Advanced search
Start date
Betweenand
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

Full text
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
Total Authors: 6
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
Total Affiliations: 3
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: 13/10498-6 - Machine learning in neuroimaging: development of methods and clinical applications in psychiatric disorders
Grantee:João Ricardo Sato
Support type: Regular Research Grants
FAPESP's process: 13/00506-1 - Time series, wavelets and functional data analysis
Grantee:Pedro Alberto Morettin
Support type: Research Projects - Thematic Grants