Synchronous beta rhythms of frontoparietal networks support only behaviorally relevant representations

Antzoulatos, Evan G.; Miller, Earl K.

doi:10.7554/elife.17822

Cited by 79 publications

(88 citation statements)

References 46 publications

(82 reference statements)

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“…This stands in contrast to prior studies reporting that long-range beta rhythmic synchronization between LPFC, ACC or STR in the primate encoded relevant task variables via the strength of beta synchrony (Antzoulatos and Miller, 2016;Babapoor-Farrokhran et al, 2017;Buschman and Miller, 2007;Buschman et al, 2012;Dean et al, 2012;Salazar et al, 2012;Womelsdorf et al, 2014a). For example, some prefrontal cortex neurons synchronize stronger at beta to posterior parietal areas when subjects choose one visual category over another (Antzoulatos and Miller, 2016), or when they maintain one object over another in working memory (Salazar et al, 2012). These findings are consistent with a communication-through-coherence schema where upstream senders are more coherent with downstream readers when they successfully compete for representation (Bosman et al, 2012;Fries, 2015;.…”

Section: Multiplexing Of Information Through Phase-of-firing Encodingcontrasting

confidence: 99%

“…This beta frequency is thus a powerful candidate for distributed information transfer, because spike output of many neurons is concentrated at the same phase and thus activate postsynaptic membranes at similar times. This scenario of beta rhythmic information exchange within fronto-striatal networks is supported by previous nonhuman primate studies that demonstrated 10-25 Hz beta rhythmic synchronization during active task processing states between ACC and LFPC (Smith et al, 2019;Voloh and Womelsdorf, 2017a;Womelsdorf et al, 2014a), between PFC and STR (Antzoulatos and Miller, 2014), between ACC and FEF (Babapoor-Farrokhran et al, 2017), between LPFC and FEF (Antzoulatos and Miller, 2016;Salazar et al, 2012), and between lateral PFC or FEF with posterior parietal cortex (Antzoulatos and Miller, 2016;Buschman and Miller, 2007;Buschman et al, 2012;Dean et al, 2012;Salazar et al, 2012).…”

Section: Distributed Encoding Of Learning Variables At a Shared Beta supporting

confidence: 78%

“…Such sitespecific selectivity of neuronal synchronization has been reported in previous studies (e.g. (Antzoulatos and Miller, 2016;Salazar et al, 2012;Womelsdorf et al, 2010). To test this possibility, we compared spike-LFP synchronization (indexed with the PPC), in those two trial conditions that were predicted to have the maximal firing rate difference.…”

Section: Preferred Spike Phase and Encoding Phase Differ For Predictimentioning

confidence: 61%

See 2 more Smart Citations

Phase of Firing Coding of Learning Variables across Prefrontal Cortex, Anterior Cingulate Cortex and Striatum during Feature Learning

Voloh

Oemisch

Womelsdorf

2019

Preprint

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KeywordsLearning; beta frequency oscillation; neuronal synchronization; prediction error phase code; anterior cingulate cortex; striatum; lateral prefrontal cortex; reward prediction error, reversal learning Highlights • Lateral prefrontal cortex, anterior cingulate cortex and striatum show phase-of-firing encoding for outcome, outcome history and reward prediction errors.• Neurons with phase-of-firing code synchronize long-range at 10-25 Hz.• Spike phases encoding reward prediction errors deviate from preferred synchronization phases.• Anterior cingulate cortex neurons show strongest long-range effects. AbstractThe prefrontal cortex and striatum form a recurrent network whose spiking activity encodes multiple types of learning-relevant information. This spike-encoded information is evident in average firing rates, but finer temporal coding might allow multiplexing and enhanced readout across the connected the network. We tested this hypothesis in the fronto-striatal network of nonhuman primates during reversal learning of feature values. We found that neurons encoding current choice outcomes, outcome prediction errors, and outcome history in their firing rates also carried significant information in their phase-of-firing at a 10-25 Hz beta frequency at which they synchronized across lateral prefrontal cortex, anterior cingulate cortex and striatum. The phase-of-firing code exceeded information that could be obtained from firing rates alone, was strong for inter-areal connections, and multiplexed Combined Manuscript File 2 information at three different phases of the beta cycle that were offset from the preferred spiking phase of neurons. Taken together, these findings document the multiplexing of three different types of information in the phase-of-firing at an interareally shared beta oscillation frequency during goal-directed behavior.

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Section: Multiplexing Of Information Through Phase-of-firing Encodingcontrasting

confidence: 99%

Section: Distributed Encoding Of Learning Variables At a Shared Beta supporting

confidence: 78%

Section: Preferred Spike Phase and Encoding Phase Differ For Predictimentioning

confidence: 61%

See 1 more Smart Citation

Phase of Firing Coding of Learning Variables across Prefrontal Cortex, Anterior Cingulate Cortex and Striatum during Feature Learning

Voloh

Oemisch

Womelsdorf

2019

Preprint

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“…We also found beta rhythms. Beta activity has been observed in PFC (Antzoulatos and Miller, 2016; Buschman et al, 2012; Kawasaki and Yamaguchi, 2012; Lundqvist et al, 2016; Stanley et al, 2016). Our approach further revealed connectivity across different spatial scales for theta vs beta frequencies.…”

Section: Discussionmentioning

confidence: 99%

On memories, neural ensembles and mental flexibility

2017

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Memories are assumed to be represented by groups of co-activated neurons, called neural ensembles. Describing ensembles is a challenge: complexity of the underlying micro-circuitry is immense. Current approaches use a piecemeal fashion, focusing on single neurons and employing local measures like pairwise correlations. We introduce an alternative approach that identifies ensembles and describes the effective connectivity between them in a holistic fashion. It also links the oscillatory frequencies observed in ensembles with the spatial scales at which activity is expressed. Using unsupervised learning, biophysical modeling and graph theory, we analyze multi-electrode LFPs from frontal cortex during a spatial delayed response task. We find distinct ensembles for different cues and more parsimonious connectivity for cues on the horizontal axis, which may explain the oblique effect in psychophysics. Our approach paves the way for biophysical models with learned parameters that can guide future Brain Computer Interface development.

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“…As an example of motifs underlying a temporally evolving network, consider how feedforward and feedback surprise predictions may be mechanistically communicated with gamma versus beta/alpha cycles (Antzoulatos & Miller, 2016;Arnal & Giraud, 2012;Michalareas et al, 2016). Slower oscillations such as theta may be long-term readers of fast activities (Buzsáki, 2010;Jensen & Colgin, 2007;Womelsdorf, Vinck, Leung, & Everling, 2010), and later low-frequency bursts may be low-entropy attractor states of integrated processes (Womelsdorf & Everling, 2015).…”

Section: Algorithmmentioning

confidence: 99%

Electrophysiology as a theoretical and methodological hub for the neural sciences

Cavanagh

2018

Psychophysiology

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Electrophysiology is a direct measure of neuronal processes, and it is uniquely sensitive to canonical neural operations that underlie emergent psychological operations. These qualities make it well suited for discovery of aberrant neural mechanisms that underlie complicated disease states. This technique is routinely utilized in vitro, in vivo, and in outpatient neurological clinics, offering a translatable bridge between animal models and human patients. The bench-to-bedside potential of this approach is unparalleled, yet it also remains undeveloped due to the slow inertia of legacy techniques and interpretations. In this review, I discuss these strengths of the method, and I detail compelling reasons why future advancements can have a direct and tangible influence over clinical practice. I hope to motivate a blurring of traditional boundaries between preclinical, computational, imaging, and clinical fields by advancing electrophysiology as a common hub for methodological integration and theoretical advancement.

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Synchronous beta rhythms of frontoparietal networks support only behaviorally relevant representations

Cited by 79 publications

References 46 publications

Phase of Firing Coding of Learning Variables across Prefrontal Cortex, Anterior Cingulate Cortex and Striatum during Feature Learning

Phase of Firing Coding of Learning Variables across Prefrontal Cortex, Anterior Cingulate Cortex and Striatum during Feature Learning

On memories, neural ensembles and mental flexibility

Electrophysiology as a theoretical and methodological hub for the neural sciences

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