Biology subjects

Yerly, J.

Publications and source records attributed to Yerly, J..

4 recordsLinked to original sources

bioRxiv2023

Hi-Fi fMRI: High-resolution, fast-sampled and sub-second whole-brain functional MRI at 3T in humans

Functional magnetic resonance imaging (fMRI) is a methodological cornerstone of neuroscience. Most studies measure blood-oxygen-level-dependent (BOLD) signal using echo-planar imaging (EPI), Cartesian sampling, and image reconstruction with a one-to-one correspondence between the number of acquired volumes and reconstructed images. However, EPI schemes are subject to trade-offs between spatial and temporal resolutions. We make strides in overcoming these limitations by measuring BOLD with a gradient recalled echo (GRE) with a 3D radial-spiral phyllotaxis trajectory at a high sampling rate (28.49ms) on standard 3T field strength. The framework enables the reconstruction of 3D signal time courses with whole-brain coverage at simultaneously higher nominal spatial (1mm3) and temporal (up to 250ms) resolutions, as compared to optimized EPI schemes. Additionally, we apply motion correction directly to the k-space raw data, enabling flexible motion-corrected reconstructions; the desired temporal resolution to observe hemodynamic responses can be chosen after scanning. By showing activation in the calcarine sulcus of 20 participants completing an ON-OFF visual paradigm, we demonstrate the reliability of our method for applications in cognitive neuroscience research.

neuroscience↗

bioRxiv2022

Characterization of cardiac-induced noise in R2* maps of the brain

PurposeCardiac pulsation increases the noise level in brain maps of the transverse relaxation rate R2*. Cardiac-induced noise is challenging to mitigate during the acquisition of R2* mapping data because its characteristics are unknown. In this work, we therefore aim to characterize cardiac-induced noise in brain maps of the MRI parameter R2*. MethodsWe designed a sampling strategy to acquire multi-echo 3D data in 12 intervals of the cardiac cycle, monitored with a fingertip pulse-oximeter. We measured the amplitude of cardiac-induced noise in this data and assessed the effect of cardiac pulsation on R2* maps computed across echoes. The area of k-space that contains most of the cardiac-induced noise in R2* maps was then identified. Based on these characteristics, we introduced a tentative sampling strategy that aims to mitigate cardiac-induced noise in R2* maps of the brain. ResultsIn inferior brain regions, cardiac pulsation accounts for R2* variations of up to 3s-1 across the cardiac cycle, i.e. [~]35% of the overall variability. Cardiac-induced fluctuations occur throughout the cardiac cycle, with a reduced intensity during the first quarter of the cycle. 50-60% of the overall cardiac-induced noise is localized near the k-space centre (k < 0.074 mm-1). The tentative cardiac noise mitigation strategy reduced the variability of R2* maps across repetitions by 11% in the brainstem and 6% across the whole brain. ConclusionWe provide a characterisation of cardiac-induced noise in brain R2* maps that can be used as a basis for the design of mitigation strategies during data acquisition.

neuroscience↗

bioRxiv2022

Connectome spectrum electromagnetic tomography: a method to reconstruct electrical brain sources at high-spatial resolution

Connectome Spectrum Electromagnetic Tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal. These results demonstrate that CSET offers high spatio-temporal accuracy, enabling neuroscientists to extend their research beyond the current limitations of low sampling frequency in functional MRI and the poor spatial resolution of M/EEG.

neuroscience↗

bioRxiv2020

3-Dimensional Magnetic Resonance Imaging of the Freely Moving Human Eye

Abstract Eye motion is a major confound for magnetic resonance imaging (MRI) in neuroscience or ophthalmology. Currently, solutions toward eye stabilisation include participants fixating or administration of paralytics/anaesthetics. We developed a novel MRI protocol for acquiring 3-dimensional images while the eye freely moves. Eye motion serves as the basis for image reconstruction, rather than an impediment. We fully reconstruct videos of the moving eye and head. We quantitatively validate data quality with millimetre resolution in two ways for individual participants. First, eye position based on reconstructed images correlated with simultaneous eye-tracking. Second, the reconstructed images preserve anatomical properties; the eye’s axial length measured from MRI images matched that obtained with ocular biometry. The technique operates on a standard clinical setup, without necessitating specialized hardware, facilitating wide deployment. In clinical practice, we anticipate that this may help reduce burden on both patients and infrastructure, by integrating multiple varieties of assessments into a single comprehensive session. More generally, our protocol is a harbinger for removing the necessity of fixation, thereby opening new opportunities for ethologically-valid, naturalistic paradigms, the inclusion of populations typically unable to stably fixate, and increased translational research such as in awake animals whose eye movements constitute an accessible behavioural readout.Author contributions B.F., L.D.S., M.S., and M.M.M. conceptualised the problem. B.F. and L.D.S. developed, implemented, and tested the protocol. A.M. provided optometry assessments and assisted with eye movement analysis. S.I., D.Z., and M.P.N. assisted with installation of the eye-tracking system within the MRI scanner. J.A.M.B., J.J. and J.Y. contributed with the MRI sequences and compressed sensing framework. B.F., L.D.S. and M.M.M. drafted the manuscript, and all authors contributed to internal review.Competing interests B.F., L.D.S., J.A.M.B., J.Y., M.S., and M.M.M. declare the following competing financial interest: a patent application for the protocol described in this manuscript has been filed (patent application: EP19160832). A.M., S.I., D.Z., M.P.N. and J.J. declare no competing financial interests.Competing Interest StatementB.F., L.D.S., J.A.M.B., J.Y., M.S., and M.M.M. declare the following competing financial interest: a patent application for the protocol described in this manuscript has been filed (patent application: EP19160832). A.M., S.I., D.Z., M.P.N. and J.J. declare no competing financial interests.View Full Text

bioengineering↗