Biology subjects

Lenglet, C.

Publications and source records attributed to Lenglet, C..

8 recordsLinked to original sources

bioRxiv2026

A retinotopic wiring principle of the human brain

Summary paragraphA central challenge in neuroscience is to understand how anatomical connectivity links neural representations to behavior. In sensory systems, cortical areas encode structured maps of the external world, yet it remains unclear whether and how this representational geometry constrains long-range connectivity in the living human brain. Here, we show that structural connectivity in the human visual cortex preserves retinotopic organization, revealing a retinotopic wiring principle; cortical regions representing the same locations in visual space preferentially connect. Across more than 1,700 participants spanning ages 2 to 88 years, we further show that well-known perceptual asymmetries correspond to systematic asymmetries in connectivity within early visual cortex, but not in connections linking visual cortex to the rest of the brain. We introduce a scalable approach to map retinotopic connectivity when tractography alone is unreliable. Together, these findings demonstrate that anatomical connectivity preserves the geometry of neural representations, providing a general principle linking brain structure, function, and behavior.

neuroscience↗

bioRxiv2025

10.5 Tesla High-Resolution Macaque Brain MRI for Connectivity Studies

Mapping brain connectivity in primates remains a major challenge due to difficulties in resolving microscopic white matter architecture, while maintaining whole-brain coverage. Increasing imaging spatial resolution is key for disambiguating fibre configurations within smaller anatomical volumes. Here, we present novel developments that allow high-resolution diffusion MRI of the macaque brain using one of the world's highest-field human MRI scanners operating at 10.5 Tesla, allowing both in vivo and ex vivo macaque brain imaging. Our approach achieves very high spatial resolution across both tissue states, (up to 580 m)3 in vivo and (300 m)3 ex vivo, with diffusion weighting up to b = 6000 s/mm2. We detail methodological advances in data acquisition, image reconstruction, processing and whole-brain tractography that overcome critical challenges associated with ultra-high-field imaging. This work establishes a new framework for high-resolution in vivo and ex vivo neuroimaging of the NHP brain at 10.5 T using a human bore scanner, paving the way for subsequent analyses of brain connectivity across species and tissue states at unprecedented detail. The dataset, along with all processing pipelines, containerised workflows, and reusable web services, is openly shared to support reproducibility and future integration with microscopy for studying white matter microstructure and connections at the mesoscale.

neuroscience↗

bioRxiv2025

Diffusion-weighted steady-state free precession imaging in the ex vivo macaque brain on a 10.5T human MRI scanner

Diffusion MRI provides a non-invasive probe of local fibre bundles and long-range anatomical connections to characterise the structural connectome. One way to achieve very high spatial resolution diffusion MRI data for connectivity investigations is to scan ex-vivo brains over many hours or days, ideally at ultra-high field strength to boost signal levels. However, conventional diffusion MRI acquisition techniques do not generally deliver good data quality for the challenging conditions of ex-vivo tissue, characterised by reduced diffusivities and relaxation times when compared to in vivo. In this work, we investigate the potential of the diffusion-weighted steady-state free precession (DW-SSFP) sequence for ex vivo diffusion imaging of the macaque brain using a 10.5 T human MRI scanner with a conventional (Gmax = 70 mT/m) gradient set. SNR-efficiency optimisations incorporating experimental relaxation times demonstrate that the DW-SSFP sequence is predicted to achieve improved or similar SNR efficiency compared to a diffusion-weighted spin- and stimulated-echo sequence. Importantly, DW-SSFP can achieve this with the additional benefit of negligible geometric distortions, unlike conventional diffusion MRI using an echo-planar imaging readout. Using optimised DW-SSFP sequence parameters, we propose a protocol at 0.4 mm isotropic resolution using a two-shell multi-orientation protocol (effective b-values of 3200 s/mm2 and 5600 s/mm2). We fit the data using Tensor, Ball and 3-Sticks and Constrained Spherical Deconvolution signal representations. The results demonstrate high-quality diffusivity estimates across the entire brain with the ability to resolve multiple fibre populations in challenging crossing-fibre regions. The data will be made fully open source and multimodal as part of the Center for Mesoscale Connectomics, providing a resource for future connectivity investigations.

neuroscience↗

bioRxiv2025

A Numerical Alternative to MR Thermometry for Safety Validation of Multi-Channel RF Transmit Coils

PurposeThis study proposes an alternative approach to MR thermometry (MRT) for the safety validation of multi-channel RF transmit coils and demonstrates its use to enable human studies at 10.5T. MethodsTo ensure patient safety, specific absorption rate (SAR) limits established under international guidelines must not be exceeded. Predicting SAR on state-of-the-art parallel transmit systems relies on electromagnetic simulations, which require extensive experimental validation. Despite a well-established validation workflow, SAR prediction errors are unavoidable and must be quantified as a safety margin. While MRT tests are commonly used for this purpose, their technical challenges necessitate an alternative. The proposed technique propagates the error between experimentally and numerically acquired B+distributions to the uncertainty in simulated peak local SAR using Monte-Carlo simulations without the need for MRT. This method was validated using a 16-channel transceiver coil for imaging the human torso (henceforth referred to as a "body" coil) at 10.5T with two excitation scenarios, as well as an 8-channel 10.5T head coil with four excitation scenarios. ResultsThe proposed numerical technique proved more conservative than existing MRT-based SAR error quantification methods across all tested scenarios. Its application to validate three state-of-the-art head coils (16Tx/32Rx, 16Tx/80Rx, and 16Tx/128Rx) led to regulatory approval for human head imaging and high-quality functional as well as diffusion MRI results at 10.5T. ConclusionA numerical alternative to MRT requires only the experimental acquisition of B+ maps for comparison with simulations, enabling the quantification of uncertainty in SAR prediction. This technique was applied to three 16-channel transmit arrays, each used in conjunction with high-channel-count receive arrays for in vivo imaging.

neuroscience↗

bioRxiv2024

Experimental Design Optimization for Brain Microstructure Imaging via Automatic Differentiation

Accurate characterization of the brain microstructure using diffusion MRI (dMRI) relies on optimal (i.e. information-rich) scanning protocols. Presently, without such protocols, extensive datasets are necessary to image the intricate microarchitecture of the brain by fitting complex biophysical models to the data. This impedes the full realization of dMRIs potential, particularly in time-constrained clinical scans. To tackle this, we introduce a novel framework based on the Cramer-Rao lower bound (CRLB) to optimize dMRI protocols for any multi-compartment biophysical models, to accurately capture features like axonal diameter index and multiple fiber orientations in a voxel. Unlike previous methods, limited in model complexity or parameter scope, our framework handles all model parameters, including water diffusivities, enhancing estimation fidelity. Leveraging automatic differentiation and parallel computing via TensorFlow, this approach systematically explores the entire parameter space for comprehensive scanning protocol optimization. By optimizing dMRI protocols, this work stands to significantly enhance biophysical modeling accuracy, thereby deepening our understanding of the brain microstructure in health and disease.

neuroscience↗

bioRxiv2024

Mapping polarization-sensitive optical coherence tomography and ultra-high-field diffusion MRI in the macaque brain

This paper provides comparisons between microstructure and two-dimensional fiber orientations measured optically using polarization sensitive optical coherence tomography (PS-OCT) and those estimated from ultra-high-field diffusion MRI (dMRI) at 10.5T in the macaque brain. The PS-OCT imaging is done at an in-plane resolution of [~]10 microns in and around the thalamus. Whole brain dMRI is acquired at an isotropic resolution of 0.75 mm. We provide comparisons between cross-polarization and optical orientation from PS-OCT with the fractional anisotropy and two-dimensional orientations extracted from dMRI using a diffusion tensor model. The orientations from PS-OCT are also extracted computationally using a structure tensor. Additionally, we demonstrate the utility of mesoscale, PS-OCT imaging in improving the MRI resolution by learning the mapping between these contrasts using a super-resolution Generative Adversarial Network.

neuroscience↗

bioRxiv2020

NOise Reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing

Diffusion-weighted magnetic resonance imaging (dMRI) has found great utility for a wide range of neuroscientific and clinical applications. However, high-resolution dMRI, which is required for improved delineation of fine brain structures and connectomics, is hampered by its low signal-to-noise ratio (SNR). Since dMRI relies on the acquisition of multiple different diffusion weighted images of the same anatomy, it is well-suited for denoising methods that utilize correlations across the image series to improve the apparent SNR and the subsequent data analysis. In this work, we introduce and quantitatively evaluate a comprehensive framework, NOise Reduction with Distribution Corrected (NORDIC) PCA method for processing dMRI. NORDIC uses low-rank modeling of g-factor-corrected complex dMRI reconstruction and non-asymptotic random matrix distributions to remove signal components which cannot be distinguished from thermal noise. The utility of the proposed framework for denoising dMRI is demonstrated on both simulations and experimental data obtained at 3 Tesla with different resolutions using human connectome project style acquisitions. The proposed framework leads to substantially enhanced quantitative performance for estimating diffusion tractography related measures and for resolving crossing fibers as compared to a conventional/state-of-the-art dMRI denoising method. HighlightsO_LIWe propose a framework, NORDIC, for denoising complex valued dMRI data using Gaussian statistics C_LIO_LIThe effectiveness of the proposed denoising method is distinguished by the ability to remove only signal which cannot be distinguished from thermal noise C_LIO_LIThe proposed method outperforms a state-of-art method for denoising dMRI in terms of fiber orientation dispersion C_LIO_LIQuantitative evaluation of NORDIC across different resolutions and SNR using human connectome type acquisitions and analysis shows up to 6 fold improvement in apparent SNR for 0.9mm whole brain dMRI at 3T. C_LI

neuroscience↗

bioRxiv2017

Network Curvature as a Hallmark of Brain Structural Connectivity

Studies show that while brain networks are remarkably robust to a variety of adverse events, such as injuries and lesions due to accidents or disease, they may be fragile when the disturbance takes place in specific locations. This seems to be the case for diseases in which accumulated changes in network topology dramatically affect certain sensitive areas. To this end, previous attempts have been made to quantify robustness and fragility of brain functionality in two broadly defined ways: (i) utilizing model-based techniques to predict lesion effects, and (ii) studying empirical effects from brain lesions due to injury or disease. Both directions aim at assessing functional connectivity changes resulting from structural network variations. In the present work, we follow a more geometric viewpoint that is based on a notion of curvature of networks, the so-called Ollivier-Ricci curvature. A similar approach has been used in recent studies to quantify financial market robustness as well as to differentiate biological networks corresponding to cancer cells from normal cells. The same notion of curvature, defined at the node level for brain networks obtained from MRI data, may help identify and characterize the effects of diseases on specific brain regions. In the present paper, we apply the Ollivier-Ricci curvature to brain structural networks to: i) Demonstrate its unique ability to identify robust (or fragile) brain regions in healthy subjects. We compare our results to previously published work which identified a unique set of regions (called structural core) of the human cerebral cortex. This novel characterization of brain networks, complementary to measures such as degree, strength, clustering or efficiency, may be particularly useful to detect and monitor candidate areas for targeting by surgery (e.g. deep brain stimulation) or pharmaco-therapeutic agents; ii) Illustrate the power our curvature-derived measures to track changes in brain connectivity with healthy development/aging and; iii) Detect changes in brain structural connectivity in people with Autism Spectrum Disorders (ASD) which are in agreement with previous morphometric MRI studies.

neuroscience↗