Estimation of the intrinsic dimensionality of fMRI data

Dietmar Cordes, Rajesh R. Nandy

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


A new method based on an autoregressive noise model of order 1 is introduced to the problem of detecting the number of components in fMRI data. Unlike current information-theoretic criteria like AIC, MDL, and related PPCA, which do not incorporate autocorrelations in the noise, the new method leads to more consistent estimates of the model order, as illustrated in simulated and real fMRI resting-state data.

Original languageEnglish
Pages (from-to)145-154
Number of pages10
Issue number1
StatePublished - 1 Jan 2006


  • Data reduction
  • Dimensionality estimation
  • PCA, Principal Component Analysis
  • fMRI, functional Magnetic Resonance Imaging


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