Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Statistical Analysis of Neuroimaging Data

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_539-1


The main goal of “statistical analysis of neuroimaging data” is to use statistical methods to extract useful neuroscientific information, from neuroimaging data such as fMRI, EEG, and MEG, which are derived from measurement of the dynamics of electrical and magnetic activity generated from the neural and vascular activities inside the live brain.

Detailed Description

A typical example is the statistical analysis of fMRI data, where the activation and deactivation of voxels (as measured by the BOLD signal) in response to some specific task of interest may be elucidated in a map called the activation map. Similarly for EEG/MEG data analysis, one of the main interests of statistical analysis is to estimate the activation of areas in response to specific stimuli to the live brain. It provides the basis for further elaborate statistical analysis, such as the analysis of dynamic causalities between the activated voxels in the estimated activation map.

“The statistical methods used...


fMRI Data Independent Component Analysis Statistical Parametric Mapping Bold Signal Dynamical System Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access


  1. Akaike H (1973) Information theory and an extension of maximum likelihood principle. In: Petrov BN, Csaki F (eds) Proceeding of 2nd international symposium on information theory. Akademiai-Kiado, Bdapest, pp 267–281Google Scholar
  2. Doob JL (1953) Stochastic processes. Wiley, New YorkGoogle Scholar
  3. Friston KJ, Holmes AP, Worsley KJ, Poline JB, Frith C, Frackowiak RSJ (1995) Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp 2:189–210CrossRefGoogle Scholar
  4. Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. Neuroimage 19:1273–1303PubMedCrossRefGoogle Scholar
  5. Galka A, Yamashita O, Ozaki T, Biscay R, Valdes-Sosa P (2004) A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering. Neuroimage 23:435–453PubMedCrossRefGoogle Scholar
  6. Li X (2014) Functional magnetic resonance processing. Springer, DordrechtCrossRefGoogle Scholar
  7. Ozaki T (2012) Time series modeling of neuroscience data. Chapman & Hall/CRC Interdisciplinary StatisticsGoogle Scholar
  8. Poldrack RA et al (2011) Handbook of functional MRI data analysis., Cambridge University PressGoogle Scholar
  9. Riera J, Yamashita O, Kawashima R, Sadato N, Okada T, Ozaki T (2004) fMRI activation maps based on the NN-ARX model. Neuroimage 23:680–697PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Institute of Statistical MathematicsTokyoJapan