Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination.

The intra-axonal water exchange time (τi), a parameter associated with axonal permeability, could be an important biomarker for understanding and treating demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI (DW-MRI) is sensitive to changes in permeability; however, the parameter has so far remained elusive due to the lack of general biophysical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters. Recently, for the first time, a theoretical framework using a random forest (RF) regressor suggests that this is a promising new approach for permeability estimation. In this study, we adopt such an approach and for the first time experimentally investigate it for demyelinating pathologies through direct comparison with histology. We construct a computational model using Monte Carlo simulations and an RF regressor in order to learn a mapping between features derived from DW-MRI signals and ground truth microstructure parameters. We test our model in simulations, and find strong correlations between the predicted and ground truth parameters (intra-axonal volume fraction f: R2 =0.99, τi: R2 =0.84, intrinsic diffusivity d: R2 =0.99). We then apply the model in-vivo, on a controlled cuprizone (CPZ) mouse model of demyelination, comparing the results from two cohorts of mice, CPZ (N=8) and healthy age-matched wild-type (WT, N=8). We find that the RF model estimates sensible microstructure parameters for both groups, matching values found in literature. Furthermore, we perform histology for both groups using electron microscopy (EM), measuring the thickness of the myelin sheath as a surrogate for exchange time. Histology results show that our RF model estimates are very strongly correlated with the EM measurements (ρ = 0.98 for f, ρ = 0.82 for τi). Finally, we find a statistically significant decrease in τi in all three regions of the corpus callosum (splenium/genu/body) of the CPZ cohort (<τi>=310ms/330ms/350ms) compared to the WT group (<τi>=370ms/370ms/380ms). This is in line with our expectations that τi is lower in regions where the myelin sheath is damaged, as axonal membranes become more permeable. Overall, these results demonstrate, for the first time experimentally and in vivo, that a computational model learned from simulations can reliably estimate microstructure parameters, including the axonal permeability .

Reducing the number of samples in spatiotemporal dMRI acquisition design.

PURPOSE: Acquisition time is a major limitation in recovering brain white matter microstructure with diffusion magnetic resonance imaging. The aim of this paper is to bridge the gap between growing demands on spatiotemporal resolution of diffusion signal and the real-world time limitations. The authors introduce an acquisition scheme that reduces the number of samples under adjustable quality loss. METHODS: Finding a sampling scheme that maximizes signal quality and satisfies given time constraints is NP-hard. Therefore, a heuristic method based on genetic algorithm is proposed in order to find suboptimal solutions in acceptable time. The analyzed diffusion signal representation is defined in the qτ space, so that it captures both spacial and temporal phenomena. RESULTS: The experiments on synthetic data and in vivo diffusion images of the C57Bl6 wild-type mouse corpus callosum reveal superiority of the proposed approach over random sampling and even distribution in the qτ space. CONCLUSIONS: The use of genetic algorithm allows to find acquisition parameters that guarantee high signal reconstruction accuracy under given time constraints. In practice, the proposed approach helps to accelerate the acquisition for the use of qτ-dMRI signal representation.

Anatomical evidence for functional diversity in the mesencephalic locomotor region of primates.

The mesencephalic locomotor region (MLR) is a highly preserved brainstem structure in vertebrates. The MLR performs a crucial role in locomotion but also controls various other functions such as sleep, attention, and even emotion. The MLR comprises the pedunculopontine (PPN) and cuneiform nuclei (CuN) but their specific roles are still unknown in primates. Here, we sought to characterise the inputs and outputs of the PPN and CuN to and from the basal ganglia, thalamus, amygdala and cortex, with a specific interest in identifying functional anatomical territories. For this purpose, we used tract-tracing techniques in monkeys and diffusion weighted imaging-based tractography in humans to understand structural connectivity. We found that MLR connections are broadly similar between monkeys and humans. The PPN projects to the sensorimotor, associative and limbic territories of the basal ganglia nuclei, the centre median-parafascicular thalamic nuclei and the central nucleus of the amygdala. The PPN receives motor cortical inputs and less abundant connections from the associative and limbic cortices. In monkeys, we found a stronger connection between the anterior PPN and motor cortex suggesting a topographical organisation of this specific projection. The CuN projected to similar cerebral structures to the PPN in both species. However, these projections were much stronger towards the limbic territories of the basal ganglia and thalamus, to the basal forebrain (extended amygdala) and the central nucleus of the amygdala, suggesting that the CuN is not primarily a motor structure. Our findings highlight the fact that the PPN integrates sensorimotor, cognitive and emotional information whereas the CuN participates in a more restricted network integrating predominantly emotional information.

Non-parametric graphnet-regularized representation of dMRI in space and time.

Effective representation of the four-dimensional diffusion MRI signal - varying over three-dimensional q-space and diffusion time τ - is a sought-after and still unsolved challenge in diffusion MRI (dMRI). We propose a functional basis approach that is specifically designed to represent the dMRI signal in this qτ-space. Following recent terminology, we refer to our qτ-functional basis as "qτ-dMRI". qτ-dMRI can be seen as a time-dependent realization of q-space imaging by Paul Callaghan and colleagues. We use GraphNet regularization - imposing both signal smoothness and sparsity - to drastically reduce the number of diffusion-weighted images (DWIs) that is needed to represent the dMRI signal in the qτ-space. As the main contribution, qτ-dMRI provides the framework to - without making biophysical assumptions - represent the qτ-space signal and estimate time-dependent q-space indices (qτ-indices), providing a new means for studying diffusion in nervous tissue. We validate our method on both in-silico generated data using Monte-Carlo simulations and an in-vivo test-retest study of two C57Bl6 wild-type mice, where we found good reproducibility of estimated qτ-index values and trends. In the hopes of opening up new τ-dependent venues of studying nervous tissues, qτ-dMRI is the first of its kind in being specifically designed to provide open interpretation of the qτ-diffusion signal.

Tractography via the ensemble average propagator in diffusion MRI.

It's well known that in diffusion MRI (dMRI), fibre crossing is an important problem for most existing diffusion tensor imaging (DTI) based tractography algorithms. To overcome these limitations, High Angular Resolution Diffusion Imaging (HARDI) based tractography has been proposed with a particular emphasis on the the Orientation Distribution Function (ODF). In this paper, we advocate the use of the Ensemble Average Propagator (EAP) instead of the ODF for tractography in dMRI and propose an original and efficient EAP-based tractography algorithm that outperforms the classical ODF-based tractography, in particular, in the regions that contain complex fibre crossing configurations. Various experimental results including synthetic, phantom and real data illustrate the potential of the approach and clearly show that our method is especially efficient to handle regions where fiber bundles are crossing, and still well handle other fiber bundle configurations such as U-shape and kissing fibers.