NODDI-SH: a computational efficient NODDI extension for fODF estimation in diffusion MRI

Diffusion Magnetic Resonance Imaging (DMRI) is the only non-invasive imaging technique which is able to detect the principal directions of water diffusion as well as neurites density in the human brain. Exploiting the ability of Spherical Harmonics (SH) to model spherical functions, we propose a new reconstruction model for DMRI data which is able to estimate both the fiber Orientation Distribution Function (fODF) and the relative volume fractions of the neurites in each voxel, which is robust to multiple fiber crossings. We consider a Neurite Orientation Dispersion and Density Imaging (NODDI) inspired single fiber diffusion signal to be derived from three compartments: intracellular, extracellular, and cerebrospinal fluid. The model, called NODDI-SH, is derived by convolving the single fiber response with the fODF in each voxel. NODDI-SH embeds the calculation of the fODF and the neurite density in a unified mathematical model providing efficient, robust and accurate results. Results were validated on simulated data and tested on \textit{in-vivo} data of human brain, and compared to and Constrained Spherical Deconvolution (CSD) for benchmarking. Results revealed competitive performance in all respects and inherent adaptivity to local microstructure, while sensibly reducing the computational cost. We also investigated NODDI-SH performance when only a limited number of samples are available for the fitting, demonstrating that 60 samples are enough to obtain reliable results. The fast computational time and the low number of signal samples required, make NODDI-SH feasible for clinical application

Investigating Diffusion-MRI based neurite density estimation model dependency: an in-vivo study on the HCP dataset

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On the Viability of Diffusion MRI-Based Microstructural Biomarkers in Ischemic Stroke

Recent tract-based analyses provided evidence for the exploitability of 3D-SHORE microstructural descriptors derived from diffusion MRI (dMRI) in revealing white matter (WM) plasticity. In this work, we focused on the main open issues left: (1) the comparative analysis with respect to classical tensor-derived indices, i.e., Fractional Anisotropy (FA) and Mean Diffusivity (MD); and (2) the ability to detect plasticity processes in gray matter (GM). Although signal modeling in GM is still largely unexplored, we investigated their sensibility to stroke-induced microstructural modifications occurring in the contralateral hemisphere. A more complete picture could provide hints for investigating the interplay of GM and WM modulations. Ten stroke patients and ten age/gender-matched healthy controls were enrolled in the study and underwent diffusion spectrum imaging (DSI). Acquisitions at three and two time points (tp) were performed on patients and controls, respectively. For all subjects and acquisitions, FA and MD were computed along with 3D-SHORE-based indices [Generalized Fractional Anisotropy (GFA), Propagator Anisotropy (PA), Return To the Axis Probability (RTAP), Return To the Plane Probability (RTPP), and Mean Square Displacement (MSD)]. Tract-based analysis involving the cortical, subcortical and transcallosal motor networks and region-based analysis in GM were successively performed, focusing on the contralateral hemisphere to the stroke. Reproducibility of all the indices on both WM and GM was quantitatively proved on controls. For tract-based, longitudinal group analyses revealed the highest significant differences across the subcortical and transcallosal networks for all the indices. The optimal regression model for predicting the clinical motor outcome at tp3 included GFA, PA, RTPP, and MSD in the subcortical network in combination with the main clinical information at baseline. Region-based analysis in the contralateral GM highlighted the ability of anisotropy indices in discriminating between groups mainly at tp1, while diffusivity indices appeared to be altered at tp2. 3D-SHORE indices proved to be suitable in probing plasticity in both WM and GM, further confirming their viability as a novel family of biomarkers in ischemic stroke in WM and revealing their potential exploitability in GM. Their combination with tensor-derived indices can provide more detailed insights of the different tissue modulations related to stroke pathology

Beta Beams

Beta Beams could address the needs of long term neutrino oscillation experiments. They can produce extremely pure neutrino beams through the decays of relativistic radioactive ions. The baseline scenario is described, together with its physics performances. Using a megaton water Cerenkov detector installed under the Frejus, Beta Beams could improve by a factor 200 the present limits on \sin^2{2 \thetaot} and discover leptonic CP violating effects if the CP phase delta would be greater than 30 degree and theta13 greater than 1 degree. These performances can be further improved if a neutrino SuperBeam generated by the SPL 4MW, 2.2 GeV, proton Linac would be fired to the same detector. Innovative ideas on higher and lower energy Beta Beams are also described.Comment: To appear in the proceedings of 21st International Conference on Neutrino Physics and Astrophysics (Neutrino 2004), Paris, France, 14-19 Jun 200

Fully-Connected Neural Network and Spherical-Harmonics Rotation Invariant Features improve the estimation of brain tissue microstructure in Diffusion MRI

Under ReviewNational audienceDiffusion Magnetic Resonance Imaging (dMRI) is the only available imaging technique for probing the brain tissue microstructure in-vivo. Through the years, dMRI has been used for both estimating brain connectivity via the use of tractography algorithms [1] and to obtain indices that represent numerically the brain tissue microstructure. Examples of such indices are the Fractional Anisotropy and Mean Diffusivity [2] which are commonly used in clinical practice. In order to better investigate the brain tissue, the dMRI community is interested in the estimation of more fine-scaled indices such as the intracellular volume fraction, and the extracellular parallel and perpendicular diffusivity. These indices are calculated by fitting a multi-compartment non-linear model to the diffusion signal, which has been proved to be challenging [3]. Microstructural indices are inherently rotation invariant, meaning for example, that the intracellular volume fraction in a voxel does not depend on the orientation of the axonal bundles underneath it. However, dMRI signal is very sensitive to the neurons orientation. The same axonal bundle oriented in two different directions has the same microstructural indices but completely different diffusion signal. To overcome this limitation, our group developed a series of algebraic independent rotation-invariant features (RIF) from the diffusion signal Spherical Harmonics (SH) expansion [4]. The use of our RIF in combination with multicompartmental models was able to increase the accuracy of the microstructural indices estimation [4]. Fully connected neural networks (FC-NN) have also been successfully trained on the diffusion signal in each brain voxel to fit microstructural indices [5]. Golkov and colleagues [5] were able to achieve the same performance of a multi-compartment model with FC-NN using fewer diffusion signal samples as input. In this work, we propose to combine the FC-NN and the new invariants testing if the combination of these two approaches improves the estimation of the microstructural indices with respect to each method taken by itself. In order to test this hypothesis, we created a set of 300000 synthetic voxels simulated using the state of the art multi-compartment models to train 12 FC-NN with an increasing number of perceptrons and hidden layers. The output of the networks are three microstructural features, namely the intracellular volume fraction, the extracellular parallel diffusivity, and the extracellular perpendicular diffusivity. We considered three inputs for the FC-NN: the raw diffusion signal (rds) as in [5], the 15 SH coefficients representing the rds approximated by a 4th-order SH, and the 12 RIF derived from the same 15 SH coefficients. We considered FC-NN with 2, 3, 4, and 5 hidden layers with 16, 32, 64, and 128 perceptrons per layer respectively. We split the dataset into 80% training and 20% testing considering batches of 100 voxels and trained the networks for 100 epochs using the Adam optimizer and MSE loss. Our results show that all the networks are able to outperform the classical fitting using multi-compartment models. RIF-based FC-NN is able to obtain better performances with respect to SH-coefficients and signal based FC-NN for all the networks with less than 64 perceptrons per hidden layer. Increasing the number of perceptrons leads to a convergence of the accuracy of the estimation of the microstructural indices for the three networks. In conclusion, increasing the number of hidden layers from 2 to 5 leads to a general improvement of the estimation of the indices for all the inputs

Investigating the effect of DMRI signal representation on fully-connected neural networks brain tissue microstructure estimation

International audienceIn this work, we evaluate the performance of three different diffusion MRI (dMRI) signal representations in the estimation of brain microstructural indices in combination with fully connected neural networks (FC-NN). The considered signal representations are the raw samples on the sphere, the spherical harmonics coefficients, and a novel set of recently presented rotation invariant features (RIF). To train FC-NN and validate our results, we create a synthetic dMRI dataset that mimics the signal properties of brain tissues and provides us a real ground truth for our experiments. We test 8 different network configurations changing both the depth of the networks and the number of perceptrons. Results show that our new RIF are able to estimate the brain microstructural indices more precisely than the diffusion signal samples or its spherical harmonics coefficients in all the tested network configurations. Finally, we apply the best-performing FC-NN in-vivo on a healthy human brain

Brain Tissue Microstructure Characterization Using dMRI Based Autoencoder Neural-Networks

OPAL-MesoInternational audienceIn recent years, multi-compartmental models have been widely used to try to characterize brain tissue microstructure from Diffusion Magnetic Resonance Imaging (dMRI) data. One of the main drawbacks of this approach is that the number of microstructural features needs to be decided a priori and it is embedded in the model definition. However, the number of microstructural features which is possible to obtain from dMRI data given the acquisition scheme is still not clear. In this work, we aim at characterizing brain tissue using autoencoder neural networks in combination with rotation-invariant features. By changing the number of neurons in the autoencoder latent-space, we can effectively control the number of microstructural features that we obtained from the data. By plotting the autoencoder reconstruction error to the number of features we were able to find the optimal trade-off between data fidelity and the number of microstructural features. Our results show how this number is impacted by the number of shells and the bvalues used to sample the dMRI signal. We also show how our technique paves the way to a richer characterization of the brain tissue microstructure in-vivo

A Unified Framework for Multimodal Structure-function Mapping Based on Eigenmodes

International audienceCharacterizing the connection between brain structure and brain function is essential for understanding how behaviour emerges from the underlying anatomy. A number of studies have shown that the network structure of the white matter shapes functional connectivity. Therefore, it should be possible to predict, at least partially, functional connectivity given the structural network. Many structure-function mappings have been proposed in the literature, including several direct mappings between the structural and functional connectivity matrices. However, the current literature is fragmented and does not provide a uniform treatment of current methods based on eigendecompositions. In particular, existing methods have never been compared to each other and their relationship explicitly derived in the context of brain structure-function mapping. In this work, we propose a unified computational framework that generalizes recently proposed structure-function mappings based on eigenmodes. Using this unified framework, we highlight the link between existing models and show how they can be obtained by specific choices of the parameters of our framework. By applying our framework to 50 subjects of the Human Connectome Project, we reproduce 6 recently published results, devise two new models and provide a direct comparison between all mappings. Finally, we show that a glass ceiling on the performance of mappings based on eigenmodes seems to be reached and conclude with possible approaches to break this performance limit

A Riemannian Revisiting of Structure–Function Mapping Based on Eigenmodes

International audienceUnderstanding the link between brain structure and function may not only improve our knowledge of brain organization, but also lead to better quantification of pathology. To quantify this link, recent studies have attempted to predict the brain's functional connectivity from its structural connectivity. However, functional connectivity matrices live in the Riemannian manifold of the symmetric positive definite space and a specific attention must be paid to operate on this appropriate space. In this work we investigated the implications of using a distance based on an affine invariant Riemannian metric in the context of structure–function mapping. Specifically, we revisit previously proposed structure–function mappings based on eigendecomposition and test them on 100 healthy subjects from the Human Connectome Project using this adapted notion of distance. First, we show that using this Riemannian distance significantly alters the notion of similarity between subjects from a functional point of view. We also show that using this distance improves the correlation between the structural and functional similarity of different subjects. Finally, by using a distance appropriate to this manifold, we demonstrate the importance of mapping function from structure under the Riemannian manifold and show in particular that it is possible to outperform the group average and the so–called glass ceiling on the performance of mappings based on eigenmodes

Analysis of Neuropeptide S Receptor Gene (NPSR1) Polymorphism in Rheumatoid Arthritis

Polymorphism in the neuropeptide S receptor gene NPSR1 is associated with asthma and inflammatory bowel disease. NPSR1 is expressed in the brain, where it modulates anxiety and responses to stress, but also in other tissues and cell types including lymphocytes, the lungs, and the intestine, where it appears to be up-regulated in inflammation. We sought to determine whether genetic variability at the NPSR1 locus influences the susceptibility and clinical manifestation of rheumatoid arthritis (RA).From the Epidemiological Investigation of Rheumatoid Arthritis (EIRA) case-control study, 1,888 rheumatoid arthritis patients and 888 controls were genotyped for 19 single-nucleotide polymorphisms (SNPs) spanning the entire NPSR1 gene and 220 KB of DNA on chromosome 7p14. The association between individual genetic markers and their haplotypic combinations, respectively, and diagnosis of RA, presence of autoantibodies to citrullinated proteins (ACPA), and disease activity score based on 28 joints (DAS28) was tested. There was no association between diagnosis of RA and NPSR1 variants. However, several associations of nominal significance were detected concerning susceptibility to ACPA-negative RA and disease activity measures (DAS28). Among these, the association of SNP rs324987 with ACPA-negative RA [(p=0.004, OR=0.674 (95% CI 0.512-0.888)] and that of SNP rs10263447 with DAS28 [p=0.0002, OR=0.380 (95% CI 0.227-0.635)] remained significant after correction for multiple comparisons.NPSR1 polymorphism may be relevant to RA susceptibility and its clinical manifestation. Specific alleles at the NPSR1 locus may represent common risk factors for chronic inflammatory diseases, including RA