Multimodal microstructure imaging: joint T2-relaxometry and diffusometry to estimate myelin, intracellular, extracellular, and cerebrospinal fluid properties

We propose a multimodal joint estimation that aims at exploiting the complementary information of diffusion and multi-echo spin echo data to disentangle the contributions and properties of the main tissue microstructure compartments. We recovered T2, diffusion coefficient, and volume fractions values of myelin, intracellular, extracellular, and cerebrospinal fluid compartments within an ex vivo spinal cord sample by means of diffusometry and relaxometry. A g-ratio map was also calculated

Model-Informed Machine Learning for Multi-component T2 Relaxometry

Recovering the T2 distribution from multi-echo T2 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 T2 distribution from the signal) approaches to T2 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 \textit{a priori} knowledge of \textit{in vivo} distributions. The proposed approach, called Model-Informed Machine Learning (MIML), takes as input the MR signal and directly outputs the associated T2 distribution. We evaluate MIML in comparison to non-parametric and parametric approaches 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 non-parametric and parametric methods, respectively.Comment: Preprint submitted to Medical Image Analysis (July 14, 2020

What we learn about bipolar disorder from large-scale neuroimaging:Findings and future directions from the ENIGMA Bipolar Disorder Working Group

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness

Reproducibility in the absence of selective reporting : An illustration from large-scale brain asymmetry research

Altres ajuts: Max Planck Society (Germany).The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an "ideal publishing environment," that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

In this study, we developed a robust inversion algorithm to estimate the Neurite Orientation Dispersion and Density Imaging (NODDI) model. It is based on the Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework. However, in contrast to AMICO, the proposed method relies on realistic MRI noise models. Moreover, it allows to take into account the underlying spatial continuity of the brain image by including a total variation regularization term. In simulated data the new method was effective in reducing the outliers, producing results more close to the ground-truth and with lower variability. The method was also evaluated on real data

Multimodal microstructure imaging: combining relaxometry and diffusometry to estimate myelin, intracellular, extracellular, and cerebrospinal fluid properties

please visit https://infoscience.epfl.ch/record/26484

pHARDI: accelerated reconstruction toolkit for estimating the white matter fiber geometry from diffusion MRI data

Diffusion magnetic resonance imaging is a non-invasive technique capable of quantifying the diffusion process of water molecules in living biological tissues. Its main application is the study of the local geometry and wiring pattern of the human brain white matter. A large number of neuroscience research studies and clinical applications have been conducted in the last decade. Many of these studies are based on different intra-voxel models of molecular diffusion, which in turn, require different sampling schemes to collect the data and fitting algorithms. In order to facilitate the widespread use of this technique, we developed a novel software called pHARDI. The purpose of pHARDI is twofold: (1) to provide in a single toolkit an extensive and diverse set of reconstruction methods for different sampling protocols, and (2) to accelerate the reconstruction process by means of high quality linear algebra libraries. The toolkit has a layer-based design allowing to parallelise the computations via multiple accelerators in a wide range of devices, including co-processors, multi-core CPU, and GPU devices.The experimental evaluation shows that pHARDI attains, on average, a speed-up of 8X over equivalent Matlab implementations

Is it feasible to directly access the bundle’s specific myelin content, instead of averaging? A study with Microstructure Informed Tractography

Diffusion MRI connectometry is a widely used tool to investigate features of structural connectomes that reflect differences in white matter tracks integrity. It consists in averaging microstructural tissues properties (obtained from any voxel-wise map) along streamlines recovered with diffusion tractography. Nevertheless, the average of a microstructural measure is a weak information about an entire bundle. Using microstructure-informed tractography (COMMIT), we were able to simultaneously estimate fiber’s specific myelin water fraction, intra-axonal volume fraction, and g-ratio. We also computed new connectomes with bundles’ specific measures instead of the commonly used averages

Unified multi-modal characterization of microstructural parameters of brain tissue using diffusion MRI and multi-echo T2 data

In this study, a unified framework to estimate different microstructure indices from diffusion MRI and multi-echo T2 (MET2) data was proposed.The new methodology takes into account the common and complementary information provided by both modalities. While the MET2 data enable us to model the myelin compartment, the diffusion data allow us to better characterize the intra-axonal and extra-axonal compartments. Numerical results support the hypothesis that the proposed approach is more accurate than the alternative approach based on the individual sequential fitting of both modalities. The performance was stable for noise levels commonly found in clinical protocols

Robust myelin water imaging from multi-echo T2 data using second-order Tikhonov regularization with control points

Myelin water imaging is an MRI technique used to quantify myelination in the brain. The state-of-the-art reconstruction method is based on non-negative least squares optimization with zero-order Tikhonov regularization. In this study, a second-order Tikhonov regularization approach with control points was examined. This penalty term is more efficient for promoting smooth solutions while minimizing the contamination between myelin and non-myelin components. The performance of the proposed algorithm was investigated on in-vivo and ex-vivo multi-echo T2 data. It exhibited a higher correlation with histology than the state-of-the-art method. Its stability was studied using scan-rescan data