Intermittent tACS during a visual task impacts neural oscillations and LZW complexity

Little is known about how transcranial alternating current stimulation (tACS) interacts with brain activity. Here, we investigate the effects of tACS using an intermittent tACS-EEG protocol and use, in addition to classical metrics, Lempel–Ziv–Welch complexity (LZW) to characterize the interactions...

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Veröffentlicht in:Experimental brain research 2020-06, Vol.238 (6), p.1411-1422
Hauptverfasser: Castellano, Marta, Ibañez-Soria, David, Kroupi, Eleni, Acedo, Javier, Campolo, Michela, Soria-Frisch, Aureli, Valls-Sole, Josep, Verma, Ajay, Ruffini, Giulio
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Sprache:eng
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Zusammenfassung:Little is known about how transcranial alternating current stimulation (tACS) interacts with brain activity. Here, we investigate the effects of tACS using an intermittent tACS-EEG protocol and use, in addition to classical metrics, Lempel–Ziv–Welch complexity (LZW) to characterize the interactions between task, endogenous and exogenous oscillations. In a cross-over study, EEG was recorded from thirty participants engaged in a change-of-speed detection task while receiving multichannel tACS over the visual cortex at 10 Hz, 70 Hz and a control condition. In each session, tACS was applied intermittently during 5 s events interleaved with EEG recordings over multiple trials. We found that, with respect to control, stimulation at 10 Hz ( tACS 10 ) enhanced both α and γ power, γ -LZW complexity and γ but not α phase locking value with respect to tACS onset ( α -PLV, γ -PLV), and increased reaction time (RT). tACS 70 increased RT with little impact on other metrics. As trials associated with larger γ -power (and lower γ -LZW) predicted shorter RT, we argue that tACS 10 produces a disruption of functionally relevant fast oscillations through an increase in α -band power, slowing behavioural responses and increasing the complexity of gamma oscillations. Our study highlights the complex interaction between tACS and endogenous brain dynamics, and suggests the use of algorithmic complexity inspired metrics to characterize cortical dynamics in a behaviorally relevant timescale.
ISSN:0014-4819
1432-1106
DOI:10.1007/s00221-020-05820-z