Automated classification of amyotrophic lateral sclerosis using multi-level whole-brain volumes from structural magnetic resonance imaging

We proposed and validated a fully-automated classification procedure for amyotrophic lateral sclerosis (ALS) using structural magnetic resonance imaging; specifically, T1-weighted images from 28 ALS subjects and 28 healthy control (HC) subjects were used. The raw features were obtained from a validated multi-granularity whole-brain analysis pipeline, providing multi-level whole-brain segmentation volumes. We employed the support vector machine as our classification algorithm with several feature selection techniques analyzed. According to our leave-one-out cross validation experiment results, the whole-brain structural volumes from Level 4, followed by a feature selection utilizing the standardized Wilcoxon two-sample rank sum statistic, yielded the best classification performance; overall accuracy: 83.93%, sensitivity: 85.71%, specificity: 82.14%, and the area under the receiver operating characteristic curve: 0.8380. The feature selection procedure revealed that the volumes of the thalamus, especially that on the left hemisphere, are the most important (of highest ranking) in the ALS-vs-HC discrimination.