[ad_1]
Ermentrout, G. B. & Kleinfeld, D. Traveling electrical waves in cortex: insights from phase dynamics and speculation on a computational role. Neuron 29, 33–44 (2001).
Google Scholar
Muller, L., Chavane, Frédéric, Reynolds, J. & Sejnowski, T. J. Cortical travelling waves: mechanisms and computational principles. Nat. Rev. Neurosci. 19, 255–268 (2018).
Google Scholar
Lubenov, E. V. & Siapas, A. G. Hippocampal theta oscillations are travelling waves. Nature 459, 534–539 (2009).
Google Scholar
Davis, Z. W., Muller, L., Martinez-Trujillo, J., Sejnowski, T. & Reynolds, J. H. Spontaneous travelling cortical waves gate perception in behaving primates. Nature 587, 432–436 (2020).
Google Scholar
Benucci, A., Frazor, R. A. & Carandini, M. Standing waves and traveling waves distinguish two circuits in visual cortex. Neuron 55, 103–117 (2007).
Google Scholar
Hamid, A. A., Frank, M. J. & Moore, C. I. Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment. Cell 184, 2733–2749 (2021).
Google Scholar
Hernández-Pérez, J. Jesús, Cooper, K. W. & Newman, E. L. Medial entorhinal cortex activates in a traveling wave in the rat. eLife 9, e52289 (2020).
Google Scholar
Bahramisharif, A. et al. Propagating neocortical gamma bursts are coordinated by traveling alpha waves. J. Neurosci. 33, 18849–18854 (2013).
Google Scholar
Zhang, H., Watrous, A. J., Patel, A. & Jacobs, J. Theta and alpha oscillations are traveling waves in the human neocortex. Neuron 98, 1269–1281.e4 (2018).
Google Scholar
Alexander, D. M. et al. Traveling waves and trial averaging: the nature of single-trial and averaged brain responses in large-scale cortical signals. NeuroImage 73, 95–112 (2013).
Google Scholar
Sato, T. K., Nauhaus, I. & Carandini, M. Traveling waves in visual cortex. Neuron 75, 218–229 (2012).
Google Scholar
Adrian, E. D. & Matthews, B. H. C. The Berger rhythm: potential changes from the occipital lobes in man. Brain 57, 355–385 (1934).
Google Scholar
Nauhaus, I., Busse, L., Carandini, M. & Ringach, D. L. Stimulus contrast modulates functional connectivity in visual cortex. Nat. Neurosci. 12, 70–76 (2009).
Google Scholar
Muller, L., Reynaud, A., Chavane, F. & Destexhe, A. The stimulus-evoked population response in visual cortex of awake monkey is a propagating wave. Nat. Commun. 5, 3675 (2014).
Google Scholar
Muller, L. et al. Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night. eLife 5, e17267 (2016).
Google Scholar
Massimini, M., Huber, R., Ferrarelli, F., Hill, S. & Tononi, G. The sleep slow oscillation as a traveling wave. J. Neurosci. 24, 6862–6870 (2004).
Google Scholar
Takahashi, K. et al. Large-scale spatiotemporal spike patterning consistent with wave propagation in motor cortex. Nat. Commun. 6, 7169 (2015).
Google Scholar
Roberts, J. A. et al. Metastable brain waves. Nat. Commun. 10, 1056 (2019).
Google Scholar
Bhattacharya, S., Cauchois, M. B. L., Iglesias, P. A. & Chen, Z. S. The impact of a closed-loop thalamocortical model on the spatiotemporal dynamics of cortical and thalamic traveling waves. Sci. Rep. 11, 14359 (2021).
Google Scholar
Kopell, N. J., Gritton, H. J., Whittington, M. A. & Kramer, M. A. Beyond the connectome: the dynome. Neuron 83, 1319–1328 (2014).
Google Scholar
Breakspear, M. Dynamic models of large-scale brain activity. Nat. Neurosci. 20, 340–352 (2017).
Google Scholar
Salinas, E. & Sejnowski, T. J. Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci. 2, 539–550 (2001).
Google Scholar
Alamia, A. & VanRullen, R. Alpha oscillations and traveling waves: signatures of predictive coding? PLoS Biol. 17, e3000487 (2019).
Google Scholar
Pang, Z., Alamia, A. & VanRullen, R. Turning the stimulus on and off changes the direction of α traveling waves. eNeuro 7, ENEURO.0218-20.2020 (2020).
Google Scholar
Alamia, A., Terral, L., d’Ambra, M. R. & Van Rullen, R. Distinct roles of forward and backward alpha-band waves in spatial visual attention. eLife 12, e85035 (2023).
Google Scholar
Engel, A., Fries, P. & Singer, W. Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704–716 (2001).
Google Scholar
Linde-Domingo, J., Treder, M. S., Kerrén, C. & Wimber, M. Evidence that neural information flow is reversed between object perception and object reconstruction from memory. Nat. Commun. 10, 179 (2019).
Google Scholar
Sederberg, P. B., Kahana, M. J., Howard, M. W., Donner, E. J. & Madsen, J. R. Theta and gamma oscillations during encoding predict subsequent recall. J. Neurosci. 23, 10809–10814 (2003).
Google Scholar
Burke, J. F. et al. Synchronous and asynchronous theta and gamma activity during episodic memory formation. J. Neurosci. 33, 292–304 (2013).
Google Scholar
Fisher, N. I. Statistical Analysis of Circular Data (Cambridge Univ. Press, 1993).
Zhang, H. & Jacobs, J. Traveling theta waves in the human hippocampus. J. Neurosci. 35, 12477–12487 (2015).
Google Scholar
Polyn, S. M., Norman, K. A. & Kahana, M. J. A context maintenance and retrieval model of organizational processes in free recall. Psychol. Rev. 116, 129–156 (2009).
Google Scholar
Burke, J. F. et al. Human intracranial high-frequency activity maps episodic memory formation in space and time. NeuroImage 85, 834–843 (2014).
Google Scholar
Canolty, R. T. et al. High gamma power is phase-locked to theta oscillations in human neocortex. Science 313, 1626–1628 (2006).
Google Scholar
Jacobs, J., Kahana, M. J., Ekstrom, A. D. & Fried, I. Brain oscillations control timing of single-neuron activity in humans. J. Neurosci. 27, 3839–3844 (2007).
Google Scholar
Luczak, A., McNaughton, B. L. & Harris, K. D. Packet-based communication in the cortex. Nat. Rev. Neurosci. 16, 745–755 (2015).
Google Scholar
Hahn, G., Ponce-Alvarez, A., Deco, G., Aertsen, A. & Kumar, A. Portraits of communication in neuronal networks. Nat. Rev. Neurosci. 20, 117–127 (2019).
Google Scholar
Heitmann, S., Boonstra, T. & Breakspear, M. A dendritic mechanism for decoding traveling waves: principles and applications to motor cortex. PLoS Comput. Biol. 9, e1003260 (2013).
Google Scholar
Sato, N. Cortical traveling waves reflect state-dependent hierarchical sequencing of local regions in the human connectome network. Sci. Rep. 12, 334 (2022).
Google Scholar
Sherfey, J., Ardid, S., Miller, E. K., Hasselmo, M. E. & Kopell, N. J. Prefrontal oscillations modulate the propagation of neuronal activity required for working memory. Neurobiol. Learn. Mem. 173, 107228 (2020).
Google Scholar
Girard, P., Hupé, J. M. & Bullier, J. Feedforward and feedback connections between areas v1 and v2 of the monkey have similar rapid conduction velocities. J. Neurophysiol. 85, 1328–1331 (2001).
Google Scholar
González-Burgos, G., Barrionuevo, G. & Lewis, D. A. Horizontal synaptic connections in monkey prefrontal cortex: an in vitro electrophysiological study. Cereb. Cortex 10, 82–92 (2000).
Google Scholar
Chiang, Chia-Chu, Shivacharan, R. S., Wei, X., Gonzalez-Reyes, L. E. & Durand, D. M. Slow periodic activity in the longitudinal hippocampal slice can self-propagate non-synaptically by a mechanism consistent with ephaptic coupling. J. Physiol. 597, 249–269 (2019).
Google Scholar
Kleen, J. K. et al. Bidirectional propagation of low frequency oscillations over the human hippocampal surface. Nat. Commun. 12, 2764 (2021).
Google Scholar
Heitmann, S., Gong, P. & Breakspear, M. A computational role for bistability and traveling waves in motor cortex. Front. Comput. Neurosci. 6, 67 (2012).
Google Scholar
Zabeh, E., Foley, N. C., Jacobs, J. & Gottlieb, J. P. Beta traveling waves in monkey frontal and parietal areas encode recent reward history. Nat. Commun. 14, 5428 (2023).
Google Scholar
Place, R., Farovik, A., Brockmann, M. & Eichenbaum, H. Bidirectional prefrontal–hippocampal interactions support context-guided memory. Nat. Neurosci. 19, 992–994 (2016).
Google Scholar
Tomita, H., Ohbayashi, M., Nakahara, K., Hasegawa, I. & Miyashita, Y. Top-down signal from prefrontal cortex in executive control of memory retrieval. Nature 401, 699–703 (1999).
Google Scholar
Rajasethupathy, P. et al. Projections from neocortex mediate top-down control of memory retrieval. Nature 526, 653–659 (2015).
Google Scholar
Felleman, D. J. & Van Essen, D. C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).
Google Scholar
Markov, N. T. et al. Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex. J. Comp. Neurol. 522, 225–259 (2014).
Google Scholar
Bastos, A. M. et al. Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron 85, 390–401 (2015).
Google Scholar
Fries, P. Rhythms for cognition: communication through coherence. Neuron 88, 220–235 (2015).
Google Scholar
Buffalo, E. A., Fries, P., Landman, R., Liang, H. & Desimone, R. A backward progression of attentional effects in the ventral stream. Proc. Natl Acad. Sci. USA 107, 361–365 (2010).
Google Scholar
Friston, K. Hierarchical models in the brain. PLoS Comput. Biol. 4, e1000211 (2008).
Google Scholar
Rubino, D., Robbins, K. A. & Hatsopoulos, N. G. Propagating waves mediate information transfer in the motor cortex. Nat. Neurosci. 9, 1549–1557 (2006).
Google Scholar
Balasubramanian, K. et al. Propagating motor cortical dynamics facilitate movement initiation. Neuron 106, 526–536 (2020).
Google Scholar
Bhattacharya, S., Brincat, S. L., Lundqvist, M. & Miller, E. K. Traveling waves in the prefrontal cortex during working memory. PLoS Comput. Biol. 18, e1009827 (2022).
Google Scholar
Li, J. et al. Anterior–posterior hippocampal dynamics support working memory processing. J. Neurosci. 42, 443–453 (2021).
Google Scholar
Michalareas, G. et al. Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron 89, 384–397 (2016).
Google Scholar
Contreras, D., Destexhe, A., Sejnowski, T. J. & Steriade, M. Spatiotemporal patterns of spindle oscillations in cortex and thalamus. J. Neurosci. 17, 1179–1196 (1997).
Google Scholar
Muller, L. & Destexhe, A. Propagating waves in thalamus, cortex and the thalamocortical system: experiments and models. J. Physiol. Paris 106, 222–238 (2012).
Google Scholar
Halgren, M. et al. The generation and propagation of the human alpha rhythm. Proc. Natl Acad. Sci. USA 116, 23772–23782 (2019).
Google Scholar
Breakspear, M., Heitmann, S. & Daffertshofer, A. Generative models of cortical oscillations: neurobiological implications of the Kuramoto model. Front. Hum. Neurosci. 4, 190 (2010).
Google Scholar
Fries, P., Reynolds, J. H., Rorie, A. E. & Desimone, R. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291, 1560–1563 (2001).
Google Scholar
Barzegaran, E. & Plomp, G. Four concurrent feedforward and feedback networks with different roles in the visual cortical hierarchy. PLoS Biol. 20, e3001534 (2022).
Google Scholar
King, J.-R. & Wyart, V. The human brain encodes a chronicle of visual events at each instant of time through the multiplexing of traveling waves. J. Neurosci. 41, 7224–7233 (2021).
Google Scholar
Hanslmayr, S., Volberg, G., Wimber, M., Dalal, S. S. & Greenlee, M. W. Prestimulus oscillatory phase at 7 Hz gates cortical information flow and visual perception. Curr. Biol. 23, 2273–2278 (2013).
Google Scholar
Sauseng, P. et al. EEG alpha synchronization and functional coupling during top-down processing in a working memory task. Hum. Brain Mapp. 26, 148–155 (2005).
Google Scholar
Hanslmayr, S. et al. The relationship between brain oscillations and BOLD signal during memory formation: a combined EEG–fMRI study. J. Neurosci. 31, 15674–15680 (2011).
Google Scholar
Busch, N. A., Dubois, J. & Van Rullen, R. The phase of ongoing EEG oscillations predicts visual perception. J. Neurosci. 29, 7869–7876 (2009).
Google Scholar
Mathewson, K. E., Gratton, G., Fabiani, M., Beck, D. M. & Ro, T. To see or not to see: prestimulus α phase predicts visual awareness. J. Neurosci. 29, 2725–2732 (2009).
Google Scholar
Dugué, L., Marque, P. & Van Rullen, R. The phase of ongoing oscillations mediates the causal relation between brain excitation and visual perception. J. Neurosci. 31, 11889–11893 (2011).
Google Scholar
Patten, T. M., Rennie, C. J., Robinson, P. A. & Gong, P. Human cortical traveling waves: dynamical properties and correlations with responses. PLoS ONE 7, e38392 (2012).
Google Scholar
Lozano-Soldevilla, D. & Van Rullen, R. The hidden spatial dimension of alpha: 10-Hz perceptual echoes propagate as periodic traveling waves in the human brain. Cell Rep. 26, 374–380 (2019).
Google Scholar
Stolk, A. et al. Electrocorticographic dissociation of alpha and beta rhythmic activity in the human sensorimotor system. eLife 8, e48065 (2019).
Google Scholar
Kastner, S., Pinsk, M. A., De Weerd, P., Desimone, R. & Ungerleider, L. G. Increased activity in human visual cortex during directed attention in the absence of visual stimulation. Neuron 22, 751–761 (1999).
Google Scholar
Buschman, T. J. & Miller, E. K. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315, 1860–1862 (2007).
Google Scholar
Gazzaley, A. & Nobre, A. C. Top-down modulation: bridging selective attention and working memory. Trends Cogn. Sci. 16, 129–135 (2012).
Google Scholar
Haegens, S., Cousijn, H., Wallis, G., Harrison, P. J. & Nobre, A. C. Inter- and intra-individual variability in alpha peak frequency. NeuroImage 92, 46–55 (2014).
Google Scholar
Mahjoory, K., Schoffelen, J.-M., Keitel, A. & Gross, J. The frequency gradient of human resting-state brain oscillations follows cortical hierarchies. eLife 9, e53715 (2020).
Google Scholar
Mueller, S. et al. Individual variability in functional connectivity architecture of the human brain. Neuron 77, 586–595 (2013).
Google Scholar
Pang, J. C. et al. Geometric constraints on human brain function. Nature 618, 566–574 (2023).
Google Scholar
Fischl, B. R., Sereno, M. I., Tootell, R. B. H. & Dale, A. M. High-resolution inter-subject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272–284 (1999).
Google Scholar
Steinmetz, N. A., Koch, C., Harris, K. D. & Carandini, M. Challenges and opportunities for large-scale electrophysiology with neuropixels probes. Curr. Opin. Neurobiol. 50, 92–100 (2018).
Google Scholar
Khodagholy, D. et al. Neurogrid: recording action potentials from the surface of the brain. Nat. Neurosci. 18, 310–315 (2015).
Google Scholar
Ribary, U. et al. Magnetic field tomography of coherent thalamocortical 40-Hz oscillations in humans. Proc. Natl Acad. Sci. USA 88, 11037–11041 (1991).
Google Scholar
Boto, E. et al. A new generation of magnetoencephalography: room temperature measurements using optically-pumped magnetometers. NeuroImage 149, 404–414 (2017).
Google Scholar
Said, C. P., Egan, R. D., Minshew, N. J., Behrmann, M. & Heeger, D. J. Normal binocular rivalry in autism: implications for the excitation/inhibition imbalance hypothesis. Vis. Res. 77, 59–66 (2013).
Google Scholar
Smith, E. H. et al. Human interictal epileptiform discharges are bidirectional traveling waves echoing ictal discharges. eLife 11, e73541 (2022).
Google Scholar
Talairach, J. & Tournoux, P. Co-planar Stereotaxic Atlas of the Human Brain (G. Thieme, 1988).
Das, A. et al. Spontaneous neuronal oscillations in the human insula are hierarchically organized traveling waves. eLife 11, e76702 (2022).
Google Scholar
Berens, P. Circstat: a MATLAB toolbox for circular statistics. J. Stat. Softw. 31, 1–21 (2009).
Google Scholar
Kempter, R., Leibold, C., Buzsáki, G., Diba, K. & Schmidt, R. Quantifying circular–linear associations: hippocampal phase precession. J. Neurosci. Methods 207, 113–124 (2012).
Google Scholar
Masseran, N., Razali, A. M., Ibrahim, K. & Latif, M. T. Fitting a mixture of von Mises distributions in order to model data on wind direction in peninsular Malaysia. Energy Convers. Manage. 72, 94–102 (2013).
Google Scholar
Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.-M. Fieldtrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 156869 (2011).
Google Scholar
Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164, 177–190 (2007).
Google Scholar
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Google Scholar
Nilearn contributors. nilearn. GitHub https://github.com/nilearn/nilearn (2007–2023).
Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14 (2014).
Google Scholar
[ad_2]
Source link