Introduction

Electrodes are the first technical interface in a system for recording bioelectrical potentials. The electrochemical and biological processes at the material–tissue interface determine the signal transfer properties and are of utmost importance for the long-term behavior of a chronic implant. Here, “electrode” is used for the whole device that consists of one or multiple active recording sites, a substrate that carries these active sites, as well as interconnections, wires, insulation layers and the connectors to the next stage of a complete recording system, whether it is wire bound or wireless. The application of the electrodes in fundamental neuroscience, diagnosis, therapy, or rehabilitation determines their target specifications. The most important factors are the application site, extracorporal device or implant, acute or chronic contact, size of the electrode (device) and the recording sites, number of active sites on a device, geometrical arrangement of electrodes and type of signal to be recorded. They influence the selection process of electrodes suitable for an envisioned application and help engineers as well as neuroscientists to choose the very best materials for the active sites, substrate and insulation and the most appropriate manufacturing technique. The properties of the recorded signals are also strongly related to this selection process since the tailoring of the transfer characteristics helps to pick up the “right” signal components and to ignore, neglect and reject the “wrong” electrical potentials that might be due to the body itself or the surrounding environment or interference caused by noise from the electrode sites and the amplifier of the recording system.

Introduction

Modern neuroimaging and computational neuroscience are two recent neuroscience disciplines that are very important for understanding brain mechanisms. Optical imaging gives the opportunity of observing the brain in activity at the level of large populations of neurons with high resolution. Many types of optical imaging techniques exist, but only two are usually used in vivo (see Grinvald et al., 1999, for a detailed review): the first is based on intrinsic optical signals and records brain activity indirectly, the second is based on voltage-sensitive dyes (VSDs) and reports postsynaptic neuronal activation in real time. In this review, we focus on the second technique, aiming at a better understanding of the origin of the optical signal. Extensive reviews of VSDI have been published elsewhere (Roland, 2002; Grinvald and Hildesheim, 2004).

This amazing technique is based on complex interaction with the system which is not yet fully understood. Indeed, the recorded signal (VSD signal) originates from a large amount of intermingled neuronal and glial membrane components and it seems difficult to isolate the contributions from the different components. Combined intracellular recording with VSDI has demonstrated a linear correspondence between the VSD signal and membrane potential of an individual neuron, but so far no studies have focused on what exactly the VSD signal actually measures when applied to a cortical population in vivo.

Experimental approaches are not really feasible because the available methodologies do not offer the possibility to inspect simultaneously all the components that may contribute to the signal.

Introduction

Extracellular recordings have been, and still are, the main workhorse when measuring neural activity in vivo. In single-unit recordings sharp electrodes are positioned close to a neuronal soma, and the firing rate of this particular neuron is measured by counting spikes, that is, the standardized extracellular signatures of action potentials (Gold et al., 2006). For such recordings the interpretation of the measurements is straightforward, but complications arise when more than one neuron contributes to the recorded extracellular potential. For example, if two firing neurons of the same type are at about the same distance from their somas to the tip of the recording electrode, it may be very difficult to sort the spikes according to from which neuron they originate.

The use of two (stereotrode (McNaughton et al., 1983)), four (tetrode (Recce and O'Keefe, 1989;Wilson andMcNaughton, 1993; Gray et al., 1995; Jog et al., 2002)) or more (Buzsáki, 2004) close-neighbored recording sites allows for improved spike sorting, since the different distances from the electrode tips or contacts allow for triangulation. With present recording techniques and clustering methods one can sort out spike trains from tens of neurons from single tetrodes and from hundreds of neurons with multi-shank electrodes (Buzsáki, 2004).

Information about spiking is typically extracted from the high-frequency band (≳500 Hz) of extracellular potentials. Since these high-frequency signals generally stem from an unknown number of spiking neurons in the immediate vicinity of the electrode contact, this is called multi-unit activity (MUA).

Introduction

Functional magnetic resonance imaging (fMRI) allows the non-invasive measurement of neural activity nearly everywhere in the brain. The structural predecessor, MRI, was invented in the early 1970s (Lauterbur, 1973) and has been used clinically since the mid-1980s to provide high-resolution structural images of body parts, including rapid successions of images for example of the beating heart. However, it was the advent of blood oxygenation level dependent (BOLD) functional imaging developed first by Ogawa et al. (1990) that made the method crucial especially for the human neurosciences, leading to a vast expansion of both the method of fMRI as well as the field of human neurosciences. fMRI is now a mainstay of neuroscience research and by far the most widespread method for investigations of neural function in the human brain as it is entirely harmless, relatively easy to use, and the data are relatively straightforward to analyze. It is therefore no surprise that fMRI has provided a wealth of information about the functional organization of the human brain. While many publications initially confirmed knowledge derived from invasive animal experiments or from clinical studies, it is now frequently fMRI that opens up a new field of investigation that is then later followed up by invasive methods.

It is important to note that fMRI does not measure electrical or neurochemical activity directly. Physically, it relies on decay time-constants of water protons, which are affected by brain tissue and the concentration of deoxyhemoglobin.

Over the past 30 years calcium-sensitive fluorescent dyes have emerged as powerful tools for optical imaging of cell function. Calcium ions subserve a variety of essential functions in all cell types. For example, changes in intracellular free calcium concentration ([Ca2+]i) underlie fundamental cellular processes such as muscle contraction, cell division, exocytosis, and synaptic plasticity. Most of these processes rely on the steep gradient of calcium ion concentration that is actively maintained across the plasma membrane. Moreover, cells store calcium ions in intracellular organelles, enabling them to release a surge of Ca2+ into the cytosol where and when needed. Calcium ions act through molecular binding to various Ca2+-binding proteins, inducing conformational changes and thereby activating or modulating protein function. The development of optical reporters of calcium concentration has opened great opportunities to read out [Ca2+]i directly as a crucial intracellular messenger signal. A major application of calcium indicators is the quantitative study of a specific calcium-dependent process X, for example, neurotransmitter release, with the goal to reveal the function X = X([Ca2+]i). However, this is not the only type of application. Because neuronal excitation in the form of receptor activation or generation of action potentials typically is linked to calcium influx, calcium indicators are also used to reveal neural activation patterns, either within the dendritic tree of individual cells or within cell populations.

Introduction

Intracellular recording is the measurement of voltage or current across the membrane of a cell. It typically involves an electrode inserted in the cell and a reference electrode outside the cell. The electrodes are connected to an amplifier to measure the membrane potential, possibly in response to a current injected through the intracellular electrode (current clamp), or the current flowing through the intracellular electrode when the membrane potential is held at a fixed value (voltage clamp). Ionic and synaptic conductances can be measured indirectly with these two basic recording modes. While spike trains can be recorded with extracellular electrodes (see Chapter 4), subthreshold events in single neurons can only be recorded with intracellular electrodes. Intracellular recordings have been used for many applications: measuring membrane potential distribution in vivo (DeWeese et al., 2003), membrane potential correlations between neurons (Lampl et al., 1999), changes in effective membrane time constant with network activity (Pare et al., 1998; Leger et al., 2005), excitatory and inhibitory synaptic conductances in response to visual stimulation (Borg-Graham et al., 1998; Anderson et al., 2000; Monier et al., 2003), current–voltage relationships during spiking activity (Badel et al., 2008), the reproducibility of neuron responses (Mainen and Sejnowski, 1995) dendritic computation mechanisms (Stuart et al., 1999), gating mechanisms in thalamocortical circuits (Bal and McCormick, 1996), oscillations of membrane potential (Engel et al., 2001; Volgushev et al., 2002), stimulus-dependent modulation of the spike threshold (Azouz and Gray, 1999; Henze and Buzsaki, 2001; Wilent and Contreras, 2005), and many others.

In the nineteenth century, Julius Bernstein invented an ingenious device called the “differential rheotome,” a rotating wheel which could record the time course of action potentials (see Chapter 3). Since then, many sophisticated techniques have been introduced to measure correlates of neural activity: measurements of electricity produced by single neurons (Chapters 3 and 4) or multiple neurons (Chapters 5–7 and 9), measurements based on brain metabolism (Chapters 8 and 11) or on calcium dynamics (Chapter 10). These techniques are always more or less indirect measurements of neural activity, and they have diverse spatial and temporal resolutions, and spatial scales. Each chapter in this book has described the quantitative relationship between neural activity (e.g. membrane potential or synaptic activity) and the measured quantity, as it is currently understood. This effort serves two purposes: to give a better understanding and interpretation of the measurements, and to help enhance existing techniques or develop new ones. To conclude this book, the authors of all the chapters describe ongoing developments in their field, open questions to be addressed, and new emerging techniques.

Extracellular recording

Substrate-integrated microelectrode arrays (MEAs) are planar arrays of microelectrodes used to record electrical activity in neuronal cell cultures or acute brain slices (Taketani and Baudray, 2006; Egert et al., 2010; Gross, 2010). While their history goes back to the 1970s, the rapid development of photolithographic techniques (stimulated by the needs of the computer industry) has now made prefabricated high-density MEA chips a popular research tool.

Introduction

In magnetoencephalography (MEG) and electroencephalography (EEG), scalp potentials and extracranial magnetic fields generated by electrical activity in the brain are detected non-invasively (Berger, 1929; Cohen, 1972) (for an overview of the methodology see, e.g., Hamalainen et al., 1993; Niedermeyer and Lopes da Silva, 1999; Michel et al., 2009; Hansen et al., 2010). MEG and EEG signals are superpositions of contributions from sources at different locations in the brain. Source estimation (also known as inverse modeling) refers to the problem of determining the spatiotemporal patterns of neural activity on the basis of the recorded signals (Figure 7.1). The specific goal in source estimation can be stated in two closely related ways: (a) to identify the locations of the sources of the measured signals as a function of time, or (b) to disentangle the contributions from different brain regions in the measured time-varying signals. The often used term source localization refers to the former, whereas spatiotemporal imaging emphasizes the latter, reflecting the use of MEG and EEG source estimation in the analysis of the dynamical activity in networks of brain areas.

A given source in the brain generates a characteristic spatial pattern of signals in arrays of MEG and EEG sensors. These patterns can be calculated by using a forward model (see Chapter 6). In source estimation, the measured spatial patterns of signals are analyzed in order to make inferences about the distribution of the sources in the brain.

Most of what we know about the biology of the brain has been obtained using a large variety of measurement techniques, from the intracellular electrode recordings used by Hodgkin and Huxley to understand the initiation of action potentials in squid axons to functional magnetic resonance imaging (fMRI), used to explore higher cognitive functions. To extract meaningful information from these measurements, one needs to relate them to neural activity, but this relationship is usually not trivial. For example, electroencephalograms (EEG) measure the summed electrical activity of many neurons, and relating the electrical signals of the electrodes to neural activity in specific brain areas requires a deep understanding of how these signals are formed. Therefore, the interpretation of measurements relies not only on an understanding of the physical measurement devices (what physical quantity is measured), but also on our current understanding of the brain (the relationship between the measured quantity and neural activity).

The biophysics of neurons is explained in great detail in a number of books. This book deals with the biophysical and mathematical principles of neural activity measurement, and provides models of experimental measures. We believe this should be useful for at least three broad categories of scientists: (1) neuroscientists who use these techniques in their own experimental protocols and need to interpret the results precisely, (2) computational neuroscientists who use the experimental results for their models, (3) scientists who want to develop new techniques or enhance existing techniques.

Introduction

The electroencephalogram (EEG) represents potential differences recorded from the scalp as function of time (Niedermayer and Lopes da Silva, 1987). The generators of the EEG consist of time-varying ionic currents generated in the brain by biochemical sources. These current sources also generate a small but measurable magnetic induction field, which can be recorded with magnetoencephalographic (MEG) equipment (Hämäläinen et al., 1993). When EEG and MEG are studied in the time or frequency domain, several rhythms can be discriminated that contain valuable information about the collective behavior of the living human brain as a neural network. In this chapter EEG and MEG are discussed in the spatial domain. We consider that these signals are recorded from multiple sensors with known positions and study the spatial distribution of EEG and MEG (in the sequel abbreviated as MEEG) in relation to the spatial distribution of the underlying sources.

More precisely, we consider the mathematical problem of predicting the spatial distribution of MEEG, from several physiological assumptions on the current sources. This problem is commonly named the “forward problem.” Solutions of the forward problem that are fast, accurate and practical are indispensable ingredients for the solution of the “inverse problem” or “backward problem,” which is the problem of extracting as much information as possible about the cerebral current sources, on the basis of MEEG data. Both the forward and the inverse problems are formulated within the framework of a certain mathematical model, wherein the underlying physiological assumptions are precisely formulated.

Introduction

Extracellular electric potentials, such as local field potentials (LFPs) or the electroencephalogram (EEG), are routinely measured in electrophysiological experiments. LFPs are recorded using micrometer-size electrodes, and sample relatively localized populations of neurons, as these signals can be very different for electrodes separated by 1 mm (Destexhe et al., 1999a) or by a few hundred micrometers (Katzner et al., 2009). In contrast, the EEG is recorded from the surface of the scalp using millimeter-scale electrodes and samples much larger populations of neurons (Niedermeyer and Lopes da Silva, 1998). LFPs are subject to much less filtering compared to EEG, because EEG signals must propagate through various media, such as cerebrospinal fluid, dura mater, cranium, muscle and skin. LFP signals are also filtered, because the recording electrode is separated from the neuronal sources by portions of cortical tissue. Besides these differences, EEG and LFP signals display the same characteristics during wake and sleep states (Steriade, 2003).

The observation that action potentials have a limited participation in the genesis of the EEG or LFPs dates from early studies. Bremer (1938, 1949) was the first to propose that the EEG is not generated by action potentials, based on the mismatch of the time course of EEG waves with action potentials. Eccles (1951) proposed that LFP and EEG activities are generated by summated postsynaptic potentials arising from the synchronized excitation of cortical neurons. Intracellular recordings from cortical neurons later demonstrated a close correspondence between EEG/LFP activity and synaptic potentials (Klee et al., 1965; Creutzfeldt et al., 1966a, 1966b).

Introduction

The most popular technique for investigating the functional organization and plasticity of the cortex involves the use of a single microelectrode. It offers the advantage of recording action potentials and subthreshold activity directly from cortical neurons with high spatial (point) and temporal (millisecond) resolution sufficient to follow real-time changes in neuronal activity at any location along a volume of cortex, with the disadvantage that recordings are invasive to the cortex. In order to assess the functional representation of a sensory organ (e.g. a finger, a whisker), neurons are recorded from different cortical locations and the functional representation of the organ is then defined as the cortical region containing neurons responsive to stimulation of that organ (i.e. neurons that have receptive fields localized at the sensory organ). A change in the spatial distribution of neurons responsive to a given sensory organ and/or in their amplitude of response is typically taken as evidence for plasticity in the functional representation of that sensory organ (Merzenich et al., 1984). As a cortical functional representation could comprise thousands to millions of neurons distributed over a volume of cortex, the use of a single microelectrode to map a functional representation and its plasticity requires many recordings across a large cortical region, recordings that can only be obtained in a serial fashion and require many hours to complete, thus the animal is typically anesthetized.