The neural mechanisms underlying the emergence of functional connectivity in resting-state fMRI remain poorly understood. Recent studies suggest that resting-state activity consists of brief periods of strong co-fluctuations among brain regions, which reflect overall functional connectivity. Others report a continuum in co-fluctuations over time, with varying degree of correlation to functional connectivity. These findings raise the critical question: what neural processes underlie the temporal structure of resting-state activity? To address this, we used a biophysically realistic whole-brain computational model in which resting-state activity emerged from temporal variations in the ion concentrations of potassium ( K + ) and sodium ( Na + ), intracellular chloride ( Cl - ), and the activity of the Na + / K + ATPase. The model reproduced transient periods of high co-fluctuations, and the functional connectivity at different co-fluctuation levels correlated to varying degrees with the connectivity measured over the entire simulation, in line with experimental observations. The periods of high co-fluctuations were aligned with large changes in extracellular ion concentrations. Furthermore, critical parameters governing ion dynamics strongly affected both the timing of these transient events and the spatial structure of the resulting functional connectivity. The balance of excitatory and inhibitory activity further modulated their frequency and amplitude. Together, these results suggest that intrinsic fluctuations in ion dynamics could serve as a plausible neural mechanism for the temporal organization of co-fluctuations and resting-state functional connectivity.

Department of Integrative Physiology University of Colorado Boulder Boulder CO USA

Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA

Georgia Institute of Technology Atlanta Georgia USA

Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic

National Institute of Mental Health Klecany Czech Republic

School of Medicine University of California San Diego La Jolla CA USA

Zobrazit více v PubMed

Friston K. J., Functional and effective connectivity: a review. Brain Connect 1, 13–36 (2011). PubMed

Canario E., Chen D., Biswal B., A review of resting-state fMRI and its use to examine psychiatric disorders. Psychoradiology 1, 42–53 (2021). PubMed PMC

Zimmermann J., Griffiths J. D., McIntosh A. R., Unique Mapping of Structural and Functional Connectivity on Cognition. J Neurosci 38, 9658–9667 (2018). PubMed PMC

Mill R. D., Ito T., Cole M. W., From connectome to cognition: The search for mechanism in human functional brain networks. Neuroimage 160, 124–139 (2017). PubMed PMC

Fox M. D., Mapping Symptoms to Brain Networks with the Human Connectome. N Eng I J Med 379, 2237–2245 (2018).

Buckner R. L., Krienen F. M., The evolution of distributed association networks in the human brain. Trends Cogn Sci 17, 648–665 (2013). PubMed

van den Heuvel M. P., Hulshoff Pol H. E., Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20, 519–534 (2010). PubMed

Hiltunen T. et al. , Infra-slow EEG fluctuations are correlated with resting-state network dynamics in fMRI. J Neurosci 34, 356–362 (2014). PubMed PMC

Logothetis N. K., Pauls J., Augath M., Trinath T., Oeltermann A., Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001). PubMed

Ma Y. et al. , Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons. Proc Natl Acad Sci U S A 113, E8463–E8471 (2016). PubMed PMC

Pan W. J., Thompson G. J., Magnuson M. E., Jaeger D., Keilholz S., Infraslow LFP correlates to resting-state fMRI BOLD signals. Neuroimage 74, 288–297 (2013). PubMed PMC

Scholvinck M. L., Maier A., Ye F. Q., Duyn J. H., Leopold D. A., Neural basis of global resting-state fMRI activity. Proc Natl Acad Sci U S A 107, 10238–10243 (2010). PubMed PMC

Lurie D. J. et al. , Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw Neurosci 4, 30–69 (2020). PubMed PMC

Hutchison R. M. et al. , Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013). PubMed PMC

Allan T. W. et al. , Functional Connectivity in MRI Is Driven by Spontaneous BOLD Events. PLoS One 10, e0124577 (2015). PubMed PMC

Pope M., Fukushima M., Betzel R. F., Sporns O., Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics. Proc Natl Acad Sci USA 118 (2021).

Tagliazucchi E., Balenzuela P., Fraiman D., Montoya P., Chialvo D. R., Spontaneous BOLD event triggered averages for estimating functional connectivity at resting state. Neurosci Lett 488, 158–163 (2011). PubMed PMC

Tagliazucchi E., Siniatchkin M., Laufs H., Chialvo D. R., The Voxel-Wise Functional Connectome Can Be Efficiently Derived from Co-activations in a Sparse Spatio-Temporal Point-Process. Front Neurosci 10, 381 (2016). PubMed PMC

Zamani Esfahlani F. et al. , High-amplitude cofluctuations in cortical activity drive functional connectivity. Proc Natl Acad Sci U S A 117, 28393–28401 (2020). PubMed PMC

Ladwig Z. et al. , BOLD cofluctuation ‘events’ are predicted from static functional connectivity. Neuroimage 260, 119476 (2022). PubMed PMC

Laumann T. O. et al. , On the Stability of BOLD fMRI Correlations. Cereb Cortex 27, 4719–4732 (2017). PubMed PMC

Novelli L., Razi A., A mathematical perspective on edge-centric brain functional connectivity. Nat Commun 13, 2693 (2022). PubMed PMC

Allen E. A., Damaraju E., Eichele T., Wu L., Calhoun V. D., EEG Signatures of Dynamic Functional Network Connectivity States. Brain Topogr 31, 101–116 (2018). PubMed PMC

Chang C., Liu Z., Chen M. C., Liu X., Duyn J. H., EEG correlates of time-varying BOLD functional connectivity. Neuroimage 72, 227–236 (2013). PubMed PMC

Matsui T., Murakami T., Ohki K., Neuronal Origin of the Temporal Dynamics of Spontaneous BOLD Activity Correlation. Cereb Cortex 29, 1496–1508 (2019). PubMed

Tagliazucchi E., Laufs H., Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep. Neuron 82, 695–708 (2014). PubMed

Liegeois R., Laumann T. O., Snyder A. Z., Zhou J., Yeo B. T. T., Interpreting temporal fluctuations in resting-state functional connectivity MRI. Neuroimage 163, 437–455 (2017). PubMed

Breakspear M., Dynamic models of large-scale brain activity. Nat Neurosci 20, 340–352 (2017). PubMed

Deco G., Jirsa V. K., Robinson P. A., Breakspear M., Friston K., The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS Comput Biol 4, e1000092 (2008). PubMed PMC

D’Angelo E., Jirsa V., The quest for multiscale brain modeling. Trends Neurosci 45, 777–790 (2022). PubMed

Roberts J. A., Boonstra T. W., Breakspear M., The heavy tail of the human brain. Curr Opin Neurobiol 31, 164–172 (2015). PubMed

Krishnan G. P., Gonzalez O. C., Bazhenov M., Origin of slow spontaneous resting-state neuronal fluctuations in brain networks. Proc Natl Acad Sci U S A 115, 6858–6863 (2018). PubMed PMC

Bakker R., Wachtler T., Diesmann M., CoCoMac 2.0 and the future of tract-tracing databases. Front Neuroinform 6, 30 (2012). PubMed PMC

Skoch A. et al. , Human brain structural connectivity matrices-ready for modelling. Sci Data 9, 486 (2022). PubMed PMC

Biswal B., Yetkin F. Z., Haughton V. M., Hyde J. S., Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34, 537–541 (1995). PubMed

Cordes D. et al. , Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR Am J Neuroradiol 21, 1636–1644 (2000). PubMed PMC

Fox M. D., Raichle M. E., Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8, 700–711 (2007). PubMed

Nir Y. et al. , Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nat Neurosci 11, 1100–1108 (2008). PubMed PMC

Hartman D., Hlinka J., Palus M., Mantini D., Corbetta M., The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks. Chaos 21, 013119 (2011). PubMed PMC

Hlinka J., Palus M., Vejmelka M., Mantini D., Corbetta M., Functional connectivity in resting-state fMRI: is linear correlation sufficient? Neuroimage 54, 2218–2225 (2011). PubMed PMC

Laumann T. O. et al. , Functional System and Areal Organization of a Highly Sampled Individual Human Brain. Neuron 87, 657–670 (2015). PubMed PMC

Chang C., Glover G. H., Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50, 81–98 (2010). PubMed PMC

Hlinka J., Hadrava M., On the danger of detecting network states in white noise. Front Comput Neurosci 9, 11 (2015). PubMed PMC

Leonardi N., Van De Ville D., On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage 104, 430–436 (2015). PubMed

Lindquist M. A., Xu Y., Nebel M. B., Caffo B. S., Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. Neuroimage 101, 531–546 (2014). PubMed PMC

Liu X., Duyn J. H., Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proc Natl Acad Sci U S A 110, 4392–4397 (2013). PubMed PMC

Faskowitz J., Esfahlani F. Z., Jo Y., Sporns O., Betzel R. F., Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nat Neurosci 23, 1644–1654 (2020). PubMed

Betzel R. F., Faskowitz J., Sporns O., Living on the edge: network neuroscience beyond nodes. Trends Cogn Sci 27, 1068–1084 (2023). PubMed PMC

Jones H. M., Yoo K., Chun M. M., Rosenberg M. D., Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention. J Neurosci 44 (2024).

Merritt H., Mejia A., Betzel R., The dual interpretation of edge time series: Time-varying connectivity DOI

Tanner J. C. et al. , Synchronous high-amplitude co-fluctuations of functional brain networks during movie-watching. Imaging Neuroscience 1, 1–21 (2023).

Rabuffo G., Fousek J., Bernard C., Jirsa V., Neuronal Cascades Shape Whole-Brain Functional Dynamics at Rest. eNeuro 8 (2021).

Ponce-Alvarez A. et al. , Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. PLoS Comput Biol 11, e1004100 (2015). PubMed PMC

Erecinska M., Silver I. A., Ions and energy in mammalian brain. Prog Neurobiol 43, 37–71 (1994). PubMed

Pivovarov A. S., Calahorro F., Walker R. J., Na(+)/K(+)-pump and neurotransmitter membrane receptors. Invert Neurosci 19, 1 (2018). PubMed PMC

Sanda P. et al. , Cholinergic modulation supports dynamic switching of resting state networks through selective DMN suppression. PLoS Comput Biol 20, e1012099 (2024). PubMed PMC

Bazhenov M., Timofeev I., Steriade M., Sejnowski T. J., Potassium model for slow (2–3 Hz) in vivo neocortical paroxysmal oscillations. J Neurophysiol 92, 1116–1132 (2004). PubMed PMC

Frohlich F., Bazhenov M., Coexistence of tonic firing and bursting in cortical neurons. Phys Rev E Stat Nonlin Soft Matter Phys 74, 031922 (2006). PubMed

González O. C. et al. , Modeling of Age-Dependent Epileptogenesis by Differential Homeostatic Synaptic Scaling. The Journal of Neuroscience 35, 13448–13462 (2015). PubMed PMC

Krishnan G. P., Bazhenov M., lonic dynamics mediate spontaneous termination of seizures and postictal depression state. J Neurosci 31, 8870–8882 (2011). PubMed PMC

Krishnan G. P., Filatov G., Shilnikov A., Bazhenov M., Electrogenic properties of the Na(+)/K(+) ATPase control transitions between normal and pathological brain states. J Neurophysiol 113, 3356–3374 (2015). PubMed PMC

Tzourio-Mazoyer N. et al. , Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002). PubMed