title: Reduced structural connectivity within a prefrontal-motor-subcortical network in amyotrophic lateral sclerosis.
abstract: BACKGROUND
  To investigate white matter structural connectivity changes associated with amyotrophic lateral sclerosis (ALS) using network analysis and compare the results with those obtained using standard voxel-based methods, specifically Tract-based Spatial Statistics (TBSS).
  
  
  METHODS
  MRI data were acquired from 30 patients with ALS and 30 age-matched healthy controls. For each subject, 85 grey matter regions (network nodes) were identified from high resolution structural MRI, and network connections formed from the white matter tracts generated by diffusion MRI and probabilistic tractography. Whole-brain networks were constructed using strong constraints on anatomical plausibility and a weighting reflecting tract-averaged fractional anisotropy (FA).
  
  
  RESULTS
  Analysis using Network-based Statistics (NBS), without a priori selected regions, identified an impaired motor-frontal-subcortical subnetwork (10 nodes and 12 bidirectional connections), consistent with upper motor neuron pathology, in the ALS group compared with the controls (P = 0.020). Reduced FA in three of the impaired network connections, which involved fibers of the corticospinal tract, correlated with rate of disease progression (P ≤ 0.024). A novel network-tract comparison revealed that the connections involved in the affected network had a strong correspondence (mean overlap of 86.2%) with white matter tracts identified as having reduced FA compared with the control group using TBSS.
  
  
  CONCLUSIONS
  These findings suggest that white matter degeneration in ALS is strongly linked to the motor cortex, and that impaired structural networks identified using NBS have a strong correspondence to affected white matter tracts identified using more conventional voxel-based methods.
authors.affiliation: Doctoral Training Center in Neuroinformatics and Computational Neuroscience, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom; Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Colin R
authors.initials: CR
authors.lastname: Buchanan
authors.forename: Lewis D
authors.initials: LD
authors.lastname: Pettit
authors.forename: Amos J
authors.initials: AJ
authors.lastname: Storkey
authors.forename: Sharon
authors.initials: S
authors.lastname: Abrahams
authors.forename: Mark E
authors.initials: ME
authors.lastname: Bastin
mesh_terms: D000690:Amyotrophic Lateral Sclerosis / Q000473:pathology*; D063132:Connectome / Q000379:methods; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008297:Male; D008875:Middle Aged; D009044:Motor Cortex / Q000473:pathology*; D009415:Nerve Net / Q000473:pathology*; D009434:Neural Pathways / Q000473:pathology; D017397:Prefrontal Cortex / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D066127:White Matter / Q000473:pathology
keywords: amyotrophic lateral sclerosis; brain networks; connectome; tractography; white matter