User:Michaelrchen/sandbox

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Electroencephalogram (EEG) displaying burst suppression patterns. Onset of bursts are indicated by solid arrows; offset, by open arrows. In both A and B, the interval between each vertical dotted line is one second.

Burst suppression is an electroencephalography (EEG) pattern that is characterized by periods of high-voltage electrical activity alternating with periods of no activity in the brain. The pattern is found in patients with inactivated brain states, such as from general anesthesia, coma, or hypothermia.[1] The pattern is used in monitoring level of inactivity in brains in order to prevent further damage as well as an indicator of certain diseases, such as Ohtahara syndrome.

History[edit]

The burst suppression pattern was first observed by Derbyshire et al. while studying anesthetic effect on feline cerebral cortices in 1936. The researchers noticed mixed slow and fast electrical activity with decreasing amplitude as anesthesia deepened.[2] In 1948, Swank and Watson coined the term "burst-suppression pattern" to describe the alternation of spikes and flatlines in electrical activity in deep anesthesia.[3] Steriade et al. found that suppression episodes were associated with silence in up to 70% of thalamic neurons.[2]

Characteristics[edit]

The pseudo-rhythmic pattern of burst suppression is dictated by extracellular calcium depletion and the ability of neurons to restore the concentration.[3] Bursts are accompanied by depletion of extracellular cortical calcium ions to levels that inhibit synaptic transmission, which leads to suppression periods.[3] During suppression, neuronal pumps restore the calcium ion to normal levels, thus causing the cortex to be subject to the process again.[3] As the brain becomes more inactive, burst periods become shorter and suppression periods become longer.[4] This is caused by the central nervous system's inability to properly regulate calcium levels due to increased blood-brain permeability.[4]

Burst episodes are associated with excitatory activity in cortical neurons.[5] Suppression is caused by the absence of synaptic activity of cortical neurons; however, some thalamocortical neurons exhibit oscillations in the delta frequency range during these periods.[6] The burst suppression pattern varies with the brain anesthetic concentration when pharmacologically inducing coma.[7] Level of suppression is adjustable by decreasing or increasing anesthetic infusion rate, thus adjusting the level of inactivation.[8]

Electrophysiology[edit]

Bursts are identifiable on EEG readings by their high amplitude (75-250μV), typically short period of 1-10 seconds, and have frequency ranges of 0-4Hz (δ) and 4-7Hz (θ).[9] Suppression episodes are identifiable by their low amplitude (< 5μV) and typically long period (> 10s).[9]

EEG recordings of burst-suppression pattern differ between adults and neonates because of diverse pattern fluctuations found in the EEG of neonates.[9] These fluctuations, along with sudden changes in synchronous neuron firing, are caused by development of the newborn's brain.[9] Burst suppression patterns also occur spontaneously during neonatal development, rather than as a characteristic of inactivated brains as in adults.[7]

While burst suppression has typically been viewed as a homogenous brain state, recent studies have shown that bursts and suppressions can occur in specific regions while other regions are unaffected.[10] Even when a burst appears to be homogenous across the brain, the timing of onset of bursts in different regions may differ.[10]

Quantification[edit]

In order to quantify the burst suppression pattern, the EEG signal must be subject to thresholding and segmentation.[11] This process separates burst and suppression episodes based on a set voltage level. When the voltage of a particular EEG segment is below the threshold level, it is classified as suppression, and when it exceeds the threshold, it is considered a burst.

Quantifying the burst suppression pattern allows for calculation of the burst suppression ratio (BSR) by assigning binary values of 0 to bursts and 1 to suppression episodes.[11] Thus, a burst suppression ratio of 1 is associated with a state of the brain that shows no electrical activity, while a ratio of 0 indicates that the brain is active. The burst suppression ratio measures the amount of time within an interval spent in the suppressed state.[7] This ratio increases as the brain becomes increasingly inactive.[12] Because of the direct relationship between burst suppression ratio and brain inactivity, the ratio is an indicator of suppression intensity.[7] Using the same binary assignments to the burst suppression pattern, another measure of the depth of burst suppression, the burst suppression probability (BSP), can be determined.[7]

Clinical Benefits[edit]

Because the burst suppression pattern is characteristic of inactivated brains, the pattern can be used as a marker for the level of coma a patient is in, with persistence of the pattern commonly associated with poor prognosis.[11] When inducing coma to protect the brain post trauma, the pattern assists in maintaining the necessary level of coma so that no further damage occurs to the brain.[8] The pattern is also used to test the ability of anesthetic arousal agents to induce emergence from comas.[11] The burst suppression pattern can also be used to track ascent into and descent out of hypothermia through observing changes in the pattern.[11]

Monitoring the burst suppression ratio aids medical personnel in adjusting suppression intensity for therapeutic purposes; however, medical personnel currently rely on visually monitoring the EEG and arbitrarily assessing the depth of burst suppression.[7] Not only is the evaluation of the EEG signal for burst suppression done manually, but also the infusion rate of anesthetic to adjust suppression intensity.[8] The introduction of machines makes maintaining proper levels of inactivity more precise through the use of algorithms. This is done through the use of measures such as burst suppression probability[7] for real-time tracking of burst suppression or brain-machine interfaces to automate maintaining proper levels of inactivity[8].

References[edit]

  1. ^ Ching, Shinung; Purdon, Patrick L.; Vijayan, Sujith; Kopell, Nancy J.; Brown, Emery N. (7 February 2012). "A neurophysiological-metabolic model for burst suppression". Proceedings of the National Academy of Sciences. 109 (8): 3095–3100. doi:10.1073/pnas.1121461109. PMID 22323592.
  2. ^ a b Niedermeyer, E (2009 Dec). "The burst-suppression electroencephalogram". American Journal of Electroneurodiagnostic Technology. 49 (4): 333–41. doi:10.1080/1086508X.2009.11079736. PMID 20073416. S2CID 8752000. {{cite journal}}: Check date values in: |date= (help)
  3. ^ a b c d Amzica, Florin (1 December 2009). "Basic physiology of burst-suppression". Epilepsia. 50: 38–39. doi:10.1111/j.1528-1167.2009.02345.x. PMID 19941521. S2CID 662127.
  4. ^ a b Tétrault, Samuel; Chever, Oana; Sik, Attila; Amzica, Florin (1 October 2008). "Opening of the blood-brain barrier during isoflurane anaesthesia". European Journal of Neuroscience. 28 (7): 1330–1341. doi:10.1111/j.1460-9568.2008.06443.x. PMID 18973560. S2CID 28555021.
  5. ^ Kroeger, Daniel; Florea, Bogdan; Amzica, Florin (18 September 2013). "Human Brain Activity Patterns beyond the Isoelectric Line of Extreme Deep Coma". PLOS ONE. 8 (9): e75257. doi:10.1371/journal.pone.0075257. PMID 24058669.
  6. ^ Steriade, M.; Amzica, F.; Contreras, D. (1994 Jan). "Cortical and thalamic cellular correlates of electroencephalographic burst-suppression". Electroencephalography and Clinical Neurophysiology. 90 (1): 1–16. doi:10.1016/0013-4694(94)90108-2. PMID 7509269. {{cite journal}}: Check date values in: |date= (help)
  7. ^ a b c d e f g Brandon Westover, M.; Shafi, Mouhsin M.; Ching, Shinung; Chemali, Jessica J.; Purdon, Patrick L.; Cash, Sydney S.; Brown, Emery N. (1 September 2013). "Real-time segmentation of burst suppression patterns in critical care EEG monitoring". Journal of Neuroscience Methods. 219 (1): 131–141. doi:10.1016/j.jneumeth.2013.07.003. PMC 3939433. PMID 23891828.
  8. ^ a b c d Shanechi, Maryam M.; Chemali, Jessica J.; Liberman, Max; Solt, Ken; Brown, Emery N. (31 October 2013). "A Brain-Machine Interface for Control of Medically-Induced Coma". PLOS Computational Biology. 9 (10): e1003284. doi:10.1371/journal.pcbi.1003284. PMC 3814408. PMID 24204231.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  9. ^ a b c d Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar (1 November 2013). "Detection of artifacts from high energy bursts in neonatal EEG". Computers in Biology and Medicine. 43 (11): 1804–1814. doi:10.1016/j.compbiomed.2013.07.031. PMID 24209926.
  10. ^ a b Lewis, Laura D.; Ching, Shinung; Weiner, Veronica S.; Peterfreund, Robert A.; Eskandar, Emad N.; Cash, Sydney S.; Brown, Emery N.; Purdon, Patrick L. (25 July 2013). "Local cortical dynamics of burst suppression in the anaesthetized brain". Brain. 136 (9): 2727–2737. doi:10.1093/brain/awt174. PMC 3754454. PMID 23887187.
  11. ^ a b c d e Chemali, Jessica; Ching, Shinung; Purdon, Patrick L.; Solt, Ken; Brown, Emery N. (1 October 2013). "Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression". Journal of Neural Engineering. 10 (5): 056017. doi:10.1088/1741-2560/10/5/056017. PMC 3793904. PMID 24018288.
  12. ^ Vijn, P. C.; Sneyd, J. R. (1 September 1998). "I.v. anaesthesia and EEG burst suppression in rats: bolus injections and closed-loop infusions". British Journal of Anaesthesia. 81 (3): 415–421. doi:10.1093/bja/81.3.415. PMID 9861133.
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