Literature DB >> 34033187

Assessing brain injury topographically using MR neurite orientation dispersion and density imaging in multiple sclerosis.

Amalie Chen1,2, Sijin Wen3, Dhairya A Lakhani1,4, Si Gao3, Keejin Yoon1,5, Seth A Smith6, Richard Dortch6,7, Junzhong Xu6, Francesca Bagnato1,8.   

Abstract

BACKGROUND AND
PURPOSE: Axonal injury is a key player of disability in persons with multiple sclerosis (pwMS). Yet, detecting and measuring it in vivo is challenging. The neurite orientation dispersion and density imaging (NODDI) proposes a novel framework for probing axonal integrity in vivo. NODDI at 3.0 Tesla was used to quantify tissue damage in pwMS and its relationship with disease progression.
METHODS: Eighteen pwMS (4 clinically isolated syndrome, 11 relapsing remitting, and 3 secondary progressive MS) and nine age- and sex-matched healthy controls underwent a brain MRI, inclusive of clinical sequences and a multi-shell diffusion acquisition. Parametric maps of axial diffusivity (AD), neurite density index (ndi), apparent isotropic volume fraction (ivf), and orientation dispersion index (odi) were fitted. Anatomically matched regions of interest were used to quantify AD and NODDI-derived metrics and to assess the relations between these measures and those of disease progression.
RESULTS: AD, ndi, ivf, and odi significantly differed between chronic black holes (cBHs) and T2-lesions, and between the latter and normal appearing white matter (NAWM). All metrics except ivf significantly differed between NAWM located next to a cBH and that situated contra-laterally. Only NAWM odi was significantly associated with T2-lesion volume, the timed 25-foot walk test and disease duration.
CONCLUSIONS: NODDI is sensitive to tissue injury but its relationship with clinical progression remains limited.
© 2021 American Society of Neuroimaging.

Entities:  

Keywords:  MRI; NODDI; axons; multiple sclerosis; neurodegeneration

Mesh:

Year:  2021        PMID: 34033187      PMCID: PMC8440411          DOI: 10.1111/jon.12876

Source DB:  PubMed          Journal:  J Neuroimaging        ISSN: 1051-2284            Impact factor:   2.324


  29 in total

1.  Automated model-based tissue classification of MR images of the brain.

Authors:  K Van Leemput; F Maes; D Vandermeulen; P Suetens
Journal:  IEEE Trans Med Imaging       Date:  1999-10       Impact factor: 10.048

Review 2.  Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria.

Authors:  Alan J Thompson; Brenda L Banwell; Frederik Barkhof; William M Carroll; Timothy Coetzee; Giancarlo Comi; Jorge Correale; Franz Fazekas; Massimo Filippi; Mark S Freedman; Kazuo Fujihara; Steven L Galetta; Hans Peter Hartung; Ludwig Kappos; Fred D Lublin; Ruth Ann Marrie; Aaron E Miller; David H Miller; Xavier Montalban; Ellen M Mowry; Per Soelberg Sorensen; Mar Tintoré; Anthony L Traboulsee; Maria Trojano; Bernard M J Uitdehaag; Sandra Vukusic; Emmanuelle Waubant; Brian G Weinshenker; Stephen C Reingold; Jeffrey A Cohen
Journal:  Lancet Neurol       Date:  2017-12-21       Impact factor: 44.182

Review 3.  MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation.

Authors:  J-Donald Tournier; Robert Smith; David Raffelt; Rami Tabbara; Thijs Dhollander; Maximilian Pietsch; Daan Christiaens; Ben Jeurissen; Chun-Hung Yeh; Alan Connelly
Journal:  Neuroimage       Date:  2019-08-29       Impact factor: 6.556

4.  Reduced neurite density in the brain and cervical spinal cord in relapsing-remitting multiple sclerosis: A NODDI study.

Authors:  Sara Collorone; Niamh Cawley; Francesco Grussu; Ferran Prados; Francesca Tona; Alberto Calvi; Baris Kanber; Torben Schneider; Lucas Kipp; Hui Zhang; Daniel C Alexander; Alan J Thompson; Ahmed Toosy; Claudia Am Gandini Wheeler-Kingshott; Olga Ciccarelli
Journal:  Mult Scler       Date:  2019-11-04       Impact factor: 6.312

5.  Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS).

Authors:  J F Kurtzke
Journal:  Neurology       Date:  1983-11       Impact factor: 9.910

6.  Evolution of T1 black holes in patients with multiple sclerosis imaged monthly for 4 years.

Authors:  Francesca Bagnato; Neal Jeffries; Nancy D Richert; Roger D Stone; Joan M Ohayon; Henry F McFarland; Joseph A Frank
Journal:  Brain       Date:  2003-06-23       Impact factor: 13.501

Review 7.  Pathological mechanisms in progressive multiple sclerosis.

Authors:  Don H Mahad; Bruce D Trapp; Hans Lassmann
Journal:  Lancet Neurol       Date:  2015-02       Impact factor: 44.182

Review 8.  Imaging outcomes for trials of remyelination in multiple sclerosis.

Authors:  Shahrukh Mallik; Rebecca S Samson; Claudia A M Wheeler-Kingshott; David H Miller
Journal:  J Neurol Neurosurg Psychiatry       Date:  2014-04-25       Impact factor: 10.154

9.  Detecting white matter alterations in multiple sclerosis using advanced diffusion magnetic resonance imaging.

Authors:  Sourajit M Mustafi; Jaroslaw Harezlak; Chandana Kodiweera; Jennifer S Randolph; James C Ford; Heather A Wishart; Yu-Chien Wu
Journal:  Neural Regen Res       Date:  2019-01       Impact factor: 5.135

10.  Probing axons using multi-compartmental diffusion in multiple sclerosis.

Authors:  Francesca Bagnato; Giulia Franco; Hua Li; Enrico Kaden; Fei Ye; Run Fan; Amalie Chen; Daniel C Alexander; Seth A Smith; Richard Dortch; Junzhong Xu
Journal:  Ann Clin Transl Neurol       Date:  2019-08-13       Impact factor: 4.511

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  1 in total

1.  Transcallosal and Corticospinal White Matter Disease and Its Association With Motor Impairment in Multiple Sclerosis.

Authors:  Keejin Yoon; Derek B Archer; Margareta A Clarke; Seth A Smith; Ipek Oguz; Gary Cutter; Junzhong Xu; Francesca Bagnato
Journal:  Front Neurol       Date:  2022-06-15       Impact factor: 4.086

  1 in total

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