Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra

Alpha blocking, a phenomenon where the alpha rhythm is reduced by attention to a visual, auditory, tactile or cognitive stimulus, is one of the most prominent features of human electroencephalography (EEG) signals. Here we identify a simple physiological mechanism by which opening of the eyes causes attenuation of the alpha rhythm. We fit a neural population model to EEG spectra from 82 subjects, each showing different degrees of alpha blocking upon opening of their eyes. Although it is notoriously difficult to estimate parameters from fitting such models, we show that, by regularizing the differences in parameter estimates between eyes-closed and eyes-open states, we can reduce the uncertainties in these differences without significantly compromising fit quality. From this emerges a parsimonious explanation for the spectral changes between states: Just a single parameter, pei, corresponding to the strength of a tonic, excitatory input to the inhibitory population, is sufficient to explain the reduction in alpha rhythm upon opening of the eyes. When comparing parameter estimates across different subjects we find that the inferred differential change in pei for each subject increases monotonically with the degree of alpha blocking observed. In contrast, other parameters show weak or negligible differential changes that do not scale with the degree of alpha attenuation in each subject. Thus most of the variation in alpha blocking across subjects can be attributed to the strength of a tonic afferent signal to the inhibitory cortical population. Author summaryOne of the most striking features of the human electroencephalogram (EEG) is the presence of neural oscillations in the range of 8-13 Hz. It is well known that attenuation of these alpha oscillations, a process known as alpha blocking, arises from opening of the eyes, though the cause has remained obscure. In this study we infer the mechanism underlying alpha blocking by fitting a neural population model to EEG spectra from 82 different individuals. Although such models have long held the promise of being able to relate macroscopic recordings of brain activity to microscopic neural parameters, their utility has been limited by the difficulty of inferring these parameters from fits to data. Our approach is to fit both eyes-open and eyes-closed EEG spectra together, minimizing the number of parameter changes required to transition from one spectrum to the other. Surprisingly, we find that there is just one parameter, the external input to the inhibitory neurons in cortex, that is responsible for attenuating the alpha oscillations. We demonstrate how the strength of this inhibitory input scales monotonically with the degree of alpha blocking observed over all 82 subjects.