INTRODUCTION

The measurement of electrodermal activity (EDA) or palmar sweat gland activity in children involves many of the same issues as in adults. There are, however, some special problems that can arise with children, all of which are inversely proportional to age. The most fundamental problem has to do with possible differences in which stimuli elicit electrodermal responses. This topic has not been well researched, but infants and toddlers appear to respond to a more restricted range of stimuli and children may not respond to some stimuli as well as adults do. The second problem has to do with difficulties in timing the presentation of stimuli, especially in toddlers and very young children for whom compliance with experimental instructions is substantially less than for older children and adults. A third problem, also related to problems with compliance, is managing the stress associated with attaching electrodes in a strange laboratory setting. This chapter will begin with the nature and measurement of the electrodermal effector system, followed by the problems specific to children.

For readers interested in a more thorough coverage of this topic than is provided by the present chapter, there are a number of reference sources. Introductions to psychophysiology, including EDA, are available in the texts by Stern, Ray, and Quigley (2001) and Hugdahl (1995). Consensus recommendations for how to record EDA are offered by Fowles and colleagues (1981).

INTRODUCTION

Visual abilities undergo major transformation during infancy and childhood. Although infants arrive in the world both able to see and to learn about what they see, many aspects of vision and visual cognition continue to develop well into childhood (e.g., Chung & Thomson, 1995; Lewis & Maurer, 2005). Event-related potentials (ERPs) are a useful tool for investigating the neurophysiological correlates of these developmental changes as they can provide information not available from behavioral measures alone. In particular, they provide precise information about the timing and some information about the spatial distribution of the brain events underlying visual processing. Since ERPs can be obtained in “passive” tasks, where participants simply look at visual displays without any requirement to make a verbal or behavioral response, they allow use of the same procedure across a wide range of age and ability levels. For example, visual ERPs have been used to study face processing in infants only a few months old (e.g., Halit, de Haan, & Johnson, 2003) and have been used to investigate aspects of visual processing in children with various developmental disorders, including autism spectrum disorder (e.g., Dawson et al., 2002; Kemner, van der Gaag, Verbaten & van Engeland, 1999), Down syndrome (e.g., Karrer et al., 1998), and attention deficit-hyperactivity disorder (reviewed in Barry, Johnstone, & Clarke, 2003). Along with these distinct advantages, however, ERPs also present challenges both in terms of experimental design and data collection, and analysis and interpretation.

INTRODUCTION

A sudden noise occurs while you are concentrating and you respond quickly and automatically – your body muscles flex, your eyes blink, and your facial expression registers a grimace of surprise. You have just experienced a startle reflex. The startle reflex (or startle response) is commonly measured in research studies by a blink response in humans, elicited by some startling stimulus such as a loud noise. The blink response is an early and reliable component of startle in humans. It occurs to stimuli in various sensory modalities (e.g., auditory, visual, cutaneous) and often begins within 30 ms after the onset of a sudden and intense stimulus.

The word “reflex” often seems to bring to mind a stable, simple, and unchanging response elicited under specific circumstances. But in the case of the startle reflex, this view is overly simplistic. Though this reflex can be reliably elicited, it turns out to also be highly modifiable by an extensive variety of stimuli, circumstances, and clinical conditions. The wide range of studies examining this process of modification is generally referred to as startle modification research. Fundamentally, the paradigms employed in this research involve situations in which the startle reflex is modulated or modified in amplitude, latency, or probability by another non-startling variable of interest. The remarkably wide range of factors that can modify startle is what has generated such a broad interest in its study.

INTRODUCTION

Developmental psychophysiological research is a relatively young field that is rapidly expanding partly because sophisticated, cost-effective technology now allows researchers to collect physiological data much more efficiently and effectively. This volume of developmental psychophysiology reflects both the newness as well as the growth of the field. As alluded to by many of the authors included in this volume, researchers collecting valid psychophysiological data in children face challenges that are magnified when compared to the collection of these same data in adults. However, developmental psychophysiologists are not alone in addressing these challenges as we can readily draw upon the experiences from specialists working in other related fields.

The fields of psychology and education have also contributed to our general knowledge about effective methods of assessing children. Notably, the number of texts written on behavioral and neuropsychological assessment of children is plentiful, and we can apply this knowledge to assessment of psychophysiological information as well. For example, the recent editions of assessment of children (Sattler 2001, 2002) comprehensively discuss skills necessary for test administrators to have in order to successfully assess children. Some of these skills include effective listening, building rapport with the child, and how to handle difficult behaviors and individual temperaments. A researcher who develops these assessment skills discussed by psychologists, neuropsychologists, and education professionals, along with the technical skills necessary for obtaining the desired psychophysiological measurements will be much more successful in obtaining reliable and valid research data.

INTRODUCTION

The field of developmental psychophysiology provides the methodology for examination of age-related changes in the functioning of the brain. The electroencephalogram (EEG) is an efficient, non-invasive, and relatively inexpensive method for studying brain development in infants and children and for relating brain development to changes in cognitive behaviors. Utilizing EEG allows for examination of these developmental changes without dramatic interference with normal ongoing behaviors. All of these characteristics make the EEG one of the more favorable methods for investigating brain-behavior relations with young populations (Casey & de Haan, 2002; Taylor & Baldeweg, 2002).

The EEG discussed in this chapter is sometimes called “quantitative EEG” and is used for basic research on brain activity during cognition or emotion and for basic research on brain maturation. Typically, quantitative EEGs used for basic research are digital records that are converted from the time domain to the frequency domain by means of spectral analysis, yielding spectral power at specific frequencies, or by means of phase coherence analysis, yielding the degree to which the EEG signals at two distinct scalp locations are in phase at a specific frequency. This quantitative methodology differs from the traditional use of the EEG in the clinical setting to localize seizures or tumors. It also differs from event-related potentials, or ERPs, which are brain electrical responses that are time locked to a specific set of stimuli. ERP methodology and research is reviewed in Chapters 2, 3, and 4 of this volume.

CONCEPTUAL FRAMEWORK

Recently, event-related neuroelectric oscillations have provided important tools with which to study information processing in the brain and with which to enrich our knowledge of brain maturation and cognitive development. The essential advantages of this approach are the ability to (1) analyze neuroelectric responses reflecting mechanisms of stimulus information processing in comparison to electrical activity in a passive state reflecting the neurobiological maturation of the brain; (2) refine electrophysiological correlates of information processing by separating functionally different but simultaneously generated responses from different frequency ranges; and (3) reveal differential developmental dynamics of the power and synchronization of neuroelectric responses, thus providing information about independent neurophysiological mechanisms during biological and cognitive development.

In this chapter, the conceptual background of event-related oscillations will be presented with a major focus on their relevance for developmental research, followed by methods, analytic tools, and parameters for assessment of event-related oscillations. Finally, major findings on the development of the delta, theta, alpha, and gamma response systems in the brain will be described.

EVENT-RELATED POTENTIALS

The electroencephalogram (EEG) is a time-varying signal reflecting the summated neuroelectric activity from various neural sources in the brain during rest or functional activation. An EEG response that occurs in association with an eliciting event (sensory or cognitive stimulus) is defined as an event-related potential (ERP). However, the ERP may contain EEG activity not related to specific event processing, as well as electric activity from non-neural sources.

The prerequisite for discussing changes in responsiveness and sculpting inhibitory processes of neocortical neurons during different behavioural states (see Chapters 6 and 7) is the description of various neuronal types and their functional properties, which is the subject of this chapter.

Varieties, immunoreactivity and connectivity of neocortical neuronal classes

The mammalian neocortex is a laminated structure that contains up to 28 × 109 neurons that are connected by about 1012 synapses. The attempt to simplify the functional complexity of the neocortex started with the description of the columnar organization into modules that have a basic similarity of internal design and operation (see Mountcastle, 1997, 1998). The neocortex consists of a large population of long-axon (output) neurons that are excitatory and reciprocally connected to each other in the same and/or opposite hemisphere as well as to thalamocortical (TC) neurons, and a smaller population of local-circuit inhibitory neurons.

Besides morphological techniques that distinguish these two neuronal classes (Figures 2.1–2.3), physiological identification of output neurons is possible using antidromic and orthodromic activations (Figures 2.4 and 2.5), which determine the sources of synaptic inputs and neuronal targets (Evarts, 1964, 1965; Steriade et al., 1974a) thus leading to systematizations with a limited number of neuronal categories. These are rather difficult techniques in behaving animals; with some exceptions (Steriade et al., 2001a, b), they are rarely used nowadays. Neocortical neurons have been classified into four categories according to their intrinsic electrophysiological properties, as determined by responses to intracellular current pulses (see Section 2.3).

This chapter describes the structure and connections of the amygdala, emphasizing three of its main components: the basolateral complex, the central nucleus and the intercalated cell masses. As we shall see in detail below, the basolateral complex is a cortex-like structure that receives most sensory inputs to the amygdala and projects to the central nucleus. In turn, the central nucleus contributes most amygdala projections to brainstem nuclei generating components of fear responses. However, because it would be would be maladaptive if all sensory inputs triggered fear, impulse traffic between the basolateral complex and central nucleus must be flexibly gated as a function of the particular combination of sensory inputs and environmental signals confronting the organism. This is the function of the intercalated cell masses, clusters of GABAergic neurons that receive glutamatergic inputs from the basolateral complex and project to central nucleus. This chapter will describe the connectivity and physiological properties underlying this process. Their system-level consequences will be considered later in this book (Chapter 9).

Is the amygdala a valid anatomical concept?

The amygdala is a nucleated structure located in the depth of the temporal lobe (Figures 3.1 and 3.2). Early anatomists divided the amygdala into three groups of nuclei (Johnston, 1923; Humphrey, 1936; Fox, 1940; Crosby & Humphrey, 1941): (1) the basolateral complex, comprised of the lateral, basolateral and basomedial nuclei; (2) the corticomedial group including the central, medial and cortical nuclei; and (3) an ill-defined anterior group including the anterior amygdaloid area, the nucleus of the lateral olfactory tract and the intercalated cell masses.

We now discuss the neuronal processes that account for the increased responsiveness of brainstem, thalamic and cortical neurons during wakefulness and REM sleep. The closing of thalamic gates during slow-wave sleep (see Chapter 6) is modified upon transition to both brain-active states under the influence of increased neuronal activities in ascending activating modulatory (mainly glutamatergic and cholinergic) systems. In addition, the different types of low-frequency rhythms that occur during slow-wave sleep in the disconnected brain are transformed into faster (beta and gamma) oscillations, which are thought to play a significant role in sustaining the mental activity that characterizes brain-active states.

Similarities and basic differences between waking and REM sleep

Global electrical signs differentiating waking from REM sleep

The global signs that characterize the two brain-active states are EEG rhythms, muscular tone, and eye movements associated with sharp waves in brainstem–thalamocortical systems. The tonic EEG activation associated with fast rhythms (usually 20–60 Hz) in waking is virtually identical to that in REM sleep, and distinguishes both of these states from the low-frequency (less than 15 Hz) oscillations that prevail during slow-wave sleep. The other tonic aspect, muscular atonia, specifically distinguishes REM sleep from the other two major states of vigilance (Jouvet & Michel, 1959) and is taken as the cardinal sign of REM sleep. Finally, phasic eye movements are voluntary during waking and occur as involuntary ocular saccades in REM sleep, when they are accompanied by spiky ponto-geniculo-occipital (PGO) potentials in brainstem and thalamocortical systems.

In neuroscience, the term gating can assume various meanings depending on the level of analysis. At the level of ionic channels, gating refers to the transition between two or more conformational states of channel proteins. At the neuronal or networks level, gating refers to changes in responsiveness and in inhibitory processes during different behavioral states. In both instances, the causative events and their functional consequences can vary widely. This book focuses on gating in the thalamocortical and amygdalocortical systems.

In the thalamocortical system, gating was often used to describe the blockade of signal transmission from the external world to the cerebral cortex during disconnected states, such as slow-wave sleep. In this monograph, we also discuss evidence that, despite absence of information from the external world, the behavioural state of slow-wave sleep is associated with the processing of internally generated signals and with synaptic plasticity. We also argue that these events may lead to the consolidation of memory traces acquired during the waking state as well as to a form of consciousness expressed by dreaming mentation. The opening of thalamic gates during brain-active states of waking and REM sleep changes the excitability of cortical neurons and leads to different forms of mentation.

In the amygdalocortical system, gating refers to how the transmission of sensory inputs is modulated according to their emotional significance. This process leads to alterations not only in behavioural responsiveness, but also in memory consolidation.