Amyotrophic lateral sclerosis (ALS) is a progressive degenerative motor neuron disease involving the motor cortex, corpus callosum, cortical spinal tract and spinal anterior horn neurons. The disease has a uniformly fatal outcome, although the clinical presentation and course is quite heterogeneous, with median survival times between 2 - 4 years. Approximately 30,000 people in the United States are living with ALS. There is no definitive diagnostic test for ALS. Confident diagnosis is primarily based on clinical assessment and relies on the detection of upper motor neuron (UMN) and lower motor neuron (LMN) signs in multiple body segments, together with a history of progression of symptoms. Evaluation of LMN pathology may be supplemented by electromyography, but UMN pathology can remain occult as it is only assessed using clinical examination which can lead to diagnostic uncertainty. Unfortunately, there is on average a one- year delay between the onset of symptoms and diagnosis for this rapidly progressive disease;this delay prevents early treatment with emerging disease-modifying drugs. Thus, reliable biomarkers for the early diagnosis and disease prognostication are needed. Conventional magnetic resonance imaging techniques provide limited and inconsistent information in ALS patients. Therefore, there has been and continues to be great interest in using advanced neuroimaging techniques to establish improved markers of the disease. Although advanced neuroimaging techniques such as magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and resting state functional connectivity (fcMRI) have identified differences between ALS patients and healthy controls, they lack sufficient accuracy to reliably classify individual patients. To meet this important unmet need, the proposed study will use novel advanced neuroimaging techniques to develop a multimodal biomarker of ALS, and validate a discrimination and prediction model to refine the diagnostic clinical workup for ALS.
There are no definitive tests for amyotrophic lateral sclerosis and many of these patients have a delayed diagnosis preventing early intervention with new emerging treatments. Furthermore, disease prognosis is challenging due to the variability of the natural history of amyotrophic lateral sclerosis. This study will use multiple advanced neuroimaging methods to build a robust diagnostic test and prognostic model of amyotrophic lateral sclerosis. We will use a novel statistical approach to develop and validate the models.