composition and architecture, revealing an unexpected ability of Schwann cells to sustain myelination despite disruption of CTL1-dependent choline metabolism

neuroscience↗

bioRxiv2019

A sparse EEG-informed fMRI model for hybrid EEG-fMRI neurofeedback prediction

Measures of brain activity through functional magnetic resonance imaging (fMRI) or Electroencephalography (EEG), two complementary modalities, are ground solutions in the context of neuro-feedback (NF) mechanisms for brain rehabilitation protocols. While NF-EEG (real-time neurofeedback scores computed from EEG signals) have been explored for a very long time, NF-fMRI (real-time neurofeedback scores computed from fMRI signals) appeared more recently and provides more robust results and more specific brain training. Using simultaneously fMRI and EEG for bi-modal neurofeedback sessions (NF-EEG-fMRI, real-time neurofeedback scores computed from fMRI and EEG) is very promising to devise brain rehabilitation protocols. However, fMRI is cumbersome and more exhausting for patients. The original contribution of this paper concerns the prediction of bi-modal NF scores from EEG recordings only, using a training phase where EEG signals as well as the NF-EEG and NF-fMRI scores are available. We propose a sparse regression model able to exploit EEG only to predict NF-fMRI or NF-EEG-fMRI in motor imagery tasks. We compared different NF-predictors steaming from the proposed model. We showed that predicting NF-fMRI scores from EEG signals adds information to NF-EEG scores and significantly improve the correlation with bi-modal NF sessions, compared to classical NF-EEG scores.

neuroscience↗

bioRxiv2018

Automated Electrodes Detection during simultaneous EEG/fMRI

1The coupling of Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) enables the measure of brain activity at high spatial and temporal resolution. The localisation of EEG sources depends on several parameters including the knowledge of the position of the electrodes on the scalp. An accurate knowledge about this information is important for source reconstruction. Currently, when acquiring EEG and fMRI together, the position of the electrodes is generally estimated according to fiducial points by using a template. In the context of simultaneous EEG/fMRI acquisition, a natural idea is to use magnetic resonance (MR) images to localise EEG electrodes. However, most MR compatible electrodes are built to be almost invisible on MR Images. Taking advantage of a recently proposed Ultra short Echo Time (UTE) sequence, we introduce a fully automatic method to detect and label those electrodes in MR images. Our method was tested on 8 subjects wearing a 64-channel EEG cap. This automated method showed an average detection accuracy of 94% and the average position error was 3.1 mm. These results suggest that the proposed method has potential for determining the position of the electrodes during simultaneous EEG/fMRI acquisition with a very light cost procedure.

neuroscience↗