Oscillations and Filtering Networks Support Flexible Routing of Information

Akam, Thomas; Kullmann, Dimitri M.

doi:10.1016/j.neuron.2010.06.019

Cited by 247 publications

(264 citation statements)

References 67 publications

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Section: Discussionsupporting

confidence: 68%

“…3b). The observation that mid-and high-gamma activity were related to the phase consistency of beta and theta activity, respectively, extends the ideas that gamma frequency variations reflect information routing 32,34,45,46 and that message-passing is indexed by specific hierarchical combinations of slow and high frequencies (low/high frequency ratio) 47 .The current data demonstrate that violating intermodal expectations changes the neural dynamics of slow (delta and theta) brain activity, and increases the coordination between local low-beta and high-gamma oscillatory activity. Our data suggest that this transition occurs in brain regions where audio-visual predictions are likely updated (STS) and new prediction errors generated (auditory and visual cortices).…”

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confidence: 70%

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Transitions in neural oscillations reflect prediction errors generated in audiovisual speech

2011

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According to the predictive coding theory, top-down predictions are conveyed by backward connections and prediction errors are propagated forward across the cortical hierarchy. Using MEG in humans, we show that violating multisensory predictions causes a fundamental and qualitative change in both the frequency and spatial distribution of cortical activity. When visual speech input correctly predicted auditory speech signals, a slow delta regime (3-4 Hz) developed in higher-order speech areas. In contrast, when auditory signals invalidated predictions inferred from vision, a low-beta (14-15 Hz) / high-gamma (60-80 Hz) coupling regime appeared locally in a multisensory area (area STS). This frequency shift in oscillatory responses scaled with the degree of audio-visual congruence and was accompanied by increased gamma activity in lower sensory regions. These findings are consistent with the notion that bottom-up prediction errors are communicated in predominantly high (gamma) frequency ranges, whereas top-down predictions are mediated by slower (beta) frequencies.© 2011 Nature America, Inc. All rights reserved.7 9 8 VOLUME 14 | NUMBER 6 | JUNE 2011 nature neurOSCIenCe a r t I C l e S converge-that is, where multisensory predictions are generatedwhereas gamma activity was seen in lower sensory cortices where prediction errors emerge and are propagated forward. RESULTSWe presented 15 subjects with stimuli in one of three conditions: videos (audio-visual: AV condition) of a speaker pronouncing the syllables /pa/, / a/, /la/, /ta/, /ga/ and /fa/ (International Phonetic Alphabet notation); an auditory track of these videos combined with a still face (auditory: A condition); or a mute visual track (visual: V condition). The videos could be either natural or a random combination of auditory and visual tracks, creating conditions in which auditory and visual tracks were congruent (AVc condition) and ones in which they were incongruent (AVi condition; see Online Methods and Supplementary Fig. 1). Incongruent combinations yielding fusion illusory percepts-that is, McGurk stimuli-were excluded 8,11 . Subjects performed an unrelated target detection task on the syllable /fa/ that was presented in A, V or AVc form in 13% of the trials (97% correct detection). These trials were subsequently excluded from the analyses. The five other syllables were chosen because they yielded graded recognition accuracy when presented visually (Fig. 1a), resulting from an increasing predictiveness 10 . The phonological prediction conveyed by mouth movements (visemes) varies in specificity depending on the pronounced syllable. Typically, syllables beginning with a consonant that is formed at the front of the mouth (/p/, /m/) convey a more specific prediction than those formed at the back (/g/, /k/, /r/, /l/) 8 . Our second experimental factor pertained to the validity of the visual prediction with respect to the auditory input. Physically, the audio-visual stimuli could be either congruent (valid prediction) or incongruent (invalid prediction), w...

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Section: Discussionsupporting

confidence: 68%

supporting

confidence: 70%

Transitions in neural oscillations reflect prediction errors generated in audiovisual speech

2011

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“…S5), indicating that patterns of phase differences should not be confused with patterns of directed connectivity or, indeed, information flow. Instead, one could hypothesize that the net outflow of information from posterior regions is due to an increase in encoded information at higher firing rates (52), as simulations have shown that hubs also possess the highest levels of neuronal activity in the network, that is, the highest firing rates (53) and the highest power (22,53).…”

Section: Discussionmentioning

confidence: 99%

“…Finally, modeling work suggests that the relationship between power and directionality may not simply be caused by differences in SNR but may be due to a neuronal mechanism that drives this correlation (52). Akam and Kullmann (52) showed that the amount of synchronous activity in a sending neuronal population, reflected in the amplitude of oscillations, is strongly related to the amount of information available to the receiving neuronal population. At the same time, interneuron motifs within the receiving neuronal population can act as bandpass filters to selectively gate incoming signals.…”

Section: Discussionmentioning

confidence: 99%

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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“…Granule cells and their surrounding interneurons are tuned to respond differentially to particular oscillatory frequencies of input from EC [110], hence the net impact of PP input on GC firing could be adaptively filtered according to its pattern and does not depend solely on excitation. Models suggest that filtering of this kind by dynamically tuned inhibition may be used to divert information via different routes during different behavioral states [111,112]. For example, novelty induces a significant increase in the firing rates of inhibitory interneurons in the DG and a slight decrease in granule cell firing rates [113].…”

Section: Box3: Inhibitory Influencesmentioning

confidence: 99%

Updating hippocampal representations: CA2 joins the circuit

Jones

McHugh

2011

Trends in Neurosciences

114

106

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The hippocampus integrates the encoding, storage and recall of memories, binding the spatiotemporal and sensory information that constitutes experience and keeping episodes in their correct context. The rapid and accurate processing of such daunting volumes of continuouslychanging data relies on dynamically assigning different aspects of mnemonic processing to specialized, interconnected networks corresponding to the anatomical subfields of dentate gyrus (DG), CA3 and CA1. However, differentially processed information ultimately has to be reintegrated into conjunctive representations, and this is unlikely to be achieved by unidirectional, sequential steps through a DG-CA3-CA1 loop. In this Review, we highlight recently discovered anatomical and physiological features that are likely to necessitate updates to the hippocampal circuit diagram, particularly by incorporating the oft-neglected CA2 region.3

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Oscillations and Filtering Networks Support Flexible Routing of Information

Cited by 247 publications

References 67 publications

Transitions in neural oscillations reflect prediction errors generated in audiovisual speech

Transitions in neural oscillations reflect prediction errors generated in audiovisual speech

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

Updating hippocampal representations: CA2 joins the circuit

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