Beta And Gamma Rhythms

Beta and gamma rhythms were first studied in the human electroencephalogram (EEG) recorded from the scalp.

Beta rhythm is defined in general as any EEG rhythm over 13 Hz (The International Federation of Societies for Electroencephalography and Clinical Neurophysiology [IFSECN] 1974). Typically, it is a rhythm from 13 to 35 Hz. Gamma rhythm is commonly used by neuroscientists to designate neural activity of frequency of about 30-100 Hz, including the 40-Hz oscillations. Beta and gamma EEGs are of relatively low amplitude (less than 30 ||V) in the EEG, and their quantification normally requires computer analysis with careful separation of muscle artifacts (Niedermeyer, 1999).

Three main types of beta rhythms are commonly observed in the scalp EEG of human adult subjects: (1) a fronto-central beta rhythm that can be blocked by contralateral movement or tactile stimulation, (2) a diffused beta rhythm without specific reactivity, and (3) a posterior beta rhythm that can be blocked by visual activity, similar to the occipital alpha rhythm (Kuhlo, 1976). An increase in beta rhythm has been reported in neuropsychiatric patients, but Kuhlo (1976) concluded that "no adequate evidence exists at present of any relationship between normal or excessive beta activity and psychiatric disorders." Apro-nounced increase in beta-frequency EEG was found with drugs that enhance gamma-aminobutyric acid-A(GABA-A) receptor functions, including sedative doses of barbiturates and benzodiazepines (Kozelka & Pedley, 1990) and the anesthetic propofol. Neural circuitry that involves GABAergic interneurons in the cortex is probably responsible for the generation of the drug-induced beta and gamma rhythms (Leung, 1998; Traub, Jefferys, & Whit-tington, 1999). The regional loss of the spontaneous or the barbiturate-induced beta rhythm is a sign of local cortical dysfunction.

After Jasper and Andrews (1938), the term gamma rhythm has not been adopted for use in clinical EEG (IFSECN, 1974). The recent interest in gamma rhythm stems from animal experiments that have shown the importance of gamma rhythm in sensory information processing in the brain (Freeman, 1991; Singer & Gray, 1995). In the visual cortex, single neurons may code for various features of a visual object, like size, form, and orientation. It is proposed that the spatially dispersed neurons that code for different features may synchronize through gamma oscillations, thus forming a dynamic assembly of neurons that represents an object uniquely (Singer & Gray, 1995). Similar processes may exist in the olfactory, auditory, somatosensory, and motor cortices. Gamma rhythms have also been found in subcortical structures, including the thalamus (Ribary et al., 1991; Steriade, Contreras, Amzica, & Timofeev, 1996) and basal forebrain nuclei. In the hippocampus, gamma waves may mediate neural processing and enhance interactions among the entorhinal cortex and various subfields of the hippocampus (Leung, 1998; Bragin et al., 1995). An increase in hippocampal gamma waves after seizure or phencyclidine is thought to drive behavioral hyperactivity, a feature of psychosis in animals (Ma & Leung, 2000; Ma &

Leung, 2002). Gamma waves are implicated in the maintenance of consciousness (Engel & Singer, 2001), and gamma waves are suppressed during general anesthesia (Traub et al., 1999; Ma, Shen, Stewart, Herrick, & Leung, 2002).

Multiple mechanisms underlie the high-frequency oscillations in the brain. Synaptic interactions among excitatory and inhibitory neurons (Freeman, 1991) or among inhibitory interneurons only (Traub et al., 1999) have been purported to generate gamma oscillations. Llinas, Grace, and Yarom (1991) discovered that single neurons may oscillate at various frequencies including beta and gamma frequencies. In the brain, local neural circuits generate beta or gamma activity that may synchronize with other local and distant circuits. Many parts of the brain respond preferentially to gamma rather than other frequencies, and thus temporal synchronization across spatially distributed domains may be achieved dynamically.

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