Title:Frontiers for the Early Diagnosis of AD by Means of MRI Brain Imaging and Support Vector Machines
VOLUME: 13 ISSUE: 5
Author(s):Christian Salvatore, Petronilla Battista and Isabella Castiglioni
Affiliation:Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.lli Cervi, 93, 20090 Segrate, MI, Italy.
Keywords:Alzheimer’s disease, automatic classification, automatic diagnosis, machine learning, magnetic resonance
imaging, mild cognitive impairment, structural neuroimaging biomarkers, support vector machine.
Abstract:The emergence of Alzheimer’s Disease (AD) as a consequence of increasing aging population
makes urgent the availability of methods for the early and accurate diagnosis. Magnetic Resonance
Imaging (MRI) could be used as in vivo, non invasive tool to identify sensitive and specific
markers of very early AD progression. In recent years, multivariate pattern analysis (MVPA) and machine-
learning algorithms have attracted strong interest within the neuroimaging community, as they
allow automatic classification of imaging data with higher performance than univariate statistical
analysis. An exhaustive search of PubMed, Web of Science and Medline records was performed in
this work, in order to retrieve studies focused on the potential role of MRI in aiding the clinician in early diagnosis of AD
by using Support Vector Machines (SVMs) as MVPA automated classification method. A total of 30 studies emerged,
published from 2008 to date. This review aims to give a state-of-the-art overview about SVM for the early and differential
diagnosis of AD-related pathologies by means of MRI data, starting from preliminary steps such as image pre-processing,
feature extraction and feature selection, and ending with classification, validation strategies and extraction of MRI-related
biomarkers. The main advantages and drawbacks of the different techniques were explored. Results obtained by the reviewed
studies were reported in terms of classification performance and biomarker outcomes, in order to shed light on the
parameters that accompany normal and pathological aging. Unresolved issues and possible future directions were finally
pointed out.
