Directional patterns of cross frequency phase and amplitude coupling within the resting state mimic patterns of fMRI functional connectivity
Open accessAbstract
Functional imaging investigations into the brain's resting state interactions have yielded a wealth of insight into the intrinsic and dynamic neural architecture supporting cognition and behavior. Electrophysiological studies however have highlighted the fact that synchrony across large-scale cortical systems is composed of spontaneous interactions occurring at timescales beyond the traditional resolution of fMRI, a feature that limits the capacity of fMRI to draw inference on the true directional relationship between network nodes. To approach the question of directionality in resting state signals, we recorded resting state functional MRI (rsfMRI) and electrocorticography (ECoG) from four human subjects undergoing invasive epilepsy monitoring. Using a seed-point based approach, we employed phase-amplitude coupling (PAC) and biPhase Locking Values (bPLV), two measures of cross-frequency coupling (CFC) to explore both outgoing and incoming connections between the seed and all non-seed, site electrodes. We observed robust PAC between a wide range of low-frequency phase and high frequency amplitude estimates. However, significant bPLV, a CFC measure of phase-phase synchrony, was only observed at specific narrow low and high frequency bandwidths. Furthermore, the spatial patterns of outgoing PAC connectivity were most closely associated with the rsfMRI connectivity maps. Our results support the hypothesis that PAC is relatively ubiquitous phenomenon serving as a mechanism for coordinating high-frequency amplitudes across distant neuronal assemblies even in absence of overt task structure. Additionally, we demonstrate that the spatial distribution of a seed-point rsfMRI sensorimotor network is strikingly similar to specific patterns of directional PAC. Specifically, the high frequency activities of distal patches of cortex owning membership in a rsfMRI sensorimotor network were most likely to be entrained to the phase of a low frequency rhythm engendered from the neural populations at the seed-point, suggestive of greater directional coupling from the seed out to the site electrodes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Electrophysiological infraslow fluctuations are believed to coordinate large-scale intrinsic connectivity network organization and long-range information exchange.
“and are believed to coordinate large-scale ICN organization and long-range information exchange [68, 83–91].”
From Infraslow closed-loop brain training for anxiety and depression (ISAD): a protocol for a randomized, double-blind, sham-controlled pilot trial in adult females with internalizing disorders (2022) · cites this paper as [89]
The Introduction links electrophysiological infraslow oscillations to intrinsic networks, and the Discussion describes low-frequency phase–amplitude coupling as a possible mechanism for long-distance resting-state interactions. But the coordinating oscillations studied here are chiefly in the 1–10 Hz range; the paper does not establish that infraslow electrophysiological fluctuations specifically coordinate network organization or long-range exchange. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812 · see citing works
Neuronal Oscillations in Cortical Networks
Buzsáki, G., & Draguhn, A. (2004) · Science · doi:10.1126/science.1099745 · Cited by 6,576 · see citing works
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150 · see citing works
BCI2000: A General-Purpose Brain-Computer Interface (BCI) System
Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827072 · Cited by 3,022 · see citing works
Toward discovery science of human brain function
Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911855107 · Cited by 2,970 · see citing works
Dynamic functional connectivity: Promise, issues, and interpretations
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962 · see citing works
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 · see citing works
Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network
Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn059 · Cited by 2,175 · see citing works
The functional role of cross-frequency coupling
Canolty, R. T., & Knight, R. T. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.09.001 · Cited by 2,101 · see citing works
Electrophysiological signatures of resting state networks in the human brain
Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700668104 · Cited by 1,938 · see citing works
Lisman, J. E., & Jensen, O. (2013) · Neuron · doi:10.1016/j.neuron.2013.03.007 · Cited by 1,609 · see citing works
Intrinsic and task-evoked network architectures of the human brain
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.014 · Cited by 1,564 · see citing works
Measuring Phase-Amplitude Coupling Between Neuronal Oscillations of Different Frequencies
Tort, A. B. L., Komorowski, R. W., Eichenbaum, H., & Kopell, N. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00106.2010 · Cited by 1,365 · see citing works
Normal and pathological oscillatory communication in the brain
Schnitzler, A., & Groß, J. (2005) · Nature reviews. Neuroscience · doi:10.1038/nrn1650 · Cited by 1,229 · see citing works
Bullmore, E. T., Suckling, J., Overmeyer, S., Rabe-Hesketh, S., Taylor, E., & Brammer, M. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.750253 · Cited by 1,088 · see citing works
Manning, J. R., Jacobs, J., Fried, I., & Kahana, M. J. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.2041-09.2009 · Cited by 973 · see citing works
Investigating the electrophysiological basis of resting state networks using magnetoencephalography
Brookes, M. J., Woolrich, M., Luckhoo, H., Price, D., Hale, J. R., Stephenson, M. C., Barnes, G. R., Smith, S. M., & Morris, P. G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1112685108 · Cited by 938 · see citing works
Temporal dynamics of spontaneous MEG activity in brain networks
de Pasquale, F., Della Penna, S., Snyder, A. Z., Lewis, C., Mantini, D., Marzetti, L., Belardinelli, P., Ciancetta, L., Pizzella, V., Romani, G. L., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0913863107 · Cited by 698 · see citing works
High-frequency gamma oscillations and human brain mapping with electrocorticography
Crone, N. E., Sinai, A., & Korzeniewska, A. (2006) · Progress in Brain Research · doi:10.1016/s0079-6123(06)59019-3 · Cited by 526 · see citing works
Role of local network oscillations in resting-state functional connectivity
Cabral, J., Hugues, E., Sporns, O., & Deco, G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.04.010 · Cited by 515 · see citing works
Murthy, V. N., & Fetz, E. E. (1996) · Journal of Neurophysiology · doi:10.1152/jn.1996.76.6.3968 · Cited by 464 · see citing works
Hillebrand, A., Barnes, G. R., Bosboom, J. L., Berendse, H. W., & Stam, C. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.005 · Cited by 416 · see citing works
Voytek, B. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00191 · Cited by 401 · see citing works
Gamma Power Is Phase-Locked to Posterior Alpha Activity
Osipova, D. V., Hermes, D., & Jensen, O. B. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003990 · Cited by 329 · see citing works
Miller, K. J., Honey, C. J., Hermes, D., Rao, R. P. N., den Nijs, M., & Ojemann, J. G. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.070 · Cited by 275 · see citing works
Self-citationFontolan, L., Morillon, B., Liégeois‐Chauvel, C., & Giraud, A. (2014) · Nature Communications · doi:10.1038/ncomms5694 · Cited by 265 · see citing works
Automated electrocorticographic electrode localization on individually rendered brain surfaces
Hermes, D., Miller, K. J., Noordmans, H. J., Vansteensel, M. J., & Ramsey, N. F. (2009) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2009.10.005 · Cited by 257 · see citing works
Keller, C. J., Bickel, S., Honey, C. J., Groppe, D. M., Entz, L., Craddock, R. C., Lado, F. A., Kelly, C., Milham, M., & Mehta, A. D. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.4837-12.2013 · Cited by 245 · see citing works
Testing for nested oscillation
Penny, W. D., Düzel, E., Miller, K. J., & Ojemann, J. G. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.06.035 · Cited by 236 · see citing works
Self-citationStatistical Method for Detection of Phase-Locking Episodes in Neural Oscillations
Hurtado, J. M., Rubchinsky, L. L., & Sigvardt, K. A. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00853.2003 · Cited by 236 · see citing works
Dominant frequencies of resting human brain activity as measured by the electrocorticogram
Groppe, D. M., Bickel, S., Keller, C. J., Jain, S. K., Hwang, S., Harden, C. L., & Mehta, A. D. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.044 · Cited by 223 · see citing works
Causal effect of disconnection lesions on interhemispheric functional connectivity in rhesus monkeys
O’Reilly, J. X., Croxson, P. L., Jbabdi, S., Sallet, J., Noonan, M. P., Mars, R. B., Browning, P. G. F., Wilson, C. R. E., Mitchell, A. S., Miller, K. L., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1305062110 · Cited by 220 · see citing works
Infra-Slow EEG Fluctuations Are Correlated with Resting-State Network Dynamics in fMRI
Hiltunen, T., Kantola, J., Elseoud, A. A., Lepola, P., Suominen, K., Starck, T., Nikkinen, J., Remes, J. J., Tervonen, O. A., Palva, S., et al. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0276-13.2014 · Cited by 215 · see citing works
Measuring synchronization in coupled model systems: A comparison of different approaches
Kreuz, T., Mormann, F., Andrzejak, R. G., et al. (2006) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2006.09.039 · Cited by 214 · see citing works
Singer, W. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.006 · Cited by 206 · see citing works
Synchronized neuronal oscillations and their role in motor processes
MacKay, W. A. (1997) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(97)01059-0 · Cited by 202 · see citing works
Zhang, D., Johnston, J. M., Fox, M., Leuthardt, E. C., Grubb, R. L., Chicoine, M. R., Smyth, M. D., Snyder, A. Z., Raichle, M. E., & Shimony, J. S. (2009) · Operative Neurosurgery · doi:10.1227/01.neu.0000350868.95634.ca · Cited by 194 · see citing works
Florin, E., & Baillet, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.054 · Cited by 184 · see citing works
Topographic Electrophysiological Signatures of fMRI Resting State Networks
Jann, K., Kottlow, M., Dierks, T., Boesch, C., & Koenig, T. W. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012945 · Cited by 167 · see citing works
Quantifying phase–amplitude coupling in neuronal network oscillations
Onslow, A. C. E., Bogacz, R., & Jones, M. W. (2010) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2010.09.007 · Cited by 142 · see citing works
Regulation of Motor Representation by Phase–Amplitude Coupling in the Sensorimotor Cortex
Yanagisawa, T., Yamashita, O., Hirata, M., Kishima, H., Saitoh, Y., Goto, T., Yoshimine, T., & Kamitani, Y. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.2929-12.2012 · Cited by 134 · see citing works
Corticocortical Evoked Potentials Reveal Projectors and Integrators in Human Brain Networks
Keller, C. J., Honey, C. J., Entz, L., Bickel, S., Groppe, D. M., Toth, E., Ulbert, I., Lado, F. A., & Mehta, A. D. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4289-13.2014 · Cited by 127 · see citing works
Resting oscillations and cross-frequency coupling in the human posteromedial cortex
Foster, B. L., & Parvizi, J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.019 · Cited by 102 · see citing works
The contribution of electrophysiology to functional connectivity mapping
Schölvinck, M. L., Leopold, D. A., Brookes, M. J., & Khader, P. H. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.010 · Cited by 96 · see citing works
Robust, long-term control of an electrocorticographic brain-computer interface with fixed parameters
Blakely, T. M., Miller, K. J., Zanos, S. P., Rao, R. P. N., & Ojemann, J. G. (2009) · Neurosurgical FOCUS · doi:10.3171/2009.4.focus0977 · Cited by 84 · see citing works
Self-citationDarvas, F., Miller, K. J., Rao, R. P. N., et al. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.3688-08.2009 · Cited by 66 · see citing works
Self-citationEssays in Econometrics: Collected Papers of Clive W. J. Granger
Ghysels, É., Swanson, N. R., & Watson, M. W. (2001) · Cited by 52 · see citing works
Bi-phase locking — a tool for probing non-linear interaction in the human brain
Darvas, F., Ojemann, J. G., & Sorensen, L. B. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.034 · Cited by 44 · see citing works
Self-citationIdentifying Functional Networks Using Endogenous Connectivity in Gamma Band Electrocorticography
Ko, A. L., Weaver, K. E., Hakimian, S., & Ojemann, J. G. (2013) · Brain Connectivity · doi:10.1089/brain.2013.0157 · Cited by 34 · see citing works
Self-citationAmplitude and phase dynamics associated with acoustically paced finger tapping
Boonstra, T. W., Daffertshofer, A., Peper, C. E., & Beek, P. J. (2006) · Brain Research · doi:10.1016/j.brainres.2006.06.039 · Cited by 24 · see citing works
Hutt, A., Daffertshofer, A., & Steinmetz, U. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.68.036219 · Cited by 23 · see citing works
High-resolution intersubject averaging and a coordinate system for the cortical surface
Fischl, B., Sereno, M. I., Tootell, R. B. H., & Dale, A. M. (1999) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1999)8:4
Flagged first, then by citations
Cited by
Sorted by citations
Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function
Zelano, C., Jiang, H., Zhou, G., Arora, N., Schuele, S., Rosenow, J., & Gottfried, J. A. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.2586-16.2016 · Cited by 599 · see citing works
Perez, T. M., Glue, P., Adhia, D. B., Navid, M. S., Zeng, J., Dillingham, P., Smith, M., Niazi, I. K., Young, C. K., & De Ridder, D. (2022) · Trials · doi:10.1186/s13063-022-06863-z · Cited by 8 · see citing works
- Authors
- Weaver, K. E., Wander, J. D., Ko, A. L., Casimo, K., Grabowski, T. J., Ojemann, J. G., & Darvas, F.
- Affiliations
- University of Washington (US)
- Corresponding author
- Weaver, Kurt E.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 128
- Pages
- 238-251
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4762762
- OpenAlex
- W2218504993
- Cited by
- 44
- References
- 52
- Citations by year
- 2026: 1 · 2025: 4 · 2023: 3 · 2022: 7 · 2021: 5 · 2020: 3 · 2019: 6 · 2018: 2 · 2017: 8 · 2016: 5
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- NeuroImage (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5KOI NS065186-035, T32 656052, NS065186, NS012542)
- National Science Foundation (EEC-1028725)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- cross-frequency coupling, phase-amplitude coupling, resting-state functional connectivity, directional coupling, electrocorticography, sensorimotor network, neural entrainment, epilepsy monitoring
- MeSH terms
- Drug Resistant Epilepsy, Electrocorticography, Adolescent, Adult, Brain, Brain Mapping, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Synaptic Transmission, Rest, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review