Using within-subject pattern classification to understand lifespan age differences in oscillatory mechanisms of working memory selection and maintenance

Neuroimage. 2015 Sep;118:538-52. doi: 10.1016/j.neuroimage.2015.04.038. Epub 2015 Apr 27.

Abstract

In lifespan studies, large within-group heterogeneity with regard to behavioral and neuronal data is observed. This casts doubt on the validity of group-statistics-based approaches to understand age-related changes on cognitive and neural levels. Recent progress in brain-computer interface research demonstrates the potential of machine learning techniques to derive reliable person-specific models, representing brain behavior mappings. The present study now proposes a supervised learning approach to derive person-specific models for the identification and quantification of interindividual differences in oscillatory EEG responses related to working memory selection and maintenance mechanisms in a heterogeneous lifespan sample. EEG data were used to discriminate different levels of working memory load and the focus of visual attention. We demonstrate that our approach leads to person-specific models with better discrimination performance compared to classical person-nonspecific models. We show how these models can be interpreted both on an individual as well as on a group level. One of the key findings is that, with regard to the time dimension, the between-person variance of the obtained person-specific models is smaller in older than in younger adults. This is contrary to what we expected because of increased behavioral and neuronal heterogeneity in older adults.

Keywords: EEG; Lifespan age differences; Prediction; Single-trial analysis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aging / physiology*
  • Attention / physiology
  • Brain / physiology*
  • Brain-Computer Interfaces
  • Child
  • Electroencephalography
  • Female
  • Humans
  • Machine Learning
  • Male
  • Memory, Short-Term / physiology*
  • Models, Neurological*
  • Signal Processing, Computer-Assisted*
  • Young Adult