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

Aigrot, M-S

Alexander, D

Branzoli, F

Drobnjak, I

Hill, I

Lehericy, S

Palombo, M

Petiet, A

Philippe, A-C

Santin, MD

Stankoff, B

Wassermann, D

Zhang, H

English

UCL Discovery

08/11/2017, 09*32Page 1 of 4https://submissions.mirasmart.com/ISMRM2018/ViewSubmission.aspx?sbmID=7098Machine learning based estimation of axonal permeability: validation on cuprizonetreated in-vivo mouse model of axonal demyelinationMarco Palombo , Ioana Hill , Mathieu Santin , Francesca Branzoli , Anne-Charlotte Philippe , Demian Wassermann ,Marie-Stephane Aigrot , Bruno Stankoff , Hui Zhang , Stephan Lehericy , Alexandra Petiet , Daniel C. Alexander ,and Ivana DrobnjakComputer Science Department and Centre for Medical Imaging Computing, University College London, London, United Kingdom, Hôpital de laPitié Salpêtrière, Sorbonne Universités, UPMC Paris 06 UMR S 1127, Inserm UMR S 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelleépinière, Paris, France, CENIR, Centre de NeuroImagerie de Recherche, CENIR, Paris, France, INRIA, Université Côte d'Azur, Sophia-Antipolis,France, Parietal, CEA, INRIA, Saclay, France, CENIR, Centre de NeuroImagerie de Recherche, Paris, France, AP-HP, Hôpital Saint-Antoine, Paris,France, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, FranceSynopsisEstimating axonal permeability reliably is extremely important, however not yet achieved because mathematical models that express itsrelationship to the MR signal accurately are intractable. Recently introduced machine learning based computational model showed tooutperforms previous approximate mathematical models. Here we apply and validate this novel method experimentally on a highlycontrolled in-vivo mouse model of axonal demyelination, and demonstrate for the first time in practice the power of machine learning as amechanism to construct complex biophysical models for quantitative MRI.IntroductionQuantitative MRI relies on biophysical models to relate MR signals to tissue properties, but mathematical models rapidly become intractable beyondvery simple descriptions of tissue, and parameters such as permeability remain elusive. A recent study  demonstrated that using machine learning wecan construct a computational model that outperforms approximate mathematical models in estimating permeability via the residence time τ  of waterinside axons. τ  is a potentially important biomarker for white matter pathologies, as myelin damage is hypothesized to affect axonal permeability, andthus τ .Here we for the first time apply and validate this novel idea on a highly controlled in-vivo mouse model of axonal demyelination. We use Monte Carlosimulations and random forest (RF) regression  to build a mapping between diffusion-weighted MR signals and ground-truth microstructureparameters. We then estimate differences in τ  between in-vivo healthy and cuprizone-treated (CPZ) mice brain, a well-known model of white matter(WM) demyelination.MethodsIn-vivo data. We used a diffusion-weighted Pulsed-Gradients-Spin-Echo (DW-PGSE) protocol (Tab.1). We scanned sixteen C57BL6J mice onBrukerBioSpec 11.7T: eight CPZ mice at 6 weeks post intoxication (0.2% cuprizone) and eight healthy age-matched wild-type (WT) mice of the samebackground (data available on https://zenodo.org/record/996889#.WgH5E9vMx24). Images are corrected for eddy-currents using FSL-eddy. DTI andNODDI  maps are computed for comparison.Simulation data. We used MC simulations in CAMINO . To mimic the in-vivo mouse brain, WM was modelled as a collection of non-abutting parallelcylinders with gamma-distributed radii with parameters randomly selected for 11000 substrates in the ranges: mean and standard deviation of axonalradius μ ∈[0.1,1.0]μm and σ ∈[min(0.1,μ /5),μ /2]μm, intra-axonal volume fraction f ∈[0.40,0.75], τ ∈[0.02,1.00]s, intrinsic diffusivityd∈[0.8,2.2]μm /ms. Signal-to-noise ratio 30.Data quality test. We assessed the sensitivity of our imaging protocol to τ  using synthetic data as in , and the quality of the synthetic data by directcomparison with the in-vivo data.Machine Learning. We adapted the well-known RF regressor in the scikit-learn toolkit  to learn the mapping between the microstructure parametersdefining our simulation substrates and the normalized DW-MRI signal (Fig.1). The RF had decision trees/maximum depth=100/20. We trained the RFon simulated data using 11000 feature vectors (synthetic diffusion-weighted signals) through a greedy splitting process that constructs a linearmapping from the features to the corresponding ground truth parameters. The trained RF estimates f , τ  and d, which we tested on simulated datausing 1500 previously unseen feature vectors and then applied on in-vivo data. We manually segmented the corpus callosum (CC) and Fornix andcompared WT and CPZ mice estimates using 2-tailed t-test with equal variance.ResultsData quality test. Fig.2A shows that our imaging protocol has good sensitivity to τ , with sufficient change in DW-MRI signal to provide reliable τestimates for τ ≤500 ms. Fig.2B shows an excellent match between the simulated and in-vivo diffusion-weighted signals, demonstrating that ourtraining data set is a good representation of the in-vivo data.Simulation data estimates. Testing shows strong correlations between RF estimates and the ground truth parameters (R =0.86/0.94/0.88 for f /τ /drespectively). A comprehensive sensitivity/accuracy analysis of the proposed method is the subject of another abstract that we submitted to thissymposium.1 1 2,3 2,3 2,3 4,56 6,7 1 2,3,8 2,3 111 23 45 6 781iii1i23R R R R i i2i 45i ii ii2 i i08/11/2017, 09*32Page 2 of 4https://submissions.mirasmart.com/ISMRM2018/ViewSubmission.aspx?sbmID=7098Machine Learning. Fig.3 shows parametric maps from the in-vivo data (WT versus CPZ) of conventional DTI, NODDI orientation-dispersion index(ODI) and using RF approach. RF estimates in CPZ mice show a statistical significant reduction in f , and τ  compared to WT (Fig.4). DTI showsreduction in FA and increase in RD, in agreement with . ODI is not statistical different between the two groups (Fig.4). An electron microscopy imageof CC is also reported to show the underpinning microstructural changes in the tissue: demyelination and partial axonal loss.Discussion and ConclusionOur analysis of DW-MRI from a well-known mouse model of WM demyelination (Fig.3,4) shows that machine learning approach allows an accurateand robust estimation of the expected changes in tissue-microstructure. While DTI gave only indirect information, our model found: 1) a decrease in fas direct measure of partial axonal loss; 2) a decrease in τ  as direct measure of axonal membrane permeability increase due to demyelination.Furthermore, our estimates show no bias due to fiber orientation dispersion, as shown by the negligible changes in ODI. Finally, our results match theexpected tissue-microstructure changes (electron microscopy in Fig.3).Standard methods for estimating permeability so far have been using the Karger model , however in  has been shown that machine learningapproach works much better. Here we provide a clear validation of the machine learning approach introduced in  affirming a wider potential of theapproach as a mechanism to construct complex biophysical models for quantitative MRI.AcknowledgementsThis work was supported by EPSRC (EP/G007748, EP/I027084/01, EP/L022680/1, EP/M020533/1, N018702), EPSRC-funded UCL Centre forDoctoral Training in Medical Imaging (EP/L016478/1), Spinal Research, UK MS Society and NIHR Biomedical Research Centre at University CollegeLondon Hospitals. The research leading to these results received funding from the programs 'Institut des neurosciences translationnelle' ANR-10-IAIHU-06 and 'Infrastructure d’avenir en Biologie Santé' ANR-11-INBS-0006.References Nedjati-Gilani, G.L., Schneider, T., Hall, M.G., Cawley, N., Hill, I., Ciccarelli, O., Drobnjak, I., Wheeler-Kingshott, C.A.M.G., Alexander, D.C. : Machinelearning based compartment models with permeability for white matter microstructure imaging, NeuroImage,http://dx.doi.org/10.1016/j.neuroimage.2017.02.013 (2017). Zhang G, et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 2012; 61 (4): 1000-1016. Cook, P. A., Bai, Y., Nedjati-Gilani, S., Seunarine, K.K., Hall, M.G., Parker, G.J., Alexander, D.C. : Camino: Open-Source Diffusion-MRIReconstruction and Processing, 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, Seattle, WA, USA, p. 2759,May 2006. Drobnjak, I., Zhang, H., Ianuş, A., Kaden, E., & Alexander, D. C.: PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from asimulation study. Magnetic resonance in medicine, 75 (2), 688-700 (2015) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine Learning in Python. JMLR 12, 2825-30 (2011). Zhang, J., Jones, M. V., McMahon, M. T., Mori, S., Calabresi, P. A. : In vivo and ex vivo diffusion tensor imaging of cuprizone‐induced demyelinationin the mouse corpus callosum. Magnetic resonance in medicine, 67(3), 750-759 (2012). Karger, J., Pfeifer, H., Wilfried, H.: Principles and application of self-diffusion measurements by nuclear magnetic resonance. Adv Mag Res 12(1), 1-89 (1988)i i6ii7 111234567FiguresFig.1 A schematic overview of how testing, training and estimation is done with random forest regression.08/11/2017, 09*32Page 3 of 4https://submissions.mirasmart.com/ISMRM2018/ViewSubmission.aspx?sbmID=7098Tab.1 Summary of the PGSE EPI sequence parameters used to simulate and acquire data.Fig.2 A) DW-MR signal sensitivity to changes in τ , for different tissue substrates simulated (f  = 0.4,0.7; μ =0.1,1.0 μm). Sensitivity is defined as ΔS(τ ) = |S(τ )-S(τ+h)| / h, where h was set equal to 0.05 s. Red planes denote the noise level. B) Comparison between experimental and simulatedsignal intensity as a function of the angle between the diffusion gradients and the cylindrical fibres axis θ (in degrees), for different diffusion gradientstrength, G  = 50-500 mT/m, and diffusion gradient separation Δ=10.8 ms (blue lines) and 20.0 ms (green lines). Dashed black line in experimentaldata represent the noise level.Fig.3 Parametric maps obtained from conventional DTI at b=1500 s/mm  (axial and radial diffusivity, AD and RD, and fractional anisotropy FA), RFapproach (f , τ , d) and NODDI ODI. Diffusivities are in µm /ms and τ  in seconds. The mask of the CC (and Fornix) is obtained by applying thei i R hi i i1-72i i 2 i(3)08/11/2017, 09*32Page 4 of 4https://submissions.mirasmart.com/ISMRM2018/ViewSubmission.aspx?sbmID=7098following constraints: C =(λ -λ )/λ >0.5, C =(λ -λ )/λ <0.3, third component of the main DTI eigenvector, |e |>0.8. Some partial volume effects arevisible at the border of the masked regions. For this reason, only the central portions of the masked CC and Fornix were considered for the statisticalanalysis in Fig.4.Fig.4 Box-and-whisker plots of region-specific comparison between WT (N=8) and CPZ (N=8). DTI metrics (AD, RD, FA), RF metrics (f , τ , d) andNODDI ODI were evaluated within the splenium (S-CC), body (B-CC) and genu (G-CC) of the Corpus Callosum (CC) and Fornix. Statisticalsignificance was assessed by using 2-tailed t-test with equal variance and significance level *=0.01,**=0.005,***=0.001. N.s. stands for non-significant.l 1 2 1 p 2 3 1 1(3)i i

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

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

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