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Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings. Funding Agencies|European Research Council (ERC) under the European UnionEuropean Research Council (ERC) [694665]; French government, through the 3IA Cote DAzur Investments in the Future project [ANR-19-P3IA-0002]; EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/N018702/1, MR/T020296/1, ISLRA-2009]; European Space AgencyEuropean Space AgencyEuropean Commission; Belgian Science Policy Office-ProdexBelgian Federal Science Policy Office; Research Foundation Flanders (FWO Vlaanderen)FWO [12M3119N, G0D7216N]; Wellcome Trust Investigator AwardWellcome Trust [096646/Z/11/Z]; Wellcome Trust Strategic AwardWellcome Trust [104943/Z/14/Z]; Polish National Agency for Academic ExchangePolish National Agency for Academic Exchange (NAWA) [PN/BEK/2019/1/00421]; Ministry of Science and Higher Education (Poland)Ministry of Science and Higher Education, Poland [692/STYP/13/2018]; AGH Science and Technology, Poland [16.16.120.773]; Linkoping University (LiU) Center for Industrial Information Technology (CENIIT); LiU Cancer [VINNOVA/ITEA3 17021 IMPACT]; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research [RMX18-0056]; "la Caixa" FoundationLa Caixa Foundation [100010434]; European UnionEuropean Commission [847648, LCF/BQ/PI20/11760029]; Ministerio de Ciencia e Innovacion" of SpainSpanish Government [RTI2018-094569-B-I00]; National Institute for Biomedical Imaging [5R01EB027585-02]

International audience; In 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.

State-of-the-art multi-compartment microstructural models of diffusion MRI (dMRI) in the human brain have limited capability to model multiple tissues at the same time. In particular, the available techniques that allow this multi-tissue modelling are based on multi-TE acquisitions. In this work we propose a novel multi-tissue formulation of classical multi-compartment models that relies on more common single-TE acquisitions and can be employed in the analysis of previously acquired datasets. We show how modelling multiple tissues provides a new interpretation of the concepts of signal fraction and volume fraction in the context of multi-compartment modelling. The software that allows to inspect single-TE diffusion MRI data with multi-tissue multi-compartment models is included in the publicly available Dmipy Python package.

OPAL-Meso; International audience; In 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.

International audience; This paper highlights a systematic bias in white matter tissue microstructure modelling via diffusion MRI that is due to the common, yet inaccurate, assumption that all brain tissues have a similar T2 response. We show that the concept of "signal fraction" is more appropriate to describe what have always been referred to as "volume fraction". This dichotomy is described from the theoretical point of view by analysing the mathematical formulation of the diffusion MRI signal. We propose a generalized multi tissue modelling framework that allows to compute the actual volume fractions. The Dmipy implementation of this framework is then used to verify the presence of this bias in four classical tissue microstructure models computed on two subjects from the Human Connectome Project database. The proposed paradigm shift exposes the research field of brain tissue microstructure estimation to the necessity of a systematic review of the results obtained in the past that takes into account the difference between the concept of volume fraction and tissue fraction.

International audience; Characterizing 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.

Diffusion weighted magnetic resonance signals convey information about tissue microstructure and cytoarchitecture. In the last years, many models have been proposed for recovering the diffusion signal and extracting information to constitute new families of numerical indices. Two main categories of reconstruction models can be identified in diffusion magnetic resonance imaging (DMRI): ensemble average propagator (EAP) models and compartmental models. From both, descriptors can be derived for elucidating the underlying microstructural architecture. While compartmental models indices directly quantify the fraction of different cell compartments in each voxel, EAP-derived indices are only a derivative measure and the effect of the different microstructural configurations on the indices is still unclear. In this paper, we analyze three EAP indices calculated using the 3D Simple Harmonic Oscillator based Reconstruction and Estimation (3D-SHORE) model and estimate their changes with respect to the principal microstructural configurations. We take advantage of the state of the art simulations to quantify the variations of the indices with the simulation parameters. Analysis of in-vivo data correlates the EAP indices with the microstructural parameters obtained from the Neurite Orientation Dispersion and Density Imaging (NODDI) model as a pseudo ground truth for brain data. Results show that the EAP derived indices convey information on the tissue microstructure and that their combined values directly reflect the configuration of the different compartments in each voxel.

International audience; Rotation invariant features are an indispensable tool for characterizing diffusion Magnetic Resonance Imaging (MRI) and in particular for brain tissue microstructure estimation. In this work, we propose a new mathematical framework for efficiently calculating a complete set of such invariants from any spherical function. Specifically, our method is based on the spherical harmonics series expansion of a given function of any order and can be applied directly to the resulting coefficients by performing a simple integral operation analytically. This enable us to derive a general closed-form equation for the invariants. We test our invariants on the diffusion MRI fiber orientation distribution function obtained from the diffusion signal both in-vivo and in synthetic data. Results show how it is possible to use these invariants for characterizing the white matter using a small but complete set of features.

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.

Diffusion Magnetic Resonance Imaging (DMRI) has been widely used to characterize the principal directions of white matter fibers, also known as fiber Orientation Distribution Function (fODF), and axonal density in brain tissues.

To which extent connectivity measures are able to characterize subjective features? The pipeline leading from the signal acquisition to the connectivity matrix allows numerous degrees of freedom each having an impact on the final result. In this paper, we investigated the sensitivity and specificity of the connectivity models within a machine learning framework through the assessment of the detectability of repeated measures of the same subject versus other subjects. Two fiber Orientation Distribution Function (fODF) reconstruction methods, one of which firstly proposed in this paper, three tractography algorithms and four connectivity features were considered and performance was expressed in terms of Area Under the Curve of the test-retest recognition task. Results suggest that there is a trade-off between the selectivity of the fODF reconstruction methods and the conservativeness of the fiber tracking algorithms across all microstructural indices . The best solution was provided by using an high angular resolution fODF estimation method and the most restrictive deterministic tractography algorithm.

New analytical reconstruction techniques of diffusion weighted signal have been proposed. A previous work evidenced the exploitability of some indices derived from the simple harmonic oscillator-based reconstruction and estimation (3D-SHORE) model as numerical biomarkers of neural plasticity after stroke. Here, the analysis is extended to two additional indices: return to the plane/origin (RTPP/RTOP) probabilities. Moreover, several motor networks were introduced and the results were analyzed at different time scales. Ten patients underwent three diffusion spectrum imaging (DSI) scans [1 week (tp1), 1 month (tp2) and 6 months (tp3) after stroke]. Ten matched controls underwent two DSI scans 1 month apart. 3D-SHORE was used for reconstructing the signal and the microstructural indices were derived. Tract-based analysis was performed along motor cortical, subcortical and transcallosal networks in the contralesional area. The optimal intra-class correlation coefficient (ICC) was obtained in the subcortical loop for propagator anisotropy (ICC $$=$$ 0.96), followed by generalized fractional anisotropy (ICC $$=$$ 0.94). The new indices reached the highest stability in the transcallosal network and performed well in the cortical and subcortical networks with the exception of RTOP in the cortical loop (ICC $$=$$ 0.59). They allowed discriminating patients from controls at the majority of the timescales. Finally, the regression model using indices calculated along the subcortical loop at tp1 resulted in the best prediction of clinical outcome. The whole set of microstructural indices provide measurements featuring high precision. The new indices allow discriminating patients from controls in all networks, except for RTPP in the cortical loop. Moreover, the 3D-SHORE indices in subcortical connections constitute a good regression model for predicting the clinical outcome at 6 months, supporting their suitability as numerical biomarkers for neuronal plasticity after stroke.

International audience; In Diffusion MRI, q-space indices are scalar quantities that describe properties of the ensemble average propagator (EAP). Their values are often linked to the axonal diameter – assuming that the diffusion signal originates from inside an ensemble of parallel cylinders. However, histological studies show that these assumptions are incorrect, and axonal tissue is often dispersed with various tissue compositions. Direct interpretation of these q-space indices in terms of tissue change is therefore impossible, and we must treat them as scalars that only give non-specific contrast – just as DTI indices. In this work, we analyze the sensitivity of q-space indices to tissue structure changes by simulating axonal tissue with changing axonal diameter, dispersion and tissue compositions. Using human connectome project data, we then predict which indices are most sensitive to tissue changes in the brain. We show that, in both multi-shell and single-shell (DTI) data, q-space indices have higher sensitivity to tissue changes than DTI indices in large parts of the brain. Based on these results, it may be interesting to revisit older DTI studies using q-space indices as markers for pathology.

International audience; Diffusion magnetic resonance imaging (dMRI) is the modality of choice for investigating in-vivo white matter connectivity and neural tissue architecture of the brain. The diffusion-weighted signal in dMRI reflects the diffusivity of water molecules in brain tissue and can be utilized to produce image-based biomarkers for clinical research. Due to the constraints on scanning time, a limited number of measurements can be acquired within a clinically feasible scan time. In order to reconstruct the dMRI signal from a discrete set of measurements, a large number of algorithms have been proposed in recent years in conjunction with varying sampling schemes, i.e., with varying b-values and gradient directions. Thus, it is imperative to compare the performance of these reconstruction methods on a single data set to provide appropriate guidelines to neuroscientists on making an informed decision while designing their acquisition protocols. For this purpose, the SPArse Reconstruction Challenge (SPARC) was held along with the workshop on Computational Diffusion MRI (at MICCAI 2014) to validate the performance of multiple reconstruction methods using data acquired from a physical phantom. A total of 16 reconstruction algorithms (9 teams) participated in this community challenge. The goal was to reconstruct single b-value and/or multiple b-value data from a sparse set of measurements. In particular, the aim was to determine an appropriate acquisition protocol (in terms of the number of measurements, b-values) and the analysis method to use for a neuroimaging study. The challenge did not delve on the accuracy of these methods in estimating model specific measures such as fractional anisotropy (FA) or mean diffusivity, but on the accuracy of these methods to fit the data. This paper presents several quantitative results pertaining to each reconstruction algorithm. The conclusions in this paper provide a valuable guideline for choosing a suitable algorithm and the corresponding data-sampling scheme for clinical neuroscience applications.

International audience; Diffusion MRI (dMRI) is used to characterize the directional-ity and microstructural properties of brain white matter (WM) by measuring the diffusivity of water molecules. In clinical practice the number of dMRI samples that can be obtained is limited, and one often uses short scanning protocols that acquire just 32 to 64 different gradient directions using a single gradient strength (b-value). Such 'single shell' scanning protocols restrict one to use methods that have assumptions on the radial decay of the dMRI signal over different b-values, which introduces estimation biases. In this work we show, that by simply spreading the same number of samples over multiple b-values (i.e. multi-shell) we can accurately estimate both the WM directionality using 3D-SHORE and characterize the radially dependent diffusion microstructure measures using MAP-MRI. We validate our approach by undersampling both noisy synthetic and human brain data of the Human Connectome Project, proving this approach is well-suited for clinical applications.