Defining Multivariate Normative Rules for Healthy Aging using Neuroimaging and Machine Learning: An Application to Alzheimer's Disease

Author(s)

de Oliveira, Ailton Andrade ^[1] ; Carthery-Goulart, Maria Teresa ^[1] ; de Magalhaes Oliveira Junior, Pedro Paulo ^[2] ; Carrettiero, Daniel Carneiro ^[3] ; Sato, Joao Ricardo ^{[1, 2]} ; Initi, Alzheimer's Dis Neuroimaging

Affiliation

^[1] Univ Fed ABC, Ctr Math Computat & Cognit, BR-09210580 Santo Andre, SP - Brazil

^[2] Univ Sao Paulo, Fac Med, Dept Radiol, NIF LIM44, Sao Paulo - Brazil

^[3] Univ Fed ABC, Ctr Nat & Human Sci, BR-09210580 Santo Andre, SP - Brazil

• Document type: Journal article
• Source: JOURNAL OF ALZHEIMER'S DISEASE; v. 43, n. 1, p. 201-212, 2015.
• Web of Science Citations: 7

Abstract

Background: Neuroimaging techniques combined with computational neuroanatomy have been playing a role in the investigation of healthy aging and Alzheimer's disease (AD). The definition of normative rules for brain features is a crucial step to establish typical and atypical aging trajectories. Objective: To introduce an unsupervised pattern recognition method; to define multivariate normative rules of neuroanatomical measures; and to propose a brain abnormality index. Methods: This study was based on a machine learning approach (one class classification or novelty detection) to neuroanatomical measures (brain regions, volume, and cortical thickness) extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI)'s database. We applied nu-One-Class Support Vector Machine (nu-OC-SVM) trained with data from healthy subjects to build an abnormality index, which was compared with subjects diagnosed with mild cognitive impairment and AD. Results: The method was able to classify AD subjects as outliers with an accuracy of 84.3% at a false alarm rate of 32.5%. The proposed brain abnormality index was found to be significantly associated with group diagnosis, clinical data, biomarkers, and future conversion to AD. Conclusion: These results suggest that one-class classification may be a promising approach to help in the detection of disease conditions. Our findings support a framework considering the continuum of brain abnormalities from healthy aging to AD, which is correlated with cognitive impairment and biomarkers measurements. (AU)

• FAPESP's process: 13/00506-1 - Time series, wavelets and functional data analysis
• Grantee: Pedro Alberto Morettin
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 13/10498-6 - Machine learning in neuroimaging: development of methods and clinical applications in psychiatric disorders
• Grantee: João Ricardo Sato
• Support Opportunities: Regular Research Grants