Brain disease classification and progression using machine learning techniques

Abstract

In the past two decades, many machine learning techniques have been applied to the detection of neurologic or neuropsychiatric disorders such as Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI), based on different modalities of biomarkers including structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF), etc. This chapter presents some latest developments in application of machine learning tools to AD and MCI diagnosis and progression. We divide our discussions into two parts, pattern classification and pattern regression. We will discuss how the cortical morphological change patterns and the ensemble sparse classifiers can be used for pattern classification and then discuss how the multi-modal multi-task learning (M3T) and the semi-supervised multi-modal relevance vector regression can be applied to pattern regression.