Spinal Cord Imaging in Amyotrophic Lateral Sclerosis: Historical Concepts—Novel Techniques

Frontiers in Neurology

Mohamed Mounir El Mendili¹  Peter Bede²  Pierre-François Pradat⁴  Giorgia Querin⁴ 

[1] Biomedical Imaging Laboratory (LIB), Sorbonne University, CNRS, INSERM, Paris, France;Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland;Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States;Department of Neurology, Pitié-Salpêtrière University Hospital (APHP), Paris, France;

关键词: ALS (Amyotrophic lateral sclerosis);    MRI—magnetic resonance imaging;    MND;    spinal cord;    neuroimaging;   

DOI  :  10.3389/fneur.2019.00350

来源: DOAJ

【 摘 要 】

Amyotrophic lateral sclerosis (ALS) is the most common adult onset motor neuron disease with no effective disease modifying therapies at present. Spinal cord degeneration is a hallmark feature of ALS, highlighted in the earliest descriptions of the disease by Lockhart Clarke and Jean-Martin Charcot. The anterior horns and corticospinal tracts are invariably affected in ALS, but up to recently it has been notoriously challenging to detect and characterize spinal pathology in vivo. With recent technological advances, spinal imaging now offers unique opportunities to appraise lower motor neuron degeneration, sensory involvement, metabolic alterations, and interneuron pathology in ALS. Quantitative spinal imaging in ALS has now been used in cross-sectional and longitudinal study designs, applied to presymptomatic mutation carriers, and utilized in machine learning applications. Despite its enormous clinical and academic potential, a number of physiological, technological, and methodological challenges limit the routine use of computational spinal imaging in ALS. In this review, we provide a comprehensive overview of emerging spinal cord imaging methods and discuss their advantages, drawbacks, and biomarker potential in clinical applications, clinical trial settings, monitoring, and prognostic roles.

【 授权许可】

Unknown