Gamma Rhythms in the Brain

Brain rhythms are activity fluctuations shared in populations of neurons. They are evident in extracellular electric fields and detectable through recordings performed within the brain or on the scalp. The gamma rhythm, a relatively high frequency (30– 80 Hz) component of these fluctuations, has received a great deal of attention. Gamma is modulated by sensory input and internal processes such as working memory and attention. Numerous theories have proposed that gamma contributes directly to brain function, but others argue that gamma is better viewed as a simple byproduct of network activity. Here we provide a basic introduction to this enigmatic signal, the mechanisms that generate it, and an accompanying paper in PLoS Biology attempting to elucidate its potential function. Hans Berger first successfully measured the brain waves of humans in 1924 using the electroencephalogram (EEG) [1]. His goal was to demonstrate that the electromagnetic fields of the human brain could be used for telepathy. Although the signals he detected were unsuccessful for this purpose, the EEG was widely adopted by clinicians and scientists. This is because the recordings are easy to perform and the rhythms detected are informative of brain state. For example, when we are in a deep sleep, the EEG consists of low-frequency, large-amplitude oscillations; when we are awake and attentive, it consists primarily of fast, small amplitude rhythms. Brain rhythms are evident as extracellular voltage fluctuations. These arise from summed electrical activity (primarily, but not exclusively, inputs) in populations of neurons, and are shaped by the geometry and alignment of those neurons [2]. The resultant fluctuations can be measured on the scalp by EEG or magnetoencephalography (MEG), and intracranially with subdural electrodes (electrocorticography). They can also be measured, on a more local basis, with a high impedance electrode placed in the brain (Figure 1A). The voltage fluctuations detected are then low-pass filtered (,250 Hz) to capture the slower fluctuations of brain rhythms (Figure 1B). The resultant signal—termed the local field potential (LFP)—was frequently used to study brain function, until it fell in popularity with the advent of single-cell electrophysiology in the late 1950s. Over the last decade, however, LFPs have attracted renewed interest as a potentially useful signal for studying the behavior of ensembles of neurons. The LFP is a continuous voltage signal that can vary in amplitude and frequency content. Like the EEG, it can be decomposed into different frequency components—delta (,4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), gamma (30–80 Hz), and high-gamma or high-frequency activity (.80 Hz)—although the precise frequency ranges associated with these terms vary across studies. The relative contribution of these different components to the measured signal is quantified by their relative power (Figure 2). In quiescent networks, most of the power in the LFP is found at low frequencies, indicating that rhythms like delta and theta contribute more significantly than high frequency ones. This is still the case when networks are activated, but less so: the power in higher frequencies increases, whereas that in lower frequencies is suppressed. The enhancement of gamma power in this driven state is particularly striking and is evident as a distinct ‘‘bump’’ in the power spectrum (Figure 2; right panel, solid line). A prominent gamma rhythm provides a signature of engaged networks. Gamma has been observed in a number of cortical areas, as well as subcortical structures, in numerous species. In sensory cortex, gamma power increases with sensory drive [3,4], and with a broad range of cognitive phenomena, including perceptual grouping [5] and attention [6]. At a given recording site, gamma is stronger for some stimuli than others, generally displaying selectivity and a preference similar to that of nearby neuronal spiking activity [7,8]. In higher cortex, gamma power is elevated during working memory [9] and learning [10]. Interestingly, irregular gamma activity has been observed in neurological disorders such as Alzheimer’s disease, Parkinson’s disease, schizophrenia, and epilepsy [11]. To interpret the meaning of changes in gamma requires an understanding of the cellular and network mechanisms that generate it. Fast-spiking GABAergic inhibitory interneurons are known to be crucial, with their activity being both necessary and sufficient to generate gamma [12–14]. Network models suggest that this process may be enhanced by interactions with excitatory neurons [15] and that local gamma-generating networks can be coupled by long-range horizontal connections [16] or gap junctions among inhibitory interneurons [17]. Such coupling would seem necessary, as gamma has been shown to be coherent across millimeters of cortex [18–20]. It is well established that gamma correlates with engaged or driven networks, but it is less clear whether it is a simple byproduct of network activity or has an important functional role. This is not for lack of proposals: numerous functions have been attributed to this rhythm. Most of these hinge on a relationship between gamma and the timing of spiking activity in nearby neurons. Spikes are actively generated signals in individual neurons and relay information between neural networks. Gamma activity is not actively propagated. It is a component of an extracellular field