Deep neural network based framework for in-vivo axonal permeability estimation

This study introduces a novel framework for estimating permeability from diffusion-weighted MRI data using deep learning. Recent work introduced a random forest (RF) regressor model that outperforms approximate mathematical models (Kärger model). Motivated by recent developments in machine learning, we propose a deep neural network (NN) approach to estimate the permeability associated with the water residence time. We show in simulations and in in-vivo mouse brain data that the NN outperforms the RF method. We further show that the performance of either ML method is unaffected by the choice of training data, i.e. raw diffusion signals or signal-derived features yield the same results

Machine learning based estimation of axonal permeability: validation on cuprizone treated in-vivo mouse model of axonal demyelination

Estimating axonal permeability reliably is extremely important, however not yet achieved because mathematical models that express its relationship to the MR signal accurately are intractable. Recently introduced machine learning based computational model showed to outperforms previous approximate mathematical models. Here we apply and validate this novel method experimentally on a highly controlled in-vivo mouse model of axonal demyelination, and demonstrate for the first time in practice the power of machine learning as a mechanism to construct complex biophysical models for quantitative MRI

California Rental Price Prediction Using Machine Learning Algorithms

Rental price prediction (price recommendation) is a practical topic in the current online marketplace. In order to support hosts with less experience to set up the competitive rental prices, we utilize the techniques, such as feature engineering and machine learning algorithm, to select useful features and conduct models to predict possible rental prices based on the property information provided by the hosts. In this paper, machine learning algorithms are implemented on the same dataset which contains all the properties in California listed on Airbnb. After feature analysis, we notice the number of bedrooms and property types are the most important features that are highly associated with the rental prices. Among all the methods, XGBoost gives the most satisfying prediction results of rental prices based on RMSE, MAE, and R squared

Investigating exchange, structural disorder and restriction in Gray Matter via water and metabolites diffusivity and kurtosis time-dependence

Water diffusion MRI is a very powerful tool for probing tissue microstructure, but disentangling the contribution of compartment-specific structural disorder from cellular restriction and inter-compartment exchange remains an open challenge. Here, we use diffusion MR spectroscopy (dMRS) of water and metabolites as a function of diffusion time in vivo in mouse Gray Matter (GM) to shed light on: which of these concomitant mechanisms dominates the MR measurements and with which specific signature. We report the diffusion time-dependence of water with excellent SNR conditions up to 500 ms. Water kurtosis decreases with increasing diffusion time, showing the concomitant influence of both structural disorder and exchange. Despite the excellent SNR, we were not able to identify clearly the nature of the structural disorder (i.e. 1D versus 2D/3D short-range disorder). Measurements of intracellular metabolites diffusion time-dependence (up to 500 ms) show opposite behavior to water, with metabolites kurtosis increasing as a function of diffusion time. We show that this is a signature of diffusion restricted in the intracellular space from which cellular microstructural features can be estimated. Finally, by comparing water and metabolites diffusion time-dependencies, we attempt to disentangle the effect of intra/extracellular exchange and structural disorder of the extracellular space (both impacting water diffusion only). Our results suggest a relatively short intra/extracellular exchange time (1-50 ms) and short-range disorder (still unclear if 1D or 2D/3D) most likely coming from the extracellular compartment. This work provides novel insights to interpret water diffusion time-dependent measurements in terms of the underlying GM microstructure and suggests that diffusion time-dependent measurements of intracellular metabolites may offer a new way to quantify microstructural restrictions in GM

SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI

This work introduces a compartment-based model for apparent soma and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI). The existing conjecture in brain microstructure imaging trough DW-MRI presents water diffusion in white (WM) and grey (GM) matter as restricted diffusion in neurites, modelled by infinite cylinders of null radius embedded in the hindered extra-neurite water. The extra-neurite pool in WM corresponds to water in the extra-axonal space, but in GM it combines water in the extra-cellular space with water in soma. While several studies showed that this microstructure model successfully describe DW-MRI data in WM and GM at b<3 ms/{\mum^2}, it has been also shown to fail in GM at high b values (b>>3 ms/{\mum^2}). Here we hypothesize that the unmodelled soma compartment may be responsible for this failure and propose SANDI as a new model of brain microstructure where soma (i.e. cell body of any brain cell type: from neuroglia to neurons) is explicitly included. We assess the effects of size and density of soma on the direction-averaged DW-MRI signal at high b values and the regime of validity of the model using numerical simulations and comparison with experimental data from mouse (bmax = 40 ms/{/mum^2}) and human (bmax = 10 ms/{\mum^2}) brain. We show that SANDI defines new contrasts representing new complementary information on the brain cyto- and myelo-architecture. Indeed, we show for the first-time maps from 25 healthy human subjects of MR soma and neurite signal fractions, that remarkably mirror contrasts of histological images of brain cyto- and myelo-architecture. Although still under validation, SANDI might provide new insight into tissue architecture by introducing a new set of biomarkers of potential great value for biomedical applications and pure neuroscience

ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation

This paper presents Contextual Fibre Growth (ConFiG), an approach to generate white matter numerical phantoms by mimicking natural fibre genesis. ConFiG grows fibres one-by-one, following simple rules motivated by real axonal guidance mechanisms. These simple rules enable ConFiG to generate phantoms with tuneable microstructural features by growing fibres while attempting to meet morphological targets such as user-specified density and orientation distribution. We compare ConFiG to the state-of-the-art approach based on packing fibres together by generating phantoms in a range of fibre configurations including crossing fibre bundles and orientation dispersion. Results demonstrate that ConFiG produces phantoms with up to 20% higher densities than the state-of-the-art, particularly in complex configurations with crossing fibres. We additionally show that the microstructural morphology of ConFiG phantoms is comparable to real tissue, producing diameter and orientation distributions close to electron microscopy estimates from real tissue as well as capturing complex fibre cross sections. Signals simulated from ConFiG phantoms match real diffusion MRI data well, showing that ConFiG phantoms can be used to generate realistic diffusion MRI data. This demonstrates the feasibility of ConFiG to generate realistic synthetic diffusion MRI data for developing and validating microstructure modelling approaches

Investigating exchange, structural disorder and restriction in Gray Matter via water and metabolites diffusivity and kurtosis time-dependence

Water diffusion-weighted MRI is a very powerful tool for probing tissue microstructure, but disentangling the contribution of compartment-specific structural disorder from cellular restriction and inter-compartment exchange remains an open challenge. In this work we use diffusion-weighted MR spectroscopy (dMRS) of water and metabolite as a function of diffusion time in vivo in mouse gray matter to shed light on: i) which of these concomitant mechanisms (structural disorder, restriction and exchange) dominates the MR measurements and ii) with which specific signature. We report the diffusion time-dependence of water with excellent SNR conditions as provided by dMRS, up to a very long diffusion time (500 ms). Water kurtosis decreases with increasing diffusion time, showing the concomitant influence of both structural disorder and exchange. However, despite the excellent experimental conditions, we were not able to clearly identify the nature of the structural disorder (i.e. 1D versus 2D/3D short-range disorder). Measurements of purely intracellular metabolites diffusion time-dependence (up to 500 ms) show opposite behavior to water, with metabolites kurtosis increasing as a function of diffusion time. We show that this is a signature of diffusion restricted in the intracellular space, from which cellular microstructural features such as soma’s and cell projections’ size can be estimated. Finally, by comparing water and metabolite diffusion time dependencies, we attempt to disentangle the effect of intra/extracellular exchange and structural disorder of the extracellular space (both impacting water diffusion only). Our results suggest a relatively short intra/extracellular exchange time (~1-50 ms) and short-range disorder (still unclear if 1D or 2D/3D) most likely coming from the extracellular compartment. This work provides novel insights to help interpret water diffusion-time dependent measurements in terms of the underlying microstructure of gray matter and suggests that diffusion-time dependent measurements of intracellular metabolites may offer a new way to quantify microstructural restrictions in gray matter