The role of cortical beta oscillations in time estimation

Abstract

Estimation of time is central to perception, action, and cognition. Human functional magnetic resonance imaging (fMRI) and positron emission topography (PET) have revealed a positive correlation between the estimation of multi-second temporal durations and neuronal activity in a circuit of sensory and motor areas, prefrontal and temporal cortices, basal ganglia, and cerebellum. The systems-level mechanisms coordinating the collective neuronal activity in these areas have remained poorly understood. Synchronized oscillations regulate communication in neuronal networks and could hence serve such coordination, but their role in the estimation and maintenance of multi-second time intervals has remained largely unknown. We used source-reconstructed magnetoencephalography (MEG) to address the functional significance of local neuronal synchronization, as indexed by the amplitudes of cortical oscillations, in time-estimation. MEG was acquired during a working memory (WM) task where the subjects first estimated and then memorized the durations, or in the contrast condition, the colors of dynamic visual stimuli. Time estimation was associated with stronger beta (β, 14 - 30 Hz) band oscillations than color estimation in sensory regions and attentional cortical structures that earlier have been associated with time processing. In addition, the encoding of duration information was associated with strengthened gamma- (γ, 30 - 120 Hz), and the retrieval and maintenance with alpha- (α, 8 - 14 Hz) band oscillations. These data suggest that β oscillations may provide a mechanism for estimating short temporal durations, while γ and α oscillations support their encoding, retrieval, and maintenance in memory. Hum Brain Mapp 37:3262-3281, 2016. © 2016 Wiley Periodicals, Inc.

Exploring the relationship of phase and peak-frequency EEG alpha-band and beta-band activity to temporal judgments of stimulus duration

“…Interestingly, the particular involvement of beta oscillations is consistent with the association of beta amplitude to temporal judgments in other timing paradigms (e.g. Kononowicz & van Rijn, 2015;Kulashekhar et al, 2016). The exact role of beta oscillations in timing tasks is not yet clear although it has been suggested they might underlie the accumulation of duration evidence (Kulashekhar et al, 2016) or that they relate to starting parameters for decision processes (Kononowicz & van Rijn, 2015).…”

“…Kononowicz & van Rijn, 2015;Kulashekhar et al, 2016). The exact role of beta oscillations in timing tasks is not yet clear although it has been suggested they might underlie the accumulation of duration evidence (Kulashekhar et al, 2016) or that they relate to starting parameters for decision processes (Kononowicz & van Rijn, 2015). Although our results did not show an effect of beta-or alpha-band amplitude, the results still implicate pre-stimulus oscillatory activity in timing tasks and give support to the proposed notion that early brain states play a role in the trajectory of temporal decisions.…”

“…As previous research on the perception of time has implicated beta power in the estimation (Kulashekhar, Pekkola, Palva, & Palva, 2016) and production (Kononowicz & van Rijn, 2015) of time, we analysed whether there were effects of alphaand beta-band amplitude alongside those observed for phase.…”

Neural correlates of time distortion in a preaction period

“…The present data, however, do not exclude a possibility that oscillatory signals other than alpha band are involved in time perception. It has been shown that neural oscillations of various frequencies, such as delta (Arnal, Doelling, & Poeppel, ; Cravo, Rohenkohl, Wyart, & Nobre, ), beta (Bartolo & Merchant, ; Fujioka et al, ; Kononowicz & van Rijn, ; Kulashekhar et al, ), and gamma bands (Bartolo, Prado, & Merchant, ; Sperduti, Tallon‐Baudry, Hugueville, & Pouthas, ), contribute to accurate estimation and prediction of time. A recent view, therefore, is that a dominant frequency for timing behaviors is context‐dependent (Wiener & Kanai, ); the brain recruits different frequencies of oscillation signals based on the temporal features of the task being performed.…”

“…Based on those behavioral data, here we investigate neural mechanisms of this simplest type of time distortion induced by action preparation (Hagura et al, 2012;Iwasaki et al, 2017). A whole-brain imaging with magnetoencephalography (MEG) would allow us to record neural responses related to time perception, such as a slow build-up (climbing) activity in the medial frontal region (Elbert, Ulrich, Rockstroh, & Lutzenberger, 1991;Macar, Vidal, & Casini, 1999;Merchant, Zarco, Perez, Prado, & Bartolo, 2011;Parker, Chen, Kingyon, Cavanagh, & Narayanan, 2014;Pfeuty, Ragot, & Pouthas, 2003;Wittmann, 2013) and neural oscillations at various frequencies (Bartolo & Merchant, 2015;Fujioka, Ross, & Trainor, 2015;Kulashekhar, Pekkola, Palva, & Palva, 2016;Wiener & Kanai, 2016). Our previous results (Iwasaki et al, 2017) predict some sort of visual-motor interaction occurring in the time-processing regions in the brain (Harrington, Haaland, & Knight, 1998;Ivry & Spencer, 2004;Lewis & Miall, 2003;Merchant, Harrington, & Meck, 2013).…”