Strong long‐range temporal correlations of beta/gamma oscillations are associated with poor sustained visual attention performance

Abstract

Neuronal oscillations exhibit complex amplitude fluctuations with autocorrelations that persist over thousands of oscillatory cycles. Such long-range temporal correlations (LRTC) are thought to reflect neuronal systems poised near a critical state, which would render them capable of quick reorganization and responsive to changing processing demands. When we concentrate, however, the influence of internal and external sources of distraction is better reduced, suggesting that neuronal systems involved with sustained attention could benefit from a shift toward the less volatile sub-critical state. To test these ideas, we recorded electroencephalography (EEG) from healthy volunteers during eyes-closed rest and during a sustained attention task requiring a speeded response to images deviating in their presentation duration. We show that for oscillations recorded during rest, high levels of alpha-band LRTC in the sensorimotor region predicted good reaction-time performance in the attention task. During task execution, however, fast reaction times were associated with high-amplitude beta and gamma oscillations with low LRTC. Finally, we show that reduced LRTC during the attention task compared to the rest condition correlates with better performance, while increased LRTC of oscillations from rest to attention is associated with reduced performance. To our knowledge, this is the first empirical evidence that 'resting-state criticality' of neuronal networks predicts swift behavioral responses in a sensorimotor task, and that steady attentive processing of visual stimuli requires brain dynamics with suppressed temporal complexity.

Controlling the Temporal Structure of Brain Oscillations by Focused Attention Meditation

“…As a consequence, frequent switching between focusing on the sensations of breathing and mind-wandering may have occurred. This, in turn, may have resulted in more complex variability in oscillatory brain activity and a higher DFA exponent in the control cohort, which is analogous to recent findings of reduced LRTC when subjects successfully attend to a sustained visual attention task (Irrmischer et al, 2017).…”

“…Critical‐state dynamics in the brain have been related to optimal information processing (Shew & Plenz, ) and the degree to which the brain remains capable of quick reorganization (Deco, Jirsa, & Mcintosh, ; Singer, ; Tognoli & Kelso, ) and, thus, is responsive to natural environments (Beggs & Plenz, ). It has been found that the temporal structure of these oscillations are influenced by performing a task (Irrmischer, Sangiuliano Intra, Mansvelder, Poil, & Linkenkaer‐Hansen, ; Palva & Palva, , Palva, et al, ) and that these oscillations are associated with trial‐by‐trial variability in performance (He & Zempel, ; Linkenkaer‐Hansen, Nikulin, Palva, Kaila, & Ilmoniemi, ), and dynamics of finger‐tapping errors (Smit, Linkenkaer‐Hansen, & de Geus, ). So far it has not been shown if these dynamics in neuronal oscillations are also influenced in the absence of an overt task, by the mere intention to switch our focus from a thinking resting state to a meditative focused attention state.…”

Intrinsic activity temporal structure reactivity to behavioural state change is correlated with depressive symptoms

“…Time windows corresponded to equally spaced points on a logarithmic scale of data lengths corresponding to 5-14 s for the theta band, 2-14 s for the alpha band, and 1-14 s for beta (50% window overlap). These window sizes were selected to exclude temporal autocorrelations introduced by the band-pass filters (Irrmischer et al, 2018b). The DFA exponent is the slope of the line fitted through the fluctuation amplitudes against window lengths plotted on a logarithmic scale .…”

“…Ongoing brain activity appears to function close to an unstable equilibrium point (Deco and Jirsa, 2012). This critical state, where the system is on the boundary between order and disorder (Beggs and Timme, 2012), has been linked to efficient information processing and optimal behavioural performance (Irrmischer et al, 2018b;Palva et al, 2013;Palva and Palva, 2018). A system in a critical state is characterised by scale-free activity distributions and long-range temporal correlations (LRTC; Plenz and Thiagarajan, 2007), which can be measured with techniques such as detrended fluctuation analysis (Peng et al, 1995).…”

“…The ability to effectively shift between dynamic cortical states depending on behavioural state has been linked to cognitive performance (Irrmischer et al, 2018b). This may reflect an ability to move easily from a critical state, where there is the maximum potential to respond flexibly to changing demands, to a sub-critical one where processing can be focussed on a single task (Beggs and Plenz, 2003;Irrmischer et al, 2018b). Extending this principle to rumination, we could hypothesis that such continuous thoughts would constitute a task state that individuals remain in.…”