The Utility of Independent Component Analysis and Machine Learning in the Identification of the Amyotrophic Lateral Sclerosis Diseased Brain

Abstract

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.

Structure and dynamics of functional networks in child-onset schizophrenia

“…Indeed, our classification of patients with child-onset schizophrenia achieved a sensitivity of 90% and specificity of 74%. This finding supports the notion that analysis based on network measures and data mining methods may present a possible strategy for automatic diagnostics for neurological disorders Previous studies on restingstate fMRI achieved similar levels of specificity and accuracy, not only for schizophrenia in adults (Tang et al, 2012;Venkataraman et al, 2012;Shen et al, 2010), but also for other neurological disorders (Welsh et al, 2013;Zeng et al, 2013;Tang et al, 2013).…”

“…The analysis of brain network properties may provide a way to facilitate and accelerate diagnosis of such disorders. Neural network properties derived from resting-state fMRI data have already successfully been used for distinguishing between healthy subjects and patients with different neurological disorders, such as amyotrophic lateral sclerosis (Welsh et al, 2013) major depression (Zeng et al, 2013) antisocial personality disorder (Tang et al, 2013). In our study we focused on child-onset schizophrenia, which is a rare form of schizophrenia having its onset before age of 13.…”

Neuroimaging as a New Diagnostic Modality in Amyotrophic Lateral Sclerosis

“…As an example, machine learning was compared with univariate techniques for classification of patients with ALS from healthy controls, using resting-state fMRI data (i.e., a phase 2 diagnostic study). The machine learning algorithm had a higher classification accuracy of 72 % compared with univariate analysis, which had a classification accuracy of 54 % [109]. Although this example used only one MRI modality, the goal would be to incorporate several neuroimaging modalities.…”

Genetics of Amyotrophic Lateral Sclerosis

“…2 Of note, advanced machine learning tools have been shown to achieve promising (Ͼ70%) accuracy for disease state classification by use of these techniques in combination. 10 …”