Pattern analysis in neuroimaging: Beyond two-class categorization

Roman Filipovych*, Ying Wang, Christos Davatzikos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


One of the many advantages of multivariate pattern recognition approaches over conventional mass-univariate group analysis using voxel-wise statistical tests is their potential to provide highly sensitive and specific markers of diseases on an individual basis. However, a vast majority of imaging problems addressed by pattern recognition are viewed from the perspective of a two-class classification. In this article, we provide a summary of selected works that propose solutions to biomedical problems where the widely-accepted classification paradigm is not appropriate. These pattern recognition approaches address common challenges in many imaging studies: high heterogeneity of populations and continuous progression of diseases. We focus on diseases associated with aging and propose that clustering-based approaches may be more suitable for disentanglement of the underlying heterogeneity, while high-dimensional pattern regression methodology is appropriate for prediction of continuous and gradual clinical progression from magnetic resonance brain images.

Original languageEnglish
Pages (from-to)173-178
Number of pages6
JournalInternational Journal of Imaging Systems and Technology
Issue number2
Publication statusPublished - Jun 2011
Externally publishedYes


  • aging
  • Alzheimer's disease
  • clustering
  • high-dimensional pattern analysis
  • MCI
  • MRI
  • pattern regression


Dive into the research topics of 'Pattern analysis in neuroimaging: Beyond two-class categorization'. Together they form a unique fingerprint.

Cite this