Oscillatory multiplexing of population codes for selective communication in the mammalian brain

Akam, Thomas; Kullmann, Dimitri M.

doi:10.1038/nrn3668

Cited by 317 publications

(313 citation statements)

References 84 publications

(123 reference statements)

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“…Such cross-frequency interactions could be studied by applying the PTE to data filtered in different frequency bands for the senders and receivers (35). In addition, we only estimated information flow for relatively long time windows, such that we could not establish whether a single region switched from a sending to receiving state on short time scales or whether a region was simultaneously a sender in one frequency band and a receiver in another, consistent with the idea of oscillatory multiplexing for selective communication (58). This could be addressed by developing a dynamic dPTE approach.…”

Section: Discussionmentioning

confidence: 79%

Direction of information flow in large-scale resting-state networks is frequency-dependent

Hillebrand

Tewarie

Dellen

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

332

366

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Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-toposterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequencydependent reentry loops that are dominated by flow from parietooccipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.information flow | phase transfer entropy | resting-state networks | magnetoencephalography | atlas-based beamforming T he brain is an extremely complex system (1-3) containing, at the macroscopic scale, interconnected functional units (4) with more-or-less specific information processing capabilities (5). However, cognitive functions require the coordinated activity of these spatially separated units, where the oscillatory nature of neuronal activity may provide a possible mechanism (6-9). A complete description of these interactions, in terms of both strength and directionality, is therefore necessary for the understanding of both normal and abnormal brain functioning.Functional interactions may be inferred from statistical dependencies between the time series of neuronal activity at different sites, so-called functional connectivity (10). Indeed, interactions in large-scale functional networks have been observed using Electroencephalography, Magnetoencephalography (EEG/MEG) and functional Magnetic Resonance Imaging (fMRI) (e.g., refs. 11-14). However, as yet, little is known about the directionality of these interactions in large-scale functional networks during the resting state. Estimating directionality from fMRI is challenging due to its limited temporal resolution and indirect relation to neuronal activity (15, 16). In contrast, EEG studies in healthy controls have revealed a front-to-back pattern of directed connectivity, particularly in the alpha band (17-22), consistent with modeling studies that have shown that such patterns may arise due to differences in the number of anatomical connections (the degree) of anterior and posterior regions (22, 23). However, modeled patterns of information flow depend on the a...

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Section: Discussionmentioning

confidence: 79%

Direction of information flow in large-scale resting-state networks is frequency-dependent

Hillebrand

Tewarie

Dellen

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

332

366

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“…The coherent relationship we observe between the gamma and beta activity suggests that decision signals in the PPC arise in the interaction between these two types of influences. An intriguing possibility is that PPC spiking may multiplex decision signals into beta and gamma channels (53).…”

Section: Discussionmentioning

confidence: 99%

Temporal coding of reward-guided choice in the posterior parietal cortex

Hawellek

Wong

Pesaran

2016

Proc. Natl. Acad. Sci. U.S.A.

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Making a decision involves computations across distributed cortical and subcortical networks. How such distributed processing is performed remains unclear. We test how the encoding of choice in a key decision-making node, the posterior parietal cortex (PPC), depends on the temporal structure of the surrounding population activity. We recorded spiking and local field potential (LFP) activity in the PPC while two rhesus macaques performed a decision-making task. We quantified the mutual information that neurons carried about an upcoming choice and its dependence on LFP activity. The spiking of PPC neurons was correlated with LFP phases at three distinct time scales in the theta, beta, and gamma frequency bands. Importantly, activity at these time scales encoded upcoming decisions differently. Choice information contained in neural firing varied with the phase of beta and gamma activity. For gamma activity, maximum choice information occurred at the same phase as the maximum spike count. However, for beta activity, choice information and spike count were greatest at different phases. In contrast, theta activity did not modulate the encoding properties of PPC units directly but was correlated with beta and gamma activity through cross-frequency coupling. We propose that the relative timing of local spiking and choice information reveals temporal reference frames for computations in either local or large-scale decision networks. Differences between the timing of task information and activity patterns may be a general signature of distributed processing across large-scale networks.decision making | phase-of-firing | neural code | saccade | reach T he posterior parietal cortex (PPC) integrates sensory signals for impending actions. Firing rates of neurons in the PPC encode movement intention and the temporal evolution of movement choices (1-13) as well as decision variables such as expected rewards, the subjective desirability during reward-guided decisions (14-18), and the certainty in perceptual decisions (19). Decisions are made within a network that extends across many regions of the brain (20-25), so efficient and flexible mechanisms are required to enable distributed computations. Dynamic and frequency-specific correlations in activity between brain areas are ubiquitous and offer potential physiological mechanisms supporting distributed computations in decision networks (13,21,(26)(27)(28)(29)(30)(31)(32)(33)(34). We hypothesize that the encoding of decisions in the PPC should depend on the temporal structure present in neuronal activity. Previous studies have shown that coherently active ensembles of cells in the PPC predict reaction times (9) and movement choices (13, 21) better than neurons without coherent dynamics. However, whether and how the coding of decisions depends on the temporally structured firing of PPC neurons remains unknown. We examined temporal structure in the encoding of look-reach decisions by PPC neurons. ResultsWe analyzed the activity of 149 units [97 units (31 single units and 66 multiun...

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“…LFO are known to orchestrate periods of inhibition and excitation for HFA, notably in the beta and gamma bands. This is achieved via cross-frequency coupling, meaning that an increase in HFA power occurs at particular phases of LFO (Akam and Kullmann 2014;Canolty et al 2006;Canolty and Knight 2010;Hyafil et al 2015b;Lakatos et al 2005). During speech listening, HFA has been predicted to be enhanced as syllables and words unfold over time but inhibited at their boundaries (Ding and Simon 2014;Giraud and Poeppel 2012;Hyafil et al 2015a).…”

mentioning

confidence: 99%

High-frequency neural activity predicts word parsing in ambiguous speech streams

Kösem

Basirat

Azizi

et al. 2016

Journal of Neurophysiology

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Kösem A, Basirat A, Azizi L, van Wassenhove V. High-frequency neural activity predicts word parsing in ambiguous speech streams. J Neurophysiol 116: 2497-2512, 2016. First published September 7, 2016 doi:10.1152/jn.00074.2016.-During speech listening, the brain parses a continuous acoustic stream of information into computational units (e.g., syllables or words) necessary for speech comprehension. Recent neuroscientific hypotheses have proposed that neural oscillations contribute to speech parsing, but whether they do so on the basis of acoustic cues (bottom-up acoustic parsing) or as a function of available linguistic representations (top-down linguistic parsing) is unknown. In this magnetoencephalography study, we contrasted acoustic and linguistic parsing using bistable speech sequences. While listening to the speech sequences, participants were asked to maintain one of the two possible speech percepts through volitional control. We predicted that the tracking of speech dynamics by neural oscillations would not only follow the acoustic properties but also shift in time according to the participant's conscious speech percept. Our results show that the latency of high-frequency activity (specifically, beta and gamma bands) varied as a function of the perceptual report. In contrast, the phase of low-frequency oscillations was not strongly affected by top-down control. Whereas changes in low-frequency neural oscillations were compatible with the encoding of prelexical segmentation cues, high-frequency activity specifically informed on an individual's conscious speech percept.

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Oscillatory multiplexing of population codes for selective communication in the mammalian brain

Cited by 317 publications

References 84 publications

Direction of information flow in large-scale resting-state networks is frequency-dependent

Direction of information flow in large-scale resting-state networks is frequency-dependent

Temporal coding of reward-guided choice in the posterior parietal cortex

High-frequency neural activity predicts word parsing in ambiguous speech streams

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