Metabolic spatial connectivity in amyotrophic lateral sclerosis as revealed by independent component analysis.

Abstract

Positron emission tomography (PET) and volume of interest (VOI) analysis have recently shown in amyotrophic lateral sclerosis (ALS) an accuracy of 93% in differentiating patients from controls. The aim of this study was to disclose by spatial independent component analysis (ICA) the brain networks involved in ALS pathological processes and evaluate their discriminative value in separating patients from controls. Two hundred fifty-nine ALS patients and 40 age- and sex-matched control subjects underwent brain 18F-2-fluoro-2-deoxy-D-glucose PET (FDG-PET). Spatial ICA of the preprocessed FDG-PET images was performed. Intensity values were converted to z-scores and binary masks were used as data-driven VOIs. The accuracy of this classifier was tested versus a validated system processing intensity signals in 27 brain meta-VOIs. A support vector machine was independently applied to both datasets and the 'leave-one-out' technique verified the general validity of results. The 8 components selected as pathophysiologically meaningful discriminated patients from controls with 99.0% accuracy, the discriminating value of bilateral cerebellum/midbrain alone representing 96.3%. Among the meta-VOIs, right temporal lobe alone reached an accuracy of 93.7%. Spatial ICA identified in a very large cohort of ALS patients distinct spatial networks showing a high discriminatory value, improving substantially on the previously obtained accuracy. The cerebellar/midbrain component accounted for the highest accuracy in separating ALS patients from controls. Spatial ICA and multivariate analysis perform better than univariate semi-quantification methods in identifying the neurodegenerative features of ALS and pave the way for inclusion of PET in clinical trials and early diagnosis.