174 research outputs found
Diversity of Cortico-descending Projections: Histological and Diffusion MRI Characterization in the Monkey
No full textThe axonal composition of cortical projections originating in premotor, supplementary motor (SMA), primary motor (a4), somatosensory and parietal areas and descending towards the brain stem and spinal cord was characterized in the monkey with histological tract tracing, electron microscopy (EM) and diffusion MRI (dMRI). These 3 approaches provided complementary information. Histology provided accurate assessment of axonal diameters and size of synaptic boutons. dMRI revealed the topography of the projections (tractography), notably in the internal capsule. From measurements of axon diameters axonal conduction velocities were computed. Each area communicates with different diameter axons and this generates a hierarchy of conduction delays in this order: a4 (the shortest), SMA, premotor (F7), parietal, somatosensory, premotor F4 (the longest). We provide new interpretations for i) the well-known different anatomical and electrophysiological estimates of conduction velocity; ii) why conduction delays are probably an essential component of the cortical motor command; and iii) how histological and dMRI tractography can be integrated
ActiveAx(ADD): Toward non-parametric and orientationally invariant axon diameter distribution mapping using PGSE
No full textPurpose Non-invasive axon diameter distribution (ADD) mapping using diffusion MRI is an ill-posed problem. Current ADD mapping methods require knowledge of axon orientation before performing the acquisition. Instead, ActiveAx uses a 3D sampling scheme to estimate the orientation from the signal, providing orientationally invariant estimates. The mean diameter is estimated instead of the distribution for the solution to be tractable. Here, we propose an extension (ActiveAx(ADD)) that provides non-parametric and orientationally invariant estimates of the whole distribution. Theory The accelerated microstructure imaging with convex optimization (AMICO) framework accelerates mean diameter estimation using a linear formulation combined with Tikhonov regularization to stabilize the solution. Here, we implement a new formulation (ActiveAx(ADD)) that uses Laplacian regularization to provide robust estimates of the whole ADD. Methods The performance of ActiveAx(ADD) was evaluated using Monte Carlo simulations on synthetic white matter samples mimicking axon distributions reported in histological studies. Results ActiveAx(ADD) provided robust ADD reconstructions when considering the isolated intra-axonal signal. However, our formulation inherited some common microstructure imaging limitations. When accounting for the extra axonal compartment, estimated ADDs showed spurious peaks and increased variability because of the difficulty of disentangling intra and extra axonal contributions. Conclusion Laplacian regularization solves the ill-posedness regarding the intra axonal compartment. ActiveAx(ADD) can potentially provide non-parametric and orientationally invariant ADDs from isolated intra-axonal signals. However, further work is required before ActiveAx(ADD) can be applied to real data containing extra-axonal contributions, as disentangling the 2 compartment appears to be an overlooked challenge that affects microstructure imaging methods in general.LTS
Bridging the 3D geometrical organisation of white matter pathways across anatomical length scales and species
No full textWe used diffusion MRI and x-ray synchrotron imaging on monkey and mice brains to examine the organisation of fibre pathways in white matter across anatomical scales. We compared the structure in the corpus callosum and crossing fibre regions and investigated the differences in cuprizone-induced demyelination in mouse brains versus healthy controls. Our findings revealed common principles of fibre organisation that apply despite the varying patterns observed across species; small axonal fasciculi and major bundles formed laminar structures with varying angles, according to the characteristics of major pathways. Fasciculi exhibited non-straight paths around obstacles like blood vessels, comparable across the samples of varying fibre complexity and demyelination. Quantifications of fibre orientation distributions were consistent across anatomical length scales and modalities, whereas tissue anisotropy had a more complex relationship, both dependent on the field-of-view. Our study emphasises the need to balance field-of-view and voxel size when characterising white matter features across length scales.Captital Region of Denmark Research FoundationEuropean Research CouncilLundbeck FoundationIndependent Research Fund DenmarkScleroseforeningen http://dx.doi.org/10.13039/100008361Lundbeck Foundatio
Bridging the 3D geometrical organisation of white matter pathways across anatomical length scales and species
No full textWe used diffusion MRI and x-ray synchrotron imaging on monkey and mice brains to examine the organisation of fibre pathways in white matter across anatomical scales. We compared the structure in the corpus callosum and crossing fibre regions and investigated the differences in cuprizone-induced experimental demyelination mouse brains versus healthy controls. Our findings revealed common principles of fibre organisation in the two species; small axonal fasciculi and major bundles formed laminar structures with varying angles, according to the characteristics of major pathways. Individual axon fasciculi exhibited tortuous paths around obstacles like blood vessels, but in a manner independent of fibre complexity and demyelination. A quantitative analysis of tissue anisotropies and fibre orientation distributions gave consistent results for different anatomical length scales and modalities, while being dependent on the field-of-view. Our study emphasises the need to balance field-of-view and voxel size when characterising white matter features across anatomical length scales
Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data
No full textAbstractMicrostructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders
Topological principles and developmental algorithms might refine diffusion tractography
No full textThe identification and reconstruction of axonal pathways in the living brain or “ex-vivo” is promising a revolution in connectivity studies bridging the gap from animal to human neuroanatomy with extensions to brain structural–functional correlates. Unfortunately, the methods suffer from juvenile drawbacks. In this perspective paper we mention several computational and developmental principles, which might stimulate a new generation of algorithms and a discussion bridging the neuroimaging and neuroanatomy communities.</p
Diagnostic Approach to Functional Recovery: Diffusion-Weighted Imaging and Tractography
No full textInternational audienc
Modelling Brain Tissue using Magnetic Resonance Imaging
No full textDiffusion MRI, or diffusion weighted imaging (DWI), is a technique that measures the restricted diffusion of water molecules within brain tissue. Different reconstruction methods quantify water-diffusion anisotropy in the intra- and extra-cellular spaces of the neural environment. Fibre tracking models then use the directions of greatest diffusion as estimates of white matter fibre orientation. Several fibre tracking algorithms have emerged in the last few years that provide reproducible visualizations of three-dimensional fibre bundles. One class of these algorithms is probabilistic tractography. Although probabilistic tractography currently holds great promise as a powerful non-invasive connectivity-measurement tool, its accuracy and limitations remain to be evaluated. Probabilistic tractography was assessed post mortem in an in vitro environment. Postmortem DWI benefits from the possibility of using high-field experimental MR scanners and long scanning times, thereby significantly improving the signal-to-noise ratio (SNR) and anatomical resolution. Moreover, many of the degrading effects observed in vivo, such as physiological noise, are no longer present. However, the post mortem environment differs from that of in vivo both due to a lowered environmental temperature and due to the fixation process itself. We argue that the perfusion fixation procedure employed in this thesis ensures that the postmortem tissue is as close to that of in vivo as possible. Different fibre reconstruction models were tested on a range of different b-values (a b-value is a summary measurement of the strength of the applied diffusion gradients). We conclude that for robust reconstruction of fibre directions, and subsequently for tractography, b-values in the range of ~2000 s/mm2 and ~8000 s/mm2 should be used. Within a two year period, no statistical inter- or intra-brain difference in the diffusion coefficient was found in perfusion fixated minipig brains. However, a decreasing tendency in the diffusion coefficient was found at the last time points about 24 months post mortem and might be explained by an ongoing chemical reaction due to the fixative used. Short-term instabilities within the first 15 hours of DWI scanning were observed and found likely to be caused by the preparation of the postmortem tissue prior to MR scanning. This artefact can be avoided e.g. by simply excluding DW-volumes obtained in the first time period of the scanning session. Probabilistic tractography was validated against two invasive in vivo neuronal tracers that were used to derive a gold standard. A high spatial agreement between tractography and the gold standard was found, and some of the widely known limitations of tractography methods could be confirmed e.g. uncertainty in regions containing crossing fibres, and definition of tract termination. In the thesis we delve behind the published results to describe all the practical issues that had to be considered in order to ensure a reliable outcome, and a successful experiment. This includes the selection of independent anatomical data to be used to derive a gold standard, the selection of a gyrated animal model in place of the human brain, objective selection of the seed region to initiate, and a waypoint region to constrain the tractography results
Does powder averaging remove dispersion bias in diffusion MRI diameter estimates within real 3D axonal architectures?
No full textNoninvasive estimation of axon diameter with diffusion MRI holds the potential to investigate the dynamic properties of the brain network and pathology of neurodegenerative diseases. Recent studies use powder averaging to account for complex white matter architectures, but these have not been validated for real axonal geometries from regions that contain fibre crossings. Here, we present 120 - 304 mu m long segmented axons from X-ray nanoholotomography volumes of a splenium and crossing fibre region of a vervet monkey brain. We show that the axons in the complex crossing fibre region, which contains callosal, association, and corticospinal connections, exhibit a wider diameter distribution than those of the splenium region. To accurately estimate the axon diameter in these regions, therefore, sensitivity to a wide range of diameters is required. We demonstrate how the q-value, b-value, signal-to-noise ratio and the assumed intra-axonal parallel diffusivity influence the range of measurable diameters with powder average approaches. Furthermore, we show how Gaussian distributed noise results in a wider range of measurable diameter at high b-values than Rician distributed noise, even at high signal-to-noise ratios of 100. The number of gradient directions is also shown to impose a lower bound on measurable diameter. Our results indicate that axon diameter estimation can be performed with only few b-shells, and that additional shells do not improve the accuracy of the estimate. For strong gradients available on human Connectom and preclinical scanners, Monte Carlo simulations of diffusion confirm that powder averaging techniques succeed in providing accurate estimates of axon diameter across a range of diameters, sequence parameters and diffusion times, even in complex white matter architectures. At relatively low b-values, the diameter estimate becomes sensitive to axonal microdispersion and the intra-axonal parallel diffusivity shows time dependency at both in vivo and ex vivo intrinsic diffusivities.LTS
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