Alpha Oscillatory Dynamics Index Temporal Expectation Benefits in Working Memory

Abstract

Enhanced alpha power compared with a baseline can reflect states of increased cognitive load, for example, when listening to speech in noise. Can knowledge about "when" to listen (temporal expectations) potentially counteract cognitive load and concomitantly reduce alpha? The current magnetoencephalography (MEG) experiment induced cognitive load using an auditory delayed-matching-to-sample task with 2 syllables S1 and S2 presented in speech-shaped noise. Temporal expectation about the occurrence of S1 was manipulated in 3 different cue conditions: "Neutral" (uninformative about foreperiod), "early-cued" (short foreperiod), and "late-cued" (long foreperiod). Alpha power throughout the trial was highest when the cue was uninformative about the onset time of S1 (neutral) and lowest for the late-cued condition. This alpha-reducing effect of late compared with neutral cues was most evident during memory retention in noise and originated primarily in the right insula. Moreover, individual alpha effects during retention accounted best for observed individual performance differences between late-cued and neutral conditions, indicating a tradeoff between allocation of neural resources and the benefits drawn from temporal cues. Overall, the results indicate that temporal expectations can facilitate the encoding of speech in noise, and concomitantly reduce neural markers of cognitive load.

Rhythmic Auditory Cortex Activity at Multiple Timescales Shapes Stimulus–Response Gain and Background Firing

“…Accumulating evidence suggests that low-frequency rhythms play an important role for hearing (Schroeder and Lakatos, 2009;Giraud and Poeppel, 2012; Leong and Goswami, 2014). Neuroimaging and intracranial recordings show that neural activity in the auditory cortex (A1) at frequencies below ϳ12 Hz entrains to the temporal structure of sounds and carries information about sound identity Szymanski et al, 2011;Simon, 2012, 2013; Ng et al, 2013), possibly because natural sounds contain important acoustic structures at these frequencies Doelling et al, 2014;Gross et al, 2013).…”

“…Importantly, the degree of rhythmic entrainment correlates with perceptual intelligibility (Mesgarani and Chang, 2012; Doelling et al, 2014;Peelle et al, 2013), linking the timescales relevant for acoustic comprehension with those of neural activity (Rosen, 1992;Ghitza and Greenberg, 2009; Zion Golumbic et al, 2012). Based on these results, it has been hypothesized that slow rhythmic activity in the A1 reflects key mechanisms of sound encoding that have direct consequences for hearing (Giraud and Poeppel, 2012;Peelle and Davis, 2012;Strauß et al, 2014b).…”

“…Importantly, the degree of rhythmic entrainment correlates with perceptual intelligibility (Mesgarani and Chang, 2012; Doelling et al, 2014;Peelle et al, 2013), linking the timescales relevant for acoustic comprehension with those of neural activity (Rosen, 1992;Ghitza and Greenberg, 2009; Zion Golumbic et al, 2012). Based on these results, it has been hypothesized that slow rhythmic activity in the A1 reflects key mechanisms of sound encoding that have direct consequences for hearing (Giraud and Poeppel, 2012;Peelle and Davis, 2012;Strauß et al, 2014b).This raises the central question of how precisely rhythmic auditory cortical activity shapes sensory information processing. Electrophysiological recordings showed that slow rhythms reflect fluctuations in cortical excitability (Bishop, 1933; Azouz and Gray, 1999; Womelsdorf et al, 2014; Pachitariu et al, 2015; Reig et al, 2015) and the strength of neuronal firing Belitski et al, 2008; Haegens et al, 2011).…”

“…contributed equally to this work. Neuroscience, May 20, 2015 • 35(20):7750 -7762 (Schroeder andLakatos, 2009;. However, whether and how precisely network activity affects the computations by which auditory neurons represent sensory inputs remains unknown.…”

“…To distinguish these two timescales of network properties, here we use the term "cortical state" for properties of network dynamics on scales of seconds or longer (such as the synchronized and desynchronized states that can be detected from the LFP spectrum) and use the term LFP state to denote cortical dynamics indexed by the power or phase of different LFP bands, which persist on shorter timescales. This nomenclature is conceptually similar to that of Curto et al (2009), who distinguished between "dynamic states" of cortex referring to properties of network dynamics on a timescale of seconds or more and "activity states" describing cortical states that persist on shorter timescales of few tens to few hundred milliseconds.We quantified cortical state using the ratio of low-frequency (0.25-5 Hz) LFP power divided by the total LFP power (0.25-50 Hz) and using the correlation of firing rates across distant electrodes (Curto et al, 2009;Sakata and Harris, 2012; Pachitariu et al, 2015). These measures were correlated across sessions and times, and, when plotted over the experimental time axis, they both indicated that cortical states were stable for all but one of six experiments (Experiment 1), during which we observed more pronounced variations in LFP power ratio than for the others (see Fig.…”