![Histology, Polarized Light Imaging (PLI) and Diffusion MRI all confirm the presence of axon orientation dispersion in the corpus callosum of rats and humans. The left image is from Leergaard et al. [2010], showing a close-up of dispersed axon in the corpus collosum of a rat. The right image is from Mollink et al. [2016], where PLI and diffusion MRI both confirm the presence of significant orientation dispersion in the human corpus callosum.](https://www.researchgate.net/profile/Rutger-Fick-2/publication/317419497/figure/fig1/AS:676177526157312@1538224693080/Histology-Polarized-Light-Imaging-PLI-and-Diffusion-MRI-all-confirm-the-presence-of_Q320.jpg)
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Advanced dMRI Signal Modeling for Tissue Microstructure Characterization
Abstract and Figures
This thesis is dedicated to furthering neuroscientific understanding of the human brain using diffusion-sensitized Magnetic Resonance Imaging (dMRI). Within dMRI, we focus on the estimation and interpretation of microstructure-related markers, often referred to as ``Microstructure Imaging''. This thesis is organized in three parts. Part I focuses on understanding the state-of-the-art in Microstructure Imaging. We start with the basic of diffusion MRI and a brief overview of diffusion anisotropy. We then review and compare most state-of-the-art microstructure models in PGSE-based Microstructure Imaging, emphasizing model assumptions and limitations, as well as validating them using spinal cord data with registered ground truth histology. In Part II we present our contributions to 3D q-space imaging and microstructure recovery. We propose closed-form Laplacian regularization for the recent MAP functional basis, allowing robust estimation of tissue-related q-space indices. We also apply this approach to Human Connectome Project data, where we use it as a preprocessing for other microstructure models. Finally, we compare tissue biomarkers in a ex-vivo study of Alzheimer rats at different ages. In Part III, we present our contributions to representing the qt-space - varying over 3D q-space and diffusion time. We present an initial approach that focuses on 3D axon diameter estimation from the qt-space. We end with our final approach, where we propose a novel, regularized functional basis to represent the qt-signal, which we call qt-dMRI. Our approach allows for the estimation of time-dependent q-space indices, which quantify the time-dependence of the diffusion signal.
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... The left panel illustrates the different composition of the GM and the WM (adapted from Fick 2017). The nuclei, the dendrites and the somata are confined in the GM region, while the axonal tracts stem from the GM, traverse the GM-WM interface and develop in the WM. ...
The human brain is a complex multiscale biomechanism composed of nearly a hundred billion neurons.Using diffusion magnetic resonance imaging (dMRI), in this thesis we contributed to probing in-vivo the microstructural composition of brain tissues with multi-compartment (MC) models, the trajectories of the axonal pathways connecting different cortical regions using dMRI-based tractography, and the topology of the brain networks (a.k.a. connectomes) that regulate the functioning of the brain by describing the so-called structural connectome as a graph.Microstructure: Recent studies highlighted how all the available multi-compartment (MC) models of white matter tissue microstructure via dMRI are defined in such a way that they are transparent to differences between the T2 times of the modelled tissues. In this work we show how this corresponds to assuming that all the considered tissues have the same S0 response and they model signal fractions instead of volume fractions. Also, we define multi-tissue (MT)-MC models that fit the volume fractions using single-TE multi-shell dMRI data, in contrast with the state-of-the-art techniques that use multi-TE data. We provide a complete theoretical presentation of the motivations and formalisation of our MT-MC model and propose a generalised framework for modelling multiple tissues with MC models.Tractography: Several works exposed the limitation of dMRI-based tractography, in particular in the context of structural connectivity analysis, where a non-trivial quantity of false positive connections have been shown to be present and detrimental. Some methods that address this issue go under the name of tractography filtering techniques (TFTs). These techniques act as post-processing of a pre-computed tractogram and assign a coefficient to each streamline. This coefficient represents the amount of signal explained by the streamline or the connectivity strength associated to it depending on the employed technique, and is used for the definition of the so-called weighted connectome. In this thesis we present a review of the most common TFTs, focusing on how they can be framed into a more general formulation. Additionally, we assess if and how the state-of-the-art TFTs have an effect on the topology of the connectomes that they produce in the pathological (traumatic brain injury) an d the healthy cases (high resolution data and low resolution healthy controls from a clinical setting). Also, we propose a novel TFT that integrates structural and functional criteria in the filtering process. Our method extends the approaches of the state-of-the-art TFTs by simultaneously fitting the dMRI signal (or some transformation of it) and penalising the magnitude of coefficients associated to streamlines that are not coherent with the co-activation patterns measured with resting-state functional MRI.Brain Network Topology: In structural connectomes, nodes are parcels from a predefined cortical atlas. In this thesis we aim at providing a novel perspective on the evaluation of brain atlases by modeling it as a network alignment problem, with the goal of tackling the following question: given an atlas, how robustly does it capture the network topology across different subjects? To answer this question, we introduce two novel concepts arising as natural generalizations of previous ones. First, the graph Jaccard index (GJI), a graph similarity measure based on the well-established Jaccard index between sets; the GJI exhibits natural mathematical properties that are not satisfied by previous approaches. Second, we devise WL-align, a new technique for aligning connectomes obtained by adapting the Weisfeiler-Lehman (WL) graph-isomorphism test. We validated the GJI and WL-align on data from the Human Connectome Project database, inferring a strategy for choosing a suitable parcellation for structural connectivity studies.
... We provide the graphical abstract Dmipy in Figure 1. Dmipy's design is based on the observation that one can view different formulations biophysical models as "building blocks, " which could be assembled in any combination, and whose meaning can change depending on the application (e.g., Panagiotaki et al., 2012;Fick, 2017). This is despite the fact that the individual tissue models, such as various approximations of restricted geometries like cylinders and spheres (e.g., Balinov et al., 1993;Vangelderen et al., 1994;Callaghan, 1995), can have quite complex mathematical formulations. ...
Non-invasive estimation of brain microstructure features using diffusion MRI (dMRI)-known as Microstructure Imaging-has become an increasingly diverse and complicated field over the last decades. Multi-compartment (MC)-models, representing the measured diffusion signal as a linear combination of signal models of distinct tissue types, have been developed in many forms to estimate these features. However, a generalized implementation of MC-modeling as a whole, providing deeper insights in its capabilities, remains missing. To address this fact, we present Diffusion Microstructure Imaging in Python (Dmipy), an open-source toolbox implementing PGSE-based MC-modeling in its most general form. Dmipy allows on-the-fly implementation, signal modeling, and optimization of any user-defined MC-model, for any PGSE acquisition scheme. Dmipy follows a "building block"-based philosophy to Microstructure Imaging, meaning MC-models are modularly constructed to include any number and type of tissue models, allowing simultaneous representation of a tissue's diffusivity, orientation, volume fractions, axon orientation dispersion, and axon diameter distribution. In particular, Dmipy is geared toward facilitating reproducible, reliable MC-modeling pipelines, often allowing the whole process from model construction to parameter map recovery in fewer than 10 lines of code. To demonstrate Dmipy's ease of use and potential, we implement a wide range of well-known MC-models, including IVIM, AxCaliber, NODDI(x), Bingham-NODDI, the spherical mean-based SMT and MC-MDI, and spherical convolution-based single-and multi-tissue CSD. By allowing parameter cascading between MC-models, Dmipy also facilitates implementation of advanced approaches like CSD with voxel-varying kernels and single-shell 3-tissue CSD. By providing a well-tested, user-friendly toolbox that simplifies the interaction with the otherwise complicated field of dMRI-based Microstructure Imaging, Dmipy contributes to more reproducible, high-quality research.
In this study, we assessed the evolution of diffusion MRI (dMRI) derived markers from different white matter models as progressive neurodegeneration occurs in transgenic Alzheimer rats (TgF344-AD) at 10, 15 and 24 months. We compared biomarkers reconstructed from Diffusion Tensor Imaging (DTI), Neurite Orientation Dispersion and Density Imaging (NODDI) and Mean Apparent Propagator (MAP)-MRI in the hippocampus, cingulate cortex and corpus callosum using multi-shell dMRI. We found that NODDI’s dispersion and MAP-MRI’s anisotropy markers consistently changed over time, possibly indicating that these measures are sensitive to age-dependent neuronal demise due to amyloid accumulation. Conversely, we found that DTI’s mean diffusivity, NODDI’s isotropic volume fraction and MAP-MRI’s restriction-related metrics all followed a two-step progression from 10 to 15 months, and from 15 to 24 months. This two-step pattern might be linked with a neuroinflammatory response that may be occurring prior to, or during microstructural breakdown. Using our approach, we are able to provide—for the first time—preliminary and valuable insight on relevant biomarkers that may directly describe the underlying pathophysiology in Alzheimer’s disease.
Effective representation of the diffusion signal’s dependence on diffusion time is a sought-after, yet still unsolved, challenge in diffusion MRI (dMRI). We propose a functional basis approach that is specifically designed to represent the dMRI signal in this four-dimensional space—varying over gradient strength, direction and diffusion time. In particular, we provide regularization tools imposing signal sparsity and signal smoothness to drastically reduce the number of measurements we need to probe the properties of this multi-spherical space. We illustrate a novel application of our approach, which is the estimation of time-dependent q -space indices, on both synthetic data generated using Monte-Carlo simulations and in vivo data acquired from a C57Bl6 wild-type mouse. In both cases, we find that our regularization approach stabilizes the signal fit and index estimation as we remove samples, which may bring multi-spherical diffusion MRI within the reach of clinical application.
The non-Gaussian noise distribution in magnitude Diffusion-Weighted Images (DWIs) can severely affect the estimation and reconstruction of the true diffusion signal. As a consequence, also the estimated diffusion metrics can be biased. We study the effect of phase correction, a procedure that re-establishes the Gaussianity of the noise distribution in DWIs by taking into account the corresponding phase images. We quantify the debiasing effects of phase correction in terms of diffusion signal estimation and calculated metrics. We perform in silico experiments based on a MGH Human Connectome Project dataset and on a digital phantom, accounting for different acquisition schemes, diffusion-weightings, signal to noise ratios, and for metrics based on Diffusion Tensor Imaging and on Mean Apparent Propagator Magnetic Resonance Imaging, i.e. q-space metrics. We show that phase correction is still a challenge, but also an effective tool to debias the estimation of diffusion signal and metrics from DWIs, especially at high b-values.
Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.
The published journal version is also available at https://www.nature.com/articles/s41467-017-01285-x
We review, systematize and discuss models of diffusion in neuronal tissue, by putting them into an overarching physical context of coarse-graining over an increasing diffusion length scale. From this perspective, we view research on quantifying brain microstructure as occurring along the three major avenues. The first avenue focusses on the transient, or time-dependent, effects in diffusion. These effects signify the gradual coarse-graining of tissue structure, which occurs qualitatively differently in different brain tissue compartments. We show that studying the transient effects has the potential to quantify the relevant length scales for neuronal tissue, such as the packing correlation length for neuronal fibers, the degree of neuronal beading, and compartment sizes. The second avenue corresponds to the long-time limit, when the observed signal can be approximated as a sum of multiple non-exchanging anisotropic Gaussian components. Here the challenge lies in parameter estimation and in resolving its hidden degeneracies. The third avenue employs multiple diffusion encoding techniques, able to access information not contained in the conventional diffusion propagator. We conclude with our outlook on the future research directions which can open exciting possibilities for developing markers of pathology and development based on methods of studying mesoscopic transport in disordered systems.
Full abstract: http://indexsmart.mirasmart.com/ISMRM2016/PDFfiles/0928.html
The full dataset (MRI + histology registered) of this study can be downloaded there: www.neuro.polymtl.ca/downloads
In this work, we propose to validate and compare AxCaliber/ActiveAx/Noddi/MTV in the spinal cord using full slice histology with axon/myelin segmentation. High-resolution data (150µm/px) were acquired on an ex vivo spinal cord and compared voxel by voxel with histology. We found that q-space metrics were precise enough to distinguish between various fiber distributions. A correlation coefficient of r=0.62 was found between AxCaliber and histology for axon diameter metric. Also, good agreement was found between the different q-space models and with MTV.
One of the major limitations of diffusion MRI tractography is that the fiber tracts recovered by existing algorithms are not truly quantitative. Local techniques for estimating more quantitative features of the tissue microstructure exist, but their combination with tractography has always been considered intractable. Recent advances in local and global modeling made it possible to fill this gap and a number of promising techniques for microstructure informed tractography have been suggested, opening new and exciting perspectives for the quantification of brain connectivity. The ease-of-use of the proposed solutions made it very attractive for researchers to include such advanced methods in their analyses; however, this apparent simplicity should not hide some critical open questions raised by the complexity of these very high-dimensional problems, otherwise some fundamental issues may be pushed into the background. The aim of this article is to raise awareness in the diffusion MRI community, notably researchers working on brain connectivity, about some potential pitfalls and modeling choices that make the interpretation of the outcomes from these novel techniques rather cumbersome. Through a series of experiments on synthetic and real data, we illustrate practical situations where erroneous and severely biased conclusions may be drawn about the connectivity if these pitfalls are overlooked, like the presence of partial/missing/duplicate fibers or the critical importance of the diffusion model adopted. Microstructure informed tractography is a young but very promising technology, and by acknowledging its current limitations as done in this paper, we hope our observations will trigger further research in this direction and new ideas for truly quantitative and biologically meaningful analyses of the connectivity.
Objectives
Our goal was to estimate the diagnostic accuracy of substantia nigra fractional anisotropy (SN-FA) for Parkinson’s disease (PD) diagnosis in a sample similar to the clinical setting, including patients with essential tremor (ET) and healthy controls (HC). We also performed a systematic review and meta-analysis to estimate mean change in SN-FA induced by PD and its diagnostic accuracy. Methods
Our sample consisted of 135 subjects: 72 PD, 21 ET and 42 HC. To address inter-scanner variability, two 3.0-T MRI scans were performed. MRI results of this sample were pooled into a meta-analysis that included 1,432 subjects (806 PD and 626 HC). A bivariate model was used to evaluate diagnostic accuracy measures. ResultsIn our sample, we did not observe a significant effect of disease on SN-FA and it was uninformative for diagnosis. The results of the meta-analysis estimated a 0.03 decrease in mean SN-FA in PD relative to HC (CI: 0.01–0.05). However, the discriminatory capability of SN-FA to diagnose PD was low: pooled sensitivity and specificity were 72 % (CI: 68–75) and 63 % (CI: 58–70), respectively. There was high heterogeneity between studies (I2 = 91.9 %). ConclusionsSN-FA cannot be used as an isolated measure to diagnose PD. Key Points• SN-FA appears insufficiently sensitive and specific to diagnose PD.• Radiologists must be careful when translating mean group results to clinical practice.• Imaging protocol and analysis standardization is necessary for developing reproducible quantitative biomarkers.