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Di Domenicantonio, G.

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

2 recordsLinked to original sources

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↗

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↗