Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference.

Original publication

DOI

10.1098/rstb.1999.0477

Type

Journal article

Journal

Philosophical transactions of the Royal Society of London. Series B, Biological sciences

Publication Date

07/1999

Volume

354

Pages

1239 - 1260

Addresses

Department of Clinical Neuroscience, Karolinska Institute, Karolinska Hospital, Stockholm, Sweden. karlmp@neuro.ks.se

Keywords

Brain, Humans, Oxygen, Tomography, Emission-Computed, Magnetic Resonance Imaging, Multivariate Analysis, Models, Statistical, Bayes Theorem, Biometry, Cerebrovascular Circulation, Models, Neurological, Signal Processing, Computer-Assisted