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Emerging Magnetic Resonance Imaging Techniques and Analysis Methods in Amyotrophic Lateral Sclerosis.
Barritt, Andrew W; Gabel, Matt C; Cercignani, Mara; Leigh, P Nigel.
Afiliação
  • Barritt AW; Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Falmer, United Kingdom.
  • Gabel MC; Hurstwood Park Neurological Centre Haywards Heath, West Sussex, United Kingdom.
  • Cercignani M; Department of Neuroscience, Trafford Centre for Biomedical Research Brighton and Sussex Medical School, Falmer, United Kingdom.
  • Leigh PN; Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Falmer, United Kingdom.
Front Neurol ; 9: 1065, 2018.
Article em En | MEDLINE | ID: mdl-30564192
ABSTRACT
Objective markers of disease sensitive to the clinical activity, symptomatic progression, and underlying substrates of neurodegeneration are highly coveted in amyotrophic lateral sclerosis in order to more eloquently stratify the highly heterogeneous phenotype and facilitate the discovery of effective disease modifying treatments for patients. Magnetic resonance imaging (MRI) is a promising, non-invasive biomarker candidate whose acquisition techniques and analysis methods are undergoing constant evolution in the pursuit of parameters which more closely represent biologically-applicable tissue changes. Neurite Orientation Dispersion and Density Imaging (NODDI; a form of diffusion imaging), and quantitative Magnetization Transfer Imaging (qMTi) are two such emerging modalities which have each broadened the understanding of other neurological disorders and have the potential to provide new insights into structural alterations initiated by the disease process in ALS. Furthermore, novel neuroimaging data analysis approaches such as Event-Based Modeling (EBM) may be able to circumvent the requirement for longitudinal scanning as a means to comprehend the dynamic stages of neurodegeneration in vivo. Combining these and other innovative imaging protocols with more sophisticated techniques to analyse ever-increasing datasets holds the exciting prospect of transforming understanding of the biological processes and temporal evolution of the ALS syndrome, and can only benefit from multicentre collaboration across the entire ALS research community.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Guideline Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Guideline Idioma: En Ano de publicação: 2018 Tipo de documento: Article