As noted in Chapter 1, since writing the last edition of this text in 2019, the world has undergone rapid changes and continues to transform at an accelerated pace. Social work, often informed by social movements and community experience, aims to anticipate and respond to emerging social issues. Perhaps this is one of the defining hallmarks of the social work profession – its capacity to evolve to address new challenges and opportunities. Throughout this book, and especially in Chapter 2, we explore some of the global social forces and discourses that characterise the rapidly changing contexts in which social work operates. These changes have created new challenges that require critical responses, in some cases generating new fields of practice. In this chapter, our major focus will be on: (1) the increasing urgency of climate change, threats to the planet (and humanity) and the implications of climate change for social work; (2) global pandemics and their impacts for people and service delivery; and (3) increasing wealth inequality and associated poverty and homelessness.

We begin with a case study of a traditional small-scale face memory experiment. The chapter deconstructs the elements that prevent this experiment from being generalizable, and poses potential ways in which we can expand this experiment into a "Big Data" experiment. The chapter discusses the replication crisis in psychology as an important motivator for broadening psychological experiments, as well as common issues with data fishing or "p-hacking." At the same time, there are limitations to our ability to run perfect Big Data experiments. The chapter describes the beneficial relationship of hypothesis-driven research and data-driven research in psychology, presenting the pros and cons of each. Finally, the chapter discusses different formats of Big Data – wide, deep, and long data.

Computational neuroimaging is defined broadly as the use of neuroimaging to investigate the localization and representation of parameters in formal mathematical models. We focus upon models of behavior and neural processing that have been adopted widely in behavioral sciences and cognitive neuroscience, including reinforcement learning, predictive coding, decision theory (drift diffusion and evidence accumulation), population receptive field models, and encoding models (including artificial neural networks). The aim is not to explain all the technical details of the various models, but illustrate and discuss the added value of combining such models with neuroimaging.

Chapter 11 introduces basic EEG and MEG data analysis methods. It begins with an explanation of the noise components in EEG and MEG signals and discusses various methods of noise reduction, including filtering and independent component analysis (ICA). Spectral analysis, event-related response (ERR) analysis, and steady-state evoked response (ssER) analysis are then introduced. Each method is explained in plain language, followed by more detailed explanations to meet the different needs of beginners and advanced readers. Relevant statistical methods and data presentation formats are also introduced, using various data analysis platforms.

We present the main methods that are used to modulate brain activity directly. These methods are often used in combination or following up on neuroimaging experiments, in a means to test causal hypotheses. We include microstimulation, deep brain stimulation, focused ultrasound stimulation (FUS), transcranial magnetic stimulation (TMS) and its sub-types like single- and double-pulse and repetitive TMS. We end with transcranial current stimulation (TCS), also known as trancranial electric stimulation (TES), which comes in several variants such as transcranial direct current stimulation (TDCS) and transcranial alternating current stimulation (TACS).

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.