Poster No:
1625
Submission Type:
Abstract Submission
Authors:
Peter Robinson1, Rawan El-Zghir1, Natasha Gabay1
Institutions:
1University of Sydney, Sydney, NSW
First Author:
Co-Author(s):
Introduction:
Berger first recorded human EEG on 6 July 1924, noting the ~10 Hz alpha rhythm to be the most prominent brain activity [1]. Alpha is concentrated over visual cortex, sometimes displays two frequency peaks, and is suppressed by visual inputs [2]; the beta rhythm occurs at its harmonic. Later, the ~10 Hz mu rhythm was discovered, concentrated over sensorimotor cortex, suppressed by motor activity, and sometimes associated with ~20 Hz activity [2]. The ~10 tau rhythm is concentrated over auditory cortex and is suppressed by sound. Early theories argued that separate groups of neurons fire at ~10 Hz at the relevant locations, but these were ad hoc and lacked explanatory power [3]. More recently, the alpha rhythm was argued to be a natural mode of activity in the cortex [3] or of the corticothalamic (CT) system [4,5], and has been analyzed using neural field theory (NFT), whose predictions for the basic 10 Hz alpha frequency and its relationship to beta have been confirmed experimentally [8]. So far, however, no unified theory of the alpha, mu, and tau rhythms has been proposed that would account for their frequency structure, scalp topography, and reactivity to stimuli. The present work advances such a unified description, shows that it is consistent with experimental data, and makes predictions for future experimental test and analysis.
Methods:
NFT averages over the activity of large numbers of neurons to predict mean quantities such as firing rates and their effects on EEG. The NFT equations predict the existence of natural modes of activity, each associated with a characteristic spatial pattern on the cortex and resonant frequencies; these are analogous to the notes of a violin string and their associated spatial patterns. The first few NFT modes dominate activity and its spatial and frequency structure, so we employ them to explain the main characteristics of the alpha, mu, and tau rhythms in a unified way. The results are compared with data published over the last 100 years on frequency structure, topography, and reactivity of the rhythms, while additional predictions are made for cases in which observations are not yet available.
Results:
We show that: (i) The first 4 CT activity eigenmodes suffice to explain the key features of alpha, mu, and tau rhythms, including their frequency structure and topography. CT loop delays account for the basic 10 Hz frequency of alpha-band rhythms, for observed alpha-beta correlations, and for observations of harmonic activity associated with the mu rhythm. Additionally, we predict the existence of activity at the harmonic of the tau rhythm. (ii) Frequency splitting arises from the differential effects of cortical folding on various CT modes, consistent with prior observations of split-alpha and split-beta rhythms. We also predict the existence of splitting in the mu and tau rhythms and their harmonics. (iii) Strong suppression, or blocking, of each rhythm and its harmonic occurs when CT loop gains decrease by as little as 10%. This is consistent with positive correlations between alpha and beta peak power. (iv) Spatial peaks of rhythms are due to constructive interference of modes in the relevant sensory region, supported by enhanced CT gains and are suppressed when gains are reduced by attention, consistent with prior work [6].
Conclusions:
The theory provides the first unified description of the alpha, mu, and tau rhythms and accounts for their main spectral, spatial, and reactivity properties. Several predictions remain to be confirmed experimentally and fits of theory to data will enable brain states to be probed in real time, as is the case for the basic alpha rhythm [7]. NFT links these results to work on attention [6] and other phenomena such as evoked responses, opening the way to much richer analyses than have previously been possible.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Perception, Attention and Motor Behavior:
Attention: Auditory/Tactile/Motor
Attention: Visual 2
Keywords:
Computational Neuroscience
Cortex
Electroencephaolography (EEG)
MEG
Modeling
NORMAL HUMAN
Systems
Thalamus
Other - Neural Field Theory
1|2Indicates the priority used for review
Provide references using author date format
[1] Berger, H, (1929) Ueber das elektroenkephalogramm des menschen, Archiv f. Psychiatrie, vol. 9, pp. 527-570.
[2] Niedermeyer, E and Lopes da Silva F. H (1999). Electroencephalography (Philadelphia: Lippincott).
[3] Nunez P. L and Srinivasan R (2006). Electric Fields of the Brain (New York: Oxford Univ. Press).
[4] Robinson, P. A, Rennie, C. J, and Rowe, D. L (2002). Dynamics of large-scale brain activity in normal arousal states and epileptic seizures. Phys. Rev. E. Vol. 65, Art. 041924.
[5] El Zghir, R. K, Gabay, N. C, and Robinson, P. A (2023). NFT of Alpha-Band Rhythms via Eigenmodes of Brain Activity, submitted.
[6] Babaie-Janvier, T and Robinson, P. A (2020). NFT of evoked response potentials with attentional gain dynamics. Front. Human Neurosci. Vol. 14, Art. 293.
[7] Abeysuriya, R. G, Rennie, C. J, and Robinson, P. A (2015). Physiologically based arousal state estimation and dynamics. J. Neurosci. Meth. Vol. 253, pp. 55-69.
[8] van Albada, S. J. and Robinson, P. A. (2013). Relationships between EEG spectral peaks across frequency bands, Front. Hum. Neurosci., 7, Art. 56.