Affiliations: [a] Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo André, Brazil | [b] NIF-LIM44, Department of Radiology, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil | [c] Center of Natural and Human Sciences, Universidade Federal do ABC, Santo André, Brazil
Correspondence:
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Correspondence to: João Ricardo Sato (CMCC), Universidade Federal do ABC, Av. dos Estados, 5001, Bairro Bangu, Santo André, SP, CEP 09210-580, Brasil. Tel.: +55 11 49968437; E-mail: joao.sato@ufabc.edu.br.
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 a ν-One-Class Support Vector Machine (ν-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.
