The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
The contribution of frequency-specific activity to hierarchical information processing in the human auditory cortex
Open accessAbstract
The fact that feed-forward and top-down propagation of sensory information use distinct frequency bands is an appealing assumption for which evidence remains scarce. Here we obtain human depth recordings from two auditory cortical regions in both hemispheres, while subjects listen to sentences, and show that information travels in each direction using separate frequency channels. Bottom-up and top-down propagation dominates in γ- and δ–β (<40 Hz) bands, respectively. The predominance of low frequencies for top-down information transfer is confirmed by cross-regional frequency coupling, which indicates that the power of γ-activity in A1 is modulated by the phase of δ–β activity sampled from association auditory cortex (AAC). This cross-regional coupling effect is absent in the opposite direction. Finally, we show that information transfer does not proceed continuously but by time windows where bottom-up or top-down processing alternatively dominates. These findings suggest that the brain uses both frequency- and time-division multiplexing to optimize directional information transfer.
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Cites
Investigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989
Nonparametric statistical testing of EEG- and MEG-data
Maris, E., & Oostenveld, R. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.03.024 · Cited by 8,496
Neuronal Oscillations in Cortical Networks
Buzsáki, G., & Draguhn, A. (2004) · Science · doi:10.1126/science.1099745 · Cited by 6,576
The cortical organization of speech processing
Hickok, G., & Poeppel, D. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2113 · Cited by 5,495
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · cited 2×
“Functionally, predictive coding is a possible realization of the anticipatory function of the brain 1, 4, 5 .”
Investigating Causal Relations by Econometric Models and Cross-Spectral Methods
Granger, C. W. J. (2010) · Essays in Econometrics · doi:10.1017/cbo9780511753978.002 · Cited by 4,762
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578
The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes
Buzsáki, G., Anastassiou, C. A., & Koch, C. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3241 · Cited by 4,225
Measuring phase synchrony in brain signals
Lachaux, J.-P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1999)8:4<194::aid-hbm4>3.0.co;2-c · Cited by 3,798
CircStat : A MATLAB Toolbox for Circular Statistics
Berens, P. (2009) · Journal of Statistical Software · doi:10.18637/jss.v031.i10 · Cited by 3,099
Cited by
Rhythms for cognition: communication through coherence
Fries, P. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.034 · Cited by 2,725
Michalareas, G., Vezoli, J., van Pelt, S., Schoffelen, J.-M., Kennedy, H., & Fries, P. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.018 · Cited by 696
Miller, E. K., Lundqvist, M., & Bastos, A. M. (2018) · Neuron · doi:10.1016/j.neuron.2018.09.023 · Cited by 797
Neural Mechanisms of Sustained Attention Are Rhythmic
Helfrich, R. F., Fiebelkorn, I. C., Szczepanski, S. M., Lin, J. J., Parvizi, J., Knight, R. T., & Kastner, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.07.032 · Cited by 447
Park, H., Ince, R. A. A., Schyns, P. G., Thut, G., & Gross, J. (2015) · Current Biology · doi:10.1016/j.cub.2015.04.049 · Cited by 356
Metadata
- Authors
- Fontolan, L., Morillon, B., Liegeois-Chauvel, C., & Giraud, A.-L.
- Venue
- Nature Communications
- Published
- 2014-09-02
- Volume
- 5
- Issue
- 1
- Pages
- 4694-4694
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2017347948
- Cited by
- 276
- References
- 67
- FWCI
- 12.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
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