In the next chapter, we will look at conceptual and mathematical models that underpin the operational issues covered here. This chapter focuses on the design and analysis of neuroimaging studies. Conversely, EEG and MEG (electro- and magnetoencephalography) present special problems for model inversion, however, many of the basic principles are shared by fMRI and EEG, because they are both caused by distributed neuronal dynamics. Generally, the analyses of structural and PET (positron emission tomography) data are simpler because they do not have to deal with correlated errors from one scan to the next. We will focus on the analysis of fMRI time-series because this covers many of the issues that are encountered in other modalities. The principles of data analysis are very similar for all of these applications and constitute the subject of this and subsequent chapters. fMRI or functional magnetic resonance imaging). in voxel-based morphometry) ( Ashburner and Friston, 2000) or neurophysiological measures of brain functions (e.g. They may pertain to structural differences (e.g. Inferences in neuroimaging may be about differences expressed when comparing one group of subjects to another or, within subjects, changes over a sequence of observations. The final three parts address biophysical models of distributed neuronal responses, closing with analyses of functional and effective connectivity.Ĭharacterizing a regionally specific effect rests on estimation and inference.

These parts focus on identifying, and making inferences about, regionally specific effects in the brain. The first three parts follow the key stages of analysis: image transformations, modelling, and inference. These chapters have been organized into six parts.

It provides sufficient background to understand the principles of experimental design and data analysis and serves to introduce the main themes covered by subsequent chapters.

This chapter summarizes the ideas and procedures used in the analysis of brain imaging data. Friston, in Statistical Parametric Mapping, 2007 INTRODUCTION

Download imagej software