Biology subjects

Di Domenicantonio, G.

Publications and source records attributed to Di Domenicantonio, G..

4 recordsLinked to original sources

bioRxiv2023

Statistical analyses of motion-corrupted MRI relaxometry data

Consistent noise variance across data points (i.e. homoscedasticity) is required to ensure the validity of statistical analyses of MRI data conducted using linear regression methods. However, head motion leads to degradation of image quality, introducing noise heteroscedasticity into ordinary-least square analyses. The recently introduced QUIQI method restores noise homoscedasticity by means of weighted least square analyses in which the weights, specific for each dataset of an analysis, are computed from an index of motion-induced image quality degradation. QUIQI was first demonstrated in the context of brain maps of the MRI parameter R2*, which were computed from a single set of images with variable echo time. Here, we extend this framework to quantitative maps of the MRI parameters R1, R2*, and MTsat, which are computed from multiple sets of images. QUIQI allows for optimization of the noise model by using metrics quantifying heteroscedasticity and free energy. QUIQI restores homoscedasticity more effectively than insertion of an image quality index in the analysis design and yields higher sensitivity than simply removing the datasets most corrupted by head motion from the analysis. In sum, QUIQI provides an optimal approach to group-wise analyses of a range of quantitative MRI parameter maps that is robust to inherent homoscedasticity.

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

In-vivo estimation of axonal morphology from MRI and EEG data

PurposeWe present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in-vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity. TheoryThe proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on 1) the distribution of axonal radius - P(r)- and 2) the g-ratio of the individual axons that compose this tract - g(r). P(r)is assumed to follow a Gamma distribution with mode and scale parameters, M and{theta} , and g(r) is described by a power-law with parameters and {beta}. MethodsMRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract. ResultsThe estimated interhemispheric transfer time was 11.72{+/-}2.87 ms, leading to an average conduction velocity across subjects of 13.22{+/-}1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated{theta} - the width of the right tail of the axonal radius distribution and {beta} - the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter{theta} was 0.40{+/-}0.07 {micro}m and the parameter {beta} was 0.67{+/-}0.02 {micro}m-. ConclusionsThe estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in-vivo histological studies of the human brain.

neuroscience↗

bioRxiv2021

Restoring statistical validity in group analyses of motion-corrupted MRI data

Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterize disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data-driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of brain MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality.

neuroscience↗