title: Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images.
abstract: The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a "proof of concept" about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis.
authors.affiliation: Department of Biomedical Engineering, University of Florida Gainesville, FL, USA.
authors.forename: Ashkan
authors.initials: A
authors.lastname: Ebadi
authors.affiliation: Brain and Language Lab, Department of Clinical Neuroscience, University of Geneva Geneva, Switzerland.
authors.forename: Josué L
authors.initials: JL
authors.lastname: Dalboni da Rocha
authors.affiliation: Department of Computer and Information Science and Engineering, University of Florida Gainesville, FL, USA.
authors.forename: Dushyanth B
authors.initials: DB
authors.lastname: Nagaraju
authors.affiliation: D'Or Institute for Research and Education (IDOR)Rio de Janeiro, Brazil; Institute for Biomedical Sciences, Federal University of Rio de JaneiroRio de Janeiro, Brazil.
authors.forename: Fernanda
authors.initials: F
authors.lastname: Tovar-Moll
authors.affiliation: D'Or Institute for Research and Education (IDOR) Rio de Janeiro, Brazil.
authors.forename: Ivanei
authors.initials: I
authors.lastname: Bramati
authors.affiliation: D'Or Institute for Research and Education (IDOR)Rio de Janeiro, Brazil; Institute for Biomedical Sciences, Federal University of Rio de JaneiroRio de Janeiro, Brazil.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Coutinho
authors.affiliation: Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, and Department of Psychiatry and Section of Neuroscience, Pontificia Universidad Católica de ChileSantiago, Chile; Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de ChileSantiago, Chile.
authors.forename: Ranganatha
authors.initials: R
authors.lastname: Sitaram
authors.affiliation: Department of Biomedical Engineering, University of Florida Gainesville, FL, USA.
authors.forename: Parisa
authors.initials: P
authors.lastname: Rashidi
keywords: Alzheimer's disease; diffusion tensor images; ensemble classification; graph measures; machine learning