Neural Mechanisms of Sustained Attention Are Rhythmic

Helfrich, Randolph F.; Fiebelkorn, Ian C.; Szczepanski, Sara M.; Lin, Jack J.; Parvizi, Josef; Knight, Robert T.; Kästner, Sabine

doi:10.1016/j.neuron.2018.07.032

Cited by 372 publications

(467 citation statements)

References 84 publications

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“…The neural processes at the origin of this 22 rhythmic sampling of space by attention are still poorly understood. Recent works propose that neural 23 oscillations in the fronto-parietal network organize alternating attentional states that in turn modulate 24 perceptual sensitivity 22,32 . 25 In the present study, we provide evidence reconciling these two seemingly contradictory views of 26 spatial attention.…”

Section: Introductionmentioning

confidence: 99%

Prefrontal attentional saccades explore space rhythmically

Gaillard

Hassen

Bello

et al. 2019

Preprint

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Recent studies suggest that attention samples space rhythmically through oscillatory interactions in the frontoparietal network. However, the precise mechanism through which the prefrontal cortex, at the source of attention control signals, organizes this rhythmic exploration of space remains unknown. We show that, when decoded at a high spatial (0.1°) and temporal resolution (50ms), the prefrontal covert attentional spotlight, aka the mind's eye, continuously explores space at an alpha 7-12 Hz rhythm. When sensory events are presented at a specific optimal phase (resp. anti-phase) with respect to this rhythm, sensory encoding and behavioral report are accurate (resp. poor). We propose that this rhythmic prefrontal attentional spotlight dynamics corresponds to a continuous overt exploration of space via alpha-clocked attentional saccades. These attentional saccades are highly flexible, their pattern of space exploration depending both on within-trial and across-task contingencies. These results are discussed in the context of exploration and exploitation strategies and prefrontal top-down attentional control. Highlights: The decoded prefrontal attentional spotlight samples visual space in rhythmic cycles  This rhythmic attentional exploration predicts neuronal sensory processing accuracy  This rhythmic attentional exploration predicts overt behavioral accuracy  These rhythmic cycles define alpha-clocked attentional saccades  Space exploration by attentional saccades is highly flexible and under top-down control 130 +/-4 (resp. 58%+/-2). At trough, these values dropped to 44% +/-3 (resp. 47%+/-1.5). In contrast with the low 131 degree of inter-session variability that we report for PFC attention information locking to cue onset ( fig. 2D), phase 132 lag between signal and optimal target processing was quite variable (fig. 4B, inset). This variability correlated with 133 intersession behavioral variability in reaction times ( fig. 5, discussed below). Overall, these results demonstrate a 134 direct modulation of FEF target encoding by the ongoing alpha oscillations that we characterize on the PFC attention 135 information. 136 137 157 Again, this difference is highly systematic as illustrated in fig. 4E for each session and each monkey independently. 158The average target detection at peak for monkey M1 (resp. monkey M2) was of 75 +/-1.5 (resp. 52%+/-2). At trough, 159 these values dropped to 64.5% +/-1.5 (resp. 41.5%+/-2). As a result, when hit rates are calculated, across all session

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Section: Introductionmentioning

confidence: 99%

Prefrontal attentional saccades explore space rhythmically

Gaillard

Hassen

Bello

et al. 2019

Preprint

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“…On the one hand, these results retrospectively explain why studies investigating periodic temporal attention are consistently designed with rhythms in the 1-2 Hz frequency range 2,6-27 . On the other hand, they fuel recent frameworks postulating that the functional architecture of cognition is inherently rhythmic and underpinned by neural oscillatory activity generated at the population level [60][61][62][63] .…”

Section: Discussionmentioning

confidence: 91%

Attention to rhythms: sensory-specific constrained sampling of temporal regularities

Zalta

Petkoski

Morillon

2019

Preprint

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words)That attention is a fundamentally rhythmic process has recently received abundant empirical evidence. The essence of temporal attention, however, is to flexibly focus in time. Whether this function is hampered by an underlying rhythmic mechanism is unknown. In six interrelated experiments, we behaviourally quantify the sampling capacities of periodic temporal attention during auditory or visual perception. We reveal the presence of limited attentional capacities, with an optimal sampling rate of ~1.4 Hz in audition and ~0.7 Hz in vision. Investigating the motor contribution to temporal attention, we show that it scales with motor rhythmic precision, maximal at ~1.7 Hz. Critically, the motor modulation is beneficial to auditory but detrimental to visual temporal attention. These results are captured by a computational model of coupled oscillators, that reveals the underlying structural constraints governing the temporal alignment between motor and attention fluctuations.

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“…Oscillatory electrocortical activity-or more precisely, the periodic fluctuations of extracellular current in coherent groups of neurons (Buzsáki & Draguhn, 2004)-has been noted since Hans Berger's first description of EEG in humans nearly a century ago (Berger, 1929). Since then, the observed electrical oscillations have become a central focus of EEG research (Buzsaki, 2006;Buzsáki & Draguhn, 2004;Buzsáki & Watson, 2012;Helfrich et al, 2018;Helfrich & Knight, 2016;Kelso, 1995) with the functional correlates (i.e., behavior) of such oscillations becoming an increasingly important factor to consider (Başar & Güntekin, 2008;Buzsáki & Watson, 2012). Quantifying oscillations is often difficult due to electrocortical variations that are not obviously attributable to the measured phenomenon (Buzsáki & Draguhn, 2004), which is commonly considered noise and viewed as an adulteration of the true underlying signal (Stam, 2005).…”

Section: Dynamics Of Electrocortical Oscillationsmentioning

confidence: 99%

Electrocortical dynamics differentiate athletes exhibiting low‐ and high‐ ACL injury risk biomechanics

et al. 2020

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Anterior cruciate ligament (ACL) injuries are physically and emotionally debilitating for athletes,while motor and biomechanical deficits that contribute to ACL injury have been identified, limited knowledge about the relationship between the central nervous system (CNS) and biomechanical patterns of motion has impeded approaches to optimize ACL injury risk reduction strategies. In the current study it was hypothesized that high‐risk athletes would exhibit altered temporal dynamics in their resting state electrocortical activity when compared to low‐risk athletes. Thirty‐eight female athletes performed a drop vertical jump (DVJ) to assess their biomechanical risk factors related to an ACL injury. The athletes' electrocortical activity was also recorded during resting state in the same visit as the DVJ assessment. Athletes were divided into low‐ and high‐risk groups based on their performance of the DVJ. Recurrence quantification analysis was used to quantify the temporal dynamics of two frequency bands previously shown to relate to sensorimotor and attentional control. Results revealed that high‐risk participants showed more deterministic electrocortical behavior than the low‐risk group in the frontal theta and central/parietal alpha‐2 frequency bands. The more deterministic resting state electrocortical dynamics for the high‐risk group may reflect maladaptive neural behavior—excessively stable deterministic patterning that makes transitioning among functional task‐specific networks more difficult—related to attentional control and sensorimotor processing neural regions.

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Neural Mechanisms of Sustained Attention Are Rhythmic

Cited by 372 publications

References 84 publications

Prefrontal attentional saccades explore space rhythmically

Prefrontal attentional saccades explore space rhythmically

Attention to rhythms: sensory-specific constrained sampling of temporal regularities

Electrocortical dynamics differentiate athletes exhibiting low‐ and high‐ ACL injury risk biomechanics

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