Difference between revisions of "EEG"

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Electroencephalography (EEG), which primarily measures neurophysiological changes related to postsynaptic activity in the neocortex [26], has proven to be a powerful tool for studying complex neuropsychiatric disorders [27-30]. EEG has been the primary measure used to capture and characterize epileptiform and abnormal paroxysmal activity through the detection of focal spikes, which occur with increased frequency in ASD [31,32]. Resting EEG studies have shown that 20% of individuals with ASD show epileptiform discharges at rest, typically without the presence of clinical seizures [33,34]. Higher rates of epileptiform activity have also been reported in sleep studies; for example, Chez, et al. [35] reported that 61% of individuals with ASD and no clinical history of seizures displayed epileptiform abnormalities.
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**Intro**
  
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The most common way to characterize resting EEG is by breaking down the oscillatory patterns into bands of frequencies that share physiological properties. The typical clinically relevant frequency bands of EEG range from 0.3 to 100 Hz. Within the scope of the current paper, we focus on five frequency bands ranging from 1 to 100 Hz: delta (1 to 3 Hz), theta (4 to 7 Hz), alpha (8 to 12 Hz), beta (13 to 35 Hz), and gamma (>35 Hz). These historically documented frequency bands have attracted rapidly growing interest in clinical and cognitive neuroscience fields, and are believed to govern different cognitive processes [36]. Delta dominates deep sleep, and is thought to underlie the event-related slow waves seen in tasks for detection of attention and salience [37]. Theta is most commonly studied in relation to memory processes [38]. Alpha waves are present in relaxed awake individuals, and are associated with precise timing of sensory and cognitive inhibition [39]. Beta waves are associated with alertness, active task engagement, and motor behavior [40]. Finally, gamma waves, present during working-memory matching [41] and a variety of early sensory responses, are believed to facilitate feature binding in sensory processing [42,43].
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Electroencephalography (EEG), which primarily measures neurophysiological changes related to postsynaptic activity in the neocortex [26], has proven to be a powerful tool for studying complex neuropsychiatric disorders [27-30].
 +
EEG has been the primary measure used to capture and characterize epileptiform and abnormal paroxysmal activity through the detection of focal spikes, which occur with increased frequency in ASD [31,32].
 +
 
 +
**Medical Utility**
 +
Resting EEG studies have shown that 20% of individuals with ASD show epileptiform discharges at rest, typically without the presence of clinical seizures [33,34]. Higher rates of epileptiform activity have also been reported in sleep studies; for example, Chez, et al. [35] reported that 61% of individuals with ASD and no clinical history of seizures displayed epileptiform abnormalities.
 +
 
 +
**Frequency Band Classification**
 +
The most common way to characterize resting EEG is by breaking down the oscillatory patterns into bands of frequencies that share physiological properties. The typical clinically relevant frequency bands of EEG range from 0.3 to 100 Hz.  
 +
Within the scope of the current paper, we focus on five frequency bands ranging from 1 to 100 Hz:  
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* delta (1 to 3 Hz),  
 +
* theta (4 to 7 Hz),  
 +
* alpha (8 to 12 Hz),  
 +
* beta (13 to 35 Hz), and  
 +
* gamma (>35 Hz).  
 +
 
 +
These historically documented frequency bands have attracted rapidly growing interest in clinical and cognitive neuroscience fields, and are believed to govern different cognitive processes [36].  
 +
 
 +
**Frequency Band Use Cases**
 +
 
 +
Delta dominates deep sleep, and is thought to underlie the event-related slow waves seen in tasks for detection of attention and salience [37].  
 +
Theta is most commonly studied in relation to memory processes [38].  
 +
 
 +
Alpha waves are present in relaxed awake individuals, and are associated with precise timing of sensory and cognitive inhibition [39]. Beta waves are associated with alertness, active task engagement, and motor behavior [40].  
 +
 
 +
Finally, gamma waves, present during working-memory matching [41] and a variety of early sensory responses, are believed to facilitate feature binding in sensory processing [42,43].
 +
source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847481/ (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847481/)
  
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Additionally, EEG recordings can be used to assess functional connectivity between different brain regions over time via EEG coherence, and quantitative measurement of the relationship of frequency spectra between two EEG signals [44]. This advantageous feature can further our understanding of the impaired interactions between brain regions of individuals with ASD that have been suggested by functional MRI studies [45-51].
 
  
 
source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847481/   
 
source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847481/   
  
 
need: add links to citations
 
need: add links to citations

Revision as of 18:15, 26 March 2018

    • Intro**

Electroencephalography (EEG), which primarily measures neurophysiological changes related to postsynaptic activity in the neocortex [26], has proven to be a powerful tool for studying complex neuropsychiatric disorders [27-30]. EEG has been the primary measure used to capture and characterize epileptiform and abnormal paroxysmal activity through the detection of focal spikes, which occur with increased frequency in ASD [31,32].

    • Medical Utility**

Resting EEG studies have shown that 20% of individuals with ASD show epileptiform discharges at rest, typically without the presence of clinical seizures [33,34]. Higher rates of epileptiform activity have also been reported in sleep studies; for example, Chez, et al. [35] reported that 61% of individuals with ASD and no clinical history of seizures displayed epileptiform abnormalities.

    • Frequency Band Classification**

The most common way to characterize resting EEG is by breaking down the oscillatory patterns into bands of frequencies that share physiological properties. The typical clinically relevant frequency bands of EEG range from 0.3 to 100 Hz. Within the scope of the current paper, we focus on five frequency bands ranging from 1 to 100 Hz:

  • delta (1 to 3 Hz),
  • theta (4 to 7 Hz),
  • alpha (8 to 12 Hz),
  • beta (13 to 35 Hz), and
  • gamma (>35 Hz).

These historically documented frequency bands have attracted rapidly growing interest in clinical and cognitive neuroscience fields, and are believed to govern different cognitive processes [36].

    • Frequency Band Use Cases**

Delta dominates deep sleep, and is thought to underlie the event-related slow waves seen in tasks for detection of attention and salience [37]. Theta is most commonly studied in relation to memory processes [38].

Alpha waves are present in relaxed awake individuals, and are associated with precise timing of sensory and cognitive inhibition [39]. Beta waves are associated with alertness, active task engagement, and motor behavior [40].

Finally, gamma waves, present during working-memory matching [41] and a variety of early sensory responses, are believed to facilitate feature binding in sensory processing [42,43]. source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847481/ (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847481/)


source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847481/

need: add links to citations

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