Beta wave

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Beta waves

Beta waves, or beta rhythm, are neural oscillations (brainwaves) in the brain with a frequency range of between 12.5 and 30 Hz (12.5 to 30 cycles per second). Several different rhythms coexist, with some being inhibitory and others excitory in function. [1]

Contents

Beta waves can be split into three sections: Low Beta Waves (12.5–16 Hz, "Beta 1"); Beta Waves (16.5–20 Hz, "Beta 2"); and High Beta Waves (20.5–28 Hz, "Beta 3"). [2] Beta states are the states associated with normal waking consciousness.

History

Beta waves were discovered and named by the German psychiatrist Hans Berger, who invented electroencephalography (EEG) in 1924, as a method of recording electrical brain activity from the human scalp. Berger termed the larger amplitude, slower frequency waves that appeared over the posterior scalp when the subject's eye were closed alpha waves. The smaller amplitude, faster frequency waves that replaced alpha waves when the subject opened their eyes were then termed beta waves. [3]

Function

Low-amplitude beta waves with multiple and varying frequencies are often associated with active, busy or anxious thinking and active concentration. [4]

Over the motor cortex, beta waves are associated with the muscle contractions that happen in isotonic movements and are suppressed prior to and during movement changes, [5] with similar observations across fine and gross motor skills. [6] Bursts of beta activity are associated with a strengthening of sensory feedback in static motor control and reduced when there is movement change. [7] Beta activity is increased when movement has to be resisted or voluntarily suppressed. [8] The artificial induction of increased beta waves over the motor cortex by a form of electrical stimulation called Transcranial alternating-current stimulation consistent with its link to isotonic contraction produces a slowing of motor movements. [9]

Investigations of reward feedback have revealed two distinct beta components; a high beta (low gamma) component, [10] and low beta component. [11] In association with unexpected gains, the high beta component is more profound when receiving an unexpected outcome, with a low probability. [12] However the low beta component is said to be related to the omission of gains, when gains are expected. [11]

During rest, fast beta oscillations are prevalent over lateral prefrontal cortex (LPFC) in humans, following a posteroanterior increase in frequency. This increase beta frequency at more anterior subregions corresponds to an anatomical model of LPFC function wherein cognitive control is hierarchically organized, with more abstract and sophisticated control mechanisms subserved by the most anterior regions and more direct//concrete control of goal-directed action at posterior sites. This is further in agreement with the fact that posterior LPFC beta is slower in frequency, similar to that observed over resting motor cortex. [13]

Relationship with GABA

Diffuse beta waves present alongside other frequencies in spontaneous EEG recorded from a 28-month-old child with Dup15q syndrome. Dup15q EEG signature.png
Diffuse beta waves present alongside other frequencies in spontaneous EEG recorded from a 28-month-old child with Dup15q syndrome.

Beta waves are often considered indicative of inhibitory cortical transmission mediated by gamma aminobutyric acid (GABA), the principal inhibitory neurotransmitter of the mammalian nervous system. Benzodiazepines, drugs that modulate GABAA receptors, induce beta waves in EEG recordings from humans [14] and rats. [15] Spontaneous beta waves are also observed diffusely in scalp EEG recordings from children with duplication 15q11.2-q13.1 syndrome (Dup15q) who have duplications of GABAA receptor subunit genes GABRA5 , GABRB3 , and GABRG3 . [16] Similarly, children with Angelman syndrome with deletions of the same GABAA receptor subunit genes feature diminished beta amplitude. [17] Thus, beta waves are likely biomarkers of GABAergic dysfunction, especially in neurodevelopmental disorders caused by 15q deletions/duplications.

See also

Brainwaves

Related Research Articles

<span class="mw-page-title-main">Delta wave</span> High amplitude low frequency brain wave

Delta waves are high amplitude neural oscillations with a frequency between 0.5 and 4 hertz. Delta waves, like other brain waves, can be recorded with electroencephalography (EEG) and are usually associated with the deep stage 3 of NREM sleep, also known as slow-wave sleep (SWS), and aid in characterizing the depth of sleep. Suppression of delta waves leads to inability of body rejuvenation, brain revitalization and poor sleep.

A gamma wave or gamma rhythm is a pattern of neural oscillation in humans with a frequency between 25 and 140 Hz, the 40 Hz point being of particular interest. Gamma rhythms are correlated with large-scale brain network activity and cognitive phenomena such as working memory, attention, and perceptual grouping, and can be increased in amplitude via meditation or neurostimulation. Altered gamma activity has been observed in many mood and cognitive disorders such as Alzheimer's disease, epilepsy, and schizophrenia.

The sensorimotor rhythm (SMR) is a brain wave. It is an oscillatory idle rhythm of synchronized electric brain activity. It appears in spindles in recordings of EEG, MEG, and ECoG over the sensorimotor cortex. For most individuals, the frequency of the SMR is in the range of 7 to 11 Hz.

Alpha waves, or the alpha rhythm, are neural oscillations in the frequency range of 8–12 Hz likely originating from the synchronous and coherent electrical activity of thalamic pacemaker cells in humans. Historically, they are also called "Berger's waves" after Hans Berger, who first described them when he invented the EEG in 1924.

Brainwave entrainment, also referred to as brainwave synchronization or neural entrainment, refers to the observation that brainwaves will naturally synchronize to the rhythm of periodic external stimuli, such as flickering lights, speech, music, or tactile stimuli.

In the field of computational neuroscience, the theory of metastability refers to the human brain's ability to integrate several functional parts and to produce neural oscillations in a cooperative and coordinated manner, providing the basis for conscious activity.

<span class="mw-page-title-main">Slow-wave sleep</span> Period of sleep in humans and other animals

Slow-wave sleep (SWS), often referred to as deep sleep, is the third stage of non-rapid eye movement sleep (NREM), where electroencephalography activity is characterised by slow delta waves.

<span class="mw-page-title-main">Neural oscillation</span> Brainwaves, repetitive patterns of neural activity in the central nervous system

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity.

Theta waves generate the theta rhythm, a neural oscillation in the brain that underlies various aspects of cognition and behavior, including learning, memory, and spatial navigation in many animals. It can be recorded using various electrophysiological methods, such as electroencephalogram (EEG), recorded either from inside the brain or from electrodes attached to the scalp.

<span class="mw-page-title-main">Electrocorticography</span> Type of electrophysiological monitoring

Electrocorticography (ECoG), a type of intracranial electroencephalography (iEEG), is a type of electrophysiological monitoring that uses electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. In contrast, conventional electroencephalography (EEG) electrodes monitor this activity from outside the skull. ECoG may be performed either in the operating room during surgery or outside of surgery. Because a craniotomy is required to implant the electrode grid, ECoG is an invasive procedure.

<span class="mw-page-title-main">Mu wave</span> Electrical activity in the part of the brain controlling voluntary movement

The sensorimotor mu rhythm, also known as mu wave, comb or wicket rhythms or arciform rhythms, are synchronized patterns of electrical activity involving large numbers of neurons, probably of the pyramidal type, in the part of the brain that controls voluntary movement. These patterns as measured by electroencephalography (EEG), magnetoencephalography (MEG), or electrocorticography (ECoG), repeat at a frequency of 7.5–12.5 Hz, and are most prominent when the body is physically at rest. Unlike the alpha wave, which occurs at a similar frequency over the resting visual cortex at the back of the scalp, the mu rhythm is found over the motor cortex, in a band approximately from ear to ear. People suppress mu rhythms when they perform motor actions or, with practice, when they visualize performing motor actions. This suppression is called desynchronization of the wave because EEG wave forms are caused by large numbers of neurons firing in synchrony. The mu rhythm is even suppressed when one observes another person performing a motor action or an abstract motion with biological characteristics. Researchers such as V. S. Ramachandran and colleagues have suggested that this is a sign that the mirror neuron system is involved in mu rhythm suppression, although others disagree.

Recurrent thalamo-cortical resonance or Thalamocortical oscillation is an observed phenomenon of oscillatory neural activity between the thalamus and various cortical regions of the brain. It is proposed by Rodolfo Llinas and others as a theory for the integration of sensory information into the whole of perception in the brain. Thalamocortical oscillation is proposed to be a mechanism of synchronization between different cortical regions of the brain, a process known as temporal binding. This is possible through the existence of thalamocortical networks, groupings of thalamic and cortical cells that exhibit oscillatory properties.

<span class="mw-page-title-main">Spike-and-wave</span>

Spike-and-wave is a pattern of the electroencephalogram (EEG) typically observed during epileptic seizures. A spike-and-wave discharge is a regular, symmetrical, generalized EEG pattern seen particularly during absence epilepsy, also known as ‘petit mal’ epilepsy. The basic mechanisms underlying these patterns are complex and involve part of the cerebral cortex, the thalamocortical network, and intrinsic neuronal mechanisms.

<span class="mw-page-title-main">Electroencephalography</span> Electrophysiological monitoring method to record electrical activity of the brain

Electroencephalography (EEG) is a method to record an electrogram of the spontaneous electrical activity of the brain. The biosignals detected by EEG have been shown to represent the postsynaptic potentials of pyramidal neurons in the neocortex and allocortex. It is typically non-invasive, with the EEG electrodes placed along the scalp using the International 10–20 system, or variations of it. Electrocorticography, involving surgical placement of electrodes, is sometimes called "intracranial EEG". Clinical interpretation of EEG recordings is most often performed by visual inspection of the tracing or quantitative EEG analysis.

The neuroscience of rhythm refers to the various forms of rhythm generated by the central nervous system (CNS). Nerve cells, also known as neurons in the human brain are capable of firing in specific patterns which cause oscillations. The brain possesses many different types of oscillators with different periods. Oscillators are simultaneously outputting frequencies from .02 Hz to 600 Hz. It is now well known that a computer is capable of running thousands of processes with just one high-frequency clock. Humans have many different clocks as a result of evolution. Prior organisms had no need for a fast-responding oscillator. This multi-clock system permits quick response to constantly changing sensory input while still maintaining the autonomic processes that sustain life. This method modulates and controls a great deal of bodily functions.

<span class="mw-page-title-main">Large irregular activity</span>

Large (amplitude) irregular activity (LIA), refers to one of two local field states that have been observed in the hippocampus. The other field state is that of the theta rhythm. The theta state is characterised by a steady slow oscillation of around 6–7 Hz. LIA has a predominantly lower oscillation frequency but contains some sharp spikes, called sharp waves of a higher frequency than that of theta. LIA accompanies the small irregular activity state to which the term LIA has been used to describe overall.

Sharp waves and ripples (SWRs) are oscillatory patterns produced by extremely synchronised activity of neurons in the mammalian hippocampus and neighbouring regions which occur spontaneously in idle waking states or during NREM sleep. They can be observed with a variety of imaging methods, such as EEG. They are composed of large amplitude sharp waves in local field potential and produced by tens of thousands of neurons firing together within 30–100 ms window. They are some of the most synchronous oscillations patterns in the brain, making them susceptible to pathological patterns such as epilepsy.They have been extensively characterised and described by György Buzsáki and have been shown to be involved in memory consolidation in NREM sleep and the replay of memories acquired during wakefulness.

Corticomuscular coherence relates to the synchrony in the neural activity of brain's cortical areas and muscle. The neural activities are picked up by electrophysiological recordings from the brain and muscle (EMG). It is a method to study the neural control of movement.

Transcranial random noise stimulation (tRNS) is a non-invasive brain stimulation technique and a form of transcranial electrical stimulation (tES). Terney et al from Göttingen University was the first group to apply tRNS in humans in 2008. They showed that by using an alternate current along with random amplitude and frequency in healthy subjects, the motor cortex excitability increased for up to 60 minutes after 10 minutes of stimulation. The study included all the frequencies up to half of the sampling rate i.e. 640 Hz, however the positive effect was limited only to higher frequencies. Although tRNS has shown positive effects in various studies the optimal parameters, as well as the potential clinical effects of this technique, remain unclear.

<span class="mw-page-title-main">High-frequency oscillations</span> Brainwaves with frequencies larger than 80 Hz

High-frequency oscillations (HFO) are brain waves of the frequency faster than ~80 Hz, generated by neuronal cell population. High-frequency oscillations can be recorded during an electroencephalagram (EEG), local field potential (LFP) or electrocorticogram (ECoG) electrophysiology recordings. They are present in physiological state during sharp waves and ripples - oscillatory patterns involved in memory consolidation processes. HFOs are associated with pathophysiology of the brain like epileptic seizure and are often recorded during seizure onset. It makes a promising biomarker for the identification of the epileptogenic zone. Other studies points to the HFO role in psychiatric disorders and possible implications to psychotic episodes in schizophrenia.

References

  1. ↑ Rassi, Elie; Lin, Wy Ming; Zhang, Yi; Emmerzaal, Jill; Haegens, Saskia (2023). "β Band Rhythms Influence Reaction Times". eNeuro. 10 (6). doi:10.1523/ENEURO.0473-22.2023. ISSN   2373-2822. PMC   10312120 . PMID   37364994.
  2. ↑ Rangaswamy M, Porjesz B, Chorlian DB, Wang K, Jones KA, Bauer LO, Rohrbaugh J, O'Connor SJ, Kuperman S, Reich T, Begleiter (2002). "Beta power in the EEG of alcoholics". Biological Psychology. 52 (8): 831–842. doi:10.1016/s0006-3223(02)01362-8. PMID   12372655. S2CID   26052409.
  3. ↑ Buzsáki, György (2006). Rhythms of the Brain . New York: Oxford University Press. p.  4. ISBN   978-0-19-530106-9.
  4. ↑ Baumeister J, Barthel T, Geiss KR, Weiss M (2008). "Influence of phosphatidylserine on cognitive performance and cortical activity after induced stress". Nutritional Neuroscience. 11 (3): 103–110. doi:10.1179/147683008X301478. PMID   18616866. S2CID   45936526.
  5. ↑ Baker, SN (2007). "Oscillatory interactions between sensorimotor cortex and the periphery". Current Opinion in Neurobiology. 17 (6): 649–55. doi:10.1016/j.conb.2008.01.007. PMC   2428102 . PMID   18339546.
  6. ↑ Easthope, Eric; Shamei, Arian; Liu, Yadong; Gick, Bryan; Fels, Sidney (17 August 2023). "Cortical control of posture in fine motor skills: evidence from inter-utterance rest position". Frontiers in Human Neuroscience. 17: 1139569. doi: 10.3389/fnhum.2023.1139569 . PMC   10469778 . PMID   37662639.
  7. ↑ Lalo, E; Gilbertson, T; Doyle, L; Di Lazzaro, V; Cioni, B; Brown, P (2007). "Phasic increases in cortical beta activity are associated with alterations in sensory processing in the human". Experimental Brain Research. Experimentelle Hirnforschung. Experimentation Cerebrale. 177 (1): 137–45. doi:10.1007/s00221-006-0655-8. PMID   16972074. S2CID   24685610.
  8. ↑ Zhang, Y; Chen, Y; Bressler, SL; Ding, M (2008). "Response preparation and inhibition: the role of the cortical sensorimotor beta rhythm". Neuroscience. 156 (1): 238–46. doi:10.1016/j.neuroscience.2008.06.061. PMC   2684699 . PMID   18674598.
  9. ↑ Pogosyan, A; Gaynor, LD; Eusebio, A; Brown, P (2009). "Boosting cortical activity at Beta-band frequencies slows movement in humans". Current Biology. 19 (19): 1637–41. Bibcode:2009CBio...19.1637P. doi:10.1016/j.cub.2009.07.074. PMC   2791174 . PMID   19800236.
  10. ↑ Marco-Pallerés, J., Cucurell, D., Cunillera, T., García, R., Andrés-Pueyo, A., Münte, T. F., et al. (2008).Human oscillatory activity associated to reward processing in a gambling task, Neuropsychologia, 46, 241-248. doi : 10.1016/j.neuropsychologia.2007.07.016
  11. 1 2 Yaple, Z., Martinez-Saito, M., Novikov, N., Altukhov, D., Shestakova, A., Klucharev, V. (2018). Power of feedback-induced beta oscillations reflect omission of rewards: Evidence from an EEG gambling study, Frontiers in Neuroscience, 12, 776. doi : 10.3389/fnins.2018.00776
  12. ↑ HajiHosseini, A., Rodriguez-Fornells, A., and Marco-Pallerés, J. (2012). The role of beta-gamma oscillations in unexpected rewards processing, Neuroimage, 60, 1678-1685. doi : 10.1016/j.neuroimage.2012.01.125
  13. ↑ Capilla, A., Arana, L., García-Huéscar, M., Melcón, M., Gross, J., & Campo, P. (2022). The natural frequencies of the resting human brain: An MEG-based atlas. NeuroImage, 258, 119373.
  14. ↑ Feshchenko, V; Veselis, R; Reinsel, R (1997). "Comparison of the EEG effects of midazolam, thiopental, and propofol: the role of underlying oscillatory systems". Neuropsychobiology. 35 (4): 211–20. doi:10.1159/000119347. PMID   9246224.
  15. ↑ Van Lier, Hester; Drinkenburg, Wilhelmus; Van Eeten, Yvonne; Coenen, Anton (2004). "Effects of diazepam and zolpidem on EEG beta frequencies are behavior-specific in rats". Neuropharmacology. 47 (2): 163–174. doi:10.1016/j.neuropharm.2004.03.017. PMID   15223295. S2CID   20725910.
  16. ↑ Frohlich, Joel; Senturk, Damla; Saravanapandian, Vidya; Golshani, Peyman; Reiter, Lawrence; Sankar, Raman; Thibert, Ronald; DiStefano, Charlotte; Huberty, Scott; Cook, Edwin; Jeste, Shafali (December 2016). "A Quantitative Electrophysiological Biomarker of Duplication 15q11.2-q13.1 Syndrome". PLOS ONE. 11 (12): e0167179. Bibcode:2016PLoSO..1167179F. doi: 10.1371/journal.pone.0167179 . PMC   5157977 . PMID   27977700.
  17. ↑ Hipp, Joerg F.; Khwaja, Omar; Krishnan, Michelle; Jeste, Shafali S.; Rotenberg, Alexander; Hernandez, Maria-Clemencia; Tan, Wen-Hann; Sidorov, Michael S.; Philpot, Benjamin D. (2019-01-18). "Electrophysiological Phenotype in Angelman Syndrome Differs Between Genotypes". Biological Psychiatry. 85 (9): 752–759. doi:10.1016/j.biopsych.2019.01.008. ISSN   0006-3223. PMC   6482952 . PMID   30826071.
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