Amyotrophic lateral sclerosis (ALS) is a devastating disease with a lifetime risk of ∼1 in 2000. Presently, diagnosis of ALS relies on clinical assessments for upper motor neuron and lower motor neuron deficits in multiple body segments together with a history of progression of symptoms. In addition, it is common to evaluate lower motor neuron pathology in ALS by electromyography. However, upper motor neuron pathology is solely assessed on clinical grounds, thus hindering diagnosis. In the past decade magnetic resonance methods have been shown to be sensitive to the ALS disease process, namely: resting-state connectivity measured with functional MRI, cortical thickness measured by high-resolution imaging, diffusion tensor imaging (DTI) metrics such as fractional anisotropy and radial diffusivity, and more recently magnetic resonance spectroscopy (MRS) measures of gamma-aminobutyric acid concentration. In this present work we utilize independent component analysis to derive brain networks based on resting-state functional magnetic resonance imaging and use those derived networks to build a disease state classifier using machine learning (support-vector machine). We show that it is possible to achieve over 71% accuracy for disease state classification. These results are promising for the development of a clinically relevant disease state classifier. Future inclusion of other MR modalities such as high-resolution structural imaging, DTI and MRS should improve this overall accuracy.
Keywords: amyotrophic lateral sclerosis; disease-state classification; independent component analysis; machine learning; resting-state functional connectivity; support vector machine.