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On the Physiological Modulation and Potential Mechanisms Underlying Parieto-Occipital Alpha Oscillations

Overview of attention for article published in Frontiers in Computational Neuroscience, April 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Title
On the Physiological Modulation and Potential Mechanisms Underlying Parieto-Occipital Alpha Oscillations
Published in
Frontiers in Computational Neuroscience, April 2018
DOI 10.3389/fncom.2018.00023
Pubmed ID
Authors

Diego Lozano-Soldevilla

Abstract

The parieto-occipital alpha (8-13 Hz) rhythm is by far the strongest spectral fingerprint in the human brain. Almost 90 years later, its physiological origin is still far from clear. In this Research Topic I review human pharmacological studies using electroencephalography (EEG) and magnetoencephalography (MEG) that investigated the physiological mechanisms behind posterior alpha. Based on results from classical and recent experimental studies, I find a wide spectrum of drugs that modulate parieto-occipital alpha power. Alpha frequency is rarely affected, but this might be due to the range of drug dosages employed. Animal and human pharmacological findings suggest that both GABA enhancers and NMDA blockers systematically decrease posterior alpha power. Surprisingly, most of the theoretical frameworks do not seem to embrace these empirical findings and the debate on the functional role of alpha oscillations has been polarized between the inhibition vs. active poles hypotheses. Here, I speculate that the functional role of alpha might depend on physiological excitation as much as on physiological inhibition. This is supported by animal and human pharmacological work showing that GABAergic, glutamatergic, cholinergic, and serotonergic receptors in the thalamus and the cortex play a key role in the regulation of alpha power and frequency. This myriad of physiological modulations fit with the view that the alpha rhythm is a complex rhythm with multiple sources supported by both thalamo-cortical and cortico-cortical loops. Finally, I briefly discuss how future research combining experimental measurements derived from theoretical predictions based of biophysically realistic computational models will be crucial to the reconciliation of these disparate findings.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 126 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 126 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 23%
Student > Ph. D. Student 27 21%
Student > Master 20 16%
Student > Bachelor 8 6%
Professor 6 5%
Other 10 8%
Unknown 26 21%
Readers by discipline Count As %
Neuroscience 46 37%
Psychology 14 11%
Medicine and Dentistry 11 9%
Engineering 5 4%
Agricultural and Biological Sciences 4 3%
Other 8 6%
Unknown 38 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 May 2020.
All research outputs
#5,637,831
of 23,577,761 outputs
Outputs from Frontiers in Computational Neuroscience
#239
of 1,380 outputs
Outputs of similar age
#95,746
of 330,454 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#6
of 24 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,380 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done well, scoring higher than 82% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 330,454 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.