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Slow Cortical Dynamics and the Accumulation of Information over Long Timescales
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Making sense of the world requires us to process information over multiple timescales. We sought to identify brain regions that accumulate information over short and long timescales and to characterize the distinguishing features of their dynamics. We recorded electrocorticographic (ECoG) signals from individuals watching intact and scrambled movies. Within sensory regions, fluctuations of high-frequency (64-200 Hz) power reliably tracked instantaneous low-level properties of the intact and scrambled movies. Within higher order regions, the power fluctuations were more reliable for the intact movie than the scrambled movie, indicating that these regions accumulate information over relatively long time periods (several seconds or longer). Slow (<0.1 Hz) fluctuations of high-frequency power with time courses locked to the movies were observed throughout the cortex. Slow fluctuations were relatively larger in regions that accumulated information over longer time periods, suggesting a connection between slow neuronal population dynamics and temporally extended information processing.
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“this topography has previously been mapped either by temporally scrambling the stimulus at different timescales to see which regions’ responses are disrupted (Hasson, Yang, Vallines, Heeger, & Rubin, 2008; Honey et al., 2012; Lerner, Honey, Silbert, & Hasson, 2011) or by examining the power spectrum of intrinsic dynamics within each region (Honey et al., 2012; Murray et al., 2014; Stephens et al.,”
From Discovering Event Structure in Continuous Narrative Perception and Memory (2017) · cites this paper as Honey et al., 2012
In the citing paper
“The topographical hierarchy of processing timescales along the cortical surface (2) has been supported and replicated across methodologies, including single-unit analysis (4), electrocorticography analysis (7), fMRI analysis (5, 6, 25), magnetoencephalography analysis (1), computational models (3), and resting state functional connectivity (30, 31).”
From Amplification of local changes along the timescale processing hierarchy (2017) · cites this paper as [7]
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“Previous work has suggested that long-TRW areas have intrinsically slower neural dynamics compared with short-TRW areas, and that these slower cortical dynamics may be related to an area’s ability to accumulate information over time (7, 8).”
From Amplification of local changes along the timescale processing hierarchy (2017) · cites this paper as [7]
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
Short-Term Synaptic Plasticity
Zucker, R. S., & Regehr, W. G. (2002) · Annual Review of Physiology · doi:10.1146/annurev.physiol.64.092501.114547 · Cited by 5,068
The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764
Testing for nonlinearity in time series: the method of surrogate data
Theiler, J. P., Eubank, S., Longtin, A., Galdrikian, B., & Farmer, J. D. (1992) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(92)90102-s · Cited by 3,759
Kleiner, M., Brainard, D. H., & Pelli, D. G. (2007) · MPG.PuRe (Max Planck Society) · Cited by 3,602
High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex
Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., Berger, M. S., Barbaro, N. M., & Knight, R. T. (2006) · Science · doi:10.1126/science.1128115 · Cited by 2,650
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- Weisskoff, 1993, Power spectrum analysis of functionally-weighted MR data: what's in the noise, Proc. Int. Soc. Magn. Reson. Med., 1, 7
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Murray, J. D., Bernacchia, A., Freedman, D. J., Romo, R., Wallis, J. D., Cai, X., Padoa-Schioppa, C., Pasternak, T., Seo, H., Lee, D., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3862 · Cited by 990
Discovering Event Structure in Continuous Narrative Perception and Memory
Baldassano, C., Chen, J., Zadbood, A., Pillow, J. W., Hasson, U., & Norman, K. A. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.041 · Cited by 952
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Huntenburg, J. M., Bazin, P., & Margulies, D. S. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.11.002 · Cited by 989
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He, B. J. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.04.003 · Cited by 827
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Christiansen, M. H., & Chater, N. (2015) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1500031x · Cited by 803
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Self-citationBurt, J. B., Demirtaş, M., Eckner, W. J., Navejar, N. M., Ji, J. L., Martin, W. J., Bernacchia, A., Anticevic, A., & Murray, J. D. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0195-0 · Cited by 657
Shared memories reveal shared structure in neural activity across individuals
Chen, J., Leong, Y. C., Honey, C. J., Yong, C. H., Norman, K. A., & Hasson, U. (2017) · Nature Neuroscience · doi:10.1038/nn.4450 · Cited by 645
Self-citationNaturalistic Stimuli in Neuroscience: Critically Acclaimed
Sonkusare, S., Breakspear, M. J., & Guo, C. C. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.05.004 · Cited by 645
A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex
Chaudhuri, R., Knoblauch, K., Gariel, M.-A., Kennedy, H., & Wang, X. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.008 · Cited by 618
The modular and integrative functional architecture of the human brain
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Shine, J. M., Breakspear, M. J., Bell, P. T., Martens, K. A. E., Shine, R., Koyejo, S., Sporns, O., & Poldrack, R. A. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0312-0 · Cited by 533
Criticality in the brain: A synthesis of neurobiology, models and cognition
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van Diessen, E., Numan, T., van Dellen, E., van der Kooi, A. W., Boersma, M., Hofman, D., van Lutterveld, R., van Dijk, B. W., van Straaten, E. C. W., Hillebrand, A., et al. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.11.018 · Cited by 417
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Self-citationAn electrophysiological marker of arousal level in humans
Lendner, J. D., Helfrich, R. F., Mander, B. A., Romundstad, L. G., Lin, J. J., Walker, M. P., Larsson, P. G., & Knight, R. T. (2020) · eLife · doi:10.7554/elife.55092 · Cited by 425
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Demirtaş, M., Burt, J. B., Helmer, M., Ji, J. L., Adkinson, B. D., Glasser, M. F., Essen, D. C. V., Sotiropoulos, S. N., Anticevic, A., & Murray, J. D. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.017 · Cited by 343
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Cikara, M., & Bavel, J. J. V. (2014) · Perspectives on Psychological Science · doi:10.1177/1745691614527464 · Cited by 378
Distinct timescales of population coding across cortex
Runyan, C. A., Piasini, E., Panzeri, S., & Harvey, C. D. (2017) · Nature · doi:10.1038/nature23020 · Cited by 345
Hierarchical dynamics as a macroscopic organizing principle of the human brain
Raut, R. V., Snyder, A. Z., & Raichle, M. E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2003383117 · Cited by 307
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Dmochowski, J., Bezdek, M. A., Abelson, B. P., Johnson, J. S., Schumacher, E. H., & Parra, L. C. (2014) · Nature Communications · doi:10.1038/ncomms5567 · Cited by 314
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Gao, R., van den Brink, R. L., Pfeffer, T. A., & Voytek, B. (2020) · eLife · doi:10.7554/elife.61277 · Cited by 320
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Northoff, G., Wainio‐Theberge, S., & Evers, K. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2019.05.002 · Cited by 272
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Self-citationHeeger, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619788114 · Cited by 220
Self-citationWehbe, L., Murphy, B., Talukdar, P. P., Fyshe, A., Ramdas, A., & Mitchell, T. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112575 · Cited by 276
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Self-citationNorthoff, G., & Huang, Z. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.07.013 · Cited by 229
The brain and its time: intrinsic neural timescales are key for input processing
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Gollo, L. L., Zalesky, A., Hutchison, R. M., van den Heuvel, M. P., & Breakspear, M. J. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0165 · Cited by 189
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Frankland, S., & Greene, J. D. (2019) · Annual Review of Psychology · doi:10.1146/annurev-psych-122216-011829 · Cited by 198
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A Spatial Map of Onset and Sustained Responses to Speech in the Human Superior Temporal Gyrus
Hamilton, L. S., Edwards, E., & Chang, E. F. (2018) · Current Biology · doi:10.1016/j.cub.2018.04.033 · Cited by 183
Expanding the scope of memory search: Modeling intralist and interlist effects in free recall.
Lohnas, L. J., Polyn, S. M., & Kahana, M. J. (2015) · Psychological Review · doi:10.1037/a0039036 · Cited by 190
Atypical intrinsic neural timescale in autism
Watanabe, T., Rees, G., & Masuda, N. (2019) · eLife · doi:10.7554/elife.42256 · Cited by 150
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A place for time: The spatiotemporal structure of neural dynamics during natural audition
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Self-citationChang, L. J., Jolly, E., Cheong, J. H., Rapuano, K. M., Greenstein, N., Chen, P.-H. A., & Manning, J. R. (2021) · Science Advances · doi:10.1126/sciadv.abf7129 · Cited by 177
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Vinck, M. A., Uran, C., Spyropoulos, G., Onorato, I., Broggini, A. C., Schneider, M., & Canales‐Johnson, A. (2023) · Neuron · doi:10.1016/j.neuron.2023.03.015 · Cited by 170
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Self-citationNorthoff, G., & Duncan, N. W. (2016) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2016.08.003 · Cited by 163
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Chen, J., Honey, C. J., Simony, E., Arcaro, M. J., Norman, K. A., & Hasson, U. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv155 · Cited by 135
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The Timescale of Perceptual Evidence Integration Can Be Adapted to the Environment
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Mapping how local perturbations influence systems-level brain dynamics
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Meyniel, F., Maheu, M., & Dehaene, S. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005260 · Cited by 145
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Pérez, P., Madsen, J., Banellis, L., Türker, B., Raimondo, F., Perlbarg, V., Valente, M., Niérat, M.-C., Puybasset, L., Naccache, L., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109692 · Cited by 139
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Honey, C. J., Newman, E. L., & Schapiro, A. C. (2017) · Network Neuroscience · doi:10.1162/netn_a_00024 · Cited by 115
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Zilio, F., Gómez‐Pilar, J., Cao, S., Zhang, J., Zang, D., Qi, Z., Tan, J., Hiromi, T., Wu, X., Fogel, S. M., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117579 · Cited by 110
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López‐González, A., Panda, R., Ponce‐Alvarez, A., Zamora‐López, G., Escrichs, A., Martial, C., Thibaut, A., Gosseries, O., Kringelbach, M. L., Annen, J., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02537-9 · Cited by 116
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Huang, Z., Liu, X., Mashour, G. A., & Hudetz, A. G. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2545-17.2018 · Cited by 110
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Shinn, M., Hu, A., Turner, L., Noble, S., Preller, K. H., Ji, J. L., Moujaes, F., Achard, S., Scheinost, D., Constable, R. T., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01299-3 · Cited by 115
Organizing principles of pulvino-cortical functional coupling in humans
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Compressive Temporal Summation in Human Visual Cortex
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Metadata
- Authors
- Honey, C. J., Thesen, T., Donner, T. H., Silbert, L. J., Carlson, C. E., Devinsky, O., Doyle, W. K., Rubin, N., Heeger, D. J., & Hasson, U.
- Affiliations
- Princeton University (US)
- New York University (US)
- University of Amsterdam (NL)
- Corresponding author
- Honey, Christopher J.
- ORCID
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2012-10
- Volume
- 76
- Issue
- 2
- Pages
- 423-434
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC3517908
- OpenAlex
- W2152009360
- Cited by
- 532
- References
- 73
- Citations by year
- 2026: 52 · 2025: 50 · 2024: 56 · 2023: 56 · 2022: 53 · 2021: 47 · 2020: 48 · 2019: 36 · 2018: 36 · 2017: 37 · 2016: 20 · 2015: 18 · 2014: 15 · 2013: 7
- FWCI
- 6.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH094480)
- National Institute on Drug Abuse (R21DA024423)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- information accumulation, electrocorticography, sensory processing, higher-order visual processing, film viewing, neural population dynamics, temporal integration
- MeSH terms
- Acoustic Stimulation, Adult, Brain Mapping, Cerebral Cortex, Electrodes, Electroencephalography, Female, Humans, Male, Mental Processes, Middle Aged, Photic Stimulation, Reaction Time, Spectrum Analysis, Time Factors, Reproducibility of Results, Epilepsy, Complex Partial, Young Adult, Brain Waves
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