This book is divided into four main sections. The first section describes noninvasive measures of cerebral aging, including structural (e.g., volumetric MRI), chemical (e.g., dopamine PET), electrophysiological (e.g., ERPs), and hemodynamic (e.g., fMRI), and discusses how they can be linked to behavioral measures of cognitive aging. The second section reviews evidence for the effects of aging o...

Data mining techniques allow users to discover novelty in huge amounts of data. Frequent pattern methods have proved to be efficient, but the extracted patterns are often too numerous and thus difficult to analyse by end-users. In this paper, we focus on sequential pattern mining and propose a new visualization system, which aims at helping end-users to analyse extracted knowledge and to highli...

This book is divided into four main sections. The first section describes noninvasive measures of cerebral aging, including structural (e.g., volumetric MRI), chemical (e.g., dopamine PET), electrophysiological (e.g., ERPs), and hemodynamic (e.g., fMRI), and discusses how they can be linked to behavioral measures of cognitive aging. The second section reviews evidence for the effects of aging o...

Alzheimers Disease (AD) is the most common neurodegenerative disorder associated with aging. Early diagnosis of AD is key to the development, assessment, and monitoring of new treatments for AD. Machine learning approaches are increasingly being applied on the diagnosis of AD from structural MRI. However, the high feature-dimension and imbalanced data learning problem is two major challenges in...

This paper investigates the use of semi-supervised learning (SSL) for predicting Alzheimers Disease (AD) conversion in Mild Cognitive Impairment (MCI) patients based on Magnetic Resonance Imaging (MRI). SSL methods differ from standard supervised learning methods in that they make use of unlabeled data in this case data from MCI subjects whose final diagnosis is not yet known. We compare two wi...