DOI: 10.1002/alz.080590 ISSN: 1552-5260

A multi‐stage feature selection method to improve classification of Super‐Ager and Cognitive Decliner using Structural brain MRI data – A UK Biobank study

Mohammadiarvejeh Parvin, Mohammad Fili, Guiping Hu, Auriel A Willette
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience
  • Geriatrics and Gerontology
  • Neurology (clinical)
  • Developmental Neuroscience
  • Health Policy
  • Epidemiology

Abstract

Background

Cognitive aging is described as the age‐related declines in multiple cognitive abilities such as processing speed, memory, and executive function. However, old individuals with cognitive reserve are observed. Moreover, there are older adults aged more than 80 years, called Super‐Agers, who show cognitive abilities at least as good as middle‐aged adults, particularly in memory. Understanding the differences in brain structure between Super‐agers and Cognitive decliners can provide practical knowledge for middle‐aged adults to reserve the cognitive functions as they age and decrease Alzheimer’s Disease risk.

Method

Using the longitudinal cognitive tests of the UK Biobank, principal component analysis (PCA) was applied to combine the performance of the multiple cognitive domains to define the general cognitive ability (GCA) which was used to identify Super‐Ager and Cognitive Decline individuals. GCA 1 scores were extracted by processing speed, verbal and numeric reasoning, prospective memory, and visual declarative memory exams, and GCA 2 scores were extracted by executive function and processing speed exams. Random Forest (RF) classification models with a multi‐stage feature selection method were developed to distinguish Super‐Agers from Cognitive Decliners using the structural Magnetic Resonance (sMRI) features for two GCA 1 and 2 labels individually. Models’ performance and feature importance ranking were analyzed.

Result

RF model achieved AUC of 73% with 54 selected sMRI predictors by GCA 1 labels, and age, education, area of total surface, area of pars orbitalis, mean intensity of thalamus, area of superior frontal were the top six features in classification. Also, RF models achieved AUC of 76% with 42 selected sMRI features by GCA 2 labels, and age, grey‐white contrast in pars triangularis, volume of CA3‐head, grey‐white contrast in pars opercularis, grey‐white contrast in frontal pole, and mean intensity of choroid plexus were the top six features in classification.

Conclusion

our results suggest that using PCA to combine various cognitive domains is an optimal method to analyze GCA in older adults and identify latent cognitive trajectory type. Furthermore, the multi‐stage feature selection model could find the most relevant sMRI features to classify Super‐Agers and Cognitive Decliners.

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