Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

© Oxford University Press 2001. All rights reserved. This chapter describes the various preprocessing steps necessary to take raw data from the scanner and prepare it for the 'heart' of functional magnetic resonance imaging analysis, namely statistical analysis. These preprocessing steps take the raw MR data, convert it into images that actually look like brains, then reduce unwanted noise of various types and precondition the data in order to aid the later statistics. The chapter also discusses how it is much easier to 'automate' the preprocessing steps than the statistical analysis because optimal tuning of preprocessing algorithms is less dependent on the details of any particular experiment than is the case with later statistics. It discusses the reasons for applying spatial filtering as a preprocessing step and results clearly show the blurring of activation areas as spatial filtering extent increases, even causing 'activation' outside of the brain.

Original publication





Book title

Functional Magnetic Resonance Imaging: An Introduction to Methods

Publication Date