Elda Fischi Gomez

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions

Elena Beanato, Julia Brügger, Andéol Geoffroy Cadic-Melchior, Erick Jorge Canales Rodriguez, Alessandro Daducci, Elda Fischi Gomez, Gabriel Girard, Tom Hilbert, Friedhelm Christoph Hummel, Tobias Kober, Philipp Johannes Koch, Takuya Morishita, Chang-Hyun Park, Gian Franco Piredda, Marco Pizzolato, Jean-Philippe Thiran, Maximilian Jonas Wessel, Thomas Yu

Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acquired in a healthy population. This study focuses on multi-echo T2 relaxometry, a qMRI technique that probes the complex tissue microstructure by differentiating compartment-specific T2 relaxation times. However, estimation methods are still limited by their sensitivity to the underlying noise. Moreover, estimating the model's parameters is challenging because the resulting inverse problem is ill-posed, requiring advanced numerical regularization techniques. As a result, the estimates from distinct regularization strategies are different. In this work, we aimed to investigate the variability and reproducibility of different techniques for estimating the transverse relaxation time of the intra- and extra-cellular space (TIE2 ) in gray (GM) and white matter (WM) tissue in a clinical setting, using a multi-site, multi-session, and multi-run T2 relaxometry dataset. To this end, we evaluated three different techniques for estimating the T2 spectra (two regularized non-negative least squares methods and a machine learning approach). Two independent analyses were performed to study the effect of using raw and denoised data. For both the GM and WM regions, and the raw and denoised data, our results suggest that the principal source of variance is the inter-subject variability, showing a higher coefficient of variation (CoV) than those estimated for the inter-site, inter-session, and inter-run, respectively. For all reconstruction methods studied, the CoV ranged between 0.32 and 1.64%. Interestingly, the inter-session variability was close to the inter-scanner variability with no statistical differences, suggesting that TIE2 is a robust parameter that could be employed in multi-site neuroimaging studies. Furthermore, the three tested methods showed consistent results and similar intra-class correlation (ICC), with values superior to 0.7 for most regions. Results from raw data were slightly more reproducible than those from denoised data. The regularized non-negative least squares method based on the L-curve technique produced the best results, with ICC values ranging from 0.72 to 0.92.

2022

Ultrahigh field in vivo characterization of microstructural abnormalities in the orbitofrontal cortex and amygdala in autism

Guillaume Bonnier, Elda Fischi Gomez, Cristina Granziera, Nouchine Hadjikhani

There are currently no biomarkers for autism spectrum disorder (ASD). This neurodevelopmental condition has previously been associated with histopathological findings, including increased neuronal packing density in the amygdala, abnormal laminar cytoarchitecture and increased average neuronal density in the prefrontal cortex. The present study examined whether new brain imaging technologies could reveal in vivo, in adults with ASD, the manifestation of previously described histopathological changes. Using quantitative mapping at ultrahigh field (7 Tesla), we show that we can observe microstructural alterations in the right lateral orbitofrontal cortex and the bilateral amygdala in adult individuals with ASD in vivo. These imaging alterations point to an abnormal laminar cytoarchitecture and to an increased neuronal density, similar to what has been previously described in post-mortem data in ASD. Our data demonstrate that it is possible to visualize, in vivo and at the individual level, alterations of cortical and subcortical microstructure in ASD. Future studies will be needed to extend these findings to a larger group of individuals and evaluate their association with symptomatology as well as their specificity among the different neurodevelopmental disorders.

WILEY2021

Model-informed machine learning for multi-component T2 relaxometry

Meritxell Bach Cuadra, Muhamed Barakovic, Erick Jorge Canales Rodriguez, Elda Fischi Gomez, Cristina Granziera, Tom Hilbert, Tobias Kober, Gian Franco Piredda, Marco Pizzolato, Jean-Philippe Thiran, Thomas Yu

Recovering the distribution from multi-echo magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to combine machine learning and aspects of parametric (fitting from the MRI signal using biophysical models) and non-parametric (model-free fitting of the distribution from the signal) approaches to relaxometry in brain tissue by using a multi-layer perceptron (MLP) for the distribution reconstruction. For training our network, we construct an extensive synthetic dataset derived from biophysical models in order to constrain the outputs with a priori knowledge of in vivo distributions. The proposed approach, called Model-Informed Machine Learning (MIML), takes as input the MR signal and directly outputs the associated distribution. We evaluate MIML in comparison to a Gaussian Mixture Fitting (parametric) and Regularized Non-Negative Least Squares algorithms (non-parametric) on synthetic data, an ex vivo scan, and high-resolution scans of healthy subjects and a subject with Multiple Sclerosis. In synthetic data, MIML provides more accurate and noise-robust distributions. In real data, MWF maps derived from MIML exhibit the greatest conformity to anatomical scans, have the highest correlation to a histological map of myelin volume, and the best unambiguous lesion visualization and localization, with superior contrast between lesions and normal appearing tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than the non-parametric and parametric methods, respectively.

2020

Limits to anatomical accuracy of diffusion tractography using modern approaches

Muhamed Barakovic, Alice Philippa Bates, Erick Jorge Canales Rodriguez, Alessandro Daducci, Maxime Descoteaux, Tim Bjørn Dyrby, Elda Fischi Gomez, Gabriel Girard, Chao Huang, Marco Pizzolato, Gaëtan Olivier D Rensonnet, David Paul Roger Romascano, Jean-Philippe Thiran

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets – a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

2018

A Longitudinal Study of Neurite Orientation Dispersion and Density Imaging in Relapsing-Remitting Multiple Sclerosis

Guillaume Bonnier, Alessandro Daducci, Elda Fischi Gomez, Cristina Granziera, Gunnar Krüger, David Paul Roger Romascano, Jean-Philippe Thiran

We explored the sensitivity of a novel diffusion MRI method i.e. “Neurite orientation dispersion and density imaging”, to detect and characterize brain microstructure alterations in relapsing-remitting multiple sclerosis patients that we followed up over 2 years. Cross-sectionally, NODDI revealed that an increase in orientation dispersion and a decrease in neurite density in NAWM and in lesions of RRMS patients compared to healthy subjects. Longitudinally, NODDI measured a decreased dispersion and an increased neurite density in MS lesions at 2 years follow-up. Also, NODDI metrics at baseline were highly related to cognition at both baseline and follow-up.

2017

Brain network characterization of high-risk preterm-born school-age children

Elda Fischi Gomez, Alessandra Griffa, François Lazeyras, Jean-Philippe Thiran

Higher risk for long-term cognitive and behavioral impairments is one of the hallmarks of extreme prematurity (EP) and pregnancy-associated fetal adverse conditions such as intrauterine growth restriction (IUGR). While neurodevelopmental delay and abnormal brain function occur in the absence of overt brain lesions, these conditions have been recently associated with changes in microstructural brain development. Recent imaging studies indicate changes in brain connectivity, in particular involving the white matter fibers belonging to the cortico-basal ganglia-thalamic loop. Furthermore, EP and IUGR have been related to altered brain network architecture in childhood, with reduced network global capacity, global efficiency and average nodal strength. In this study, we used a connectome analysis to characterize the structural brain networks of these children, with a special focus on their topological organization. On one hand, we confirm the reduced averaged network node degree and strength due to EP and IUGR. On the other, the decomposition of the brain networks in an optimal set of clusters remained substantially different among groups, talking in favor of a different network community structure. However, and despite the different community structure, the brain networks of these high-risk school-age children maintained the typical small-world, rich-club and modularity characteristics in all cases. Thus, our results suggest that brain reorganizes after EP and IUGR, prioritizing a tight modular structure, to maintain the small-world, rich-club and modularity characteristics. By themselves, both extreme prematurity and IUGR bear a similar risk for neurocognitive and behavioral impairment, and the here defined modular network alterations confirm similar structural changes both by IUGR and EP at school age compared to control. Interestingly, the combination of both conditions (IUGR + EP) does not result in a worse outcome. In such cases, the alteration in network topology appears mainly driven by the effect of extreme prematurity, suggesting that these brain network alterations present at school age have their origin in a common critical period, both for intrauterine and extrauterine adverse conditions.

Elsevier Sci Ltd2016

Gender-specific attention system subnetwork vulnerability in prematurely born children

Elda Fischi Gomez, Jean-Philippe Thiran

Children born prematurely are at high risk for long-term abnormal development, including deficits in attentional abilities that persist throughout adolescence. The severity of these cognitive difficulties varies significantly among preterm-born children, with both the level of prematurity and sex being the most relevant factors for the determination of their neurodevelopmental outcomes. In order to understand the neurostructural correlate of these disabilities, we used a brain connectome approach to analyze the effects of gender and degree of prematurity on the attentional system network related to executive control in school age preterm children. Our results suggest that being born extremely premature, as well as being born male, differentially affects brain connectivity and development.

2016

Quantitative and qualitative analysis of transient fetal compartments during prenatal human brain development

Elda Fischi Gomez, Jonas Richiardi

The cerebral wall of the human fetal brain is composed of transient cellular compartments, which show characteristic spatiotemporal relationships with major neurogenic events (proliferation, migration, axonal growth, dendritic differentiation, synaptogenesis, cell death, and myelination). The aim of the present study was to obtain new quantitative data describing volume, surface area, and thickness of transient compartments in the human fetal cerebrum. Forty-four postmortem fetal hemispheres aged 13-40 postconceptional weeks (PCW) were included in this study. High-resolution TI MRI was acquired on 19 hemispheres and MRI images were processed using the MNI-ACE toolbox. Delineation of fetal compartments was performed semi-automatically by co-registration of MRI with histological sections and age-matched brains from Zagreb Neuroembryological Collection. Growth trajectories of transient fetal compartments were reconstructed and the composition of telencephalic wall was quantitatively assessed. Between 13 and 25 PCW, when the intensity of neuronal proliferation decreases drastically, the relative volume of proliferative (ventricular and subventricular) compartments showed pronounced decline. In contrast, synapse- and extracellular matrix-rich subplate compartment continued to grow during the first two trimesters, occupying up to 45% of telencephalon and reaching its maximum volume and thickness around 30 PCW. This developmental maximum coincides with a period of intensive growth of long cortico-cortical fibers, which enter and wait in subplate before approaching the cortical plate. Although we did not find significant age related changes in thickness of the cortical plate, the volume, gyrification index, and surface area of the cortical plate continued to exponentially grow during the last phases of prenatal development. This cortical expansion coincides developmentally with the transformation of embryonic cortical columns, dendritic differentiation, and ingrowth of axons. These results provide a normative, quantitative description of transient human fetal brain compartments observable with MRI, will improve understanding of structural-functional relationships, enable correlation between in vitro/in vivo imaging and fine structural histological studies, and will serve as normative for study of perinatal brain injuries.

Frontiers Research Foundation2016

Elda Fischi Gomez

Cortical alterations after very preterm birth and the association with socio-emotional abilities from childhood to early adolescence

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions

Ultrahigh field in vivo characterization of microstructural abnormalities in the orbitofrontal cortex and amygdala in autism

Model-informed machine learning for multi-component T2 relaxometry

Limits to anatomical accuracy of diffusion tractography using modern approaches

A Longitudinal Study of Neurite Orientation Dispersion and Density Imaging in Relapsing-Remitting Multiple Sclerosis

Brain network characterization of high-risk preterm-born school-age children

Gender-specific attention system subnetwork vulnerability in prematurely born children

Quantitative and qualitative analysis of transient fetal compartments during prenatal human brain development

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions

Cortical alterations after very preterm birth and the association with socio-emotional abilities from childhood to early adolescence

Model-informed machine learning for multi-component T2 relaxometry

Brain network characterization of high-risk preterm-born school-age children

Ultrahigh field in vivo characterization of microstructural abnormalities in the orbitofrontal cortex and amygdala in autism

Limits to anatomical accuracy of diffusion tractography using modern approaches

Gender-specific attention system subnetwork vulnerability in prematurely born children

Quantitative and qualitative analysis of transient fetal compartments during prenatal human brain development

A Longitudinal Study of Neurite Orientation Dispersion and Density Imaging in Relapsing-Remitting Multiple Sclerosis