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.

Chapter 3 covers several structural imaging methods, including T1-weighted imaging, diffusion-weighted imaging (DWI), and magnetic resonance spectroscopy.

Moral feelings (e.g., guilt, pity) and values (e.g., honesty, generosity) motivate humans to act on other people’s needs. Research over the last two decades has suggested that these complex constructs can be decomposed into specific cognitive-affective and neuroanatomical components. This chapter gives operational definitions of what distinguishes moral from other forms of social and emotional functions. The cognitive components that distinguish different moral feelings (e.g., guilt being related to self-agency and indignation to another person being the agent) are elucidated. An overview of evidence from brain lesion and functional imaging studies on moral judgement and feeling in general is presented, with a focus more specifically on recent evidence that links particular brain networks to specific moral feelings (in particular, guilt and sympathy). The implications of this evidence for understanding psychopathology are addressed. The chapter also discusses the implications of opposing models of frontal cortical function for the understanding of moral cognition. Suggestions for future avenues of research in this area are provided. The cognitive neuroscience of moral emotions and motivations may provide novel and powerful ways to gauge complex aspects of adaptive and maladaptive human social behaviour.

In this chapter, we focus on the neuronal networks underlying the socio-affective capacities empathy and compassion. We first provide definitions of empathy and compassion and give an overview of the historical development in social neuroscience related to empathy and compassion research, with a focus on differentiating between empathy, empathic distress, compassion, and related concepts of social understanding like Theory of Mind. We then examine the neuronal networks underlying these distinct social capacities and discuss the latest discoveries in this field. Next, we turn to the plasticity of the social brain and compare training approaches in their efficacy in improving socio-affective and socio-cognitive capacities. This is followed by the exploration of how psychopathological symptoms are differentially related to empathy, compassion, and socio-cognitive skills. Lastly, we conclude the main findings of this chapter and provide questions for future neuroscientific and psychological research on empathy and compassion.

Humans are inherently social beings, driven by a fundamental need to belong. To fulfill this need for social connection, neural circuits of reward processing are co-opted to value social rewards derived from social interactions. These circuits play a critical role in our pursuit of social relationships, enabling us to learn about others and strengthen connections. In this chapter, we delve into basic reward circuitry that facilitates social learning, and how such circuitry supports brain networks involved in unique social phenomena, such as theory of mind and empathy. We then explore how this understanding of neural mechanisms informs decision-making in complex social situations. Furthermore, we discuss how research into rewarding social outcomes can shed light on coping mechanisms for challenges such as isolation and pervasive social media use. By examining the interplay between our social nature and neural processes, we gain insight into navigating the complexities of human interaction and well-being.

There is growing evidence that language plays an important role in emotion because it helps people acquire emotion concept knowledge. In this chapter, we argue that language plays a mechanistic role in emotion because emotion concept knowledge, once acquired, is used by the brain to predictively and adaptively regulate a person’s subjective emotional experiences and behaviors. Building on predictive processing models of brain function, we argue that the emotion concepts learned via language during early development “seed” the brain’s emotional predictions throughout the lifespan. We review constructionist theories of emotion and their support in behavioral, physiological, neuroimaging, and lesion data. We then situate these constructionist predictions within recent neuroscience research to speculate on the neural mechanisms by which emotion concepts “seed” emotional experiences.