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dc.contributorVall d'Hebron Barcelona Hospital Campus
dc.contributor.authorJohnson, Daniel
dc.contributor.authorRicciardi, Antonio
dc.contributor.authorBrownlee, Wallace
dc.contributor.authorKanber, Baris
dc.contributor.authorPrados, Ferran
dc.contributor.authorCollorone, Sara
dc.contributor.authorGrussu, Francesco
dc.date.accessioned2022-03-22T14:29:41Z
dc.date.available2022-03-22T14:29:41Z
dc.date.issued2021-06
dc.identifier.citationJohnson D, Ricciardi A, Brownlee W, Kanber B, Prados F, Collorone S, et al. Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis. Front Neurol. 2021 Jun;12:662855.
dc.identifier.issn1664-2295
dc.identifier.urihttp://hdl.handle.net/11351/7242
dc.descriptionMicrostructure; Multiple sclerosis; Spherical mean technique
dc.description.abstractBackground: Neurite orientation dispersion and density imaging (NODDI) and the spherical mean technique (SMT) are diffusion MRI methods providing metrics with sensitivity to similar characteristics of white matter microstructure. There has been limited comparison of changes in NODDI and SMT parameters due to multiple sclerosis (MS) pathology in clinical settings. Purpose: To compare group-wise differences between healthy controls and MS patients in NODDI and SMT metrics, investigating associations with disability and correlations with diffusion tensor imaging (DTI) metrics. Methods: Sixty three relapsing-remitting MS patients were compared to 28 healthy controls. NODDI and SMT metrics corresponding to intracellular volume fraction (vin), orientation dispersion (ODI and ODE), diffusivity (D) (SMT only) and isotropic volume fraction (viso) (NODDI only) were calculated from diffusion MRI data, alongside DTI metrics (fractional anisotropy, FA; axial/mean/radial diffusivity, AD/MD/RD). Correlations between all pairs of MRI metrics were calculated in normal-appearing white matter (NAWM). Associations with expanded disability status scale (EDSS), controlling for age and gender, were evaluated. Patient-control differences were assessed voxel-by-voxel in MNI space controlling for age and gender at the 5% significance level, correcting for multiple comparisons. Spatial overlap of areas showing significant differences were compared using Dice coefficients. Results: NODDI and SMT show significant associations with EDSS (standardised beta coefficient −0.34 in NAWM and −0.37 in lesions for NODDI vin; 0.38 and −0.31 for SMT ODE and vin in lesions; p < 0.05). Significant correlations in NAWM are observed between DTI and NODDI/SMT metrics. NODDI vin and SMT vin strongly correlated (r = 0.72, p < 0.05), likewise NODDI ODI and SMT ODE (r = −0.80, p < 0.05). All DTI, NODDI and SMT metrics detect widespread differences between patients and controls in NAWM (12.57% and 11.90% of MNI brain mask for SMT and NODDI vin, Dice overlap of 0.42). Data Conclusion: SMT and NODDI detect significant differences in white matter microstructure between MS patients and controls, concurring on the direction of these changes, providing consistent descriptors of tissue microstructure that correlate with disability and show alterations beyond focal damage. Our study suggests that NODDI and SMT may play a role in monitoring MS in clinical trials and practice.
dc.language.isoeng
dc.publisherFrontiers Media
dc.relation.ispartofseriesFrontiers in Neurology;12
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceScientia
dc.subjectEsclerosi múltiple - Imatgeria
dc.subjectImatgeria per ressonància magnètica
dc.subject.meshMultiple Sclerosis
dc.subject.mesh/diagnostic imaging
dc.subject.meshDiffusion Magnetic Resonance Imaging
dc.subject.mesh/methods
dc.titleComparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.3389/fneur.2021.662855
dc.subject.decsesclerosis múltiple
dc.subject.decs/diagnóstico por imagen
dc.subject.decsimagen de resonancia magnética de difusión
dc.subject.decs/métodos
dc.relation.publishversionhttps://doi.org/10.3389/fneur.2021.662855
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.audienceProfessionals
dc.contributor.organismesInstitut Català de la Salut
dc.contributor.authoraffiliation[Johnson D] Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom. Addenbrooke's Hospital, Cambridge, United Kingdom. [Ricciardi A, Kanber B] Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom. Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom. [Brownlee W, Collorone S] Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom. [Prados F] Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom. Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom. e-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain. [Grussu F] Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom. Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
dc.identifier.pmid34194382
dc.identifier.wos000667206100001
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/634541
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/666992
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess


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