Alpha-gamma phase amplitude coupling subserves information transfer during perceptual sequence learning

Tzvi, Elinor; Bauhaus, Leon J.; Kessler, Till U.; Liebrand, Matthias; Krämer, Ulrike M.

doi:10.1016/j.nlm.2018.02.019

Cited by 18 publications

(16 citation statements)

References 48 publications

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“…The phase of low-frequency oscillations modulate with the amplitude of high-frequency oscillations (Canolty et al, 2006; Tort et al, 2008; Canolty and Knight, 2010). Some evidence of phase-to-amplitudes comodulation has come from studies conducted across different frequency bands: δ-γ (Gross et al, 2013; López-Azcárate et al, 2013; Szczepanski et al, 2014), θ-γ (Bragin et al, 1995; Chrobak and Buzsáki, 1998; Canolty et al, 2006; Tort et al, 2008; Voytek et al, 2010; Lisman and Jensen, 2013; Voloh et al, 2015), α-γ (Osipova et al, 2008; Cohen et al, 2009; Voytek et al, 2010; Spaak et al, 2012; Yanagisawa et al, 2012; van Kerkoerle et al, 2014; Bonnefond and Jensen, 2015; Park et al, 2016; Seymour et al, 2017; Tzvi et al, 2018), and β-γ (de Hemptinne et al, 2013; Kim et al, 2015; Swann et al, 2015). In particular, some of these studies investigated α-γ comodulation in the visual cortices (Osipova et al, 2008; Voytek et al, 2010; Spaak et al, 2012; van Kerkoerle et al, 2014; Bonnefond and Jensen, 2015; Seymour et al, 2017), parietal-occipital areas (Tzvi et al, 2018), lingual gyrus (Park et al, 2016), and sensorimotor cortex (Yanagisawa et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Reward Expectation Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex

Yadav

Hessburg

et al. 2019

eNeuro

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Reward modulation (M1) could be exploited in developing an autonomously updating brain-computer interface (BCI) based on a reinforcement learning (RL) architecture. For an autonomously updating RL-based BCI system, we would need a reward prediction error, or a state-value representation from the user’s neural activity, which the RL-BCI agent could use to update its BCI decoder. In order to understand the multifaceted effects of reward on M1 activity, we investigated how neural spiking, oscillatory activities and their functional interactions are modulated by conditioned stimuli related reward expectation. To do so, local field potentials (LFPs) and single/multi-unit activities were recorded simultaneously and bilaterally from M1 cortices while four non-human primates (NHPs) performed cued center-out reaching or grip force tasks either manually using their right arm/hand or observed passively. We found that reward expectation influenced the strength of α (8–14 Hz) power, α-γ comodulation, α spike-field coherence (SFC), and firing rates (FRs) in general in M1. Furthermore, we found that an increase in α-band power was correlated with a decrease in neural spiking activity, that FRs were highest at the trough of the α-band cycle and lowest at the peak of its cycle. These findings imply that α oscillations modulated by reward expectation have an influence on spike FR and spike timing during both reaching and grasping tasks in M1. These LFP, spike, and spike-field interactions could be used to follow the M1 neural state in order to enhance BCI decoding ( An et al., 2018 ; Zhao et al., 2018 ).

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Section: Discussionmentioning

confidence: 99%

Reward Expectation Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex

Yadav

Hessburg

et al. 2019

eNeuro

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“…Changes in α power were also linked to pre-stimulus preparatory processes over frontal areas during learning, as well as to memory consolidation, in occipito-parietal areas (Moisello et al, 2013). Finally, we previously showed that during visuomotor sequence learning, α power decreases over occipito-parietal areas as learning progresses (Tzvi et al, 2018(Tzvi et al, , 2016. Together, this evidence suggests that for both motor and perceptual sequence learning dynamic changes in α power at functional relevant sites may play a critical role.…”

Section: Introductionmentioning

confidence: 60%

“…We further found that α coherence between left PMC, right TPJ and cerebellar crus I was significantly weaker during SEQ compared to RND. Previously, we showed that reduced α phase coupling in SEQ compared to RND, interpreted to reflect global functional decoupling, is important for encoding the sequence into memory (Tzvi et al, 2018). We therefore suggest that α decoupling between left PMC, right TPJ and cerebellar crus I serves to integrate motor representations in motor cortical areas, attentional processes in TPJ (Corbetta and Shulman, 2002), and internal model representations in the cerebellum (Ito, 2006) during motor learning.…”

Section: Learning Modulates α Power In Premotor-and Sensorimotor Cortexmentioning

confidence: 69%

The role of alpha oscillations in a premotor-cerebellar loop in modulation of motor learning: insights from transcranial alternating current stimulation

Schubert

Dabbagh

Claßen

et al. 2020

Preprint

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Alpha oscillations (8-13 Hz) have been shown to play an important role in dynamic neural processes underlying learning and memory. The goal of this study was to scrutinize the role of α oscillations for communication within the network implicated in motor sequence learning. To this end, we conducted two experiments using the serial reaction time task. In the first experiment, we explored changes in α power and cross-channel coherence shortly before the motor response. We found a gradual decrease in learning-related α power over left premotor cortex (PMC) and somatosensory areas. Connectivity between left PMC and right cerebellum was reduced for sequence learning, possibly reflecting a functional decoupling in the premotor-cerebellar loop during the motor learning process. In the second experiment in a different cohort, we applied 10Hz transcranial alternating current stimulation (tACS), a method shown to entrain local oscillatory activity, to left M1 (lM1) and right cerebellum (rCB) during sequence learning. We observed learning deficits during rCB tACS compared to sham, but not during lM1 tACS. In addition, learning-related α power following rCB tACS was increased in PMC, possibly reflecting a decrease of neural activity. Importantly, learning-specific coherence between left PMC and a right cerebellar cluster was enhanced following rCB tACS. These findings suggest that interactions within a premotor-cerebellar loop, which are underlying motor sequence learning, are mediated by α oscillations. We show that they can be modulated through external entrainment of cerebellar oscillations, which modulates motor cortical α and interferes with sequence learning.

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“…Subjects were stimulated while performing a serial reaction time task (SRTT), entailing both sequence and non-sequence blocks, shown to induce activity within motor cortex-cerebellar loops ( Tzvi et al 2014 ; Schubert et al 2021 ). Specifically, we showed that this task induces changes in alpha power ( Tzvi et al 2016 ; Tzvi et al 2018 ; Schubert et al 2021 ) that may be more readily modulated by externally applied 10-Hz tACS. The effect of tACS on “offline” EEG signals was studied during resting-state as well as during performance of a non-sequence block of the SRTT.…”

Section: Introductionmentioning

confidence: 85%

Classification of EEG Signals Reveals a Focal Aftereffect of 10 Hz Motor Cortex Transcranial Alternating Current Stimulation

Tzvi¹,

Alizadeh²,

Schubert³

et al. 2022

Cerebral Cortex Communications

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Transcranial alternating current stimulation (tACS) modulates oscillations in a frequency- and location-specific manner and affects cognitive and motor functions. This effect appears during stimulation as well as “offline,” following stimulation, presumably reflecting neuroplasticity. Whether tACS produces long-lasting aftereffects that are physiologically meaningful, is still of current debate. Thus, for tACS to serve as a reliable method for modulating activity within neural networks, it is important to first establish whether “offline” aftereffects are robust and reliable. In this study, we employed a novel machine-learning approach to detect signatures of neuroplasticity following 10 Hz tACS to two critical nodes of the motor network: left motor cortex (lMC) and right cerebellum (rCB). To this end, we trained a classifier to distinguish between signals following lMC-tACS, rCB-tACS and sham. Our results demonstrate better classification of EEG signals in both theta (θ, 4 Hz-8 Hz) and alpha (α, 8 Hz-13 Hz) frequency bands to lMC-tACS compared to rCB-tACS/sham, at lMC-tACS stimulation location. Source reconstruction allocated these effects to premotor cortex. Stronger correlation between classification accuracies in θ and α in lMC-tACS suggested an association between θ and α efffects. Together these results suggest that EEG signals over premotor cortex contains unique signatures of neuroplasticity following 10 Hz motor cortex tACS.

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Alpha-gamma phase amplitude coupling subserves information transfer during perceptual sequence learning

Cited by 18 publications

References 48 publications

Reward Expectation Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex

Reward Expectation Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex

The role of alpha oscillations in a premotor-cerebellar loop in modulation of motor learning: insights from transcranial alternating current stimulation

Classification of EEG Signals Reveals a Focal Aftereffect of 10 Hz Motor Cortex Transcranial Alternating Current Stimulation

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