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Neuroimage Classification for Early Diagnosis of Alzheimer’s Disease
Abstract
Diagnostic criteria for neurological and psychiatric disorders are typically based on clinical and psychometric assessment, which might not be effective for early detection of the disease onset. For brain disorders such as Alzheimer’s Disease (AD), neuroimaging can potentially play an important role in the development of imaging-based biomarkers. Following voxel-wise univariate neuroimage analysis methods, machine learning and pattern recognition based neuroimage analysis techniques have been increasingly adopted in neuroimaging studies of neurological and psychiatric disorders, aiming to provide tools that classify individuals, based on their neuroimaging scans, rather than detect statistical group difference. The machine learning based methods, optimally combining information of multiple measures derived from images, have demonstrated promising performance in diagnosis of AD and early prediction of conversion of Mild Cognitive Impairment (MCI) individuals. This chapter introduces the general framework of such techniques with a focus on structural MRI analyses and their applications to studies of AD.
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