Power and temporal dynamics of alpha oscillations at rest differentiate cognitive performance involving sustained and phasic cognitive control.

Neuroimage. 2019. Vol. 188. P. 135–144.

Mahjoory K., Cesnaite E., Hohlefeld F., Villringer A., Nikulin V.

Resting state neuronal activity in EEG/MEG recordings is primarily characterized by the presence of alpha oscillations (approx. 8-12 Hz). However, their functional significance and link to cognitive task performance remains elusive. We investigated resting state neuronal activity and its relation to task performance by assessing traditional measures of alpha activity (power and individual alpha peak frequency) and dynamic properties of the signal measured by long-range temporal correlations (LRTC). Multichannel EEG was recorded at rest in 82 healthy male adults and compared to their cognitive performance, measured by tests involving executive functions, working memory, short- and long-term memory demands. Our results showed that attention-span scores positively correlated with alpha power at rest, with corresponding neuronal sources located primarily in the left-hemispheric anterior cingulate cortex, parietal regions, and bilateral suplementary motor areas. Furthermore, better working memory performance was related to increased LRTC of alpha oscillations at rest in the right hemispheric fronto-parietal, temporal, and occipital regions. Our findings suggest that resting state neuronal activity may reflect properties of brain networks that are functionally relevant for cognitive task performance. While alpha power measured at rest might relate to tasks that employ sustained inhibitory control, LRTC are suggested to reflect the capacity of neuronal networks to perform tasks that require phasic attention and quick adaptation to changing task demands.

Keywords: attention, cognitive control, long-range temporal correlations, Alpha, IAF, Resting state EEG

Publication based on the results of: Investigations of neuronal mechanisms of cognition and decision making: neuroimaging, computational models and non-invasive brain stimulation (2019)