The roles of cortical oscillations in sustained attention

Clayton, Michael S.; Yeung, Nick; Kadosh, Roi Cohen

doi:10.1016/j.tics.2015.02.004

Cited by 409 publications

(454 citation statements)

References 91 publications

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“…Recent hypotheses have stressed the importance of both alpha and theta connectivity in attention, where theta band connectivity from the medial frontal cortex to various regions plays a key role in inducing control from higher association areas over lower-level, perceptual processes, but also over the DMN (43). Our theta band findings seem to be in line with this theory, as it may explain why we found distributed theta band information flow from frontal areas to various regions, including the DMN.…”

Section: Discussionsupporting

confidence: 89%

“…The opposite pattern was observed in the theta (4-8 Hz) band (PAx = −0.50, P < 0.001). The patterns in the gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48) and delta (0.5-4 Hz) bands were more dispersed [although still significant (P < 0.001); PAx = 0.26 and −0.29, respectively]. We describe the results for the alpha2 and theta band in more detail below, as the patterns for these bands (i) were most pronounced and (ii) resulted in high PAx values.…”

Section: Resultsmentioning

confidence: 99%

“…Our theta band findings seem to be in line with this theory, as it may explain why we found distributed theta band information flow from frontal areas to various regions, including the DMN. At the same time, this model predicts that anterior-to-posterior alpha connectivity provides a gating mechanism for attention, because top-down modulation by alpha could inhibit irrelevant activity (43,44). This may seem to be in disagreement with our findings of a posterior-toanterior dominant pattern of information flow in the alpha band, although it is possible that the enhanced bottom-up signaling in the alpha band is in itself a consequence of enhanced top-down signaling in the theta band (45).…”

Section: Discussionmentioning

confidence: 99%

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Direction of information flow in large-scale resting-state networks is frequency-dependent

Hillebrand

Tewarie

Dellen

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

321

366

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Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-toposterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequencydependent reentry loops that are dominated by flow from parietooccipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.information flow | phase transfer entropy | resting-state networks | magnetoencephalography | atlas-based beamforming T he brain is an extremely complex system (1-3) containing, at the macroscopic scale, interconnected functional units (4) with more-or-less specific information processing capabilities (5). However, cognitive functions require the coordinated activity of these spatially separated units, where the oscillatory nature of neuronal activity may provide a possible mechanism (6-9). A complete description of these interactions, in terms of both strength and directionality, is therefore necessary for the understanding of both normal and abnormal brain functioning.Functional interactions may be inferred from statistical dependencies between the time series of neuronal activity at different sites, so-called functional connectivity (10). Indeed, interactions in large-scale functional networks have been observed using Electroencephalography, Magnetoencephalography (EEG/MEG) and functional Magnetic Resonance Imaging (fMRI) (e.g., refs. 11-14). However, as yet, little is known about the directionality of these interactions in large-scale functional networks during the resting state. Estimating directionality from fMRI is challenging due to its limited temporal resolution and indirect relation to neuronal activity (15, 16). In contrast, EEG studies in healthy controls have revealed a front-to-back pattern of directed connectivity, particularly in the alpha band (17-22), consistent with modeling studies that have shown that such patterns may arise due to differences in the number of anatomical connections (the degree) of anterior and posterior regions (22, 23). However, modeled patterns of information flow depend on the a...

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Section: Discussionsupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Direction of information flow in large-scale resting-state networks is frequency-dependent

Hillebrand

Tewarie

Dellen

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

321

366

View full text Add to dashboard Cite

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“…Others have suggested a generic association between frontomedial theta and those fundamental functions of monitoring and control functions that underpin the process of sustained attention (Clayton et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The effect of task demand and incentive on neurophysiological and cardiovascular markers of effort

Fairclough

Ewing

2017

International Journal of Psychophysiology

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According to motivational intensity theory, effort is proportional to the level of task demand provided that success is possible and successful performance is deemed worthwhile. The current study represents a simultaneous manipulation of demand (working memory load) and success importance (financial incentive) to investigate neurophysiological (EEG) and cardiovascular measures of effort. A 2 x 2 repeated-measures study was conducted where 18 participants performed a n-back task under three conditions of demand: easy (1-back), hard (4-back) and very hard (7-back). In addition, participants performed these tasks in the presence of performance-contingent financial incentive or in a no-incentive (pilot trial) condition.Three bands of EEG activity were quantified: theta (4-7Hz), lower-alpha (7.5-10Hz) and upper-alpha (10.5-13Hz). Fronto-medial activity in the theta band and activity in the upper-alpha band at frontal, central and parietal sites were sensitive to demand and indicated greatest effort when the task was challenging and success was possible. Mean systolic blood pressure and activity in the lower-alpha band at parietal sites were also sensitive to demand but also increased in the incentive condition across all levels of task demand. The results of the study largely support the predictions of motivational intensity using neurophysiological markers of effort. • Systolic blood pressure was sensitive to both demand and incentive page 3

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“…Spectral analyses have also revealed that the power in specific EEG frequency bands reflect the level of sustained attention (for a review see [9]). Previous studies have used the power of such bands to try and assess various mental states such as attentional and mental fatigue states [10].…”

Section: Introductionmentioning

confidence: 99%

Pre-stimulus antero-posterior EEG connectivity predicts performance in a UAV monitoring task

Senoussi

Bovo

et al. 2017

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-Long monitoring tasks without regular actions, are becoming increasingly common from aircraft pilots to train conductors as these systems grow more automated. These task contexts are challenging for the human operator because they require inputs at irregular and highly interspaced moments even though these actions are often critical. It has been shown that such conditions lead to divided and distracted attentional states which in turn reduce the processing of external stimuli (e.g. alarms) and may lead to miss critical events. In this study we explored to which extent it is possible to predict an operator's behavioural performance in a Unmanned Aerial Vehicle (UAV) monitoring task using electroencephalographic (EEG) activity. More specifically we investigated the relevance of large-scale EEG connectivity for performance prediction by correlating relative coherence with reaction times (RT). We show that long-range EEG relative coherence, i.e. between occipital and frontal electrodes, is significantly correlated with RT and that different frequency bands exhibit opposite effects. More specifically we observed that coherence between occipital and frontal electrodes was: negatively correlated with RT at 6Hz (θ band), more coherence leading to better performance, and positively correlated with RT at 8Hz (lower α band), more coherence leading to worse performance. Our results suggest that EEG connectivity measures could be useful in predicting an operator's attentional state and her/his performances in ecological settings. Hence these features could potentially be used in a neuro-adaptive interface to improve operator-system interaction and safety in critical systems.

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The roles of cortical oscillations in sustained attention

Cited by 409 publications

References 91 publications

Direction of information flow in large-scale resting-state networks is frequency-dependent

Direction of information flow in large-scale resting-state networks is frequency-dependent

The effect of task demand and incentive on neurophysiological and cardiovascular markers of effort

Pre-stimulus antero-posterior EEG connectivity predicts performance in a UAV monitoring task

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