title: Defining multivariate normative rules for healthy aging using neuroimaging and machine learning: an application to Alzheimer's disease.
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.
  
  
  CONCLUSIONS
  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.
authors: Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo André, Brazil.
authors: Ailton
authors: A
authors: Andrade de Oliveira
authors: Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo André, Brazil.
authors: Maria Teresa
authors: MT
authors: Carthery-Goulart
authors: NIF-LIM44, Department of Radiology, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
authors: Pedro Paulo de Magalhães
authors: PP
authors: Oliveira Júnior
authors: Center of Natural and Human Sciences, Universidade Federal do ABC, Santo André, Brazil.
authors: Daniel Carneiro
authors: DC
authors: Carrettiero
authors: Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo André, Brazil NIF-LIM44, Department of Radiology, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
authors: João Ricardo
authors: JR
authors: Sato
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology*; D000544:Alzheimer Disease / Q000145:classification / Q000473:pathology; D001921:Brain / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000145:classification / Q000473:pathology; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D000069558:Unsupervised Machine Learning
keywords: Dementia; neurodegeneration; neuroimaging; normative; outliers; pattern recognition; support vector machines