Neurophysiological and Neuroimaging Techniques Robin Nusslock, Christina B. Young, Narun Pornpattananangkul, and Katherine S. F. Damme Northwestern University, U.S.A. Tere has been a growing movement over the past three decades to place models of psychi- atric disorders into a biological framework. Tis movement has recently been championed by the National Institute of Mental Health with the development of the Research Domain Criteria Initiative (RDoC), which calls for new ways of classifying psychiatric illness based on core brain–behavior dimensions (Insel et al., 2010). Examining these brain–behavior dimensions can inform our understanding of the pathophysiology of psychiatric illness, facilitate the identifcation of biological mark- ers that can aid scientifc investigation and diferential diagnosis, and generate targeted treatment protocols. Tis movement has been spearheaded by methodological advancements in the feld of neuroscience and bioengineering. Tree techniques for investigating brain function that have made important contributions to clinical psychological research are quantitative electroencephalography (EEG), event-related potentials (ERPs), and functional magnetic resonance imaging (fMRI). Each of these tech- niques has pros and cons, largely involving a trade-of between spatial and temporal reso- lution. Spatial resolution involves the ability to identify the biological source of a particular signal and to distinguish two separate struc- tures close to each other in space. Temporal resolution refers to the ability to determine the order of occurrence of two events close to each other in time. Te Encyclopedia of Clinical Psychology, First Edition. Edited by Robin L. Cautin and Scott O. Lilienfeld. © 2015 John Wiley & Sons, Inc. Published 2015 by John Wiley & Sons, Inc. DOI: 10.1002/9781118625392.wbecp557 Quantitative Electroencephalography Te frst EEG recording in humans was per- formed in 1924 by a German psychiatrist named Hans Berger. Using two electrodes, Berger observed spontaneous rhythmic activ- ity oscillating at approximately 10 Hz during relaxed wakefulness in the absence of sensory input. Tis rhythmic activity would become known as alpha activity, and Berger was among the frst to relate fuctuations in human EEG to diferent psychological states. Te feld of human neurophysiology has come a long way since Berger’s landmark observation. It is now established that scalp-recorded EEG oscillations are generated by the summation of both excitatory and inhibitory postsynaptic potentials in tens of thousands of cortical pyramidal neurons. Placing electrodes at the scalp allows measurement of these small but reliable potentials. Over the decades, researchers have developed techniques for reducing raw EEG signals into metrics that refect the activation or deactiva- tion of various brain regions. Tese techniques, referred to as spectral analyses, typically sum- marize EEG data into conventionally defned frequency bands. Te delta band refects low-frequency activity (1–4 Hz) typically asso- ciated with the deepest stages of sleep in healthy humans, also known as slow-wave sleep. Teta activity involving EEG activity within the 4–8 Hz range is also prominent during sleep. Elevated activity in the alpha band (8–13 Hz) is indicative of less cortical neuronal activity. Support for this claim comes from research combining EEG and positron emission tomog- raphy (PET) that demonstrates an inverse relationship between glucose metabolism (an index of neuronal activity) and alpha activity in cortical regions underlying the specifed EEG electrode. Both beta (13–30 Hz) and gamma