Neurons generate electromagnetic fields as they communicate with each other. Chapter 9 introduces the electromagnetic field as a key concept overarching different electrophysiological brain activities. The concept corrects common misconceptions (e.g. "EEG is the sum of action potentials") and provides a common basis for data analysis of field signals. Basic properties of the field signal, amplitude, phase and frequency, are explained in plain language.

In addition, two major noninvasive techniques for measuring field activity, electroencephalography (EEG) and magnetoencephalography (MEG), are introduced. The advantages and disadvantages of the methods are discussed with a brief history of the techniques.

This method explains in an accessible way what the underlying principles are of magnetic resonance imaging, which underlies all structural imaging methods that are described in the next chapter.

This chapter sets the stage. We provide several examples of how neuroimaging findings have been covered in popular media, and the criticism that such coverage has elicited. This book intends to provide the knowledge and facts to understand the potential and proper use and mis-use of neuroimaging methods. We end with a brief overview of the different types of neuroimaging methods, and how they are organized with respect to spatial resolution, temporal resolution, and level of invasiveness.

We provide an introduction in the main pre-processing steps that are involved when analyzing imaging data: Correction for slice timing, motion correction, coregistration, normalization, and spatial smoothing

We discuss three more advanced statistical analysis approaches. First, the analysis of functional connectivity, including topics like directional and effective functional connectivity, modulations of connectivity by task (psychophysiological interactions), and resting-state fMRI. Second, we cover multivariate analyses and multi-voxel pattern analyses, and we discuss their potential and limitations to understand information processing in the brain. Third, we introduce the use of functional MRI adaptation as a means to measure neural selectivity.

Chapter 12 covers selected advanced data analysis methods for EEG and MEG data. In time-frequency analysis, two relevant techniques, the Short Time Fourier Transform (STFT) and the Wavelet Transform (WT), are explained in a flat language.

Phase analysis begins with the calculation of phase, which is made easy with graphical representations. The nature of the phase signal is explained using simple circular statistics. This makes phase synchronization and functional network analysis easy to understand.

In addition, event-related analysis of phase signals (Inter-trial Phase Coherence) is introduced to complete the family of event-related brain response analyses.

In addition to correlation-based phase synchronization analysis, autoregression analysis is introduced as a method of causality inference.

This chapter explains the physical and biological principles behind the main imaging methods that measure hemodynamics, including Blood-oxygenation-level-dependent (BOLD) functional MRI, arterial spin labeling fMRI, positron emission tomography (PET), and functional near-infrared spectroscopy (fNIRS). Molecular neuroimaging is also covered in the discussion of PET and MRS.

We introduce basic principles of the statistical analysis of hemodynamic imaging data, including concepts like the General Linear Model, data cleaning, efficiency, parametric hypothesis testing, correction for multiple comparisons, first- and second-level analyses, region of interest analysis, double dipping, and the issue of statistical inference with reference to forward and reverse inferences.

Chapter 10 explains how electroencephalography (EEG) and magnetoencephalography (MEG) work. EEG and MEG equipment is explained component by component in plain language. EEG and MEG signal acquisition procedures are explained along with the basics of digital signal processing to bridge the conceptual understanding of the signals to practical EEG and MEG data analysis. In addition to traditional methods, dry electrode EEG and optically pumped magnetometer MEG (OPM-MEG) methods are introduced.

In this chapter we discuss how multiple imaging modalities can be conbined and the benefits of such combinations. We illustrate such multi-modal imaging with several examples, including the fusion of fMRI and MEG, simultaneous acquisition of EEG and fMRI, source localization, the combination of analyses of functional connectivity and multi-voxel pattern analyses, and potential benefits of multi-modal imaging for clinical diagnostics.

We discuss how to design a hemodynamic imaging experiment. We present the main designs, including block and event-related designs. We discuss the subtraction method, and consider the relevance of baseline conditions.

This concluding chapter discusses the potential and limitations of the wide diversity of neuroimaging methods. The introductory chapter I was going into such questions but does not yet provide an informed answer because at that point the reader does not yet have any technical knowledge. It is relevant to come back to some of the earlier examples and provide a more in-depth and informed evaluation of neuroimaging. This concluding chapter avoids most technicalities (which received ample attention in the other chapters) and focuses more upon the broader picture.