Serial MRI studies over 12 months using manual and atlas-based region of interest in patients with amyotrophic lateral sclerosis

BMC Med Imaging. 2020 Aug 3;20(1):90. doi: 10.1186/s12880-020-00489-w.

Abstract

Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by loss of upper and lower motor neurons. There is a need for an imaging biomarker to track disease progression. Previously, magnetic resonance imaging (MRI) has shown loss of grey and white matter in the brain of patients with ALS compared to controls. We performed serial diffusion tractography imaging (DTI) study of patients with ALS looking for changes over time.

Methods: On all subjects (n = 15), we performed three MRI studies at 6 month intervals. DTI changes were assessed with tract-based spatial statistics (TBSS) and region of interest (ROI) studies. Cortic-spinal tract (CST) was selected for our ROI at the upper level; the posterior limb of internal capsule (PLIC), and a lower level in the pons.

Results: There was no significant change in DTI measures over 12 months of observation. Better correlation of manual and atlas-based ROI methods was found in the posterior limb of the internal capsule than the pons.

Conclusion: While previous DTI studies showed significant differences between ALS subjects and controls, within individual subjects there is little evidence of progression over 12 months. This suggests that DTI is not a suitable biomarker to assess disease progression in ALS.

Keywords: ALS; Amyotrophic lateral sclerosis; DTI; Diffusion tensor imaging; MND; Motor neuron disease; ROI; Region of interest; TBSS; Tract-based spatial statistics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Amyotrophic Lateral Sclerosis / diagnostic imaging*
  • Databases, Factual
  • Diffusion Tensor Imaging / methods*
  • Disease Progression
  • Female
  • Humans
  • Internal Capsule / diagnostic imaging*
  • Male
  • Middle Aged
  • Neuroimaging
  • Pons / diagnostic imaging*
  • Radiographic Image Interpretation, Computer-Assisted
  • Sensitivity and Specificity