Thalamic Involvement in the Generation of the Alpha Rhythms

Alpha… it’s not a simple idling rhythm… let’s look at alpha generators:

The thalamic involvement in the generation of the alpha rhythm is being under-valued when looking at the LORETA images of alpha current source generators. The alpha power may come from the sources that LORETA identifies, but the thalamus is intimately involved in alpha rhythm generation, and this is not part of the LORETA image of the sources.

The polarization within the thalamus sets the base frequency of the alpha, but the cortical rhythm requires a complex multi-layer feedback loop from the thalamus to the cortex, and back to the thalamus. Without the cortex, there is a total disruption of the normal spatio-temporal distribution of the alpha wave’s spike trains within the thalamus, and cortical damage often disturbs coherence due to this mechanism.

The thalamus distributes the alpha posteriorly via specific sensory relays, which have a simple return circuit. Like the white matter relay from the lateral geniculate of the thalamus to the occipital lobe’s primary visual areas, and directly back. This thalamo-cortical-thalamic loop is relatively faster than the loop seen frontally. The frontal return circuitry is not simple, but the descending routes are complex and somewhat circuitous, taking more time, and thus it is common for the frontal lobe’s alpha to be at the slower end of the individual’s alpha frequency range. The frontal lobe has a return path through the striatum.

The five divisions of the frontal-striatal pathways are the motor circuit, the oculomotor circuit (from the frontal eye fields), the dorsolateral prefrontal circuit (cortical gating), lateral orbito-frontal circuit (emotive), and the anterior cingulate circuit (emotional and cognitive flexibility). The striatal-thalamic pathways are divided into two descending pathways which both start from the cortex to the head of the caudate and then the putamen, and then this pathway divides between the globus pallidus and substantia nigra, and then these both go to the thalamus. The thalamo-cortical completion of the circuit projects to both the premotor and motor cortex directly.

Not all circuits are simple thalamus-to-cortex-to-thalamus “echoic” returns to the original source…

A cortico-thalamo-cortical projection system exists which originates from the primary visual cortex, relayed by the lateral posterior nucleus of the thalamus, projecting to the suprasylvian visual area (which is involved in highest levels of visual integration and comprehension). This finding suggests that the thalamus modulates transmission of cortical signals from one cortical area to another… the coherence or “connectivity” of the cortex is not cortical-cortical, but cortical-thalamo-cortical.

With maturation, the cortex provides a stimulatory effect on the alpha frequency, raising it to a slightly faster frequency tuning through feedback to the thalamus, but the basic frequencies of alpha are generated by the reticular nucleus of the thalamus providing acetylcholine to the thalamic nuclei, and by the underlying polarization within the thalamus, which is effected by the NE levels from the brainstem, and by fluctuating DC field strength levels in the brain. The other effects are the thalamo cortical transmission times, and an effect of the cortical-thalamic processing time for any given pathway…. Longer time needed for frontal than posterior circuits.

Crudely stated: The frequencies of alpha are set in the thalamus, and the spatial and temporal distribution of alpha are controlled by the cortex, with rhythmic “initiation” (phase reset) done by the DC system’s “modulatory” influence on the AC rhythms of the EEG.

The thalamus can provide rhythms in the range from 3 to 16, with the common range of 8-12 representing an adult group’s “average”. Hyperpolarization of the thalamus slows the alpha, and hypopolarization speeds it up until it desynchronizes at about 16 Hz, and becomes a low voltage fast EEG.

The addition of some GABA (an inhibitory neurotransmitter) easily acquired with the addition of some alcohol will slow the alpha back into a rhythmic pattern. This basic mechanism is the reason alpha-theta training works so well on the low voltage fast EEGs seen so commonly in alcohol addiction.

LORETA may show a generator in the precuneus/cuneus area for the occipital alpha component, and the posterior cingulate for the parietal component (when alpha modulators are identified with ICA analysis and then source localized)… but these localizations miss the full beauty of the real mechanism’s complexity and especially the primary importance of the thalamus.

The thalamus gates our perceptions into “perceptual packets”, with the “thalamic gate” being open during the negative half-wave (up-side of the waveform), and less open during the positive half wave (the downward going half). Two stimuli presented within 75 to 100 milliseconds of each other will be “perceptually synchronized”, or though of as being instantaneously simultaneous.

The alpha frequency is the perceptual sampling rate… how many perceptual packets are evaluated per unit time, with a better semantic or declarative memory function seen with faster alpha frequencies. This is from the work on IAF (individual alpha frequency) from Professor Dr. Wolfgang Klimesch’s lab in Salzburg Austria, with significant contributions from Drs Michael Doppelmayr and Simon Hanselmayr.

The databases have difficulty characterizing alpha frequency tuning issues, with many identifying too much power at a slower frequency (like 7 Hz)… although the power values would be healthy and normal if alpha were only faster (like 9 Hz)… the databases seldom tell you it is merely 2 Hz slow. The normal alpha coherence values, if the alpha is slowed, are seen as hypercoherence, although they are perfectly normal for alpha. Databases that rely on predetermined band’s peak frequency may miss a shift if it exceeds their defined band, and this will miss the mean frequency if the peak is good but the band width has less faster content than slower content.

Faster alpha may cause similar issues (too much 12-15 Hz power or 12-15 Hz hypercoherence) when it is not “too much” of either that is really wrong, just alpha being too fast.

Thus when there are tuning issues, databases often have difficulty characterizing the core issue of tuning. When a tuning issue is noted, the coherence and power values may be “off” according to the database, when the real values are not really abnormal, just that they are too slow or too fast.

Theoretically, these issues may prove to be an area where Z-score training may have difficulty, flagging red herrings of power and coherence… though this is an empirical question that will be answered with time and experience

1 thought on “Thalamic Involvement in the Generation of the Alpha Rhythms”

  1. Jay,

    Thanks for the excellent outline!

    I think this is an important contribution, indeed the real source of Alpha is not part of LORETA’s solution space which is important to be aware of.

    Furthermore, I agree that most research and databases have maybe not looked at alpha the way they should. Individualizing alpha is in my opinion very important. However, this is very hard to automate. Auto-scoring of the iAPF is really hard to do given that there are multiple alpha like sources e.g. central Mu rhythm, often 1-2 Hz faster; parietal alpha, sometimes occipital alpha which is faster tuned than the parietal alpha; frontal alpha which as you stated as well is often slower tuned and other variants such as temporal alpha and tau rhythm when using MEG. I think the only reliably way to score the iAPF is manually. This intrinsically makes it hard for researchers and databases to really incorporate personalized frequency bands based on APF.

    Regarding coherence… well… what is coherence….? I think there is not enough standardization in the research and QEEG field to clinically use coherence. The first step is to have guidelines about which methods of coherence to use and understand the implications of for example establishing coherence deviations using QEEG hardware and database X and feeding back coherence with equipment Y… these might both use different expressions for calculating coherence hence implicating that you might feedback something completely different then measured in the QEEG.

    Rob Coben and I are trying to get more insight into this by running the same EEG data from a group of Dyslexics through all databases and checking what the differences are. I hope this sheds more light onto this topic…

    All the best!

    Kind regards,

    Martijn Arns
    Director / QEEG-D

    Brainclinics Diagnostics B.V.
    Brainclinics Treatment B.V.
    Bijleveldsingel 34
    6524 AD Nijmegen
    The Netherlands

    Tel: +31(0)24-7503505
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